Encyclopedia of Education Economics and Finance (2 Volume Set) [1-2] 9781452281858

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E N C Y C L O P E DI A O F

Education Economics & Finance

Editorial Board Editors Dominic J. Brewer New York University Lawrence O. Picus University of Southern California

Managing Editor Rochelle Hardison University of Southern California

Editorial Board Bruce D. Baker Rutgers University Eric Bettinger Stanford University Eric R. Eide Brigham Young University Margaret E. Goertz University of Pennsylvania Douglas N. Harris Tulane University Guilbert C. Hentshcke University of Southern California Jennifer Imazeki San Diego State University Kieran M. Killeen University of Vermont Tatiana Melguizo University of Southern California Anthony Rolle University of South Florida

E N C Y C L O P E DI A O F

Education Economics & Finance Editors

Dominic J. Brewer New York University

Lawrence O. Picus

VOLUME

University of Southern California

1

Copyright © 2014 by SAGE Publications, Inc.

FOR INFORMATION: SAGE Publications, Inc. 2455 Teller Road Thousand Oaks, California 91320 E-mail: [email protected] SAGE Publications Ltd. 1 Oliver’s Yard

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Library of Congress Cataloging-in-Publication Data Encyclopedia of education economics and finance / editors, Dominic J. Brewer, New York University, Lawrence O. Picus, University of Southern California. pages cm

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ISBN 978-1-4522-8185-8 (hardcover : alk. paper) 1. Education—Finance—Encyclopedias. I. Dominic J. Brewer, II. Picus, Larry, 1954– III. Title. Acquisitions Editor: Jim Brace-Thompson Developmental Editor: Shirin Parsavand Production Editor: Jane Haenel Reference Systems Manager: Leticia Gutierrez

LB2824.E53 2014 379.1'3—dc23

Reference Systems Coordinator: Anna Villaseñor Copy Editors: QuADS Prepress (P) Ltd. Typesetter: Hurix Systems Private Ltd. Proofreaders: Kristin Bergstad, Pam Suwinsky Indexer: Virgil Diodato Cover Designer: Anupama Krishnan Marketing Manager: Carmel Schrire

14 15 16 17 18 10 9 8 7 6 5 4 3 2 1

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Contents Volume 1 List of Entries vii Reader’s Guide xi About the Editors xvii List of Contributors xix Introduction xxv Entries A B C D E

1 63 93 191 233

F G H I J

327 363 381 395 427

Volume 2 List of Entries vii Reader’s Guide ix L M N O P Q

431 451 463 495 511 591

R S T U V W

601 629 723 817 825 835

Appendix A: Resource Guide 839 Appendix B: Chronology 845 Appendix C: Glossary 849 Index 855

List of Entries Ability-to-Pay and Benefit Principles Access to Education Accountability, Standards-Based Accountability, Types of Accreditation Achievement Gap Adequacy Adequacy: Cost Function Approach Adequacy: Evidence-Based Approach Adequacy: Professional Judgment Approach Adequacy: Successful School District Approach Adequate Yearly Progress Administrative Spending Adult Education Age-Earnings Profile Agency Theory Allocative Efficiency American Association of School Administrators Association for Education Finance and Policy Auxiliary Services

Charter Schools College Choice College Completion College Costs. See Tuition and Fees, Higher Education College Dropout College Enrollment College Rankings College Savings Plan Mechanisms College Selectivity Common Core State Standards Community Colleges Finance Comparative Wage Index Compensating Differentials. See Hedonic Wage Models Compound Annual Growth Rate Comprehensive School Reform Compulsory Schooling Laws Continuing Education Contracting for Services Cost Accounting Cost of Education Cost-Benefit Analysis Cost-Effectiveness Analysis Credential Effect Cultural Capital Cumulative Annual Growth Rate. See Compound Annual Growth Rate

Baumol’s Cost Disease Behavioral Economics Benefits of Higher Education Benefits of Primary and Secondary Education Bilingual Education Block Grants Bonds in School Financing Brown v. Board of Education Budgeting Approaches

Data Envelopment Analysis Demand for Education Department of Defense Schools Deregulation Desegregation Difference-in-Differences Digital Divide Discount Rate Distance Learning District Power Equalizing District Size

Capacity Building of Organizations Capital Budget Capital Financing for Education Capitalist Economy Categorical Grants Central Office, Role and Costs of Centralization Versus Decentralization Charter Management Organizations vii

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List of Entries

Dropout Rates Dual Enrollment Dual Labor Markets Due Process Early Childhood Education Econometric Methods for Research in Education Economic Cost Economic Development and Education Economic Efficiency Economics of Education Economies of Scale Economies of Scope. See Economies of Scale Education and Civic Engagement Education and Crime Education Finance Education Management Organizations Education Production Functions and Productivity Education Spending Education Technology Educational Equity Educational Innovation Educational Vouchers Effect Size Elasticity Elementary and Secondary Education Act Enrollment Counts Enrollment Management in Higher Education Equalization Models Evolution in Authority Over U.S. Schools Expenditures and Revenues, Current Trends of Extended Day External Social Benefits and Costs Factor Prices Faculty Contracts. See Faculty in American Higher Education Faculty in American Higher Education Faculty Tenure. See Faculty in American Higher Education Family Educational Rights and Privacy Act Federal Perkins Loan Program Federal Work-Study Program Financial Literacy and Cognitive Skills Fiscal Disparity Fiscal Environment Fiscal Neutrality Fixed-Effects Models Foregone Earnings For-Profit Higher Education Fund Accounting

Gainful Employment General Educational Development (GED®) General Obligation Bonds GI Bill Globalization Governmental Accounting Standards Board Guaranteed Tax Base Hedonic Wage Models Higher Education Finance Homeschooling Horizontal Equity Human Capital Income Inequality and Educational Inequality Individuals with Disabilities Education Act Infrastructure Financing and Student Achievement Instrumental Variables Intergovernmental Fiscal Relationships Internal Rate of Return International Assessments International Datasets in Education International Organizations Investing in Innovation Fund (i3) Job Training Labor Market Rate of Return to Education in Developing Countries Licensure and Certification Local Control Lotteries for School Funding Lotteries in School Admissions Market Signaling Markets, Theory of Measurement Error Median Voter Model Merit Pay. See Pay for Performance Moral Hazard Nation at Risk, A National Assessment of Educational Progress National Board Certification for Teachers National Center for Education Statistics National Datasets in Education National Science Foundation Neighborhood Effects: Values of Housing and Schools New Institutional Economics

List of Entries

No Child Left Behind Act Nonwage Benefits

Reliability Risk Factors, Students

Omitted Variable Bias Online Learning Opportunity Costs Opportunity to Learn Ordinary Least Squares Organisation for Economic Co-operation and Development Outsourcing. See Contracting for Services

Salary Schedule San Antonio Independent School District v. Rodriguez SAT School Boards School Boards, School Districts, and Collective Bargaining School District Budgets School District Cash Flow School District Wealth School Finance Equity Statistics School Finance Litigation School Quality and Earnings School Report Cards School Size School-Based Management Schools, Private Schools, Religious Segmented Labor Market. See Dual Labor Markets Selection Bias Serrano v. Priest Service Consolidation Sheepskin Effect. See Credential Effect Social Capital Socioeconomic Status and Education Special Education Finance Spillover Effects Stafford Loans State Education Agencies State Education Codes Student Financial Aid Student Incentives Student Loans Student Mobility Supplemental Educational Services

Parcel Tax Parental Involvement Partial and General Equilibrium Pay for Performance Peer Effects Pell Grants Percentage Power Equalizing. See Guaranteed Tax Base Performance Evaluation Systems Permanent Income Philanthropic Foundations in Education Policy Analysis in Education Portfolio Districts Preschool. See Early Childhood Education Present Value of Earnings Price Discrimination Principal-Agent Problem Private Contributions to Schools Private Fundraising in Postsecondary Education Private School Associations Privatization and Marketization Professional Development Program Budgeting Progressive Tax and Regressive Tax Propensity Score Matching Property Taxes Public Choice Economics Public Good Public-Private Partnerships in Education Pupil Weights Quantile Regression Quasi-Experimental Methods Race Earnings Differentials Race to the Top Randomized Control Trials Reduction in Force Regression-Discontinuity Design

Tax Burden Tax Elasticity Tax Incidence Tax Limits Tax Yield Teacher Autonomy Teacher Certification. See Licensure and Certification Teacher Compensation Teacher Effectiveness Teacher Evaluation

ix

x

List of Entries

Teacher Experience Teacher Intelligence Teacher Pensions Teacher Performance Assessment Teacher Supply Teacher Training and Preparation Teacher Value-Added Measures Teachers’ Unions and Collective Bargaining Technical Efficiency Theory of the Firm Tiebout Sorting Title I Tracking in Education Tragedy of the Commons Transaction Cost Economics Tuition and Fees, Higher Education

Tuition and Fees, K-12 Private Schools Tuition Tax Credits Two or Three Tier Funding Programs. See Equalization Models Unfunded Mandates University Endowments U.S. Department of Education Validity Value-Added Model. See Teacher Value-Added Measures Vertical Equity Vocational Education Weighted Student Funding

Reader’s Guide Special Education Finance State Education Agencies State Education Codes Teacher Autonomy Teacher Effectiveness Teacher Evaluation Teacher Performance Assessment Teachers’ Unions and Collective Bargaining Tracking in Education U.S. Department of Education Weighted Student Funding

Accountability and Education Policy Access to Education Accountability, Standards-Based Accountability, Types of Accreditation Achievement Gap Adequate Yearly Progress American Association of School Administrators Association for Education Finance and Policy Brown v. Board of Education Capacity Building of Organizations Common Core State Standards Comprehensive School Reform Compulsory Schooling Laws Desegregation Educational Equity Elementary and Secondary Education Act Gainful Employment Individuals with Disabilities Education Act International Assessments International Organizations Investing in Innovation Fund (i3) Local Control Median Voter Model Nation at Risk, A National Assessment of Educational Progress National Datasets in Education National Science Foundation No Child Left Behind Act Organisation for Economic Co-operation and Development Performance Evaluation Systems Philanthropic Foundations in Education Policy Analysis in Education Portfolio Districts Race to the Top SAT School Boards School Report Cards

Budgeting and Accounting in Education Finance Adequacy: Successful School District Approach Administrative Spending American Association of School Administrators Auxiliary Services Block Grants Bonds in School Financing Budgeting Approaches Capital Budget Capital Financing for Education Categorical Grants Central Office, Role and Costs of Cost Accounting Cost of Education Cost-Benefit Analysis Cost-Effectiveness Analysis Enrollment Counts Fund Accounting Governmental Accounting Standards Board Higher Education Finance Intergovernmental Fiscal Relationships Philanthropic Foundations in Education Program Budgeting School District Budgets xi

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Reader’s Guide

School District Cash Flow Service Consolidation Weighted Student Funding

Education Markets, Choice, and Incentives Agency Theory Capitalist Economy Centralization Versus Decentralization Charter Management Organizations Charter Schools Compound Annual Growth Rate Comprehensive School Reform Deregulation Dual Labor Markets Economic Efficiency Education Management Organizations Education Production Functions and Productivity Educational Equity Educational Innovation Educational Vouchers Evolution in Authority Over U.S. Schools Factor Prices Globalization Homeschooling Local Control Lotteries in School Admissions Market Signaling Markets, Theory of Median Voter Model Moral Hazard Neighborhood Effects: Values of Housing and Schools New Institutional Economics Opportunity to Learn Parental Involvement Partial and General Equilibrium Pay for Performance Philanthropic Foundations in Education Portfolio Districts Principal-Agent Problem Private Contributions to Schools Private Fundraising in Postsecondary Education Private School Associations Privatization and Marketization Public Choice Economics Public Good Public-Private Partnerships in Education Risk Factors, Students Salary Schedule School-Based Management Schools, Private

Schools, Religious Spillover Effects State Education Codes Student Incentives Student Mobility Theory of the Firm

Equity and Adequacy in School Finance Ability-to-Pay and Benefit Principles Access to Education Achievement Gap Adequacy Adequacy: Cost Function Approach Adequacy: Evidence-Based Approach Adequacy: Professional Judgment Approach Adequacy: Successful School District Approach Allocative Efficiency Association for Education Finance and Policy Bilingual Education Brown v. Board of Education Comparative Wage Index Desegregation District Power Equalizing District Size Due Process Education Finance Educational Equity Equalization Models Expenditures and Revenues, Current Trends of Guaranteed Tax Base Horizontal Equity Infrastructure Financing and Student Achievement Lotteries for School Funding Progressive Tax and Regressive Tax Property Taxes San Antonio Independent School District v. Rodriguez School District Wealth School Finance Equity Statistics School Finance Litigation Serrano v. Priest Special Education Finance Title I Unfunded Mandates Vertical Equity Weighted Student Funding

Financing of Higher Education Baumol’s Cost Disease Benefits of Higher Education

Reader’s Guide

College Choice College Completion College Dropout College Enrollment College Rankings College Savings Plan Mechanisms College Selectivity Community Colleges Finance Dual Enrollment Enrollment Management in Higher Education Faculty in American Higher Education Federal Perkins Loan Program Federal Work-Study Program For-Profit Higher Education Gainful Employment GI Bill Higher Education Finance Pell Grants Private Fundraising in Postsecondary Education Stafford Loans Student Financial Aid Student Loans Tuition and Fees, Higher Education Tuition Tax Credits University Endowments

Key Concepts in the Economics of Education Age-Earnings Profile Agency Theory Baumol’s Cost Disease Behavioral Economics Capitalist Economy Centralization Versus Decentralization Cultural Capital Demand for Education Deregulation Discount Rate Economic Development and Education Economic Efficiency Economics of Education Economies of Scale Education Production Functions and Productivity Education Spending Educational Equity Educational Vouchers Elasticity External Social Benefits and Costs Factor Prices Foregone Earnings Human Capital Internal Rate of Return

Market Signaling Markets, Theory of Moral Hazard New Institutional Economics Opportunity Costs Partial and General Equilibrium Permanent Income Policy Analysis in Education Price Discrimination Principal-Agent Problem Progressive Tax and Regressive Tax Public Choice Economics Public Good Public-Private Partnerships in Education Social Capital Socioeconomic Status and Education Spillover Effects Tax Burden Tax Elasticity Tax Incidence Tax Limits Tax Yield Technical Efficiency Theory of the Firm Tiebout Sorting Tracking in Education Tragedy of the Commons Transaction Cost Economics Vertical Equity

Private and Social Returns to Human Capital Investments Access to Education Achievement Gap Adult Education Age-Earnings Profile Benefits of Higher Education Benefits of Primary and Secondary Education College Completion College Dropout College Enrollment College Rankings College Savings Plan Mechanisms College Selectivity Comparative Wage Index Continuing Education Credential Effect Demand for Education Discount Rate Dropout Rates Early Childhood Education

xiii

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Reader’s Guide

Economic Development and Education Education and Civic Engagement Education and Crime Education Spending Federal Work-Study Program Financial Literacy and Cognitive Skills Foregone Earnings Gainful Employment General Educational Development (GED®) GI Bill Human Capital Income Inequality and Educational Inequality Internal Rate of Return Job Training Labor Market Rate of Return to Education in Developing Countries Market Signaling Nonwage Benefits Present Value of Earnings Race Earnings Differentials Risk Factors, Students School Quality and Earnings Service Consolidation Spillover Effects Student Mobility Vocational Education

Production and Costs of Schooling Ability-to-Pay and Benefit Principles Adequacy Adequacy: Cost Function Approach Adequacy: Evidence-Based Approach Adequacy: Professional Judgment Approach Adequacy: Successful School District Approach Administrative Spending Adult Education Allocative Efficiency Baumol’s Cost Disease Capacity Building of Organizations Capitalist Economy Central Office, Role and Costs of Compound Annual Growth Rate Contracting for Services Cost Accounting Cost of Education Cost-Benefit Analysis Cost-Effectiveness Analysis Data Envelopment Analysis Department of Defense Schools Digital Divide Distance Learning

District Size Dual Enrollment Economic Cost Economies of Scale Education Production Functions and Productivity Education Technology Educational Innovation Elasticity Enrollment Counts Evolution in Authority Over U.S. Schools Extended Day External Social Benefits and Costs Hedonic Wage Models Homeschooling Infrastructure Financing and Student Achievement Intergovernmental Fiscal Relationships Online Learning Peer Effects Price Discrimination Professional Development School Boards School District Budgets School Size Social Capital Socioeconomic Status and Education Supplemental Educational Services Teacher Compensation Teacher Experience Technical Efficiency

Revenue and Aid for Schools Bilingual Education Block Grants Bonds in School Financing Capital Financing for Education Categorical Grants Early Childhood Education Education Finance Enrollment Counts Equalization Models Fiscal Environment Fiscal Neutrality General Obligation Bonds Guaranteed Tax Base Individuals with Disabilities Education Act Infrastructure Financing and Student Achievement Lotteries for School Funding Parcel Tax Private Contributions to Schools

Reader’s Guide

Progressive Tax and Regressive Tax Property Taxes Pupil Weights School District Cash Flow School District Wealth Special Education Finance State Education Agencies Tax Burden Tax Elasticity Tax Incidence Tax Limits Tax Yield Title I Tuition and Fees, K-12 Private Schools Tuition Tax Credits Unfunded Mandates

Statistical Methods in the Economics of Education Data Envelopment Analysis Difference-in-Differences Econometric Methods for Research in Education Economic Cost Effect Size Family Educational Rights and Privacy Act Fiscal Disparity Fixed-Effects Models Instrumental Variables International Datasets in Education Measurement Error Median Voter Model National Center for Education Statistics National Datasets in Education Omitted Variable Bias Ordinary Least Squares Organisation for Economic Co-operation and Development Peer Effects Present Value of Earnings

xv

Propensity Score Matching Pupil Weights Quantile Regression Quasi-Experimental Methods Randomized Control Trials Regression-Discontinuity Design Reliability Selection Bias Tiebout Sorting Validity

Teachers and Teacher Labor Markets Comparative Wage Index Dual Labor Markets Faculty in American Higher Education Hedonic Wage Models Licensure and Certification National Board Certification for Teachers Nonwage Benefits Pay for Performance Performance Evaluation Systems Private Fundraising in Postsecondary Education Professional Development Reduction in Force Salary Schedule School Boards, School Districts, and Collective Bargaining Teacher Autonomy Teacher Compensation Teacher Effectiveness Teacher Evaluation Teacher Experience Teacher Intelligence Teacher Pensions Teacher Performance Assessment Teacher Supply Teacher Training and Preparation Teacher Value-Added Measures Teachers’ Unions and Collective Bargaining

About the Editors Policy for 2014–2015; past coeditor of Educational Evaluation and Policy Analysis (EEPA), 2010−2012; and a fellow of the American Educational Research Association (AERA), elected in 2011. Brewer holds a bachelor’s degree in philosophy, politics, and economics from the University of Oxford, a master’s degree in economics from the University of Wisconsin–Milwaukee, and a PhD in labor economics from Cornell University.

Dominic J. Brewer is Gale and Ira Drukier Dean of New York University’s Steinhardt School of Culture, Education, and Human Development. A labor economist specializing in the economics of education and education policy, Brewer has overseen major projects focusing on educational productivity and teacher issues in both K-12 and higher education. Before joining NYU in 2014, Brewer was a professor in the Rossier School of Education at the University of Southern California for nine years and was named Clifford H. and Betty C. Allen professor in 2007. Prior to coming to USC, he was the vice president at the RAND Corporation where he directed RAND’s education policy research program for more than 5 years. He spearheaded RAND’s effort to assist in major K-12 reform in Qatar, the centerpiece of which is a system of charter-like government-funded schools. He is coauthor of a book detailing this effort, Education for a New Era: Design and Implementation of K-12 Education Reform in Qatar (2007). Brewer also co-led a state-sponsored evaluation of California’s charter schools and is one of the authors of Rhetoric Versus Reality: What We Know and What We Need to Know About Vouchers and Charter Schools (2001). His most recent publications include a coedited book, Economics of Education (2010), and one on urban education, Urban Education: A Model for Leadership and Policy (2011). His earlier work includes empirical analyses of the effects of teachers on student achievement, class size (including a review of the research literature published in Scientific American), and a book on competition in higher education, In Pursuit of Prestige: Strategy and Competition in U.S. Higher Education (2001). Brewer served as codirector of Policy Analysis for California Education (PACE), a policy research collaboration of USC, University of California, Berkeley, and Stanford University. He is president of the Association for Education Finance and

Lawrence O. Picus is professor of education finance and policy at the Rossier School of Education at the University of Southern California. His current research interests focus on adequacy and equity in school finance as well as efficiency and productivity in the provision of educational programs for K-12 school children. Picus is past president of the Association for Education Finance and Policy (AEFP) and is the president of the board of EdSource, a California-based education research organization. Picus is the coauthor of School Finance: A Policy Perspective (5th edition) with Allan R. Odden. He has authored, coauthored, or edited several other books including Where Does the Money Go? Resource Allocation in Elementary and Secondary Schools (1995), In Search of More Productive Schools: A Guide to Resource Allocation in Education (2001), Developing Community-Empowered Schools (2001) coauthored with Mary Ann Burke, and Principles of School Business Administration (1995) with R. Craig Wood, David Thompson, and Don I. Tharpe. He has also published numerous articles in professional journals. Picus studies how educational resources are allocated and used in schools across the United States. He has conducted studies of the impact of incentives on school district performance. Picus maintains close contact with the superintendents and chief business officers of school districts throughout California and the nation and is a member of a xvii

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About the Editors

number of professional organizations dedicated to improving school district management. He is a former member of the Editorial Advisory Committee of the Association of School Business Officials International, and he has served as a consultant to the National Education Association, American Federation of Teachers, the National Center for Education Statistics, and WestEd. He served as the principal consultant for the design of school funding systems in Wyoming and Arkansas and has

conducted equity, adequacy, and resource allocation studies in Arizona, Arkansas, Washington, Vermont, Oregon, South Carolina, Louisiana, Kansas, Kentucky, Montana, New Jersey, Nebraska, Texas, North Dakota, Ohio, Wisconsin, and Maine. Picus holds a bachelor’s degree in economics from Reed College and master’s degrees from the University of Chicago and the Pardee RAND Graduate School. He received his PhD in public policy analysis from the Pardee RAND Graduate School.

List of Contributors Michael F. Addonizio Wayne State University

Marianne Bitler University of California, Irvine

Tommaso Agasisti Politecnico di Milano

Daniel H. Bowen Rice University

June Ahn University of Maryland, College Park

Alex J. Bowers Columbia University

Nicola A. Alexander University of Minnesota

Kevin P. Brady North Carolina State University

Jonathon Attridge Vanderbilt University

Brian O. Brent University of Rochester

John G. Augenblick Augenblick, Palaich and Associates, Inc.

Klaus Breuer Goethe University Frankfurt am Main

Katherine Baird University of Washington, Tacoma

Dominic J. Brewer New York University

Stephen Ballard University of Oklahoma

Katherine Bryant University of Maryland, Baltimore

Nathan Barrett University of North Carolina, Chapel Hill

Richard Buddin ACT, Inc.

Daphna Bassok University of Virginia

Sa A. Bui Cornell University

William J. Baumol New York University, Stern; and Princeton University

Patricia E. Burch University of Southern California

Clive Belfield City University of New York

Tyrone Bynoe University of the Cumberlands

Mark Berends University of Notre Dame

Christine Campbell University of Washington, Seattle

Samantha Bernstein University of Southern California

Fatima Capinpin University of Southern California

Sharla Berry University of Southern California

Martin Carnoy Stanford University

Eric Bettinger Stanford University

Matthew J. Carr Walton Family Foundation xix

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List of Contributors

Celeste K. Carruthers University of Tennessee

Thomas Downes Tufts University

Jay G. Chambers American Institutes of Research

Alex Duke University of Southern California

Eric W. Chan Teachers College, Columbia University

Matthew Duque University of Southern California

Duncan Chaplin Mathematica Policy Research

Max Eden American Enterprise Institute

Albert Cheng University of Arkansas

Anna J. Egalite University of Arkansas

Amita Chudgar Michigan State University

Laura Egan University of Maryland

Sean P. Corcoran New York University

Ronald G. Ehrenberg Cornell University

José Manuel Cordero Ferrera Universidad de Extremadura

Eric R. Eide Brigham Young University

Sarah Cordes New York University, Wagner

Todd L. Ely University of Colorado, Denver

Ilja Cornelisz Columbia University

Jason Evans University of Missouri

Luke M. Cornelius University of North Florida

Mark L. Fermanich Augenblick, Palaich and Associates, Inc.

Michelle Croft ACT, Inc.

Carlena K. Ficano Hartwick College

Bradley Curs University of Missouri

Frederick Freking University of Southern California

Rajeev Darolia University of Missouri

Emma García Economic Policy Institute

Karen J. DeAngelis University of Rochester

Hovanes Gasparian University of Southern California

Matthew R. Della Sala Clemson University

Annie Georges JBS International, Inc.

Thomas A. DeLuca University of Kansas

Lawrence S. Getzler Commonwealth of Virginia

Elizabeth Dhuey University of Toronto

Timothy W. Giles University of North Florida

Lisa M. Dickson University of Maryland, Baltimore

Brian Gill Mathematica Policy Research

Thurston Domina University of California, Irvine

Philip Gleason Mathematica Policy Research

Alicia C. Dowd University of Southern California

William Glenn Virginia Tech

List of Contributors

Margaret E. Goertz University of Pennsylvania

Mariesa Ann Herrmann Mathematica Policy Research

Robert K. Goertz Retired, State of New Jersey

Frederick M. Hess American Enterprise Institute

Mike Goetz Research on Social and Educational Change, LLC

Nicholas W. Hillman University of Wisconsin, Madison

Linda Goetze Utah State University

Paula Lee Hobson Truckee Meadows Community College

Michael Gottfried University of California, Santa Barbara

Marc J. Holley Walton Family Foundation

Alan G. Green University of Southern California

Kathleen Mulvaney Hoyer University of Maryland, College Park

Judith A. Green Southern Illinois University at Carbondale

Alice Huguet University of Southern California

Shawna Grosskopf Oregon State University

Jennifer Imazeki San Diego State University

Cassandra Guarino Indiana University

Scott A. Imberman Michigan State University

Charisse Gulosino University of Memphis

W. Kyle Ingle Bowling Green State University

Michelle Hall University of Southern California

Su Jin Jez California State University, Sacramento

Benjamin Hansen University of Oregon

Oscar Jimenez-Castellanos Arizona State University

Eric A. Hanushek Stanford University

Demetra Kalogrides Stanford University

Tenice Hardaway University of Southern California

Alvin Kamienski North Park University, Chicago

Douglas N. Harris Tulane University

Rita Karam RAND Corporation

Cassandra M. D. Hart University of California, Davis

Venessa A. Keesler Michigan Department of Education

William Hartman Penn State College of Education

Robert Kelchen Seton Hall University

Angela Hasan University of Southern California

Jeongmi Kim South Dakota State University

Matthew D. Hendricks The University of Tulsa

Robert C. Knoeppel Clemson University

Guilbert C. Hentschke University of Southern California

Cory Koedel University of Missouri

Carolyn D. Herrington Florida State University College of Education

Rajindar K. Koshal Ohio University

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List of Contributors

Cathy Krop University of Southern California

José Felipe Martinez University of California, Los Angeles

Andrew L. LaFave University of Southern California

Paco Martorell RAND Corporation

Robin Lake University of Washington, Seattle

William J. Mathis University of Colorado, Boulder

Josephine M. LaPlante University of Southern Maine

Jordan Matsudaira Cornell University

Eric Larsen American Institutes for Research

Brian P. McCall University of Michigan

Matthew F. Larsen Tulane University

Andrew McEachin North Carolina State University

Quynh Tien Le University of Southern California

Allison McKie Mathematica Policy Research

Lars Lefgren Brigham Young University

Walter W. McMahon University of Illinois

Henry M. Levin Columbia University

Michael Q. McShane American Enterprise Institute

Jesse D. Levin American Institutes for Research

Raegen Miller Teach for America

Jane Arnold Lincove University of Texas at Austin

David Mitch University of Maryland, Baltimore

Peter Lovegrove JBS International, Inc., Aguirre Division

Inés P. Murillo University of Extremadura

Thomas F. Luschei Claremont Graduate University

Phuong Nguyen-Hoang University of Iowa

Maricar Mabutas Cornell University

Yongmei Ni University of Utah

Jeffrey Maiden University of Oklahoma

Ira Nichols-Barrer Mathematica Policy Research

Nat Malkus American Institutes for Research

Jeffrey B. Nugent University of Southern California, Dornsife

Michelle Turner Mangan Concordia University Chicago

Irina S. Okhremtchouk Arizona State University

Dave E. Marcotte University of Maryland, Baltimore

Harold F. O’Neil University of Southern California

Julie A. Marsh University of Southern California

La’Tara Osborne-Lampkin Florida State University

Brandon Martinez University of Southern California

Maria G. Ott University of Southern California

David Martinez Arizona State University

Gary Painter University of Southern California

List of Contributors

Toby J Park Florida State University College of Education

Anthony Rolle University of South Florida

Shirley C. Parry University of Southern California

Christine Ross Mathematica Policy Research

Richard Patterson Cornell University

Justin Roush University of Tennessee

Matea Pender The College Board

Marguerite Roza Georgetown University

Andrew Penner University of California, Irvine

Jenna R. Sablan University of Southern California

Emily Penner University of California, Irvine

Lucrecia Santibañez RAND Corporation

Rachel Perera Teach For America

Stephen J. Schmidt Union College

Paige C. Perez Texas A&M University

Gabriel R. Serna University of Northern Colorado

Nicholas Perry University of Southern California

M. Najeeb Shafiq University of Pittsburgh

Michael C. Petko National Education Association

Kenneth Shores Stanford University

Lawrence O. Picus University of Southern California

David L. Sjoquist Georgia State University

David N. Plank Policy Analysis for California Education

John Brooks Slaughter University of Southern California

Margaret Plecki University of Washington, Seattle

Joanna Smith University of Oregon

Michael Podgursky University of Missouri

Claire Smrekar Vanderbilt University

Morgan S. Polikoff University of Southern California

William E. Sparkman University of Nevada

Michael Puma Chesapeake Research Associates, LLC

Richard Startz University of California, Santa Barbara

Robert Ream University of California, Riverside

Luke J. Stedrak Seton Hall University

Michael A. Rebell Teachers College, Columbia University

Stefanie Stern RAND Corporation

Jennifer King Rice University of Maryland, College Park

LaShonda M. Stewart Southern Illinois University Carbondale

C. Edward Richards Columbia University

Leanna Stiefel New York University

Gary Ritter University of Arkansas

Andrei Streke Mathematica Policy Research

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Katharine O. Strunk University of Southern California

Margaret Weston Public Policy Institute of California

Min Sun Virginia Tech

Emilyn Ruble Whitesell New York University

Iwan Syahril Michigan State University

Priscilla Wohlstetter Teachers College, Columbia University

Lori L. Taylor Texas A&M University

Patrick J. Wolf University of Arkansas

David C. Thompson Kansas State University

Stephani L. Wrabel University of Southern California

Eugenia F. Toma University of Kentucky

Di Xu Columbia University

Philip Trostel University of Maine

Ryan Yeung State University of New York, Brockport

Brendesha Tynes University of Southern California

Bryan Young University of Oklahoma

Fatih Unlu Abt Associates

Peter Youngs Michigan State University

Rachel A. Valentino Stanford University

Gema Zamarro University of Southern California

Brian R. Walkup University of Tulsa

Dara B. Zeehandelaar Thomas B. Fordham Institute

Spencer C. Weiler University of Northern Colorado

Yuan Zhang University of Pittsburgh

Jilleah G. Welch University of Tennessee

Ron Zimmer Vanderbilt University

Richard O. Welsh University of Southern California

Olga Zlatkin-Troitschanskaia Johannes Gutenberg University Mainz

Introduction and understand the effects of education in general and how specific policies and programs affect individuals, governments, and private businesses. With increasing demands on education, global competition, and tight public resources, the importance of understanding these topics has never been greater. Although economists have referenced education for centuries, and school professionals have studied resource use, the proliferation of research on education economics and education finance dates to the 1960s. First, economists who traditionally focused on how firms, consumers, and governments allocate scarce resources to competing ends began to become more interested in education. Human capital theorists such as the Nobel prize–winning economists Theodore Schultz and Gary Becker argued that schooling decisions should be viewed as investments—much like how firms buy machinery and equipment to produce future revenue, individuals acquire schooling with the intent that it will lead to better paying jobs, higher wages, and other benefits in the future. This notion generated, and continues to generate, a large amount of research that has helped our understanding of the relationship between education and the economic and other benefits associated with it, as well as the contribution of education to national development. Second, education became widely viewed as a critical tool of social and economic policy. Following the seminal Equality of Educational Opportunity report (commonly known as the Coleman Report) in 1966, social scientists became interested in the relationship between school resources and funding, and educational outcomes. If education was to be the primary policy vehicle for achieving social and economic equality, understanding its organization and finance became more critical. In the United States, the federal government began investing resources in schools that served large numbers of low-income children under Title I of the Elementary

There is little doubt that education is critically important both for individual and national development in the 21st century. For individuals, education is seen as essential for economic and social success. Schooling helps students acquire academic and nonacademic skills that help them become productive employees, thoughtful and engaged citizens, and healthy human beings. For nations, an educated populace is seen as an essential driver of economic growth and social cohesion. Over the past 50 years, as globalization and new technologies have shifted the world economy and society, the attention given to education has increased. Since the beginning of humankind, education has been integral to development. In the past two centuries in particular, formal education systems have been developed and have expanded dramatically to touch virtually every citizen in developed countries. Today, the education enterprise is vast and complex. Learning takes place in both formal and informal settings. It occurs from birth to death, in different forms from early learning centers and kindergarten, through compulsory primary and secondary schooling, to myriad tertiary educational offerings, including training and certificate programs, college and university degree programs, and workplace-based training. Some of these educational settings are privately financed and operated, others are publicly financed and operated, and still others are a combination. In the United States, the public primary and secondary system alone served nearly 50 million students, in 100,000 schools, at a cost of more than $600 billion in 2012–2013. As formal schooling has expanded, more and more public and private resources have been devoted to education. This has led to a steadily growing interest on the part of policymakers and practitioners to understand how best to organize schooling, raise and allocate resources for schools, and measure individual and organizational learning xxv

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Introduction

and Secondary Education Act of 1965, based on the theory that the additional resources were necessary to give those children equal access to the success enjoyed by the children of better-off families. Beginning in the late 1960s and continuing through the present, lawsuits in most states have challenged the constitutionality of existing school funding practices on the basis of individual state constitutional education clauses on the issues of equity (equal access to educational resources) and adequacy (how much schools need to help students reach the performance standards established by state policymakers). This spurred further scholarship in the education finance arena with the development of measures of funding equity. In 1975, the American Education Finance Association (now the Association for Education Finance and Policy) was formed, indicating this new interest among both academics and practitioners in the equitable distribution of school resources. Third, following economic challenges in the United States and the publication in 1983 of A Nation at Risk—a federal report that warned that American students were falling behind those of other countries—the volume of research on the economics of education has expanded tremendously. New and richer sources of education data (at international, federal, and state levels) and sophisticated empirical research designs have allowed academics to study a wide array of topics, including the social and nonmarket returns to education; the effects of a multitude of school, classroom, neighborhood, and family influences on student success; teacher labor markets; and market- and incentive-based policies for school improvement. In tandem, as policy attention shifted to holding schools accountable for performance, education finance scholars turned their attention to what level of resources might be adequate to achieve this performance.

Rationale for the Encyclopedia Today, education economics and education finance is a vibrant scholarly field. Insights from economics help decision makers at the state level understand how to raise and distribute funds for public schools in an equitable manner for both schools and taxpayers. Economics can assist researchers in analyzing effects of school spending and teacher compensation on student outcomes. And economics can provide important insights into public debates on issues ranging from vouchers for subsidizing student attendance at private schools to how to measure the impact of teachers on student performance. What is

the appropriate role of government (federal, state, local) in funding education? Should gambling—or corporate advertising in the school cafeteria—be a revenue source for schools? Should students be charged fees for music or sports? What are the benefits and drawbacks of pay for performance for teachers, and what sorts of metrics are used to determine who qualifies? When crafting state aid formulas, what constitutes a fair formula? More generally, how does education affect economic and social outcomes, and how can economics inform public policy directed at education? These questions and more are explored within the pages of this encyclopedia. The economics of education and education finance is a wide-ranging field covering many disparate topics. Previously no single extant source had covered the field in a comprehensive manner. Much of the research literature is technical in nature, building on economic theories or empirical analyses of data using complex statistical techniques, limiting its accessibility to a general audience. This encyclopedia is designed as an introduction to the field for practitioners who work in schools or policy settings, for undergraduates and beginning graduate students seeking an introduction to the field, and for the general lay reader interested in better understanding some of the debates about education funding and policy. We have tried to emphasize nontechnical, intuitive explanations of key concepts and empirical findings. In entries that are necessarily more complex, we have encouraged authors to provide clear nontechnical explanations before going deeper for interested readers. We should note that no single topic is given an in-depth treatment, but suggested readings provide an opportunity to explore a topic further. Finally, the primary focus of this volume pertains to the economics of education and education finance in the United States. Education is a multifaceted and complex topic, and much of the finance and delivery of educational services depends on how it is organized. It is therefore to some extent geographically specific, depending on the set of arrangements made within a city, state, or country. Although many of the entries have broad applicability to other settings, most of the illustrative examples and some of the entries will be of most interest to the U.S. readers.

Content and Organization To help readers locate the topics they are most interested in reviewing, we have organized a Reader’s Guide into 11 thematic areas. Undoubtedly, our

Introduction

classification is somewhat arbitrary, but we hope it will facilitate the development of a strong understanding of the important economic concepts that undergird economic and financial analyses of education in the 21st century. In school finance, broad topic areas include Budgeting and Accounting, which will likely be of particular interest to school finance practitioners, and sections covering the critical topics of Equity and Adequacy, Revenue and Aid for Schools, and the Financing of Higher Education. In the economics of education, we include a section on Key Concepts covering core theoretical ideas and on Statistical Methods for commonly used empirical concepts. We group remaining entries around the chief “buckets” of the field: Production and Costs of Schooling; Private and Social Returns to Human Capital Investments; Education Markets, Choice, and Incentives; Teachers and Teacher Labor Markets; and Accountability and Education Policy. The latter grouping is perhaps the most diffuse, and we have selected those topics that occur most often in economics- and finance-related policy debates.

Development of the Encyclopedia The encyclopedia was developed under the direction of the general editors and an editorial board

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consisting of leading academics in the field of education economics and education finance. The editors first compiled a list of topics based on their own prior work in the field and by reviewing other volumes and recent major field journals. The list of possible topics was then reviewed and edited by the editorial board to create a master list. The editors then identified potential contributors based on their expertise, with input from the editorial board.

Acknowledgments Producing a comprehensive volume like this requires the contributions of a huge number of individuals— the authors, the editorial board, the production assistants, and SAGE personnel. We are indebted to Rochelle Hardison and Sonya Black-Williams, who oversaw the logistics of the project. Rochelle, in particular, deserves singular credit for managing the project overall, moving things forward, working closely with the editors, the editorial board, and contributors, and keeping track of entries; without her efforts, this encyclopedia would not have been completed. We also thank our team of graduate research assistants, Matthew Duque, Richard Welsh, Michelle Hall, Tenice Hardaway, and Quynh Tien Le, who provided reviews of every entry prior to editor review, giving us an extra layer of input.

A ABILITY-TO-PAY PRINCIPLES

AND

skating rink—a flat fee may be charged for admission. In other cases, charges are intended to fund the full cost of service provision. For example, rates for water systems are set to cover operating and capital expenses including depreciation. Customers’ bills reflect an established usage rate applied against actual water consumption. In all cases, the fee is proportional to the benefits received and is paid only by the users. With its emphasis on the equivalence of the benefits received and the financing burden imposed, the benefit principle has great appeal for citizens and policymakers who favor a businesslike approach to government finance. However, many programs require funding beyond the amounts that may be acquired through voluntary payments. In addition, some public policies transfer income between more and less economically advantaged citizens. Ensuring adequate resources for priority purposes necessitates revenue instruments that compel payment, which has led to taxation. Taxes are legally enforceable financial obligations imposed on taxpayers whom governments judge to have the requisite ability to pay. Taxes are levied on the taxable value of items like income and property and on purchases of goods and services. The ability-to-pay principle guides tax policy making. The remainder of this entry explains how the ability-to-pay and benefit principles may be used to inform financing choices. The entry concludes with a discussion of how challenging fiscal environments are reshaping state and local revenue systems and

BENEFIT

Deciding who should pay for government is a crucial public policy concern. The ability-to-pay principle and the benefit principle provide important guidance. The ability-to-pay principle directs us to distribute financing responsibilities in accordance with ability to pay. In other words, higher income households are expected to pay more of the costs of government than households with less ability to pay. The ability-to-pay principle advises further that the burden of government finance should not be permitted to become excessive. The benefit principle says that financing shares should be allocated in proportion to the value of benefits received from government activities. Under the benefit principle, financing shares are allocated according to service usage. Implicit in the benefits standard is the expectation that people who do not participate in a particular government activity will not be asked to pay. Although the two principles are complementary, they offer distinct views about government financing that have contributed to the development of governmental revenue systems. Governments use two broad classes of revenue, user charges and taxes, to finance diverse public purposes. A user charge is a fee paid by a consumer for a specific service. The benefit principle is especially relevant to the design and implementation of fee-based financing. In some cases—for example, a public ice

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Ability-to-Pay and Benefit Principles

making vulnerable education and social programs that cannot pay their own way with user charges.

Deciding Funding Methods Applying the principles to practical financing questions is aided by knowledge of how market failures, deficiencies, and inefficiencies encourage government intervention in the private marketplace. The private sector and some government programs offer goods and services that are “private” in nature. “Private” refers to products for which beneficiaries can be distinguished, benefit values are quantified, and prices negotiated that are acceptable to sellers and buyers. In addition, purchasing an item establishes individual ownership. These essential elements of private goods make possible voluntary exchanges. A public service activity that has all these traits is well suited to benefit-based financing. For example, when a prison store sells to patrons merchandise handcrafted by inmates, the transactions are similar to those occurring in a privately owned gift shop. At the opposite end of the spectrum, a class of benefits known as purely public goods meets none of the requirements for private, voluntary exchange. National defense is a purely public good that illustrates how market failings hinder private sales. First, once a decision is made to provide national defense, it is impossible to prevent nonpayers from benefiting. Secondly, it is difficult to assign benefit shares to individuals, which stymies quantification and pricing. In these cases, compulsory payment through taxation is used to ensure revenues adequate to support expenditures. Another important aspect of purely public goods is nonrival collective consumption: A large number of people may participate in a program such as national defense without diminishing individual benefits. A beautiful sunset viewed and enjoyed by hundreds of people epitomizes the notion of nonrival public use. Because public goods may be consumed collectively with no loss of benefit, public access is more efficient than private ownership. Governments may choose to purchase access for citizens to parks, beaches, and forests. Equity also is enhanced by government intervention because people who might never have been able to afford access to these amenities are able to take pleasure in these natural resources. Public goods are financed primarily with taxes but may be subsidized partially, for example, with entrance fees for parks.

Between purely private and purely public goods falls a class of services called mixed goods. Mixed goods share traits of both private and public goods. Security services are a good example of a mixed good that is offered for sale in the private marketplace but is also produced by the public sector. However, a market deficiency called nonexclusion, which is an inability to secure payment from all users, discourages widespread private provision. For example, once a security guard has been hired by a few homeowners, all homes in close physical proximity benefit even though the owners have not paid for the service. Were the paying homeowners to seek financing from these neighbors, they would be likely to hear negative responses. There simply is no incentive for nonpayers to contribute once a service has been put in place. The spillover of benefits fosters the “free rider problem” in public finance, wherein people who do not pay nonetheless receive service. This problem is common at the local government level, where commuters use services in cities where they work but pay taxes only in the communities where they reside. Government might not step in to offer police services and other mixed goods were it not for a second set of market issues. If police services were available only in locations where people were willing to pay for them, spatial and interpersonal mismatches would arise. Only privileged neighborhoods would purchase security, leaving low-income neighborhoods without patrols and crime investigation. Wealthy individuals would be protected, while other citizens would be forced to fend for themselves. If crime victims were required to pay police officers to investigate, only prosperous victims would receive help. The disparity between service requirements and who can pay encourages government to provide programs where and when they are most needed. Mixed goods may be financed with taxes or a combination of taxes and user charges. Merit goods are another class of governmentfinanced activities that can be and often are delivered privately. Merit goods offer societal benefits that equal or exceed the price of service provision. Vaccination programs, well-child clinics, and public education are examples of merit goods. Public education is offered widely in the private market, sometimes at a profit. However, if only families who could afford to purchase private education did so, society as a whole would be worse off. In economics terminology, not ensuring widespread access to meritorious programs imposes a large opportunity

Ability-to-Pay and Benefit Principles

cost, which means extensive social benefits would be forgone should government fail to act. To ensure broad access, merit goods traditionally have been financed with tax dollars. However, as tax resources have become increasingly constrained, supplementation with user charges has become more common. Higher education presents a particularly relevant merit-good-financing question. States desire broad access to postsecondary education for citizens and accordingly provide tax-financed appropriations to public higher education institutions. Colleges supplement state appropriations with tuition revenues. When deciding tuition rates, colleges may subsidize all students by keeping tuition low. Conversely, schools may charge higher tuition while providing more scholarship assistance to needy students. The benefit and ability-to-pay principles may be used to decide a balance between subsidized tuition and financial aid. A final set of government programs strives to address inequities caused by market forces. By providing financial rewards to those who achieve career success and bestowing little on those who do not, the free market exacerbates differences in wealth and personal traits over which individuals have little or no control. Social programs that provide financial resources for the poor are financed primarily with taxes. Compensatory programs that address other inequities, such as vocational rehabilitation services, usually are funded with tax dollars but may be augmented with fees paid by users. For example, an individual who gains employment after having a vehicle specially outfitted by a vocational rehabilitation program might reimburse over time some of the expenses incurred in making the modifications.

Using the Principles to Evaluate Revenue Approaches Taxation is evaluated along two dimensions of the ability-to-pay principle: burden and equity. Burden considers the weight of the economic claim of taxation. Equity considers the distribution of financing shares across households. Horizontal equity analysis compares taxes paid by similarly situated households, or the treatment of “likes.” Horizontal equity is achieved when individuals with similar incomes pay similar taxes. Vertical equity analysis examines tax burdens of households in different income groups, from the lowest to the highest levels. Vertical

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equity is realized when higher income taxpayers pay more tax than those with lower incomes. Tax policy making often involves debates about the extent to which taxes paid by the rich should exceed taxes paid by other income groups. A flat or proportional tax rate claims the same percentage of income from all households. This approach meets the ability standard because higher income households pay more tax in dollar terms. However, many people believe that an equal percentage of income does not impose the same degree of sacrifice on all income groups. An “equal sacrifice” objective is the basis for progressive taxation. Progressive taxes, for example, the U.S. federal individual income tax, use a graduated rate structure that applies increasing rates to increments of income called brackets. The first bracket of taxable income might be taxed at 2%, the second bracket at 3%, and so on until a specified top bracket rate is reached. Under a progressive rate structure, the average or “effective” tax rate increases as income increases. The equal sacrifice doctrine is rooted in the economic concept of declining marginal utility of income. According to this theory, the usefulness of an additional dollar of income is somewhat less than that of the previous dollar earned. Although most people would agree that the utility of income begins to diminish after basic needs are met, precisely when utility begins to decrease and how quickly it declines are subject to debate. Hence, whether to use progressive taxation and how best to structure tax rates are matters debated and decided by elected representatives. The benefit principle largely guides the design and implementation of beneficiary-based financing strategies, but the ability-to-pay principle is nonetheless relevant. Fees for services may be unduly burdensome for some payers and may impede achieving vertical equity goals. Excessive burden or inequities in the distribution of funding shares may be addressed through policy actions that eliminate or mitigate burdens. Sliding fee schedules, offering free or reduced prices for admission to qualifying people, and tax credit programs are examples of policy offsets that can make beneficiary-based financing more equitable and less burdensome. The ability-to-pay principle primarily informs tax policy making, but when a tax finances services that provide benefits to identifiable parties, the benefit principle becomes relevant. For example, property taxes often finance public improvements and services that benefit property owners and affect home value.

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Access to Education

Conclusion Challenging fiscal environments are forcing budgetary decisions that focus on closing expenditurerevenue gaps rather than using a reasoned approach to distributing financing shares. To bring unwieldy budgets into balance, both the federal government and the states have reduced aid to local governments and to elementary, secondary, and higher education. At the local level, citizen dissatisfaction with property taxes continues to constrain access to the very revenue source on which public schools and other local functions depend. Many programs financed with taxes are being downsized or have been eliminated. Services that require new tax dollars face great difficulty securing budgetary approval. On the other hand, programs funded with user charges have been increasing as a component of state and local finance. Although this trend sometimes is viewed negatively, it is important to recognize that user charges provide an important complement to tax-based financing. Revenues from user charges may free up scarce tax dollars, which can be directed to other programs. For a relevant service like a municipal golf course, charging customers directly rather than funding programs with property taxes reduces the pressure on property tax while also increasing equity because those who benefit support the program. While there are no easy solutions to resource scarcity, the benefit and ability-to-pay principles provide essential benchmarks against which financing options may be assessed. Josephine M. LaPlante See also Fiscal Environment; Horizontal Equity; Progressive Tax and Regressive Tax; Tax Burden; Tax Limits; Vertical Equity

Further Readings Burman, L. E., & Slemrod, J. (2013). Taxes in America: What everyone needs to know. New York, NY: Oxford University Press. McCarthy, K. C., Neels, K., Rydell, P. R., Stucker, J. P., & Pascal, A. (1984). Exploring benefit-based finance for local government services: Must user charges harm the disadvantaged? A RAND note. Santa Monica, CA: RAND Corporation. Mikesell, J. (2010). Fiscal administration: Analysis and applications for the public sector (8th ed.). Independence, KY: Cengage Learning. Simonsen, B., & Robbins, M. D. (1999). The benefit equity principle and willingness to pay for city services. Public Budgeting and Finance, 19(2), 90–110.

Tannenwald, R. (1990). Taking charge: Should New England increase its reliance on user charges? New England Economic Review, January/February, 56–74.

ACCESS

TO

EDUCATION

Access to education refers to an individual’s capacity to attend school and, in the aggregate, the rate at which children attend and complete school. Increased education, particularly for females, is associated with reductions in fertility and child mortality and improvements in child health, as well as other drivers of economic growth. Access refers to the most basic ability to attend school and is therefore crucial to obtaining the many individual benefits and social benefits of education. This entry will define and globally measure access to schooling as well as access to education, discuss how it is measured, and describe major movements to increase access and reduce inequality in access.

Definitions The term access to education is often used to describe basic physical access to a school. For example, access can be defined as having a school close enough to a child’s home to make the child’s attendance feasible. Typically, both economists and policymakers define access more broadly in terms of both physical and economic capacity to attend a school that is appropriate for the child’s needs. With this broader definition, access to education encompasses both the provision of school facilities and availability of accessible seats and the feasibility of attending school given the economic and social contexts of the family. Children may have physical access to a school seat but may remain out of school due to factors such as poverty, the need for child labor, parents’ distrust of the school, or social norms that discourage schooling. Additionally, for children with disabilities or special needs, schools may not provide adequate accommodations to enable attendance. Similarly, children from minority ethnic or religious groups may be excluded from schools they can physically access by formal or informal admission rules and/or language or cultural differences.

The Education for All Movement Access to education is incorporated into the Millennium Development Goals (MDGs) adopted by the United Nations in 2000. Goal No. 2,

Access to Education

“Achieve universal primary education,” sets the ambitious target that by 2015 all children will complete primary education. Goal No. 3, “Promote gender equality and empower women,” also addresses access to schooling, with a target to eliminate gender disparities in primary and secondary education by 2005, and at all levels by 2015. At the World Education Conference in Dakar, Senegal, in 2000, 164 countries signed on to the MDGs, committing to provide education for all (EFA). The conference also created a unified EFA campaign headed by the United Nations Educational, Scientific and Cultural Organization, with partnership from the World Bank, United Nations Development Programme, United Nations Children’s Fund, United Nations Population Fund, and multiple countries’ ministries of education. The closely related Global Partnership for Education Fund was created in 2002 to mobilize international financial support for countries implementing EFA initiatives, and it has provided more than $3.5 billion for EFA projects.

Measuring Access to Education There are many ways to measure access to schooling. Statistics on enrollment reveal the intent to go to school, statistics on attendance reveal the ability to go to school, and completion statistics reveal the acquisition of educational benchmarks. The MDGs rely on statistics of completion and equity in enrollment, while others report enrollment statistics. There is strong evidence that many countries are approaching MDG goals for education and that global access to education is improving. The World Bank, in its World Development Indicators, reports that global completion of primary education increased from 81% to 91% between 1991 and 2011. (In most countries, the primary education cycle is 5 or 6 years.) Despite these impressive gains, many children are still not completing primary school. Most of the noncompleters are concentrated in low-income countries in less developed regions. Based on the World Development Indicators, Latin America, Europe, and East Asia achieved full primary completion by 2011. In the same year, the primary completion rate was 69% in sub-Saharan Africa, 88% in South Asia, and 91% in the Middle East. Primary completion in low-income countries was 67% in 2011. Another common strategy to measure access to education is through enrollment. The World Development Indicators estimate that approximately 27 million

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boys and 31 million girls of primary school age do not have access to school, as measured by enrollment statistics. Of these children, nearly 97% live in low- and middle-income countries. Gross enrollment rates report the number of all children in school divided by the school-age population. Thus, gross enrollment can exceed 100% when overage children attend school. Net enrollment rates report the number of school-age children divided by the school-age population. Net enrollment is easier to interpret because it cannot exceed 100%, but enrollment by overage children is not counted. It can be interpreted as the percentage of the age-appropriate population that has access to school. Overall, net enrollment statistics highlight that access to education, and particularly secondary and tertiary education, is not universal. Globally, net enrollment in 2011 was 89% for primary school, 63% for secondary school, and 30% for tertiary school. Rates are lowest in low-income countries, where net enrollment in 2011 was 81% for primary school, 36% for secondary school, and 9% for tertiary school.

Improving Access to Education The EFA movement and the many social and economic benefits of education have generated many policy strategies to expand access to education. One strategy is simply to build more schools in locations where access is low. This strategy addresses physical access to school only. Additional policies address economic and social obstacles to schooling that commonly inhibit access even when sufficient school seats exist. Poverty may be the most common obstacle to education access, as poor parents cannot afford to pay tuition and other costs of attendance. Free primary education seeks to expand access by eliminating tuition and fees. Free primary education policies replace private tuition with government subsidies, often providing additional resources for food, uniforms, textbooks, and supplies. School subsidies may be offered universally or targeted to populations with the greatest need for increased access. Another policy strategy attempts to make education more accessible to families by linking schooling with other resources. This can include providing free food for the child and his or her family or providing health services. Beyond the direct costs of schooling, poverty can also inhibit access to education if children

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Accountability, Standards-Based

participate in income-generating activities or domestic labor outside school. The International Labour Organisation provides guidelines for legal restrictions on child labor designed to protect children and to facilitate participation in education. Prohibitions must be accompanied by replacement income for families to make education an economically accessible alternative to child labor. Conditional cash transfer programs, such as Opportunidades in Mexico and Bolsa Familias in Brazil, provide extra income to families of children who attend school, by linking cash assistance with requirements for children to attend and complete school. A final strategy addresses matching the characteristics of the school program to the needs of the out-of-school community. In addition to economic obstacles, children will remain out of school if parents have concerns about faculty, curriculum, safety, or other school characteristics. Recommendations to improve access include siting schools in underserved communities, adjusting school calendars and hours to accommodate farming or migration, providing safe transportation to and from school, providing adequate sanitation for boys and girls, providing culturally and linguistically appropriate curricula, and hiring well-trained teachers from the local community. Community-designed schools, which involve parents in the design and implementation of a new school, improve access by ensuring that the school is aligned with parents’ values. These strategies are particularly useful for improving access among isolated communities or ethnic or religious minorities, who may have preferences for school that differ from national norms. Jane Arnold Lincove See also Benefits of Primary and Secondary Education; Demand for Education; Economic Development and Education; Educational Equity; Tuition and Fees, K-12 Private Schools

Further Readings Cohn, E., & Geske, T. (Eds.). (1990). Education and economic growth. In The economics of education (3rd ed.). Oxford, UK: Pergamon Press. Herz, B., & Sperling, G. (2004). What works in girls’ education? Evidence and policies from the developing world. New York, NY: Council on Foreign Relations. United Nations. (2013). The global partnership for development: The challenge we face (Report by the MDG Gap Task Force). Retrieved from http://www.

un.org/millenniumgoals/2013_Gap_Report/MDG%20 GAP%20Task%20Force%20Report%202013_English. pdf United Nations Children’s Fund. (2013). The state of the world’s children 2013: Children with disabilities. Retrieved from http://www.unicef.org/sowc2013/files/ SWCR2013_ENG_Lo_res_24_Apr_2013.pdf United Nations Educational, Scientific, and Cultural Organization. (2012). Youth and skills: Putting education to work (EFA Global Monitoring Report 2012). Retrieved from http://www.unesco.org/new/en/ education/themes/leading-the-international-agenda/ efareport/reports/2012-skills/ World Bank. (2013). World development indicators 2013. Washington, DC: Author. Retrieved from http:// databank.worldbank.org/data/download/WDI-2013ebook.pdf

ACCOUNTABILITY, STANDARDS-BASED The seminal 1983 A Nation at Risk report, which warned of a “rising tide of mediocrity” in U.S. K-12 education, was among the primary stimulators of the standards-based accountability movement in the United States. Several waves of standards-based accountability followed this report throughout the 1990s. However, standards-based reform and accountability, a subset of systemic school reform, was not formalized at the federal level until the implementation of the No Child Left Behind Act (NCLB) in 2002. Standards-based accountability policies moved the federal policy focus away from educational inputs to the outcomes produced. These policies have proven long-lasting, enduring in many states for more than two decades with no end in sight. This entry provides an overview of standards-based accountability and reviews the progression and effects of such policies following their implementation.

The Concept of Standards-Based Accountability In 1990, Marshall Smith and Jennifer O’Day presented one of the first descriptions of standardsbased accountability. They identified several reasons why previous reforms had been unsuccessful. First, they described the fragmented, complex, and multilayered educational governance system that involved multiple levels of government influencing the provision of education. Second, because of the fragmented governance structure, they argued, schools

Accountability, Standards-Based

operate within a web of duplicative and conflicting policies and expectations that send unclear messages to schools and teachers about where to target their efforts. As schools identify different priorities, reform efforts are not implemented as intended and thus do not elicit the expected or desired outcomes. Finally, they described how education policy is created within a political system where long-term, transformative school improvement outcomes are often sacrificed for short-term results that yield greater political gain. Furthermore, short-term efforts largely focus on small clusters of schools or districts and, as such, may not be adaptable to schools across the country or schools in different contexts. Standards-based accountability was introduced as a mechanism to address the incoherence and fragmentation found within school governance structures by creating a tightly aligned system of education. There are six essential components of standards-based accountability systems: 1. Clear, well-defined, and measurable academic standards to describe what students are to know and what they will be able to do 2. Tight alignment of curriculum and instruction with the content specified in the standards 3. Assessment of student mastery of the content specified in the standards 4. Devolution of resource allocation, curriculum, and instructional practices to the school level 5. Accountability based on demonstrated performance 6. Technical assistance and support from the state and district leadership to low-performing schools

Together, these six features are intended to send consistent messages to teachers about the key content to teach, leading teachers to align their instruction with state standards. The ultimate outcome envisioned is that student achievement on aligned assessments will rise, leading to concomitant improvement in other student outcomes (e.g., attainment). Unlike the patchwork reform efforts previously instituted, standards-based accountability takes a systems approach to changing the way education is provided.

The History of Standards-Based Accountability Within the United States, national efforts to shape education using the principles of standards-based

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accountability are largely guided by the Elementary and Secondary Education Act (ESEA). Originally established in 1965, ESEA was intended to reduce achievement gaps by providing all students access to high-quality education. In its original form, the legislation outlined a funding structure (Title I funds) and associated expectations for schools serving large proportions of students from low socioeconomic backgrounds. Because of the fierce opposition to federal involvement in education at the time, there was little regulation for outcomes or standards within the legislation. Thus, while the funding provided additional resources for professional development of teachers and the development of instructional materials, there was no clear connection between how the money had to be spent and the goal of reducing the achievement gaps. The initial ESEA legislation and related federal policy focused on inputs and the development of basic skill competency for all students. The next several decades of ESEA reauthorizations were mainly focused on programmatic oversight. Eventually, A Nation at Risk and other research and advocacy in the 1980s began to change the policy focus to the outcomes of schooling. This shift was largely in response to the growing dissatisfaction with educational performance and related concerns about international competitiveness. In 1989, an education plan known as America 2000 was drafted by President George H. W. Bush and governors from across the country. This plan outlined a goal of establishing “world-class” standards and developing national assessments aligned to the standards. While the policy was never enacted, many of the ideas from this proposed plan were later incorporated into the Goals 2000: Educate America Act (Goals 2000), which was signed into law by President Clinton in 1994.

Goals 2000 Goals 2000 was a reauthorization of the ESEA legislation that, for the first time, set national expectations for the educational performance of students. Specifically, students in the 4th, 8th, and 12th grades were expected to demonstrate grade-level competency in subjects such as English, mathematics, science, foreign language, and government. Also, high schools were expected to raise graduation rates to at least 90%. Schools, however, were not yet held accountable through federal policy; as such, Goals 2000 largely failed to initiate the wide-scale

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Accountability, Standards-Based

improvement in educational outcomes hoped for in the policy’s design. Although not required by federal law, by 1998, most states had implemented or were in the process of implementing content standards in mathematics and reading. Additionally, individual states began to proactively develop school accountability frameworks in anticipation of future federal policy mandates. These state, and occasionally district, initiatives set various goals and expectations for schools to meet. In later years, once the ESEA was reauthorized to include school-level accountability, the presence of separate state accountability systems sometimes created policy conflicts.

No Child Left Behind The 2001 reauthorization of the ESEA, known as NCLB, altered the role of the federal government in education policy. Previously, education policy was largely a state issue, and the federal role in education was one of providing resources to support state reform efforts. Now, the U.S. Department of Education was changing its role and increasing control and oversight of U.S. K-12 education. Though many states were already testing students in accordance with state academic standards prior to NCLB, the U.S. Department of Education had not required schools to be held responsible for student performance with respect to these standards. Under NCLB, schools became accountable for their students’ achievement. States were required to adopt grade-level content standards, conduct annual assessments, and establish annual performance expectations by which schools would be measured. The long-term goal of the policy was to bring all students to grade-level proficiency by 2014. The theory of action underlying standards-based accountability comes from principal-agent theory in economics. Specifically, principal-agent theory suggests that the principal (the policy creator) develops incentives that motivate agents (those implementing the policy) to align practice with policy goals and increase efforts toward meeting those goals. For NCLB, this meant that the accountability measures would incentivize schools and teachers to align curriculum and instruction with challenging academic standards to raise student achievement. Additionally, the provision of student performance data would help schools identify where to focus improvement efforts. Implicit in the policy design is the belief that educators need both information and incentives to improve instruction and achievement.

As a systemic reform, NCLB also requires modest capacity-building and system-changing reform in the states. When schools fail to meet performance targets, states are required to use established interventions to incentivize or assist low-performing schools to improve student performance. These interventions are specific to schools receiving federal Title I funds for disadvantaged students and can include school improvement plans, supplemental tutoring services, opportunities for students to transfer to higher performing public schools in the district, or even changes in leadership or school management. Overall, NCLB went much further than Goals 2000 in aligning federal education policy under the principles of standards-based accountability. A decade after the implementation of NCLB, research has shown that K-12 schools continue to operate in a fragmented governance system. Whereas the policy was federally designed, the implementation was left to the individual states. As such, vast discrepancies in implementation and alignment between standards, curriculum, instruction, and assessment continue to exist. And with many states having designed accountability systems prior to NCLB that remained in effect, the policy environment continued to be multilayered, with competing priorities.

Next-Generation Accountability Building on NCLB, the Obama administration has pursued several policy initiatives to reform standards-based accountability policy. The latest policies focus further on creating well-aligned systems of standards and assessments and help states move away from conflicts caused by dual (state and federal) accountability systems. In other words, the next generation of accountability policies looks to reduce the fragmentation of education policy that NCLB could not resolve. Race to the Top

The first break from NCLB came under the American Recovery and Reinvestment Act of 2009 with a program known as Race to the Top (RTT). This program was guided by four principles: (1) developing benchmarked standards and assessments, (2) building longitudinal data systems to track performance, (3) increasing teacher effectiveness and equitably distributing teachers across schools and classrooms, and (4) turning around the lowest performing schools. Of the 46 states that applied to

Accountability, Standards-Based

participate in RTT, only 12 received grants to pursue reform efforts. RTT differed from NCLB in that RTT encouraged the adoption of a national set of content standards—the Common Core state standards—to avoid the inconsistency in expectations found under NCLB. Similarly, RTT incentivized states to adopt new teacher accountability systems based in part on student performance, bringing accountability to a new level. RTT did not release states from the requirements of NCLB. Thus, where the RTT policy may have improved on some of the flaws of NCLB’s version of standards-based accountability, it also failed to consolidate or reduce competing policy structures. ESEA Flexibility Waivers

In response to congressional inaction in reauthorizing the ESEA as scheduled, the U.S. Department of Education offered states the opportunity to waive key components of NCLB policy in 2011. In exchange for a waiver, states were required to propose comprehensive standards-based accountability systems. States were expected to establish rigorous and comprehensive plans to reduce achievement gaps, improve instruction, and advance educational outcomes for all students. In the waiver application process, states had to spell out new accountability systems, many of which showed improvement over NCLB’s version of standards-based accountability. As with RTT, the waiver process included educator accountability systems. Thus, the waiver system removed the onesize-fits-all standards-based accountability system of NCLB and gave states the flexibility to create better accountability systems that suited their educational goals. As of January 2014, 42 states, the District of Columbia, and Puerto Rico had received waivers, as had a group of eight school districts in California that includes the state’s largest district, Los Angeles Unified. Whether these waivers will reduce K-12 policy fragmentation remains to be seen.

Effects of Standards-Based Accountability Neither the federal government nor any U.S. state has fully implemented standards-based accountability as originally described in this entry. Still, there were modest improvements in educational achievement and attainment during the standardsbased accountability era. Overall, research suggests improvements in mathematics and (to a lesser extent) English achievement, as well as improvements in

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high school graduation and college enrollment rates. However, there is little evidence of policy effects on achievement gaps between different racial and socioeconomic groups, and there remain substantial problems with high school dropout rates, college readiness, and college completion. By almost any measure, it is clear that standards-based accountability has not led to the large-scale systemic improvements its authors envisioned.

Unintended Consequences and Design Issues A primary reason for current dissatisfaction with standards-based accountability as a reform effort stems from policy design choices and the resulting unintended negative consequences of the policies. One design choice in NCLB policy was the focus on proficiency rates as a measure of school performance. This intense focus on proficiency incentivized schools to narrowly target instructional efforts on students just below proficient (referred to as “bubble students”) rather than on students who are far above or below the proficiency goal. Furthermore, the focus on proficiency rates ignores schools’ contributions to student learning (which would be more accurately measured by growth models such as value-added models). Thus, the measures used for accountability under NCLB were poor indicators of a school’s actual performance in meeting the intended objectives of the policy. A second design choice was that NCLB’s accountability measures were based on student proficiency in mathematics and English but not in any other subjects. Predictably, schools and teachers responded to the accountability pressure by focusing instruction on these two subjects at the expense of other content areas. While test scores in these two subjects are undoubtedly important school performance indicators, there are many other academic and nonacademic goals that are unaccounted for with this narrow focus. A third design choice under NCLB was to set weak parameters for states seeking to demonstrate the alignment of state tests with their corresponding content standards. This alignment is a central feature of standards-based accountability policy, but several studies under NCLB showed that state assessments were, in fact, weakly aligned with their target standards. This lack of coherence undermined the policy messages of the content standards, sending teachers conflicting messages about what to teach. There is some evidence that this incoherence indeed resulted in weaker than expected instructional responses to

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Accountability, Standards-Based

standards-based policy (i.e., teachers did not align their instruction with the standards as expected). A fourth design choice was to allow states to construct their own tests and set their own benchmarks for “proficiency” on their state tests. This decision had several ramifications, each of which undermined the goals of standards-based accountability. First, it encouraged states to set relatively low benchmarks for proficiency so that more schools would pass. Second, it led to a wide degree of state-to-state variation in proficiency, so that the word itself became nearly meaningless. Third, it led to the creation of cheap, largely multiple-choice assessments that tended to measure predominantly lower level skills, reducing the rigor of the standards. A fifth design choice was to offer states weak guidance about certain key provisions of the accountability policy. Without this information, some states responded with poor decisions that undermined the strength of the accountability policy. For instance, while the federal government offered some guidance on the minimum number of students schools should have in a given subgroup (e.g., Hispanic) for that subgroup to be included in accountability, states were allowed to set substantially different minimums. These and many other decisions meant that whether a school failed or not was often based more on the state where the school was located than on how well it was educating its students. In short, NCLB was the federal government’s first attempt to compel states to implement standardsbased accountability in all schools across the country. Unfortunately, the implementation suffered from numerous design problems that substantially undermined the intent of standards-based accountability to improve education. The future of federal accountability policy, discussed below, may address some of the unintended consequences of NCLB and reinforce the positive outcomes associated with accountability.

standards-based accountability systems and will likely do so in the foreseeable future. This makes standards-based accountability relatively long lived by the standards of typical K-12 education policies. While initial implementations of standards-based accountability have not yet achieved the systemic changes intended in the design, there is evidence that educational outcomes for students have improved over the past two decades. Whether these improvements will persist or even increase in the waiver era is not clear. The extent to which standards-based accountability truly improves educational outcomes almost certainly depends on the detailed design choices made by states and the federal government in implementing the policy. Early examinations of the waiver systems and the forthcoming assessments suggest that there is some hope that next-generation accountability systems have incorporated a few of the lessons learned from earlier reform efforts. At the national level, the Common Core standards have created the needed coherence in the policy system by moving states toward uniform standards and by creating a national market for aligned curriculum materials and professional development, and there is hope that the new aligned assessments will be improvements over the existing tests. However, the progress at the state level appears to be uneven. Some states are taking steps forward (e.g., by testing in additional subjects and using growth models to measure school performance), but others are not (e.g., by continuing to rely on proficiency rates as measures of school performance and by using only English and mathematics for accountability purposes). Furthermore, there is weak evidence that standards-based accountability of any kind can make meaningful progress in closing long-standing achievement gaps. For U.S. schools to achieve the kind of educational improvement desired by policymakers, it is likely the case that standards-based accountability is only part of the policy formula. Morgan S. Polikoff and Stephani L. Wrabel

Conclusion and Unanswered Questions More than two decades into its implementation, there is no evidence that standards-based accountability is losing its place in the portfolio of state and national education policies designed to improve school performance. Quite the contrary, despite many of the well-known issues mentioned above, the federal government continues to push standardsbased accountability through the ESEA waivers and the RTT program. All states continue to implement

See also Adequate Yearly Progress; Common Core State Standards; Elementary and Secondary Education Act; No Child Left Behind Act; Principal-Agent Problem; Race to the Top

Further Readings DeBray-Pelot, E., & McGuinn, P. (2009). The new politics of education: Analyzing the federal education policy

Accountability, Types of landscape in the post-NCLB era. Educational Policy, 23(1), 15–42. Elmore, R. (2004). School reform from the inside out: Policy, practice, and performance. Cambridge, MA: Harvard Education Press. Figlio, D. N., & Ladd, H. F. (2010). The economics of school accountability. In D. J. Brewer & P. J. McEwan (Eds.), Economics of education (pp. 351–356). New York, NY: Elsevier. Forte, E. (2010). Examining the assumptions underlying the NCLB federal accountability policy on school improvement. Educational Psychologist, 45(2), 76–88. Fuhrman, S. H., & Elmore, R. F. (Eds.). (2004). Redesigning accountability systems for education. New York, NY: Teachers College Press. Hamilton, L. S., Stecher, B. M., & Yuan, K. (2008). Standards-based reform in the United States: History, research, and future directions. Santa Monica, CA: RAND Corporation. O’Day, J. A., & Smith, M. S. (1993). Systemic reform and educational opportunity. In S. H. Fuhrman (Ed.), Designing coherent education policy: Improving the system (pp. 250–312). San Francisco, CA: Jossey-Bass. Smith, M. S., & O’Day, J. (1990). Systemic school reform. In S. Fuhrman & B. Malen (Eds.), The politics of curriculum and testing: The 1990 yearbook of the Politics of Education Association (pp. 233–267). New York, NY: Falmer Press. Vinovskis, M. A. (2009). From a Nation at Risk to No Child Left Behind: National education goals and the creation of federal education policy. New York, NY: Teachers College Press.

ACCOUNTABILITY, TYPES

OF

Accountability in public education has been at the center of policy reform since the 1983 report A Nation at Risk. Since then, the academic performance of public school students in the United States has slipped relative to student performance in other nations. The No Child Left Behind Act of 2001 sought to hold all schools accountable for student outcomes. Yet, despite several reform initiatives intended to improve student performance outcomes, the United States continues to lag behind many other developed countries on international tests. In December 2013, U.S. Education Secretary Arne Duncan lamented the performance of U.S. students on the Programme for International Student Assessment test in 2012. The Programme for International Student Assessment is an international

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comparison of student academic performance in core subject areas. In his remarks, Duncan stated that the U.S. results were a “picture of educational stagnation.” The pressure for accountability on states, school districts, charter schools, principals, teachers, parents, and students has never been higher. Given the centrality of accountability to education, how can we better understand its role in school reform? Accountability is a complex concept with influential contributions from economics, psychology, political science, sociology, evaluation, and industrial engineering. At its core, accountability implies three nested ideas: 1. That each person is accountable to someone else and that whatever activity one is engaged in, the outcome matters to others 2. That failure to produce a satisfactory outcome has consequences 3. That socially accepted methods are available for determining whether or not one has met the relevant standard—a method of assessment that has validity for us and for those who hold us accountable

In practice, one or all of these aspects of accountability may be contested or may fail to be realized. A good definition of accountability would entail that we are accountable for outcomes that matter to other stakeholders. Such outcomes can be validly assessed, and failure to meet the standards to which we are held has consequences. This entry examines the ecology of accountability in education from the various social science disciplines that have sought to hold schools, teachers, and school leaders accountable.

Modeling Accountability To hold principals and teachers accountable, we need to have a well-thought-out theoretical model of the factors that produce high student achievement. Using analogies to microeconomics and the factory, we need to know what mix of capital (schools, technology, and instructional materials), labor (principals, deans, teachers, social workers, nurses), and processes (instructional methods, classes, work flow) produces optimal outcomes. In economics, the literature on cost-effectiveness seeks to compare cost against outcomes—typically student achievement outcomes. It focuses on efficient use of resources. In

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Accountability, Types of

the human capital literature, performance, expertise, and productivity are also important. This research focuses on teacher qualifications, intelligence, and education. Literally hundreds of studies have been conducted on variables such as busing, school size, class size, teacher qualifications, teacher experience, teacher verbal ability, whether master’s degrees improve teacher quality, salaries, hours of instruction, and so on. Whether any of these variables are associated with student outcomes depends on several contextual variables, including parental background, student health, school culture, community culture, whether students are reading on grade level, attendance, and other ecological factors. The complexity of these factors and their relative influence on student achievement are studied as a result of the influential Coleman Report, which was commissioned by the U.S. Department of Education and released in 1966. According to the report, officially titled Equality of Educational Opportunity, student background and socioeconomic status (SES) played a prominent role in student achievement as compared with school funding alone. The report highlighted the question of to what degree student achievement is influenced by schools as opposed to race and SES. It thus directly challenges the assumption that society can legitimately hold schools and teachers primarily accountable for student achievement. The best studies put the within-school variance at about 40%. About 40% of differences in student achievement are attributable to race and social class, about 40% to schools (teacher quality, leadership, and resources), and the remaining 20% to other variables and interaction effects. The findings of the report thus indicate that an effective strategy for improving student outcomes must move beyond the classroom and into the wider society. For example, universal preschool is common among the highest performing nations but still haphazardly available in the United States. An economist trained in human capital theory would argue that academic outcomes are both individually and jointly produced. They are the result of many contributions, both formal and informal, by individuals, families, institutions, and the wider culture. If, for example, some of the largest determinants of future achievement are a year of breast feeding, childhood health care, and high-quality day care, all of which occur prior to formal public schooling, then it is problematic to hold schools, principals, and teachers accountable for outcomes

over state and national policies they do not control. That is not to say that schools are unimportant. In fact, a school with strong leadership, dedicated and well-trained teachers, a positive school culture, a strong mission of academic excellence, and the necessary resources will likely deliver greater gains in student achievement than a school lacking in such resources. Schools matter, but our ability to produce highly effective schools is limited by the lack of highquality teachers, unequal school resources, poor leadership, and a dysfunctional school climate. The complexity of the relationship between educational contributions to learning (in and out of school), the complexity of school and classroom processes, and the complexity of educational outcomes invalidate most simple causal models of accountability. Can a principal be held accountable for teacher quality when most of the school’s teachers were hired long before the principal’s arrival at the school and when the barriers to removal are costly because teachers are protected by both union contracts and tenure provisions? Even when a principal has an opportunity to hire a new teacher, the teachers may be selected at the district level, the pool of candidates may vary in quality and preparation depending on salary, and the teacher’s willingness to take the job may depend on the race and ethnicity of students, the poverty level of the community, the working conditions at the school, and the quality of a candidate’s preparation to teach. Thus, even in this narrow instance of principal accountability for teacher quality, the complexity of the factors at work in shaping the overall quality of the teachers in a particular school mitigate against a direct causal relationship between leadership and student achievement. To summarize, the validity of schoollevel accountability is weakened by many internal and external influences—some are difficult to manage and many are beyond the control of individual teachers and school principals.

Do Consequences Improve Accountability? In psychology, the literature on intrinsic and extrinsic motivation and sanctions (rewards and punishments) also makes important contributions to our understanding. A fundamental assumption of accountability is that those who hold us accountable believe that the rewards (salaries, merit pay, positive evaluations) and punishments (termination, loss of merit pay, negative evaluations) are causally related

Accountability, Types of

to behavior in the expected direction—rewards improve our performance, and punishments call us to task for poor performance. In fact, the literature on merit pay and evaluations suggests that teachers may be either neutrally or negatively affected by external rewards and negative evaluations may decrease rather than increase motivation to excel in teaching. Thus, intrinsic and extrinsic motivation and individual psychological orientation introduce more complex feedback loops when the consequences interact with accountability. Misapplying motivational factors with ill-informed theoretical models of teacher behavioral sanctions can actually undermine teacher effectiveness. The MetLife Survey of the American Teacher (2013) found that teacher job satisfaction decreased by 5 percentage points from the previous year, reaching the lowest level of job satisfaction seen in the survey series in more than two decades. This decline in teacher satisfaction is coupled with large increases in the number of teachers who indicate that they are likely to leave teaching for another occupation and in the number who do not feel that their jobs are secure. These data show a correlation between job satisfaction and the increased pressure of accountability and consequences for teachers, but it would be more interesting to know how those teacher responses are correlated with teacher effectiveness. For example, if relatively ineffective teachers are dissatisfied and seeking to leave the profession, that would be a productive outcome of accountability; however, if the most effective teachers were the most dissatisfied, that would be an unproductive consequence. Social psychology and sociology also make thoughtful contributions by examining organizational culture, group norms, risk, and uncertainty. The chief executives of urban districts tend to have brief tenures. The American School Superintendent: 2010 Decennial Study From the Council of the Great City Schools states that the average tenure of urban superintendents increased from 2.3 years in 1999 to 3.6 years in 2010, an increase of 56%, but from the perspective of leadership stability and influence, it was hardly long enough to implement a reform, let alone assess its effectiveness. Given that fact, many teachers quickly realize that the district leadership may change before a leader has a chance to implement new policies. How hard should teachers be expected to work to implement new curricula in such an uncertain policy environment? Risk-averse

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teachers often elect to stay within the norms of current practice.

Stakeholders The political science literature uses median voter analysis to assess the level of political support as a form of accountability. Public sentiments around school reform issues are difficult to measure. In many school board elections, 5% or fewer of voters cast ballots, so that elections are often decided by narrow-interest groups, teacher associations, taxpayer associations, and religious groups who oppose some specific feature of the curriculum. Should school boards and superintendents conclude that nonvoting is a vote of confidence or indifference? Voter turnout is influenced more by fiscal accountability concerns than by dissatisfaction with student achievement. Furthermore, studies of the relationship between student achievement and board election outcomes have not demonstrated a significant correlation. Superintendents are then forced to navigate school improvement initiatives with board members who are elected by these narrow interests and who may not represent the best interests of children and their families. Given these difficulties, those seeking to understand accountability often shift their analysis to a more specialized and subjective approach from the evaluation literature called stakeholder analysis. Another factor to consider is school finance adequacy and equity. Accountability presumes fairness. If a school is being asked to compete for student outcomes against another school or even a state standard of student achievement, then it reasonably follows that the schools should have a level playing field for the competition, especially in school funding. One concern is whether the amount of funding available from local, state, and federal sources combined is adequate to deliver a quality education. The means of determining adequacy vary considerably and are widely debated by experts. Expert panels, estimates of average cost for a given level of student achievement (cost-effectiveness), and average cost comparisons with schools that are high achievers are among the most common approaches. Once adequacy is determined, equity moves to the foreground. Excellent schools with high student achievement are typically (but not always) spending above state-defined levels of adequacy. Equity is typically assessed in two directions. The horizontal equity claim argues that to achieve the same student

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Accreditation

outcomes, the same level of per-pupil expenditure must be provided. The vertical equity argument takes things one step further, maintaining that the highest standard of equity is to determine the real educational supports each child needs to reach the highest level of performance and to provide that level of per-pupil expenditure. In practice, per-pupil spending varies widely among the states, between districts within states, and even among schools and classrooms in the same district. Perhaps the most significant variable from a student’s perspective is the quality of the teacher in the classroom. Finally, the effects of the home environment on school performance must be considered. Parents with greater financial resources can provide their children with experiences that poorer families cannot afford, such as high-quality day care by age 2, an all-day preschool program by age 4, summer camps, access to tutors, and private music and sports lessons. Higher SES also makes it more likely that mothers will receive prenatal care and have adequate nutrition, while poverty contributes to stress and is correlated with unhealthy behaviors, such as smoking and physical inactivity, which negatively affect children. Among families of higher SES, educational technology and games are more likely to be available and used in the home. Activities such as reading to children also are more common among wealthier families, and children from higher SES homes also have a significantly larger vocabulary when they start school. These factors convey a cultural advantage with respect to school achievement for children with higher SES backgrounds. Now imagine two children born on the same day, one who has access to these environmental and family supports and one who does not. Both arrive in the same kindergarten class at age 5 or 6 with vastly differing opportunities for learning. Long-term studies of children who received high-quality earlychildhood supports at home and in preschool show that these children attain lifetime positive outcomes in student achievement, high school graduation, and college completion rates. These children have access to a host of benefits not available to children from financially and culturally disadvantaged homes. These advantages accumulate as schooling progresses, and the language, achievement, and experiential gap between privileged and disadvantaged students widens. School finance equity is not able to overcome such disparities; only a more egalitarian society with strong commitments to public social supports is likely to do so. Thus, accountability in

education requires attention not only to what happens in the school but also to student experience outside the school. As this brief review of accountability in public education shows, the issues are indeed complex and touch every aspect of societal, economic, cultural, political, and psychological development. The failure of children to attain their academic and, therefore, economic and social potential is a failure with profound implications for their future and the future of our nation. In truth, are we all not accountable for these outcomes? C. Edward Richards See also Accountability, Standards-Based; Adequacy; Educational Equity; Pay for Performance; Teacher Effectiveness

Further Readings Baker, B. D., Green, P., & Richards, C. E. (2008). Financing education systems. Upper Saddle River, NJ: Prentice Hall. Carnoy, M., & Rothstein, R. (2013, January). What do international tests really show about U.S. student performance? Washington, DC: Economic Policy Institute. Retrieved from http://www.epi.org/publication/ us-student-performance-testing/ Hanushek, E. A. (2014, January 8). Why the U.S. results on PISA matter. Education Week. Retrieved from http:// www.edweek.org/ew/articles/2014/01/08/15hanushek. h33.html MetLife Survey of the American Teacher. (2013). Education Week Teacher. Retrieved from http://www.edweek.org/ tm/collections/metlife/index.htm Tyack, D. B. (1974). The one best system: A history of American urban education. Cambridge, MA: Harvard University Press. U.S. Department of Education. (2013). The threat of educational stagnation and complacency: Remarks of U.S. Secretary of Education Arne Duncan at the release of the 2012 Program for International Student Assessment (PISA). Retrieved from http://www.ed.gov/ news/speeches/threat-educational-stagnation-andcomplacency

ACCREDITATION The term accreditation refers to a process of continuous improvement wherein educational institutions are externally evaluated for both processes and measures of student performance. The accreditation

Accreditation

process involves a cyclical review of these institutions to ensure that applicable standards are being met. In the United States, the process is more than 100 years old and can be traced to concerns about protecting public health and safety and serving the public interest. Accreditation relates to education finance and the economics of education because it involves the use of resources. Furthermore, since education is linked to economic growth, the process of accreditation is one way to provide public accountability and quality assurance. This entry describes the accreditation process for K-12 schools and institutions of higher education and reviews some of the accrediting bodies at each level. The entry concludes with an overview of the advantages and disadvantages of accreditation as well as a list of resources on the accreditation process.

Accreditation of K-12 Schools Typically, the process of accreditation in K-12 schools is done by an external body; the process is governed by the respective states through their accreditation standards. If schools meet the specified standards, accreditation status is granted by the state. Six regional accrediting bodies exist in the United States. They include the Middle States Association of Colleges and Schools, the New England Association of Schools and Colleges, the North Central Association of Colleges and Schools, the Northwest Accreditation Commission, the Southern Association of Colleges and Schools, and the Western Association of Schools and Colleges. Typically, the process of accreditation involves a self-study by the educational institutions to compare their current practices with research-based standards. In theory, this process of self-reflection leads to organizational change and continuous improvement. After conducting a self-study, schools are visited by an external review team that is empowered to validate the findings of the self-study, offer suggestions for improvement, and make a recommendation to the accrediting body for accreditation status. Similarities in the standards are found across the six regional accrediting bodies. Each accrediting body has between five and seven standards that are focused on mission and direction; governance and leadership; curriculum, assessment, and instruction; resources and support systems; and the use of data for continuous improvement. Typically, the mission is focused on high expectations for student learning;

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it is measurable and includes elements about appropriate social behavioral norms. Governance and leadership standards tend to focus on the creation of a culture of success that is aligned with the mission; governance standards encourage educational leadership that supports student achievement and includes processes of supervision and evaluation to improve professional practice. Curriculum, teaching, and assessment standards focus on access for all to a challenging curriculum that is delivered, monitored, and adjusted as a result of multiple assessments; it is expected that teachers will meet regularly in collaborative professional communities to reflect on teaching effectiveness and student growth. Furthermore, schools are expected to create grading policies that reflect student growth and to regularly report student progress to families and the community. School resource and support systems standards are focused on the need to hire and retain highly qualified teachers and other support personnel to meet the educational, physical, and emotional needs of all children. Resource standards also require sufficient facilities, technology, equipment, and access to media centers for all children. Standards defining the use of data to support continuous improvement set forth the means by which schools should collect and analyze student data for the purposes of evaluating student learning, programs and instruction, and the operation of the school. The accreditation process in K-12 schools is governed by state policy, but it is typically conducted in cooperation with an outside agency, for example, a regional accrediting body. Whereas regional accreditation standards are only focused on educational practices aligned with research-based strategies, state accreditation standards include both educational practices and measures of student performance. State standards are more specific with regard to educational practices; student performance measures are expressed in terms of the criterionreferenced exams included in state accountability policies. For example, state accreditation standards establish processes and set recommended limits on specific resources, such as class size or the use of instructional time. In the area of governance and leadership, state accreditation standards describe the procedures required for hiring district superintendents, school administrators, teachers, library media specialists, school counselors, and other school personnel. Furthermore, these standards establish class-size limits in each grade and specify the number of students required to warrant the

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Accreditation

hiring of additional administrators, counselors, and media specialists. In addition, state standards with regard to personnel require that professional development be offered to educators and that it be monitored and evaluated; the standards also suggest that such professional development be aligned to school goals. Some states include a standard on educational opportunity. These standards require that all students in the state be given the opportunity to attend educational institutions regardless of race, gender, culture, religious affiliation, disability, or political affiliation. These same standards establish policies for programs in education for the gifted and talented as well as programs for special education. Nearly all state accreditation standards include a description of the academic program to be offered. More detail is typically found at the middle and high school levels, where accreditation standards describe what classes should be offered, the required units or credits, and graduation requirements. However, all state standards include the requirement for elementary education with content aligned to state curriculum standards. Educational program standards describe in broad terms the content to be delivered in each subject area and establish broad student learner outcomes. Many states also include, by subject area, a required amount of time on the task, expressed as hours or days in their educational program standards. In addition, educational program goals include the requirement for the creation and storage of student academic as well as discipline records. In addition to standards that describe practices and procedures, most state accreditation standards specify a level of performance on the state assessment program. Student pass rates are established on the basis of an aggregate school score or are expressed as a percentage of students passing the exam. The accreditation standards determine the criteria for who must take the exam and describe procedures for accommodating transfer students and children receiving special education services. Based on student performance and adherence to standards describing policies and practices, states typically use levels to accredit schools. Schools are required to pay to the accrediting agency yearly dues, which vary according to the level of the school—elementary, middle, or high school, or college. In addition, there is an application fee associated with the accrediting process as well as fees associated with the site visit. These fees include a payment to each member of the

review team as well as the travel costs incurred by the team.

Accreditation in Higher Education In higher education, the accreditation process involves nongovernmental entities as well as governmental agencies. These accrediting organizations are private national and regional agencies that develop evaluation criteria and conduct periodic reviews of institutions to assess whether the criteria are met; accreditation is granted by the agencies if the standards are met. The U.S. Department of Education does not accredit higher education institutions or programs. Rather, the secretary of education is required by law to publish a list of nationally recognized accrediting agencies that the secretary has determined to be reliable authorities in assessing the quality of education provided by the institution as well as the programs that have received accreditation. State agencies establish policies that require accreditation of institutions of higher education in their respective states; these state agencies must obtain recognition from the U.S. secretary of education. Recognition is achieved by meeting the secretary’s criteria and is granted after a review by the National Advisory Committee. As with K-12 schools, the accreditation of institutions of higher education involves a self-study to discern if applicable standards are being met. This process can be complicated and multifaceted at institutions of higher education, especially if state regulations require that the entire institution be accredited by a regional agency and that individual programs meet the requirements of specialized accrediting agencies. In education, the accreditation process involves the collection of data that are aggregated at the unit level (school or college of education), although most of the data collected apply to individual programs within the unit. Each school/college of education, termed educator preparation provider by National Council for Accreditation of Teacher Education, is tasked with the creation of multiple assessments in all programs that lead to certification as a teacher, principal, or other school personnel. Individual programs must create six to eight assessments that are aligned to specific standards; these assessments are used to measure candidate knowledge and skill, candidate pedagogical knowledge, and candidate disposition. These assessments are used to satisfy standards for content and pedagogical knowledge established by the Council for the Accreditation of

Achievement Gap

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Educator Preparation. Currently, the Council for the Accreditation of Educator Preparation has five standards in draft form: (1) Content and Pedagogical Knowledge; (2) Clinical Partnerships and Practice; (3) Candidate Quality, Recruitment, and Selectivity; (4) Program Impact; and (5) Provider Quality, Continuous Improvement, and Capacity. A sliding scale is used to determine fees associated with the accreditation process and is based on the total number of program completers. Institutions of higher education are also required to incur the costs of visitors who travel to campuses to conduct the on-site review, which is the final stage of the accreditation process.

Eaton, J. S. (2013). Accreditation and the next reauthorization of the Higher Education Act. Inside Accreditation With the President of CHEA, 9(3). Retrieved from http://www.chea. org/ia/IA_2013.05.31.html Ewell, P. T. (2001). Accreditation and student learning outcomes: A proposed point of departure. Washington, DC: Council for Higher Education Accreditation. Vergari, S., & Hess, F. M. (2002). The accreditation game. Education Next, 2(3), 48–57.

Advantages and Disadvantages

Achievement gaps in education refer to disparities in student outcomes, such as academic test score gaps between different groups of students. In addition to standardized test scores, researchers have examined group differences in course grades; course enrollments; dropout rates; college application, attendance, and persistence; and adult occupations, income, wealth, and health. A substantial body of research examines test score gaps between different racial-ethnic, socioeconomic, and gender groups—the focus of this entry—although research has also addressed differences among students with disabilities and English Language Learners. What follows is a description of trends in test score differences for students in nationally representative data, a description of the key factors that researchers have examined to explain the achievement gaps, and the possibilities for future research.

According to each of the six regional accrediting bodies, the accreditation process is a way to ensure the integrity of the educational program at each respective institution, to certify to the public that the schools are trustworthy institutions of learning, to foster a culture of continuous improvement to support student learning, to assure the public that the school has an appropriate mission and that the mission is being accomplished through appropriate educational practices, and, finally, to assist in prioritizing areas of needed change and improvement as a result of data collection and self-study. Critics of the accreditation process contend that the process is costly, that accreditation fails to differentiate between levels of quality in institutions, that the process is burdensome and intrusive, and that accreditation fails to support innovation despite the claim of continuous improvement. Robert C. Knoeppel and Matthew R. Della Sala See also Accountability, Types of; Economic Development and Education; Opportunity to Learn; Policy Analysis in Education; School Quality and Earnings

Further Readings Financial aid for postsecondary students: Accreditation in the United States. (n.d.). Retrieved from https://www2. ed.gov/admins/finaid/accred/index.html?exp=0 Eaton, J. S. (2003). Is accreditation accountable? The continuing conversation between accreditation and the federal government. Washington, DC: Council for Higher Education Accreditation. Eaton, J. S. (2009). An overview of U.S. accreditation. Washington, DC: Council for Higher Education Accreditation.

ACHIEVEMENT GAP

Racial-Ethnic Achievement Gaps Data from the 2012 National Assessment of Educational Progress (NAEP) trend assessment reveal that with respect to achievement scores in mathematics and reading, 17-year-old students in the United States were scoring about the same in 2012 as they were in the early 1970s. But these overall trends mask the significant progress made among certain groups. For instance, over the past 40 years, minority students have made substantial steps toward closing the minority-nonminority test score gap in both mathematics and reading. In 2012, Black students scored 18 points higher (or 19 percentile points) on the NAEP mathematics test and about 30 points higher (or 24 percentile points) in reading than those in the early 1970s. Similarly, Latino students made improvements in achievement, gaining 18 percentile points on the NAEP trend mathematics test and 17 percentile points in reading.

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Achievement Gap

In the 1990s and 2000s, however, the reading scores of Black and Latino students did not increase as much as they did during the 1970s and 1980s. Because minority students are performing markedly higher than what similar students did 40 years ago, this progress has helped close the gap with White students, whose achievement levels have changed little over this same time period. Whereas the Black-White gap on the NAEP reading assessment was 53 points in 1971, it narrowed to 26 points in 2012—a closing of the gap by about 22 percentile points; the mathematics gap closed by about 15 percentile points during this time period. The reading test score gap between Latinos and Whites was 41 points in 1975 but narrowed to 21 points in 2012 (a closing of the gap by about 16 percentile points), and the Latino-White mathematics gap closed by 15 percentile points between 1971 and 2012 (from a 33-point difference in 1971 to a 19-point difference in 2012).

Socioeconomic Achievement Gaps Socioeconomic background—including parents’ education, family income, and occupations—has always been one of the strongest predictors of students’ academic achievement and educational attainment. Compared with achievement gaps by race-ethnicity—which reveal a significant narrowing of the Black-White and Latino-White test score differences over the past four decades—the achievement gap between higher and lower socioeconomic groups has remained large and persistent. When the NAEP began to measure parents’ educational attainment in the late 1970s, the mathematics test score gap between 17-year-olds whose parents graduated college and those whose parents only had a high school degree was 24 percentile points. In 2012, this gap increased to 27 percentile points. The test score gap was even greater in comparisons of NAEP mathematics scores for 17-year-old students with college-educated parents and students whose parents did not graduate high school; this achievement gap was 39 percentile points in 1978 and narrowed to 28 percentile points in 2012. The achievement gaps between students from wealthy families and those from poor families appear to be increasing over time. Researcher Sean Reardon shows in several nationally representative datasets that the academic achievement gaps between children from high-income and low-income families has been growing over the past 50 years. For cohorts with more reliable data, the wealthy-poor achievement gap was

30% to 40% larger in the 2000s than for students born in the 1970s. When the income achievement gap is defined as the difference between a student from a family at the 90th percentile of the family income distribution and a student from a family at the 10th percentile, Reardon shows that the income achievement gap is currently more than twice as large as the Black-White achievement gap. Moreover, the relationship between family income and student achievement is stronger now than it was several decades ago, particular for students from families above the median income level. Reardon (2011) notes the importance of family income vis-à-vis parents’ educational attainment: The growing income achievement gap does not appear to be a result of a growing achievement gap between children with highly educated and lesseducated parents. In fact, the relationship between parental education and children’s achievement has remained relatively stable during the last fifty years, while the relationship between income and achievement has grown sharply. Family income is now nearly as strong as parental education in predicting children’s achievement. (p. 93)

Gender Achievement Gaps Although women typically make less money than men and remain underrepresented in the most prestigious positions in corporations and government, they have made substantial gains in education, including college attendance and graduation and enrolling in graduate school. In standardized test scores over time, the achievement gap by gender has also narrowed, although the gap has never been as large as the achievement gaps by race-ethnicity or socioeconomic status (SES). The trend NAEP reveals is that females have typically outperformed males in reading, while scoring lower on mathematics. For 17-year-olds, the reading gap between males and females in 1971 was about 10 percentile points (with females scoring higher), but this gap narrowed to about 6.5 percentile points in 2012—due primarily to the larger gains over time for male students compared with female students. In mathematics, 17-year-old males scored higher than females in 1971 by 6.5 percentile points, but females closed this gap in 2012 to 3 percentile points.

Explaining Achievement Gaps Researchers have extensively examined achievement gaps by race-ethnicity, socioeconomic background,

Achievement Gap

and gender. Although the theoretically relevant factors differ by the particular achievement gap under consideration, there have been some broad sets of factors applicable to several types of achievement gaps—including parenting styles, parental involvement, and family resources; stereotype threat; summer learning loss; ability grouping and tracking; teacher quality; school composition; and school sector (Catholic or charter schools). Parenting Styles, Parental Involvement, and Family Resources

Parenting styles, parental involvement, and family resources have been examined with respect to racial-ethnic, socioeconomic, and gender achievement gaps. Annette Lareau finds that middle-class parents engage in concerted cultivation to improve their children’s talents and skills, in contrast to lowincome parents, who allow their children to develop spontaneously. Concerted cultivation refers to middle-class parents’ attempts to further their children’s development by involving them in organized activities during the schooling years to improve their educational and occupational opportunities as adults. For example, parents will engage their children in organized sports, music lessons, or other extracurricular activities to encourage development of language, critical thinking, and social skills that provide advantages in elementary, secondary, and tertiary schooling. Moreover, compared with low-income parents, middle-class parents will interact more comfortably with professionals such as teachers and physicians, and these interactions may provide future advantages to their children. Finally, middleclass parents interact with their children in different ways by using elaborated speech with their children, compared with working-class parents, who tend to use direct commands and have more restricted speech patterns. Stereotype Threat

Stereotype threat is another social-psychological factor that researchers have examined in relationship to achievement gaps. According to Claude Steele and Joshua Aronson (1998), “when a negative stereotype about one’s group becomes relevant to the situation that one is in, it signals the risk of being judged or treated stereotypically, or of doing something that would inadvertently confirm the stereotype” (p. 403). For example, they showed in experimental conditions that when their race was

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emphasized, Black college students performed more poorly on standardized tests than Whites. When race was not emphasized, Black students performed similarly or even better than White students on the same test, indicating that academic performance can be inhibited when students become aware that their behavior may be viewed through racial stereotypes. This line of research has shown that stereotype threat can harm academic achievement for any individual for whom the situation involves an expectation of poor performance based on stereotypes related to race-ethnicity, SES, or gender. Summer Learning Loss

Summer learning loss refers to the loss of academic knowledge and skills when schools are out of session during the summer months, and some researchers have pointed to this phenomenon as an explanation for racial-ethnic and socioeconomic achievement gaps. That is, there are several studies that show that lower SES and minority students experience greater losses in academic achievement than higher SES and majority White students during summer vacations. The literature on the extent to which achievement gaps between students from different racialethnic and socioeconomic groups narrow during the school year is inconclusive. Using nationally representative data, Douglas Downey, Paul von Hippel, and Beckett Broh (2004) find that schools help close the achievement gap between high- and low-SES students, widen the Black-White gap, and have no effect on the gender gap—suggesting that SES gaps in achievement may be due to nonschool factors and racial-ethnic gaps may be due to school and nonschool factors. Dennis Condron extends this research by showing that nonschool factors (indicators of health, cultural and social capital) account for the socioeconomic differences in achievement during the school year; school factors (e.g., curriculum tracking) explain more of the Black-White achievement gap than do nonschool factors. Schooling Factors

Schooling factors involve the social relationships and processes within schools that students experience with teachers, school staff and administration, and their peers. Some schooling process that have been examined to address achievement gaps are ability grouping or tracking—the division of students for instructional purposes purportedly based

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Achievement Gap

on their prior academic achievement levels and interests—and teacher quality. Tracking

Research suggests that high-track placement is associated with greater test score gains than low-track placement, leading to greater inequality between students placed in high- and low-track classes over time. Some research has examined whether changes in track placement over time are related to the racial-ethnic achievement gap. For example, Mark Berends and Roberto Penaloza found that there was a significant increase in the proportion of Black students reporting academictrack placement, from 0.28 in 1972 to 0.53 in 2004. Although White students tended to report academictrack placement more than did Black students, these Black-White differences decreased significantly since the early 1970s and in turn were associated with a significant reduction in the Black-White test score gap. Teacher Quality

Recent research has pointed to the importance of high-quality teachers for promoting student achievement and reducing achievement gaps related to raceethnicity, SES, and gender. Researchers have pointed to teacher quality as a critical educational policy issue, given the evidence that teachers are the most important school-related factor affecting student achievement and that a large proportion of education dollars are devoted to compensating teachers. However, there is substantial disagreement about how to measure teacher quality in valid and reliable ways, as shown by the recent Measures of Effective Teaching Project. Although the quality of research on teacher quality has increased significantly over the past several years, it remains unclear whether teacher quality will have robust effects on reducing the achievement gaps among different groups (e.g., racial-ethnic, socioeconomic, or gender). Having a high-quality teacher may benefit all students, although some argue that teachers with more directed pedagogy will help address the achievement gaps. Socioeconomic Composition

A sizeable body of research has confirmed that schools with higher proportions of students from high socioeconomic backgrounds have higher achievement, higher graduation rates, and more

college-bound graduates. The question of interest, however, is whether these relationships reflect contextual effects above and beyond the individual-level relationships between SES and achievement. This topic has received attention for several decades, and generally, the research reveals that although students’ scores may be higher in schools that have greater percentages of higher status students, the net effect of school SES on achievement is not as strong as the association between individual students’ SES and achievement. Racial-Ethnic Composition

A finding of the 1966 Coleman Report was that the achievement of minority students is higher in racially integrated schools even after controlling for individual and other school and community characteristics. However, like other school effects, minority composition was not as strongly related to student achievement scores when compared with the strong net effects of individual measures for students’ racial-ethnic and socioeconomic background. One of the concerns in recent years about changes in school composition has been the increases in patterns of racial-ethnic segregation. Historical patterns reveal that most of the desegregation in U.S. schools took place between 1968 and 1972. However, since that period of court-ordered desegregation, there has been a developing trend toward resegregation, particularly among Black and Latino youth. This trend, while present over the past 25 years, has become more pronounced in the past decade. Thus, it is important to continuously examine changes in school composition over time and how such changes are related to differences in student test scores among different racial-ethnic, socioeconomic, and gender groups. School Sector

Many have argued that public schools are outperformed by private schools, particularly Catholic schools. However, the magnitude of these effects and their implications are often the center of heated debate. Much of this debate has focused on whether students attending Catholic schools score higher on achievement tests than students in public schools. Recent evidence suggests that Catholic schools have positive effects on achievement at the high school level, but at the elementary level, Catholic school students perform no differently than their public school counterparts and may even score lower in mathematics.

Achievement Gap

In addition to the average effects of school sector on the general population of high school students, there is some research that shows that Catholic schools help address racial-ethnic and socioeconomic achievement gaps. However, Christopher Jencks (1985) argued that “the evidence that Catholic schools are especially helpful for initially disadvantaged students is quite suggestive, but not conclusive” (p. 134). Thus, the question whether Catholic schools contribute to the closing of the gaps for students by race-ethnicity, SES, or gender remains open for empirical research. Charter schools are another school sector that has received attention for potentially addressing achievement gaps for students from different racial-ethnic and socioeconomic backgrounds. Charter schools are public schools funded by the government, but their governance structure differs from traditional public schools in that they are established under a charter by parents, educators, community groups, or private organizations to encourage school autonomy and innovation. In exchange for such autonomy and flexibility, charter schools are held accountable to current state and federal accountability standards. When a charter school has more students applying than there are seats available (i.e., oversubscription), the school is required to hold a lottery to select students for open seats at random. Research shows that charter schools have mixed effects on achievement (some positive, some negative, and some neutral), but it is important to note that some studies have found significant and substantial positive effects of charter schools, particularly in urban areas where it has been difficult to implement meaningful educational reforms. For example, comparing students who won and lost charter school lotteries, Hoxby, Murarka, and Kang (2009) found that charter students outscored the comparison group in both mathematics and English. The authors noted that students who attended charter schools in New York City over a longer period of time (e.g., kindergarten through eighth grade) matched the mathematics performance gains of their peers in affluent suburban schools—what they called the closing of the “Harlem-Scarsdale” achievement gap. Will Dobbie and Roland Fryer (2011) examined students who won and lost the charter school lotteries in the Harlem Children’s Zone and found that the effects of charter elementary schools were large enough to close the racial achievement gap across subjects: Students gained about 0.20 of a standard deviation a year in both mathematics and

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English language arts. Whether such effects can be sustained across a large number of charter schools at scale remains an open question.

Closing Achievement Gaps Although many researchers have speculated as to why the test score gaps closed between the early 1970s and today, only a few have been able to empirically study how changes in individual, family, and school factors are related to changes in achievement gaps. The main reason for this is the lack of data for multiple student cohorts—information necessary for the examination of such relationships. Another challenge in examining individual, family, and school factors and how they contribute to trends in achievement gaps is that isolating these relationships takes not only data over time but also careful theoretical thinking about the trends under consideration. For example, whatever the trends in the achievement gap, it is important to empirically link the explanatory factor to the outcome of interest (e.g., test scores). Moreover, the explanatory factor must have changed over the time period of interest (e.g., the early 1970s–2000s). Also, the explanatory factor must affect the progress of the disadvantaged group (e.g., Black or Latino students) compared with the advantaged group (non-Hispanic White students). Despite these challenges, more data and research are critical to further our understanding of the trends in achievement gaps and the factors that explain these trends. There is more to learn in terms of persistent inequalities and educational opportunities. The challenge will be to conduct empirical research that is rigorous and theoretically driven and reveals the conditions under which achievement gaps persist or are ameliorated. Mark Berends See also Income Inequality and Educational Inequality; National Assessment of Educational Progress; National Center for Education Statistics; Race Earnings Differentials; Tracking in Education

Further Readings Aronson, J., & Steele, C. M. (2005). Stereotypes and the fragility of human competence, motivation, and selfconcept. In C. Dweck & E. Elliot (Eds.), Handbook of competence and motivation (pp. 436–457). New York, NY: Guilford Press. Berends, M., & Penaloza, R. V. (2010). Increasing racial isolation and test score gaps in mathematics: A 30-year

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Adequacy

perspective. Teachers College Record, 112(4), 978–1007. Bryk, A. S., Lee, V., & Holland, P. (1993). Catholic schools and the common good. Cambridge, MA: Harvard University Press. Coleman, J. S., Campbell, E. Q., Hobson, C. J., McPartland, J., Mood, A. M., Weinfield, F. D., & York, R. L. (1966). Equality of educational opportunity. Washington, DC: Government Printing Office. Condron, D. J. (2009). Social class, school and non-school environments, and Black/White inequalities in children’s learning. American Sociological Review, 74(5), 685–708. DiPrete, T. A., & Buchmann, C. (2013). The rise of women: The growing gender gap in education and what it means for American schools. New York, NY: Russell Sage Foundation. Dobbie, W., & Fryer, R. G. (2011). Are high quality schools enough to close the achievement gap? Evidence from a social experiment in Harlem. American Economic Journal: Applied Economics, 3, 158–187. Downey, D., von Hippel, P., & Broh, B. (2004). Are schools the great equalizer? Cognitive inequality during the summer months and the school year. American Sociological Review, 69(5), 613–635. Hoxby, C. M., Murarka, S., & Kang, J. (2009). How New York City’s charter schools affect achievement. Cambridge, MA: New York City Charter Schools Evaluation Project. Retrieved from http://www.nber. org/~schools/charterschoolseval/how_NYC_charter_ schools_affect_achievement_sept2009.pdf Jencks, C. (1985). How much do high school students learn? Sociology of Education, 58, 128–135. Measures of Effective Teacher Project. (2012). Ensuring fair and reliable measures of effective teaching: Culminating findings from the MET project’s three-year study. Seattle, WA: Bill & Melinda Gates Foundation. Orfield, G., & Frankenberg, E. (2008). Lessons in integration: Realizing the promise of racial diversity in America’s public schools. Charlottesville: University of Virginia Press. Reardon, S. F. (2011). The widening academic achievement gap between the rich and the poor: New evidence and possible explanations. In G. J. Duncan & R. J. Murnane (Eds.), Whither opportunity? Rising inequality, schools, and children’s life chances (pp. 91–115). New York, NY: Russell Sage Foundation. Reardon, S. F., Cheadle, J., & Robinson, J. (2009). The effect of Catholic schooling on math and reading development in kindergarten through fifth grade. Journal of Research on Educational Effectiveness, 2(1), 45–87. Steele, C. M., & Aronson, J. (1995). Stereotype threat and the intellectual test performance of African Americans.

Journal of Personality and Social Psychology, 69, 797–811. Steele, C. M., & Aronson, J. (1998). Stereotype threat and the test performance of academically successful African Americans. In C. Jencks & M. Phillips (Eds.), BlackWhite test score gap (pp. 401–427). Washington, DC: Brookings Institution Press.

ADEQUACY The central question of education finance—how much funding is enough—is a complex one, because it relies on reaching agreement on what the school system should achieve. If we define the goal as providing an adequate level of resources so that there is a reasonable expectation that all students can meet the Common Core State Standards—or a related set of state curriculum standards—it is clear that the term adequacy is something of a misnomer and our true goal is establishment of a world-class educational system. Adequacy involves the provision of a set of strategies, programs, curriculum, and instruction, with appropriate adjustments for students, districts, and schools with particular needs, and their full financing sufficient to provide all students an equal opportunity to learn in order to meet high performance standards. This entry will describe how adequacy has been defined in school finance theory and the four methods currently used to estimate adequate funding levels in the states.

Defining Adequacy For most of the 20th century, state school finance systems focused on providing equity in the funding of schools within a state. The goal was to ensure that school districts had roughly equal levels of revenue per pupil, regardless of the wealth of the district as measured, in most states, by property value per pupil. An equitable distribution of educational resources is still an important focus of state school finance systems. However, simply considering equity does not answer the complex question of how much money a school or a school district needs to ensure that all students can perform at state standards. In fact, until recently, school funding levels in most states were often a function of how much money was available for appropriation at the state level and how much local taxpayers were willing to tax themselves to fund schools.

Adequacy

With the growth of the standards movement in the late 20th century, there was increasing attention paid to how much money is needed to educate students adequately. Starting in 1989, with the Kentucky Supreme Court’s decision in Rose v. Council for Better Education, the issue of adequacy has taken on increased importance in school finance. Courts in a number of states have required these states to define what an adequate education would be and then to fund the resources necessary to ensure that most, if not all, children can meet those standards. It is a complex and uncertain task to estimate how much money a school or district needs to ensure that a student has the opportunity to meet his or her state’s proficiency standards, because children are not all alike and they come to school with varied experiences and backgrounds. Today, adequacy is a key focus of school finance policy, and often litigation. Adequate school funding can be defined as the funding needed for the level of inputs—programs, services, curriculum, instruction, classroom, and school organization—that would ensure that students can meet state education standards or the Common Core State Standards. This funding is to be applied to curriculum content, student performance standards, and changes in school management, organization, finance, and accountability. Adequate school funding must also include a range of appropriate adjustments for students requiring special education, children from lowincome households, and children who are English Language Learners. In addition, adequate funding would include adjustments for specific characteristics of schools and school districts (enrollment, population density, regional cost differences, climate variations, etc.). Once a set of programs, services, and other educational elements are identified and estimated to meet the standard of adequacy, it is straightforward to price them and calculate a dollar amount that should be available for each district or school. This level of resources could be used to define the foundation level of funding in a school finance formula. In addition to consideration of educational inputs as described above, the notion of adequacy as outputs can also be argued. Nearly all written discussion of adequacy includes the notion of students achieving to some set of performance standards, implying that adequate education funding could also be defined as funding a set of educational strategies

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that enable students to attain some set of achievement standards. Some economists, including William Duncombe, John Yinger, Andrew Reschovsky, and Jennifer Imazeki, have suggested that this means moving to a “performance-based funding system” that formally links spending levels and adjustments for particular student needs to a specified level of system output. One major difference between equity and adequacy is that equity implies something about a relative difference while adequacy implies something about an absolute level. For example, a state school finance system could have base resources distributed quite equally, as is the case in California and Alabama, but still not fund all schools adequately. Similarly, one could conceive of a state or education system (perhaps New Jersey when its response to its 1998 court case is fully implemented) with substantial differences in resources but with the lowest spending districts still spending above some adequacy level. Finally, given all these issues, adequacy requires some link between inputs and outputs, a set of inputs that should lead to certain outputs, or some level of spending that should be sufficient to produce some level of student achievement. This highlights the need to learn more about input-output linkages.

Determining the Adequacy Level There has been both conceptual and empirical research on educational adequacy. William Clune has produced particularly thorough conceptual analyses of how educational adequacy and school finance can be linked. Although he emphasized the importance of adequacy for low-income students, his work addresses the adequacy issue for all children. Even before Clune’s work in the 1990s, there were school finance researchers who were concerned with adequacy. Their work focused on the question of how high the foundation funding level should be. Four methods have been used to determine an adequate foundation expenditure level: (1) the input or professional judgment approach, (2) the successful district approach, (3) the cost function approach, and (4) the evidence-based approach. Input or Professional Judgment Approach

The professional judgment approach, which relies on panels of education professionals to develop a description of the resources needed for a school to

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Adequacy

produce students who are likely to meet state standards, was first applied nearly three decades ago when the Washington state school finance system was declared unconstitutional. When that state’s top court required the state to identify and fund a “general and uniform” education program, the state responded by identifying the average staffing (teachers, professional support staff, administrators, etc.) in a typical district and, using statewide average costs, determined a spending level using those staffing averages. A 2005 study in Washington found that substantial new resources were needed to fund schools at an adequate level for the needs of the more demanding global economy of the 21st century. Subsequently, the legislature modified the ratios that were in the former Washington model to include those from an evidence-based analysis conducted by Allan Odden and Lawrence O. Picus, but it did not fund the new ratios. In 2012, the Washington Supreme Court ordered that the program be funded in the future. Another input approach is the resource cost model, created by Jay Chambers and Thomas Parrish. Using groups of professional educators, the resource cost model first identifies base staffing levels for the regular education program and then identifies effective program practices, along with their staffing and resource needs, for compensatory, special, and bilingual education. All ingredients are priced using average price figures, but in determining the base dollar amount for each district, the totals are adjusted by an education price index. This method was used in Illinois and Alaska in the 1980s, but the proposals were never implemented. This method is similar to what has been termed “activityled staffing” in England. James Guthrie and colleagues made a further advance in the professional judgment approach as part of a response to the Wyoming Supreme Court’s finding that the state’s finance system was unconstitutional. The Wyoming work used a panel of professional education experts to identify the base staffing level for typical elementary, middle, and high schools. Guthrie and colleagues used the findings of the Project STAR, or Student/Teacher Achievement Ratio, class-size reduction study in Tennessee to set a class size of 16 in elementary schools and then used the panels of education professionals to determine additional resources for compensatory, special, and bilingual education. They adjusted the estimates they derived by a constructed price index that modified

the cost of resources based on estimates of the variation in costs of education inputs across the state. The Wyoming Supreme Court upheld part of this model but ruled part of it unconstitutional, and Wyoming has since moved toward the use of an evidencebased funding model (described in a subsequent section). As previously noted, similar types of adequacy studies have been conducted recently for Kansas, Kentucky, Maryland, Massachusetts, New York, and South Carolina. The advantage of this input approach is that it identifies a set of elements that can be used to produce a strong education program in each school district and estimates their cost; it then uses that figure as the funding level for that district. The disadvantage of this approach is that the resource levels are connected to student achievement results indirectly through professional judgment and not directly to actual measures of student performance. Moreover, expert judgments can vary both across and within states, and they often vary based on the way a professional judgment analysis is carried out. Successful District Approach

The second approach to determining an adequate spending level attempts to remedy the key deficiency of the input approach—variation in professional judgment about how much is needed to reach a given level of student performance—by identifying a spending level directly linked to a specified level of student performance. The successful district approach first determines a desired level of performance using state tests of student performance. The method then identifies school districts that produce that level of student performance, and from that group, it selects those districts with characteristics comparable or close to the state average. The average spending per pupil is then computed and considered the basic adequacy level. Successful district studies have been conducted in Illinois, Maryland, Ohio, and Washington. In most of these studies, the level of spending identified was approximately the median spending per pupil in the state. In Washington, however, the successful district level was substantially above the level supported by state funds. A major advantage of this approach is that it identifies the spending level that is linked to a specified student performance level. A disadvantage is

Adequacy

that the method does not indicate how the funds should be spent to produce the desired student achievement results. Furthermore, atypical districts are often eliminated from successful district analyses. The districts not included often include the highest and lowest spending and highest and lowest wealth districts, as well as large urban districts. The result is that the districts identified are usually nonmetropolitan districts of average size and relatively homogeneous demographic characteristics. These districts often spend below the state average, making the adequacy level established through this approach difficult to relate to the fiscal adequacy needs of big city and small rural districts, even if the resulting formula accounts for differences in pupil needs and geographic price differentials. Cost Function Approach

The third method used to estimate adequacy uses the economic cost function approach. Cost functions are used to identify a per-pupil spending level sufficient to produce a given level of performance, adjusting for characteristics of students and other socioeconomic status characteristics of districts. This method can also be used to calculate how much additional money is required to produce the specified level of performance for students with particular needs, adjusting for scale economies related to the size of schools and districts, the effect of input prices in different regions of a state, or other factors likely to affect the operation of a school district. The result of a cost function analysis is an adequate expenditure level per pupil for the average district. This figure can then be adjusted by the factors in the cost function model to estimate the adequate spending level for districts that vary from the average. The expenditure level estimated by the model is higher or lower as the desired performance level used in the model is increased or reduced. This analysis usually produces an adjustment for city districts of two to three times the average district adequacy level, which, when combined with the complex statistical analysis needed to estimate the adequacy level, makes its use problematic in the political context. No state currently uses this approach, though cost function research has been conducted for several states, including New York, Wisconsin, Texas, California, and Illinois. Research has shown that there is substantial variation in the average

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adequacy level estimated for individual districts based on the variation in student and district needs. These findings have suggested adequacy levels for individual school districts that range from a low of 49% to a high of 460% of the average adequacy level in Wisconsin and from a low of 75% to a high of 158% of the average adequacy level in Texas. In both states, the estimated adequacy levels for large urban districts were the highest in the state. Another concern with the current cost function studies is that they used different statistical methodologies and relied on varying definitions of “adequate” performance levels (i.e., test scores). In Wisconsin, adequate performance was defined as teaching students to enable them to perform at the average level on state tests. In two other states, adequacy was defined as at least 70% of students meeting the state proficiency standards. All studies sought to identify a spending level that was associated with a desired, substantive education result— student achievement to a specified standard—and in general, that spending level was close to the respective state’s median spending level. Evidence-Based Approach

The fourth approach to determining an adequate expenditure level, the evidence-based approach, identifies research- or other evidence-based educational strategies that have been successful in raising student performance. The resources necessary to put these strategies in place and the costs associated with these resources are then estimated to determine an adequate funding level. This approach more directly identifies educational strategies that produce the desired results, so it also helps to guide schools in how to use dollars in the most effective ways. The evidence-based approach has been used to calculate adequate school spending levels in Kentucky, Arizona, Arkansas, Maine, New Jersey, North Dakota, Ohio, Texas, Wyoming, Washington, and Wisconsin. Today, Arkansas and Wyoming have enacted school finance reforms using the evidencebased model, and in 2009, the governor of Ohio recommended using this approach to reform school funding in that state. The evidence-based approach defines prototypical elementary, middle, and high schools, estimates the resources (and their costs) needed to implement strategies that will help all students reach identified performance objectives, and then uses that figure as

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the base spending level for the state funding formula. As with the other models, the evidence-based model includes adjustments for student needs as well as school and district characteristics. The level of funding derived from an evidence-based analysis should allow schools to deploy the strategies research has shown to have statistically significant impacts on student learning.

Conclusion One of the most challenging questions facing educational policymakers and school leaders is how much money is needed to ensure that all students have the ability to reach high levels of student performance as measured by assessments designed for the Common Core State Standards or other state-established curriculum standards. The complexity of the educational process makes linking education spending to student achievement extremely challenging. The four methods outlined in this entry offer alternative approaches to estimating what is needed. Each has its strengths and weaknesses, and each may be better suited for different state policy environments. Regardless, developing a better understanding of what schools need to help all students succeed is critical to the success of the U.S. educational system, and the first step is identifying adequate levels of educational resources. Lawrence O. Picus See also Adequacy: Cost Function Approach; Adequacy: Evidence-Based Approach; Adequacy: Professional Judgment Approach; Adequacy: Successful School District Approach; Cost of Education; School Finance Equity Statistics

Further Readings Baker, B. D., Taylor, L., & Vedlitz, A. (2005). Measuring educational adequacy in public schools (Report prepared for the Texas Legislature Joint Committee on Public School Finance, The Texas School Finance Project). Retrieved from http://bush.tamu.edu/research/ faculty/TXSchoolFinance/papers/ MeasuringEducationalAdequacy.pdf Clune, W. (1994). The shift from equity to adequacy in school finance. Educational Policy, 8(4), 376–394. Odden, A., & Clune, W. (1998). School finance systems: Aging structures in need of renovation. Educational Evaluation and Policy Analysis, 20(3), 157–177. Odden, A. R., & Picus, L. O. (2014). School finance: A policy perspective (5th ed.). New York, NY: McGraw-Hill.

ADEQUACY: COST FUNCTION APPROACH Cost function refers to one of several analytical approaches researchers have used over the past couple of decades to provide cost estimates of the provision of an adequate education. Fundamentally, the cost function process employs econometric analyses using extant data (primarily expenditure and achievement data), often coupled with data derived from estimates, to produce cost figures. This entry provides background on the concept of an adequate education, efforts to define an adequate education, and litigation addressing the issue. It then describes the cost function approach, including some of the strengths and weaknesses as identified in the research literature.

Background A universally accepted definition of adequacy has been somewhat difficult to articulate precisely, which may account for the amount of litigation to interpret the relevant language in state constitutions. Each state’s constitution includes an education clause that mandates or suggests free public education for eligible children and suggests (to varying degrees) a responsibility to provide adequate support for education. Traditionally, local districts have borne the responsibility of providing the necessary education for students to become productive citizens in a democratic society. Part of the responsibility includes providing adequate resources to ensure a constitutionally viable free public education. The establishment of a clear definition of an adequate education may have been aided by the No Child Left Behind Act of 2001, which required states to test students annually in reading and mathematics. Under the Common Core State Standards, adopted by 45 states and the District of Columbia, states have worked with two testing consortia to develop tests aligned with these common standards. With improvements in data collection and consistency, perhaps even at the interstate level, it may be possible to find common ground on defining what constitutes an adequate education. Schools in most states are financially supported by a combination of locally generated revenue and state funding, often through a foundation or other type of state funding formula, with a modest contribution from the federal government. Traditionally,

Adequacy: Cost Function Approach

most education funding was local, resulting in large variances in school funding between wealthy and poor communities. With a shift toward greater state funding of schools since the 1970s, some of these naturally occurring interdistrict funding inequities have been mitigated. However, considerable litigation at the state level has addressed the inequities that have persisted. In recent years, the focus of school finance litigation has shifted from inequities between districts to whether states are providing the funding needed for an adequate education. Rose v. Council for Better Education (1989) specifically dealt with the issue of adequacy and serves as a standard for education reform. The Kentucky Supreme Court found that the state’s school finance system violated Kentucky’s education clause and required a systemic reform. Kentucky completely overhauled its educational funding system, along with its curriculum, which may have provided an impetus for what we now know as standards-based education reform. Since Rose, courts in most of the 50 states have grappled with interpreting state education clauses, and with the meaning and assessment of educational adequacy as a result. Often, studies utilizing models to determine the cost of an adequate education, including the cost function approach, stemmed from the work of technical advisory teams employed by plaintiffs or state governments to support their side in school finance lawsuits.

Cost Function as an Adequacy Estimation Procedure The establishment of a universally accepted cost estimate process for determining the financial support necessary for an adequate level of education remains elusive. Researchers have developed a series of processes to estimate levels of adequate financial support of education. In addition to the cost function model, these have included the resource cost model, the successful school district model, the professional judgment model, and the expert judgment model. The overall premise of these models is to ascertain the dollar amount necessary to enable schools to provide the educational resources for all students to reach established state educational standards. The cost function model has been used with some frequency as part of adequacy litigation over the past decade. Essentially, this approach uses school and school district data to project necessary levels of financial support so that schools can educate

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students to the levels required by their state’s constitution, given defined demographic and organizational characteristics. The data used most frequently include school and district expenditures and achievement data, such as test scores, coupled with other types of data depending on the specific model. Similar to other economic projection procedures, the cost function relies heavily on extant, historical data to make future projections. Individual cost function models will include unique features, based on variant state constitutional requirements, local district and school community characteristics, student demographic characteristics, and the like. However, the procedures have common characteristics. In many respects, the cost function approach has features prevalent in the production function models that were more commonly used years ago. However, while production function models include statistical modeling that uses expenditure data as a primary independent variable and outcome data, such as test scores, as an outcome variable, in cost functions, the output data are a critical input, and expenditure data are a dependent variable. As with other adequacy estimation procedures, cost function has not only advantages but also some glaring shortcomings. The increased prevalence of education data has enabled increased use of econometric modeling such as cost function. However, some of the statistical procedures may be sophisticated and may therefore be difficult for policymakers and lay persons without sufficient analytical training to understand. Additionally, these procedures rely heavily on expenditure data, which are used to estimate costs. Some researchers have criticized the use of expenditures to estimate costs, as the two are technically different (expenditures include inefficiencies), which leads to problematic error terms in the modeling. Advocates of the cost function approach, however, often point out that expenditures are highly related to costs, and often researchers include estimates of inefficacies in their cost function modeling. Cost function procedures have been championed by some researchers as being more objective than some of the other procedures prevalent in the literature. The professional judgment and expert judgment models depend on either practitioner or researcher expertise and the accumulated knowledge of the educational needs of students. However, these models are particularly subjective, and opinions (and concomitant cost estimates) potentially vary

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among experts. The cost function relies on statistical data rather than on judgments. However, cost function studies, even in single states, have yielded meaningfully different results, which would appear to militate against the model’s claim of producing an objective reality. Recent litigation in Texas (West Orange-Cove Consolidated ISD v. Neeley, 2005) and Missouri (Committee for Educational Equality v. State, 2009) provides two prominent examples. In each case, expert teams used cost function to produce estimates that were dramatically different from each other. Clearly, cost functions are nonexperimental procedures and therefore are subject to criticism for being insufficient to specify causal relationships between costs and outcomes. This is certainly a valid criticism of any type of economic forecasting analysis. Advocates, however, counter that though these procedures use historical data to estimate future projections, they, like other econometric estimation procedures, do have a practical utility for policymakers. Each model for estimating the resources needed for an adequate education has particular strengths and weaknesses. Certainly, cost function models are not without critics. Hybrids of the varying models might certainly take advantage of the strengths while minimizing the weaknesses of individual models, but additional weaknesses may be introduced when combining fundamentally differing estimation approaches. Though conceptualizing, litigating, and estimating the costs of providing an adequate level of education is a continuing area of education finance research, it is clear that much more exploratory research is warranted. Jeffrey Maiden and Bryan Young See also Adequacy; Adequacy: Evidence-Based Approach; Adequacy: Professional Judgment Approach; Adequacy: Successful School District Approach; Cost of Education; Education Spending; Evolution in Authority Over U.S. Schools; Expenditures and Revenues, Current Trends of; School Finance Litigation; Tax Burden

Further Readings Baker, B. D. (2005). The emerging shape of educational adequacy: From theoretical assumptions to empirical evidence. Journal of Education Finance, 30(3), 259–287. Rose v. Council for Better Education, 790 S.W.2d 186 (1989).

ADEQUACY: EVIDENCE-BASED APPROACH One of the continuing challenges of education finance is identifying links between levels of spending and student achievement. While it makes conceptual sense that spending more money on schools would lead to improved student outcomes, it has not always been possible to identify those links in a clear way that would indicate how much money is needed to ensure that all students could meet a high level of performance. The field of school finance adequacy attempts to address this issue. The evidence-based model is one of four approaches commonly used in “costing out” studies to estimate the level of funding that will successfully link resources to outcomes. This entry provides details on how the evidence-based model estimates adequate levels of education spending and how it has been used to help several states determine funding levels for their school finance systems.

Background and Context The evidence-based approach to estimating school finance adequacy was developed by Allan Odden and Lawrence Picus through their work with the states. The evidence-based approach identifies a cohesive set of school-level resources required to deliver a comprehensive and high-quality instructional program within a school and describes the research-based evidence on their individual and collective effectiveness. The resources needed to implement these programs and the cost of those resources are then estimated and aggregated to the district level and then the state level to develop an estimated statewide cost for education. The research base for the evidence-based model continues to expand as we learn more about programs that are successful in helping improve student learning under a variety of educational conditions. The evidence-based model relies on three general sources of research: 1. Research with randomized assignment to the treatment (the “gold standard” of evidence) 2. Research with other types of controls using statistical procedures that can help separate the impact of a treatment from other variables 3. Best practices either as codified in comprehensive school design or from studies of schools that have improved student performance

Adequacy: Evidence-Based Approach

A school system that is adequately funded, under the evidence-based model, generally would have the following: • Preschool for 3- and 4-year-olds, at least for children from lower income backgrounds, with a teacher and an educational assistant for every 15 students • An extended teacher year to include at least 180 days on pupil instruction and at least 10 days for professional development • One principal for each school • Two and a half FTE instructional facilitators, coaches, or mentors for each school • Enough teachers for a full-day kindergarten program at each school • Enough core teachers to provide for class sizes of 15 students in Grades K-3 and class sizes of 25 for all other grades • Additional teachers (called elective teachers in the text that follows) numbering 20% of the number of core teachers and 33% of the core teachers in high schools to teach art, music, physical education, and other noncore academic classes and to provide for planning and preparation time for the above teachers, with the requirement that a substantial portion of such time be used by regular classroom teachers for collaborative instructional improvement work • Tutors (professionally licensed teachers) for struggling students, at a rate of one tutor for every 100 students from low-income backgrounds • One teacher for every 30 students from lowincome backgrounds to provide an extended day program, and the same resources to provide a summer school for students who need it, so that schools can vary the learning time while holding rigorous performance standards constant • Sufficient funds for all students with disabilities—students with mild and moderate disabilities are funded through the general model, and it is assumed that the state will pay the costs for children with severe disabilities • About $100 per pupil for teacher trainers for professional development • About $250 per pupil for computer technologies (hardware and software), to cover purchase, upgrades, and repair

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• One to five positions at each school for a pupil support/family outreach strategy, with one position for every 100 students from a low-income background, one counselor position for every 250 students in secondary schools, and a nurse for every 750 students • Other resources for materials, equipment, and supplies; operation and maintenance; and clerical and secretarial support at the school and district levels • A strategy to provide additional resources to meet the needs of particularly small schools and school districts • A cost adjustment index to accommodate variation in costs across a state applied to the professional salary component of the model In the evidence-based approach, necessary resources are estimated at the school level based on the enrollment and characteristics of the students enrolled in the school. But to develop the model and to conduct an initial cost estimate, three prototypical schools are used—an elementary (PreK-5), a middle (6–8), and a high (9–12) school. The enrollment of the prototypical schools is assumed to be the number of students per grade times the number of classes at each grade level. A prototypical elementary school would have 450 students—75 students in each grade K-5, which results in five K-3 classrooms of 15 students each and three Grades 4–5 classrooms of 25 students each. A prototypical middle school and high school would have 150 students per grade level, or 450 students in a middle school and 600 in a high school. To estimate the resources needed for students who are at risk or have special needs, the statewide average is used. In reality, few, if any, schools have the actual characteristics of the prototypical schools. Consequently, the resources allocated to a school at each level are prorated up or down based on the actual enrollment and characteristics of the students. Staffing (core and elective teachers, coaches, other staff members) and other resources are then estimated for each school, and the costs are aggregated to the district level. Resources for the central management or administration of the district are included, and totals for all districts are summed to determine the total funding needs of the state. In practice, two approaches to estimating the total costs of the evidence-based model have been used. Under the first, the staffing and educational resource

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needs of each prototypical school are estimated and then applied to the actual characteristics of each school in each district in a state. For schools with very small student populations, additional resources are allocated to accommodate the diseconomies of scale that exist. For larger schools, the resources identified through the prototypical model are prorated upward, with the exception that beyond an initial principal, additional site administrators are assistant principals. This approach results in a different funding total for each school and each school district. Although it provides a potentially accurate estimate of the actual educational resource needs of each school, using this approach is often impractical in states that are used to determining a single perpupil funding level and then making adjustments for student characteristics and other variations across school districts. As a result, an alternative approach for computing the evidence-based costs for a state is to estimate the costs of a prototypical school at each level using state cost factors. This estimate would not include additional resources for students with special characteristics but instead would provide a base or foundation funding level for each student. Separate per-pupil funding levels are estimated for elementary, middle, and high school students, along with the costs of central administration. These are then used separately in a funding formula, or a weighted average based on the state’s total student enrollment is computed. Separate computations are then made for the additional costs of meeting the needs of lowincome students, English Language Learners, and special education students. To estimate a district’s total funding, the per-pupil funding level is multiplied by the number of students in the district. Additional funds for the actual number of low-income students, English Language Learners, and special education students are then included to reach a district’s, and thus a state’s, total cost of education. The advantage of the second approach is that it more closely approximates the way states currently fund schools. It establishes a base or foundation funding level for each student (this figure would include core and elective teachers as well as site administration, central administration, maintenance and operations, utilities, etc.) and adds the estimated additional costs of serving students with additional needs. It can be used in a state foundation program or a state guaranteed tax base funding program and can be used to estimate the weights in a weighted pupil formula as well.

Evidence-Based Model in Practice The evidence-based approach has been used to calculate adequate school spending levels in Kentucky, Arizona, Arkansas, Maine, New Jersey, North Dakota, Ohio, Texas, Wyoming, Washington, and Wisconsin. To understand how the evidence-based model can be used in practice, brief descriptions of its use in Arkansas and Wyoming are provided. Arkansas

Faced with a court ruling holding the state’s school funding system unconstitutional, Arkansas embarked on an evidence-based costing-out study in 2003. The study estimated the school-level costs of an evidence-based model for every school and district in the state. The study was widely accepted, and a strategy to raise an additional $800 million in educational revenues over several years was developed and implemented. To distribute funds to schools, the evidence-based cost components were combined into a prototypical K-12 school district of 500 students, and a per-pupil foundation level was established. The only adjustment to this figure was to provide additional resources for children from low-income families. An adjustment of approximately an additional $410 was made for each low-income student, and that amount was doubled for the number of lowincome students exceeding 70% of a district's total enrollment and tripled for the number of low-income students exceeding 90% of a district’s total enrollment. The actual foundation amount is reviewed annually and adjusted for the costs of inflation. Initially, school districts were provided the new funds with no restrictions on their use. A subsequent study in 2006 showed little change in the pattern of use of educational resources across the schools and very little change in student performance. Following that study, the state legislature enacted legislation to hold districts more accountable for the use of funding and for student outcomes. The system remained in place as of 2013. Wyoming

In 1997, the Wyoming State Supreme Court held the state’s school funding system to be unconstitutional and ordered the state legislature to determine a set of educational goods and services that would meet the needs of the state’s students, determine the cost of that set of goods and services, and fund it. The initial approach to meeting this requirement relied on a professional judgment analysis to fund

Adequacy: Professional Judgment Approach

schools. The court also ordered the state to recalibrate the funding formulas at least every 5 years. Beginning in 2005, the state shifted to an evidencebased funding formula. The Wyoming funding formula uses the evidencebased approach to estimate the funding needs of each school and each school district, and then it funds each district through a combination of property taxes and state resources. Because of the challenges of a large, sparsely populated state, special education and pupil transportation are fully reimbursed by the state. Between 2005 and 2010, when the funding system was recalibrated, two studies of district use of educational resources were conducted. Both found that most districts elected to allocate their resources using theories of action that differed from the theory embedded in the evidence-based model—something that was clearly acceptable under Wyoming’s strong local control culture. However, the studies also noted that student performance had not changed much over time, and they recommended that the districts consider implementation of the evidencebased strategies before additional funds were allocated to schools—particularly in light of the fact that Wyoming was, and remains, among the states with the highest rates of spending on education.

Conclusion The evidence-based model is one of four approaches currently used to estimate adequate levels of funding for education. The model relies on extant educational research to identify practices that have been successful in improving student achievement, estimate the resources needed to implement those strategies, and then use the costs of those strategies to determine total school, district, and state costs of education. Evidence-based estimates of total costs have been developed in several states, and two states, Arkansas and Wyoming, have used the evidence-based model to distribute funds to schools. Lawrence O. Picus See also Adequacy; Adequacy: Cost Function Approach; Adequacy: Professional Judgment Approach; Adequacy: Successful School District Approach; Cost of Education; School Finance Equity Statistics

Further Readings Baker, B., Taylor, L., & Vedlitz, A. (2004). Measuring educational adequacy in public schools (Report prepared

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for the Texas Legislature Joint Committee on Public School Finance, The Texas School Finance Project). Retrieved from http://bush.tamu.edu/research/faculty/ TXSchoolFinance/papers/MeasuringEducational Adequacy.pdf Odden, A. R., & Picus, L. O. (2014). School finance: A policy perspective (5th ed.). New York, NY: McGraw-Hill. Odden, A. R., Picus, L. O., & Fermanich, M. (2003). An evidence-based approach to school finance adequacy in Arkansas (Prepared for the Arkansas Joint Committee on Educational Adequacy). Retrieved from http:// picusodden.com/wp-content/uploads/2013/09/ AR_2003_EB_Report.pdf Odden, A. R., Picus, L. O., Goetz, M., Fermanich, M., Seder, R. C., Glenn, W., & Nelli, R. (2005). An evidence based approach to recalibrating Wyoming’s Block Grant School Funding Formula (Prepared for the Wyoming Legislative Select Committee on Recalibration). Retrieved from http://picusodden.com/wp-content/ uploads/2013/09/WY_Recalibration_report_Final_ November_30_05.pdf

ADEQUACY: PROFESSIONAL JUDGMENT APPROACH This entry introduces the professional judgment (PJ) approach to determining the cost of an adequate education. It describes the process for defining the concept of adequacy, the selection of educators to serve on PJ panels, the role of the research team in facilitating the panel deliberations, the role of experts and evidence in the approach, and the criticisms of the approach. In the United States, the financing of public schooling is largely left to the individual states. Most state constitutions include clauses stating that it is the responsibility of the state to provide education free of charge to all kindergarten through 12th-grade students, and many use terms such as adequate, sufficient, or thorough and efficient to describe their obligation. In an attempt to hold states accountable for fulfilling their obligation, there have been several lawsuits maintaining that the constitutional mandate of providing an adequate education has not been met. Some states have also been proactive in determining whether they are providing an adequate education. For both of these reasons, over the past several decades, there has been a burgeoning literature developed around costing out educational

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adequacy, where studies regularly attempt to answer the following two fundamental questions: 1. What is the total cost of providing an adequate education to all students in a state public school system? 2. How should resources be distributed in an equitable manner such that all students are afforded an adequate education regardless of their need or circumstance?

Studies in this area have used at least one of the following four different methodologies to answer the first question: (1) cost functions, (2) evidence based, (3) PJ, and (4) successful districts/schools. This entry details one of these four main approaches used to cost out an adequate education—namely, the PJ approach. It is important to note that there is no set definition of what constitutes an adequate education. Rather, the definition of adequacy must necessarily be determined for each study that attempts to answer the two fundamental questions put forth earlier. The section “Defining Adequacy” addresses how public engagement has been used to define an adequate education.

responsible for providing an adequate or thorough and efficient public education to all students. Such statements provide vague guidance at best to the costing-out process. Some recent costing-out studies have used public engagement strategies in an attempt to provide a more concrete definition of an adequate education. The rationale for a public engagement approach is that it is the public’s conception of what is “adequate” for its children that matters most in a democratic society. Public town hall meetings as well as surveys of legislators and the general public have been used to specify those educational goals that should be expected of a public school system that provides an adequate education. Using public engagement to establish these goals helps promote buy-in, capture priorities for various cognitive and noncognitive outcomes and programs, and assess the willingness of the public and its legislative representatives to commit funding to support education. Both cognitive and noncognitive outcomes can be included in such a statement of goals. Cognitive outcomes would include academic knowledge and skills, while noncognitive outcomes would include things such as good citizenship, emotional and physical well-being, social skills, and work ethic.

Educational Cost Factors Addressed by PJ The PJ approach was designed to estimate the cost of an adequate education and to account for two factors that influence the variations in costs across local educational agencies—differences in student needs and the scale of school and district operations. For example, the PJ model has been used to estimate the incremental costs of an adequate education for students at different grade levels and for students who are from low-income families or who are English Language Learners. The PJ approach also provides a way of measuring the differential costs associated with students at different grade levels and with students located in smaller, more remote communities, which necessarily have to operate schools subject to the diseconomies associated with the small scale of operations. This process of estimating these costs is commonly referred to in the school finance literature as “costing out.”

Defining Adequacy The first step in the costing-out process is to define what we mean by the concept of adequacy. School finance is for the most part under the purview of the states, and most state constitutions hold the state

Determining the Cost of an Adequate Education Once state policymakers have defined an adequate education, the next step is to determine how much it costs to ensure that every student has an equal opportunity to an adequate education. To answer this question, the PJ approach provides a process that identifies the programs, services, and corresponding resources deemed necessary to allow students to achieve the goals that have been established and then calculates their costs using existing market prices. The complexity of costing out becomes evident when one realizes that to make this determination requires general knowledge and experience of what works in education—that is, what combinations of programs, services, and resources will achieve the goals. Furthermore, the difficulty of this task is even more apparent with the recognition of the wide variety of needs faced by student populations that require specific types of supports and resources. Costing out an adequate education therefore not only requires determining how much is required to achieve the common objectives for all students but also must take into account the

Adequacy: Professional Judgment Approach

systematic differences in need among students, which is where the concepts of adequacy and equity come together. Costing out must address horizontal equity (treating similar students in systematically similar ways) as well as vertical equity (treating students with different needs in systematically distinctive ways). In effect, vertical equity takes into account the differences in the cost of serving different types of children and providing them with an equal opportunity to achieve the goals.

Elements of the PJ Approach PJ represents one approach to costing out an adequate education and ensuring an equitable distribution of resources to communities and schools serving diverse student populations. PJ Panel Deliberations

The PJ approach relies on comprehensive panels of educators to take part in a costing-out exercise. Such panels will generally include superintendents, school business officers, elementary and secondary school principals, regular classroom teachers at each grade level, and program specialists who are knowledgeable about at-risk populations, English Language Learners, or students with disabilities. Because the PJ approach draws heavily on the previous professional experience of the panelists, a carefully structured nomination process is used to recruit the most recognized educators from the state and to ensure that the best and the most experienced individuals are included on the panels. Multiple PJ panels are often selected within a single state to represent educators from different categories of districts, such as urban, suburban, or rural systems. Having different PJ panels for the various types of districts helps account for the potential differences in technology that may be utilized in rural versus large urban or suburban systems. Moreover, research teams will often create multiple panels for each type of district to minimize the impact of any single panel on the costing-out results. The research team provides each panel with a goals statement derived from state law and/or a public engagement process that may have been implemented as part of the project. The goals statement essentially defines the concept of adequacy in preparation for the costing-out exercise. With the goals statement in hand, the members of each individual PJ panel are then asked to work through a structured deliberation process to specify the resources

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(e.g., the quantities of teachers, administrators, aides, student or instructional support personnel, and supplies and materials) they believe, based on their own knowledge and professional experiences, are necessary to deliver an “adequate” education at a minimum cost across a diverse group of students, classified according to their need (e.g., poverty or English language proficiency), and across schools of varying sizes. Each panel is taken through a series of exercises that asks them to specify the quantities of labor and nonlabor inputs at hypothetical schools with differing levels of student need (e.g., high vs. lower levels of student poverty, English Language Learners, etc.) and size (enrollment). These proposed quantities of labor and nonlabor inputs are then used in conjunction with information on the prevailing input prices (e.g., compensation rates for categories of school personnel) to estimate the costs of an adequate education. The research team can then use the differences in the costs of educational services across the exercises carried out by each PJ panel to estimate the differential costs of serving various populations of students classified according to their need (e.g., poverty or English language proficiency) and the size of the school in which they are served. In some instances, prior to the process of specifying the resources, research teams ask the PJ panels to develop a narrative program design that describes the educational philosophies and practices they believe to be essential to achieving the goals set out by the state for the K-12 school system. The research team then asks the PJ panel to use the narrative program design as a guide to specify the quantities and characteristics of the labor and nonlabor inputs necessary to achieve the desired goals. Use of Expert Briefs and Empirical Information to Support the PJ Approach

To support the deliberations of the PJ panels, some research teams have provided expert briefs prior to the meeting of the panels that summarize the research literature on best practices to meet the educational needs of various student populations. In addition, previous studies provide the PJ panels resource profiles of the average personnel and nonpersonnel resources used across schools deemed to be “beating the odds” (i.e., performing better than expected given the composition of student needs being served). These materials assist the panels in

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developing instructional program designs that are supported by both mainstream educational research and empirical data (i.e., the materials help the panels develop resource specifications by incorporating programmatic elements such as length of school day and year, full-time equivalents of specific staff, and nonlabor expenses for supplies, technology, etc.). Jay Chambers and Jesse Levin have previously referred to combining the PJ approach with the use of expert briefs and beating-the-odds resource profiles as the hybrid approach. Researchers’ Role in PJ

The research team plays an important role in keeping the panel on track. It reviews the panel members’ qualifications carefully before choosing members and may provide them with an extensive package of background materials. As mentioned above, the materials incorporate elements of the other methodologies by including mainstream research literature on effective schooling practices and sets of average resource profiles (i.e., average staffing ratios, nonpersonnel dollars) derived from data on schools that have successfully beaten the odds. Using these materials and the goals statement, the panels are then asked to develop prototypical instructional programs that are expected to achieve the goals in several school settings that vary with respect to student needs and size. During the panel meeting, research team members serve to facilitate the deliberations. All decisions are ultimately those of the panel members, who are asked to arrive at these decisions through a consensus. A key role of the facilitators is to help ensure that the panels stay focused on achieving the goals at a minimum cost and that they develop realistic program designs and resource specifications that are appropriate to the conditions and realities of their state. After the panel deliberations are completed, the research team is then responsible for translating the final school-level resource specifications developed by the panels into dollar figures. The variations in calculated dollars across the different exercises are then used to project costs for each school based on the school’s individual needs and enrollment. The projected school-level costs are then aggregated to the district level, at which point the costs of districtlevel ancillary services, central administration, and maintenance and operations are added. Finally, these district-level figures are adjusted for geographic cost

variations and then aggregated across all districts to determine the total cost estimate to provide an adequate education in the state. The last step in the process is for the researchers to use the district-level cost figures to develop a basic per-pupil foundation amount to ensure that all students have access to adequate educational services. The foundation amount is defined as the funding for a student with no special needs in an average district in the state (i.e., a district with average student needs and size). Then, various algorithms can be used to supplement this foundation according to the percentage of various special needs populations and for differences in district size. In effect, the costing-out results provide data to create a student-weighted formula that adjusts the per-pupil foundation amount for differential student needs and scale of district operations. Additive or multiplicative formulas may be designed for this purpose. By design, the projected per-pupil funding for each district should be sufficient to meet the standard of adequacy for the state. In addition, the formula should be transparent (i.e., understandable to the general public and policymakers), minimize incentives to overidentify students as having specific needs that require additional funding, be predictable, and have reasonable administrative burden.

Critiques of the PJ Approach A major criticism of the PJ approach is that it results in nothing more than an educator’s “wish list” that includes inefficient and inflated cost estimates. Several studies employing the PJ approach have acknowledged this pitfall and have attempted to incorporate a series of checks and balances that mitigate this “wish list” mentality. First, multiple panels for the same types of districts have been set up to operate independently of one another so that there is some pressure not to be an outlier. Second, a final review panel is formed, which can be made up of representatives of the original panels or external reviewers, whose purpose is to carefully scrutinize the program designs, and the subsequent costs stemming from the panel deliberations, and ensure that they are as efficient as possible in achieving the stated goals. Third, PJ panels are asked to present their program designs to and answer the questions of a wider group of external independent stakeholder panels that have been established to review the results of PJ panel deliberations. Jay G. Chambers and Jesse D. Levin

Adequacy: Successful School District Approach See also Adequacy: Cost Function Approach; Adequacy: Evidence-Based Approach; Adequacy: Successful School District Approach; Cost of Education; Education Finance; Vertical Equity; Weighted Student Funding

Further Readings Baker, B. (2006). Evaluating the reliability, validity, and usefulness of education cost studies. Journal of Education Finance, 32(2), 170–201. Chambers, J., & Levin, J. (2006). Funding California’s schools: Part II. Resource adequacy and efficiency. In H. Hatami (Ed.), Crucial issues in California education 2006: Rekindling reform. Berkeley: University of California, Policy Analysis for California Education. Chambers, J., Levin, J., & Delancey, D. (2006). Efficiency and adequacy in California school finance: A professional judgment approach (American Institutes for Research report prepared for Getting Down to Facts Project). Stanford, CA: Stanford University, Institute for Research on Education Policy and Practice. Chambers, J., Levin, J., Delancey, D., & Manship, K. (2008). An independent comprehensive study of the New Mexico public school funding formula: Vol. 1. Final report (Report prepared for New Mexico State Legislature Funding Formula Study Task Force). Palo Alto, CA: American Institutes for Research. Chambers, J., Parrish, T., Levin, J., Guthrie, J., Smith, J., & Seder, R. (2004). The New York adequacy study: Determining the cost of providing all children in New York an adequate education: Vol. 1. Final report (Report prepared for the Campaign for Fiscal Equity). Palo Alto, CA: American Institutes for Research/ Management Analysis and Planning. Hanushek, E. (2005, October). The alchemy of “costing out” an adequate education. Paper presented at the Conference on Adequacy Lawsuits, Harvard University, Cambridge, MA. Hanushek, E. (Ed.). (2006). Courting failure: How school finance lawsuits exploit judges’ good intentions and harm our children. Stanford, CA: Hoover Institution Press. Perez, M., Anand, P., Speroni, C., Parrish, T., Esra, P., Socias, M., & Gubbins, P. (2007). Successful California schools in the context of educational adequacy (American Institutes for Research report prepared for Getting Down to Facts Project). Stanford, CA: Stanford University, Institute for Research on Education Policy and Practice. Rothstein, R. (2004). Class and schools: Using social, economic, and educational reform to close the BlackWhite achievement gap. Washington, DC: Economic Policy Institute.

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Sonstelie, J., Altman, I., Battersby, S., Benelli, C., Dhuey, E., Gardinali, P., . . . Lipscomb, S. (2007). Aligning school finance with academic standards: A weighted-student formula based on a survey of practitioners (American Institutes for Research report prepared for Getting Down to Facts Project). Stanford, CA: Stanford University, Institute for Research on Education Policy and Practice.

ADEQUACY: SUCCESSFUL SCHOOL DISTRICT APPROACH The successful school district approach is one of several methodologies—such as the professional judgment approach, the evidence-based approach, or the cost function approach—that states have used to estimate the total amount individual school districts would need to spend so students have a reasonable chance of meeting a state’s student performance expectations. All of these approaches are rational ways to estimate district costs: The successful school district approach examines the spending of districts identified as meeting state education standards; the professional judgment approach relies on calculating the costs of resources deemed necessary by a panel of educators; the evidence-based approach focuses on research that estimates the value of specific resources in improving student performance; and the cost function approach examines the statistical relationships between resources and student performance. A few states have adopted the successful school district approach as a key parameter in the school finance formulas used to allocate state funding to all school districts. In some states, the cost figure produced by the successful school district approach has served as a starting point for allocating state aid when the state is moving toward a higher figure over time, where the higher figure has been calculated using a different methodology to estimate adequate spending (as in Maryland). In most cases, but not all, the cost figure that results from applying the successful school district approach is lower than the cost figures produced by alternative approaches (in New Jersey, the figure produced by the successful school district approach was higher than the figure produced by an alternative approach). This entry discusses how the successful school district approach was developed, what costs are included in

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Adequacy: Successful School District Approach

the method, and how successful school districts are identified.

Development of the Successful School District Approach The successful school district approach, like the other approaches that have been developed in the past 20 years to calculate school finance formula funding levels, is considered to be a way to link school improvement and school accountability with school funding. Prior to that time, states typically set the parameters for their school finance formulas on the basis of political considerations, particularly the available state and/or local revenue or the yearto-year rate of inflation, rather than on the revenue needs of school districts. The most widespread form of school finance formula is the foundation program, under which a state sets a target revenue level for each school district and pays as state aid the difference between that amount and the local revenue generated using a state-specified, and typically constant, tax rate. Prior to the 1990s, under most foundation programs, the foundation target revenue level that was set for each school district was unrelated to the spending level needed to meet state education standards. Some states did use personnel ratios and statewide minimum personnel salary schedules as the basis for determining the target revenue levels of school districts. Even then, such ratios and salaries were not directly related to state standards but, rather, reflected education practice adjusted by available revenue. The successful school districts methodology was developed in the 1990s as states were implementing what has come to be called standards-based reform, under which the states began to create academic performance objectives for students, develop assessment procedures to measure how well students were performing, and establish consequences for schools and districts that did not meet those objectives. Ohio was the first state to use the successful school districts approach. In the mid-1990s, the state was implementing standards-based reform at the same time as the litigation focused on the constitutionality of the school finance system was in process, which required the state to develop an educational rationale for the parameters used in the formula. After the 2001 passage of the federal No Child Left Behind Act, which embraced standards-based reform and effectively required each state to do so or risk a loss of federal support, states grew more

focused on what resources school districts needed to meet state standards. The successful school district approach has also been used by plaintiffs in school finance litigation to calculate how much school districts would have to spend to meet state standards. Plaintiffs in these cases argued that these estimates showed current spending was inadequate to meet the standards and that the lack of a direct link between state education standards and the amount of funding the state provided to school districts violated the state constitution. (An example of this was the plaintiffs’ argument in Lobato v. State of Colorado; a district court found the plaintiffs’ argument to be persuasive in 2011, although the state supreme court later overturned the verdict and ruled that the state’s finance system was constitutional.) The philosophical basis of the successful school district approach is the concept that all districts should be able to meet state education standards if they spend as much, on a per-student basis, as the average amount spent by those districts that do meet the standard. This logic appeals to state legislatures because it is relatively simple to understand, does not require enormous amounts of data, and does not use analytic procedures that are overly complicated.

Costs Included in the Successful School District Approach The successful school district approach can currently only be used to estimate the base cost in a school finance formula; that is, it is only capable of estimating the cost of serving students with no special, high-cost needs and not the cost of serving at-risk students, English Language Learners, or students participating in special education programs. Critics of the successful school district approach argue that it includes a variety of expenditures that should be excluded to avoid double counting when the base cost is used in a school finance formula that makes additional adjustments for students with special needs. In fact, if done properly, the base cost should not only exclude spending for students with special needs, but it should also exclude cost adjustments for district characteristics that are beyond the control of the districts, such as size or location (which may produce geographic cost-of-living differences among school districts analogous to those that exist across states). In addition, a number of other expenditures should not be included in a base cost figure, such as those for transportation, food

Adequacy: Successful School District Approach

services, community services, and capital outlay and debt services. It is appropriate to use the successful school district approach only if other approaches are used to address the cost implications of student and district needs. In the simplest terms, the figure that emerges from the successful school district approach is the average per-student current operating spending of those districts that meet, nearly meet, or appear to be on a path to meet state education standards. Districts that do not fully meet state standards at the time the calculation is being made are included to ensure that the spending levels of a reasonable number of districts are used in calculating the cost figure, rather than relying on what might be a very small number of districts that meet state standards, some of which may be unusual in a particular way (e.g., they may serve a homogeneous population that is unique, such as Amish students, or be located in a unique place, such as an island). Districts that are included in the calculation do not need to be statistically representative of all the districts in a state. As mentioned above, to be sure that the figure is a basic cost, expenditures for students with special, highcost needs and expenditures associated with district characteristics need to be excluded. This can be done either directly, when a state’s accounting system is capable of separating out such expenditures, or indirectly. The indirect method entails excluding state aid that is provided specifically for student and district cost factors by making assumptions about how much districts spend for such purposes, either by using student weights or by using factors that the state already includes in its school finance system to recognize such costs (which might overestimate the actual spending).

Identifying Successful School Districts An important issue that has arisen in selecting successful school districts is whether identifying such districts should be based on their meeting student performance objectives in an absolute sense (at one point in time) or in a relative sense (movement over time). Districts could be selected because students perform at high levels or because student performance has grown at a high rate over some period of time even if, after the growth has occurred, the level of performance is still low in an absolute sense. In general, given the persistent relationship between student performance and family income, districts identified as being successful in an absolute way will

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tend to have higher income. If there is a desire for other districts to be selected, it may be necessary to use a measure of performance growth separate from, and in addition to, absolute measures. Given that policymakers tend to want to be inclusive and not base calculations on a small number of districts with elite credentials, usually both procedures to identify successful school districts are used. In practice, the successful school district approach has attempted to directly address the efficiency of school district operations by focusing on districts that not only are successful in meeting state education standards but also are not unusually high or unusually low in their use of resources. To do this, expenditures have been disaggregated into three categories: (1) instruction (including instructional support), (2) administration (combining spending at both the school and the district level), and (3) plant maintenance and operation (excluding capital outlay). Then, in the case of instruction, for example, all districts are ranked by their number of teachers per 1,000 students, and districts that are more than, perhaps, 1 standard deviation above or below the statewide average on this measure are excluded; the districts used in calculating the statewide instructional expenditures per student are those that are both successful and within one standard deviation of the statewide average. This figure is then added to similarly calculated figures for administration (based on the number of administrators per 1,000 students) and plant maintenance and operation (typically based on per-student spending, not staffing). Once the figure has been calculated, it can be incorporated into the state school finance system and adjusted as necessary by student weights for students with special needs and district characteristics, so that the state sets revenue levels for all districts that reflect their full costs of operation. This means that student weights for high-needs student populations may not reflect the expenditures thought to be necessary for such students to meet state education standards; the professional judgment approach can be used to calculate weights that take student performance into consideration. The successful school district approach can also be based on school site, rather than school district, expenditures. That is, rather than using school district average expenditures, the per-student spending of schools considered to be successful can be analyzed as the basis for creating a base cost figure for use in a state aid distribution formula. This is a way to more precisely link expenditures to student

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Adequate Yearly Progress

performance since districtwide expenditures may include spending for schools that are not considered to be successful. This also allows for recognition of different types of school organization, including elementary, middle, and high schools as well as magnet schools, charter schools, or online schools. If this approach is used, funds for district costs must be included in the final assessment of total statewide costs of funding adequacy.

Conclusion The successful school district approach has persisted over time because policymakers are attracted by its underlying philosophy and its ease of calculation. It has been used both to set the base cost parameter in a state’s school finance formula and as the basis for determining whether the spending of every school district in a state is at or above what is considered to be a reasonable level. The fact that the successful school district approach also includes a way to address the efficiency of education service delivery makes it attractive. Some states that have wanted to analyze the relationship between school district spending and state education standards have required that several approaches be used to determine costs, specifically calling for the use of the successful school districts approach. John G. Augenblick See also Accountability, Standards-Based; Adequacy: Professional Judgment Approach; Cost of Education; Economic Efficiency

Further Readings Augenblick, J. G., Myers, J. L., & Anderson, A. B. (1997). Equity and adequacy in school funding. The Future of Children: Financing Schools, 7(3), 63–78. Retrieved from http://futureofchildren.org/futureofchildren/ publications/docs/07_03_04.pdf Fermanich, M., Picus, L. O., Odden, A., Mangan, M. T., Gross, B., & Rudo, Z. (2006). A successful-districts approach to school finance adequacy in Washington (Analysis prepared for the K-12 Advisory Committee of Washington Learns). North Hollywood, CA: Lawrence O. Picus. Guthrie, J. W., & Rothstein, R. (1999). Enabling adequacy to achieve reality: Translating adequacy into state school finance distribution arrangements: In H. Ladd, R. Chalk, & J. Hansen (Eds.), Equity and adequacy in education finance: Issues and perspectives (chap. 7). Washington, DC: National Research Council,

Committee on Education Finance, Equity, and Adequacy. Lobato v. State, 304 P.3d 1132 (Colo. 2013). National Access Network. (2006, June). A costing out primer. Retrieved from http://www.schoolfunding.info/ resource_center/costingoutprimer.php3 Picus, L. O., & Blair, L. (2004). School finance adequacy: The state role. Insights on Education Policy, Practice, and Research, 16. Retrieved from http://www.sedl.org/ policy/insights/n16/insights16.pdf Taylor, L. L., Baker, B. D., & Vedlitz, A. (2005, September). Measuring educational adequacy in public schools (Bush School Working Paper No. 580). College Station, TX: Bush School of Government and Public Service. WestEd. (2000, July). Policy brief: School funding, from equity to adequacy. Retrieved from http://www.wested. org/resources/school-funding-from-equity-to-adequacy/

ADEQUATE YEARLY PROGRESS The No Child Left Behind Act of 2001 (NCLB)— the most recent reauthorization of the Elementary and Secondary Education Act—established adequate yearly progress (AYP) as the primary measure of school performance for the purpose of federal accountability. NCLB requires that all students are grade-level proficient in English language arts (ELA) and mathematics by the 2013–2014 academic year. Schools are expected to demonstrate adequate progress toward this goal each year by meeting increasingly higher targets for the proportion of students who are proficient in the tested subjects. The U.S. Department of Education established the overall accountability framework for states to measure school performance, while state education departments decided how the criteria would be administered within each state. Separately, many states had their own state accountability policies; these policies generally conflicted with AYP in that they were based on different measures and produced different ratings of school performance. This entry details the accountability framework established in NCLB as well as the state-level decisions. It concludes with a discussion of what we have learned from a decade of research on AYP and where future policy on school accountability is headed.

Federal Legislation and State Decisions Undergirding NCLB are four aspects of school performance measurement. First, testing must occur in

Adequate Yearly Progress

mathematics and ELA annually in Grades 3 through 8 and at least once in Grades 10 through 12. Science testing was added during the 2005–2006 academic year but is not included in school performance measurement. Second, student performance in both math and ELA must be reported for the entire school population and broken down by numerically significant subgroups. The federal legislation states that the school subgroups should include major racial/ ethnic groups, English Language Learners, students with disabilities, and socioeconomically disadvantaged students. Third, 95% of the enrolled students must be included in the school performance calculation. The 95% participation rate must also be met by each subgroup for each tested subject. Finally, graduation rates must be included as an indicator of high school performance, and an additional indicator of performance must be identified for schools without a graduating class. This additional indicator is commonly average daily attendance or alternative state examination performance. Subgroup Determinations

States determine which ethnic/racial groups to include in AYP calculations based on state demographics. States also identify how large a particular group of students needs to be within the tested population in a school for the group to be considered significant. Nationally, subgroup sizes range from 10 to 100 students and average around 30 to 40 students. If a state chooses a subgroup size of 35 and a school has 34 Latino students, the Latino students would be included in the school’s overall proficiency rate, but the group’s performance would not be separately reported. In contrast, if the same state chose a subgroup size of 30, the school with 34 Latino students would have to report on the performance and participation rate of the Latino students in both mathematics and ELA (four additional performance criteria). Clearly, state subgroup decisions affect which schools are subject to accountability and for what. Annual Measurable Objectives

The “annual measurable objectives” (AMOs) are the targets a school must meet to make AYP annually. Every school has a minimum of five AMOs: overall school proficiency in ELA and mathematics, school participation rate in ELA and mathematics, and performance on the additional indicator. For every additional numerically significant subgroup,

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a school has an additional four AMOs as described above. The subgroups for which a school is accountable may change from year to year if a school’s enrollment changes in demographic characteristics. Proficiency Rate Targets

Each state set its own proficiency rate baseline and annual targets. The baseline was set as the higher of (a) the proficiency level of the lowest performing student subgroup in 2001–2002 and (b) the schoolwide proficiency rate of the school at the 20th percentile. Once a state had set a baseline proficiency rate, the state selected how the proficiency targets would increase toward the 100% goal. One option was to increase proficiency targets in equal increments on an annual basis from 2003 to 2014. For states that did not select an equalincrement increase, there were two rules: First, the proficiency target must increase within the first 2 years of NCLB’s enactment, and second, targets could never be held constant for more than 3 consecutive years. Under NCLB, schools determine their annual proficiency rates by dividing the number of grade-level proficient students by the total number of students enrolled in the tested grades. For a subgroup, the calculation is the number of grade-level proficient students in the subgroup divided by the total number of students who are members of that subgroup. Importantly, states set dramatically differing expectations of what achievement level students must meet to be considered proficient. This led to criticism of NCLB for putting in place incentives for states to set low thresholds for what is considered grade-level proficiency. Making AYP

For a school to make AYP in a given year, every AMO for the school must be successfully met: 95% of all students and students in each significant subgroup are tested, the proficiency targets are met by each subgroup and the overall school population, and the additional indicator or the graduation rate criterion is met. Failure to meet one or more criteria results in identification as failing AYP for the given year. For Title I schools (those with many students from low-income families who receive federal support), failing to make AYP is associated with sanctions. After 2 consecutive years of not making AYP, a Title I school is labeled as “in need of improvement.” The associated sanctions are intended to incentivize schools to meet AYP goals in the following years.

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Adequate Yearly Progress

After multiple years of failing AYP, a school may be taken over by the state or a charter organization. Alternative Methods to Making AYP

There are also several alternative methods that schools can use to meet individual AMOs. Alternative methods serve two purposes: (1) to ensure statistical reliability of calculations, particularly for small schools or subgroups, and (2) to not unduly penalize schools with larger proportions of students below proficiency levels when NCLB began. Two common alternative methods are confidence intervals and safe harbor. Confidence intervals adjust proficiency rate targets for small schools or subgroups. Safe harbor applies when a subgroup does not meet the annual target but shows a 10% reduction in the proportion of students scoring below proficiency levels from the previous year. For example, a school with 40% of Asian students who are proficient would have to raise the percentage proficient for Asian students by 6%. States vary in their interpretations of these two alternative methods, with some making it substantially easier for schools to meet the AMOs. Lessons Learned

AYP has several advantages as an accountability metric. Most notably, the use of the percentage proficient measure is conceptually simple and transparent to both parents and educators. Status-based measures of performance are familiar and widely understood (e.g., grades, test scores).The simplicity and transparency of AYP, however, have been overshadowed by several critiques of the measure that have contributed to the unintended consequences of NCLB accountability. A primary critique is of the daunting target of 100% proficiency by 2014. Research has supported this criticism, demonstrating that large proportions of schools nationwide were well below the targeted proficiency goal with 1 year remaining before the 2014 deadline. While proficiency rates among almost all student subgroups have shown improvement over the 2002 baseline rates, more schools failed to make AYP during each year of NCLB’s implementation. Furthermore, schools that are making AYP increasingly rely on alternative methods, such as safe harbor, to meet their AMOs. A second criticism of AYP is of the calculation used to represent school performance. AYP is based on a status measure of performance—a snapshot of a school during the testing period without any

consideration of the students’ prior achievement or the school’s contribution to that achievement. When student demographic characteristics and prior achievement are incorporated into school performance calculations, measuring the academic growth of students in a year rather than using a status measure of performance, the result is a fairer identification of schools that are failing to improve performance. This is because such a system more closely measures those factors under a school’s control, as opposed to measuring student demographics and prior performance. Researchers have therefore recommended achievement measures that combine status- and growth-based measures of achievement to give a fuller picture of school performance. AYP has also been criticized by researchers who find failure to make AYP to be highly related to a school’s demographic composition and the number of AMOs required to be met. Schools with large enrollments and schools located in urban districts fail to make AYP more frequently than smaller schools and suburban schools. High schools fail to make AYP more frequently than elementary schools. Schools with more AMOs to meet each year fail to make AYP more frequently than schools with fewer goals to meet. In other words, the more diverse a school’s student population, the more students a school serves, or the more performance benchmarks a school must meet, the more difficult it is for that school to make AYP. These findings vary considerably from state to state, however, because of variation in the decisions around the racial/ethnic groups included in the policy, subgroup size, proficiency targets, and alternative methods for meeting AMOs. Several other critiques have been proffered in response to AYP measures. For example, many have critiqued NCLB’s exclusive focus on ELA and mathematics. The focus on these two subjects has incentivized schools—particularly those serving more disadvantaged students—to narrow the curriculum and reduce teaching in other subject areas. AYP’s use of proficiency rates also encourages teachers to focus on improving the performance of students just below the proficiency targets (so-called bubble kids) and to give less attention to students who are far above or below the proficiency target. Finally, AYP has been criticized for conflicting with the separate accountability systems that exist in many states. Some of these accountability systems, such as California’s Public Schools Accountability

Administrative Spending

Act, predate NCLB. When states have two or more accountability systems by which schools are assessed, teachers, parents, and students can receive mixed messages about school performance. Where some schools may be failing to make AYP, they may be meeting state-level accountability targets, or vice versa. Competing accountability systems have also created competing goals within schools, which give teachers conflicting messages about the appropriate focus of improvement efforts. In short, when two accountability systems have different measures, targets, and reporting requirements, the public is left to wonder which accountability system is actually identifying a struggling school.

The Future of AYP The next reauthorization of the Elementary and Secondary Education Act has been pending since 2007. While waiting for a decision on the next version of the legislation, the Education Department announced in 2011 the opportunity for states to be released from NCLB accountability measures through a waiver process. Nearly all states applied for flexibility waivers, outlining the new methods by which they would identify low-performing schools and address underperformance. As of January 2014, 42 states, the District of Columbia, Puerto Rico, and a group of eight school districts in California have received waivers. Several other states have applications under review and have been released from raising proficiency targets while waiting for a response. Those states that have not submitted a request for a waiver or been granted one will remain subject to AYP until the reauthorization of NCLB. These new waivers have offered states substantial flexibility around accountability requirements. For instance, the waivers offer opportunities to incorporate growth measures of school performance, to modify subgroup definitions for accountability determination, and to outline new proficiency goals. The 100% proficiency target is no longer required. These waivers give states the freedom to create new accountability systems that avoid some of the criticisms and problems of AYP. However, early analyses of these waivers indicate that many of the state waiver systems have made some of the same mistakes as AYP. For example, many states continue to utilize test scores only in the subjects of math and ELA and rely solely on proficiency rates to identify low-performing schools. The future of AYP may well depend on how well the state waiver systems

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fairly and reliably identify low-performing schools for improvement. Morgan S. Polikoff and Stephani L. Wrabel See also Accountability, Standards-Based; Achievement Gap; Elementary and Secondary Education Act; No Child Left Behind Act

Further Readings Balfanz, R., Legters, N., West, T. C., & Weber, L. M. (2007). Are NCLB’s measures, incentives, and improvement strategies the right ones for the nation’s low-performing high schools? American Educational Research Journal, 44(3), 559–593. Dee, T. S., & Jacob, B. (2011). The impact of No Child Left Behind on student achievement. Journal of Policy Analysis and Management, 30(3), 418–446. Kim, J. S., & Sunderman, G. L. (2005). Measuring academic proficiency under the No Child Left Behind Act: Implications for educational equity. Educational Researcher, 34(8), 3–13. Linn, R. L. (2003). Accountability: Responsibility and reasonable expectations. Educational Researcher, 32(7), 3–13. McEachin, A., & Polikoff, M. S. (2012). We are the 5%: Which schools would be held accountable under a proposed revision of the Elementary and Secondary Education Act? Educational Researcher, 41(7), 243–251. Polikoff, M. S., & Wrabel, S. L. (2013). When is 100% not 100%? The use of safe harbor to make adequate yearly progress. Education Finance and Policy, 8(2), 251–270. Porter, A. C., Linn, R. L., & Trimble, C. S. (2005). The effects of state decisions about NCLB adequate yearly progress targets. Educational Measurement: Issues and Practice, 24(4), 32–39.

ADMINISTRATIVE SPENDING One of the most common criticisms of public schools in the United States is that they are “inefficient” and overly bureaucratic, with too many resources devoted to administrative bloat instead of instruction and students. In several states, such concerns have resulted in policies that limit administrative or noninstructional spending (or, conversely, require a minimum share of the budget to be spent on classroom instruction). However, one must first define and measure administrative spending, which, as discussed in the first section below, is not always

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Administrative Spending

a straightforward task. This entry explores some of the measurement issues that arise when policymakers try to restrict the share of spending on administrative versus classroom purposes. Next, there is a review of the research on whether such spending should really be considered an “inefficient” use of resources. The entry concludes with a summary of the questions that policymakers should ask when considering imposing restrictions on administrative spending.

What Constitutes Administrative Spending? Many people think of school and district administrative spending as any expenditure that is not directly related to classroom instruction, but there are many staff positions and spending categories where the distinction is not so obvious. Superintendents, principals, assistant principals, and office staff are clearly part of the administrative apparatus of a district and its schools. However, some expenditures that might be considered “administrative” are still related to instruction, though not necessarily directly tied to classrooms, such as spending on libraries, counselors, or professional specialists. Other spending categories are not instructional but not traditionally thought of as administration either, such as transportation, food services, or maintenance and operations. Although these expenditures are not directly tied to the instruction of students, they are clearly important for ensuring an environment in which students can learn effectively. Because “administrative” spending and “noninstructional” spending are not necessarily interchangeable concepts, any discussion of administrative spending must define terms carefully at the outset. A common option for researchers is to use the categories defined by the National Center for Education Statistics (NCES). For example, in Condition of Education, an annual NCES publication that reports trends in schools, all current expenditures are divided into subcategories. Instruction includes “activities dealing directly with the interaction between teachers and students,” such as the work of teachers and teaching assistants, use of instructional materials, and instructional services provided under contract. There is a separate category for Instructional Staff Services, which includes curriculum development, staff training, libraries, and media centers. Student Support also is a separate category, encompassing guidance, health, attendance, and speech pathology services.

Transportation, Operation and Maintenance, and Food Services are additional categories, with all other expenditures grouped under Administration. Given that NCES data are often used in discussions of school spending, researchers and policymakers should pay careful attention to which of these categories are being used. A related issue is whether administrative costs at the central office (i.e., district costs) are separated from costs at individual sites (i.e., school costs). Revenues and expenditures are generally reported for districts, not schools, and it is relatively rare to see a distinction made between central and site costs. However, it is sometimes argued that a large central office administration is a stronger indicator of inefficiency, representing more bureaucracy that hinders those at school sites from serving students most effectively.

The “65 Percent Solution” How one defines administrative spending can be critical for discussions of the size of administrative “bloat.” For example, in the mid-2000s, a number of states proposed adopting the so-called “65 percent solution,” a requirement that at least 65% of district budgets be spent on classroom instruction. The 65% number came, in part, from data showing that “instruction” constitutes around 60% of the budget in most school districts. The implication by many supporters of this policy was that any district that was spending less than 65% in the classroom must be “wasting” resources (see below). Those who opposed the idea countered that many expenditures outside the classroom are still clearly serving the instructional mission of schools and that an arbitrary cap on such spending could mean closing libraries, laying off instructional aides and counselors, or reducing transportation and letting buildings fall apart. It is also worth noting that if one focuses on the share of a district’s budget that is devoted to administration, then it is important to define the denominator just as carefully as the numerator. That is, one must be clear about how one is defining total spending. For example, Kansas was one of the states to adopt the “65 percent solution” in 2006, and districts are required to spend 65% of current spending in the classroom. However, there has been much debate over whether bond payments and capital spending should be included in “current spending.” The NCES definition excludes such spending,

Administrative Spending

and with lower spending (a smaller denominator), the share spent on classroom instruction will appear higher.

Is Administrative Spending Inefficient? In policy discussions, references to administrative spending are often closely tied to concerns about inefficiency and “wasteful” bureaucracy. In the school finance literature, inefficiency generally refers to allocations that do not maximize student performance (however that might be measured). That is, an inefficient district is one in which resources could be reduced or reallocated so that student performance would increase. There are multiple ways in which higher administrative spending might reflect such an inefficient use of resources. One possibility is that administrators are inherently bad for students and exhibit negative productivity; that is, having more administration expenditures reduces student performance directly, perhaps because the bureaucracy creates such a regulatory burden on teachers that it makes schools less effective. More realistically, administrative spending “crowds out” instructional spending, so that student outcomes are reduced because administrators are simply less productive than teachers. A more nuanced argument, mentioned earlier, is that increased district administration reduces productivity, while school site administrators do not. Production Function Studies

Although policymakers (and the general public) often equate administrative spending with inefficiency, the research evidence is quite mixed. Production function studies that examine the relationship between administrative inputs and student outcomes tend to find either no relationship or a positive one (i.e., higher administrative spending is correlated with improved student performance). For example, one study used data from New York and not only found weak evidence that, all else being equal, a larger number of district administrators negatively affects student performance but also found that a larger number of school site administrators may positively affect student performance. In a meta-analysis of production function studies, no statistically significant negative impact of administrative inputs on student outcomes was found, and seven studies in the analysis found a positive relationship. In one of the only studies to try to directly assess the claims made about the

43

“65 percent solution,” researchers at Standard & Poor’s found that there was no relationship between higher instructional spending and student proficiency rates on state math and reading tests across a number of states that adopted, or were considering adopting, that particular policy (cited in American Federation of Teachers, 2006). Efficiency Studies

A handful of studies have tried to estimate the relationship between administration and inefficiency directly, and these provide some support for a positive correlation. In these studies, the authors first generated a direct measure of school or district inefficiency and then investigated whether and how that measure is correlated with expenditures. For example, one 1997 study used data from Illinois and measured inefficiency using the nonparametric estimation method of data envelopment analysis. District inefficiency was found to be correlated with higher ratios of administrative to instructional spending. More recently, researchers used data from Georgia and measured inefficiency with the modified quadriform method (in which districts are placed in discrete input-output categories and compared across multiple variables); they found a positive correlation between district inefficiency and the percentage of the budget allocated to administration. On the other hand, a study using data from Texas and measuring inefficiency with an input distance function found that instructional share was positively correlated with student test scores (controlling for student characteristics) but schools spending a larger share of their budget on instruction were significantly less technically efficient (i.e., they could reduce inputs without reducing output) and neither more nor less allocatively efficient (i.e., they were neither overusing nor underusing different types of inputs). Charter School Studies

Finally, a somewhat indirect way to assess claims about administrative spending and efficiency is to look at charter schools. Because charter schools are more decentralized and must compete for students, they are often expected to be more efficient. However, researchers have found that charters in Michigan spend relatively more on administration than traditional public schools. This is also reflected in work done in Texas, where charters are less allocatively efficient than traditional public schools, with a tendency to “overuse” administrators.

44

Administrative Spending

Level or Share of Spending?

One issue raised by the mixed results in the literature is whether it is the level of administrative spending or the share of spending that matters. There are clearly legitimate reasons why the share of spending allocated to administration and noninstructionional services will vary across districts. District size is one of the most important factors; many noninstructional costs are relatively fixed, so the proportion of the budget for smaller districts will be larger. Increasing instructional budget share is only possible by increasing the total size of the budget. At the same time, two districts could have similar shares of administrative spending but may be allocating very different dollar amounts to administration if their total budgets are of different sizes (i.e., 35% of $12,000 equals more dollars than 35% of $8,000). There is also some evidence that central administrative spending is higher in districts with more poor students and students with limited English proficiency, which would be consistent with these students requiring more support services.

Policies to Restrict Administrative Spending If there is one thing that is clear from the research literature, it is that widespread generalizations about administrative inefficiency in schools are unwarranted. However, that seems unlikely to stop policymakers from periodic attempts to restrict administrative spending. For these policymakers, and for researchers who are investigating how districts allocate their resources, it is worth keeping the following issues in mind: • How is administrative spending defined? If a policy is intended to limit administrative costs, it should be explicit about what are considered “legitimate” expenditures. This can be a difficult balancing act. Policymakers may want to define administrative spending broadly so districts cannot simply move money into different categories; on the other hand, there are many expenditures that fall outside the classroom but are still closely tied to instruction, so restricting such spending could have negative effects on students. If the focus is on the administrative share of spending, then it is also important to clearly define what is or isn’t included in the total budget. • What is the appropriate policy measure: the level of administrative or instructional spending

per pupil or the share of a budget spent on a particular category? And what is the appropriate policy target? Some states (e.g., Kansas, Georgia) that adopted the “65 percent solution” in 2006 are now realizing that 65 is a fairly arbitrary number, particularly when there are legitimate reasons for budget shares to vary, even across districts with similar levels of spending. Jennifer Imazeki See also Allocative Efficiency; Central Office, Role and Costs of; Data Envelopment Analysis; Economic Efficiency; Technical Efficiency

Further Readings American Federation of Teachers. (2006). 65 percent solution. Retrieved from http://www.aft.org/issues/ economy/65percent/ Arsen, D., & Yi, Y. (2012). Is administration leaner in charter schools? Resource allocation in charter and traditional public schools. Education Policy Analysis Archives, 20(31). Retrieved from http://epaa.asu.edu/ojs/ article/view/1016 Baker, B. (2003). State policy influences on the internal allocation of school district resources: Evidence from the Common Core of Data. Journal of Education Finance, 29(1), 1–24. Brewer, D. J. (1996). Does more school district administration lower educational productivity? Some evidence on the “Administrative Blob” in New York public schools. Economics of Education Review, 15(2), 111–124. Chalos, P. (1997). An examination of budgetary inefficiency in education using data envelopment analysis. Financial Accountability and Management, 13(1), 55–69. Hedges, L. V., Laine, R. D., & Greenwald, R. (1994). Does money matter? A meta-analysis of studies of the effects of differential school inputs on student outcomes. Educational Researcher, 23(3), 5–14. Houck, E., Rolle, R. A., & He, J. (2010). Examining school district efficiency in Georgia. Journal of Education Finance, 35(4), 331–357. National Center for Education Statistics. (2009). Financial accounting for local and state school systems: 2009 edition. Retrieved from http://nces.ed.gov/pubsearch/ pubsinfo.asp?pubid=2009325 Taylor, L. L., Grosskopf, S., & Hayes, K. J. (2007). Is a low instructional share an indicator of school inefficiency? Exploring the 65-percent solution (Bush School Working Paper No. 590). Retrieved from http://bush. tamu.edu/research/workingpapers/ltaylor/The_65_ Percent_Solution.pdf

Adult Education

ADULT EDUCATION Adult education, broadly conceived, encompasses the basic, secondary, and postsecondary education of individuals who are older than traditional students or who have social roles (e.g., parenthood, work) that are more consistent with adulthood than with adolescence or youth. Adult education is motivated by educational philosophies on the value of an educated citizenry and the individual economic returns to continued education. Adults are a large and growing segment of secondary and postsecondary education. Adult learners have different objectives, challenges, and opportunities compared with traditional students. These differences have implications for every area of education policy and finance, from curriculum and program development to accountability and budgeting. In the modern-day United States, secondary adult education is primarily focused on providing diplomas and equivalent credentials to individuals who have not completed high school. At the postsecondary level, adult enrollees account for a large share of the student population, particularly among part-time students, community college students, and students enrolled in for-profit institutions. Adult postsecondary learners include first-time freshmen who entered the labor force immediately after high school, returning students who did not complete college the first time they enrolled, and other adults whose circumstances (e.g., parenthood, military service, or prison) prevented a seamless transition from high school to college. Students who pursue advanced degrees that take them well into adulthood are another large class of adult learners. The entry proceeds as follows. First, adult education is described in the context of life-cycle investments in education and various philosophies of education. This is followed by an outline of basic and secondary-level adult education. Next, summary statistics for older college students are discussed, alongside a small discussion of research on nontraditional students’ success at the college level. Last, the returns to adult education are profiled.

Adult Education: Theory Life Cycle Investments in Education

Beyond the age of compulsory schooling, which currently ends between the ages of 15 and 18 in the United States, continued education is a choice

45

that each individual makes. In economic theory, the choice to pursue additional schooling at the secondary or postsecondary level is weighed against the alternatives, chiefly work and leisure. The opportunity cost of schooling in a given year is the value of the next best alternative. Consider a recent high school graduate who is deciding between work and college for the coming year. The opportunity cost of college is the value of work to the student, including the wages she would bring home, plus the value of tuition, fees, and other expenses that must be paid to attend college. Enrolling in college reduces earnings in the very short term by limiting the time that she can work and by delaying the labor market gains that will come from promotion. Yet abundant research shows that postsecondary schooling increases earnings over a lifetime. And so the opportunity cost of working in a given year rather than continuing her education includes the present discounted value of the incremental impact that a year of schooling would have on her lifetime earnings. In theory, this student will weigh the costs and benefits of schooling against the costs and benefits of working throughout her life, updating as new information or shocks affect the perceived net value of schooling relative to the alternatives. Within the construct of this simple conceptual model, returning adult learners are those who choose work or leisure over school, and later choose school over work and leisure. More complex models recognize the overlap between work and school, which describes part-time adult learners. The model of life cycle investments in education is useful for understanding why adult learners choose a timeline that blends work and schooling, as well as the policy levers that can help adults accomplish their objectives. Students who drop out of high school or forgo college to enter the labor force do so because the perceived lifetime returns to continued schooling are overshadowed by the value of earnings that can be garnered at the present time or the psychic value of leisure. Adults who return to school do so because of an unexpected shock to earnings (unemployment, technological change, imprisonment) or an updated, higher perception of the value of schooling. A policy response to draw adults back to school, for example, would be a campaign that advertises the benefits of schooling and updates individual perceptions. Institutions can facilitate returning adults’ successful transition through schooling and back to work by offering career-focused programs that ameliorate the

46

Adult Education

individual circumstances that led to lower earnings (e.g., inadequate or outdated skills). Another important factor in the college/work decision over the life cycle is the existence of credit constraints or debt aversion, which can prevent prospective students from borrowing to finance college. If students cannot adequately borrow against future earnings, they may be constrained to choose work over college despite high returns to schooling. These circumstances motivate financial aid, tax-financed subsidies for higher education institutions, and public job training programs.

Philosophies of Adult Education The human capital theory of investment in education defines the broad parameters of student demand for education but says nothing about the social values of education or the means of practicing those values. To complete the picture, philosophies of education can be used to cast the purpose of educating adult learners. Three educational philosophies—liberal, progressive, and behavioral—are introduced below alongside their role in adult education. Liberal education philosophies, dating back to the time of Socrates, Plato, and Aristotle, aim to produce virtuous, principled statesmen through rigorous intellectual explorations into logic, the sciences, and the humanities. Though much of today’s liberal education systems focus on youth, adults have much to gain from academic training, especially in light of their experience and maturity. In practice, however, adult demand for education is typically tied to job opportunities. Therefore, the ideals of adult education are more naturally couched in practical educational philosophies. The progressive philosophy of education, for instance, is very relevant to adult education. Progressive education philosophy was most influential during the first half of the 20th century and emphasizes vocational training (but not to the exclusion of intellectual training), contextualized learning, inductive reasoning, and democratic access to formal education. These virtues are well suited to career-focused adults who face hurdles to continuing education. The behavioral philosophy of education is perhaps the most broadly applied across youth and adult education, emphasizing positive reinforcement, clear standards of evaluation, student and instructor accountability, and competency-based education. The overriding principles of behavioral education

are to change student behavior (including the use of skills) through the use of reinforcing incentives (e.g., grades) and to cultivate each student’s ability to adapt to new circumstances. These themes apply strongly to adult learners who have been hindered by insufficient skills or adaptability.

Basic and Secondary Adult Education Adult education movements date back to the 18th- to 19th-century Industrial Revolution. Early examples from the United States are the lyceum and Chautauqua movements, which were typified by assemblies that combined entertainment, religious instruction, and lectures on a variety of topics. In 19th-century Great Britain and Australia, Mechanics’ Institutes—often funded by private industrialists—provided vocational training to working-age men. In the United States, federal support for adult education began with the Adult Basic Education Program contained in the Economic Opportunity Act of 1964. This program funded training for individuals of 18 years and older who lacked basic skills such as literacy and numeracy. It was followed in 1966 by the Adult Education Act, an outgrowth of President Lyndon B. Johnson’s War on Poverty and Great Society movement. The act evolved over time to expand eligibility to 16- and 17-year-olds (1968), secondary students (1970), and individuals with limited English proficiency (1988). A centerpiece of adult secondary education is preparation for the GED® exam. The GED exam dates back to 1942, when it was introduced as a test of “general educational development” for World War II veterans who had not completed high school before entering the war. GED test taking has increased tremendously since that time due to public job training programs and prison education programs, as well as its greater use by teenagers who view the GED credential as a substitute for completing high school. In 2007, 12% of high school credentials were obtained by passing the GED exam. Research has demonstrated that the labor market does not treat the GED credential as equivalent to a traditional high school diploma. Similarly, job training programs for high school dropouts have had a mixed record of success that rarely passes cost-benefit examination. These findings underscore the importance of completing secondary education on time.

Adult Education

Postsecondary Adult Education Table 1 lists the share of the 2011 U.S. college student population who were more than 25 years of age (a common but blunt benchmark for demarcating between adults and the traditionally younger agegroup of college students), overall and for several subgroups. The striking conclusion from Table 1 is that older students are in the minority but hardly “nontraditional” among the college-attending population at large. Students more than 25 years of age account for one quarter of all 2011 undergraduates, more than one third of all part-time students, and 40.2% of students in for-profit colleges and universities. The bottom panel of Table 1 lists the rate of adult learners by Carnegie Basic Classification. The Carnegie Classification system groups higher education institutions according to their highest degree awarded, their research mission, and other features. Adults are more common in colleges granting the 2-year associate’s degree and in colleges granting the 4-year master’s degree than they are in researchintensive universities or in liberal arts colleges granting a bachelor’s degree.

Table 1 Postsecondary Students of Ages 25 and Older in 2011, by Subgroup and Type of College or University Adult Students as a Percentage of Total Students, by Subgroup All students

28.3%

Women

29.3%

Part-time students

36.5%

Undergraduate students

25.0%

Graduate students

43.7%

Adult Students as a Percentage of Total Students, by Type of College or University Associate’s Carnegie class

29.6%

Bachelor’s Carnegie class

22.4%

Master’s Carnegie class

29.2%

Research and doctoral Carnegie class

25.7%

For-profit institutions

40.2%

Source: Calculations by Celeste K. Carruthers, using data from the Integrated Postsecondary Education Data System, National Center for Education Statistics, Institute for Education Sciences, U.S. Department of Education.

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Older students enter college with inherent disadvantages. Adult college students are more likely than traditional students to work and attend college at the same time and more likely to have dependent children. These factors contribute to binding financial constraints and have been shown to decrease the likelihood of degree completion. Not surprisingly, then, adult enrollees are less apt to complete a bachelor’s degree than their younger counterparts on average. Some circumstances have been shown to improve the completion rate of postsecondary adults. Researchers James E. Rosenbaum and Janet Rosenbaum engaged in extensive quantitative and qualitative analyses to draw out the lessons of successful “occupational colleges,” which are typically private, for-profit institutions that cater to nontraditional students. The more successful institutions in this class (a) emphasize sub-bachelor’s credentials such as certificates and associate’s degrees that are attained within tightly structured programs of study, (b) omit remedial education, and (c) align programs with specific occupational skills and job placement pathways. The individual returns to postsecondary education have been documented in a variety of settings, and the benefits of higher education typically extend to adult students. A study by Duane E. Leigh and Andrew M. Gill (1997) found that students returning to school as community college students realize roughly the same wage premium as students who transition from high school directly into community college. Emerging research on the returns to enrolling in a for-profit college—where adults are common—shows mixed results. Relative to students in public and nonprofit institutions, for-profit students exhibit not only higher completion rates 6 years after entry but also higher rates of unemployment, lower earnings, and larger debt burdens. Celeste K. Carruthers Note: GED® is a registered trademark of the American Council on Education (ACE) and is administered exclusively by GED Testing Service LLC under license. This content is not endorsed or approved by ACE or GED Testing Service. Celeste K. Carruthers and this work are not affiliated with or endorsed by the American Council on Education (ACE) or GED Testing Service LLC. Any reference to “GED” in the title or body of this work is not intended to imply an affiliation with, or sponsorship by, ACE, GED Testing Service LLC, or any other entity authorized to provide GED®-branded goods or services.

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Age-Earnings Profile

See also Benefits of Higher Education; Benefits of Primary and Secondary Education; Continuing Education; For-Profit Higher Education; General Educational Development (GED®); Vocational Education

Further Readings Becker, G. S. (1993). Human capital: A theoretical and empirical analysis with special reference to education (3rd ed.). Chicago, IL: University of Chicago Press. Deming, D. J., Goldin, C., & Katz, L. F. (2012). The forprofit postsecondary school sector: Nimble creatures or agile predators? Journal of Economic Perspectives, 26(1), 139–164. Elias, J. L., & Merriam, S. (2004). Philosophical foundations of adult education (3rd ed.). Malabar, FL: Krieger. Heckman, J. J., Humphries, J. E., LaFontaine, P. A., & Rodriguez, P. L. (2012). Taking the easy way out: How the GED testing program induces students to drop out. Journal of Labor Economics, 30(3), 495–520. Heckman, J. J., Humphries, J. E., & Mader, N. S. (2011). The GED. In E. A. Hanushek, S. Machin, & L. Woessmann (Eds.), Handbook of the economics of education (Vol. 3, pp. 423–483). Amsterdam, Netherlands: North-Holland. Leigh, D. E., & Gill, A. M. (1997). Labor market returns to community colleges: Evidence for returning adults. Journal of Human Resources, 32(2), 334–353. Rosenbaum, J. E., & Rosenbaum, J. (2013). Beyond BA blinders: Lessons from occupational colleges and certificate programs for nontraditional students. Journal of Economic Perspectives, 27(2), 153–172. Taniguchi, H., & Kaufman, G. (2005). Degree completion among nontraditional college students. Social Science Quarterly, 86(4), 912–927.

AGE-EARNINGS PROFILE The age-earnings profile describes the relationship between average individual earnings and an individual’s age. At any age, earnings depend on personal characteristics of the earner that contribute to that person’s human capital, most particularly the individual’s level of education. For a given level of education, earnings generally rise with age as a worker gains more experience. Similarly, at a given age, more highly educated people typically earn more than do the less educated. From the viewpoint of an educator, understanding the quantitative nature of the relationship between earnings, on the one

hand, and age and education, on the other, is useful in helping students understand the economic value of an education. More education leads not only to higher starting salaries but also to more rapid growth of earnings in the early decades of a student’s working life. For economists and policymakers, looking at earnings throughout a lifetime is central to understanding the economic value of investment in education. This entry begins with looking at the age-earnings profile both graphically and numerically and then discusses some of the complications in creating the underlying statistical estimates. The left panel in Figure 1 shows an estimate of the age-earnings profile for male, full-time workers. Age is shown on the horizontal axis, and the estimated mean of annual earnings is shown on the vertical axis. Separate profiles are graphed for men with less than a high school education, for men whose education ended with a high school diploma, and for men with a bachelor’s degree or more. The immediately striking characteristic of the lines is that the earnings are much higher for men with more education. The age-earnings profile shows visually that more education not only leads to higher starting salaries but also leads to much higher earnings through the life course. A second characteristic typical of the age-earnings profile is that an individual’s earnings rise rapidly in the years following completion of education and then level off. Notably, when compared with less educated groups, incomes of the college educated rise more rapidly. Table 1 shows annual earnings at selected ages. At age 22, earnings for male workers with a college education are approximately twice the earnings of a high school dropout. By age 42, a typical college-educated male earns thrice the amount earned by a high school dropout. The age-earnings profile for any individual will depend on many characteristics, some observable (e.g., education) and others not observable (e.g., luck in the job market). Because of the unobservable characteristics, the earnings of any particular individual may be considerably above or below the estimated averages as shown in Figure 1. Economists often draw separate age-earnings profiles to account for important differences in observable characteristics. For example, the right-hand side of Figure 1 shows age-earning profiles for women. Women earn less than men at all ages and at all levels of education. The picture also illustrates that earnings for women peak a few years earlier than do men’s earnings. In practice, age-earnings profiles computed by

Age-Earnings Profile

Age-Earnings Profile: Men

Age-Earnings Profile: Women 1,00,000

90,000

90,000

80,000

80,000

70,000

70,000

60,000

60,000

50,000

50,000

40,000

40,000

30,000

30,000

20,000

20,000

Annual Earnings

1,00,000

20

30

40 Age

49

50

60

20

30

40 Age

50

60

less than high school high school diploma bachelor’s or more

Figure 1

Age-Earnings Profiles for Men and Women

Note: Calculations by Richard Startz based on Integrated Public Use Microdata Series, Current Population Survey.

Table 1

Age-Earnings Profiles (Annual Earnings, Full-Time Workers) Men

Age (Years)

Less Than High School High School ($) Diploma ($)

Women Bachelor’s or More ($)

Less Than High School High School($) Diploma ($)

Bachelor’s or More ($)

22

26,793

35,613

51,758

16,783

25,510

39,044

32

29,162

39,714

80,677

17,524

27,796

55,810

42

29,536

44,031

90,886

18,150

30,027

61,600

52

29,765

43,239

85,904

18,194

29,171

61,072

Source: Richard Startz, based on Integrated Public Use Microdata Series, Current Population Survey.

economists are averages estimated from a statistical model that takes into account a limited set of observable variables such as gender and race/ethnicity. The profiles computed above reflect the earnings of full-time workers, as is commonly done. This means that the profiles predict earnings for workers who are employed full-time throughout their

working life but do not reflect lower earnings due to periods of unemployment or withdrawal from the labor force. Since people with less education are more likely to be out of work than those with more education, age-earnings profiles for all individuals would show even larger earnings gaps than do the profiles shown.

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Agency Theory

Technical Considerations The age-earnings profile originated in the work of Jacob Mincer, who raised many of the issues that economists have investigated since and continue to explore. Mincer suggested that earnings rise as workers obtain increased human capital (the ability to produce more and therefore earn more) and that the increase in human capital is due to increased work experience rather than simply due to aging. For this reason, economists sometimes prepare an experience-earnings profile rather than an ageearnings profile. However, because there is relatively little data on experience and because fully employed workers add 1 year of experience with each year of additional age, economists often compute “potential experience,” defined as age minus years of schooling minus 6. For example, a 22-year-old high school graduate would be assumed to have 4 years of work experience, or 22 − 12 − 6. Age-earnings profiles can be estimated from either cross-sectional or longitudinal data. A crosssectional age-earnings profile is computed from earnings data for workers of different ages and education levels in a given year. This provides a snapshot comparing the earnings of workers of different ages at a point in time. But someone who is 22 today will likely not earn the same amount in 30 years as does a current 52-year-old, if only because earnings change over time. A longitudinal age-earnings profile tries to adjust for changes over time by using cross-sectional data drawn from many years or in some cases by following the earnings of specific individuals for many years. A longitudinal age-earnings profile may give a more accurate picture of the likely earnings of an individual over his or her life course, at the expense of a less accurate representation of the age-earnings relationship of the current-day workforce. Richard Startz See also Economics of Education; Human Capital; Permanent Income; School Quality and Earnings

Further Readings King, M., Ruggles, S., Alexander, J. T., Flood, S., Genadek, K., Schroeder, M. B., . . . Vick, R. (2010). Integrated public use microdata series, current population survey (Version 3.0) [Machine-readable database]. Minneapolis: University of Minnesota.

Lemieux, T. (2005). The “Mincer equation” thirty years after schooling, experience, and earnings. In S. Grossbard (Ed.), Jacob Mincer: A pioneer of modern labor economics (pp. 127–145). New York, NY: Springer. Mincer, J. (1974). Schooling, experience, and earnings. New York, NY: National Bureau of Economic Research. Murphy, K. M., & Welch, F. (1990). Empirical age-earnings profiles. Journal of Labor Economics, 8, 202–229. Taubman, P. J., & Wales, T. (1974). Age-earnings profiles. In P. J. Taubman & T. Wales (Eds.), Higher education and earnings: College as an investment and screening device (pp. 113–122). New York, NY: National Bureau of Economic Research. Thornton, R. J., Rodgers, J. D., & Brookshire, M. L. (1997). On the interpretation of age-earnings profiles. Journal of Labor Research, 18, 351–365.

AGENCY THEORY Agency theory starts with the idea that a firm, or organization, is the sum of a number of agreements between a principal (e.g., the firm, a manager, and society in the case of public organizations) and agents (e.g., employees). In general, the goal of the firm, or the sum of agreements, is to maximize output using as few resources as possible. In the typical firm, it is impossible for the principal to completely monitor the actions and behaviors of the agents within the firm. Instead, firms put in place a set of contracts, inducements, or sanctions (hereafter referred to as contracts, for simplicity) to incentivize actors to align their behavior with the goals of the firm. The contracts establish a set of performance goals or targets and provide a means of ensuring that the agents’ actions maximize the firm’s output, thereby “solving” the principal-agent problem. The design of the contracts and incentives has a specific impact on the potential agent’s intended or unintended actions. There are generally two important decisions in the design of the performance contract: (1) the degree to which the contracts and incentives are based on subjective versus objective measures of performance and (2) whether the contracts and incentives are static (i.e., an agent’s performance is based on this year’s output only) or dynamic (i.e., an agent’s performance is based on this and the previous year’s output). This entry first discusses the design of aspects of incentives and contracts (e.g., subjective or objective, static or dynamic) and how these influence the expected behavior of agents.

Agency Theory

It then covers many of the unintended consequences that arise when performance incentives and contracts are inadequately implemented. Last, it applies these ideas to education research.

Subjective Versus Objective Performance Measures In a simple principal-agent model, the principal can monitor and evaluate the agent’s performance subjectively and/or objectively. Objective methods evaluate an agent’s performance based on a predetermined measurable criterion. For example, in the public education sector, test scores are the most ubiquitous objective method of evaluating the performance of teachers (agents) within educational institutions. Subjective methods evaluate an agent’s performance based on the impressions of a superior. Subjective methods are often used in professional sports. Instead of objectively paying a professional baseball pitcher according to the number of strikeouts he records, which may cause the pitcher to increase his strikeout rate to the detriment of his overall productivity, a subjective evaluation can be used to capture a more comprehensive measure of performance. However, a drawback of subjective measures is that they often cannot be validated by outsiders or third parties. It is also quite possible that an agent’s job has multiple dimensions. Many parents and other educational stakeholders would argue that a teacher’s job of educating children and young adults includes many more dimensions than typical achievement tests measure. In the presence of multiple, or multidimensional, tasks, incentive structures not only motivate agents to increase their efforts but also direct the allocation of an agent’s effort among the many tasks or dimensions. If the incentive structure is not properly organized, the agent may be incentivized to act in a way that maximizes personal gain at the expense of the firm. Prior research proposes two possible solutions to the “multitasking” problem. The first solution is to provide low-powered incentives, or incentives that are loosely related to performance, when the outcome of interest is multidimensional and difficult to measure. The low-powered incentives will reduce the agent’s likelihood of undertaking dysfunctional behaviors in the hope of distorting his or her actual output. For example, school administrators could receive small bonuses if their school reaches and/or maintains a certain level of quality.

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The cash bonus would have to be large enough to incentivize the school administrator to incorporate the goals of the reform but small enough that the school administrator would not be overtaken by the desire to distort his or her actual output through dysfunctional behavior. The second solution is to use multiple instruments in measuring the quality of the output. The use of multiple instruments will reduce the agent’s ability to maximize productivity according to a single criterion and instead encourage the agent to focus on jointly maximizing his or her multiple tasks. For school administrators, this could include holding them accountable not only for their students’ achievement but also for the overall environment of the school (e.g., safety), parental involvement with the school’s decision-making processes, community valuation of the school’s ability to provide an equitable education, and other factors that are important for educational stakeholders.

Static Versus Dynamic Relationships In dynamic relationships where the principals and agents have repeated interactions over time, the agents’ reputation can be used as an incentive mechanism. Even in the absence of a performance-based contract, there is an incentive for a Major League Baseball pitcher to maximize his output since his next contract will depend in part on his past performance. A similar phenomenon occurs within public education between parents and students and their schools.

Unintended Consequences While incentives and contracts have been used to solve the principal-agent problem, they can also lead to unintended consequences or agents’ maladaptive behavior in certain circumstances. There is a robust body of empirical research that demonstrates how incentive structures that use objectively measured goals to gauge output quality can create a moral hazard problem, especially when the output quality is difficult to measure or multidimensional. For example, the quality of a lightbulb manufacturer’s output could be measured in two ways: (1) the number of lightbulbs the worker produces in a given amount of time or (2) the average quality of the lightbulbs produced in a given time. Suppose the owner of a lightbulb firm wanted to maximize the production of its agents (or lightbulb makers). An incentive structure that paid workers based on the number of lightbulbs produced in a given amount

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Agency Theory

of time may incentivize workers to make as many bulbs as possible, forsaking the average quality of the bulbs. On the other hand, an incentive structure that paid workers based on the average quality of their lightbulbs may incentivize workers to spend too much time on each bulb. As the production of a given output becomes more difficult to measure, because it includes more than one dimension, the use of a single objective measure of output quality may lead to dysfunctional behavior changes. There is a long documented history of this multitasking phenomenon occurring in many private and public sector industries. For example, the communications company AT&T implemented a bonus program that rewarded computer programmers by the number of lines of code they were able to produce. Not surprisingly, the programmers’ codes became unnecessarily long. There is evidence that incentive schemes that require executives to hit a minimum profit threshold in order to receive an earningsbased bonus create an incentive for the executives to misreport the companies’ income. For example, executives whose company’s income is well above the threshold are more likely to underreport their earnings in order to defer the unnecessary earnings for the following year. In an analysis of the Job Training Partnership Act, researchers found that job training centers would selectively release trainees to companies based on the centers’ proximity to the objective employment rate threshold. Under the performance measure, a center that had already met its yearly goal had little incentive to release more trainees until the following year. The use of simple quota- or count-based reward systems can promote agents’ maladaptive behavior as well. Quota-based reward systems in risky industries (e.g., medicine) that reward individuals for reaching an objectively set level of success create an incentive for dysfunctional behavioral responses. For example, a system that grades cardiac surgeons based on their mortality rate encourages the surgeons to only accept patients that are most likely to live through the surgery. Similarly, job training programs that are evaluated based on the employment outcomes of their trainees have an incentive to only accept applicants on the margin of needing the training program—a phenomenon known as “cream skimming.” Similar phenomena exist in education. Performance-based accountability systems that grade schools based on annual proficiency or passing rates can create perverse incentives for the education of continuously low-performing students.

This occurs because the marginal costs (MCs) of educating students with consistently low scores on annual achievement tests exceed the school’s marginal benefit. These students are seen as high risk for the school because their probability of passing the exam is close to zero. The economic incentive research literature predicts that if there is an opportunity for schools to avoid educating these students in favor of focusing on students on the margin of passing, or somehow removing the low-performing students from the proficiency rate count, the schools will act accordingly.

Examples of Unintended Consequences in Education Another common theme in both the education accountability literature and the principal-agent theories is the phenomenon where managers under a pay-for-performance system shift their effort from helping all individuals to helping only those on the margin of helping the managers meet their performance goal. In a firm-level experiment, researchers found that managers working under a pay-for-performance structure shifted their focus toward the highest performing employees, while seemingly ignoring or dismissing the lower performing employees. This also occurs in education, where teachers and schools focus on the “bubble students,” those on the margin of helping a school meet its accountability goal, at the expense of students either well above or well below the margin. In a study of the Chicago public schools’ accountability system, researchers found that the average increases in student achievement were driven by students closest to the proficiency threshold, with the students at the tails, or those who previously scored far above or far below the proficiency threshold, making no progress or losing ground. Furthermore, students at the tails, especially high-performing students, score significantly lower than expected when their school is in jeopardy of sanctions under the No Child Left Behind Act. The distributional effects of incentives also depend on the design of the accountability structure. The presence of high stakes, including the threat of sanctions or the promise of rewards, attached to accountability policies is linked to examples of teachers and school administrators partaking in dysfunctional behaviors that seek to maximize short-term education outcomes. The most blatant dysfunctional response is teachers and school

Allocative Efficiency

administrators cheating on annual tests by providing answers or inappropriate help to students prior to or during the exams, erasing students’ wrong answers and replacing them with correct answers, or using other creative methods to falsely improve students’ scores. There is also evidence that schools will give low-performing students longer suspensions than higher performing students near the testing window. The dysfunctional behavior of teachers and school administrators does not stop with excluding students from the school or cheating on state tests. There is a small, but growing, body of literature that links accountability pressure to changes in the health and well-being of students. For example, prior research shows that some schools facing accountability pressure have manipulated their students’ food programs and physical education regimens. A group of schools in Florida that provided empty-calorie food for students during the testing windows received small increases in student achievement compared with similar schools that did not. Furthermore, researchers have found suggestive evidence that low-performing schools have significantly higher obesity rates. In descriptive followup surveys, the authors found that schools on the margin of passing their accountability targets are more likely to sell junk food in vending machines and spend more time on test-taking activities at the cost of physical education. Schools facing financial pressure are also more likely to use junk food sales and food and beverage advertising as a means of increasing revenue, and these behaviors are linked to increases in the students’ body mass index. Last, there is also evidence that school accountability policies have led to an increase in the prescription of psychostimulants (e.g., attention deficit/hyperactivity disorder medication) for students. Agency theory has a rich history in economics and public education. The premise of the theory is that the only efficient method of monitoring agents’ behavior is through the use of performance guidelines or contracts. The design of the contracts has a direct impact on the agents’ behavioral responses. The literature provides a number of useful results for the possible intended or unintended consequences that can occur from the use of contracts to solve the principal-agent problem. Andrew McEachin Author’s Note: Portions of this entry were adapted from McEachin, A. (2012, October). Incentives, information, and ideals: The use of economic theory to evaluate

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educational accountability policies. Paper presented at the Association for Public Policy Analysis and Management international conference in Rome, Italy.

See also Accountability, Standards-Based; Behavioral Economics; Economics of Education; Performance Evaluation Systems; Principal-Agent Problem; Student Incentives

Further Readings Courty, P., & Marschke, J. (1997). Measuring government performance: Lessons from a federal job-training program. American Economic Review, 87, 383–388. Gibbons, R. (1998). Incentives in organizations. Journal of Economic Perspectives, 12(4), 115–132. Holmstrom, B. (1999). Managerial incentive problems: A dynamic perspective. Review of Economic Studies, 66(1), 169–182. Holmstrom, B., & Costa, J. R. (1986). Managerial incentives and capital management. Quarterly Journal of Economics, 101(4), 835–860. Holmstrom, B., & Milgrom, P. (1991). Multitask principalagent analyses: Incentive contracts, asset ownership, and job design. Journal of Law, Economics, & Organization, 7, 24–52. Holmstrom, B., & Milgrom, P. (1994). The firm as an incentive system. American Economic Review, 84(4), 972–991. Prendergast, C. (1999). The provision of incentives in firms. Journal of Economic Literature, 37(1), 7–63.

ALLOCATIVE EFFICIENCY Scarcity is a central concept in the allocation and organization of resources in an economy. If a society had an infinite amount of everything, there would be no need to choose between different resource uses. There are various ways in which resources can be allocated to different outcomes for different individuals in terms of gains and losses. Allocative efficiency occurs when markets optimally distribute goods and services taking into account consumers’ preferences, so that the level of output demanded by society is satisfied by the firms in the market. Allocative efficiency represents the optimum production and consumption point when the benefit of an extra unit of a good consumed is equal to the cost of producing it. Allocative efficiency is an important issue in the economics of education for several reasons. The concept is an instrumental tool for researchers investigating the contribution

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Allocative Efficiency

of schooling to economic growth. Policymakers and administrators also use the concept to analyze the efficiency of schooling, or the effectiveness of the use of resources to meet parents’ demands. This entry first briefly outlines the concept of economic efficiency and then examines several technical considerations with respect to allocative efficiency. The entry concludes with a description of applications of allocative efficiency, including its use in the education marketplace.

Economic Efficiency Every choice in resource allocation has consequences and inevitably produces winners and losers. The outcomes, or the impact of this resource allocation on different subgroups, can be reasonably identified and measured. Some allocations may be better than others in terms of distribution of gains and losses. There are significant differences, however, in how comparisons of the winners and losers of resource allocation are interpreted. Efficiency is an important concept in economics that addresses these issues of the allocation of resources and the resultant distributional gains and losses. Overall, efficiency refers to the allocation of scarce resources in a manner that adequately satisfies the demands of consumers in a cost-saving fashion. In essence, efficiency addresses how effectively an economy allocates its available resources. Efficiency can be defined, measured, and interpreted in alternative ways. For example, Pareto efficiency is a state in which no one can be made better off without someone being worse off. Kaldor efficiency occurs when the gains to some individuals outweigh the losses to others. Allocative efficiency is one of the major types of economic efficiency. Unlike other kinds of efficiency such as productive efficiency, allocative efficiency also encompasses consumer preferences. For example, consider a society that devotes the majority of its resources to producing satellite dishes. Production may be productively efficient when the maximum output is attained for the minimum cost. However, the economy will likely not be allocatively efficient as it produces only one good. It is readily conceivable that consumers’ preferences extend beyond satellite dishes, regardless of whether firms are producing them at the lowest cost. For an economy to be allocatively efficient, the production of goods must be in sync with and satisfy the preferences and needs of consumers.

Technical Considerations Prices and Marginal Cost

Allocative efficiency occurs when price (P) is equal to marginal cost (MC). Prices reflect the value consumers place on goods or the marginal benefits (MB) consumers receive from a product. In other words, the price that consumers are willing to pay represents the marginal utility they receive from consuming the good. Put another way, MB is the additional happiness, benefit, or utility that consumers gain from enjoying one more unit of a good. MC is the cost of resources expended in the production of an additional unit of a good. Stated differently, it is the extra cost of utilizing scarce inputs toward the production of an additional unit of a finished product. When the condition P = MC is met, the market is allocatively efficient and total economic welfare is maximized. At this level of output, the price paid by the consumers is the same as the MC faced by the producers. Figure 1 graphically illustrates allocative efficiency. MC is represented by the supply curve, which slopes upward to show that the cost of production increases as more units of a good are produced. MB is represented by the demand curve, which slopes downward, indicating that the additional happiness or utility of consumption decreases as more of a good is consumed. At equilibrium price and quantity, the MB of consumers is equal to the MC of producers. When a quantity less than the equilibrium quantity is produced, there is underallocation of resources. At this level of output, Q1, MB is greater than MC, and society would be better off allocating more resources to the production of this good. In other words, if output increases (above Q1) and prices decreases (below P1), society would experience welfare gains and benefit more from the consumption of the good. The exact opposite situation occurs in overallocation. In this case, the MC exceeds the MBs, and hence, an excess of goods is being produced by the market. Consumers are made worse off as total welfare is reduced in spite of the increase in quantity. Consumer and Producer Surplus

Figure 1 also demonstrates consumer and producer surplus. Consumer surplus is the difference between the maximum price consumers are willing to pay and the price they actually pay, or equilibrium price. Producer surplus is the difference between the

Allocative Efficiency

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Supply Curve (S) P1 Consumer Surplus

A

Allocative Efficiency

Equilibrium Price B

Producer Surplus

Demand Curve (D)

Q1

Quantity Equilibrium Quantity

Figure 1

An Illustration of Allocative Efficiency

Source: Dominic J. Brewer.

minimum price firms are willing to pay and the price they receive. When there is allocative efficiency, consumer surplus and producer surplus are maximized efficiently. There is no deadweight loss as the value consumers place on a good is equal to the value of the resources used in its production. In situations of underallocation and overallocation, the market is inefficient. When the market allocates resources inefficiently, there is deadweight loss. For example, when resources are underallocated, the market is inefficient because the increase in producer surplus from producing the quantity Q1 at a price P1 is at the expense of consumer surplus. Thus, some consumers will experience a reduction in consumer surplus to facilitate this additional producer surplus. As Figure 1 shows, there is also a “deadweight loss” (A + B) as society (both consumers and producers) would benefit more when consumption and production occur at equilibrium price and quantity. Market Failures

Perfectly competitive markets are typically allocatively efficient. In perfect competitive markets, firms are maximizing profits at a price that equals MC. Although firms in perfect competition are allocatively efficient, cases of imperfect competition such as monopolies and oligopolies are not allocatively efficient. When markets do not allocate resources efficiently, a market failure occurs. Examples of market failures include monopoly, oligopoly, and monopsony. For instance, due to market power,

monopolies can set prices (normally above the MC) to reduce consumer surplus and increase producer surplus. Thus, price is not equal to MC, and resources are not allocated efficiently. Externalities

In free markets, externalities, or the external costs of consumption and production, are typically ignored. Thus, supply (S) is equal to private marginal cost (PMC) or the marginal cost faced by firms (S = PMC), while demand (D) is equal to private marginal benefit (PMB) or the utility derived by individuals consuming an additional unit of a good (D = PMB). Allocative efficiency occurs when PMC = PMB, as Figure 1 illustrates. When there are externalities, the concept of allocative efficiency is extended to incorporate society’s preferences, and thus, social costs and benefits are considered. In this scenario, demand is equal to marginal social benefit (MSB), and supply is equal to marginal social cost (MSC). MSB is the sum of all individual consumer demands, resulting in a demand curve for an industry’s good. Put another way, MSB represents the benefits to all society of increased consumption of a product. In parallel fashion, MSC is the supply curve of an industry’s good. Social efficiency, or allocative efficiency from society’s perspective, occurs when the condition MSB = MSC is satisfied. This is the level of output for the optimum production and consumption of goods that accounts for externalities. From society’s

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American Association of School Administrators

perspective, allocative efficiency occurs when producers specialize in goods in the highest demand that contribute the most to social welfare. Stated differently, producers produce only the types of goods and services that are in high demand and desirable by society as a whole.

Applications Allocative efficiency is often used in welfare analysis to evaluate the impact of various public policies on different subpopulations in society. In other words, allocative efficiency is central to determining which subgroups are made better or worse off by policy changes. The concept is also useful in analyzing tax burden and determining tax rates according to income. Allocative efficiency is ripe for application in the economics of education given the magnitude of individual and government spending on schooling, the size of the education industry, and the increasing costs of providing schooling. Economists such as Henry Levin have applied the concept of allocative efficiency to educational production. As educational marketplaces proliferate and parents choose schools and courses from multiple providers, allocative efficiency will become even more pivotal in the economics of education. Dominic J. Brewer and Richard O. Welsh See also External Social Benefits and Costs; Markets, Theory of

Further Readings Barr, N. (2012). Economics of the welfare state (5th ed.). Oxford, UK: Oxford University Press. Levin, H. (1976). Concepts of economic efficiency and educational production. In J. T. Froomkin, D. T. Jamison, & R. Radner (Eds.), Education as an industry (pp. 149–198). Cambridge, MA: National Bureau of Economic Research.

AMERICAN ASSOCIATION OF SCHOOL ADMINISTRATORS Founded in 1865, the American Association of School Administrators (AASA) is a professional association that represents more than 13,000 school leaders across the United States and throughout the world. The association is also known as the School Superintendents Association, and it is committed to

the development and support of excellence in leadership within the nation’s public schools and districts. AASA is viewed as a national voice for superintendents across the United States on policy issues affecting public education. Superintendents utilize AASA for policy-related research, and the association provides leadership training and support to maximize the leadership skills of superintendents. The association’s website has this mission statement: “AASA, the School Superintendents Association, advocates for the highest quality public education for all students, and develops and supports school system leaders.” AASA members include chief executive officers, superintendents of school systems, and senior-level school administrators, as well as cabinet members, professors, and aspiring school system leaders. The bylaws of the association define active members as all persons who serve as school system leaders or who are in administrative positions in a public or private school system or in a regional, state, or national educational agency or association and who possess a valid license for the position. In addition, the bylaws indicate that membership for professors is open to all persons employed by or at a college or university, who serve in an administrative position, or who teach persons preparing for education or educational administration, and possess any legally required license for the position. As an association, AASA attempts to shape education policy from the perspective of school system leaders. Members of AASA utilize the strength of their advocacy voice to influence policy and shape the direction of national educational efforts to raise academic achievement. AASA maintains belief and position statements on major educational topics including public education, equity and diversity, learning environments, leadership, student learning and accountability, and collaborative partnerships. AASA claims that its position papers and research are utilized by educational leaders to advocate for policies to support implementation of highly effective educational efforts in support of student achievement. AASA operates as a nonprofit corporation and is governed by a 135-member governing board. The governing board represents seven geographic regions across the United States. Each state affiliate is eligible for at least two representatives on the governing board. Two-way communication between the governing board and state affiliates is maintained through representational governance. The governing board elects a 21-member executive committee to

American Association of School Administrators

represent each of the seven regions. The term of office for members of the executive committee is 3 years. Meetings of the entire governing board occur biannually, and the executive committee meets quarterly. The executive committee conducts the routine business of the association. Because AASA’s governance structure ensures that the viewpoints of school leaders across the nation are represented in policy positions through the governing board, the policy positions of the association are more likely to influence policymakers at the national, state, and local levels. AASA officers include the president, presidentelect, and immediate past president, and each officer holds the position for 1 year. The president-elect appoints one member-at-large to the executive committee, who represents an underrepresented element of the membership on the executive committee. The president is the presiding officer and a member of the executive committee and provides significant leadership to the association as a national spokesperson on behalf of AASA. An executive director is hired and is annually evaluated by the executive committee and serves as chief executive officer for the association and staff liaison to the governing board and executive committee. As national spokesperson, the executive director advocates for public education and for superintendents and other school system leaders. The executive director manages the AASA office in Alexandria, Virginia, and supervises the association’s staff. The executive director also works directly with the state-level executive leadership of state affiliate organizations to support their success. A national conference held in February each year invites AASA members to convene on topics significant to national educational leaders. The presentations are oriented toward the practitioner, and successful superintendents are invited to submit proposals to present their success stories in sessions conducted during the 3-day conference. In addition to successful practitioners, the AASA conference features renowned speakers who offer a national and an international perspective on education. Thousands of school-level leaders participate in the annual conference, where they are introduced to new ideas, have an opportunity to network and pursue career advancement opportunities, and share best practices in public education with job-alike colleagues from across the nation. The national conference provides a forum for presenting major awards and scholarships to recognize excellence in leadership and to honor leaders who

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have made a significant impact in improving the education of the nation’s youth. AASA recognitions include National Superintendent of the Year, Effie H. Jones Humanitarian Award, Distinguished Service Awards, Educational Administration Scholarship Award, Women in School Leadership Award, Leadership Through Communication Award, American Education Award, and Architectural Awards. The recognition program is highly regarded and prized by those working in the field of public school leadership. School superintendents value the AASA online library as a resource that ensures immediate access to articles from School Administrator, AASA’s national magazine. The online library includes topics of broad impact for school system leaders, including parent and community relations, superintendent evaluation, support for new superintendents, board relations, human resources practices, and wellness. In addition to articles from School Administrator, the online library provides quick access to blogs by respected educators and videos on topics of interest and importance in effective leadership practices. AASA provides weekly online communications to members. These communications provide highlights of important educational events occurring across the nation and offer opportunities to participate in webinars designed to keep educational leaders current on education and policy trends. In addition, AASA’s Leadership Development Department provides a vehicle for organizations and individuals to create links to support and maintain efforts around shared goals and beliefs. The Eastern States Consortium for Learning and School System Excellence and the Western States Benchmarking Consortium are examples of networks facilitated by AASA in support of increasing communication on specific areas of interest. AASA’s history as the premier association for school leaders continues to draw membership from among those interested in promoting the goals of educational excellence and effective leadership and influencing national policy. The online resources, regional meetings, annual conference, online training opportunities, and access to a highly competent professional staff ensure that AASA will continue to be a major voice for superintendents and educational leaders across the nation. Maria G. Ott See also School District Budgets; School District Wealth

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Association for Education Finance and Policy

Websites AASA: http://www.aasa.org AASA’s magazine, School Administrator: http://www .aasaconnect.com AASA’s Superintendent Resource Library: http://www .aasaconnect.com/Discover-Our-Library

ASSOCIATION FOR EDUCATION FINANCE AND POLICY The Association for Education Finance and Policy (AEFP) is a professional organization of scholars, policymakers, and practitioners engaged in producing and utilizing research on educational policy and finance. The association was founded in 1975 and was incorporated under the name American Education Finance Association. Its current new name was adopted in 2010. The purpose of the association is to provide a forum for discussion and debate on educational policy and practice regarding the use and distribution of resources for learning. According to the bylaws, key functions of the association are to promote research and development, to encourage and support experimentation and reform, and to ensure that the field of educational finance remains responsive to policymakers’ concerns and the emerging needs of educational institutions and practitioners. Although founded in North America, the membership and influence of the association are global. The association currently has 550 members from the United States, Canada, and 14 other countries. The association has two primary activities. It sponsors an annual conference held in different cities in the United States in late winter or early spring. It also hosts a scholarly journal that is published quarterly. This entry provides information on AEFP, its annual conference, and its journal, Education Finance and Policy.

Organization AEFP is a nonprofit corporation chartered in the state of Florida and recognized as tax-exempt by the Internal Revenue Service. It is governed by a 17-member board of governors, which meets twice a year; the majority of the members of the board are elected by the AEFP membership and serve staggered 3-year terms. The executive committee of the board—consisting of the president, president-elect, past president, treasurer, and

executive director—meets by phone throughout the year and is empowered to act on behalf of the board in certain circumstances. The executive director provides leadership and management for the organization. The director’s office implements the decisions of the board, develops initiatives, and manages programs designed to further the goals of the association. In addition to individual membership, the association recognizes two additional types of membership: (1) institutional members, or organizations paying annual dues, and (2) sustaining members, or organizations providing ongoing financial support, whose representatives serve as voting members of the board. Financial support for the association comes from individual membership fees, conference and workshop registration fees, contributions from institutional and sustaining members, and funds from foundations and government offices for general support and to underwrite specific projects and activities.

Annual Conference The association meets annually. Programs for the conferences consist of featured speakers, presentations of research papers, special and general interest discussion groups, and social gatherings. Featured speakers at the 2013 conference were Alice Rivlin, a leading expert on fiscal and monetary policy and former vice chair of the Federal Reserve Board, and John Deasy, superintendent of the Los Angeles Unified School District. The bulk of the conference is devoted to individual presentations of research papers exploring current issues in education finance and policy. Most of the presenters are academics from universities, governmental education analysts, or researchers at nonprofit associations such as think tanks and research and development agencies concerned with education finance and policy. Attendance for the first few decades ranged between 250 and 400. However, over the past few years, registration for the conference has been increasing, with registration for the 2013 meeting in New Orleans at 507, the highest in the association’s history. The 2013 conference consisted of 77 general sessions with three or four papers presented in each session. Although the bulk of the sessions focused on K-12 public education, the participants presented research and engaged in discussions on early childhood education, higher education, private education, alternative education, and community education, among other topics. While educational

Auxiliary Services

funding, revenue sources and distribution, and educational management have been long-standing foci of analytic attention, the past decade has seen a rapid growth of research focusing on the relation between educational resource inputs and educational outputs. In particular, teachers and the effectiveness of their instruction has become a particularly lively area of research among AEFP members. Its direct application to policy through policy innovations, such as performance-based teacher evaluation and compensation, has been one of the most active strands of engagement in the annual conference programs over the past few years.

Education Finance and Policy The association sponsors the premier journal in the field, the quarterly Education Finance and Policy, published by MIT Press. The first issue appeared in 2006. The Journal of Education Finance, currently published by the University of Illinois, was associated with AEFP from its origins until 2005. In 2005, the AEFP Board decided to adopt a journal already being published by an academic press and to expand the range of issues to include educational policy as well as finance. Issues addressed in the journal include school and district resource allocation, teacher quality, instructional policy, school choice, and equity and adequacy in school finance. The journal covers issues affecting prekindergarten through higher education in diverse settings, including public and private institutions, formal and informal providers, and local, state, national, and international settings. As of 2013, the editors were Thomas A. Downes of Tufts University and Dan Goldhaber of the University of Washington. The journal’s impact factor, a ranking given to journals based on the average number of times each article is cited, was determined for the first time in 2013. It was ranked 57 out of 216 journals by Thomson Reuters under the category of education and education research, with an impact factor of 1.070. It is highly unusual and a mark of distinction for a journal to be ranked so high after less than a decade of publication.

Trends Affecting AEFP The association’s recent increase in membership, in conference attendance, and in attention from key policy-making organizations is a result of a convergence of three trends over the past decade: (1) increased interest in educational productivity,

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(2) vastly enriched datasets on educational finance and on student and school characteristics, and (3) comparative data on learning outcomes as measured by state, federal, and international assessments. Policymakers’ increased attention to issues of educational productivity, efficiency, and impact on learning outcomes emerged from earlier research, much of it conducted by members of AEFP, documenting the complex relationship between resources and educational outcomes. The interest in this line of research is furthered as well by state, federal, and international assessments of student learning and comparative data on educational resources, student characteristics, and organizational features of educational systems. This has allowed for a much richer exploration of how student learning is affected by resources, the regulation of those resources, and how resources are applied to education. Carolyn D. Herrington See also Capital Financing for Education; CostEffectiveness Analysis; Higher Education Finance; Intergovernmental Fiscal Relationships; Policy Analysis in Education; Teacher Value-Added Measures

Further Readings Herrington, C. D. (2013). Thirty-seven years and counting: How has AEFP evolved from its origins? Education Finance and Policy, 8, 1–13.

AUXILIARY SERVICES Auxiliary services in PreK-12 schools is a term with multiple and broad meanings in the field of education economics and finance. Its definition varies from state to state, depending on the organizational structures within a given state. In some states, the definition is sweeping and can encompass the areas of transportation; food service; capital outlay and maintenance; guidance, counseling, and testing services; psychological services; services for exceptional children; remedial services; speech and hearing services; services for the educationally disadvantaged, including but not limited to teaching English as a second language; as well as other secular, neutral, nonideological services of a supplementary and remedial nature. For typical purposes in the language of PreK-12 school finance, however, the definition is narrower and encompasses primarily those areas defined as

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Auxiliary Services

selected noninstructional support services, most often funded under segregated fund accounting systems. The term auxiliary services itself is rapidly disappearing from the literature, as there is no reference to the term in many recent sources, including the 2009 edition of the National Center for Education Statistics’ Financial Accounting for Local and State School Systems. But, as detailed in other historically respected publications, at a minimum the term typically applies to school transportation services, food services, and, to a lesser extent, capital outlay and maintenance. The sections that follow focus on the definition, role, and magnitude of auxiliary services, with specific attention to the origins, purpose, operation, and issues of the two areas of transportation and food service.

Definition, Role, and Magnitude of Auxiliary Services For the purposes of this entry, the term auxiliary services is restricted to the areas of pupil transportation and school food services; other entries address infrastructure investment and physical maintenance. These two key auxiliary operations (e.g., transportation and food service) are large and complex irrespective of school district size. For example, large and/or urban districts may use many buses to transport thousands of pupils over a short distance, while rural districts often operate many buses to transport students across longer and sparsely populated distances. Regardless of enrollment size or geography, school districts face similar types of transportation costs: staffing, bus safety, vehicle maintenance and replacement, fuels, liability, and insurance. Likewise, food services employ a range of staff to plan and operate approved programs that include the following: breakfast, lunch, after-school snacks, and summer meal programs; free and reduced-price meals for economically disadvantaged children; commodity support programs meant to lower the cost of meals and aid agricultural markets; federal and state subsidies; and extensive federal oversight and regulation. Given the scope and cost of pupil transportation and food services, with national annual expenditures in the tens of billions of dollars, auxiliary services represent a major cost center for schools.

Origins, Purpose, Operation, and Issues of Transportation Systems Widespread transporting of PreK-12 students to and from school developed primarily as a result of school

district consolidation in the early 20th century. Records indicate a much earlier start, however. Public school transportation and state financial support actually began in 1869, when Massachusetts became the first state to spend public funds for transporting pupils. School transportation systems grew rapidly as compulsory attendance laws, school and district consolidation, and the invention of motor vehicles rapidly altered American society. School district consolidation alone led to major transportation changes, as the number of districts decreased from 117,108 to 14,928 over the course of the 20th century. Unlike some aspects of fiscal support for schools, from the earliest days U.S. taxpayers have chosen to spend for pupil transportation. Support came from a common view that what individuals could do only poorly could instead be done more efficiently by the community. An early key factor was geographic isolation in an agricultural nation, the vestiges of which remain in sizable proportion today as school districts in many states are geographically vast and/or complex. Developments such as the 1954 U.S. Supreme Court ruling in Brown v. Board of Education mandating school desegregation, the 1971 Supreme Court ruling in Swann v. CharlotteMecklenburg Board of Education authorizing transportation as one way to achieve desegregation, and more year-round schools, charter schools, private schools, and intradistrict and interdistrict school choice have enlarged the scope and cost of school transportation services. Effective application of transportation services is complex from legal and operational perspectives. Equal educational opportunity demands equal access to transportation, and the cost of providing services has evolved into a multibillion-dollar industry ranging from employment and training of bus drivers to insurance against myriad risks. These costs are affected by carefully calculated efficiencies such as technologies, owning versus outsourcing services (e.g., transportation that can be either district operated and owned or provided through a contract with a private transportation provider), and maintenance and replacement plans and by governmental accountability and complex legal requirements. Pupil transportation requires expensive physical facilities and equipment, and continual population shifts in communities create a pattern of ever-changing and new transportation demands. Additionally, states and school districts experience unique needs, so that no two states or school districts are identical on variables such as population density, number

Auxiliary Services

of pupils to be transported, topography, climate, road conditions, and length of routes—factors that greatly affect the number, size, and configuration of buses and the total costs of transportation systems. Further complicating circumstances are variations in the ability and willingness of individual states and local communities to pay for effective transportation systems, entailing the negotiation of adequate and equitable state and local funding systems for transporting pupils. Although today’s elaborate procedures and responsibilities may seem only distantly related to the origins of transporting children at taxpayer expense, the basic purpose—making education available to every child on an equal basis—still remains the first goal.

Origins, Purpose, Operation, and Issues of Food Service Systems The food service function similarly plays a key role in effective and efficient outcomes of PreK-12 schooling by providing a vital support system for the instructional process. Clearly, equal educational opportunity cannot exist if children’s nutritional needs are not served. Unlike many other aspects of school funding, however, food service is meant to be a cash-basis operation. As a result, food service is a highly complex operation involving systems and procedures that are driven by how meal prices are established under federal, state, and local participation requirements and guidelines for revenues and expenditures. In sharp contrast to other areas of PreK-12 school funding, food service programs are highly dependent on federal subsidies and are secondarily aided by state and local funding and student fees. Furthermore, within the context of a cash-basis operation, meal prices are established differently than any other aspect of educational services. But even more important, the food service function is a key element in equal educational opportunity given the fundamental role of nutrition in students’ readiness to learn. A disproportionate percentage of children from low-income homes suffer from learning problems, which research has shown are related to social class and economic circumstance, and this research further indicates that nutrition plays a role in the learning problems suffered by low-income children. As a result, federal, state, and local units of government have chosen to aid the food service function, although with highly variable methods and outcomes.

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Federal support is therefore the dominant fiscal force in today’s PreK-12 school food service programs across the nation. The federal government’s involvement in the provision and funding of school food services grew out of the dire economic conditions of the Great Depression of the 1930s, when farmers received very low prices for their crops, if they could sell them at all. The concept of surplus food commodities was born, and in 1935, the federal government enacted the Agricultural Adjustment Act (Pub. L. 74-320), a law that not only assisted farmers but also authorized the U.S. Department of Agriculture to distribute surplus food commodities to public and nonprofit schools. At the same time, as part of the New Deal federal legislation addressing unemployment, the Works Projects Administration provided jobs for the unemployed on public works projects, including school lunch programs. By 1941, school lunch programs organized by the Works Projects Administration were operating in every state, in Washington, D.C., and in Puerto Rico. Countless federal programs followed, and by 2013, the federal government was long known for providing the lion’s share of every food service dollar in local school districts across the nation. Federal regulations were also closely attendant regarding health and nutrition, so that the U.S. Department of Agriculture has the authority to set nutritional standards for all foods sold in schools, including vending machines, à la carte lunch lines, and school stores. Federal funding, budgeted annually via congressional act, subsidizes approximately tens of millions of daily free and reduced-price breakfasts and lunches at a cost of billions in federal aid. In contrast, state support has been only modest, with state governments acting as a pass-through for federal funds, with no standardized or uniform level of state support. Effectively, the two major PreK-12 food service price supports have been the federal government alongside the local school district, which determines how much of the unfunded cost of a market-priced meal will be subsidized by the district and how much will be left to the child’s price as the unfunded portion of each meal’s cost. The critical element of a food service program, therefore, is that—theoretically at least—a PreK-12 district’s school food service program is self-supporting. Meal prices are determined in combination with federal, state, and local aid to identify the full cost of a financially solvent program. Federal aid, of course, is established by Congress. State aid is established

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Auxiliary Services

by at-will state legislative appropriation. Local aid is thereby the more complex decision, because when a district funds a portion of a food service program, it has only two potential revenue sources in its control. First, the local district may transfer local tax revenue away from other budgeted areas to subsidize food services—a decision that can prove controversial if, for example, it reduces direct-instruction programs in order to fund food service. Second, the district may raise the price charged to students who do not qualify for free or reduced-price meals. Undesirably raising prices too much may decrease pupil participation, and as participation drops, per-meal cost rises due to diseconomy of production scale, which in turn can necessitate additional price increases, creating an unending circle. With labor and food costs consuming an average 90% of a typical food service budget, districts and their food service programs are highly vulnerable to fluctuations in costs associated with these two budget categories—a problem made worse as the price of a single meal is only part of the equation given how districts also must factor in long-term capital costs for facilities and kitchen equipment, all of which are subject to significant

geographic cost variations. These fiscal challenges will continue to affect the ability of school districts to provide healthy meals to deserving students in a cost-effective manner. David C. Thompson See also Bonds in School Financing; Brown v. Board of Education; Capital Budget; Capital Financing for Education; Cost-Benefit Analysis; Economic Efficiency; General Obligation Bonds; Infrastructure Financing and Student Achievement; School Size

Further Readings National Center for Education Statistics. (2009). Financial accounting for local and state school systems: 2009 edition. Washington, DC: Author. Retrieved from http:// nces.ed.gov/pubs2009/2009325.pdf Thompson, D. C., Crampton, F. E., & Wood, R. C. (2012). Money and schools (5th ed.). Larchmont, NY: Eye on Education. Wood, R. C., Thompson, D. C., Picus, L. O., & Tharpe, D. I. (1995). Principles of school business management (2nd ed.). Washington, DC: Association of School Business Officials International.

B tuition at a top-tier private school could cost nearly $200,000. (Baumol, 2012a, p. 3)

BAUMOL’S COST DISEASE

The cost disease analysis was first introduced in 1966 by William Baumol and William Bowen in Performing Arts: The Economic Dilemma. More recently, further research by William Nordhaus and Robert Flanagan has shown that these predictions for the future costs of education and other labor-intensive services have been borne out fully. Yet as the Cambridge economist Joan Robinson has pointed out, all industries must grow less costly in the amount of human labor they require as productivity rises. This simply is happening more slowly in some industries and more quickly in others. As the overall productivity of the economy increases, the purchasing power of consumers rises. As noted in The Cost Disease,

The cost disease (also known as “Baumol’s cost disease” or, in educational circles, “Bowen’s curse”) is the idea that costs are destined to rise in certain sectors of the economy, including education, health care, and the live performing arts, because it is difficult to reduce the labor required to produce these services. The concept is explained by William J. Baumol in the 2012 book The Cost Disease: Why Computers Get Cheaper and Health Care Doesn’t: Since the Industrial Revolution, labor-saving productivity improvements have been occurring at an unprecedented pace in most manufacturing activities, reducing the cost of making these products even as workers’ wages have risen. In the personal services industries, meanwhile, automation is not always possible, and labor-saving productivity improvements occur at a rate well below average for the economy. As a result, costs in the personal services industries move ever upward at a much faster rate than the rate of inflation. (Baumol, 2012a, p. xvii)

The key conclusion that follows from this: no matter how painful rising education and medical bills may be, society can afford them and there is no need to deny them to ourselves or to the less affluent members of our society, or indeed to the world. Overall incomes and purchasing power must rise quickly enough to keep these services affordable, despite their persistently rising costs. (Baumol, 2012a, p. xviii)

For example, In 1980, it cost $3,500 per year, on average, to attend a four-year undergraduate school in the United States (including room and board). By 2008, that figure was ancient history: a single year of undergraduate study cost nearly $20,500. That’s an average annual increase of more than 6 percent—well above the rate of inflation. If this trend continues, by 2035 annual

But suppose the future does not bring ever growing productivity. Suppose innovation grinds to a halt, we run out of natural resources, and average income levels cease their steady rise. Then what? . . . The cost disease, too, will terminate because it stems from 63

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unequal rates of productivity growth in different sectors of the economy. If productivity growth is zero in all areas of the economy, these inequalities will disappear. The cost disease would no longer be any part of the problem, but that’s the only good news: our problems—particularly poverty—would be far worse. (Baumol, 2012a, p. 181)

These are the central arguments of the cost disease: 1. Rapid productivity growth in the modern economy has led to cost trends that divide its output into two sectors, which I call “the stagnant sector” and “the progressive sector.” . . . Productivity growth is defined as a labor-saving change in a production process so that the output supplied by an hour of labor increases, presumably significantly. 2. Over time, the goods and services supplied by the stagnant sector will grow increasingly unaffordable relative to those supplied by the progressive sector. The rapidly increasing cost of a hospital stay and rising college tuition fees are prime examples of persistently rising costs in two key stagnant-sector services, health care and education. 3. Despite their ever-increasing costs, stagnant-sector services will never become unaffordable to society. This is because the economy’s constantly growing productivity simultaneously increases the community’s overall purchasing power. 4. The other side of the coin is the increasing affordability and the declining relative costs of the products of the progressive sector, including some products we may wish were less affordable and, therefore, less prevalent, such as weapons of all kinds, automobiles, and other mass-manufactured products that contribute to environmental pollution. . . . Paradoxically, it is the developments in the progressive sector that pose the greater threat to the general welfare. . . . Some of the gravest threats to humanity’s future stem from the falling costs of these products, rather than from the rising costs of services like health care and education. (Baumol, 2012a, xx–xxi)

As productivity rises overall, even if it rises more slowly in some industries than in others, then the same or fewer hours of labor will produce more goods and services. This will mean that in

societies with competitive economies, health care and education will remain affordable. The picture that emerges is not so daunting: We can have it all: better health care, good education, and even more orchestral performances. In exchange, we will not have to surrender food, clothing, shelter, or even less essential commodities such as comfortable vacations, unrestricted travel, and readily available entertainment. This is not merely naïve optimism but something we have already experienced. The exploding cost of hospital care and galloping college tuition increases since World War II have not prevented Americans from consuming these and other services and goods. Indeed, we now live longer than ever, and a continually rising share of the population attends college. (Baumol, 2012a, p. 180)

William J. Baumol Author’s Note: Portions of this entry are republished from Baumol, W. (2012). The cost disease: Why computers get cheaper and health care doesn’t. New Haven, CT: Yale University Press. Copyright © 2012 by William J. Baumol.

See also Cost of Education; Economics of Education; Education Spending; Policy Analysis in Education; Public Good; Tuition and Fees, Higher Education

Further Readings Baumol, W. (2012a). The cost disease: Why computers get cheaper and health care doesn’t. New Haven, CT: Yale University Press. Baumol, W. (2012b). We can have it all: Why health care will still be affordable. Policy Options, 33, 28–30. Baumol, W., & Bowen, W. (1966). Performing arts: The economic dilemma. New York, NY: Twentieth Century Fund. Flanagan, R. (2012). The perilous life of symphony orchestras. New Haven, CT: Yale University Press. Nordhaus, W. (2008). Baumol’s diseases: A macroeconomic perspective. B. E. Journal of Macroeconomics, 8(1), 1–39.

BEHAVIORAL ECONOMICS Historically, economic models and analytic tools assumed that humans were fully rational. The notion of full rationality includes a number of aspects that are not realistic in the world at large. For example, full rationality assumes that people can identify all available options in a given scenario, that they

Behavioral Economics

have full and relevant information at hand, and that they have explicit preferences for those options that they can rank in simple and complex scenarios. Full rationality also assumes that people maximize their own utility or benefit and are motivated by unadulterated self-interest. Given the extreme notion of full rationality, it is not surprising that the underlying assumptions have been brought into question and that alternative models of decision making have been developed. To that end, behavioral economics is a subfield of economics that explores alternatives to the standard model of economic rationality. Specifically, behavioral economics applies insights drawn primarily from the fields of cognitive and social psychology to examine how economic actors predictably deviate from rational models of decision making. Behavioral economists employ these insights to improve the application and accuracy of economic concepts and analysis. This entry describes behavioral economics in four sections. The first section describes the development of behavioral economics out of the broader disciplines of economics and psychology. The second section reviews the methods used in behavioral economics, which differ substantially from those used in classical economics. The third section reviews some of the seminal insights developed in the field. The final section discusses how behavioral economics may intersect with education policy.

Development of Behavioral Economics Criticism of the standard model of economic rationality preceded behavioral economics. In the 1950s, the Nobel Prize winner Herbert Simon criticized the assumptions of rational decision-making models and offered the notion of bounded rationality, which acknowledges the substantial limits on the rationality of human decision making. Rather than being optimizers, Simon described decision makers as “satisficers” who draw on their limited or bounded rationality to arrive at satisfactory rather than optimal solutions. Although Simon and other economists held critical views of the assumptions of unbounded rationality, calls for models of human choice and decision making that incorporate behavioral or psychological principles were not readily received in the broader field of economics. It wasn’t until the 1970s that behavioral economics began to receive recognition, thanks to the work of two psychologists, Daniel Kahneman and Amos Tversky. Kahneman and Tversky introduced

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two theses that became fundamental to behavioral economics. Their work on decision making showed that people relied on simplifications and shortcuts (i.e., heuristics) to make decisions and that while heuristics were often useful, they might often lead to less than ideal decision making. Their 1979 article “Prospect Theory” outlined a framework for how humans cast decisions from a particular reference point, and based on that reference point, they value losses differently from gains. Prospect theory provided a framework for how people make choices in complex situations, especially when faced with risk or uncertainty, and this theory allowed for several phenomena that traditional models of utility-based decision making could not accommodate. For their formative work in behavioral economics, Kahneman and Tversky received the Nobel Prize in 2002. The work of Kahneman and Tversky was instrumental in establishing the field; however, classically trained economists were also among the early principal figures in behavioral economics. Chief among these was Richard Thaler, who focused on anomalies in the rational frameworks of decision making and choice. Drawing on psychology, and explicitly on the work on Kahneman and Tversky, Thaler focused on examining anomalies with the purpose of developing realistic and empirically accurate descriptions of decisions and consumer choice. Central to Thaler’s work was his identification of the failure of actors to accept sunk costs (i.e., resources that have been used and cannot be recovered) and consider opportunity costs (i.e., the alternative choices that must be given up to pursue another) in decision making. Thaler further developed the concept of mental accounting, which detailed the ways in which consumers’ differential considerations of payment methods, such as cash versus credit cards, influenced spending, and their consideration of the source of funds influenced marginal consumption. Since the pioneering work in the 1970s by Kahneman, Tversky, Thaler, and others, behavioral economics has developed into a recognized subfield of economics, with a dedicated journal (Journal of Behavioral Economics) as well as academic societies and professorships. Behavioral economics has been increasingly applied to policy and regulation. Notable behavioral economists who apply the behavioral theories to practice include Cass Sunstein (author of the popular behavioral economics book Nudge), appointed as “regulatory czar” by President Obama, and the U.K. Cabinet Office of the Behavioral Insights Team, often called the “Nudge unit.”

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Behavioral Economics

Methods in Behavioral Economics Methodologically, behavioral economics has drawn heavily from psychology and uses randomized experiments in controlled settings to isolate ways in which human behavior runs counter to the assumptions in economic models. Randomized experiments afford high internal validity and causal attribution, but they come at the cost of external validity and criticism regarding the generalizability of narrow lab experiments to the world at large. Nonetheless, experimental findings highlight how economic assumptions may fail to reflect human behavior, and insights from this work are used to explain results that traditional economic models do not predict. Although behavioral economists have predominantly relied on lab experiments, they also have employed other methods of investigation. In some experiments, researchers have looked at the neurological level to understand departures from rational decision making. Researchers have used functional magnetic resonance imaging to examine the parts of the brain used in decision making and to identify what areas of the brain engaged in decision making can be manipulated by experimental stimuli. Additionally, behavioral economists have used field studies, both observational and quasi experimental, and have analyzed data on natural experiments. Such experiments typically preclude random assignment but have nonetheless produced compelling findings.

Insights From Behavioral Economics Behavioral economics has yielded many important insights to inform economic research. A selective list of such insights is given below followed by three education policy topics that may benefit from the inclusion of these insights. Certainty preference refers to people’s preferences for certainty over uncertainty in the face of probabilistically equivalent outcomes. Loss aversion is the idea that individuals dislike losses more than they like gains, based on a neutral reference point. Generally, individuals dislike losses twice as much as they like gains. The endowment effect is seen when people value things that they are given or own more than they would if they did not already have them. The endowment effect affect sellers’ willingness to sell

but not buyers’ willingness to pay, resulting in valuation effects that run counter to traditional assumptions of rationality. Status quo bias refers to the human tendency to prefer one’s current state, independent of transaction costs, even if the available alternatives are rationally preferable. Default effects describe the tendency people have to let the default options unduly influence decisions. For instance, enormous differences between countries’ organ donation consent rates have been attributed to whether the forms have people opt in or opt out of organ donation, with far higher percentages of consent where the default is consent and citizens must act to opt out. Framing effects are effects that result from relatively small differences in how choices are presented to actors. Framing effects can change the reference point from which actors conceptualize their options, thereby influencing outcomes. Intrinsic motivation describes how people are motivated by nonfinancial incentives. Social and market norms are generalized frames for decision making that operate on the bases of relationships or payments. For example, behavioral economists showed that once a day care center instituted a $3 fine for parents who were late to pick up their children, parents were more likely to be late, not less. Applying a market disincentive to a problem once governed by social norms (parents’ guilt) fundamentally changed parents’ decision frames and thereby the results. Fairness, altruism, and inequity aversion are three related phenomena that impact decisions in ways that run counter to the economic assumption of pure self-interest. An experiment called the ultimatum game has shown that recipients often will reject rewards that they consider unfair, even though they represent a personal net loss. The paradox of choice refers to the difficulty people have making decisions when a large number of choices are available to them. Overwhelmed with options and unequal information regarding various outcomes, people often depend on rules of thumb, heuristics, defaults, or the status quo when making choices.

Behavioral Economics

Choice architecture describes how the way choices are structured, in terms of constructing defaults, framing options as gains or losses, or presenting the time frames associated with decisions, can influence decision outcomes without addressing a person’s underlying preferences. Choice architecture combines multiple decision biases into the design of policies and practices that affect the structure of choice.

Behavior Economics Applied to Education Policy Behavioral economics has revealed a number of insights that are readily applied to education policy concerns. Following are three examples regarding (1) postsecondary attendance decisions, (2) the operation of parent choice in school choice and charter school reform, and (3) the structure of teacher compensation reforms. Decisions about college attendance and financial aid are illustrative of relevant postsecondary policy concerns. For example, high school students’ perceptions of their default or expected options for college attendance may significantly affect their decisions to attend college. It is known that students’ social or family setting can influence their expectations regarding college attendance. Insights about loss aversion may help illuminate that for some students not attending college would be framed as a loss to be averted rather than as a gain to be attained. Since loss aversion is felt more strongly than desire for comparable gains, students who frame college attendance as a gain above expectations may have a different decision algorithm compared with those who see not attending college as a loss. Furthermore, since college preparation entails several prerequisite steps (e.g., taking college entrance exams or completing financial aid forms), the difference between these frames may suggest different remedies for different students. Public school choice and charter school reforms are largely predicated on economic theories of consumer choice. While the introduction of additional schooling options may indeed introduce more choice to parents, behavioral economics has shown that such choices are not simple. Parents’ consideration of where to send their children may not be based on rationally optimized choices from available alternatives. Many parents’ choices may be affected by default effects and status quo bias, when parents may “choose” the school for which students

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are geographically zoned by not making any choice, or by the paradox of choice, when parents’ options are too numerous and the logistics of making such a choice are so complex that decisions are based on heuristics or poor information. Insights from behavioral economics could be applied to study and alter the choice architecture of public school choice options to provide appropriate information, a manageable menu of options, and straightforward application processes that would help families make school attendance decisions that reflect their true preferences. Teacher compensation reform, especially in terms of performance bonuses and merit pay, is largely based on market-oriented economic theories of labor. However, teachers’ motivations are often characterized as intrinsic rather than predominantly extrinsic motivations. Behavioral economists have shown how applying solutions based on market norms can have unintended consequences when applied to arenas where social norms play a substantial role. The set of norms applied in these incentive systems are important not only for their effect on teachers’ labor decisions but also on their practice if the incentive targets are narrowly focused and thereby reward “teaching to the test,” or even cheating. Teachers’ decisions to participate in pay reforms are also likely to be influenced by concerns about fairness, inequity aversion, the values of certainty, and aversion to loss. In terms of teacher compensation and many other education policy concerns, applying the insight of behavioral economics may provide better explanations of outcomes in the field and improved design of policy solutions than would economic theory based on assumptions of rationality. Nat Malkus See also Economics of Education; Public Choice Economics

Further Readings Ariely, D. (2009). Predictably irrational: The hidden forces that shape our decisions. New York, NY: HarperCollins. Gneezy, U., & Rustichini, A. (2000). A fine is a price. Journal of Legal Studies, 29(1), 1–17. Jabbar, H. (2011). The behavioral economics of education: New directions for research. Educational Researcher, 40, 446–453. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–292.

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Benefits of Higher Education

Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. New Haven, CT: Yale University Press.

BENEFITS

OF

HIGHER EDUCATION

The benefits of higher education accrue to both the individual and the society as a whole. When individuals decide whether to pursue higher education, they may only narrowly consider the effects of higher education on their labor market outcomes. Higher education is correlated with higher rates of employment as well as higher earnings. Individuals with more education also receive larger nonwage benefits, such as more generous health and retirement benefits. In addition to these labor market effects, individuals may reap other benefits from higher education such as better health and higher happiness levels. A more educated populace also benefits society. Higher education levels are associated with greater levels of tax revenues as well as lower expenditures on social welfare programs. In addition, higher levels of education may also generate positive externalities such as faster economic growth as well as lower levels of crime. This entry provides a review of the benefits of higher education for both the individual and the society.

Labor Market Outcomes One of the main reasons individuals choose to pursue more education is to achieve better labor market outcomes. Individuals who obtain a college degree are more likely to be employed and earn more on average than individuals without a college degree. Sandy Baum, Jennifer Ma, and Kathleen Payea document the returns to education. They report that the median earnings for a college graduate were $21,100 more than the median earnings for a high school graduate in 2011. They also report that the unemployment rate for individuals between the ages of 25 and 34 was 7.1 percentage points lower for college graduates than for high school graduates in 2012. A great deal of research has focused on whether the effects of education on these outcomes are merely a correlation or whether education causes these beneficial labor market outcomes. In addition, researchers have also sought to evaluate whether the mechanism by which education affects labor market outcomes is mainly due to the individual becoming more productive or whether it is through a signaling or sorting mechanism.

One of the most common ways by which researchers evaluate the effects of education on earnings is to estimate a regression in the form pioneered by Jacob Mincer. In the most basic model, the effects of education on earnings are captured by running a regression of log wages on individual characteristics, experience, and measures of schooling. In the past, researchers have used either years of schooling or indicator variables for completed levels of schooling in the regression. James Heckman, Lance Lochner, and Petra Todd provide a review of the use of the Mincer regression and review problems with interpreting the coefficient on schooling as the causal return to education. While the approach is very common, the estimates obtained from the Mincer regression may be biased for at least three reasons. First, it takes for granted that individuals with more education have a higher probability of employment. Individuals with lower levels of education report higher levels of unemployment and thus are more likely to earn zero dollars. Wage regressions that do not take into account the effects of education on employment are not capturing the full effects of education. Second, individuals with more education also typically report higher levels of hours worked. Without controlling for hours worked, the wage regression may overestimate the return to education. A third potential problem with the basic wage regression is that it does not take into account other types of benefits individuals may receive from their employers, including retirement benefits, health benefits, and other insurance benefits. The coefficient(s) from a Mincer equation on higher education may also be contaminated by selection bias. This selection bias stems from the fact that individuals who choose to go on to obtain more education may differ in important ways from individuals who choose not to pursue more education. If individuals who pursue more education are of higher ability than individuals who choose not to pursue more education, then the estimated returns to schooling may be contaminated by ability bias. Individuals of higher ability may be more likely to pursue higher education, because learning the material will be easier for them and they will likely have lower costs of schooling due to scholarships. They also will likely have higher returns to the investment. Ability bias may lead researchers and policymakers to overestimate the returns to schooling. The possibility of ability bias in the estimated returns to schooling has led researchers to pursue

Benefits of Higher Education

innovative methods to measure the monetary returns to schooling. One approach to remedying the potential problem of ability bias is to include a measure of ability in the regression. This approach is straightforward, though test score data may not be readily available. In addition, test scores may not capture all the dimensions of ability as both cognitive skills and noncognitive skills may affect earnings. Cognitive skills are more readily tested as they test whether an individual has specific knowledge. Noncognitive skills make the workers more productive, though these skills are not as readily tested. These noncognitive skills could include the ability to persevere as well as the person’s reliability. Both of these attributes could affect whether individuals obtain more schooling and could also affect earnings. Another approach to capture the causal effects of education on earnings is to use data on twins. The argument for using twins is that twins have the same innate ability, and therefore, differences in wages must be due to differences in schooling. The potential problem with this argument is that some factor affected whether one twin obtained more schooling and that trait of the twin may also affect earnings. Other innovative methods have focused on what factors affect whether an individual enrolls in college. In the past, researchers have used distance to the nearest college as a variable for estimating whether an individual obtains more education. This is done out of the belief that earnings should not be affected by the likelihood that individuals who live closer to a college are more likely to go to college. Researchers have also used compulsory schooling laws to estimate the causal returns to schooling. The estimates in most cases range between 7% and 10%. Over the past few decades, the returns to higher education have increased. Thomas Lemieux provides an analysis of the returns to years of schooling using quantile regressions. He finds that the returns to postsecondary education have increased over time and that the variance in earnings has also increased over time. The effects are larger for individuals who obtain 16 years of schooling rather than 14 years of schooling. This is consistent with the returns to a 4-year degree increasing faster than the returns to a 2-year degree. While there is an increasing return to higher education in the United States, there is considerable heterogeneity in the returns to education by the selectivity of the university and the choice of college major. One of the current debates in the research literature and in the popular press is how much of the

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returns to schooling vary according to where the student attended college. The empirical evidence on the returns to attending a more selective institution are mixed, with some studies demonstrating positive returns to attending a more selective institution and others demonstrating very little return. The difficulty inherent in measuring the effects of a selective institution on earnings is that selective institutions by their very nature only admit students who are of high ability. Thus, it is often difficult to separate the effects of the individual characteristics from the characteristics of the institution the student attended. Dominic J. Brewer, Eric Eide, and Ronald G. Ehrenberg demonstrate that individuals report higher returns from more selective institutions and also show that the return to selectivity may have increased over time. Stacy Dale and Alan Krueger find that once the researcher controls for where students apply to college, the returns to attending selective institutions fall to almost zero for most individuals, though the returns for Black and Hispanic students are still sizable. The higher returns to attending selective universities may be due in part to the fact that selective universities usually have more resources than universities that are less selective. It could also be due to the fact that students who attend more selective universities are more likely to complete a college degree and may also have peers who can help connect them to job opportunities. While studies have demonstrated that where a student attends college matters, it may be that the field of study that the individual chooses has a larger impact on earnings. Research has shown that individuals who pursue engineering, for example, earn substantially more than individuals who pursue education as a field of study. Similar to the returns to attending a more selective institution, there is a concern that failing to account for the ability of the student may bias the returns to college major choice. Individuals who have higher math ability are more likely to choose to be engineers, and thus, it is difficult to separate whether it is the ability of the student or the choice of major that leads to higher earnings. Daniel Hamermesh and Stephen Donald demonstrate that after accounting for the ability of the student the variations in earnings by field of study are very large even when considering graduates from only one university. The returns to higher education also vary according to the demographic characteristics of the graduate. The differences in the returns by race and ethnic

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Benefits of Higher Education

group may be related to trends where these students choose to apply to college and where they choose to attend. Individuals from families with lower family incomes are less likely to enroll in more selective institutions even after controlling for qualifications. If selectivity may account for differences in earnings, then some of the differences in earnings by race and ethnic group may be due to differences in where these students choose to attend. In addition, evidence suggests that women and minorities are more likely to major in lower paid fields of study; this also could partially explain earnings differences between individuals with the same level of education. While most research on the returns to higher education has focused on the earnings that individuals receive with more education, these returns also extend to the type of work the individual does as well as to whether the individual obtains other nonmonetary benefits. Education levels do affect occupation choice, and studies that control for occupation in earnings regressions may be underestimating the returns to education. Evidence also suggests that education affects the types of responsibilities that an individual has on the job as well as the level of autonomy for the individual on the job. In evaluating whether society should help promote investments in education, it is important to know whether the effects of education on earnings and employment are due to educated individuals becoming more productive or whether they are due to education signaling the ability of the worker. Education could serve as a signal of an individual’s ability, and it may be that some of the return to education is due to the signal of the worker’s capabilities rather than due to the productive nature of education. A. Michael Spence won the Nobel Memorial Prize in Economic Sciences for his work on signaling in labor markets. The evidence does seem to suggest that some of the monetary returns to education are due to signaling. Andrew Weiss provides an analysis of how much of a role signaling and sorting play in the returns to education.

Effects of Higher Education on Other Outcomes for the Individual Many studies demonstrate that the benefits of education go well beyond the effects on the labor market outcomes of the individual. The effects of education on the individual range from changing the consumption patterns of the individual to the self-reported happiness level of the individual. Barbara Wolfe and

Robert Haveman provide a more thorough catalog of the effects of education on the individual as well as on society. For the sake of brevity, this entry only reviews the effects of education on health and family formation. A large and growing amount of research focuses on evaluating the causal effects of education on health. The current evidence suggests that individuals who obtain more education are substantially healthier, though it is difficult to attribute these as causal effects. It is possible that education does enable individuals to learn how better to care for themselves and when to identify health problems. It may also introduce individuals to healthier behaviors. Education may also provide individuals with more income, and thus, this may provide them with the means to purchase better health care. However, it is also possible that individuals who obtain more education are able to do so because they have better health. The relationship between education and health may not be causal as individuals who value the future highly may just be more likely to invest in both education and health. Research also suggests that education affects investments in children. Individuals with more education are more likely to use contraception and are more likely to achieve their desired family size. Along with affecting the number of children individuals have, there is evidence that education also affects the types of investments individuals make in their own children. Sandy Baum, Jennifer Ma, and Kathleen Payea show that individuals with more education are more likely to invest more time in their children and are more likely to invest in developmental activities with their children. While there are enormous private benefits from having education, the effects of parents’ education on their children have societal implications as well.

Benefits of Higher Education for Society The education levels of the population have enormous effects on society. Individuals with more education contribute more in taxes and draw less from social welfare programs. Evidence also suggests that education affects economic growth, crime, civic engagement, and charitable contributions. As individuals with more education are healthier, this may also lead to other individuals in society being healthier as well. The investments educated individuals make in their children also have direct effects on society as these individuals will help determine the future of the country.

Benefits of Higher Education

Research on the effects of education on economic growth has generally taken two separate approaches. One approach taken by Eric Hanushek and Dennis Kimko is to analyze data from multiple countries over time and to see how the education (measured by test scores) of the population helps predict economic growth. Another approach taken by Enrico Moretti uses data from cities to analyze how changes in the number of college-educated workers within an area affect average earnings within a city. While the empirical approach to documenting the effects of education on economic growth does vary, the analysis does have common assumptions with regard to how education could affect economic growth. Since education makes individuals more productive, it is possible that this can foster job creation for workers. Education may also lead to the development of new technology. The amount of education the population obtains may also affect the cohesiveness of society itself. The empirical evidence does suggest that individuals with more education are less likely to commit crimes. Since education increases a person’s wages, education increases the opportunity cost of committing a crime for an individual. Lochner and Moretti find a large effect of education on crime in the order of 14% to 26% of the private return to education. The amount of education an individual obtains also affects his or her engagement in society. Individuals with more education are more likely to vote. This may be due to their having a more vested interest in the outcomes of the country. In addition, individuals with more education are also more likely to donate to charity. They may be more able to afford charitable contributions as they earn more income. Individuals with more education are also more likely to donate more of their time to charity.

Conclusion An accurate assessment of the total benefits of higher education is important for policy. The large effects of education on society do not directly enter into the individual’s decision of whether to invest in higher education, and thus, there may be an underinvestment in education from society’s viewpoint. Haveman and Wolfe provide a means to calculate the economic value of the benefit of education for outcomes and goods that are nonmarketed in nature. The optimal amount of government investment in education and subsidies to promote investment in education in society relies on an accurate

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accounting of both the benefits of education and the costs of education. Lisa M. Dickson and Katherine Bryant See also College Completion; Cost of Education; Educational Equity; Higher Education Finance

Further Readings Baum, S., Ma, J., & Payea, K. (2013). Education pays: The benefits of higher education for individuals and society (Trends in Higher Education Series). New York, NY: College Board. Brewer, D. J., Eide, E. R., & Ehrenberg, R. G. (1999). Does it pay to attend an elite private college? Cross-cohort evidence on the effects of college type on earnings. Journal of Human Resources, 34(1), 104–123. Dale, S., & Krueger, A. (2011). Estimating the return to college selectivity over the career using administrative earnings data (National Bureau of Economic Research Working Paper No. 17159) [Online]. Retrieved from http://www.nber.org/w17159 Hamermesh, D., & Donald, S. (2008). The effect of college curriculum on earnings: Account for non-ignorable nonresponse bias. Journal of Econometrics, 144, 479–491. Hanushek, E. A., & Kimko, D. D. (2000). Schooling, laborforce quality, and the growth of nations. American Economic Review, 90(5), 1184–1208. Heckman, J. J., Lochner, L. J., & Todd, P. E. (2006). Earnings functions, rates of return and treatment effects: The Mincer equation and beyond. In E. A. Hanushek & F. Welch (Eds.), Handbook of the economics of education (Vol. 1, pp. 307–458). Boston, MA: Elsevier North-Holland. Lemieux, T. (2006). Postsecondary education and increasing wage inequality. American Economic Review, 96(2), 195–199. Mincer, J. (1974). Schooling, experience, and earnings. New York, NY: National Bureau of Economic Research. Moretti, E. (2004). Estimating the social return to higher education: Evidence from longitudinal and repeated cross-sectional data. Journal of Econometrics, 121, 174–212. Spence, M. (1973). Job market signaling. Quarterly Journal of Economics, 87(3), 355–374. Weiss, A. (1995). Human capital vs. signalling explanations of wages. Journal of Economic Perspectives, 9(4), 133–154. Wolfe, B. L., & Haveman, R. H. (2002). Social and nonmarket benefits from education in an advanced economy. In Y. K. Kodrzycki (Ed.), Education in the 21st century: Meeting the challenges of a changing world (pp. 97–131). Boston, MA: Federal Reserve Bank.

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Benefits of Primary and Secondary Education

BENEFITS OF PRIMARY AND SECONDARY EDUCATION Education economists argue that rational individuals and societies invest in primary and secondary education because of their numerous benefits. An individual and his or her family enjoy private benefits of education such as improved earnings and health. Communities, states, and countries gain social benefits from an educated populace, such as larger tax revenues and savings in government expenditures on health and crime. This entry presents three conceptual models for understanding the relationship between education and its benefits and reviews the international evidence on the various private and social benefits of primary and secondary education.

Conceptual Models of Education and Benefits Social scientists typically use three conceptual models to explain the association between benefits and education: (1) the direct effects model, (2) the correlated effects model, and (3) the indirect effects model. Drawing from Nicholas Emler and Elizabeth Frazer’s work, this section explores the economic interpretations of these models. According to the direct effects model, increased quantity and quality of education enhances cognitive skills, knowledge, and tastes that lead to improved private benefits, such as greater productivity, earnings, health, and political participation. Notably, the human capital model is a direct effects model that emphasizes the positive effects of education on an individual’s labor market productivity. Furthermore, raising the average quantity and quality of education raises the population average for private benefits, thereby spurring social benefits such as tax revenues (see Figure 1). Unlike the direct effects model, the correlated effects model posits that education is influenced by the attributes of an individual, and in turn, these “third variables” are responsible for any consequent benefits. Key third variables include an individual’s innate ability (e.g., IQ) and personality (e.g., motivation, patience, and preference for risk). In other Education Cognitive skills Knowledge Tastes

Figure 1

Direct Effects Model

Benefits Private benefits Social benefits

words, in this model, the quantity and quality of education are correlated with benefits but do not actually cause the benefits. In economics, screening and signaling models are examples of correlated effects models: An individual with more innate ability pursues education credentials to signal his or her higher productivity to employers, while employers simultaneously screen job applicants and their productivity on the basis of education credentials. Other third variables include an individual’s family socioeconomic status, social capital (i.e., an individual’s relationships with parents and community members), and cultural capital (i.e., the extent to which an individual possesses the mannerisms, sense of appropriate attire, and habits of speech that impress his or her teachers and employers) (see Figure 2). Finally, the indirect effects model consists of two stages. In the first stage, education affects an individual’s socioeconomic position, which influences labor market earnings, occupation, self-esteem, and social position; for example, an individual with a higher quantity and quality of education earns more in the labor market. In the second stage, this socioeconomic position affects private and social benefits. For example, a more educated person bears larger opportunity costs (i.e., forgone labor market earnings) for committing a crime and being incarcerated. Like the correlated effects model, the indirect effects model posits that education does not produce private and social benefits. Unlike the correlated effects model, the indirect effects model does assume that education eventually affects benefits (see Figure 3). In summary, the three models provide competing conceptual explanations of the association between education and benefits. Economists have made major methodological contributions to disentangling the direct, indirect, and correlated relationships between education and benefits. Nevertheless, it remains challenging to determine which of the models is responsible for the observed association between education and benefits. One explanation for this difficulty is that in any given case, more than one model can hold.

Third variables Innate ability Personality Family socioeconomic status Social capital Cultural capital

Figure 2

Correlated Effects Model

Education

Benefits Private benefits Social benefits

Benefits of Primary and Secondary Education

Education Cognitive skills Knowledge Tastes

Figure 3

Socioeconomic position Earnings & occupation Self-esteem

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Benefits Private benefits Social benefits

Indirect Effects Model

The three models have different policy implications. The direct effects and indirect effects models suggest that educational policy (at the national, state, district, and school levels) has implications for private and social benefits. In contrast, the correlated effects model suggests that schools do not and cannot determine benefits; instead, the correlated effects model suggests that policy efforts should focus on “third variables” such as family poverty. Since researchers in education economics and finance are interested in education policy, the remainder of this entry focuses on explanations of benefits offered by direct effects and indirect effects models.

Evidence of the Benefits of Primary and Secondary Education A large body of social science research has examined the direct, indirect, and correlational relationships between primary and secondary education and various private and social benefits. Economic research often expresses these benefits in monetized forms. Furthermore, economic research is distinguished by its use of particular concepts, such as human capital theory and opportunity costs, to explain the relationship between education and benefits. Methodologically, in studies of private benefits, the unit of observation is a person, and data are gathered from numerous individuals. Next, researchers use statistical methods to establish a relationship between education (typically a quantity of education) and a benefit. For instance, high school graduation is associated with an X% lower likelihood of incarceration. In monetizing the benefits of education, a study may suggest that high school graduation results in $Y increased earnings over the lifetime. To compute a region’s or country’s social benefits, researchers consider the increases in tax revenues and reduction in government expenditure that result from high local rates of educational quality and quantity. Cross-country studies are another approach to assessing the social benefits of education. In these studies, a country is the unit of observation, and researchers investigate the relationship between education (e.g., share of individuals that

have completed secondary education) and a benefit for one or more time periods. Given that data availability is inversely related to a country’s level of development, far more research exists on industrialized countries than on developing ones. Furthermore, the research on benefits deals with educational quantity but not quality; this is because it is easier to collect data on educational quantity (e.g., attainment) than on quality measures (e.g., the many class sizes experienced over an individual’s entire formal schooling career) in large-scale surveys of adults. Productivity

As suggested earlier, foundational work on human capital articulated that education has a direct effect on an individual’s labor market productivity. In turn, educated and productive individuals earn higher wage rates in the labor market. But what is it about education that directly affects productivity? While visiting farms in developing and industrialized countries in the mid-1900s, Theodore Schultz observed that farmers with primary and secondary education were more likely to adopt new technology. A high quantity and quality of education provided farmers with the cognitive skills (e.g., literacy and numeracy) and knowledge (e.g., science) necessary to operate equipment; thus, compared with uneducated farmers, it took educated farmers less time and effort to learn new technology. To uneducated farmers, the extended time and effort required for technology adoption typically did not justify the benefits of productivity. This farming analogy can be extended to understand the benefits of primary and secondary education in manufacturing and services sectors. From a social perspective, a state or country with more educated workers should experience higher rates of economic growth. As discussed elsewhere in this encyclopedia, international research typically shows a positive relationship between individual earnings and quantity of primary and secondary education. Because earnings are often used as a measure of productivity, these findings suggest that productivity is a private benefit of education. According to Henry Levin and Clive

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Belfield’s estimates of private educational benefits, male high school graduates in the United States earn, over a career, $117,000 to $322,000 more than male high school dropouts, and female high school graduates earn $120,000 to $244,000 more than their counterparts without a diploma. The majority of other studies present productivity benefits as a rate of return. According to estimates from 52 developing countries compiled by Harry Patrinos and George Psacharopoulos, the rate of return is 23.0% for primary education (vs. attainment below primary education) and 17.9% for secondary education (vs. primary education attainment). In industrialized countries, the rate of return for secondary education varies from 7.0% to 23.9%. Given the universal rates of primary education completion in industrialized countries, returns to primary education are not typically reported for these countries. One approach to gauging the social benefits of increased productivity (shown in earnings) is to measure the additional tax revenues. Levin and Belfield estimate that male graduates pay $76,000 to $153,000 more than dropouts, and female graduates pay an additional $66,000 to $84,000. Numerous cross-country studies have analyzed the relationship between education and national productivity or economic growth (i.e., the annual increase in mean per capita income). Evidence from industrialized countries over the 1991–2000 period suggests that secondary educational quality, measured by scores on international assessments, has a stronger effect on economic growth than educational quantity. The cross-country evidence from developing countries indicates that educational quantity matters only in a high-quality education system.

ability to pay for superior health insurance, health care, and nutrition. Furthermore, more educated individuals are not discouraged by earnings forgone during health-related activities because of additional earnings accrued over a longer and more economically productive life. Evidence from industrialized countries indicates that secondary education has a negative relationship with health indicators and outcomes, such as nights spent in the hospital, trouble with stairs, disability that limits personal care, disability that limits mobility, smoking, obesity, long illness, reduced activity, hypertension, and death. In the United States, high school graduates live 6 to 9 years longer than high school dropouts. However, evidence from England, France, and Wales suggests that increases in secondary education are not associated with longer life expectancy. Some argue that education matters less in western Europe than in the United States, because Europe’s more generous government policies (especially health care access and financial support) are stronger than education as predictors of health. The economic evidence on the relationship between education and health in developing countries is limited and has focused mostly on how parental education contributes to child health; this phenomenon is discussed later in this entry. For society, the health benefits of education can result in significant savings for the public sector. In the United States, educational attainment is associated with lower enrollment in public sector health care. Levin and Belfield estimate that between the ages of 20 and 65, a high school dropout will receive a total of $60,800 in government support, whereas a high school graduate will receive only $23,200.

Health

A recent body of research examines the relationship between education and health. According to the direct effects model, education improves the cognitive skills necessary for acquiring and processing health information as well as understanding complicated instructions from doctors. Furthermore, welleducated people know more about medical science. The indirect effects model suggests that educated individuals interact with higher status peers who are more health conscious (e.g., have increased access to fitness facilities and nutritional resources) or who work in the health sector (e.g., doctors and surgeons). In addition, the indirect effects model suggests that higher socioeconomic status enhances the

Crime

According to the economic literature, education may enhance the cognitive skills that are more appropriate for legitimate work than for criminal activities. Knowledge of math and reading can also help individuals assess the benefits and costs of crime and the probability of incarceration. In addition, education instills distaste for crime, which raises the psychological cost of committing criminal activities. Similarly, education may inculcate patience, which reduces the likelihood of participating in criminal activities made attractive by immediate gratification. The indirect effects model also offers clear economic predictions; higher earnings from a greater quantity

Benefits of Primary and Secondary Education

of education make it more costly to plan and commit a crime. The would-be criminal will not only forgo his legitimate earnings while planning and engaging in this crime, he also stands to lose a large amount of future earnings if ultimately incarcerated as a consequence of that crime. Economic research from the United States and the United Kingdom has used the indirect effects model to demonstrate that crime increases in areas with rising unemployment rates and declining wage rates. Evidence from the United States indicates that completing secondary education is associated with a significant decrease in probability of incarceration. U.S. estimates indicate that high school graduates are 0.76 percentage points less likely to be incarcerated than dropouts. This benefit is considerably larger for African American graduates—incarceration probabilities become 3.4 to 8 percentage points lower on the completion of high school. The social benefits of decreased crime take the form of savings on several components of the criminal justice system: policing, trials, sentencing, and incarceration costs (e.g., parole and probation); state-funded victim costs (i.e., medical care and lost tax revenue); and expenditures by government crime prevention agencies. According to Levin and Belfield, the average cost saving per new high school graduate in the United States is $26,600.

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terms of cognitive skills, science and other inquirybased courses could instill values of openness and skepticism, which may enhance political outcomes. Pedagogy may shape students’ tastes and habits, especially if children are encouraged to ask questions and express opinions. Economic research from both the United States and the United Kingdom demonstrates that the quantity of secondary education has a direct effect on awareness of public affairs and support for free speech, both important political outcomes. The indirect effects model predicts that individuals with a higher quantity and quality of education forgo more income to engage in politics. This explanation was used in a study of New Zealand twins that found that an additional year of education resulted in a 12.5% lower likelihood of volunteering. However, a competing prediction is that more educated, richer individuals can better afford to participate politically. Both the indirect effects model and the direct effects model can explain the global research that suggests that higher levels of primary and secondary education correlate with more democratic political tendencies, such as voting and tolerance toward ethnic minorities. At the social level, however, cross-country studies show mixed evidence on the relationship between education and democracy. Furthermore, it is difficult to express the monetary benefits of most political outcomes.

Politics

Politically, education may only offer psychic benefits for an individual, but it creates important benefits for society. The work of classical philosophers and modern political economists alike—from Aristotle and Plato to Thomas Jefferson and John Stuart Mill—indicates that education has long been viewed as a prerequisite for desirable political outcomes; these outcomes are foundational to the social obligations involved in political life, and include political attitudes (e.g., tolerance and partisanship), political engagement (e.g., voting, campaigning, and protesting), political skills (e.g., collective decision making and organization, listening, speaking, and writing), political knowledge (e.g., knowledge of the Constitution, policies, ideology, current affairs, and history), and a sense of political efficacy (i.e., faith in government). According to the direct effects model, education affects political outcomes through knowledge accrued in civics and social science courses that teach facts about historical and current affairs. In

Intergenerational Benefits

Educated parents pass intergenerational benefits on to their children. Economic arguments for the intergenerational benefits of mothers’ education are based on the direct effects model. Essentially, an explanation for the positive relationship between mothers’ education and children’s outcomes is that the cognitive skills and knowledge gained from education help with parenting skills. From 1970 to 2000, infant mortality rates sharply declined in countries with higher adult literacy rates; this can be interpreted as an intergenerational benefit of primary education. Perhaps the most consistent finding in international education research is the relationship between mothers’ educational attainment and children’s educational and health outcomes, including higher birth weight, child survival, earlier entry into school, and years of schooling completed on reaching adulthood. Economic estimates of intergenerational social benefits, such as increases in tax revenues and reduction in

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Bilingual Education

government health and welfare expenditures, are unavailable but presumably large.

Conclusion Much social science research has examined the private and social benefits of primary and secondary education. Several compelling but competing conceptual models frame the relationship between education and benefits. In general, economic research typically finds strong associations between education and benefits like greater productivity, better health, lower crime, and better political and intergenerational outcomes. Several challenges remain, however. Methodologically, it is immensely challenging to disentangle the direct effects, indirect effects, and correlations. Furthermore, data limitations have prevented inquiry into educational quality, benefits in developing countries, and computation of monetized private and social benefits. M. Najeeb Shafiq See also Benefits of Higher Education; Demand for Education; Education and Civic Engagement; Education and Crime; External Social Benefits and Costs; Human Capital; Labor Market Rate of Return to Education in Developing Countries; Market Signaling; Opportunity Costs; Public Good; Social Capital; Socioeconomic Status and Education

Further Readings Colclough, C., Kingdon, G., & Patrinos, H. A. (2010). The changing pattern of wage returns to education and its implications. Development Policy Review, 28, 733–747. Emler, N., & Frazer, E. (1999). Politics: The education effect. Oxford Review of Education, 25(1–2), 251–273. Lange, F., & Topel, R. (2006). The social value of education and human capital. In E. Hanushek & F. Welch (Eds.), Handbook of economics of education (Vol. 1, pp. 459–509). Amsterdam, Netherlands: Elsevier. Levin, H., & Belfield, C. (2007). Educational interventions to raise high school graduation rates. In C. Belfield & H. Levin (Eds.), The price we pay: Economic and social consequences of inadequate education (pp. 177–199). Washington, DC: Brookings Institution Press. Lochner, L. (2011). Nonproduction benefits of education: Crime, health and good citizenship. In E. Hanushek, S. Machin, & L. Woessmann (Eds.), Handbook of economics of education (Vol. 4, pp. 183–282). Amsterdam, Netherlands: Elsevier. McMahon, W. (2002). Education and development: Measuring the social benefits. New York, NY: Oxford University Press.

Oreopoulos, P., & Salvanes, K. (2011). Priceless: The nonpecuniary benefits of schooling. Journal of Economic Perspectives, 25, 159–184. Schultz, T. P. (2008). Why governments should invest more to educate girls. World Development, 30, 207–225.

BILINGUAL EDUCATION Bilingual education is commonly defined as instruction provided in two languages. However, this simple definition is misleading since the concept of bilingual education is much more complex. For example, bilingual education may target English Language Learners (ELLs) and/or native English speakers. Moreover, the goal of the bilingual programs varies depending on the instructional model. This entry focuses on bilingual education in K-12 schools within the United States. The topic of bilingual education is particularly relevant to education economics and finance when considering funding implications of providing bilingual education to ELLs. Bilingual programs primarily serve ELLs, thus the federal government and most states allocate additional funding to serve the needs of this growing student population. This entry begins with a brief history of bilingual education outlining four major periods of bilingual education from the 1700s to the present. Then it describes several pedagogical approaches used in bilingual education and their effectiveness. Finally, the entry examines the financing implications of bilingual education at both the federal and state levels.

History of Bilingual Education The scholarly literature suggests that there have been four major periods of bilingual education in the United States: (1) permissive period (1700s–1880s), (2) restrictive period (1880s–1960s), (3) opportunist period (1960s–1980s), and (4) dismissive period (1980s to the present). A very brief synopsis of each period is provided. Permissive Period (1700s–1880s). The permissive period began in the 1700s when early European immigration was at its peak. The attitude of most immigrants and nonimmigrants alike was openminded to allow linguistic and cultural preservation through cultural enclaves. Moreover, limited informal bilingual education programs were offered in local communities that had a large number of immigrants (e.g., Germans, Italians). However, the

Bilingual Education

majority of schools still used English as the sole language of instruction. Restrictive Period (1880s–1960s). The restrictive period implemented repressive language policies directed toward most immigrant and ethnic minorities. For example, many Native Americans were forced into boarding schools, the use of German was repressed due to World War I, and Japanese internment camps were established during World War II. The factors of nationalism and urban ethnic immigrant poverty spurred nativist policies to “Americanize” ethnic and immigrant children. Opportunist Period (1960s–1980s). The opportunist period was initiated by the Cold War and contemporary immigration policy. Competition with the Soviet Union for global influence led to efforts by the United States to win over populations in countries across the world. Therefore, federal funds were allocated to teach and learn a second language as a national defense priority. Second, the Immigration Act of 1965 eliminated the national origin quota system; thus, many immigrants from Asian and Latin American countries immigrated to the United States. Consequently, the Bilingual Education Act of 1968 formalized bilingual education at the federal level to serve this growing immigrant population. Dismissive Period (1980s to the Present). The dismissive period began in the 1980s and continues today. The dismissive period directly affected the progress made during the opportunist period. For example, federal bilingual education funds began to be diverted to English-only approaches during the Reagan administration. Moreover, the No Child Left Behind Act of 2001 shifted federal policy from supporting transitional bilingual education to English-only approaches. At the state level, several states have passed antibilingual education initiatives (e.g., California, Arizona, and Massachusetts).

Pedagogical Approaches to Bilingual Education There are primarily four different types of bilingual education programs identified in the literature. Each pedagogical approach is distinct due to the target population, language usage, program length, and goal(s). These programs are the following: Structured English Immersion Program. This program is designed for ELLs of all ages. All student

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instruction is in English. The teacher promotes English comprehension through the use of second language acquisition strategies. Students are in an immersion program for 1 to 3 years. The goal of the English immersion program is monolingualism—to acquire English. Transitional Program. This program is designed for ELLs in the early grades, K-3 usually. Both the native language and English are used for instruction. The native language is used more frequently in K-1 grades to help students learn content as they acquire English. Each year, less native language is used until students are “transitioned” to English only by the end of the second or third grade. After exiting the program, they are placed into mainstream English classes. The goal of the transitional program is monolingualism—to acquire English. Maintenance Program. This program is designed for ELLs in Grades K-6. Both native language and English are used for instruction. In this program, a student’s native language is predominant in the early grades, then English use is increased every year until students are taught approximately 70% of the time in English and 30% of the time in their native language. Apart from the length of the program, there is one other fundamental difference between this method and the transitional program. The goal of the maintenance program is bilingualism—to acquire both English and the native language. Dual-Language Program. This program is designed to serve two types of students: (1) native Englishspeaking students and (2) ELLs in Grades K-6. Similar to the maintenance program, English is only taught minimally in the early grades, with instruction in English increasing every year until the fifth or sixth grade, when English is taught up to 50% of the time. The goal of the dual-language program is bilingualism for both groups. The native English speakers will learn a second language in addition to English and ELLs will acquire English as well as their native language. There has been intense debate about the effectiveness of bilingual education, especially for ELLs, over the past five decades. Empirical research clearly shows the benefits of bilingual education, especially in dual-language and maintenance models. Largescale longitudinal studies and meta-analyses conducted in the past 30 years show that, at a minimum, ELLs do as well in bilingual education programs as

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Bilingual Education

their peers in mainstream English programs. But most studies suggest a small to moderate positive difference in academic achievement in reading for ELLs enrolled in bilingual programs. Nevertheless, bilingual education is typically seen through a political lens, causing debate and controversy even when the research shows that bilingual programs work. The bilingual education debate is many times intertwined with the immigration debate, thus provoking strong emotional reactions that interfere with focusing on the empirical evidence. Voters in several states have passed antibilingual education initiatives; Arizona banned it, while California and Massachusetts severely restricted it in favor of teaching ELLs in English-only classrooms.

Funding for Bilingual Education and ELL Although bilingual education has been part of the United States since its inception, it was not formalized until 1968 when Title VII of the Elementary and Secondary Education Act, known as the Bilingual Education Act, was enacted. Soon afterward, states began to pass legislation to provide additional funding for bilingual education and/or students learning English as a second language. This section details federal- and state-level funding for bilingual education. Federal Funding

The Bilingual Education Act of 1968 earmarked federal funds for the first time for programs serving ELLs. During the initial year, the Bilingual Education Act provided $7.5 million to fund 76 programs across the country. The funding encouraged instruction in English and multicultural awareness. The Bilingual Education Act did not mandate bilingual education but instead allowed districts to develop bilingual programs. The federal government’s official policy supported a transitional bilingual education model. Only after Lau v. Nichols (1974), in which the U.S. Supreme Court ruled that limited-English-proficient students were not receiving an equal education unless districts took steps to correct their language deficiencies, did support and funding for bilingual education begin to significantly increase. Then, the Reagan administration began to decentralize government, including education, in the early 1980s, significantly decreasing the federal funds allocated for bilingual programs. Reagan federalism gave more administrative powers to the states, therefore curtailing any national movement for bilingual education and other federal educational programs. Another significant

change occurred with Title III of the No Child Left Behind Act of 2001, which took the place of Title VII of the Elementary and Secondary Education Act and promoted English-only approaches while restricting federal funding designated for bilingual education with instruction in students’ primary language. In 2005, 40 states still had bilingual education programs that used students’ native language and English, with the rest offering some type of Englishas-a-second-language instructional program. State Funding

After the Bilingual Education Act of 1968, some states began to pass their own bilingual education acts to provide additional revenue to support bilingual education. As of 2013, 37 states provided some additional funding to serve ELLs, primarily through a student-adjusted weight, in which ELLs count as more than one child for the purposes of funding, embedded in the basic school funding formula and/ or supplemental grants. However, each state handles its funding allocation differently. For example, Texas has a pupil funding weight of .10 (representing a percentage of extra funding, in this case 10% additional funding for each ELL student) for ELLs enrolled in special language programs, including bilingual programs. Consequently, ELLs not enrolled in these programs do not receive funding. Overall, the cost study literature suggests that states are not adequately funding programs to help ELLs reach grade-level proficiency on standardized tests. In fact, funding is based more on political and budgetary considerations than on actual costs needed to fund bilingual education or support ELLs to meet state standards. In other words, the funding allocated to serve ELLs in bilingual programs or other programs is not based on the available evidence from cost studies but instead based on the overall amount of revenue available for education, and the share of that devoted ELL programs, which is determined by a state’s legislature, and political negotiations.

Conclusion Bilingual education, and the primary target student group it serves (ELLs), is an important concept in education finance due to the long history of immigration in the United States. In addition, ELLs are one of the fastest growing K-12 student populations, yet one of the lowest performing. The education and academic performance of ELLs has significant long-term implications for the finances of the United

Block Grants

States and its economic development. It is important to determine the best ways to design and fund education for ELLs in bilingual and nonbilingual settings in order to maximize this population’s human resource potential. Oscar Jimenez-Castellanos See also Categorical Grants; Elementary and Secondary Education Act; Pupil Weights; Supplemental Educational Services; Vertical Equity

Further Readings August, D., & Hakuta, K. (Eds.). (1997). Improving schooling for language minority children: A research agenda. Washington, DC: National Research Council, Institute of Medicine. Baker, C., & Jones, S. P. (1998). Encyclopedia of bilingualism and bilingual education. Clevedon, UK: Multilingual Matters. Crawford, J. (1999). Bilingual education: History, politics, theory, and practice (4th ed.). Los Angeles, CA: Bilingual Education Services. Jimenez-Castellanos, O. (2010). School finance and English Language Learners: A legislative perspective. Association of Mexican-American Educators (AMAE) Journal, 3(1), 12–21. Jimenez-Castellanos, O., & Topper, A. (2012). The cost of providing an adequate education to English Language Learners: A review of the literature. Review of Educational Research, 82(2), 179–232. doi:10.3102/0034654312449872 Lau v. Nichols, 483 F.2d 791 (9th Cir. 1973); 414 U.S. 563 (1974). Ovando, C. (2003). Bilingual education in the United States: Historical development and current issues. Bilingual Research Journal, 27(1), 1–24. Verstegen, D., & Jordan, T. (2009). A fifty-state survey of school finance policies and programs: An overview. Journal of Education Finance, 3(34), 213–230.

BLOCK GRANTS Many governments around the world have introduced school block grants to improve the financing and delivery of education services. These policies transfer funds directly to schools to be managed and spent by a school council or some combination of the principal, teachers, and parents. School block grants are typically part of a broader policy called “school-based management” (SBM) that decentralizes authority from the central government to the

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school level. SBM policies identify the individual school as the primary unit of improvement and rely on the redistribution of decision-making authority as the primary means through which improvement might be stimulated and sustained. Most school block grant projects involve transfer of responsibility, usually the responsibility for school operations, from a school district or other central agency to a combination of principals, teachers, parents, and other school community members. The block grant is the instrument to empower principals and teachers, thereby enhancing their sense of ownership of the school. Through planning for the use of the block grant, these policies place special emphasis on meaningful community participation. And by placing the locus of decision making closer to the classroom, these projects aim to increase the efficiency and relevance of school funding decisions. School block grants, and SBM in general, are part of a larger movement of decentralization of education administration and authority from central governments to local entities. Decentralization is not a new idea; it was the norm in 19th-century schools in the United States. Some authors argue that SBM is just the latest incarnation of a movement to counter the broader historical trend, which occurred for most of the 20th century in the United States and in many other countries, toward central control over schooling. Decentralization reforms have been fueled by a perceived inefficiency and lack of responsiveness on the part of central governments coupled with a perceived sense of urgency to improve education quality. In countries where inefficiency or corruption of the central government could pose a threat to efficient use of resources, multilateral aid and lending agencies have largely supported the proliferation of SBM policies. This entry begins by defining school block grants, describing their main features, and detailing the types of school block grant programs. This is followed by a discussion of the logic model of school block grants, supporting how and why they would have the intended effect on education quality and other outcomes. The entry then discusses recent evidence on the effects of school block grant programs and the importance of implementation to explain program effects (or the lack thereof).

Definition and Description of School Block Grants School block grants differ from broader SBM reforms in that they provide fixed grants to schools

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Block Grants

in exchange for allowing school councils to exercise, to varying degrees, autonomy over the use of those funds. SBM includes programs such as block grants, as well as those in which parents have complete choice and control over schools and all decisions concerning school operations, finances, and education management. The latter is the case in some charter schools in the United States and in public education systems in some countries, such as the Netherlands. In this sense, block grants are a limited form of SBM in that the governance change it seeks to affect is attached to the block grant. Thus, it is temporary in nature, although some of its effects could alter future school-level governance even in the absence of the block grant. The school council or committee plays an important role in most school block grant programs. In addition to conducting the planning process to spend the grant and ensuring its proper use, school councils typically monitor school performance as well as teacher and student attendance. School block grant policies usually demand that school councils draft a school development or improvement plan (SIP). SIPs serve an important planning and community participation purpose. They set targets for learning and other outcomes as well as the steps that teachers and principals will take to reach them. Targets can be set in terms of student learning (e.g., student test scores or dropout rates); teaching, such as teacher attendance or training; and community participation. There may be other targets in terms of school construction, equity, achievement gaps, or other items that are important to schools. School councils or committees must then decide what strategies they will pursue to reach these goals. These include strategies related to teachers’ professional development, improvements to school infrastructure and equipment, purchase of textbook and other pedagogical materials, and community participation. By being explicit about what the school seeks to accomplish and how the funds will help achieve those goals, SIPs facilitate conversations between principals, parents, teachers, and sometimes even students on topics directly related to student learning. SIPs allow school council members to monitor principals and thus serve an important accountability role as well. The SIP is a road map for school councils, and it can help schools take advantage of a continuous, predictable stream of funding (the block grant) by helping school committees think strategically about where to spend these resources.

Types of School Block Grant Programs School block grant programs, understood in the context of SBM reforms, can take on different forms depending on who has the power to make decisions as well as the degree of decision making devolved to the school level. While some programs transfer authority only to school principals or teachers, others encourage or mandate parental and community participation, often in school councils. Felipe Barrera-Osorio and his coauthors refer to most block grant programs as a “weak” version of SBM. In the weaker versions of SBM, block grants provide limited autonomy to school councils by earmarking the funds for specific uses. For example, Mexico’s School Quality Program (or Programa Escuelas de Calidad) mandates that funds be used mostly for teaching and learning materials, school maintenance, teacher training, and purchase of school equipment. Stronger versions of SBM are characterized by providing almost complete autonomy to school councils. In some cases, like in El Salvador’s EDUCO (short for Educacion con Participacion de la Comunidad, or Education With Community Participation) program, school councils receive their entire school budget directly from the central education authority and have the responsibility for hiring and firing teachers, purchasing all school needs, and setting curricula. Block grant programs, for the most part, do not provide schools with this much authority, and in most cases, they do not allow schools to make staffing decisions (i.e., hiring and firing of teachers) if they do not already make them.

Pathways for Block Grant Effects Conceptually, there are at least four direct pathways through which SBM could affect learning and other education outcomes. First, block grants could encourage parents to become more involved in the school. Parental involvement is known to be a strong predictor of academic and behavioral success in elementary school and adolescence in the United States. SBM programs have been found to increase parental involvement in both traditional ways, such as participating in parent-teacher conferences, attending school events, and doing fundraising, as well as in more active ways, such as having influence over teacher hiring and firing. Yasuyuki Sawada and Andrew Ragatz found this to be the case in studies of the EDUCO program in El Salvador. In Mexico, parental involvement traditionally centered on fundraising and school maintenance activities as well

Block Grants

as on attending parent-teacher conferences. Parents can also provide oversight to ensure that funds and other resources actually reach schools. They can also monitor teacher behavior and attendance, which can result in reduced teacher absenteeism. A second possible pathway linking SBM to improved student outcomes involves how decision making affects resource efficiency. The block grants encourage all relevant local actors to get involved in decision making about key educational matters. Because these actors are closer to the school building and classroom than state or federal authorities, they should have better knowledge of the local context and needs. A third potential pathway relates to the relationship between local actors and their schools. It is possible that under SBM, school personnel and even students might develop a higher sense of “ownership” of the school, thereby becoming more committed to their schools. A fourth pathway suggests a resource effect. The defining feature of block grant programs is a transfer of funds to local schools. In some contexts, such as Mexico, these funds are vital for schools to meet their needs. By some accounts, more than 97% of school spending is tied up in teacher and administrative salaries, leaving schools with few resources for instructional materials, teacher training, or infrastructure and equipment. This is the case in many countries where block grants are implemented. Although the evidence is not conclusive, resources such as teaching and learning materials, classroom furniture and equipment, and basic infrastructure could matter for student achievement in these contexts.

Effects of Block Grants According to Barbara Bruns and her coauthors, even though block grant programs are popular around the world, few rigorous studies have been carried out to assess their effects. Most of the studies of SBM reforms around the world are nonexperimental and thus suffer from some form of selection or other biases, which could affect the external validity of their results. School block grant programs have yielded mixed results in terms of student learning and other outcomes. Although the research base is not thick, several studies are rigorously executed and provide the best analysis that is possible given program rollout and data availability. Studies that had access

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to standardized test scores found mixed evidence regarding SBM effects on outcomes. Some programs such as those in Mexico, the United States (Chicago), and El Salvador suggest some positive results, while others, such as those in Brazil, Nepal, and Pakistan have not found any statistically significant results. In their review of evaluations of SBM in more than 20 countries, Felipe Barrera-Osorio and his colleagues found mixed evidence regarding the impact of SBM on student test scores. There were positive results observed from programs in Kenya, El Salvador, the Philippines, Indonesia, and Nicaragua. Several studies showed improvement on pass rates, and to a lesser degree on dropout rates. A study from Brazil conducted by Martin Carnoy and others found no effects of program participation on student achievement. A recent randomized evaluation of a block grant program in rural areas in Mexico by Paul Gertler and colleagues called Apoyo a la Gestión Escolar (AGE), or Support to School Management, found that AGE promoted the joint participation of teachers, principals, and parents for school-level planning. AGE schools that received twice as much grant money as other treatment schools improved Spanish and math test scores for third graders and reduced dropout rates. Results from the parent-only intervention suggest that it also improved some learning outcomes. Lucrecia Santibañez and her colleagues conducted an evaluation of a larger school block grant program, Programa Escuelas de Caldidad—Fortalecimiento e Inversión Directa a las Escuelas (PEC-FIDE), whose English name is Program of Strengthening and Direct Investment in Schools, targeted at urban and rural areas in Mexico. They found positive effects of the program on Spanish test scores for third graders. The effect was only observed in a subset of program schools (i.e., those that were receiving the block grant for the first time). The program was not found to have effects on math scores or dropout rates. A recent evaluation of a pilot program in Indonesia conducted by Menno Pradhan and coauthors tested different combinations of cash grants plus one of three randomly assigned interventions: (1) training for school committee members, (2) democratic election of school committee members, or (3) facilitated collaboration between the school committee and the village council (also called the “linkage” intervention). Results suggest that interventions that change existing school organization

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Block Grants

structures, either through democratic elections or by linking committees to the larger community, were more successful. After 2 years, the grant plus linkage improved language test scores by 0.17 standard deviations. The grant plus linkage plus election intervention improved language test scores by 0.22 standard deviations. A study of another recent program in the Philippines, conducted by Nidhi Khattri and colleagues, provided schools with block grants to be used for school infrastructure, teacher training, curriculum development, and textbooks. The authors found some (nonexperimental) evidence to suggest that schools participating in the program increased school-averaged student performance on national tests, in science and English, 2 years after the program started. The increases, however, were small, around 1.2 to 1.8 percentage points.

The Importance of Implementation A comprehensive review of SBM programs around the world, which included many block grant programs, concluded that these programs can increase teacher effort, raise parental involvement, decrease repetition rates, and in some cases, improve student test scores. The review found that implementation is important for success, but many schools in countries where these programs are implemented lack the capacity to effectively take advantage of the autonomy conferred on them. For example, the Indonesia Bantuan Operasional Sekolah, or School Operational Assistance, school block grant program suffered from lack of capacity at the school level. Georges Vernez and colleagues conducted a thorough implementation study and found that the capacity of elementary schools to implement SBM was found to be relatively low. Principals and teachers indicated that they generally understood the autonomy the program provided schools to make managerial and programmatic decisions with input from other stakeholders, but they did not understand how to put the principles into operation effectively. Several studies of block grant programs, and studies of SMB programs in general, have found that the key actor is the school principal. In El Salvador, Sawada and Ragatz found that even in schools where school councils were designated to take over all administrative matters of the school, the principal was the main decision maker. The same finding arose from the implementation study of

the Indonesian Bantuan Operasional Sekolah program: School committees rarely met, and the school committee chair was simply asked to sign off on decisions already made by the principal. This was also true in Mexico. Much of the eventual success of the block grant hinges on whether the principal can make decisions that effectively improve school quality. It is unclear whether principals, particularly in low-income countries, have sufficient skills and information to make these decisions. In Indonesia, Vernez and colleagues found that only a minority of principals indicated that they were well prepared to deal with key activities such as “providing creative leadership and vision for school staff,” “planning for the school’s academic improvement in the medium term,” or “planning and managing the school budget and finances.” They also reported that whatever training they had received had been insufficient or not useful. Most of the training they received lasted only a day or two, and some reported that they had already forgotten what they learned. Parents and community members who are part of school councils also need information and training to be able to effectively participate in school planning and monitoring. One of the lessons from Mexico’s AGE, which targeted schools in rural areas, was that parent training by itself might have contributed to positive effects on student test scores.

Conclusion While the evidence is not yet conclusive, block grant programs appear to be an effective way to improve student learning as well as other outcomes related to parental participation, teacher effort, and accountability in the use of school resources. By providing schools with a financial incentive, block grants encourage broad community participation in school decision making and allow schools some degree of autonomy over how best to spend financial resources to meet pressing school needs. The available evidence also suggests that many school actors, particularly in developing and low-income countries, might lack the skills and knowledge to effectively engage in the planning and decision-making process. Some studies have found that programs that provide implementation support (i.e., training and other capacity-building activities) might have a greater chance of success. Lucrecia Santibañez

Bonds in School Financing See also Centralization Versus Decentralization; Education Finance; Local Control; Parental Involvement; School-Based Management

Further Readings Barrera-Osorio, F., Fasih, T., & Patrinos, H. (with Santibañez, L.). (2009). Decentralized decision-making in schools: The theory and evidence on school-based management. Washington, DC: World Bank. Bruns, B., Filmer, D., & Patrinos, H. A. (2011). Making schools work: New evidence on accountability reforms. Washington, DC: World Bank. Carnoy, M., Gove, A., Loeb, S., Marshall, J., & Socias, M. (2008). How schools and students respond to school improvement programs: The case of Brazil’s PDE. Economics of Education Review, 27(1), 22–38. Gertler, P., Patrinos, H., & Rodriguez-Oreggia, E. (2012). Parental empowerment in Mexico: Randomized experiment of the Apoyo a la Gestion Escolar (AGE) in rural primary schools in Mexico: Preliminary findings. Evanston, IL: Society for Research on Educational Effectiveness. Available from ERIC database. (ED530174) Gertler, P., Patrinos, H., & Rubio-Codina, M. (2006). Empowering parents to improve education: Evidence from rural Mexico (Policy Research Working Paper No. 3935). Washington, DC: World Bank. Karlsen, G. E. (2000). Decentralized centralism: Framework for a better understanding of governance in the field of education. Journal of Education Policy, 15(5), 525–538. Khattri, N., Ling, C., & Jha, S. (2010). The effects of school-based management in the Philippines: An initial assessment using administrative data (World Bank Policy Research Working Paper No. 5248). Washington, DC: World Bank. Malen, B., Ogawa, R. T., & Kranz, J. (1990). What do we know about site based management: A case study of the literature—a call for research. In J. F. Witte & W. H. Clune (Eds.), Choice and control in American education (Vol. 2, pp. 289–342). London, UK: Falmer Press. Pradhan, M., Suryadarma, D., Beatty, A., Wong, M., Alishjabana, A., Gaduh, A., & Artha, R. P. (2011). Improving educational quality through enhancing community participation: Results from a randomized field experiment in Indonesia (World Bank Policy Research Working Paper No. 5795). Washington, DC: World Bank, East Asia and Pacific Region. Sawada, Y., & Ragatz, A. B. (2005). Decentralization of education, teacher behavior and outcomes: The case of El Salvador’s EDUCO program. In E. Vegas (Ed.), Incentives to improve teaching: Lessons from Latin America (pp. 255–306). Washington, DC: World Bank.

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Vernez, G., Karam, R., & Marshall, J. H. (2012). Implementation of school-based management in Indonesia. Santa Monica, CA: RAND Corporation. Retrieved from http://www.rand.org/pubs/monographs/ MG1229

BONDS

IN

SCHOOL FINANCING

While school districts receive funding from multiple sources, school bonds provide a needed source of revenue for districts across the United States. School bonds are a form of revenue, usually at the school district level, in which a district will work to issue a municipal bond debt obligation, providing nearterm funding for specific items approved by the local community in exchange for long-term repayment of the principal to the debt holders, with interest. However, as with most school financing issues, there is substantial variety across the states in not only what a bond may fund (capital facility construction, renovations, and past debt obligations are just a few examples) but also in the ways in which bonds are funded through local elections. In examining bonds in school financing, this entry focuses first on how school facility construction is funded through local bond elections. Second, the discussion moves to the bonding process for school districts, leading to the bond election. Third, it discusses multiple strategies district administrators use when attempting to pass a bond. And finally, the entry discusses the influence that the federal government has recently begun to exert over school district bonds, which historically has been exclusively a function of local governments with some state oversight. The United States has a long history of local financing of schools, especially for acquiring, building, and renovating capital facilities (e.g., building new school buildings; renovating school buildings; purchasing land; construction of athletic, art, and performance facilities; upgrading technology infrastructure; and large equipment purchases such as new school buses). While many states have moved to state-level formulas for funding instruction and operating expenditures, the majority of states continue to fund capital facilities through local property taxes. As an example, in the state of Michigan, a set per-pupil funding formula that is distributed from the state level (but includes both state and local revenues) is used to fund current or annual expenses such as teacher

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salaries and curriculum resources, while local property taxes fund capital facility projects. Because expenditures for construction are usually large, onetime expenditures, districts rarely have enough cash on hand and, instead, issue debt in the form of bonds, receiving the funding upfront and agreeing to pay back the purchasers of the debt instrument over a specified period of time (usually between 10 and 30 years) with interest. The districts fund this added yearly expense of paying back the debt through increases in local property tax rates, which in the vast majority of states must be approved by a majority vote of the school district’s constituents. Six states impose an additional restriction of a supermajority for approval. Many states allow the school district to use the state’s credit rating, which is usually very high, helping to decrease the risk to investors on the public bond market and thus decrease the interest rate on the bond, saving the district money over the life of the debt.

Steps in the Bonding Process The bonding process for school districts typically occurs in four steps: (1) determination, (2) debt limit obligations, (3) election scheduling, and (4) issuing the bond. First is project determination, in which the school district consults with the school board and the stakeholders in the project across the community, such as teachers, parents, and students, as well as architects to do a needs assessment to determine what the project should entail and estimate the costs. Second, once the project is outlined and the costs are determined, the district determines if its costs are within the debt limits set by the state. Nearly every state imposes a ceiling on the amount of debt a school district can hold, usually as a percentage of the school district’s assessed value. Third, a bond election is scheduled. Almost every state has specific rules on the timing of the election and how the election is to be conducted. Fourth, if the referendum passes, then the school district works with legal and financial counsel to issue the bond on the municipal bond market. Funds from the sale of the bond notes are then used to pay for the planned project, and the district levies the approved new property taxes to pay back the investors.

Bond Measures and Election Strategies For many districts, the most difficult aspect of the bond funding process is the bond referendum election. Many bond elections do not pass. Passage

rates vary from state to state, with districts in some states passing bond proposals on average 70% to 80% of the time, while in other states, average passage rates are 50% or lower. When a bond referendum does not pass, the district is still faced with the costs of the needed capital projects, such as districts with growing enrollments and the need for additional schools to house all of the new students or districts with aging school buildings that may not pass state facility inspections without needed renovations. Faced with this situation, districts may opt for several different strategies. First, the district may forgo the bond; however, this does not solve the infrastructure issues identified during the determination phase. Second, the district may refloat the bond by putting the same or similar bond referendum up for election again within 1 to 3 years, in the hope that it will pass on a second or third attempt. Third, the district may opt to break up a large bond referendum into smaller projects that are then put before voters as separate smaller bonds. The problem with the last two strategies is that the research on bond elections has shown that a refloated or broken-up bond referendum has a much lower chance of passing than on the first attempt. For districts attempting to pass a bond referendum, there are additional issues that should be considered. First, the background and demographics of a school district may have a large effect on the probability of passing a bond. Historically, while it varies by state, some communities have a greater appetite for taxing themselves to pay for long-term debt for capital projects. Research suggests that school districts in small towns and rural communities may be at a disadvantage in passing bonds in comparison with those in metropolitan areas due, in part, to the link to property tax increases on owners of large farmlands. Second, election pass rates vary based on the description of the capital project on the ballot proposal. As examples, bond elections in Oklahoma pass more often when items related to technology are included in the proposal, while in Texas renovations and debt refinancing are favored over the construction of new buildings. Third, the characteristics of the election are important considerations, since bonds that are the first proposal, or the only proposal, on the ballot pass more often than bonds that are listed second, third, or lower. Most states require school districts to hold bond elections on specific dates, such as in the fall of the year at the same time as the national presidential and congressional elections, in order

Bonds in School Financing

to ensure the highest possible turnout. This may mean that in addition to electing people to office, ballots may contain other municipal bonds, or even in some states, property tax levy proposals to fund municipal operating costs. This leads to a certain level of competition on the ballot, resulting in an apparent voter fatigue, in which voters are less willing to approve each successive measure. Finally, voter turnout has long been shown to significantly influence bond election outcomes, with many studies showing that increased voter turnout is negatively associated with passing a bond measure. This effect appears to be a function of the likelihood that in low-turnout elections, most voters are positively motivated to go to the polls for the bond issue in question, whereas in high-voter turnout elections, such as during a presidential election, many more voters who are not acquainted with the bond proposal go to the polls. Voters who are uninformed about a bond proposal have a higher probability of voting against it. In the majority of states, public school district officials, as representatives of the local government, are barred from campaigning directly in support of district bond measures, or are extremely limited in how they are allowed to conduct any campaign activities. Restrictions include limiting campaign activities to before and after business hours and away from schools and district offices. Moreover, district strategies to pass a bond measure vary considerably across states and districts. District administrators in these situations face a conundrum in that they are the local experts on why the district is requesting funding for the capital project, they are usually well connected to the community, and they are highly motivated to want to pass the bond. At the same time, they face restrictions on their conduct that are designed to preserve fair and honest elections and not have district administrators overly influencing election outcomes. Thus, recommendations to district officials on how to support bond passage usually focus on a two-step strategy. This consists of first involving the community in the campaign process by bringing community members who are not affiliated with the district into the campaign, as well as discussing the purpose of the bond in the local news media and at public meetings and town halls. The second step is encouraging likely yes voters, such as district employees and parents of school-age children, to go to the polls and vote. Research in this area notes that the micropolitical context of local communities is highly varied, and

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shifts constantly, so recommendations for one district may not apply for another.

Federal Influence Over School District Bonds While the school bonding process has historically been a local and state issue, with a varying amount of state oversight of school district bonds depending on the state, the federal government has recently begun to exert some influence in this domain. The most significant shift has been with the American Recovery and Reinvestment Act of 2009, in which the federal government pledged to assist the 100 largest school districts in the United States with funding their capital facility construction bonds through paying the interest on new qualified bonds for construction projects that had already completed the determination phase. Prior to this, the only federal role had been in the small Qualified Zone Academy Bonds program of 1997, which provided a targeted tax credit for bondholders and thus did not provide funding directly to states or districts for school facilities. While many states provide assistance to districts for their bonds not only in providing the state credit rating in many instances to help back the bond but also in providing partial subsidies for construction or the bond debt service, the emerging role of the federal government is a trend that may see further expansion in the coming years. Alex J. Bowers See also Capital Budget; Capital Financing for Education; Expenditures and Revenues, Current Trends of; Infrastructure Financing and Student Achievement; Median Voter Model

Further Readings Bowers, A. J., & Lee, J. (2013). Carried or defeated? Examining the factors associated with passing school district bond elections in Texas, 1997–2009. Educational Administration Quarterly, 49(5), 732–767. Duncombe, W., & Wang, W. (2009). School facilities funding and capital-outlay distribution in the states. Journal of Education Finance, 34(3), 324–350. Ehrenberg, R. G., Ehrenberg, R. A., Smith, C. L., & Zhang, L. (2004). Why do school district budget referenda fail? Educational Evaluation and Policy Analysis, 26(2), 111–125. Ingle, W. K., Johnson, P. A., & Petroff, R. A. (2012). “Hired guns” and “legitimate voices”: The politics and participants of levy campaigns in five Ohio school districts. Educational Administration Quarterly, 48(5), 814–858.

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Piele, P. K., & Hall, J. S. (1973). Budgets, bonds, and ballots: Voting behavior in school financial elections. Lexington, MA: Lexington Books. Sielke, C. C. (2001). Financing school infrastructure: What are the options? School Business Affairs, 67(12), 12–15. Sielke, C. C., Dayton, J., Holmes, T., & Jefferson, A. L. (2001). Public school finance programs of the U.S. and Canada: 1998–99 (NCES 2001309). Washington, DC: National Center for Education Statistics. Retrieved from http://nces.ed.gov/edfin/state_financing.asp Wood, C. R., Thompson, D. C., & Crampton, F. E. (2012). Money and schools. New York, NY: Routledge.

BROWN V. BOARD

OF

“separate,” and worked vigorously to ensure that Black and White students did not intermingle, but paid little attention to the “equal” component of the Plessy ruling. As a result, Black students were relegated to inferior school buildings, were taught by poorly trained teachers, and lacked access to many basic instructional materials. At the time of the Brown ruling, there were 17 states that had legislated segregation policies and 16 states that explicitly prohibited the practice. Four states had optional segregation laws in place that allowed local governments to decide to separate the races if they desired to do so. The remaining states had no legislation regarding the practice of segregation.

EDUCATION Facts of the Case

Of all the lawsuits adjudicated by the U.S. Supreme Court that impact education, few are as well-known or influential as Brown v. Board of Education (1954). This class action lawsuit directly affected public education throughout the United States in a number of different areas, including de jure segregation and school finance. The Brown ruling is universally regarded as a landmark decision from the Supreme Court and, as such, is the subject of extensive scholarly research. This entry discusses the background of the Brown case, the details of the case, the Court’s ruling, and the significance of the Brown ruling for desegregation and school finance.

History Leading to the Brown Ruling In 1896, the Supreme Court handed down a controversial ruling in the Plessy v. Ferguson case, which established the idea of separate but equal. Homer Plessy, who was seven-eighths White and oneeighth Black, purposefully rode in a “White Only” section of a train in New Orleans, Louisiana, and was arrested for his actions. His aim was to have the Supreme Court strike down segregation laws that surfaced following the Civil War. However, the Supreme Court ruled that segregation was constitutional as long as the different facilities were equal. With this ruling, the terminology of separate but equal was created and the practice of segregation sanctioned. Following the Plessy ruling, local and state governments in certain areas of the United States began to embrace the concept of separate but equal, including public school systems and higher education. In addition, those in power embraced the concept of

Brown is named after Oliver Brown, the father of Linda Brown—a Black student in the third grade who was forced to attend a segregated school that was significantly farther away from her home than the White neighborhood elementary school. Brown, who was supported by the National Association for the Advancement of Colored People (NAACP), first attempted to enroll his daughter in the closer elementary school, and this effort was denied. As a result, the NAACP filed a lawsuit on behalf of Brown and 12 other parents against the City of Topeka Board of Education (in Kansas) in 1951. The case would take 2 years to work its way to the Supreme Court and 3 years for a final verdict. The U.S. District Court, where Brown was first argued, ruled in favor of the Topeka school board based on evidence that Black and White students received comparable services. This ruling was appealed to the Supreme Court. The Supreme Court consolidated five class action lawsuits that shared the same goal, the eradication of segregated schools, in the case it heard as Brown v. Board of Education. The NAACP supported all five lawsuits financially and with legal counsel. The other four cases that were lumped together with Brown by the Supreme Court were Briggs v. Elliott, which was filed in South Carolina; Davis v. County School Board of Prince Edward County, which was filed in Virginia; Gebhart v. Belton, which was filed in Delaware; and Bolling v. Sharpe, which was filed in Washington, D.C. The lead counsel for the NAACP was Thurgood Marshall, who would later go on to serve as a Supreme Court justice. The original Brown lawsuit was unique among the five lawsuits because the Topeka school board

Brown v. Board of Education

had actually ensured that the segregated schools were nearly equal in aspects of school buildings, curriculum, and staff qualifications. In fact, the lower court found substantial evidence supporting the claim that the separate services were equal.

Supreme Court Ruling The Brown case was argued in front of the Supreme Court in the spring of 1953. The Supreme Court justices then asked to rehear the case in the fall of 1953. During the interim, Chief Justice Fred Vinson passed away and was replaced by Earl Warren. Prior to Chief Justice Warren joining the Court, it appeared unlikely that the justices would be able to unanimously agree on a ruling related to Brown. Warren met with the other eight Supreme Court justices and argued that the only justification for supporting segregation would have to be based on a belief that Blacks were inferior to Whites. Warren worked toward a unanimous ruling, and on May 17, 1954, the Supreme Court handed down a 9–0 decision that held that the practice of separating races is inherently unequal. The Warren opinion offered the following statement: In these days it is doubtful that any child may reasonably be expected to succeed in life if he is denied the opportunity of an education. Such an opportunity, where the state has undertaken to provide it, is a right which must be made available to all on equal terms. (Brown v. Board of Education, 1954, p. 493)

The Supreme Court also established the concept of “equal educational opportunities” for all students, an idea that would prove significant in the area of school finance litigation (Brown v. Board of Education, 1954, p. 493).

Significance of the Brown Ruling for Desegregation The order from the Supreme Court to desegregate schools was met with a wide range of reactions. Some states, such as Kansas, worked diligently to comply with the legal order. Other states worked diligently to uphold the practice of segregation. In Virginia, for example, the governor attempted to privatize education. However, in 1954, the Supreme Court established the ideal of “equal educational opportunities” for all students, and states had to comply with that standard. In 1955, the Supreme

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Court was asked to determine how quickly school districts had to desegregate in Brown II, and the ruling in this case failed to establish a clear direction. The Court stated that school districts should work to desegregate “with all deliberate speed” while taking into consideration the views of all who would be affected by the changes. The Brown II ruling, which effectively allowed school districts to work toward desegregation as quickly or slowly as they desired, was the first of many roadblocks put up by the Supreme Court that collectively served to whittle away at the ideal of Brown—namely, a completely desegregated system of education. Other cases that also restricted Brown’s ability to completely desegregate schools included Keyes v. School District No. 1, Denver (1973) and Milliken v. Bradley (1974). In Keyes, the Court determined that the Brown ruling only applied to de jure, or intentional, segregation, not to de facto, or unintentional, segregation due to housing patterns. In Milliken, the Supreme Court determined that the Brown ruling did not require interschool district desegregation efforts, which effectively condemned school districts with a high concentration of minority students to remain segregated.

Significance of the Brown Ruling for School Finance As Brown’s ability to create a completely desegregated school system in the United States came into jeopardy with subsequent Supreme Court rulings, advocates for equal educational opportunities for all children discovered that school finance litigation could be another way to realize that goal. Basically stated, the justification for the shift from focusing on desegregation to concentrating on school finance is that a well-funded school, even one with a disproportionate number of minority students, is a forum where all students will have equal access to education. Brown has continued to influence public education through school finance litigation; two of the ways it has shaped school finance litigation are discussed below. The first way that Brown shaped school finance litigation was through its legal arguments, which relied, in part, on the Equal Protection Clause of the Fourteenth Amendment of the U.S. Constitution. The Brown ruling eliminated the practice of de jure segregation based on the Supreme Court justices’ interpretations of the Equal Protection Clause. The use of the Equal Protection Clause was deemed to

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Budgeting Approaches

apply to legal arguments over desegregation and, in some early cases, school finance. The legal arguments in early school finance lawsuits in the 1970s held that drastic funding disparities between school districts in the same state were a violation of the Equal Protection Clause. Examples of funding disparities included a $10 to $1 differential in Texas, where the wealthiest school district received $10 for every $1 the poorest school district received. However, that disparity paled in comparison with California, where the range was $10,000 to $1. These alarming disparities were due to the states’ overreliance on property tax revenues to fund public education up until the 1970s, when states began to move away from a state funding formula based solely on property tax revenues toward a more equitable system of financing public education. The other way that Brown influenced school finance litigation was by establishing the concept of equal educational opportunities for all students. The equal educational opportunities idea seemed to be perfectly suited for school finance reform. Do students enjoy equal educational opportunities when they might attend school in a system that allows for significant variance in the amount of funding? The answer to that question appears to be no when considering this statement by John Dayton and Anne Dupre (2004): “Property-wealthy districts can raise large amounts of money, while property-poor districts may fail to generate adequate funding for their schools even when levying the maximum legal tax rate” (p. 2356). The use of local property taxes to fund, even just a portion of, public education can result in significant opportunity gaps between school districts. A funding formula that relies on property tax limits the educational opportunities of students based on ZIP code or socioeconomic status, which is just as capricious as limiting a student’s right to learn based on skin color. Some legal scholars have voiced concern that the current standard in the area of school finance litigation—adequacy—is a far cry from the ideal of Brown. The concern is that public education has transitioned from the goal of ensuring that all students have equal access to education to now striving to guarantee that all students have access to an adequate education. However, depending on how it is defined, the adequacy standard could prove the optimal path for achieving the goal of Brown. If states fund public education at a truly adequate level, then students would receive greater access to educational opportunity.

Conclusion Brown is generally viewed as a landmark Supreme Court ruling and heralded as a ruling that has drastically altered public education for the better. However, Brown’s influence is not limited to the area of desegregation. Brown’s significance to school finance litigation became clear based on its emphasis on equal educational opportunities for all students. Spencer C. Weiler See also Property Taxes; San Antonio Independent School District v. Rodriguez; School Finance Litigation; Serrano v. Priest

Further Readings Alexander, K., & Alexander, M. D. (2012). American public school law (8th ed.). Belmont, CA: Wadsworth. Dayton, J., & Dupre, A. (2004). School funding litigation: Who’s winning the war? Vanderbilt Law Review, 57(6), 2351–2413. Heise, M. (2002). Causation, constitutional principles, and the jurisprudential legacy of the Warren Court. Washington & Lee Law Review, 59(4), 1173–1202. Kiracofe, C. (2004). The natural relationship between desegregation and school funding litigation. West’s Education Law Reporter, 184(1), 1–16. Odden, A. R., & Picus, L. O. (2014). School finance: A policy perspective (5th ed.). New York, NY: McGraw-Hill. Weiler, S. C., & Walker, S. (2009). Desegregating resegregation efforts: Providing all students opportunities to excel in advanced mathematics courses. Brigham Young University Education and Law Journal, 2009(2), 341–364.

Legal Citations Brown v. Board of Education, 347 U.S. 483 (1954). Keyes v. School District No. 1, Denver, 413 U.S. 189 (1973). Milliken v. Bradley, 418 U.S. 717 (1974). Plessy v. Ferguson, 163 U.S. 537 (1896).

BUDGETING APPROACHES Budgeting approaches are formats, models, or plans used to construct a budget. Budgeting is a multifaceted, ongoing, and cyclical process that is conducted annually and guided by federal and state law and local requirements. A budget is a financial expression of identified priorities for accomplishing an

Budgeting Approaches

organization’s vision. One example would be a school district’s vision of its educational program. Different budgeting approaches serve as tools for achieving different goals and objectives, which include, but are not limited to, control of revenues and expenditures, prioritization, financial planning, and improvement of administrative functions. An organization has a choice of adopting or modifying an existing budgeting approach or designing an approach that most closely addresses its vision and needs for achieving that vision. This entry presents a variety of cross-disciplinary budgeting approaches organized into two categories: (1) common budgeting approaches and (2) other budgeting approaches. There are common approaches to budgeting that organizations in different fields (e.g., business, economics, education, and public administration) and different levels of government have employed as part of their budgeting practices. These approaches are line-item budgeting, outcomes-focused budgeting, performance budgeting, program and planning budgeting, site-based budgeting, and zero-based budgeting. Starting in the 1980s, an increasing number of organizations began using budgeting approaches designed for efficiency and/or accountability, such as performance and program budgeting. The organization’s purpose for budgeting, its organizational structure, and the skill level of the administrator facilitating the budgeting process influence not only the selection of the budgeting approach but also the specific individuals who participate in the budgeting and decision-making processes and the manner and extent of their involvement. Over time, organizations have modified aspects of one or more of the common budgeting formats to address their own unique needs as well as to reflect new budgeting theories and to respond to external accountability measures.

Common Budgeting Approaches Line-Item Budgeting

Line-item budgeting, also known as traditional budgeting or input budgeting, was introduced in the 1920s and is used by schools and various other organizations. Line-item budgeting is considered a modest improvement over incremental budgeting, explained later in this entry. The purpose of lineitem budgeting is to describe projected expenditures, by category, and the amount allocated to each category. A specific line in the budget is assigned to each category of expense, such as equipment, materials,

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services, and salaries, and aligned to anticipated revenues. Line-item budgeting is a straightforward approach to budgeting that makes it easy to prepare, administer, and explain a budget; makes it easier to monitor revenues and expenditures; and allows flexibility in the degree of control an organization chooses to provide a given unit. Additionally, it accommodates the analysis of trends relative to purchases and costs. A criticism of this approach is that it does not facilitate long-range planning nor does it allow monitoring of expenditures associated with a particular program, area, or administrative function. Performance Budgeting

The performance budgeting approach, introduced in the 1940s, represents an attempt to address the shortcomings of the line-item budgeting process by placing emphasis on organizational outputs such as activities or services. Programs are presented in sufficient detail to describe and link goals and objectives (program components) with related activities and services (outputs) to measurable intended results. Measures or performance indicators are identified for each activity or service. Federal, state, and local governmental agencies use performance budgeting. Performance budgeting is intended as a viable option regardless of the organization’s economic stability. However, performance budgeting has been most successful in the field of business. More school districts are choosing to use performance budgeting in response to federal and state-level initiatives tied to performance pay, although past efforts to use performance pay in education have met with limited success. Performance budgeting does not determine whether the intended activities or services are appropriate for the program’s goals and objectives, nor does it accommodate long-range planning. This form of budgeting tends to be more suitable for standard or routine activities and may be used in conjunction with one of the other budgeting approaches discussed in this section. Program Budgeting or Program and Planning Budgeting

The program budgeting, or program and planning budgeting, approach is a modified budgeting approach that includes aspects of line-item budgeting and focuses on the program as a whole rather than program elements such as inputs, activities, or outcomes. Its individual history, beginning in the 1920s, is limited to some extent due to overlap with

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three other budgeting approaches: (1) program and planning budgeting, (2) planning programming budgeting system, and (3) program planning budgeting and evaluation system, given that each addresses program budgeting in some manner. Program budgeting provides a flexible form of budgeting and supports long-range planning. However, it does not offer the detail of line-item budgets. Additional criticisms of this approach are the absence of organization-wide agreement on its goals and objectives and the lack of data management systems that are adequately designed to provide appropriate data for program planning and budgeting. This approach may be combined with any one of the common budgeting approaches. Planning Programming Budgeting System

This is a modified form of zero-based budgeting, introduced in the 1960s, that does not require annual budget justification. Its purpose is to use alternative forms of resource allocation to identify program benefits (outputs) at different funding levels. These data inform and improve decision making for determining the most efficient level of funding for the attainment of program goals and objectives. This approach requires clear, measurable goals and objectives to support its planning and budget development processes, which are not in place in many organizations. Program Planning Budgeting and Evaluation System

This system is a modified budgeting approach that focuses primarily on the program as a whole. The program is created to meet identified needs, for which an educational plan is developed and implemented. The unique aspect of this model is the way long-term funding allocated to program costs is connected with the accomplishment of program goals and objectives to establish a cause-and-effect relationship. A criticism of this relatively new approach is that few organizations possess the infrastructure and organizational capacity necessary for its implementation.

more established in federal and state government agencies in the 1970s under President Carter. The purpose of zero-based budgeting is to achieve efficiency and effectiveness by annually reviewing a program to eliminate any components that are no longer considered as best practice and that are not realizing the intended benefit. Zero-based budgeting offers several strategies, one of which is the use of decision packages, a way of organizing and preparing proposed cost changes. Each package provides support for the costs associated with achieving identified program goals and objectives by combining particular products, activities, or services. In some instances, the decision packages are designed to support decisions to maintain, decrease, or increase funding. Once the budget is rebuilt from zero, a series of decision packages is developed and ranked. The strength of this approach is that it provides administrators with detailed information in the form of decision packages, which simplifies the decision-making process. This is a resourceintensive model that requires a good portion of an organization’s staff time for planning and completion of paperwork. Outcomes-Focused Budgeting

Outcomes-focused budgeting aligns an organization’s vision, goals, and objectives with the allocation of resources linked to measurable outcomes. Introduced in the 1990s, the purpose of this approach is to emphasize accountability by focusing on results. More and more governmental agencies and institutions of higher education are using this approach. Programs or services that demonstrate attainment of identified measures associated with agency/organizational goals and objectives are given funding priority. Benefits of this approach are its focus on accountability and efficiency in funding organizational priorities and the flexibility it allows for determining strategies for achieving the desired outcomes. A criticism is that it places units or programs within the organization in competition for limited resources. Site-Based Budgeting

Zero-Based Budgeting

Zero-based budgeting entails starting the budgeting process, each year, from the beginning (zero) to identify projected revenues and proposed expenditures and to justify the costs for proposed program activities and services. This approach became

The site-based budgeting approach, also known as school-site budgeting, is used most often by school districts. Under this budgeting approach, districts use building-specific data to generate a formula or system for reallocating funding and responsibility for some budgeting decisions to the school-site level.

Budgeting Approaches

Building administrators are accountable for planning and developing the budgets for their schools. Strengths of this approach are its potential for increased decision making at the levels of service delivery (the school building and classroom) and increased participation of faculty, staff, and community members in decision-making processes. The degree to which site-based staff and the community actually participate in the decision-making process is based on which model of shared decision making the administrator adopts or whether he or she chooses to use shared decision making. A criticism of this approach is the conflict that may arise among individual staff members or departments over resource allocation. Additionally, districts may have more difficulty ensuring cross-district equity in educational programming. Site-based budgeting may be combined with one of the other common budgeting approaches listed in this section. Capital Budgeting Approach (Capital-Investment Budgeting)

The capital budgeting approach focuses on the budgeting of an organization’s current and future benefits to improve and/or maintain long-term investments (assets) such as facilities, equipment, and land for a period of time, usually more than a year. Capital budgeting is widely used by various types of agencies, including educational institutions, and offers organizations four different options for determining if the benefits (return) of a proposed long-term investment will equal or exceed the associated costs. (1) The payback method refers to the period of time it will take for the investment to generate a benefit (cash or cash equivalent) that equals the amount of money allocated to originally fund the investment. While this method is easy to use, it does not consider benefits beyond the period of time it takes to recover the original investment funds. (2) The net present value method determines the current value or present-day value of a future return on an investment by applying a discount rate to the future return. (3) The internal rate of return method, unlike the net present value method, which uses a dollar amount, indicates the cost of an investment as a percentage. (4) Capital budgeting involves degrees of uncertainty around large long-term investments and requires a great deal of planning in the decision-making process. Capital budget decisions, once made, are not easily changed.

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Other Budgeting Approaches Incremental Budgeting

Incremental budgeting is one of the approaches most commonly used to prepare budgets for governments and public agencies at the local, state, and federal levels in the United States and in some other countries. Incremental budgeting uses the previous year’s budget as a starting point, focusing on individual line items, which are then increased or decreased in equalized increments. Strengths of this approach are consistency in funding across units or functions, easily identifiable changes in funding, and low levels of uncertainty. A criticism of this approach is that projected funding is based on previous year allocations, which can encourage indiscriminate spending to avoid lowered funding allocations for the following year. Additionally, decision making with this approach is minimal given that administrative units are not required to justify existing expenditures nor are program-related needs, activities, or outcomes considered. While administrators and elected officials at the organizational level determine the amount and direction of the increment, this approach limits the implementation of substantial changes to reflect a change in an organization’s vision or priorities. Bracket Budgeting

The bracket budgeting approach is a companion approach to other traditional or more common forms of budgeting that utilizes computer program– generated data. Bracket budgeting, which primarily focuses on future planning, is most often used in the field of business, but it could have relevance for units within some private or for-profit educational organizations. It involves the identification of budget elements whose uncertainty may negatively affect the ability to realize a profit. Additionally, there must be a number of options for manipulating the uncertain elements to positively affect the potential profit outcome and sufficient authority to make or influence the necessary changes. Finally, commitment to the planning and decision-making processes inherent in the model must be a priority. Formula Budgeting Approach

The formula budgeting approach uses a fixed method to determine the amount of resources allocated to an entity. Formulas utilize institutionspecific data collected using quantitative metrics

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that do not consider a program’s qualitative aspects. Federal and state governments, school districts, and institutions of higher education use this approach. There are basically five types of funding formulas: (1) base or foundational funding, (2) reward-for-effort funding, (3) categorical funding, (4) flat grant funding, and (5) equalization or guaranteed tax base funding. At face value, this approach is viewed as objective, equitable, and efficient. However, in practice, these strengths become criticisms when appropriations are not necessarily realized at the full funding level.

Conclusion Historically, school districts have used the more common approaches to budgeting: line-item budgeting, site-based budgeting, program and planning budgeting, and capital budgeting. Public and federal demands to hold schools accountable for how public funds are spent on education—and the quality of the education students receive—have encouraged an increasing number of school districts to consider two more recently developed approaches to budgeting: outcomesfocused budgeting and performance-based budgeting. However, their long-term success in schools is yet to be determined. Finally, relatively few school districts use zero-based budgeting: Time- and staff-intensive models such as zero-based budgeting are not viable for school districts faced with decreased revenues. Judith A. Green

See also Capital Budget; Education Spending; Program Budgeting; School-Based Management

Further Readings Dellavigna, S., & Pollet, J. M. (2013). Capital budgeting versus market timing: An evaluation using demographics. Journal of Finance, 14, 237–270. Ezzamel, M., Robson, K., & Stapleton, P. (2012). The logics of budgeting: Theorization and practice variation in the educational field. Accounting, Organizations and Society, 37, 281–303. Hansen, S. C. (2011). A theoretical analysis of the impact of adopting rolling budgets, activity-based budgeting and beyond budgeting. European Accounting Review, 20, 289–319. Layzell, D. T. (2007). State higher education funding models: An assessment of current and emerging approaches. Journal of Education Finance, 33, 1–19. Mullen, P. R. (2006). Performance-based budgeting: The contribution of the program assessment rating tool. Public Budgeting & Finance, 26, 79–88. Thompson, D. C., Crampton, F. E., & Wood, R. C. (2012). Money and schools (5th ed.). Larchmont, NY: Eye on Education. Zierdt, G. L. (2009). Responsibility-centered budgeting: An emerging trend in higher education budget reform. Journal of Higher Education Policy Management, 31, 345–353.

C CAPACITY BUILDING ORGANIZATIONS

Essential Building Blocks of School Organizations

OF

The essential capacity-building components emerge from the research literature on effective schools and from production function analysis of the contributions of a variety of school inputs to the outcomes of schooling, as often measured by students’ achievement and future earnings. Each building block represents a unique aspect of effective schools and supports the development of other building blocks.

The term capacity building refers to the process by which money or other resources are used to institutionalize the ability of an organization to achieve its goals. It has become a term widely used in education policy, practice, and research. To effectively guide the development and evaluation of school organizations, further clarification of the purposes, means, and impacts of capacity building is necessary. Following Beth Walter Honadle’s work, in this entry, a school’s capacity is defined by its ability to provide its core service—classroom instruction. Building school capacity means improving the school's ability to make informed, intelligent decisions about school goals and policy, develop a coherent curriculum and instructional guidance, manage both human and material resources, and forge effective links with other organizations. The purpose of capacity building is often to invest in future material, intellectual, or human returns and to anticipate and shape future actions. Therefore, the evaluation of the impact of school capacity-building efforts should be forward thinking and should focus on the predicative capacity of any investment to improve student learning outcomes. This entry discusses the essential building blocks of school capacity and the broader institutions that influence its development. It also discusses what economists can do to further help school leaders, policymakers, and researchers to develop cost-effective ways of improving school capacity.

Goals

Capable schools strive to prepare students for success in future employment and to function as meaningful participants of society. School activities center on academic excellence and aim to equip students with core skills and knowledge and to develop students’ ability to apply these skills and knowledge to novel, challenging problems. In addition, the goals of effective schools are often clear and simple, homogeneous, and coherent across grades and subjects. The homogeneous goals unify school staff and orchestrate finite resources to enact a set of coherent tasks. Curriculum and Instructional Guidance System

The curriculum and instructional guidance system specifies the subject content that students are expected to learn in any given grade and the ways in which the content is intended to build over the duration of schooling to form a coherent knowledge and skill base for students. Although states and 93

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districts specify content standards, these documents come alive when teachers embed them in classroom instruction. Capable schools support teachers in the application of these standards in the selection of textbooks and the development of other instructional materials. Teacher Professional Capacity

The effectiveness of schooling depends largely on teachers’ capacity to implement effective instruction. Individually, successful teachers have subject knowledge, apply effective pedagogies, and understand students’ backgrounds, interests, and learning styles. Collectively, they form a viable and coherent community that shares responsibility for promoting students’ learning, supports one another, embraces a belief that teachers are the agents for school improvement, and has a strong commitment to the school and to their profession. In effective schools, teachers are treated as professionals and have great autonomy over classroom activities. At the same time, to build collective capacity, effective schools institute collaboration among teachers through critical dialogues about what has been taught and what has not, how to assess student progress, and what alternative pedagogies might better support students’ learning. Clearly, teacher recruitment is critical to creating the breadth and depth of expertise necessary to achieve academic excellence. Effective schools often provide their teachers with ongoing professional development that focuses on content and instructional practices, encourages them to analyze and reflect, and fosters a collective learning community. The teacher evaluation system must aim to provide guidance and feedback to teachers for continuous improvement. The design of the compensation system might also be used to incentivize both individual excellence and collaboration among teachers, as well as linking directly to evaluation results, recruitment, and tenure decisions. Student-Centered Learning Climate

A student-centered learning environment integrates the beliefs, values, and everyday behaviors of school professionals, parents, and students and focuses on ensuring that every student can learn well. Such student-centered learning climates can have profound effects on student motivation and engagement with classroom instruction. They may include several aspects: an orderly and safe school environment to protect learning time from disruptive

behavior, teachers motivating students to achieve academic excellence, ample support to sustain student efforts, and peer support among students to achieve learning goals. Economic Resources

Although some studies have concluded that measures of economic resources (e.g., school facilities) are unrelated to school performance, it is possible that under some conditions and at certain resource levels, they may influence school performance. For example, schools that offer higher salaries should be able to attract more capable teachers. Schools can also use additional resources to purchase instructional guides or materials or to reduce class size. Similarly, schools with state-of-the-art facilities, equipment, and supplies should be able to provide better learning opportunities for students than those that are physically antiquated or dilapidated and threaten students’ and teachers’ safety or cause classroom routines to be frequently interrupted. Interorganizational Relations

Schools and teachers who make efforts to reach out to parents and communities gain critical supports for students’ learning in schools. These efforts include communicating with parents about their children’s learning status and progress, reinforcing coherent educational expectations and goals, asking parents to read to students at home and support students’ homework, inviting parents to volunteer at the school, and creating opportunities for parents to participate in school decision making. Moreover, schools and teachers engage in the community to better understand students’ needs and the community’s needs. Such a deeper understanding is instrumental to the school and to the teachers in establishing interpersonal connection with students and connecting academic learning with students’ lives. Given the fact that many services that may affect student learning are not directly provided by a school, the school’s capacity to partner with community services (e.g., museums, youth organizations, churches, libraries, the police department) has a direct impact on the effectiveness of the supplemental resources available to support learning. School Leadership

Anthony S. Bryk and his colleagues identify school leadership as the “driver” of school improvement. School leaders establish ambitious goals, reach

Capacity Building of Organizations

out to parents and the community, enhance the professional capacity of the school through recruiting and developing high-quality teachers, and develop a student-centered school culture. To be able to do this, effective instructional leaders should have knowledge about both student and teacher learning, and the managerial skills to deliberately orchestrate people, programs, and extant resources.

School Accountability and Market Mechanisms’ Influence on School Capacity Building In the United States, school accountability and market are two competing yet complementary institutions that underlie the design of educational policies and school reforms that aim to improve the aforementioned school capacity components. The essence of accountability is to evaluate school performance based on students’ achievement on standardized tests and then use administrative mechanisms to sanction low-performing schools. These schools may also receive additional resources and technical assistance to improve. An example of this form of accountability in the United States is the No Child Left Behind Act (NCLB). Proponents of NCLB school accountability argue that the social stigma and negative consequences of poor performance on accountability measures will motivate schools to provide better services, particularly for lowperforming, minority, and poor students. Because schools are required to make student achievement public, monitoring is not only from government but also from parents and communities, who now have access to the information on school performance and are able to influence school practices. In addition to the accountability pressure and the increased connections between schools and the public, states often require schools and districts to develop corrective plans for low-performing schools to build their capacity. These strategies most frequently include, in varied combination, the following elements: school improvement grants that allocate more resources to these schools, professional development for teachers and principals, new instructional materials and guidance (e.g., pacing plans, structured reading, and mathematics programs), new services or extensions of existing services (e.g., summer school, extended day, after-school programs, and individual tutoring for students), on-site instructional specialists or coaches who add expertise to the teaching, leadership coaches or change

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agents who support the school leadership in setting ambitious academic goals and developing teacher collaboration, and incentives to recruit and retain high-quality teachers and principals. There is growing evidence that these corrective plans, combined with other elements of accountability under NCLB, positively influence school capacity to improve student achievement over time. On the other hand, this consequential accountability system under NCLB is accused of provoking certain behaviors that game the system to artificially improve test scores (e.g., using simpler tests and changing the group of students subject to the test). Teachers are more inclined to narrow their curriculum to teach to the test. Moreover, critics claim, the state and district approaches for supporting school improvement often lack continuous supports over time, and thus they cannot sustain positive changes because capacity building often needs long-term objectives and continuous investment. This system of bureaucratic control over accountability systems is also criticized as too hierarchical, too rule bound, and too formalistic to allow the autonomy and professionalism that schools need to perform well. Complementary to government control, market mechanisms are also incorporated into strategies for redesigning schools designated as persistently low performing under NCLB. These mechanisms commonly include school reorganization or reconstitution as charter or magnet schools, school vouchers, or other choice programs. The market mechanisms are theorized to ensure that parents and students play much more central and influential roles in determining the allocation of school resources and the design of educational programs. Parents and students would have the freedom to choose between schools and to switch from one school to another as they wish. Schools would then have to please their clients by making better decisions and competing in the market. Moreover, principals, as personnel managers of school organizations, would have the autonomy to hire teachers with diverse professional backgrounds and those who are less likely to have union membership. Schools would have greater freedom to design teacher incentives, including both financial compensation and career advancement, to reward excellence in teaching and retain teachers in shortage areas. They would also have greater ability to dismiss ineffective teachers. Moreover, schools would have greater flexibility to implement comprehensive reforms that strive to improve the

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organization of instructional time, curriculum, specialty programs, class sizes, and resources based on student need. Last, these market-driven schools and programs are designed to better engage stakeholders, including teachers, parents, and community representatives, in school decision making. These market-based mechanisms of consumer choice, competition, and school autonomy are expected not only to induce diverse and innovative school practices, and thus improve the capacity of schools that are directly under the choice programs, but also to stimulate the cost-effectiveness of the entire educational system. However, research studies have shown mixed and inconclusive findings about whether such market mechanisms are effective in achieving these two goals. Research on charter schools uncovers that they are often ill equipped and lack the necessary human and financial resources to implement, institutionalize, and sustain comprehensive reforms. School choice programs have been criticized as creating more inequality in the educational system. Students who exercise school choice often differ systematically from those students who do not. They often are more motivated, have more educated parents, and come from families with higher income. As to whether the competition created by school choice stimulates efficiency in traditional public schools, studies have shown little or even negative impacts, often as a result of other public schools losing students to schools of choice and losing funding as a result, so that they have fewer resources to provide quality programs for the remaining students.

Contribution of Economics to Capacity Building in Schools The field of economics of education has contributed to various approaches for building school capacity. Production function analysis has increased our understanding of the factors related to the outcomes of schooling and of how family background may interact with school practices to influence learning outcomes. Economic research has also contributed to understanding the teacher and principal labor markets, school personnel incentives, school finance, school choice, and accountability. Yet there exist a number of important directions for future research. The research performed to date on educational production functions has not been able to measure many of the most important educational inputs, such as classroom activities, school leadership,

school climate, parental involvement, and formal and informal organizational structure. The lack of these measures makes the production function analysis less likely to directly contribute to the improvement of schools’ organizational capacity. Teacher labor market research has provided evidence on effective teacher personnel management strategies (e.g., licensure, compensation, and collective bargaining). Yet few studies have linked various kinds of incentives to learning outcomes, budgets, and expenditure information; thus, few studies have directly contributed to the design of cost effectiveness of these programs. Moreover, compared with empirical studies of teachers' incentive programs after they enter the profession, there is much less work that has been done to examine the effectiveness of various teacher-hiring strategies. More studies are needed to derive a clearer understanding of the matching process between teachers and schools, search costs, and asymmetric information that affects schools’ hiring choices. Additionally, we have little knowledge about principal labor markets and the impacts of personnel incentives and other management strategies on recruiting, developing, and retaining effective leaders. Another line of inquiry that could help capacity building in schools involves studies on school governance systems. For example, since charter schools and other schools of choice programs started and have gained momentum in the past two decades, many existing studies assess the short-term impacts of these programs on student learning. In future studies, it would be valuable to examine the longterm capacities of charter schools and other schools of choice to build the essential components of effective schools, in comparison with those of traditional schools. The focus of these studies should be on understanding features of school governance systems that help school organizations use resources more efficiently and helping practitioners and policymakers build more effective public school system in the long run. Finally, relatively few studies in economics have been conducted to understand how school accountability and market mechanisms can support each other. As accountability pressures merge with market mechanisms, understanding how they can help schools develop, implement, and sustain instructional improvement will be critical to the continued capacity building of schools. Min Sun

Capital Budget See also Accountability, Standards-Based; Charter Schools; Cost-Effectiveness Analysis; Education Production Functions and Productivity; Educational Vouchers; Teacher Compensation

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capital budgets. It also discusses pitfalls to avoid in capital budgeting and lists organizations that provide information and guidelines on capital budgeting.

History of Capital Budgeting Further Readings Bryk, A. S., Sebring, P. B., Allensworth, E., Luppescu, S., & Easton, J. Q. (2010). Organizing schools for improvement: Lessons from Chicago. Chicago, IL: University of Chicago Press. Chubb, J. E., & Moe, T. M. (1990). Politics, markets, and America’s schools. Washington, DC: Brookings Institution Press. Hanushek, E. A., Machin, S., & Woessmann, L. (2011). Handbook of the economics of education (Vols. 1–4). Waltham, MA: Elsevier. Honadle, B. W. (1981). A capacity-building framework: A search for concept and purpose. Public Administration Review, 41(5), 575–580. Mintrop, H., & Trujillo, T. (2005). Corrective action in low performing schools: Lessons from NCLB implementation from first-generation accountability systems. Education Policy Analysis Archives, 13(48), 1–30.

CAPITAL BUDGET In for-profit businesses, the purpose of a capital budget is to determine a project’s value to shareholders. Since capital is considered the funding source for such ventures, a capital budget focuses on the return on investment to shareholders for the long term. However, since the public sector is not driven by profit, a capital budget, from a public finance viewpoint, should compare the long-term cost of any project with future revenue sources, and consideration should be given to the impact of that project on taxpayers. The rationale for a separate capital budget is related to the policy and procedural purposes of the government (or school system). That is, the capital budget focuses more on the specialization of the expenditure, specifically related to the acquisition of capital (e.g., land, buildings). The capital budget distinguishes between recurring and nonrecurring expenses, because it considers only the capital portion of the government expenditure. A capital budget can also serve to explain government borrowing. This entry gives an overview of capital budgeting as it relates to the general budget, explains the importance of capital budgets, and discusses best practices for

Capital budgeting derives from the collective influence of civic reformers, scholars, and businesspeople. The practice of capital budgeting is found as early as the 1920s, when cities began to develop master plans for community growth. The practice of capital budgeting grew from these efforts, from other influences (e.g., the Great Depression and World War II) that required municipalities and states to plan for the collection and distribution of resources, and from the need to develop new infrastructure to accommodate population growth. Capital budgeting has remained an integral part of many states and localities, and with the involvement of organizations such as the Government Finance Officers Association (GFOA), improvements in the process continue.

Capital Budgets and Operating Budgets In public finance, the capital budget is distinct from the general operating budget. A capital budget is connected to a government’s or school district’s capital improvement program (CIP), because its focus is on the decision-making process of the CIP and it only monitors those expenses related to the CIP. Some practitioners view the capital budget as the budget for the CIP, which makes it a separate entity from the CIP. Others view it as containing the details of the CIP, which makes it a part of the CIP process. There is no clear consensus on this as states and municipalities develop their budgeting practices over time and derive their own budget categories to meet specific needs. Next, since capital projects extend over multiple years, a separate capital budget helps monitor that long-term expenditure. The capital budget adds stability to the operating budget because it assesses the long-term impact of a government’s long-term capital needs; it can be further defined as a process that analyzes and evaluates whether or not resources should be allocated for any particular project. Since capital expenses are funded over multiple years, the capital budget is a necessary budgeting practice. There is a risk of uncertainty in any long-term project, so great care and planning need to be part of capital budgeting to ensure that funds are spent wisely. Capital budgets also differ from operating budgets because they relate to a different set of revenues.

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Capital Budget

Since a capital project is generally funded with a dedicated tax collection, subject to voter approval, a capital budget helps a government prioritize and monitor its acquisition and maintenance of various fixed assets (e.g., land, buildings, and roads). Furthermore, it identifies other types of hard assets that are necessary for governmental use (e.g., movables, equipment, and vehicles). A capital budget covers planning, development, and use of infrastructure for public services and accounts for the revenues and expenditures of the capital project.

Importance of Capital Budgets Capital budgets are needed to assess future costs to the government and to evaluate future benefits. Furthermore, conflict is created in governments between the need for improvements and the constraints of limited resources. A capital budget provides the necessary tools for clear analysis of where the revenues are spent and what benefits are achieved from those expenditures. Also, the capital budget provides stability to the operating budget because it remains constant and changes are determined only by population growth or price fluctuations. Including Results and Outcomes in Capital Budgets

The National Advisory Council on State and Local Budgeting identifies certain principles of effective capital budgets, including broad goals, strategies, and financial policies; support of strategies and goals; and evaluation of performance. As part of its efforts to develop best practices in public finance, the GFOA has promoted the practice of budgeting for results and outcomes as a way to meet the National Advisory Council on State and Local Budgeting’s budgeting principles. The GFOA listed eight recommendations for creating clear results and outcomes as follows: 1. Determine how much money is available. 2. Prioritize results. 3. Allocate resources among high-priority projects. 4. Conduct analysis to determine what strategies, programs, and activities will best achieve the desired results.

7. Check on how the money was spent. 8. Communicate the performance results. Internal and external stakeholders should be informed of the results in an understandable format.

Knowing from where the funding is coming and where it is spent is important for the success of any capital budget. Thus, understanding how revenues are derived and what new revenue sources are expected is crucial for any capital budget process. Prioritizing results is crucial for the success of the capital budget. Results should be analyzed as part of the CIP process, with clear identification of what capital needs are required and a clearly defined outline of the scope of the project. Understanding the scope of the project is crucial to preparing, funding, and managing a capital budget to avoid unexpected increases in costs. By incorporating an explanation of what work is required to meet the need, a capital budget can be used to monitor the effectiveness of the CIP in containing costs and also provide a guide for utilizing expected future revenues. Keeping in mind the CIP’s intended purpose and incorporating that into the capital budget helps the project meet the stated need and protect the taxpayer’s investment. Details of Successful Capital Budgets

Details are important for a strong capital budget. These details include a definition of a capital project, the estimated costs of capital projects, and the impact of capital projects on current and future operating budgets. Other important details include identification of funding sources for every facet of the project. These would include indicating which funding requirements are necessary for the coming fiscal year. Describing the scope of the project, including what services or benefits the school district or municipality will receive, is important, as is including summary information on the project’s funding as it relates to the project’s various components. A schedule should also be included, and it should cover the phases of the project, estimation of the funding requirements for the next several years, and what acquisitions, designs, and construction activities will be forthcoming.

Some Best Practices for Capital Budgets

5. Budget available dollars to the most significant programs and activities.

Transparency in Capital Budgets

6. Set measures of annual progress, and monitor and close the feedback loop (make sure that communication reaches all the constituents involved in the monitoring).

Capital budgets should follow all state and local statutory requirements. Each capital project should have its own budget, and each capital budget should be properly planned and tracked. Although

Capital Budget

government entities do not achieve perfect separation of operating budgets and capital budgets, it is best to treat capital budgets as separate, with consideration given to the finances for that budget. Each budget should be incorporated into the annual budgets for the government entity (e.g., state, municipality, or school district) only as a line item for capital expenditures, but each should be tracked separately to ensure proper implementation and funding. Adoption of each capital budget by the legislative body is essential. This adoption should be incorporated into the operating budget but can be considered separately. Capital expenditures should be clearly defined, and costs correctly estimated and tracked. An explanation of how the financing is handled is also important. Since capital budgets should be separate from general operation budgets, there should be an established central agency to oversee the administration of capital budgets in order to avoid a lack of political accountability, which has the potential to create waste, fraud, and/or misappropriation of taxpayers’ funds. Consideration for How Resources Are Acquired

Providing a clear financing policy for capital projects is an important part of the budgeting process. Identifying which revenue sources are used for the CIP is helpful for monitoring the overall debt for the government entity. Monitoring debt is crucial for governments because most CIPs are funded through debt, and a government’s debt rating can have an impact on its borrowing costs. The higher a government’s ratio of debt to its ability to raise revenue, the greater is the negative impact on its debt rating. Since lower ratings increase the cost of borrowing, good, clear monitoring of debt within the budget will help governments in the long term. Cost Estimation

Unless the capital budgeting process is well planned, costs can quickly add up unexpectedly. The importance of viewing the CIP’s scope and cost together should not be overlooked. As a project is refined, the costs for that project are also changed. The source of additional funding should be determined whenever a cost increase is identified. In any public project, resources are generally derived from the tax revenues generated from the community. Large cost overruns can result in a strong negative reaction from the public. In public-private projects, potential increased costs that are foreseen can be

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included in the negotiations over which party pays what amount. Part of estimation is guesswork and good luck. However, there are specific components that can be estimated with some degree of accuracy. These include land acquisitions, soft costs (e.g., professional services), technology, financing costs, equipment, furniture and fixtures, moving costs, inflation, and consideration for any contingency. Consideration of changes to the overall operating cost of a project is also important in the budgeting process (e.g., an increase in the cost of services and goods can have a negative impact on the funds appropriated for the capital project and can lead to a funding crisis, where new resources will have to be raised). A good practice for avoiding any funding pitfalls is to include as many cost items as possible. Having more items to consider is better than having to add items when funding for the project has been determined. States identify capital expenditures and maintenance differently. Some identify purchases as capital expenditures only if the items purchased exceed a 1-year life span. Some identify items such as chairs, desks, and cars as capital expenditures only if they cost more than a certain amount (ranging from $500 to more than $250,000). The rationale for determining capital expenditures or maintenance costs is as varied as the number of states, years practicing budgeting, and legislators involved in the process. However, no matter what the differences of definition, determining what these costs are is important so that every group involved in the budgeting process identifies them the same way. This provides clarity for auditing and debt-rating purposes. Rating the maintenance projects helps keep costs down by minimizing deferred maintenance. It is also important to provide open communication between the legislative body and the various departments in charge of the capital budgets. Establishing oversight committees is a good step toward meeting this goal. Central oversight is important in keeping potential unexpected costs in check and also to keep close control of the budget process. Keeping Costs in Check

Keeping a CIP on task and controlling costs is an important function of any capital budget. Although the capital budget is used to monitor the CIP project, a good budgeting system will help identify the relationship between the government entity’s needs and the funding mechanisms to meet those needs.

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Capital Budget

There needs to be a strong link between the desired outcomes of a project and what expenditures are necessary to bring about those outcomes. Along with the estimation of costs, the capital budget should include a clear tracking system to monitor those costs. It is also important to monitor costs for longterm leases and collaboration with private sector participants. Public-private projects can help keep costs down while providing capital assets that the government entity needs. Part of this process will include reviewing long-term leases and maintaining an updated list of the capital assets.

Pitfalls in Capital Budgets Not all potentialities can be foreseen with absolute accuracy, but consideration of potential pitfalls can provide any capital budget a greater degree of success. With the acceptance and continual practice of capital budgeting in the public sector, it is unlikely that government entities will discard it from their budget practice in the near future. Several important factors need consideration to make a government’s capital budgeting successful. There is a danger in confusing the overall affordability of any CIP when multiple budgets are used in government budgeting. Although the capital budget is separate for administration purposes, it still affects the bottom line of the government entity’s financial stability. Careful correlation between the general budget and capital expenditures is crucial for good financial planning. Failure to establish a clear definition of what is considered a capital expenditure can lead to confusion within the budget, leading to a mixing of accounts. This potential pitfall could cause an inaccurate picture of the overall health of the budget, resulting in poor decisions that lead to an inflated budget. Finally, the issue of depreciation of assets has to be addressed. In the private sector, depreciation is reported by firms to provide a clearer idea of the need for future capital purchases. In the public sector, many government entities do not consider depreciation of capital assets. This might create an inflated view of the value of the capital in question. If textbooks, chairs, desks, and so on are considered part of the capital assets of a school district or other government entity, not depreciating them over the years may give an inaccurate picture of the financial stability of that entity and lead to an inaccurate assessment of that entity’s ability to take on debt.

Following the best practices provided by organizations such as GFOA or the National Association of State Budget Officers has helped states avoid some of these pitfalls, but officials of any government entity can be misguided into thinking that they are following a correct accounting program only to discover that a small accounting error somewhere in the past has created a huge problem in the present. By following checks and balances throughout any budgeting process, governments should be able to avoid the pitfalls inherent within the process.

The Future of Capital Budgets Capital budgets have a long, established history in both public and private finance. The use of capital budgets has changed over time, with newer practices helping to create stronger interpretations of a government entity’s financial strength. National organizations such as the GFOA and the Association of School Business Officials International have helped create budgetary best practices that many governments and school districts utilize. There are some negative considerations regarding capital budgeting that all government entities should take into account. These include the potential for abuse and fraud should the capital budget be excluded from the overall governmental expenditures. The need for political accountability is crucial in maintaining the public’s support for any government’s budget. However, clear policies and guidelines provide the necessary safeguards.

Organizations That Provide Guidance for Best Practices in Capital Budgets Several organizations provide guidance and best practices for capital budgeting. The Association of School Business Officials International focuses on the business practices of school systems at both state and local levels. GFOA has been a leader in the budgetary practices of state and local governments and provides guidelines for budget preparation. The National Conference of State Legislatures is an organization specifically created to provide national information on state legislative issues. It produces materials that are relevant and current, which can be helpful for preparing a CIP program. The GFOA was instrumental in forming the National Advisory Council on State and Local Budgeting, which was a collaboration of several organizations for a 3-year mission and developed a report on best practices. The National Association of State Budget Officers is mostly concerned with state government budget issues, but it

Capital Financing for Education

provides information on budgeting policies that affect local municipalities and schools as well. Michael C. Petko See also Budgeting Approaches; Capital Financing for Education; Education Finance; Infrastructure Financing and Student Achievement

Further Readings Burkhead, J. (1959). Government budgeting. New York, NY: Wiley. Casey, J. P., & Mucha, M. J. (2007). Capital project planning and evaluation: Expanding the role of finance officers. Chicago, IL: Government Financial Officers Association. Fisher, R. C. (2007). State and local public finance (3rd ed.). Mason, OH: Thomson South-Western. Government Finance Officers Association. (1998). Recommended budget practices: A framework for improved state and local government budgeting. Chicago, IL: Author. Retrieved from http://www.gfoa .org/services/nacslb National Association of State Budget Officers. (1999). Capital budgeting in the states. Washington, DC: Author. Poterba, J. M. (1995). Capital budgets, borrowing rules, and state capital spending. Journal of Public Economics, 56(2), 165–187. Vogt, A. J. (2004). Capital budgeting and finance: A guide for local governments. Washington, DC: International City/County Management Association.

Websites Association of School Business Officials International: http://www.asbointl.org Government Finance Officers Association: http://www.gfoa .org National Association of State Budget Officers: http://www .nasbo.org National Conference of State Legislatures: http://www.ncsl .org

CAPITAL FINANCING EDUCATION

FOR

This entry defines capital spending before providing an overview of the approaches used to finance capital spending in primary and secondary education in the United States. The approaches are then detailed by level of government (e.g., local, state,

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and federal), in order of importance for financing capital spending on education. Public schools and other educational buildings constitute a major component of public infrastructure, one that grew dramatically to accommodate the postwar baby boom. Spending accelerated through the following decades in response to successful litigation challenging the equity of existing state capital finance systems, continued enrollment growth, class-size reduction mandates, and shifts in technology needs. State governments have played an increasingly important role in financing education capital spending. According to the U.S. Census Bureau, from 2002 to 2011, school districts spent more than $580 billion on capital outlays, not accounting for inflation. The vast majority of spending was directed to new construction activity (78%), although the remainder was used to acquire land and existing structures (7%) and equipment (15%). School capital spending typically represents around 1/10 of the total education budget. Yet the manner in which school districts finance capital investments is often overlooked, due to the prominence of instructional spending in education and the long-lived nature of capital assets.

Capital Spending Capital spending by school districts can be distinguished from current, or operational, spending by the long-lived nature of the assets purchased. An asset generally refers to something of value to the school district. Whereas a teacher’s salary is paid for instructional services received in the current school year, a purchased building or constructed athletic field will continue to impart benefits to the school well beyond the current school year. While individual school districts have different expenditure thresholds for classifying an asset as a capital asset, they are often large enough to require debt financing and remain useful for more than a single year. Capital assets are often referred to broadly as “infrastructure,” although useful distinctions can be made to differentiate capital spending activities by school districts. New construction activity not only encompasses brand new buildings and grounds but also reflects the renovation or expansion of existing facilities. School districts may also purchase land or existing buildings. Finally, while capital spending is largely thought of as facilityrelated construction activity, it actually represents the acquisition of a broader set of capital assets as disparate as photocopiers, computers, and school

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Capital Financing for Education

buses. The magnitude of equipment and nonfacility assets, though, is generally small relative to expenditures on facility assets. Capital spending is uneven over time for a school district and is often characterized as “lumpy.” The main reason for this irregular spending is the previously mentioned long-lived nature of capital assets, where annual replacement is unnecessary. A secondary reason is the frequent use of borrowing, or debt, as the main source of capital financing. Despite this, most school districts have some capital spending each year, although at the school district level, capital spending is a highly variable component of the education budget. Capital improvement plans and capital budgets, as opposed to operating budgets, are used to prioritize and structure capital investment activities to smooth out the impacts on annual operating budgets and ensure that aging capital assets are replaced and new service demands can be met.

of the school district and pushing the costs off to future residents and voters. The composition of these two approaches for financing school district capital spending is not known, but evidence strongly suggests that payas-you-use is the dominant approach. According to the Census Bureau, over the past decade nearly $495 billion in long-term debt has been issued by school districts in the United States compared with capital outlays during the same period of just over $580 billion. Despite the fact that a portion of this reported long-term debt would not have been used for direct capital outlays (e.g., school districts also issue such debt to refund, or refinance, existing debt and satisfy pension obligations), the amount of debt represents more than 85% of the capital outlays and is indicative of school districts’ continued reliance on pay-as-you-use financing.

Financing Capital Spending

Local Financing of School District Capital Spending

There are two different ways, pay-as-you-go and pay-as-you-use, by which school districts can finance capital spending. The first, pay-as-you-go financing, uses existing resources, from current revenues, accumulated savings (often consisting of past surplus revenues directed to distinct capital project funds), or intergovernmental transfers, to purchase capital assets. This approach is appealing to school districts averse to borrowing or unable to borrow and is suited for relatively small capital asset purchases. The more common, and arguably more appropriate, method is pay-as-you-use, where a school district borrows to finance capital spending. Since capital assets are not consumed wholly in the current year, it is considered reasonable to match the costs of a capital asset to the length of its useful life. Repayment is, therefore, supported by the taxpayers benefiting from the asset in the years following its purchase or acquisition. This matching of costs and benefits reflects the previously mentioned long-lived nature of capital assets. The pay-as-you-use approach may also promote capital investment in education, especially when compared with the challenges of raising funds for up-front payment of capital costs. Since capital expenditures tend to be large relative to operating budgets, they may lead to inefficient temporary spikes in tax rates if funding comes from current revenues. Of course, critics argue that pay-as-youuse financing incentivizes overinvestment in capital assets by lowering the current spending requirements

Historically, capital spending by school districts has been financed primarily at the local level through the issuance of long-term, tax-exempt debt. According to the Census Bureau, evidence of this system is the nearly $17.8 billion of interest payments made on nearly $398.5 billion of outstanding long-term school district debt in the 2011 school year. The debt primarily takes the form of bonds, which are financial instruments sold to investors (lenders) that represent a promise by the issuer (borrower) to repay the borrowed amount (the principal) at a time in the future, along with interest payments. The interest payments compensate the investors for the use of their money. Long-term bonds, as opposed to shortterm borrowing for cash flow needs, are typically defined as those that mature (or will be repaid) more than a year after issuance. The interest paid to debt holders from taxexempt bonds is untaxed through the income taxes at the federal and, in most cases, state levels. More specifically, a tax-exempt bond purchased in a given state is typically exempt from that state’s income taxes. The forgone income tax revenue is a subsidy that lowers the interest rate that districts must pay relative to taxable debt instruments. The size of the benefits accruing to investors in the tax-exempt debt market depends directly on the investor’s marginal income tax rate. School district debt tends to be issued as general obligation (GO) debt, which refers to the “full faith and credit” source of the district’s

Capital Financing for Education

pledge to service the debt (both interest and principal payments) through its taxing power. Such debt is normally serviced by increases in the property taxes paid by district property owners. Referenda, elections to approve the issuance of school district debt, are required in most states. This is especially true for GO debt. School districts are far less likely than other types of governments to issue revenue bonds, the other main type of government borrowing, since they have limited local nontax revenue sources available. Referenda tend to be competitive, especially when supermajority requirements exist that require more than a simple majority (one vote greater than 50% of the votes) for passage. The issuance of municipal bonds is a highly technical process. School districts work with financial intermediaries, including financial advisors, underwriters, bond counsel, credit rating agencies, trustees, and credit enhancers, to borrow through the taxexempt debt market. The prominence of GO debt at the school district level often overshadows the fact that school districts in various states have unevenly adopted alternative local forms of capital financing in response to strained property tax bases, strict referendum requirements, and statutory limits on such debt. In states such as Georgia and Iowa, local option sales taxes have been adopted and directed to support school district capital spending. An alternative to GO debt is for school districts to form a separate public or nonprofit entity, typically referred to as a building corporation, that issues the debt for a capital project. The debt is then repaid with the lease revenue paid by the school district for the use of the asset (usually a new building). The lease-purchase bonds or certificates of participation usually avoid referendum requirements and debt limitations since the obligation by the school district is not considered long-term debt. Technically, the lease payments face appropriation risk each year from the school board. For this same reason, borrowing costs for lease-purchase arrangements are typically higher than GO debt. The traditional method of financing capital spending through local, tax-exempt debt often presents a challenge for a particular segment of schools, namely, existing and proposed charter schools. Charter schools may lack direct access to the tax base for borrowing and tend to lack control over preexisting facilities. Innovative programs in some states, as well as support from authorizing school districts, have allowed a small portion of charter schools to access the tax-exempt debt

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market. Otherwise, charter schools frequently use current revenues from state per-pupil funding to lease facilities or to service debt from local banks. Capital campaigns are also used to raise funds for capital projects from donors and supporters of the schools. The need for additional capital spending can arise from a number of sources, including the depreciation of existing capital assets or the need for additional school capacity in a growing school district. The previous discussion suggested that the pay-as-youuse approach to financing capital spending increased equity by connecting debt repayment to future beneficiaries of the asset. There is a similar geographic component to the equitable financing of school capital. The building of a new school, for example, tends to provide outsized benefits to the surrounding households. Often, a new school must be built or major upgrades are necessary to accommodate the construction of new residential development. Some school districts, similar to other local governments, have adopted impact fees; such fees are allowed in around a dozen states. Developers are required to provide payments, typically based on the number of houses to be built, or land as a site for a new school to offset the additional costs to the school district of new school buildings. Another option to finance new school construction, where available, is to create a special district (often referred to as community facilities districts), which includes the new development area and applies a special assessment to the property tax levies. The assessment revenues service the debt incurred for expansion until the debt is retired (paid off). Other, less common approaches to financing school district capital outlays include publicprivate partnerships and performance contracting, in which up-front energy efficiency improvements are financed with the resulting cost savings. Despite these many approaches, the major alternative to local debt issuance is the trend toward increased support from state governments, which has frequently been driven by the courts and is discussed in the following section.

State Government Support for School District Capital Spending To support the same amount of borrowing, districts with less property wealth must tax at a higher rate than a property-rich district. This makes equal funding of capital investment problematic at the

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local level. A majority of states now provide some financial assistance to school districts for facilities through a variety of mechanisms, although there are also states that play no role in school capital spending. State support for school district capital spending is usually in the form of grants or intergovernmental aid. These funds are distributed to assist lower levels of government, typically based on a formula to determine need, on the basis of a proposed capital project or on a per-pupil basis. Only a couple of states include capital assistance in their general school funding allocations, while others direct grant funds to school districts with rapidly growing enrollments or, most frequently, to serve an equalizing function targeting those districts with a limited tax base. In general, grant funding is used to offset debt service incurred through ongoing capital projects or to fund a share of new capital projects. States that provide grants for capital spending utilize matching grants most commonly. Matching grants require that the recipient, in this case a school district, also contributes funds to the capital project. For example, a one-to-one match would result in the school district raising $1 for every dollar received as a grant from the state. Such matching rates can be modified to reflect the financial resources of the recipient, so wealthier districts can have higher matching rates than districts with more limited tax bases. Matching grants allow the state to stimulate capital spending by school districts through both a substitution effect (meaning that capital assets are now cheaper relative to other goods, which will lead districts to purchase more capital assets) and an income effect (representing the overall increase in the district resources available to purchase all goods), as opposed to only an income effect with nonmatching grants. Much less common than grant programs are state-administered loan programs that offer low-cost borrowing, which is especially beneficial for districts that have limited access to the debt market. A handful of states have responded to legal challenges to their facilities funding systems by establishing comprehensive capital programs, where the state plays a major role in funding school facilities. Full state funding of school facilities exists in only two states, Hawaii and, more recently, Arizona. This increasing state role of funding school district capital investment can be supported through state general revenues, earmarked revenues, or state debt issuance. General revenues come from state taxes, normally income and sales tax (in comparison with school districts’ widespread dependence on

the property tax), user fees and charges, and federal intergovernmental aid. Earmarked state revenues come from sources as diverse as lottery proceeds and state trust lands, which are found primarily in the West, dedicated to education. The state’s role is not limited to such direct involvement. Indeed, nearly half of all states offer credit enhancement, akin to private municipal bond insurance, which leverages existing state resources to improve school district credit ratings and ultimately reduce borrowing costs. These credit enhancement programs have become more valuable in recent years as the private municipal bond insurance market has contracted. Overall, the scale and types of state aid for school capital spending vary greatly across the country, but the involvement of state governments continues to grow and serves as the main force for equalizing capital spending across school districts with uneven fiscal capacity.

Federal Government Support for School District Capital Spending The role of the federal government appears to be quite minor relative to school districts and states. In fact, direct federal involvement in financing school capital spending is quite limited, but the importance of the federal income tax exemption for school district debt cannot be overstated. The exemption is a federal tax expenditure initially based on a set of U.S. Supreme Court precedents from the 1800s that established the doctrine of “reciprocal immunity.” This doctrine limited the ability of the states and federal government to interfere in each other’s affairs. Subsequent passage of the law creating the income tax and the Sixteenth Amendment to the Constitution injected some uncertainty as to whether the federal government had the right to tax municipal debt. This uncertainty continued until the Supreme Court ruled in 1988, in South Carolina v. Baker, that the federal government has the constitutional right to tax state and local debt interest. Eliminating this tax exemption has been discussed as part of federal tax reform proposals and was among the proposals laid out by the National Commission on Fiscal Responsibility and Reform (also known as the Simpson-Bowles commission after its cochairs, Alan Simpson and Erskine Bowles). The tax exemption, according to supporters, promotes much-needed capital investment by lowering the cost of capital for school districts and other governments. On the other hand, the tax exemption

Capitalist Economy

is commonly criticized on both efficiency and equity bases largely due to concerns about overinvestment and the outsized benefits received by wealthy individuals with high marginal income tax rates. Over time, the federal government has also introduced two forms of tax credit bond programs for school districts, which differ from tax-exempt bonds. The issuance of Qualified Zone Academy Bonds, which became available in 1998, was restricted geographically and also required a private sector match. Investors in Qualified Zone Academy Bonds received federal income tax credits rather than the traditional tax-exempt interest payments. Qualified School Constructions Bonds were introduced later as a part of the American Recovery and Reinvestment Act of 2009. As with the overall funding of the education system, the federal government plays a relatively minor, but valued, role in the financing of school district capital spending.

Conclusion In general, the financing of school capital spending falls to the school district, with various degrees of state assistance, while the federal government’s role is one of providing an implicit subsidy to local and state borrowing through the tax code. The irregular nature and large magnitude of capital spending demand a system that can spread costs over time to minimize budgetary disruption and reflect future benefits. State assistance is especially needed where equity concerns exist over unequal access to debt markets and property tax bases across school districts. Todd L. Ely See also Bonds in School Financing; Capital Budget; General Obligation Bonds; Infrastructure Financing and Student Achievement

Further Readings Crampton, F. E., Thompson, D. C., & Vesely, R. S. (2004). The forgotten side of school finance equity: The role of infrastructure funding in student success. NASSP Bulletin, 88, 29–52. Duncombe, W., & Wang, W. (2009). School facilities funding and capital-outlay distribution in the states. Journal of Education Finance, 34, 324–350. Fisher, R. C. (2007). State and local public finance (3rd ed.). Mason, OH: Thomson South-Western. Mikesell, J. L. (2014). Fiscal administration: Analysis and applications for the public sector (9th ed.). Boston, MA: Wadsworth, Cengage Learning.

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National Commission on Fiscal Responsibility and Reform. (2010). The moment of truth. Washington, DC: Executive Office of the President. Retrieved from http:// www.fiscalcommission.gov/sites/fiscalcommission.gov/ files/documents/TheMomentofTruth12_1_2010.pdf Odden, A., & Picus, L. O. (2008). School finance: A policy perspective (4th ed.). Boston, MA: McGraw-Hill. Sielke, C. C. (2001). Funding school infrastructure needs across the states. Journal of Education Finance, 27, 653–662. U.S. Bureau of the Census. (2013). Public education finances: 2011 (G11-ASPEF). Washington, DC: Government Printing Office. U.S. General Accounting Office. (2000). School facilities: Construction expenditures have grown significantly in recent years (GAO/HEHS-00–41). Washington, DC: Author. Wang, W., Duncombe, W. D., & Yinger, J. (2011). School district responses to matching aid programs for capital facilities: A case study of New York’s building aid program. National Tax Journal, 64, 759–794.

CAPITALIST ECONOMY Capitalism describes a system of economic organization of land, labor, and capital. These factors of production are privately owned and employed in the production and distribution of goods and services. Production (or supply) and consumption (or demand) take place via market mechanisms where prices are determined and what is on offer is sold off and markets are cleared. Unique to the capitalist economic system is its emphasis on market functioning as a principal mechanism for allocating resources, which is subject to evolving trends. For instance, advancements in information technology have increased the value of knowledge in production processes. These trends have altered the relative importance of the traditional factors of production (e.g., land, labor, and capital), increasing the importance of human capital and, consequently, of education. The first section of this entry describes the key features of modern capitalist economies and their impact on market functioning, including their evolution from earlier mercantilist economic systems. The second section identifies the major influences affecting capitalist structures associated with advances in information technology and the rise of the global knowledge economy. The final section examines education and employment in capitalist economies, including human capital

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trends, the division of labor, and the evolving economic structure of capitalist societies.

Key Features of Capitalist Economic Systems Capitalism has been the dominant economic system in the Western world since the 19th century, as nation-states emerged from a history of mercantilism. Like capitalism, the mercantilist system was based on markets, but it relied more heavily on governments (e.g., local and national) to regulate trade in pursuit of favorable trade balances. Colonies typically served as markets for exports and suppliers of raw materials to the mother country. A nation’s wealth was determined by its production and trading activity, both domestic and international. Colonial exploitation by governments gave rise to resentment, particularly in the British colonies. Adam Smith’s The Wealth of Nations (1776) was written in reaction to the powerful government role inherent in the mercantilist system. Smith argued for an economy guided by individual self-interest, whereby the “invisible hand” would guide free markets to achieve the greatest good for all individuals. Smith’s free market theories and the rise of large production enterprises after the Industrial Revolution (1760–1840) facilitated Western adoption of free market capitalism. Economically strong nations had developed uniform monetary systems and property law, on which capitalist economies are based. Thus, mercantilism provided the foundation for the growth of capitalism by providing the social conditions that allowed for the concepts of private ownership and transfer of property to flourish. “Private” here can apply to individuals and various types of organizations, including proprietorships, partnerships, and limited liability corporations. Capitalism has presumed that the pursuit of selfinterest and the right of individuals to own property are both morally defensible and legally legitimate. Capitalist economies create wealth through market transactions, wherein both buying and selling parties gain value by exchanging something (e.g., a good or service) of perceived lesser value for something of perceived greater value. Thus, the transaction price of the item varies across buyers and sellers as it is reflective of subjective values. Sellers seek revenues, especially net revenues (profits), often in competition with other sellers. Competition (multiple sellers of similar goods and services) provides incentives to innovate, modifying what is on sale to attract more, different buyers.

In theory, markets are self-regulating. The role of government in capitalist economies is therefore limited to law enforcement—especially as it applies to property ownership, contracts, taxation, and maintenance of law and order—and to public provision of certain goods and services. However, market engagement is not limited to private parties; governments also enter markets in capitalist economies, as both public sector producers and sellers as well as consumers and buyers of goods and services. These transactions support the work of government, affecting the array of goods and services on offer and providing those goods and services that tend to have large positive benefits for citizens, but for which insufficient quantities would otherwise be produced or consumed. A prerequisite for functioning markets is a formal legal system of ownership that allows for protection of individual property rights. Stable and uniformly enforced legal systems foster citizen trust in the sanctity of transactional agreements. Central components of a formal property system include the creation of titles that identify property, rules for trading, and systems of formal record keeping. Hernando De Soto asserts that property law sustains market functioning in six ways. First, through a writing—title, contract, security—a formal property system fixes the value potential of assets according to their economically and socially useful qualities. Second, it integrates this value potential into a single official knowledge base. Third, it holds individuals legally accountable for breaches through widespread recording and through sanctions in the form of reputational damage. Fourth, it represents assets in terms of economic value, as opposed to their physical state, making the assets fungible, that is, able to be combined and divided to suit the particular transaction. Fifth, it creates a network of people as economic agents by improving the flow of communication about individual transactions. Finally, through public record keeping, assets are tracked and protected as they travel within the market. These six effects of a formal property system all contribute to fostering trust in the economic system, without which individuals cannot confidently participate in market transactions.

Contemporary Variations Capitalist countries in the world today vary greatly in the relative importance attributed to governments, big businesses (foreign as well as domestic), nonprofit organizations, and households. The most widely

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acknowledged issue associated with capitalism is the proper role of government. As one illustration, the role of government in U.S. capitalist society was called into question in 2008 following the subprime mortgage crisis and resulting government bailout and stimulus efforts. Such public questioning of government roles is due in large part to the democratic nature of most capitalist economies. Though the pure free market capitalist system assumes an absence of all but the most basic forms of government regulation, government rules and regulations significantly influence market behavior. Forms of capitalism vary in different countries depending on the type and extent of government regulation. Should the role of government be limited to enforcing property rights and otherwise ensuring the growth of “free markets”? Alternatively, do governments have additional responsibilities to regulate and, thus, to shape the production and distribution of goods and services? Are the negative externalities (“public bads,” e.g., air, water, and soil pollution or household income inequality) that result from free market (capitalist) behavior to be curtailed by government? The structural specifics of capitalism continue to evolve within each nation-state, even as multilateral economic harmonization draws them more closely together. A recent manifestation of the evolving role of government relates to the growing importance of nonphysical or intellectual property (IP) and whether IP should be treated differently than tangible property. As the value of knowledge continues to grow, IP rights have become a considerable source of wealth for innovative businesses and entrepreneurs. However, where information is so easily accessible by non–rights holders, enforcement of these rights becomes problematic, as does the concept of “idea” ownership. Because capitalism does not work unless who owns what is clear, an enforceable intellectual property system has become equally integral to the market-based economy as that of physical property. Given the ease with which ideas can now be transmitted, the current system of patents, copyrights, and trademarks may be ill equipped to regulate IP in the technological age. With growing transnational (global) integration of economic processes and the increasing role of specialized knowledge in production, local rules of private ownership of IP will likely be tested.

Capitalism and Education Of the three factors of production, “capital” is the least self-evident. It refers to the potential of an

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object to generate value. Capital in this sense can denote financial capital, or money used to invest in the production of wealth. It can also refer to capital goods, or goods that are used to produce other goods, such as a lathe or a tractor. “Human capital” then refers to the mental capabilities of humans to generate value. The structure and governance of education systems operate within the political context of nation-states and reflect the dual, sometime competing, roles of governments in capitalist societies: (1) recognizing property ownership by individuals and (2) acting on behalf of “the public.” Because knowledge is a public good and provision of much of education is considered a government responsibility, capitalist governments operate and finance the majority of their schools and colleges (about 70% on average). On the other hand, individuals enjoy rights to both produce and consume educational services in private markets, and governments determine the rules that address both dimensions of education. Regardless of the proportion of education services that are financed and governed publicly in a given country, each capitalist economy relies on a combination of private market forces and public rules to guide the production and consumption of knowledge goods (education), not unlike other goods and services. Because individuals with higher levels of education are associated with higher levels of economic well-being (largely through increased earnings), some individuals seek added education when they feel that it is in their interest to do so, while others do not. Over time, this results in growing variations in education and income levels among households, even as the average levels of education and income increase over time. Compulsory schooling and publicly subsidized higher education in capitalist economies not only helps integrate the young into the economic system but also seeks to address the inequalities associated with the perceived failure of private markets to adequately allocate education services and outcomes. Capitalist governments seek to increase the education levels of their citizens in part to improve the overall quality of their workforce, relying first on coercion (compulsory schooling) and then on inducements to voluntarily attend higher education. Where education policy in capitalist governments is largely decentralized (as in the United States), many schooling decisions by individuals and education providers are left to the marketplace. In more centralized capitalist nation-states (e.g., France and

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Singapore), more of those decisions (e.g., quotas, majors, costs, etc.) are made by government agencies. In both cases, the education and labor sectors of society are often mediated through markets in which human capital (i.e., the skills, knowledge, and aptitudes of individuals) is “sold” into the workplace. The primary, albeit imperfect, proxies for human capital are the degrees, credentials, and certificates awarded by educational providers to students. As capitalist nation-states evolve from industrial to knowledge economies, better educated workers increasingly become central factors of production, resulting in greater consumer demand for education by individuals for themselves, by businesses for their workers, and by governments for their taxpayers. The net result of the increased demand for education has stretched the capacities of capitalist governments to respond, fostering disproportionate growth of (excess demand absorbing) privately governed education providers. In recent years, the education role of capitalist governments is shifting from solely direct provision and financing to include additional roles of quality assurance and partial regulation of private education providers. Education provision in capitalist societies remains a public responsibility. At the same time, private markets for education services are proliferating and diversifying, driven by demands for ever higher levels of human capital for a given price. Viewed from a human capital perspective, job offshoring, worker migration across capitalist economies, and income inequality are manifestations of employment markets at work, with income as a function of the demand and supply of worker knowledge and skills. Education in capitalist nation-states, then, remains a public responsibility, while, at the same time, markets for the services of private education firms are growing in size and number, forming yet another subcategory of industry in capitalist economies. Guilbert C. Hentschke and Samantha Bernstein See also Cultural Capital; Economic Development and Education; Globalization; Human Capital; Privatization and Marketization; Social Capital

Becker, G. S. (2009). Human capital: A theoretical and empirical analysis, with special reference to education. Chicago, IL: University of Chicago Press. Bowles, S., & Gintis, H. (2013). Schooling in capitalist America: Educational reform and the contradictions of economic life. Chicago, IL: Haymarket Books. Brown, P., Lauder, H., & Ashton, D. (2011). The global auction: The broken promises of education, jobs and incomes. New York, NY: Oxford University Press. Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94, S95–S120. De Soto, H. (2000). The mystery of capital: Why capitalism triumphs in the West and fails everywhere else (1st ed.). New York, NY: Basic Books. Smith, A. (1776). An inquiry into the nature and causes of the wealth of nations. London, UK: W. Strahan & T. Cadell.

CATEGORICAL GRANTS Categorical grants are restricted funds that are intended for a specified purpose, to address a particular educational need or accomplish a specific educational objective. These funds, depending on the type of grant, are most often related to the effort to allot resources in order to address the needs various student populations present or provide an incentive for local educational agencies (LEAs) to create, sustain, support, and/or reform an educational program or initiative. To that end, the overarching aim of categorical funding mechanisms is to ensure access and maximize the potential for the success of otherwise at-risk students. To do so, categorical programs are designed to provide supplemental funding to serve students and sustain programs without any cost to LEAs’ general funds. This entry first discusses the objectives and theoretical foundation of categorical grant programs and gives an overview of the types of categorical programs. It then details the critique of categorical programs, the degrees of latitude in their use, their impact, their political economy, and their unintended consequences. Last, it discusses categorical programs in higher education.

Objectives of Categorical Grants Further Readings Baumol, W., Litan, R., & Schramm, C. (2007). Good capitalism, bad capitalism and the economics of growth and prosperity. New Haven, CT: Yale University Press.

A widely used method to counter inequalities within the public education system, categorical programs and funding can be found at the state and federal levels of education finance systems, with varying

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degrees of latitude in their uses. Most notable examples of federal categorical grants involve compensatory education or special education. The largest categorical program under the federal Elementary and Secondary Education Act, reauthorized most recently as the No Child Left Behind Act of 2001 (NCLB), is Title I, which provides funds for compensatory education for educationally disadvantaged students. Title I funding is based on the number of low-income pupil enrollments, generally those eligible for the free or reduced-priced lunch program. Federal special education funding comes from two sources: (1) NCLB and (2) Individuals with Disabilities Education Act. These funds typically constitute anywhere between 10% and 20% of local school districts’ special education budgets. State categorical funding programs vary from state to state. However, a number of categorical programs exist almost universally across all states and are typically aligned with similar categories at the federal level, for example, compensatory education, special education, English language development or bilingual education, and other state-specific targeted grants. The primary goal of categorical funding mechanisms is to supplement education and services for students with particular needs, a requirement known as “supplement not supplant.” To that end, using categorical funds to pay for existing programs and services (i.e., supplanting) is strictly prohibited. The “supplement not supplant” provision, a condition present in all categorical programs, is designed to ensure that funding for already existing programs and services funded with general education funds or through other public means is not simply replaced with categorical dollars. In other words, categorical funding mechanisms must be used only to supplement funds available for the education of students and the programs for which these funds are intended.

Theoretical Underpinnings The notion of vertical equity, as applied to education by the economists Robert Berne and Leanna Stiefel, is operationalized by the use of categorical grants. Vertical equity suggests that additional, targeted resources be distributed in relationship to the educational needs that certain student populations represent. In early applications, vertical equity was defined as “unequals treated unequally.” However, to avoid the misinterpretation of this definitional language as suggestive of second-class citizenship, vertical equity in education finance is often pursued

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through the additional resources required to address the added cost of educating certain populations. Another way of framing this theory and its operationalization via categorical grants is the distribution of resources to ensure that students of varying educational needs are able to benefit comparably from the same educational system in order to achieve more equitable outcomes.

Types of Categorical Programs All categorical programs narrowly define eligible activities and allow funding to be used only for a specific purpose. Typically, these funds are distributed by a predetermined formula and, in some cases, at the discretion of the state or federal agencies. In addition, categorical programs are guided by closely defined administrative and reporting requirements intended to ensure accountability. In a broad sense, there are three types of categorically funded programs available to LEAs: (1) entitlement categorical programs, (2) incentive categorical programs, and (3) grants. Entitlement Categorical Funding

Entitlement categorical programs are one of the most stable forms of funding that are guaranteed to renew every fiscal year. An allotment of these funds is guided by specific qualifications or formulas defined in statute, where spending of these funds is controlled and regulated by laws other than just appropriation acts. In other words, the state or federal government is legally obligated to make payments to eligible entities. As related to the specific goals of these funds, entitlement dollars are designed to achieve equity objectives that are rooted in meeting the needs of students based on specific characteristics. For instance, categorical funds distributed under the Individuals with Disabilities Education Act are designed to address the needs of students with special needs, while categorical funds distributed under Title III of NCLB are designed to assist students with limited fluency in the English language, and Title I funds are designed to meet the needs of students from low-income families. Incentive Categorical Funding

Incentive categorical programs are designed to provide incentives for the LEA to either support or establish a program designed to address a specific need. The primary goal of these programs is to advance the state’s policy objectives by providing

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resources to local entities. Although they vary from state to state, examples of programs supported by incentive categorical funding sources include the following: professional development for teaching and administrative staff to ensure appropriate implementation of academic standards, class-size reduction programs, and sustaining or expanding already existing supplemental programs. Unlike entitlement programs, incentive categorical funding mechanisms are not guaranteed to renew each fiscal year and are typically established to serve a specific objective or set of objectives outlined in the state policies guiding these funds. Grants

Grants are generally defined as contributions, either in money or in material goods, made by one governmental entity to another, for example, federal agency to state or state to LEA. Grants are available on a competitive basis: Entities must compete to obtain funding. In addition, grants are typically guided by a set of objectives that are expected to be met within a certain time frame. Based on the goals of a grant, these funds may be used for specific or general purposes.

Critique and Varying Degrees of Latitude in the Use of Categorical Grants Although well intended, categorical funding programs have been heavily criticized over the years, especially in recent years. Among these criticisms, one pertains to flaws within the funding formulas that guide distributions of categorical aid. In some cases, the funding formulas on which allocations of categorical aid are based do not meet or only marginally meet the policy objectives. Thus, these earmarked funds fail to target the student subgroups for whom these funds are intended. Likewise, in some cases, categorical funds are allocated to districts that do not have evident use for these funds and, therefore, cannot appropriate these funds properly, which inevitably leads to misuse of these dollars. Additionally, the educational policy researcher Thomas Timar, in his investigations of the uses of categorical aid, asserts that little if any accountability exists to enforce categorical regulations as these relate to how LEAs expend restrictive and purpose-driven resources. Thus, lack of accountability enforcements leads to misallocation of these earmarked dollars wherein local officials use categorical funds as they would the funds that are earmarked

for general purposes. Furthermore, the inflexibility factor and the various complexities relating to regulations associated with categorical aid often put local officials in a bind where they have to make a difficult choice between doing what is best for their students and following the appropriation guidelines outlined in the provisions of categorical funding mechanisms. In her work, Susanna Loeb asserts that if given more flexibility as this pertains to categorical aid, LEAs will be more effective and efficient in addressing the needs of the students for whom these funds are intended, due to local officials’ intimate knowledge of their unique education system, community needs, and local demographic trends. That being said, research in the area of categorical fund expenditures is scarce in the school finance literature. In other words, apart from local officials’ interview data and surveys, very little empirical evidence exists concerning how these funds are expended once distributed to LEAs, especially in the area of how LEAs allocate these resources at the school site level. Since NCLB, there has been a push toward greater flexibility and deregulation of categorical aid. School finance scholars such as Allan Odden and John Augenblick as well as William Ouchi have long deliberated whether to target educational funding to specific groups of students through categorical aid or to allow local administrators to make decisions regarding how to allocate educational resources. Proponents of deregulation attest that increased flexibility would allow districts to better align resources to local priorities, thus directing more dollars toward instructional gains and expanding local administrators’ authority to build more efficient systems. Those who oppose deregulation of categorical aid contend that it will result in greater disparities in school finance than those that already exist and make those dollars subject to negotiation among interested parties at the local level. A recent deregulation of categorical aid in California provides an opportunity to examine these competing claims. That being said, it is important to remember that as with any major reform, the reality is more complex than the theory outlined by advocates on either side of the debate.

Categorical Grants and Their Impact The best-documented account of the impacts of the largest federally funded categorical program, Title I, can be found in the works of Kenneth Wong

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and Stephen Meyer, who engaged in a historical analysis of the Title I program and its evolution. Understanding the political shifts and paradigms is the key to grasping how the largest federally funded program functions today under NCLB. In the 1960s, the federal government used equity as the primary principle of social policy—a more studentcentered, restrictive approach to how LEAs were able to expend Title I categorical aid, with a focus on individual student needs. In the 1980s, a policy revision was implemented that allowed discretionary distribution of funds by LEA officials (albeit within the outlined parameters) under the schoolwide umbrella. The goal behind this shift was to offer more flexibility under schoolwide programs in order to promote efficiency within LEAs and cut the costs associated with social welfare programs. Although years of reports considering the effectiveness of the Title I schoolwide programs were for the large part neutral and merited mere favorable results in terms of student achievement, this programmatic reform has been sustained by the current legislation. Inevitably, over the years, this approach to distribution of Title I aid to address the needs of socioeconomically disadvantaged students has led to trade-offs between locally competing priorities and value systems that directly influence appropriation and expenditures of these funds at the site level. That being said, NCLB added an additional layer that manifested itself through heightened accountability reforms and stringent quality controls as these pertain to standards, instructional quality, and student achievement. As a result, Title I categorical goals and focus have strayed further and further away from the individual needs of the students for whom these funds are intended.

Another dimension is the situation of competing interests ascribed to various categorical grants, such as special education, bilingual education, and compensatory education. As indicated by Thomas B. Parrish and colleagues, competing interests for targeted funds may prevent or stagnate the flexibility required in resource allocation at the local level to more effectively address the complex educational needs of students representing a range of characteristics. Moreover, at times, students are used to meet a threshold established by categorical programs and thus generate greater revenues for their schools. In their recent work, Jordan Matsudaira, Adrienne Hosek, and Elias Walsh found that school sites are disposed to respond to the incentives outlined in the Title I guidelines in order to secure a greater amount in federal funds. To that end, the researchers report that since the amount of Title I funds for each school district is predetermined by Census data, school sites within a district are forced to compete for this fixed pot of funds. As a result, to meet the Title I program threshold, schools within the district where the investigation took place engaged in a competition to secure these funds for their sites by manipulating the application sign-up process just enough to meet the Title I threshold. Although this allowed the schools to receive a greater amount of Title I funds, no impact on overall school-level test scores was found. More notably, the researchers found no impact on academic achievement among the subgroups of students who were identified to benefit from Title I categorical aid. Such results suggest that Title I categorical aid is used to advance the local needs of an entity as opposed to addressing the needs of those students for whom these funds are intended.

Political Economy of Categorical Grants

Unintended Consequences of Categorical Grants

Student needs are not always the overriding concern in formulating categorical grant programs. As discussed by Timar, not only do the dollars not consistently follow the needs, but when the distribution of categorical grants is politicized as a mechanism of competing interests, it is not unusual to encounter targeted programs that have a potentially disequalizing impact in favor of economically advantaged constituencies with political clout. This can occur, for instance, when categorical funds are used for schoolwide services rather than targeting certain students or groups for whom these funds are intended.

While the labeling of educational needs for the purpose of identifying the beneficiaries of entitlementbased categorical grants is standard practice in the field of education, such labeling has the inadvertent consequence of target populations being viewed primarily as deficient in their educational preparation, skills, home cultures, and even potential. Such characterizations reflect the pervasive use of cultural deficit models to identify the educational needs of children and youth for the purpose of allocating categorical grants. Yet dollars still are not always used to meet the needs of those for whom these funds are

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intended. Nevertheless, balancing the concern for avoiding deficit-based labels with accurately identifying educational needs is a conundrum that must be solved for categorical grants to address acute educational gaps and benefit students with particular needs.

Categorical Grants in the Higher Education Context There are a variety of targeted grants that are aimed at needs and educational goals associated with certain populations in the postsecondary sector. Comparable with K-12 categorical funding mechanisms, categorical aid at the higher education level is designed to support programs that can be found at the state and federal levels of the education finance system. Unlike state higher education categorical grants, which vary by type and purpose from state to state, federally funded categorical programs are open to all participating higher education institutions across the nation. The main purposes of the higher education federal categorical grants are to provide assistance to low-income students and first-generation college students, to ensure supports for students with disabilities, and to support students enrolled in educational programs that are identified as high need (e.g., teacher certification programs). Perhaps unique among contemporary higher education categorical grants for their cross-institutional model are the TRIO grant programs. The TRIO label was a result of the 1968 reauthorization of the Higher Education Act, which established a section for student support services that incorporated existing programs. Three programs—Upward Bound, Talent Search, and Student Support Services— were included in TRIO at the time of its establishment, hence the label. TRIO later expanded to include five programs, with the addition of the Ronald E. McNair Post-Baccalaureate Achievement Program of 1986 and the Educational Opportunity Centers of 1972. TRIO categorical aid is federally funded and is designed to identify and provide services to those students who come from disadvantaged backgrounds. The underlying goals of the TRIO programs are to engage in outreach and to identify as well as provide services to assist low-income students, first-generation college goers, and individuals with disabilities. Furthermore, this categorical program uses a more encompassing cross-institutional

model by identifying students early for entry into the academic pipeline and providing the necessary supports for middle school to postbaccalaureate programs. The recipients of these grants are typically institutions of higher education and, less frequently, community as well as public and private entities that have a proven track record in serving youth from low socioeconomic backgrounds and engaging in collaborative work with secondary schools. More specifically geared to low-income college students, federal Pell grants are direct grants awarded through the participating institutions to college students with financial need who have not received their first bachelor’s degree. Pell grants are also awarded to students who are enrolled in programs that are considered high need, such as teacher education and certification programs. Similar to the low-income, need-based Pell grants, the Federal Supplemental Educational Opportunity Grant is designed to assist undergraduate students with exceptional financial need and to avert attrition among low-income students. Unlike federal student aid, these two grants do not have to be repaid.

Contemporary Political Context of Higher Education Policies The Higher Education Act of 1965, reauthorized in 2008 and due for reauthorization in 2014, governs federal student aid programs. Since the start of the recession in 2007, political debates around higher education categorical aid and, to a greater extent, around federal student aid have intensified in legislative circles. The high poverty rates, climbing unemployment rates, rising tuition, and growing number of graduates struggling to find employment and repay their student loan debt have all contributed to many heated discussions in Washington. The categorical grants are typically considered first when adjustments or cuts to funding are debated. Bearing in mind that categorical higher education grants are designed to serve those college students who are typically not considered traditional or mainstream, the elimination of these programs could result in a considerable demographic shift among college-goers and significantly affect the college climate as well as the culture of higher education institutions. Irina S. Okhremtchouk See also Access to Education; Capacity Building of Organizations; Economics of Education; Education Spending

Central Office, Role and Costs of

Further Readings Berne, R., & Stiefel, L. (1984). The measurement of equity in school finance: Conceptual and methodological issues. Baltimore, MD: Johns Hopkins University Press. Berne, R., & Stiefel, L. (1999). Concepts of equity: 1970 to present. In H. F. Ladd, R. Chalk, & J. S. Hansen (Eds.), Equity and adequacy issues in education finance (pp. 7–33). Washington, DC: National Academies Press. Matsudaira, J., Hosek, A., & Walsh, E. (2012). An integrated assessment of the effects of Title I on school behavior, resources, and student achievement. Economics of Education Review, 31(3), 1–14. Parrish, T. B., Merickel, A., Pérez, M., Linquanti, R., Socias, M., Spain, A., . . . Delancey, D. (2006). Effects of the implementation of Proposition 227 on the education of English learners, K-12: Findings from a five-year evaluation (Final report for AB 56 and AB 1116). San Francisco, CA: WestEd. Retrieved from http://www.wested.org/online_pubs/227Reportb.pdf Timar, T. (2007). Financing K-12 education in California: A system overview. Stanford, CA: Stanford University, Institute for Research on Education Policy and Practice. Wong, K. K., & Meyer, S. J. (1998). Title I schoolwide programs: A synthesis of findings from recent evaluations. Educational Evaluation and Policy Analysis, 20(2), 115–136.

CENTRAL OFFICE, ROLE COSTS OF

AND

The central office is the administrative center of a school district, responsible for the operations of schools within its defined area. Its locus of control is broad and paramount to the experience of school administration, teachers, and students, with responsibility for district leadership and partnerships, finance and operations, human capital management, curriculum and instruction, and evaluation and accountability. To the extent that the central office shares its responsibilities with other levels of the educational system (e.g., the state and schools), its operating costs will vary, but they remain a small percentage of the overall education budget. The role of the central office, and subsequently the cost of its services, is highly debated in the educational arena. This entry describes the roles, responsibilities, and major functions of the central office and its primary cost components. It also discusses centralized versus decentralized approaches to central office management of school districts.

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The ultimate goal of the central office is to provide an equitable and adequate education to students within the school district. To do so, it leverages, to varying degrees, school administrative and teaching staff to produce student learning results. A central office is usually led by a superintendent and assistant superintendents, with directors and support staff for departments such as finance, technology, curriculum, student services, maintenance and operations, and transportation. It is charged with staffing schools with administrators, teachers, and support staff and provides budgets for schools, which in turn have varying degrees of freedom in controlling these budgets. The quantity of personnel and the central office budget depend heavily on the size of the school district (which may serve students from a single school to hundreds of schools) and how much autonomy is given to schools in the areas typically controlled by the central office. How a school functions depends on where it falls on a spectrum between fulfilling a traditional central office role and a decentralized role. In its traditional role, the central office is in charge of policy formulation, implementation, and accountability within all areas in its purview. Advocating an educational vision, the central office holds schools accountable to the rules and regulations of district, state, and federal education agencies. The primary, traditional roles and responsibilities of the central office can be grouped into five categories: (1) leadership and partnership, (2) human capital management, (3) finance and operations, (4) curriculum and instruction, and (5) evaluation and accountability. 1. Leadership and partnership: To increase learning results, the central office leads district personnel in all facets of district operations. Superintendents, assistant superintendents, directors, and support staff are charged with ensuring that the district runs smoothly and efficiently. Partnering with the school board, business leaders, teacher labor unions, and the community at large, the central office is the agency that implements policy, including reforms developed at the federal, state, and district levels. Central office personnel hire, supervise, and evaluate school leaders; negotiate teachers’ union contracts; respond to parents’ concerns; manage information and the relationship between the district and the community; and create the school district budget. 2. Human capital management: The central office is ultimately responsible for the recruitment,

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selection, retention, and dismissal of school and district personnel. The superintendent of the district plays a pivotal role in this, especially in hiring and in evaluating school principals. The central office develops the formulas used to determine how many teachers are assigned to each school. The central office takes a role in mandating much of the professional development that occurs in schools, as well as the evaluation system by which teacher performance is reviewed. The central office bargains with teachers’ unions and other labor unions representing school and district personnel concerning human resource policies such as compensation and working conditions. 3. Finance and operations: The central office is responsible for district and school budgets and for the maintenance and operations of schools. It oversees federal, state, and district categorical funds, or restricted funds that are intended for a specified purpose, and audits all expenditures. Major operations such as transportation, building and equipment repair, food service, facilities management, technology, and custodial services are run from and/ or outsourced from the central office. All contracted services are the responsibility of the central office. 4. Curriculum and instruction: The central office designs and implements common educational standards and curriculum for the school district. It mandates professional development tied to these standards and curriculum as well as the system of formative and summative assessments used to evaluate student and teacher performance. Policies and procedures determined and controlled by the central office allow for additional curricular and instructional support systems for students with particular needs (e.g., special education students, limited-English-proficient students, and economically disadvantaged students). 5. Evaluation and accountability: The central office is the primary collector and compiler of data for evaluation and accountability purposes. It ensures adherence to district, state, and federal education guidelines, including the use of categorical funds. It relays this and other information to state and federal agencies, such as those for auditing and enforcement of civil rights provisions, as well as to the public at large. This information is used to judge program and teacher effectiveness as well as whether or not schools, and therefore principals, are facilitating student academic growth. Central office personnel

ensure adherence to individualized education programs for special education students and to school improvement plans for the district’s schools. In contrast to a traditional central office, a decentralized central office serves more of a supporting role to those staff closer to the students receiving services. While this decentralization is sometimes part of a reform movement to give more power to community or parent-based councils, it is typically the schools that garner more responsibility in this scenario. When giving increased autonomy to schools, also referred to as school-based management, the central office may function as a service provider to school staff. Instead of creating and enforcing policies, the central office then serves as a resource for building school-level capacity to create a vision and execute this vision effectively. A decentralized central office allows schools to create and enact policy. When a central office re-forms its role to be a support to local schools’ vision and operations, the principals, teacher leaders, and school support staff become the major decision makers in the educational system. Central offices with a decentralized design allow varying degrees of autonomy and control over school resources. Some services may continue to be centralized due to economies of scale. For example, food service, transportation, and building maintenance may continue to be offered by single organizations, such as the central office or third-party contractors. In some cases, schools may find that independent contracting for these services is costefficient. School districts’ level of decentralization varies widely. Central offices may delegate some or all of these responsibilities to schools: • Control over resource use • Control over the budget (e.g., personnel selection, professional development, and/or program implementation) • Seeking third-party providers for products and services (e.g., transportation, custodians, and equipment) • Teacher and staff selection (including selection and staffing based on compensation levels) • Implementation of school improvement plans • Selection of curriculum

The cost of running a central office varies widely depending on its structure and the number of schools and students it serves. While costs do

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fluctuate, central offices typically consume about 4% to 6% of the overall district budget. This range of costs includes personnel salaries and benefits as well as miscellaneous costs such as purchasing services, communications, insurance, office supplies, legal consultation, auditing, association fees, election materials and personnel, and technology. Additional district-level costs not classified as central office costs include operations and maintenance, student transportation, food service, and debt service. Mike Goetz See also Accountability, Types of; Centralization Versus Decentralization; Evolution in Authority Over U.S. Schools; School District Budgets; School District Cash Flow; School-Based Management

Further Readings Burch, P., & Spillane, J. (2004). Leading from the middle: Mid-level district staff and instructional improvement. Chicago, IL: Cross City Campaign for Urban School Reform. Retrieved from http://eric.ed.gov/?id=ED509005 DuFour, R. B. (2003, June). Central office support for learning communities. School Administrator. Retrieved from https://www.cosa.k12.or.us/downloads/profdev/ CentralOfficeSupportforLCs.pdf Hord, S., & Smith, A. (1993). Will our phones go dead? The changing role of the central office (Issues . . . About Change, Vol. 2, No. 4). Austin, TX: Southwest Educational Development Laboratory. Retrieved from http://www.sedl.org/change/issues/issues24.html Rothman, R. (Ed.). (2009). Redesigning the “central office” (Voices in Urban Education, Winter, No. 22). Providence, RI: Annenberg Institute for School Reform. Retrieved from http://annenberginstitute.org/sites/ default/files/product/241/files/VUE22.pdf Swift, E. (2006). A central office staffing model to provide for an adequate education (Doctoral dissertation). University of Southern California. Available from Proquest database (UMI No. 3233858). Retrieved from http://books.google.com/books/about/A_Central_Office_ Staffing_Model_to_Provi.html?id=IRXqFGz7f2EC

CENTRALIZATION VERSUS DECENTRALIZATION Centralization in the context of education policy refers to shifting the locus of decision-making authority for school administration and funding

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from local educational authorities to a more centralized level. This could entail the shift from individual schools to a national educational agency or in the case of the United States from local school districts to state governments. Decentralization refers to a shift in the opposite direction, from central to more localized authorities. Many salient policy issues about the role of government versus markets in educational provision entail debates about the merits of centralization versus decentralization. This entry illustrates issues in the definition and description of centralization and decentralization. It then turns to the variation in trends in each direction throughout the world and details the forces behind first centralization and then decentralization. Finally, consideration is given to evidence on the consequences and effectiveness of trends in each direction. National educational systems vary widely in the extent to which key decisions in school administration and funding are made at the national level or at more local levels of administration and organization. In the case of France and for much of sub-Saharan Africa, most of the important decisions are made by national officials. In the case of the United States, most of the decisions are made at the state and local levels. And various intermediate possibilities prevail elsewhere. The advent of almost universal access to primary schooling in much of the world over the past two centuries has entailed a shift in the locus of decision making away from individual schools and local authorities toward national or at least more centralized authorities. In the United States, this shift has placed more authority in state governments. The standard economic arguments for centralized and hierarchical provision are that the public goods nature of schooling implies the desirability of setting threshold standards for educational outcomes and guaranteeing widespread and possibly universal access to schooling. This in turn implies the redistribution of resources within societies. However, since at least the 1980s, educational policymakers in diverse parts of the world have advocated shifting the locus of decision making away from national and more centralized authorities toward more local levels and in many instances toward the individual school. The arguments for such decentralization often have much in common with those for employing markets to make decisions: (a) more immediate information and familiarity with the local circumstances of students and their families and

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(b) more immediate responsiveness to the educational demands of students and their families insofar as compensation of educational providers depends on this response. Centralization and decentralization should be viewed as processes involving the reassignment of decision making rather than as static structures.

be occurring in some dimensions at the same time as decentralization is occurring in others. In the United States, centralizing tendencies were evident as a result of the No Child Left Behind Act of 2001, which required states to meet annual goals for student academic attainment. Yet the spread of charter schools during the same period can be seen as reflecting decentralizing tendencies.

Dimensions of Centralization and Decentralization

Trends in Centralization and Decentralization

Centralization and decentralization are abstractions, each encompassing heterogeneous aspects. Both can occur in dimensions of school finance, in establishing standards for teacher qualification, in curricular standards, in assessment of educational outcomes, in expectations for student enrollment and attendance, as well as in other aspects of the education enterprise. Studies of decentralization have distinguished the following types in terms of agency involvement: (a) deconcentration, which entails the repositioning of centralized education officials from centralized to less centralized locations; (b) delegation, which entails delegating tasks previously undertaken by centralized officials to more decentralized or localized agencies but with the basic authority behind the delegation remaining at the center; and (c) devolution, which entails surrendering centralized authority to more decentralized authorities with minimal accountability back to the center. In a related set of distinctions, it is common to distinguish political from administrative decentralization. Political decentralization entails relinquishing all control over decision making by central authorities to autonomous local authorities. Administrative decentralization is the more modest delegation of decision-making activity from the center to a lower level locus within an administrative hierarchy. Centralization as such has received far less explicit attention in the recent educational policy literature. However, one can distinguish between policies that attempt to establish a centralized schooling system ab initio and policies that endeavor to bring already existing individual schools and local schooling authorities under more centralized control. While general ideological considerations might suggest some underlying forces at work behind tendencies to either centralization or decentralization, a number of studies have noted that decentralization often occurs for diverse motives. The same can be said of centralization as well. Moreover, it has been noted that centralization in education can

A number of analyses emphasize that centralization and decentralization—whether in the educational sphere or more generally—entail redistribution of power and resources. As a result, the outcomes of movement in either direction can be influenced by the interest groups affected by the centralization or decentralization policies. In the United States as well as in other countries such as Mexico, teachers’ unions are often identified as key national interest groups that tend to block efforts at decentralization of school decision making. In developing countries, efforts at using decentralization to improve the use of information and enhance efficiency by placing decision making closer to the point of delivery are often thwarted by the regressive and corrupt practices of local politicians. This leads to the irony that decentralization in more totalitarian societies can be more effective than in more democratic ones, where the corrupt practices of local politicians are less subject to centralized supervision. Countries with especially heterogeneous populations may decentralize their schooling systems to respond to the great diversity in their populations’ educational demands. India is often cited as an example of a country with a culturally and ethnically diverse population, with resulting widespread decentralization. Spain, with its strong regional identities, is another example. On the other side, homogeneity implies more opportunity for gain from the economies of centralization. France and Denmark are commonly cited as examples on this score. Developing countries tend to be far more centralized in their provision of schooling than developed ones. Among the developed countries, there appears to be wide variation in the extent of centralization of school provision. In recent decades, the overall worldwide trend has generally been characterized as toward decentralization. The pace of decentralization has varied considerably across regions and countries. In sub-Saharan Africa, decentralization has proceeded from highly centralized national

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systems and has done so quite tentatively. In Latin America, decentralization has proceeded much further, with various mixes of national controls and school-based management systems in Chile, Brazil, and Mexico. Substantial moves toward decentralization are also in evidence throughout East and South Asia. Decentralization tendencies are arguably somewhat less in evidence in Europe. However, Spain has been moving toward increased regional autonomy in educational decision making. In Britain, the use of school-based management policies has featured prominently since the 1980s. Decentralization tendencies have surfaced to a lesser degree elsewhere in western Europe but are more prominent in eastern Europe and Russia. Although education in the United States is commonly viewed as subject to minimal control at the national level, since the 1960s trends toward increased federal influence are evident, starting with the enactment of Title I, which set up a system of federal funding for disadvantaged students, during the Johnson administration. The trend continued under the George W. Bush administration with the enactment of No Child Left Behind, even though since 2011, many states have received waivers by the Obama administration from the legislation’s standards for “adequate yearly progress” in student achievement.

Centralization Historically, the development of schooling systems in the Western developed world have arguably been based on tendencies toward centralization. The aims of such centralization include (a) increasing participation rates of populations in schooling through ensuring widespread availability of schooling and the use of attendance laws to ensure universal attendance during childhood, (b) establishing minimum and often uniform standards for teacher training, and (c) uniformity of curriculum. Such centralization generally also includes control over school finance to minimize disparities in expenditure per student. France and to a lesser degree Prussia are often held up as paradigms for the formation of national systems based on such centralizing tendencies. In the United States, much less national control is in evidence, but the same centralizing tendencies have been in evidence in state-level governments and large urban school systems. Scholars have reached conflicting conclusions over whether compulsory schooling legislation,

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frequently a key component of centralizing educational policies, has had positive causal impacts on attendance. An alternative view for the United States is that the legislation was put in place by states largely after and due to universal school attendance by the relevant school-age populations. Recent research does suggest positive causal impacts from compulsory schooling laws, albeit relatively small ones. Scholars have also debated the benevolence of the motives behind the centralization of schooling. Marxist accounts have argued that capitalist elites pushed for mass schooling as a means of indoctrinating and rendering the working classes more docile. Non-Marxist historians have argued that the evidence indicates that social class conflict was not a major determinant of support for popular schooling. Although the United States has resisted nationallevel control of education, nationalizing tendencies have been traced at least as far back as the passage of Title I by Congress in the Johnson administration. Various interest groups resisted efforts to tie these funds to more extensive federal control over schooling. Then, the George W. Bush administration formed a coalition with liberal Democrats in Congress to link federal funding to schools with standardized examination results under No Child Left Behind. The Obama administration followed up with the Race to the Top grant competition, which tied receipt of federal monies to linking teacher compensation with student educational outcomes. Over this same half-century, legal decisions in state courts held that failure to meet adequacy standards in educational provision entailed a deprivation of rights for the students affected. This led to more state-level restrictions on gaps between local school districts in spending per pupil and to state-level redistributive efforts in school finance. As noted above, developing countries are generally characterized by centralization of their educational systems. This can partly be attributed to legacies of colonial control that would emphasize top-down administration of educational systems. In addition, since World War II, organizations such as the United Nations Educational, Scientific and Cultural Organization have pressed developing nations to move toward universal literacy and access to primary schooling. This explains why a number of these governments have implemented centralized policy campaigns to facilitate universal access of their populations to basic education. The experience of both the United States and China in recent decades

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points to limits to centralizing tendencies apart from the countervailing forces of decentralization. One factor that has arisen in both cases is simply scarcity constraints on the funding that central authorities are willing to provide to local school administrations to deal with educational deficiencies. In the United States, one major objection by state-level authorities to No Child Left Behind is that it constituted an unfunded mandate; the legislation provided no additional funding to improve academic performance to the levels it required. In addition, state and local interests have wanted to retain control over key schooling decisions. In the case of China, the central government, while providing substantial subsidies to local elementary schools to ensure widespread access, has not been willing to commit to ensuring universal free primary schooling. And in many rural areas of China, poor families are still expected to pay tuition for their children’s schooling.

Decentralization Since at least the 1980s, the educational policy literature has given far more prominence to decentralization than centralization. This may reflect the increasing prominence given to promarket ideologies in schooling policies and, indeed, in public policy more generally. Advocates of decentralization of schooling generally assert that this will enhance teaching and learning outcomes by placing policy decision making closer to the point of execution and by strengthening incentives for improved performance. However, a range of actual motives seems to underlie decentralization policies regardless of the official rhetoric. In addition to improved learning outcomes, one further motive for decentralization is to shift the financial burden of school provision away from central governments onto more local levels as well as to parents and other nongovernment stakeholders. This was a key motive behind the decentralization of schooling in Argentina in the 1990s, and as noted above, it also appears to have been a factor in the decentralizing tendencies in school funding in China in recent decades. Another motive is to cut costs by trimming inefficiencies in educational bureaucracies. This was a key motive behind the decentralization in New Zealand school provision in the 1980s and 1990s. A final set of motives mentioned by many commentators is the redistribution of political power associated with decentralization. This can play out in complex ways, ranging from enhanced legitimacy in the case of the Colombian central government in the 1980s to local

authorities filling in the political vacuum that arose with the demise of communism in central and eastern Europe post-1990.

Consequences and Outcomes of Centralization and Decentralization There is no consensus on whether decentralizing educational policies has enhanced educational outcomes. Some observers came to the conclusion that there is little quantitative evidence that decentralization improves measurable student outcomes, such as raising standardized test scores. One difficulty has been the lack of rigorous empirical work on the impact of decentralization on outcomes. A major problem is simply coming up with operational measures of decentralization. However, recently, a number of studies have made efforts to empirically specify measures of decentralization and to estimate their causal impact on various measures of educational outcomes and performance. Thomas Fuchs and Ludger Woessmann found that decentralization in the specific form of school autonomy in decision making does have positive effects in raising PISA (Programme for International Student Assessment) scores after controlling for numerous factors. They found that the impact of school autonomy is substantially more positive in systems with external exit exams in place, that is, with clear accountability for student outcomes. They also found that school autonomy in what they term process decisions, such as textbook choice, hiring of teachers, and budget allocations within schools, has a positive impact on outcomes. However, they also found that centralized decision making in setting the overall school budget has positive impacts on PISA scores. They interpreted this result as indicating that school autonomy in areas particularly subject to local corruption is likely to weaken rather than strengthen test score outcomes. Francisco Gallego corroborated these results at the cross-country level with his finding that decentralization of educational administration has substantially greater positive effects on measures of educational attainment than does decentralization of school finance. Barbara Bruns, Deon Filmer, and Harry Patrinos found in their survey of empirical studies of the impact of school-based management policies in a variety of developing countries that these policies do have positive impacts on both student school attendance and examination outcomes in some but not all instances. But as with the other two studies just mentioned, the studies they surveyed show that whether the impact of school-based

Charter Management Organizations

management is positive or not depends on various details of implementation. Thus, while some empirical studies found positive impacts of decentralization, they generally underscore that these positive impacts are associated with particular types of decentralization and impact in specific ways. In other words, the evidence tends to militate against attributing broadly based enhancements in educational effectiveness to general decentralizing policies. A number of observers have argued that decentralization has tended to widen educational inequality and schooling gaps and has made it more difficult to ensure universal attainment of minimum educational standards. They attribute these tendencies both to the regressive redistribution of educational resources associated with decentralization and to the self-interested behavior of local elites. However, some commentators argue that appropriately designed offsetting policies can still allow achievement of the benefits of decentralization without greatly exacerbating initial schooling inequalities.

Conclusion Trends toward both educational centralization and decentralization, sometimes occurring simultaneously in the same location, are evident in recent educational policy. Both tendencies occur along multiple dimensions. How they each play out in affecting educational performance depends on how educational systems interact with the larger societies of which they are a part. David Mitch Further Readings Archer, M. (1979). Social origins of educational systems. London, UK: Sage. Astiz, M. F., Wiseman, A., & Baker, P. (2002). Slouching towards decentralization: Consequences of globalization for curricular control in national education systems. Comparative Education Review, 46, 66–88. Bardhan, P. (2002). Decentralization of governance and development. Journal of Economic Perspectives, 16, 185–205. Bray, M. (2003). Control of education: Issues and tensions in centralization and decentralization. In R. Arnove & C. A. Torres (Eds.), Comparative education: The dialectic of the global and the local (pp. 204–228). Lanham, MD: Rowman & Littlefield. Bruns, B., Filmer, D., & Patrinos, H. A. (2011). Making schools work: New evidence on accountability reforms. Washington, DC: World Bank.

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DeBoer, J. (2012). Twentieth-century American education reform in the global context. Peabody Journal of Education, 87, 416–435. Fiske, E. B. (1996). Decentralization of education: Politics and consensus. Washington, DC: World Bank. Fuchs, T., & Woessmann, L. (2007). What accounts for international differences in student performance? A re-examination using PISA data. Empirical Economics, 32, 433–464. Gallego, F. (2010). Historical origins of schooling: The role of democracy and political decentralization. Review of Economics and Statistics, 92, 228–243. McGinn, N., & Street, S. (1986). Educational decentralization: Weak state or strong state? Comparative Education Review, 30, 471–490. Naidoo, J. (2005). Educational decentralization in Africa: Great expectations and unfulfilled promises. In D. P. Baker & A. W. Wiseman (Eds.), International perspectives on education and society: Vol. 6. Global trends in educational policy (pp. 99–124). Amsterdam, Netherlands: Elsevier. Winkler, D. R. (1993). Fiscal decentralization and accountability in education: Experiences in four countries. In J. Hannaway & M. Carnoy (Eds.), Decentralization and school improvement: Can we fulfill the promise? (pp. 102–134). San Francisco, CA: Jossey-Bass.

CHARTER MANAGEMENT ORGANIZATIONS The charter school movement began in 1991 with Minnesota enacting the first state school law. By the 2012–2013 school year, 42 states and the District of Columbia had enacted charter school laws, and there were more than 2 million students in nearly 6,000 charter schools across the country. Over the years, the one-by-one approach to charter schooling was joined by the growth of networks of charter schools. First, for-profit education management organizations (EMOs) joined the educational landscape to provide services to traditional public schools. As the charter sector developed, EMOs were well positioned to enter the charter market. By the late 1990s, EMOs were joined by their nonprofit counterparts, charter management organizations (CMOs). A CMO is a nonprofit organization that manages multiple charter schools with a common mission and instructional design, and with a home office management team that offers ongoing support to its schools. Charter school reforms were intended

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for individual charter schools to replicate promising practices, but CMOs were an unexpected consequence of the movement; they emerged to accelerate the speed of reform by scaling up proven models of schooling. The first CMO, Aspire Public Schools, was founded in 1999; more than 12 years later, the sector has grown to include 137 nonprofit management organizations operating 793 charter schools in 26 states with charter laws and serving more than 200,000 students. According to the National Alliance of Public Charter Schools, over the past 5 years, there have been between 51 and 96 new CMO schools each year, with an average growth rate of 12% annually. In urban centers, including Chicago, Los Angeles, New Orleans, Newark, New York City, Oakland, and Washington, D.C., CMO-run schools make up more than one third of the charter market. This entry traces the growth of CMOs and discuses the economics of how they function. The concluding sections highlight the research on CMOs and what lies ahead as the population of CMOs and the number of schools within each CMO continue to grow.

The Growth of Charter Management Organizations The emergence of CMOs has grown in direct response to the financial and operational challenges faced by many stand-alone charter schools, as well as their limitations in effecting systemic change. Not too surprisingly, individual charter schools have most frequently closed because of financial and governance mismanagement issues. CMOs help combat the resource scarcity experienced by standalone charter schools by seeking to take advantage of economies of scale as service providers and by providing opportunities for collaboration across schools. For instance, the CMO home office offers member schools specific expertise in key organizational areas, such as financial management, facility acquisition, legal compliance, grants management, and human resources. By concentrating these responsibilities in a centralized management team, principals and teachers are able to focus on their responsibilities as instructional leaders at the school site. The home office personnel can also devote time and talent to growth in ways that one-off school site administrators cannot. Much of the growth among CMOs has been attributed to the infusion of dollars from the philanthropic community, from corporate giving and

family foundations to individual donors. The San Francisco–based New Schools Venture Fund has invested heavily in entrepreneurs to launch CMOs across the United States. Research shows that CMOs funded by New Schools Venture Fund relied on philanthropy for an average of 64% of their home office revenues. The Charter School Growth Fund, a nonprofit philanthropic organization, channels donations from foundations and wealthy individuals into helping charters expand, typically from a school or two into larger, multischool networks. Since 2006, the organization has given between $160 million and $170 million in grants and loans to charters, according to the organization’s chief executive officer. The Michael and Susan Dell Foundation supports charter schools and includes CMOs in its grant making because of their ability to reach greater numbers of students than stand-alone charter schools. In a similar vein, the Walton Family Foundation, which has a long history of providing support to stand-alone charter schools, also funds CMOs to build the replication capacity of proven school models. These and other philanthropic organizations also support CMO growth by funding school start-up costs, longterm capacity-building efforts, operations, facilities, and human capital organizations, such as Teach for America or New Leaders for New Schools, that feed the CMO talent pipeline. Apart from philanthropy, there has been significant federal support for the replication of high-quality charter schools that has fueled the expansion of CMOs. In 2010, the U.S. Department of Education unveiled the Charter Schools Program Grants for Replication and Expansion of High-Quality Charter Schools competition with an initial appropriation of $50 million. Eligible applicants are defined specifically as nonprofit CMOs with demonstrated success in increasing student academic achievement. CMOS and support organizations (e.g., Teach for America) also have received support from the Investing in Innovation Fund (i3) grant competition (established under the American Recovery and Reinvestment Act of 2009), which provides funding to expand and take to scale innovative models. CMO-like structures emerged in England as CMOs were growing in the United States. While England’s brand of autonomous schools preceded the U.S. charter movement, schools in England have only recently (since 2004) been able to federate under one governing body if they wished to do so. This school governance structure is known as an academy federation or chain. Like CMOs in the

Charter Management Organizations

United States, a single governing body is shared by all schools; the board makes budgetary decisions on behalf of the schools and also appoints management, such as a single “head teacher,” to oversee the schools, much like the CMO board appoints the network’s CEO or president. England’s federations are, however, different from their counterparts in the United States in significant ways. At the start, academy status was a privilege bestowed on schools that the national department of education (Ofsted, short for the Office for Standards in Education, Children’s Services and Skills) judged outstanding, and so participation in federations was limited chiefly to high-performing academies. New legislation now allows schools to acquire academy status even if they have not been judged outstanding. As a result, federations in England often have schools with different performance records, the idea being that stronger schools can help weaker schools improve through shared resources, mentoring, and opportunities for classroom observation in higher performing schools. Another difference is that federations are composed of schools that existed prior to joining the network, and perhaps as a consequence, the relations between the governing board and its affiliated schools are formalized through a servicelevel agreement. By contrast, CMOs grow their own schools. In the few instances where CMOs take over failing schools, the network closes and restructures the school before its reopening as part of the CMO.

The Economics of CMOs Although one motivation behind the creation of CMOs was to combat the pervasive resource scarcity that has challenged stand-alone charter schools, the funding environment in which CMOs operate has created unique funding challenges that counteract the economies of scale from which CMOs were expected to benefit. This funding environment is constrained by charter school laws that originally conceived of stand-alone schools—funding formulas were designed to cover the operational costs of individual charter schools and often fell short by not including the costs of facilities and home offices. As pointed out earlier, foundations have constituted a major component of the CMO funding environment, and not too surprisingly, many CMO business plans acknowledged early reliance on foundation funding to expand their network. Complicating the CMO financial environment are the range of restrictions on the money CMOs

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access. According to several national studies of CMOs, some funders required that they be given a seat on the CMO’s governing board as part of the funding arrangement, so that they could have a decision-making role in how the money was spent. Other restrictions directed external funds for a specific, funder-identified purpose, but CMOs reported that their needs were for unrestricted money to be used for the purposes identified by the CMO. This situation was exacerbated by the fact that funders often preferred funding some areas over others. There was also the problem of balancing short-term grants and long-term needs. Foundation funding often occurred through early infusions of large amounts of funds available for a specific, short period of time. At the end of the funding period, 3 or 4 years, the CMOs were expected to have reached sustainability, and the foundations pulled out. An alternative source of financial capital had to be found to replace the inevitable decline in foundation support. In addition to formal restrictions on how funding could be used, CMOs frequently mentioned that funders—in particular foundations—often had expectations that were at odds with the organization’s mission or approach to growth, at times pushing for fast scale-up of the network. In these instances, CMO funding was often contingent on meeting certain growth targets (e.g., opening two schools every year), despite most CMOs’ preference for growing new schools slowly, grade by grade. The economic model of sustainability for CMOs relies on the idea of financial equilibrium: The projected break-even point is when fees from affiliated schools cover the cost of the home offices and the services (e.g., professional development) to the schools. Attaining fee-based, financial equilibrium was viewed as a necessary condition for CMOs to meet the anticipated demand for large numbers of high-quality charter schools. However, self-funded operations have proven elusive. The need for external funding has grown at least in proportion to the number of schools served.

Research on CMOs The National Study of Charter Management Organization Effectiveness in 2010 was among the first large-scale studies of CMOs. The study described the CMO landscape and how CMOs compared with one another. At the time of the study, CMOs were young and were relatively small organizations. On average, a CMO operated slightly fewer

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than seven schools. CMOs were also regionally concentrated, with the vast majority of CMO-affiliated schools operating in nine states (e.g., Arizona, California, Florida, Illinois, Louisiana, New York, Ohio, Pennsylvania, and Texas) and the District of Columbia. CMO-affiliated charter schools were also concentrated in big cities and served primarily lowincome and minority student populations. These observations continue to be true; however, the numbers of CMO-affiliated schools have exploded in the past few years, as discussed earlier. The national study also focused on how CMOs compared with one another and with district schools. On average, CMO schools tended to be much smaller than schools in their host districts, with slightly lower student-teacher ratios. Most CMOs tended to be highly prescriptive in their educational approach, in order to ensure that all affiliated schools followed the education program, human resource functions, and student behavior and support functions. CMO office staff made frequent visits to the schools they oversaw. As the researchers concluded, CMO-affiliated charter schools are not isolated and left to sink or swim on their own. Relative to district schools, CMOs reported that their schools provided more instructional time and that teachers’ pay was more likely to be based on performance. Considerable attention in the research has examined the impacts of CMOs on student outcomes. The national study of CMO effectiveness concluded that the achievement impacts for CMOs were more positive than negative, at least at the middle school level. The conclusions from the same report further suggest that some CMOs are systematically outperforming others. According to the report, most of the variation in school-level impacts occurred between CMOs rather than within CMOs. A different study of California CMOs found that charter schools run by management organizations had significantly higher student achievement results than other charter schools, after controlling for differences in enrollment and student characteristics. Similarly, England’s academy chains or federations have been shown to have a positive impact on student achievement. However, researchers concluded that there was a time lag in this improvement, which became apparent after schools had been in a federation for 2 to 4 years. Performance federations—where higher and lower performing schools had been federated—showed the most positive impact on student outcomes.

The largest evaluation to date of the performance of CMOs included 167 CMOs operating in 25 states and examined the extent to which CMOs provided high-quality educational outcomes for their students relative both to stand-alone charter schools and to their counterparts in traditional public schools. The findings suggested that CMOs on average were not dramatically better in terms of contributing to student learning. However, CMOs did produce stronger academic gains with historically disadvantaged students (e.g., Black, Hispanic, high poverty) than traditional public schools and, to some extent, stand-alone charter schools. Indeed, the researchers concluded that there was considerable variation across CMOs in terms of quality and impact. Across the 167 CMOs, learning gains in math and reading far outpaced the gains of the local traditional public schools; however, about one third of the CMOs had portfolio average learning gains in reading that were significantly lower than the gains of the local traditional public schools.

Emerging Issues Many CMOs have ambitious growth targets, and having more schools per CMO will potentially allow CMOs to benefit from economies of scale, affecting a larger number of students and influencing systemic change in ways single charter schools have not been able to do. On the other hand, CMOs may be in danger of replicating the bureaucracies of traditional school districts if their networks become too large. Could too much scale-up be a bad thing? Is there an optimal size for CMOs, or is it possible for a CMO to become too large? Given the financial, human capital, and real estate requirements for growing these networks, how sustainable are CMOs as a longterm solution? CMOs, with their heavy dependency on external funding, are often in the chase for dollars. Will policymakers amend state charter laws to provide more equitable funding to CMOs and greater access to district-owned facilities? As CMOs grow, they may be constrained by a restricted talent pool, given the model’s demands for large numbers of high-quality teachers and school leaders (along with the high rate of burnout of both groups). A few CMOs have launched graduate schools of education to populate their own talent pool. Many other CMOs consider locating in geographic areas where Teach for America corps members are plentiful.

Charter Schools

In addition to the fiscal and human capital challenges, little attention has been given to the governance of CMOs—what are the governance structures leading to high-performing CMOs, and do these structures vary depending on the maturity or size of the CMO? How can CMOs balance their lofty growth goals with ensuring quality across current and new schools within their networks? What is the proper balance between the standardization necessary for running a system of schools and the school-level autonomy, experimentation, and innovation inherent in the charter idea? In conclusion, questions about capacity and the as yet unresolved strains associated with the kind of rapid expansion that CMOs are undertaking need to be critically examined by policymakers, funders, researchers, and the CMOs themselves in order to ensure high-quality education models whose finances and infrastructure are sustainable in the long run.

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on interim findings. Seattle, WA: Center on Reinventing Public Education. Networking and collaboration for school improvement [Special issue]. (2010). School Effectiveness & School Improvement: An Interdisciplinary Journal of Research, Policy and Practice, 21(1). Wohlstetter, P., Smith, J., & Farrell, C. (2013). Choices and challenges: Charter school performance in perspective. Cambridge, MA: Harvard Education Press. Wohlstetter, P., Smith, J., Farrell, C., Hentschke, G. C., & Hirman, J. (2011). How funding shapes the growth of charter management organizations: Is the tail wagging the dog? Journal of Education Finance, 37(2), 150–174. Woodworth, J. L., & Raymond, M. E. (2013). Charter school growth and replication (Vol. 2). Palo Alto, CA: Center for Research on Education Outcomes.

Website National Alliance for Public Charter Schools: http://www .publiccharters.org

Priscilla Wohlstetter See also Centralization Versus Decentralization; Charter Schools; Deregulation; Education Management Organizations; Philanthropic Foundations in Education

Further Readings Chapman, C. (2014). From one school to many: Reflections on the impact and nature of school federations and chains in England. Educational Management Administration and Leadership. Advance online publication. doi:10.1177/ 1741143213494883 Furgeson, J., Gill, B., Haimson, J., Killewald, A., McCullough, M., Nichols-Barrer, I., . . . Lake, R. (2011, November). The National Study of Charter Management Organization (CMO) effectiveness: Charter-school management organizations: Diverse strategies and diverse student impacts. Princeton, NJ: Mathematica Policy Research and Center on Reinventing Public Education. Retrieved from http:// www.mathematica-mpr.com/publications/pdfs/education/ cmo_final_updated.pdf Hill, R., Dunford, J., Parish, N., Rea, S., & Sandals, L. (2012). The growth of academy chains: Implications for leaders and leadership (National College for School Leadership). Retrieved from http://dera.ioe.ac.uk/14536/ 1/the-growth-of-academy-chains%5B1%5D.pdf Lake, R., Dusseault, B., Bowen, M., Demeritt, A., & Hill, P. (2010, June). The National Study of Charter Management Organization (CMO) effectiveness: Report

CHARTER SCHOOLS Charter schools represent an important experiment in U.S. public education. As privately operated public schools, charter schools depart from the “common school” model under which U.S. public schools have been operating for nearly two centuries. Charter schools differ in three key ways from the traditional model of public education in the United States. First, students or their parents choose to enroll in charter schools. Students are not assigned to a specific charter school by school districts or states, meaning that each charter school must compete with other charter schools and traditional public schools to attract students. Second, virtually all public funding for charter schools is tied directly to student enrollment, creating incentives for charter schools to offer educational options that parents will prefer over existing public schools. Third, charter schools are not directly operated by local school districts or other government agencies. Although they operate under public regulations, they function autonomously, with greater flexibility than traditional public schools to make a variety of operational changes (e.g., altering the length of the school day and school year), staffing decisions (including the hiring and dismissal of teachers), and curriculum design choices that differ from other public schools. In other words, charter schools discard direct government operation while

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retaining public funding, public regulation, and public access. This arrangement is novel on the American scene but common in Europe, where privately operated schools (including religiously affiliated schools) often receive public funding in exchange for accepting public regulation. Charter schooling is part of a global trend toward the use of contracting and private provision of public services. This entry begins by summarizing the arguments for and against charter schools and by describing trends in the characteristics and growth of charter schools nationwide. The entry then reviews the evidence on charter schools’ impacts on student achievement and other outcomes and describes the characteristics that tend to be associated with particularly high-performing charter schools. It concludes by summarizing the evidence on enrollment patterns in charter schools, the effects of charter schools on traditional public schools, and unresolved questions for future research.

Contrasting Views on Charter Schools Proponents argue that the autonomy, choice, and educational diversity inherent in charter schools benefit students and parents in multiple ways. Greater school choice may allow parents to seek out higher performing alternatives to existing public schools that are underperforming or failing to serve all students adequately. Proponents hold that competition for funds will incentivize improved academic performance at both charter and noncharter schools, as school operators become increasingly attuned to the forces of market accountability introduced by the enrollment choices of students and parents. In addition, those favoring charter schools argue that their operational flexibility will lead to greater innovation, as they serve as laboratories for improvements in school operations that could subsequently be adopted by charter and noncharter schools alike. Charter school skeptics, meanwhile, have raised several concerns about their effects. First, they have questioned whether charter schools produce better academic results than traditional public schools. Second, some have argued that charter schools disproportionately benefit the children of educated and motivated parents who learn about the schools and how to get their children admitted. If charter schools disproportionately attract advantaged students, traditional public schools may be left with a student population that is more difficult to teach. In districts where charter schools constitute a substantial

number of the available schools, critics argue that this problem will be compounded by the defunding of traditional public schools as resources will be redirected to charter schools with growing enrollment levels. In addition, some observers believe that charter schools could undermine civic socialization in public schools. One of the core functions of public education is to establish a shared foundation of citizenship-related skills and cultural knowledge among all students. The traditional common-school model is presumed to serve this purpose because the schools operate under the direct control of democratically elected officials. Skeptics worry that charter schools while pursuing their own educational visions under private operation might neglect the inculcation of civic knowledge, skills, and attitudes that constitute the raison d’être of public education. Though value differences may play a role in these disagreements between charter school supporters and skeptics, all the competing claims described above can at least potentially be informed by empirical evidence. As the number of charter schools has continued to grow, researchers have completed studies examining the impacts of charter schools. The remainder of this entry summarizes some of the key insights emerging from this body of research.

Charter School Authorization, Funding, Growth, and Characteristics Although charter schools are not directly operated by local school districts, the opening of a charter school requires the approval of an agency that has the legal authority to issue charters. Authorizers vary in different states: Some states have a single authorizer, while others have multiple authorizers of different types. School districts often have authorizing power, though many of them are reluctant to use it. Some states also vest authorizing power in universities and/or nonprofit organizations. Each charter school is authorized for a specific term (5 years in many states), after which the school’s charter must be renewed. Funding for the operation of charter schools is universally provided on a per-pupil basis, but details related to charter school funding formulas can vary substantially. In most states, charter schools receive a designated fraction of the per-pupil spending in traditional public schools, but that fraction varies by state. In some states, charter school funding is based on funding available to traditional public schools in the local district where the charter school operates,

Charter Schools

while other states fund charter schools relative to statewide average spending levels. The extent to which charter school funding comes from a central, statewide pool of funds versus a reallocation of local education funding also varies. In many states, charter schools have little or no access to capital funds or facility maintenance funds that are typically made available to traditional public schools; a small number of states address this issue by providing an additional amount of per-pupil funding to cover facility expenses or by creating charter school lending vehicles to help cover capital investments. Charter schools in most states appear to receive less total public funding than do conventional public schools. In addition to public funds, many charter schools seek charitable donations or grants, often to support initial startup costs or growth plans. A few prominent charter schools and networks receive enough private funding that their total per-pupil expenditures exceed those of local public schools. The first charter schools opened in Minnesota in the early 1990s. By the 2012–2013 school year, approximately 6,000 charter schools were operating in 42 states and the District of Columbia, representing nearly 6% of all primary and secondary schools in the United States. In cities such as New Orleans, Kansas City, and Washington, D.C., charter school enrollments are large enough to collectively rival the local school district in size. Some charter schools have expanded into multisite networks or management organizations; a few have achieved a reach that crosses state lines. High demand for entry into charter schools in many communities suggests that they provide choices that are attractive to students and parents. Approximately, 2 million students enrolled in charter schools in the 2011–2012 school year, representing about 4% of the total student population nationwide. This level of enrollment represents more than a threefold increase compared with the 2002–2003 school year, indicating a strong and consistent demand for charter school placements during this period. In addition to these overall enrollment trends, a substantial number of charter schools are attracting more applicants than the schools can accept, necessitating wait lists and admissions lotteries. One national survey of charter schools estimated that approximately 610,000 students were on charter school wait lists in the 2011–2012 school year, and a separate study found that approximately 26% of charter middle schools reported being oversubscribed in 2006–2007. Studies that include

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parental surveys have consistently found higher levels of school satisfaction among parents of charter school enrollees. By design, charter schools vary widely in educational approaches and aims. Individual charter schools may emphasize science and technology, Montessori pedagogy, performing arts, or an international focus. Charter schools are more likely than traditional public schools to say they offer special instructional approaches. Despite the wide variation in instructional approaches, there are some ways in which charter schools tend to differ systematically from traditional public schools. They are usually smaller: More than three fifths of charter schools had fewer than 300 students in 2009–2010. They often have unconventional grade configurations that eliminate the traditional boundaries between elementary and middle school or between middle and high school. Their teachers tend to be younger than teachers in traditional public schools and are rarely unionized.

Charter Schools’ Effects on the Achievement of Their Students Charter schools have had mixed impacts on their students’ test scores. Most studies of charter schools’ achievement impacts have examined scores on standardized statewide tests in mathematics and reading, either by matching charter school students to an equivalent group of comparison students enrolled in traditional public schools or by studying charter schools with student admission lotteries that identify randomly assigned treatment and control groups. On average, these studies provide little evidence to suggest that charter schools consistently outperform or underperform traditional public schools on standardized tests on a nationwide basis. Large multistate studies of charter schools have tended to find average impacts on students’ math and reading test scores that are statistically insignificant. Nationwide results for all charter schools mask a great deal of variation found in specific cities and states. Charter schools in some geographic areas, such as Boston and New York City, generally show greater achievement gains than traditional public schools locally. Some charter school networks, such as the Knowledge Is Power Program, have produced large positive test score impacts across many schools. These schools have demonstrated positive impacts not only in math and reading but also in science and social studies. At the same time,

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students in many other charter schools—including some charter networks—are falling behind their counterparts in traditional public schools. And studies have found statewide average charter school achievement impacts to be negative in some states. In short, the clearest implication of two decades of studies of charter school achievement impacts is that their performance varies widely, just as the performance of traditional public schools varies widely. Indeed, given the policy intention to permit charter schools to pursue a variety of different educational approaches, the variation in achievement effects may be exactly what should be expected.

Charter Schools’ Effects on Attainment and Other Student Outcomes Few studies have examined charter schools’ impacts on educational attainment and other outcomes beyond test scores, but the available evidence suggests that charter schools may produce stronger impacts on attainment than on test scores. Studies of charter high schools in Florida, Chicago, and Boston have all found that charter school students have higher rates of graduation from high schools and enrollment in colleges compared with students at traditional public schools. A study of charter school management organizations examined these outcomes at six different charter school networks and found mixed results: Three networks had positive impacts, and three networks had statistically insignificant or negative impacts. Researchers have also recently begun to look beyond achievement and attainment to measure the impacts of charter schools on noncognitive outcomes, such as student discipline, attendance, or other character-related indicators that may predict long-run success. One study that examined these alternative outcomes found encouraging results, suggesting that enrolling in charter schools may lead to improvements in these nonacademic measures. Meanwhile, almost no empirical evidence yet exists on the critical question of charter schools’ effects on the civic knowledge, skills, and attitudes needed for effective citizenship. But it is notable that several charter school networks—such as Democracy Prep in New York; Cesar Chavez Schools for Public Policy in Washington, D.C.; and UNO Charter School Network in Chicago—incorporate civic socialization as a core mission. A decade ago, a single study in Washington, D.C., found that charter schools appeared to produce comparable levels of political tolerance as traditional public

schools while increasing students’ exercise of civic skills (e.g., taking part in debates and community meetings) and the likelihood of participating in community service. The charter school advantage for promoting skills and voluntarism is consistent with literature on private schools, but as there is only a single study in charter schools, the issue demands additional attention from researchers.

Characteristics of High-Performing Charter Schools A new line of research has sought to identify charter school practices that are most closely associated with effectiveness in raising student achievement. While the precise causal links between charter school practices and impacts remain unclear, these studies have identified several factors that tend to be associated with the most effective charter schools. Several studies have found that the most effective charter schools tend to be located in urban school districts and serve students with low socioeconomic status and low prior achievement. Many of these same charter schools also tend to use comprehensive behavior policies for students, with a system of rewards for positive behaviors and sanctions for negative behaviors. Other researchers have found patterns of strong achievement impacts at charter schools that adopt extensive teacher coaching and monitoring systems. Finally, multiple studies have found that larger achievement impacts tend to be found in charter school networks with an extended school day and an extended school year. For example, at Knowledge Is Power Program middle schools, students attend school for an average of more than 9 hours per day, 192 days per year; in comparison, public schools in the United States have an average school day lasting 6.6 hours and a school year of 180 days. Whether these characteristics of charter schools constitute important innovations may be a matter of interpretation. In the first decade of charter schools’ existence, many observers were disappointed that most of them did not look radically different from traditional public schools—and that when they did innovate, traditional school districts largely ignored the innovations. More recently, the innovative possibilities inherent in charter schools have become more apparent, and districts and states have started paying closer attention. The first “cyber” or “virtual” schools, for example, were charter schools; a number of school districts have followed the example of charter schools in creating virtual schools. Other

Charter Schools

charter school “innovations” may not be totally new on the education scene, but charter schools have expanded their availability. These kinds of innovations might include project-based learning, a longer school year, and a particular content focus, among others. Pilot efforts to establish longer school days or longer school years have recently been initiated in traditional public schools in a few states and districts; in Houston, for example, the district’s pilot program was explicitly modeled on the practices of high-performing charter schools.

Characteristics of Charter School Students One commonly raised concern about charter schools is that, as schools of choice, charter schools may deliberately or inadvertently attract students who are disproportionately advantaged relative to other local students. If such “cream skimming” of students occurred at a large scale, critics argue, charter schools could leave nearby traditional public schools with a student population that is more difficult to teach. In fact, there is little evidence that the students who are enrolling in charter schools are systematically more advantaged than the population at local public schools. Compared with local districts, charter schools tend to attract similar numbers of low-income students, African American students, and Hispanic students. Several studies have found that charter school students also tend to have somewhat lower academic achievement prior to charter school entry compared with students in local districts who do not enroll in charter schools. The same and other studies, however, have also found that charter schools tend to enroll a lower share of special education students and students who are English Language Learners than do traditional public schools in their communities.

Charter Schools’ Effects on Students in Traditional Public Schools A separate line of research has examined the effects of introducing charter schools on student achievement in nearby traditional public schools. This is a key area of disagreement between charter school supporters and skeptics, with skeptics anticipating harms as traditional schools lose not only funding but also motivated students and parents while supporters predicting a win-win scenario, as traditional schools respond to new competition from charter schools by improving their own performance. Existing research has reached no definitive conclusions, but it provides

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some support for the competitive hypothesis that the introduction of charter schools may improve academic outcomes at nearby schools, at least in some contexts. Other studies have found no significant impacts, but there is little empirical evidence that students in traditional public schools are harmed by the existence of charter schools.

Conclusion Much remains to be learned about charter schools and their effects. Debates about their average effects on student achievement continue, but the variation in achievement effects is far larger than the average impact, and researchers are only beginning to explore explanations for that variation in the hope of identifying practices that produce successful charter schools. Despite the lengthy list of studies of charter schools’ test score impacts, their long-term effects on student attainment as measured by high school graduation, college entry, and college completion remain uncertain. Moreover, charter schools’ effects on students’ preparation for citizenship are essentially unknown. There is almost no empirical support for the hypothesis that they harm students who remain in traditional public schools (and some support for the view that charter school competition may induce improvements in nearby traditional public schools), but the implications of very high rates of charter school participation are not yet clear, since charter schools have only recently enrolled a substantial proportion of students in a handful of cities. The extent to which charter school innovations can and will be successfully adopted by traditional public schools also remains to be seen. In short, although much has been learned about charter schools in the two decades since the first one opened, many critical questions are yet to be answered. Brian Gill and Ira Nichols-Barrer See also Centralization Versus Decentralization; Charter Management Organizations; Deregulation; Lotteries in School Admissions; Privatization and Marketization

Further Readings Angrist, J. D., Pathak, P. A., & Walters, C. R. (2013). Explaining charter school effectiveness. American Economic Journal: Applied Economics, 5(4), 1–27. Bettinger, E. P. (2005). The effect of charter schools on charter students and public schools. Economics of Education Review, 24(2), 133–147.

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Bifulco, R., & Ladd, H. (2006). The impacts of charter schools on student achievement: Evidence from North Carolina. Education Finance and Policy, 1(1), 50–90. Brewer, D. J., & Hentschke, G. C. (2009). International perspective on publicly-financed, privately-operated schools. In M. Berends (Ed.), Handbook of research on school choice (pp. 227–247). New York, NY: Routledge. Buckley, J., & Schneider, M. (2004). Do charter schools promote student citizenship? (Occasional Paper No. 91). New York, NY: Columbia University, National Center for the Study of Privatization in Education. Dobbie, W., & Fryer, R. G., Jr. (2013). Getting beneath the veil of effective schools: Evidence from New York City. American Economic Journal: Applied Economics, 5(4), 28–60. Furgeson, J., Gill, B., Haimson, J., Killewald, A., McCullough, M., Nichols-Barrer, I., . . . Lake, R. (2012). Charter-school management organizations: Diverse strategies and diverse student impacts. Princeton, NJ: Mathematica Policy Research. Gill, B., Timpane, P. M., Ross, K. E., Brewer, D. J., & Booker, T. K. (2007). Rhetoric vs. reality: What we know and what we need to know about vouchers and charter schools (2nd ed.). Santa Monica, CA: RAND Corporation. Gleason, P., Clark, M., Tuttle, C. C., & Dwoyer, E. (2010). The evaluation of charter school impacts. Washington, DC: U.S. Department of Education, National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences. Imberman, S. (2011). Achievement and behavior in charter schools: Drawing a more complete picture. Review of Economics and Statistics, 93(2), 416–435. Imberman, S. (2011). The effect of charter schools on achievement and behavior of public-school students. Journal of Public Economics, 95, 850–863. Winters, M. A. (2012). Measuring the competitive effect of charter schools on public school student achievement in an urban environment: Evidence from New York City. Economics of Education Review, 31(2), 293–301. Zimmer, R., Gill, B., Booker, T. K., Lavertu, S., Sass, T. R., & Witte, J. (2009). Charter schools in eight states: Effects on achievement, attainment, integration, and competition. Santa Monica, CA: RAND Corporation.

on average, a college graduate earns $500,000 more than a high school graduate. Recent data from the U.S. Department of Labor show that the unemployment rate for college-educated workers is 3.8% compared with 7.6% for all workers and 11.1% for those without a high school degree. There are numerous decisions that prospective college students must make as they consider higher education options. Students must decide whether to attend a 2-year or a 4-year college, what type of college to apply to (e.g., public or private, or highly competitive or less competitive), which college to attend among those that admitted them, which major to pursue, how to finance their studies, and so on. Most dimensions of college choice have been studied by economists. This entry summarizes the research on various aspects of college choice and the implications they have for students’ outcomes.

College Attendance Decision According to the U.S. Bureau of Labor Statistics, more than 66% of recent high school graduates were enrolled in colleges or universities in the fall following graduation. The benefits of college attendance are well known, but individuals will consider their own interests and aptitudes in deciding whether to pursue some form of higher education. Most high school graduates are able to gain admission to some type of postsecondary education, and so the individual’s decision to apply to college is the primary determinant of college attendance. The benefits of college are so well established that when it comes to college-going behavior, the decisions concerning where and when to attend and what to study are at least as consequential as the decision about whether to attend at all. Research shows that education policy does little if anything to affect the attendance decision, but it does influence subsequent decisions after the attendance decision has been made. The following sections focus on issues related to choices faced by students once they have decided to pursue postsecondary education.

Two-Year College Attendance

COLLEGE CHOICE A college education has long been viewed as the path toward higher lifetime earnings, lower probability of unemployment, and desirable nonpecuniary job characteristics. Over the course of a worker’s career,

Two-year colleges make up a large share of postsecondary enrollment, accounting for more than 40% of first-time students. One of the primary benefits of attending a 2-year college is the relatively low attendance cost. During the 2010–2011 academic year, the average annual undergraduate tuition and fees

College Choice

at public 2-year colleges was $2,713 compared with $7,605 at public 4-year colleges. Not surprisingly, low-income students are significantly more likely to attend 2-year colleges than their high-income peers. Although many 2-year college students take a class or two for personal interest or career advancement, one of the main functions of a community college is to prepare students to transfer to a 4-year institution. Studies show that 2-year transfers generally receive similar grades and are as likely to graduate from the institution to which they transfer as are continuous attendees. Initial 2-year attendance may enable students to transfer to a more selective university than the one they would otherwise have attended. Furthermore, a 2-year attendance does not appear to limit access to elite institutions. Two-year transfers graduate from universities that are as highly selective as those attended by direct 4-year attendees. The gains to 2-year college attendance extend beyond the 4-year college preparatory role and into the labor market. There is a significant wage premium to 2-year college attendance: The average person who attended a 2-year college earned 10% more than those without any college education, even without completing an associate’s degree. The benefits of 2-year colleges in the U.S. higher education system are diverse and significant. They play an important role in providing low-cost access to higher education, preparation for continued study at a 4-year college, and higher average wages compared with peers without college education.

Four-Year College Attendance Students who decide to attend a 4-year college must then decide where to apply and attend college, how to finance their education, and what major to choose, among other decisions. College Selectivity

Most students and parents believe that to some degree more selective colleges lead to higher paying jobs and a greater chance of being admitted to strong graduate programs. When considering what makes a college selective, factors such as tuition (higher tuition associated with greater selectivity), percentage of applicants admitted, and academic qualifications of the student body are at the forefront. These elements are incorporated into commonly used college ranking systems such as U.S. News & World Report’s Best Colleges listing and Barron’s Profiles of American Colleges.

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On balance, attendance at more selective colleges is associated with higher labor market earnings. Compared with a less selective public university, typical estimates suggest a premium of about 20% for a selective public university and up to 40% for an elite private university. A notable exception in this literature is the work of Stacy Dale and Alan Krueger, who only find a positive earnings premium for more selective colleges in the case of Black and Hispanic students and students from households with less-educated parents. While the bulk of the college selectivity literature focuses on earnings measures as outcomes, college selectivity has also been found to lead to graduate school at more prominent research universities and to positive adult health behaviors. Cost and Financial Aid

College costs and financial aid are prominent in discussions about postsecondary education. These issues are especially relevant given the increasing attention to the prospect of a “higher education bubble” in which tuition costs are rising faster than the returns to a college degree, and heavily indebted students are increasingly at risk of defaulting on student loans. As an example of cost differences associated with attending different types of colleges, the Digest of Education Statistics for the 2010–2011 academic year reports the annual prices for undergraduate tuition, room, and board to be $15,918 at public 4-year colleges and $32,617 at private 4-year colleges. Furthermore, the cost of attending the most selective private colleges far exceeds the average cost of attending all other private colleges, as evidenced by the fact that the cost of tuition, room, and board for the 2011–2012 academic year at Harvard College, Princeton University, and Bates College are between $55,000 and $60,000. There are a variety of financial aid options available for students to help finance their postsecondary studies. Merit-based and need-based scholarships, grants, loans, and tax credits are a few of the options. Recent research suggests that some types of financial aid, including merit-based state scholarships and tax credits, do not affect the decision to attend college, but they do lead students to “upgrade” to more selective and costly colleges. Other research suggests that increases in the amount awarded through the Federal Pell Grant Program increased the likelihood of staying in college, and decreases in Pell grants lowered the likelihood of staying in college. This

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finding may seem straightforward, but it overturns previous research that suggested that Pell grants led students to drop out of college. College Major

A student’s choice of college major matters because it influences postcollege labor market earnings and graduate school opportunities. Some students struggle to balance a course of study that allows them to pursue their interests while also considering future job prospects with their degree. This is particularly important given the debt that some students incur during their college years. Many students change majors during their course of study as they learn more about their aptitudes and interests. Although this can result in a better ultimate job match, excessive switching is costly for students because it prolongs their studies, which requires additional tuition payments, and it also delays labor market entry, which has an opportunity cost: For every additional semester a student stays in college, it is more income that is given up that could have been earned by working. These can be difficult and complex issues. Economists often frame college major choice using the human capital model. Students choose a major based on the present value of expected lifetime utility, which is usually proxied by earnings. Their course of study raises their productive capital, which results in higher earnings. An alternative framework suggests that college majors do not actually increase productivity but, rather, that a particular major signals to prospective employers that a person possesses characteristics the employer values. In most cases, a college major likely has elements of both human capital and signaling. Data from the Digest of Education Statistics show substantial variation in the types of majors students choose. Among 25- to 29-year-old bachelor’s degree holders in 2011, a selected sampling of fields shows that 30% graduated in business or health-related fields; 15% in English, social sciences, history, liberal arts, or humanities; 5.5% in engineering, mathematics, or statistics; and 8.5% in education. Earnings of recent graduates in these fields also vary markedly around an average of $44,800. The highest average salaries among 25- to 29-year-olds were in computer engineering ($75,700) and chemical engineering ($68,480), followed by finance ($53,340) and accounting ($50,840). Graduates in secondary education, liberal arts/humanities, anthropology/ archeology, and sociology all earned $40,000 or less.

Research on the economic effects of college major choice shows substantial differences in earnings by college major, even after controlling for numerous individual and family background characteristics. In general, more technical fields receive a higher earnings premium than less technical fields. One study found that several years after college graduation, engineering majors earned hourly wages that were on average 27% higher than those of high school graduates, while education majors had hourly wages about 10% less than those of high school graduates, on average. While postcollege earnings are a primary determinant of college major for many students, anecdotal evidence suggests that some students choose to major in fields never intending to terminate their education with an undergraduate degree but rather intending to enroll in professional or academic graduate programs. This highlights the “option value” of college majors—that is, the opportunity the major provides for graduate study in the desired field. For example, a student may choose to major in philosophy in preparation for law school, with no intention of entering the labor market with the philosophy degree. Research finds that liberal arts and sciences have a strong option value component, while computer science and engineering do not. This suggests that ignoring the expected returns from graduate school attendance leads to an underestimate of the earnings of some college majors. Interaction Between College Major and College Selectivity

With all the attention paid to the earnings premium associated with attendance at the most selective colleges, students who cannot afford to attend the most prestigious schools and those who are unable to gain admittance may perceive themselves to be at a sizable disadvantage in the labor market after graduation. However, simply because the average earnings associated with an elite college are higher than those associated with a less selective college does not mean that every student at a top college will earn more than every student at a lower ranked school. Clearly, there is a distribution of earnings associated with each type of college, and these distributions overlap. Hence, some students from less selective colleges will earn more than some students from top colleges, and where students end up in the earnings distribution associated with their college type is most likely associated with their college major. For example, suppose a student wants

College Choice

to be an engineer. The question of interest is, does it matter in terms of, say, future earnings whether the engineering student attends a top-ranked college or a less selective college, or is it simply being an engineer that matters? If, on average, engineers from middle- or bottom-ranked colleges earn about the same as engineers from top-ranked colleges, then the student may be better off choosing to attend the less selective, and less expensive, college. Only a few studies have examined the interaction of college selectivity and college major. One study found that earnings differences associated with college major are larger than premiums associated with college selectivity, while another found significant wage growth for college selectivity controlling for college major and significant variation in wage growth for some, but not all, college majors, accounting for college selectivity. Persistence to Graduation

Much attention is focused on providing access to college but considerably less attention is devoted to college completion. College attendance rates have been rising in recent decades; however, college completion rates have not risen accordingly. The average 6-year graduation rate for bachelor’s degrees in the United States is about 55%, with considerable heterogeneity around this mean. Massachusetts, Rhode Island, Connecticut, and Pennsylvania have graduation rates between 65% and 70%, while Louisiana, New Mexico, and Nevada have rates between 35% and 40%. Alaska’s graduation rate is about 27%. Economic research has not provided a definitive answer as to why college graduation rates are not higher. Lower aptitude among marginal college enrollees, relatively more resources focused on less expensive 2-year colleges, and high school graduates who are less prepared for 4-year college are among the possible reasons. Colleges themselves may bear part of the blame by not providing adequate incentive to students to finish in a reasonable time period. Pell grants and similar financial aid are designed to help students complete college; however, the research on the effects of Pell grants on college completion is mixed. Some studies find that Pell grants do not facilitate college graduation as they are designed to do, and in fact, some suggest that they increase dropout rates. Other findings based on better data and more sophisticated econometric methodology find that Pell grants do accomplish their objective of promoting college completion.

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Graduate School Attendance On completion of an undergraduate degree, students are again faced with a college attendance decision. Should I enter the labor market or attend graduate school? If graduate school, where and when and in what field? What the student studied and where he or she attended as an undergraduate will greatly influence this decision. Attending a highly selective college increases the probability of attending graduate school and, more specifically, increases the likelihood of attending graduate school at a major research institution. Graduates from selective colleges are also more likely to finish their graduate degree within 4 to 5 years.

Conclusion College choice involves a multifaceted and complex set of decisions. These choices have long-term impact on a student’s education and labor market outcomes. A large body of research shows that college is a sound investment for most young people and that the return on investment is influenced by factors such as the type of college attended and the major studied. The important issues embodied in college choice, together with access to better data and evolving econometric techniques, suggest that researchers will have ample opportunity to pursue answers to these crucial research questions in the future. The answers that result from continued and improved research will hopefully provide students and policymakers with the information to make well-informed postsecondary decisions. Eric R. Eide See also College Completion; College Rankings; College Selectivity; Human Capital; Pell Grants

Further Readings Arcidiacono, P. (2004). Ability sorting and the returns to college major. Journal of Econometrics, 121(1–2), 343–375. Bettinger, E. (2004). How financial aid affects persistence. In C. M. Hoxby (Ed.), College choices: The economics of where to go, when to go, and how to pay for it (pp. 207–237). Chicago, IL: University of Chicago Press. Brewer, D. J., Eide, E. R., & Ehrenberg, R. G. (1999). Does it pay to attend an elite private college? Evidence on the effects of college type on earnings. Journal of Human Resources, 34(1), 104–123.

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Dale, S. B., & Krueger, A. B. (2002). Estimating the payoff to attending a more selective college: An application of selection on observables and unobservables. Quarterly Journal of Economics, 177(4), 1491–1527. Eide, E. R., Brewer, D. J., & Ehrenberg, R. G. (1998). Does it pay to attend an elite private college? Evidence on the effect of undergraduate college quality on graduate school attendance. Economics of Education Review, 17(4), 371–376. Eide, E. R., & Waehrer, G. (1998). The role of the option value of college attendance in college major choice. Economics of Education Review, 17(1), 73–82. Grogger, J., & Eide, E. R. (1995). Changes in college skills and the rise in the college wage premium. Journal of Human Resources, 30(2), 280–310. Hilmer, M. J. (1997). Does community college attendance provide a strategic path to a higher quality education? Economics of Education Review, 16(1), 59–68. Hoekstra, M. (2009). The effect of attending the flagship state university on earnings: A discontinuity-based approach. Review of Economics and Statistics, 91(4), 717–724. Hoxby, C. M. (2009). The changing selectivity of American colleges. Journal of Economic Perspectives, 23(4), 95–118. Manski, C. F., & Wise, D. A. (1983). College choice in America. Cambridge, MA: Harvard University Press. Snyder, T. D., & Dillow, S. A. (2012). Digest of education statistics 2011 (NCES 2012-001). Washington, DC: U.S. Department of Education, National Center for Education Statistics, Institute of Education Sciences. Turner, S. (2004). Going to college and finishing college: Explaining different educational outcomes. In C. M. Hoxby (Ed.), College choices: The economics of where to go, when to go, and how to pay for it (pp. 13–61). Chicago, IL: University of Chicago Press. Zhang, L. (2005). Advance to graduate education: The effect of college quality and undergraduate majors. Review of Higher Education, 28(3), 313–338.

COLLEGE COMPLETION College completion refers to the number of students who enroll in college and obtain either a 2-year or a 4-year degree. Over the past few decades in the United States, college completions have not kept up with college enrollments. This fact is troubling in that the returns to obtaining some college education are relatively low and that individuals recover most of the benefits of college only after obtaining a degree. This entry will provide an overview of recent trends in completion rates, issues with measurement

of completion, factors that determine college completion rates, and some of the possible reasons why individuals may fail to complete college. According to the Chronicle of Higher Education, 4.3 million students started college in the fall of 2004, and of them, approximately 1 million obtained degrees by 2010. The degrees were awarded by public universities (~48%), community colleges (~12%), private nonprofit colleges (~29%), and private for-profit colleges (~12%). Of the remaining students who did not obtain degrees, 1.2 million students were not counted, possibly because they enrolled part-time, and 2.1 million students did not graduate from the institution that they first attended and therefore were not counted as graduates. These numbers highlight some of the issues involved in the measurement of college completion. For example, college completion rates are often calculated using only traditional students who enroll in college fulltime and those students who graduate from the institution they initially enrolled in, without taking into account that some students transfer and graduate from other institutions. An analysis of recent trends in completion rates requires a method for measuring college completion. One method used by researchers is to use data on educational attainment and college attendance for a birth cohort. The completion rate is then equal to the number of individuals in the birth cohort obtaining a degree divided by those who have attended some college. This method does truncate the data for younger cohorts, as they may still choose to pursue and complete a degree in later years. Another method commonly used by 4-year colleges and universities in the United States is the 6-year completion rate. A university’s 6-year completion rate is the number of students who obtained a 4-year degree within 6 years divided by the number of students who entered the university 6 years prior. The current statistics show that rising college enrollment rates over time have not been followed by a proportional increase in college completion rates. There are many factors that affect an individual’s likelihood of completing a college education, including (a) student attributes such as academic preparation, demographic characteristics, family background, and financial resources and (b) institutional attributes such as college quality as measured by the enrolled students’ academic ability, resources per student (e.g., expenditures per students, studentfaculty ratios), academic support, and other nonacademic factors.

College Completion

On the student side, research suggests that family background, including family income and parents’ education, plays a crucial role in students’ academic success. As a result, low-income and first-generation students have lower college completion rates relative to high-income students and individuals with parents who have a college education. Large differences also exist in college completion rates by racial and ethnic groups as well as by gender. Martha Bailey and Susan Dynarski show the current trends in college enrollment and college completion by family income level. The statistics show that individuals from higher family incomes are more likely to enroll in college and complete college. For these students, college cost is usually not a barrier. Thus, individuals with more family income may have more time to focus on their studies because they may not need to work during college in order to pay tuition. The higher family income also provides the students with more adequate academic preparation and social and cultural capital to succeed in college. The reasons why students do not obtain a degree are numerous and include credit constraints, uncertainty about job prospects, imperfect information about college options, and underpreparation of students. Individuals with low levels of family income may be credit constrained and thus may be unable to finance the costs of college. Students face uncertainty about their job prospects, and this may cause students to question their investment in education and, thus, to drop out of college. Individuals with low levels of family income often lack information about their college options and thus may end up enrolling in less selective institutions than what they qualify for based on their academic ability. This is problematic as students of all academic ability levels have a higher probability of completing a degree if the selectivity level of the college they attend matches their measured academic skill level. Furthermore, the quality and intensity of academic experience prior to entering college are important determinants of college success. Thus, students with limited opportunities to engage in rigorous coursework during high school are less prepared and, as a result, are often placed in remedial courses leading to worse college outcomes relative to their peers who are exposed to the advanced coursework early on. Institutional characteristics also determine a student’s college success. Those students who choose to enroll in more selective colleges graduate at a higher rate relative to students who attend less selective postsecondary institutions. Academic requirements

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to enter selective colleges are more rigorous when compared with requirements at the less selective institutions. Thus, selective colleges enroll candidates who are better prepared academically than those enrolled by less selective institutions, resulting in higher college completion rates at these colleges. Students who attend more selective colleges also benefit from interactions with their peers and from a stronger academic environment. Furthermore, selective postsecondary institutions have greater financial resources and are thus able to offer students enriched academic and nonacademic experiences along with generous financial aid packages. Both of these factors have been linked to academic success. John Bound, Michael Lovenheim, and Sarah Turner explore several potential hypotheses for observed stagnant college completion rates over the past few decades. One of the common explanations for flat completion rates includes changes in the composition of the student body over time with a larger share of less academically prepared students being induced to attend postsecondary education relative to earlier cohorts. The increase in the proportion of high school graduates who attend college has resulted in shifts in the types of colleges students attend. The decrease in college completion rates has been concentrated among students attending openaccess or less selective public colleges and universities and community colleges. On the institutional side, reduction in institutional resources per student has also negatively affected college completion rates. Lisa M. Dickson and Matea Pender See also Benefits of Higher Education; College Dropout; College Enrollment; College Selectivity; Cultural Capital

Further Readings Bailey, M. J., & Dynarski, S. M. (2011). Inequality in postsecondary education. In G. J. Duncan & R. J. Murnane (Eds.), Whither opportunity? Rising inequality, schools, and children’s life chances (pp. 117–132). New York, NY: Russell Sage Foundation. Bound, J., Lovenheim, M. F., & Turner, S. (2010). Why have college completion rates declined? An analysis of changing student preparation and collegiate resources. American Economic Journal: Applied Economic, 2(3), 129–157. Chronicle of Higher Education. (2013). College completion: Who graduates from college, who doesn’t and why it matters. Retrieved from http://collegecompletion .chronicle.com/

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COLLEGE COSTS See Tuition and Fees, Higher Education

COLLEGE DROPOUT College dropout has emerged as a central issue in American higher education, becoming a key focus of the research community as well as that of postsecondary practitioners and education policymakers at the local, state, and federal levels. Current college dropout rates have lasting effects on the dropouts themselves, the economy, and the future of state and federal financial assistance policy for higher education. This entry discusses these effects and the influences in whether students complete college, including the level of commitment to their goals and the institution, student characteristics, financial aid, and working while in college. It ends with a discussion of current ideas to reduce the college dropout rate.

Dropout Rates Current figures from the U.S. Department of Education show that while college enrollment has swelled by 38% over the past decade to exceed 20 million, only 58% of students beginning at a 4-year institution complete a bachelor’s degree within 6 years. Furthermore, this figure drops to 38% when looking at on-time, 4-year degree completion and is again reduced to 28% and 20% for 4-year degree completion rates for Hispanic and Black students, respectively. Additionally, when examining certificate or associate degree completion rates for students beginning at a community college—a segment of American higher education that has expanded greatly over the past decade—more than 70% of students fail to complete the credential. According to the U.S. Department of Labor, 34 million Americans of age 25 years and older have some college credits but no degree—a figure that has grown by nearly 700,000 people over the past 3 years. From an international perspective, the United States has been the envy of the rest of the world for more than half a century as a leading country in the percentage of adults with postsecondary credentials; however, this situation has changed in the past decade. Statistics from the Organisation for Economic Co-operation and Development show that the lead once enjoyed by the United States has disappeared.

In fact, in 2011, the United States was ranked 12th among 37 Organisation for Economic Co-operation and Development and partner countries in the percentage of 25- to 34-year-olds who have attained a tertiary education. Furthermore, the United States was once second, behind New Zealand, in terms of college completion rates; however, in 2010, the United States ranked 13th among 25 countries with comparable data. In terms of both national trends and international comparisons, college dropout in the United States is a pressing issue.

Financial Impact Failing to complete a bachelor’s degree can have lasting impacts on students as well as on the U.S. economy. According to the U.S. Census Bureau, college graduates make, on average, nearly $1 million more than high school graduates over the course of their lifetime. For students who begin college, but do not complete a degree, this differential is made greater by the foregone earnings during the years in which the student was enrolled as well as the student loan debt acquired. The low U.S. college completion rates are particularly alarming given that student debt in the United States now exceeds $1 trillion, according to the Consumer Financial Protection Bureau. In addition, the cost of dropping out of college has soared to new heights in the past several years, with some estimates indicating that college dropouts cost nearly $4.5 billion in terms of lost wages as well as the related lost federal and state income tax revenue every year.

Factors in College Completion or Dropout Commitment to Goals and Institution

Why do students drop out of college? Historically, the study of student departure from college has its roots in models pioneered by Vincent Tinto in the 1970s that focused on the interplay between an individual’s commitment to his or her academic goals and the individual’s commitment to his or her current institution. Students with high levels of goal and institutional commitment will persist in higher education to earn a degree. Influencing these commitments are factors such as prior qualifications, individual attributes, family attributes, academic integration, and social integration, as well as debt and access to resources. Successful students are thought to develop both strong social and academic integration in order to remain in higher education through to graduation.

College Dropout

Student Characteristics

Studies have shown that the decision to drop out of college is largely influenced by characteristics such as race, sex, economic capacity, and academic preparation. Indeed, some estimates indicate that nearly half of the likelihood that a student will graduate from postsecondary education can be predicted by these characteristics, with economic capacity of the family being a strong indicator for degree completion. In addition, a recent report from the Harvard Graduate School of Education shows that among students who dropped out of college, 66% dropped out to support a family, 57% dropped out to work and earn money, and 48% dropped out because they could not afford college. In light of these statistics and with the ever-growing importance of earning a bachelor’s degree, much attention has been paid to the role of financial aid in the decision to leave college. Financial Aid

Over time, the burden of paying for college in the United States has shifted dramatically to the shoulders of students and their families as public support for higher education has diminished. Tuition levels at both public and private institutions have increased steadily over time; however, financial aid has not experienced the same rate of growth and has experienced a shift with loans becoming a much larger percentage of overall financial aid packages. Given the importance of a bachelor’s degree, many students make great effort to enroll in higher education despite these obstacles. Financial aid, therefore, has become a significant focus as a mechanism by which students may continue to stay actively enrolled and persist in degree completion. Merit-based aid—that is, financial aid given to students based on academic ability often in the form of grants and often without regard to financial need—has become a popular mechanism by which state governments, in particular, have focused efforts to increase college enrollment and decrease college dropout rates. Indeed, at least 14 states have used various merit-aid programs, starting with the Georgia HOPE program (Helping Outstanding Pupils Educationally), and several studies have shown that merit aid is associated with reducing the likelihood of college dropout. This relationship, however, is confounded by the nature of the students who typically receive this sort of aid and how these qualities also affect college dropout. Put differently,

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students receiving merit-based aid tend to be those who finish high school with impressive qualifications and, as such, are also less likely to drop out of postsecondary education, thus making it difficult to make a direct connection between merit-based aid and decreasing college dropout. Despite this difficulty, economists have developed tools by which it can become possible to identify effects between financial aid and college dropout, with a number of reports indicating a positive relationship between merit aid and lowering the likelihood of college dropout, particularly for state merit aid. At the same time, however, there is considerable concern around the unintended consequences of merit aid and the negative effects it may have on college access and dropout, particularly for low-income students. Need-based aid—that is, financial aid determined largely on the basis of financial need (as opposed to strictly on academic merit) and often in the form of loans—has received much attention, often with conflicting findings, with respect to its relationship to reducing college dropout for reasons not unlike those outlined for merit-based aid. In the form of need-based grants, the best known program is the federal Pell grant, which has provided grant-based assistance to low-income college students for many years. The amount of the Pell grant, however, has not risen at the same rate as college tuition, and as such, many students also rely on need-based loans. Many studies have documented the negative relationship between increased loans and college dropout. While this relationship is well documented, it remains unclear how to best manage economic resources at the state and federal levels in order to decrease college dropout. Working While Enrolled in College

Another economic factor influencing college dropout is that of working while concurrently enrolled. While students who qualify for needbased financial aid often make use of the federal work-study program—whereby students work on campus and receive a salary subsidized by the federal government—many students have turned to additional employment opportunities to meet growing individual and family financial needs. Often, these positions tend to have low hourly wages, thus often requiring students to devote substantial time to working, often at the expense of hours focused on academic coursework. While some students may benefit from working while enrolled, particularly

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those who gain valuable skills and experience for their future occupation, others find an increase in working hours to be a deterrent both to the number and types of academic courses taken in a given semester and the amount of time available to devote to academic endeavors—both factors that greatly increase the risk of dropping out.

Emerging Issues For many years, the onus of reducing college dropout was often placed on the postsecondary institution and the financial aid policy related to it. Still others have suggested that college dropout could be dramatically reduced—and in a cost-effective manner—by placing a stronger emphasis on better preparing students in secondary schools as well as exposing students to college enrollment and financial aid options long before the senior year in high school. By some reports, high-performing, lowincome students could benefit from early intervention in the college selection process that could better match them to a college or university in terms of academic ability and financial assistance, thereby reducing the likelihood of college dropout and its associated cost. Some estimates suggest that early interventions such as these come at a cost of only $6 per student and could greatly increase college access and decrease college dropout, particularly for low-income students. A series of ongoing studies has also examined the effect of better facilitating access to financial aid, particularly for incoming students but also for continuing students. Some estimates indicate that 40% of undergraduate students do not apply for needbased federal financial aid and that of these students nearly half (47%) would qualify for a Pell grant. One possible solution that has received considerable attention is reducing the complexity while also easing the application process for federal aid via the FAFSA (Free Application for Federal Student Aid). As many sections of the form are based on tax information, studies have explored the extent to which having these sections precompleted either by data linked from the Internal Revenue Service or compiled by a tax preparation professional may increase student enrollment and decrease college dropout by providing students with a clearer picture of the federal financial aid for which they may qualify. Another emerging issue surrounding college dropout is that of linking federal support for higher education to the performance of postsecondary institutions, with a major factor being degree completion

rates. Proponents of this initiative argue that the federal government is failing to serve college students by permitting federal aid to flow freely to those institutions that are not adequately performing in terms of graduation rates and other metrics. Skeptics of such a system worry about the unintended consequences of a federal college ranking system based on performance metrics, including a decrease in college access and an increase in college dropout, particularly for the nation’s most vulnerable students. Though the future of these, and other, proposed shifts to the distribution of federal financial aid is uncertain, it remains clear that college dropout and its associated economic impact will continue to be a key focus of higher education policy in the years to come. Toby J Park See also College Choice; College Completion; College Enrollment; Higher Education Finance; Lotteries for School Funding

Further Readings Braxton, J. M. (2000). Reworking the student departure puzzle. Nashville, TN: Vanderbilt University Press. Harvard Graduate School of Education. (2011). Pathways to Prosperity project. Cambridge, MA: Author. Retrieved from http://www.gse.harvard.edu/news_ events/features/2011/Pathways_to_Prosperity_Feb2011 .pdf Hoxby, C., & Turner, S. (2013). Expanding college opportunities for high-achieving, low income students (SIEPR Discussion Paper No. 12-014). Retrieved from http://siepr.stanford.edu/publicationsprofile/2555 Schneider, M., & Yin, L. (2011). The high cost of low graduation rates: How much does dropping out of college really cost? Washington, DC: American Institutes for Research. Retrieved from http://www.air.org/files/ AIR_High_Cost_of_Low_Graduation_Aug2011.pdf Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89–125.

COLLEGE ENROLLMENT College enrollment is defined as the number of individuals currently pursuing higher education. Higher education in the United States consists of technical colleges, community colleges, and 4-year colleges and universities. The number and characteristics of

College Enrollment

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100% 15% 80%

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29%

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Figure 1

18–24

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Age Distribution of College Enrollees

Source: Lisa M. Dickson, from data in Digest of Education Statistics (2012) published by the National Center for Education Statistics, U.S. Department of Education, Table 224. Retrieved from http://nces.ed.gov/programs/digest/d12/tables/dt12_224.asp.

students enrolled in college vary over time and by type of higher education institution. This entry provides an overview of current trends in enrollment, the factors that affect whether an individual enrolls in any college, and the factors that affect whether an individual enrolls in a specific college.

Current Trends in Enrollment Rates Traditional college students are high school completers who are between the ages of 18 and 24. Nontraditional college students are typically older students who may or may not have completed high school. Figure 1 shows the age distribution of students enrolled in higher education and how it has changed between 1970 and 2010. While the majority of students enrolled are between the ages of 18 and 24, the fraction of enrollees who are more than 30 years old is nontrivial. Between 1970 and 2010, the share of nontraditional students who are more than 30 years old grew by 12 percentage points, from 15% to 27%. During the same time period, there was an offsetting decrease of 12 percentage points in the share of traditional college students, from 69% to 57%. Colleges and universities vary substantially across the United States in terms of their mission, size, curriculum, faculty, and the characteristics of their student body. A college is defined as an institution that focuses primarily on undergraduate education and offers either 2- or 4-year degrees. A community college is typically a 2-year institution that offers an associate degree as well as other technical certificates. Four-year institutions may be either colleges or universities. The distinction between a university

and a 4-year college is that a university offers both undergraduate and graduate degrees. While colleges and universities refer to different types of institutions, many researchers, policymakers, and the popular press use the word college to encompass any type of higher education institution. The type of higher education institution the student chooses to attend has important implications for earnings as well as for educational attainment. Two-year institutions serve several functions, including preparing students to transfer to 4-year colleges as well as providing some students with vocational training. Thomas Kane and Cecilia Rouse give a historical overview of community colleges in the United States and discuss the effects of attending a community college on earnings. While many students may intend to go on to a 4-year college after attending a 2-year college, few succeed in transferring and obtaining their 4-year degree, as shown by Kane and Rouse. Over the past few decades, the fraction of traditional college students enrolling in higher education increased with three quarters of the increase occurring at 4-year institutions. Figure 2 displays the fraction of 18- to 24-year-old high school completers who enroll in higher education by type of higher education institution. Between 1975 and 2010, the percentage of high school completers between the ages of 18 and 24 enrolled at 2-year institutions grew by 4 percentage points, from 11% to 15%. In comparison, over the same time period, the percentage of high school completers between the ages of 18 and 24 enrolled at 4-year institutions grew by 12 percentage points, from approximately 21% to 33%.

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40% 35% 30% 25% 20% 15% 10% 5% 0% 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2 year

Figure 2

4 year

Enrollment Rates for Traditional Students by Type of Institution

Source: Lisa M. Dickson, from data in Digest of Education Statistics (2011) published by the National Center for Education Statistics, U.S. Department of Education, Table 213. Retrieved from http://nces.ed.gov/programs/digest/d11/tables/dt11_213.asp. Note: Traditional students are 18- to 24-year-old high school completers.

The vast majority of students in the United States are enrolled in public higher education institutions. According to the National Center for Education Statistics, approximately 21 million students were enrolled in higher education in 2010, and approximately 15 million of these students were enrolled in public higher education institutions. For the private institutions, total enrollment was 6 million with approximately one third of those students enrolling in for-profit private institutions. Claudia Goldin and Lawrence Katz provide a history of higher education in the United States and review some of the historical reasons for differences between public and private institutions. College enrollment rates vary by gender and by race and ethnicity. Since 1978, more women have enrolled in higher education than did men. Goldin, Katz, and Ilyana Kuziemko review historical trends in enrollment rates by gender and discuss some explanations for the recent rise in female enrollment rates. The large differences in enrollment rates by gender are evidenced in every demographic group. Individuals of American Indian heritage historically maintain the lowest educational attainment though they are often not included in national statistics.

Black and Hispanic students both maintain lower college enrollment rates than White students. Part of the differences in college enrollment rates can be explained by race and ethnic differences in high school completion and by differences in family income. Since a college education is often seen as a means to help improve a person’s financial well-being, it is troubling that few students from families with low family income enroll in college. Martha Bailey and Susan Dynarski analyze the college enrollment rates of recent cohorts of students by income and by gender. They find that inequality in college enrollment increased more for females than for males. Individuals from families with less family income are also less likely to enroll in selective universities. Family income also affects whether students complete college.

What Determines Whether an Individual Enrolls in Any College? Gary Becker’s work on human capital provides a model for how individuals choose whether to invest in education. According to the model, individuals

College Enrollment

should invest in a college education if the present discounted value of earnings minus the costs from higher education is higher than the present discounted value of earnings from not pursuing higher education. Becker’s work starts with individuals weighing the relative monetary benefits from investing in education, but as he notes, it can easily be adapted to individuals weighing the relative utility levels from varying education levels. As education is an investment, the earlier the investment is incurred, the longer the person has to recoup the investment. This reasoning provides one explanation for why one would expect enrollment rates to be higher for younger people than for older people. Individuals may choose different levels of education for several reasons, including (but not limited to) differences in earnings levels, ability to finance the costs of higher education, and how individuals determine the present value of future earnings. When deciding whether to invest in college, individuals base their decision on their perceived benefits and costs. The returns to a college degree may vary across individuals due to differences in ability and talent. Similarly, due to the differences in talent, individual costs of an education may vary as colleges may choose to recruit students of high ability and offer scholarships. It is important to note that individual behavior as to whether to invest in education depends on the individual’s perceptions of the returns and costs of an education. Student perceptions of the benefits of a college degree may be inaccurate due to uncertainty as well as imperfect or incomplete information about the labor market. Individuals from families with a lower socioeconomic status may have less information with regard to the returns to a college degree than individuals from families with a higher socioeconomic status. Since socioeconomic status does affect location as well as occupation, individuals from lower socioeconomic statuses may encounter fewer people with college degrees and thus may have less information with regard to the returns to a college degree. Students from lower socioeconomic statuses may also have different perceptions of the costs of a college degree. Imperfect or incomplete information about either the returns or the costs of a college degree may explain some of the differences in enrollment rates by family income. Students from lower socioeconomic statuses may believe that they will be fully responsible for the listed tuition and fees at a college or university. However, most students do not have to pay the listed tuition due to financial aid, tax credits, and

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tax deductions. To improve college affordability for families with lower socioeconomic statuses, the state and federal governments offer financial aid to these students. Yet many students are unaware that they may qualify for financial aid and often require help to file the appropriate paperwork to apply for financial aid. Eric P. Bettinger, Bridget Terry Long, Philip Oreopoulos, and Lisa Sanbonmatsu document through random assignment that assisting students to file applications significantly affects their enrollment in college. Dynarski and Judith Scott-Clayton point out the lack of transparency in the federal financial aid system and provide recommendations for how best to improve its effectiveness. A large literature exists on the effects of specific types of financial aid offered to students and how successful these offers of financial aid are at increasing the enrollment rates of students in higher education institutions. One of the difficulties inherent in measuring the effects of financial aid on enrollment is that the amount of financial aid offered to a student is often correlated with individual characteristics. Stephanie Cellini provides a review of the different methods researchers can use to measure the causal effect of financial aid on enrollment. Merit aid is an example of a type of financial aid that is awarded based on student characteristics. Individuals who achieve a certain level of academic success may be awarded financial aid. Several policymakers and researchers have expressed the concern that by offering aid to students based on their achievements rather than need, merit aid may be diverting funds away from students who require need-based financial aid to attend college. An example of a statewide merit scholarship offered to students is the Helping Outstanding Pupils Educationally (HOPE) program in Georgia. Christopher Cornwell, David Mustard, and Deepa Sridhar demonstrate that the program did affect enrollment in higher education in Georgia. Since the implementation of the HOPE program in Georgia, several other states have developed similar meritaid programs. In addition to statewide practices of merit aid, individual institutions may also offer their own merit aid as a means of recruiting high-ability students.

What Determines Whether an Individual Enrolls in a Specific College? Enrollment in a specific college is determined by a two-sided matching process. Individuals choose

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which colleges to apply to, and from those applicants, colleges must choose whom to accept. From the candidates that the college accepts, the college can then determine whether it desires to offer financial aid to those students. Finally, from the offers of acceptance and offers of financial aid, students choose whether to enroll in college and where to enroll. For many colleges and universities, students have the option to apply either early decision or early action. Early decision is typically when a college or university decides early in the admission process whether a student will be admitted, and if he or she is admitted, then the student is committed to attending that specific institution. Early action typically occurs when a college or university decides early in the admissions process whether the candidate is eligible for admission, but the student is not bound to attend that specific institution. Both early action and early decision may disadvantage students from lower socioeconomic statuses. The disadvantage occurs because individuals who are willing to make the decision to enroll at a specific college without knowing their offer of financial aid are likely to require less financial aid than students who will not make a decision until they receive offers of financial aid. Awarding admission spots early to those students who can afford to come may limit the number of available spots for students with financial need. When deciding whether to enroll in a specific college, students may consider many attributes of the college. One of the factors may be the costs of the college not only in terms of the listed tuition and fees but also in terms of the psychological costs of moving away from home. Several studies in the economics of education literature use distance to the nearest community college as an instrument for whether an individual will enroll in college. The idea is that the closer in proximity one is to a higher education institution, the more likely the individual is to pursue higher education. The decision of whether to enroll in a specific college is also related to the individual’s perceived benefit from enrolling in a specific institution. College selectivity is often cited as a potential determinant of whether an individual enrolls at a specific college. If students perceive that there will be earnings differences between colleges of greater selectivity and those that are less selective, then this may affect their probability of enrolling. A more selective university may offer a challenging academic

environment, including prestigious faculty and capable peers, which could subsequently increase student learning and lead to higher earnings after graduation. In addition, students who attend more selective undergraduate institutions may also have better access to more prestigious graduate institutions. Since the return to a college education may differ according to where individuals choose to attend college, there is concern from researchers, policymakers, and college administrators about “undermatching.” Undermatching occurs when an individual chooses to enroll in a college that may be below what his or her academic credentials would suggest. Jonathan Smith, Matea Pender, and Jessica Howell demonstrate a significant amount of undermatching in the United States. While the overall undermatch has decreased over time, students who come from families with lower socioeconomic status continue to undermatch at a disproportionately higher rate relative to students who come from families with higher socioeconomic status. Researchers hypothesize that lack of information about colleges, from parents and high schools, is a major source of undermatching. Thus, improving access to and quality of information for socioeconomically disadvantaged students has garnered a lot of attention among researchers and policymakers. Through a small-scale intervention, Caroline Hoxby and Sarah Turner demonstrate that improvements in access to basic information about college options for high-achieving, low-income students can increase their chances of selecting colleges that match their academic qualifications. In addition to recent focus on better understanding the causes and consequences of undermatching, there is also a concern about overmatching, including affirmative action policies that may lead less qualified individuals to attend more selective institutions. Affirmative action policies are typically described as policies that grant colleges and universities the ability to use race or other student demographic characteristics to affect the probability of admission. William G. Bowen and Derek Bok provide an analysis of the use of race in college admissions. The use of race in college admissions was challenged in several court cases in the past few decades. Today, colleges and universities may consider race or ethnicity as a factor in college admissions. The evidence suggests that the use of race in college admissions affects whether students apply to college, where they apply to college, and where they enroll.

College Enrollment

Individuals when deciding where to apply to college may consider other attributes of the college besides location, cost, and selectivity. For instance, students may also consider whether the peers at the institution will be similar in background to them or they may focus on the types of amenities available at the college. Examples of amenities available in college may be movie theaters, athletic teams, or student recreational centers. These types of amenities do not affect the academic rigor of the institution and are present to make the college experience more enjoyable for students. Brian Jacob, Brian McCall, and Kevin Stange analyze whether students demand these types of amenities using data from colleges on expenditures. They find that students value amenities and that some universities may want to alter their investment strategy so as to recruit more students to their institution.

Conclusion Current projections show that total college enrollment will grow over the next decade although at a slower rate relative to the previous decade. According to the National Center for Education Statistics, total enrollment in postsecondary institutions is expected to increase by 15% (from 21 million in 2010 to 24.1 million in 2021). This is in comparison with a 46% increase between 1996 and 2010 (from 14.3 to 21 million). Enrollment is expected to grow in both public and private sectors with a disproportionate growth in the number of nontraditional and underrepresented minority students enrolling in college over the next decade. Because returns to a college degree have increased over time, college education remains an important avenue of social and economic mobility. The projected growth in college enrollment and changing demographics of the student body over the next decade suggest that access to college, college choice, and equity in educational opportunity will continue to be important policy issues. Lisa M. Dickson and Matea Pender See also Benefits of Higher Education; College Completion; Cost of Education; Educational Equity; Higher Education Finance

Further Readings Bailey, M., & Dynarski, S. (2011). Inequality in postsecondary education. In G. J. Duncan & R. J. Murnane (Eds.), Whither opportunity? Rising

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inequality, schools, and children’s life chances (pp. 116–131). New York, NY: Russell Sage Foundation. Becker, G. (1964). Human capital: A theoretical and empirical analysis with special reference to education. Chicago, IL: University of Chicago Press. Bettinger, E. P., Long, B. T., Oreopoulos, P., & Sanbonmatsu, L. (2010). The role of simplification and information in college decisions: Results from the H&R Block FAFSA experiment (NBER Working Paper No. 15361) [Online]. Retrieved from http://www.nber.org/ papers/w15361 Bowen, W. G., & Bok, D. (2000). The shape of the river. Princeton, NJ: Princeton University Press. Cellini, S. (2008). Causal inference and omitted variable bias in financial aid research: Assessing solutions. Review of Higher Education, 31(3), 329–354. Cornwell, C., Mustard, D. B., & Sridhar, D. J. (2006). The enrollment effects of merit-based financial aid: Evidence from Georgia’s HOPE program. Journal of Labor Economics, 24(4), 761–786. Dynarski, S., & Scott-Clayton, J. (2006). The cost of complexity in federal student aid: Lessons from optimal tax theory and behavioral economics. National Tax Journal, 59(2), 319–356. Goldin, C., & Katz, L. F. (1999). The shaping of higher education: The formative years in the United States 1890 to 1940. Journal of Economic Perspectives, 13(1), 37–62. Goldin, C., Katz, L. F., & Kuziemko, I. (2006). The homecoming of American college women: The reversal of the college gender gap. Journal of Economic Perspectives, 20(4), 133–156. Hoxby, C., & Turner, S. (2013). Expanding college opportunities for high-achieving, low income students (SIEPR Discussion Paper No. 12-014). Stanford, CA: Stanford Institute for Economic Policy Research [Online]. Retrieved from http://siepr.stanford. edu/?q=/system/files/shared/pubs/papers/12-014paper .pdf Hussar, W. J., & Bailey, T. M. (2013). Projections of education statistics to 2021 (NCES 2013-008). Washington, DC: Government Printing Office. Jacob, B., & McCall, B., & Stange, K. (2013). College as country club: Do colleges cater to students’ preferences for consumption (NBER Working Paper No. 18745) [Online]. Retrieved from http://www.nber.org/papers/ w18745 Kane, T., & Rouse, C. E. (1999). The community college: Educating students at the margin between college and work. Journal of Economic Perspectives, 13(1), 63–84. Smith, J., Pender, M., & Howell, J. (2013). The full extent of student-college academic undermatch. Economics of Education Review, 32, 247–261.

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College Rankings

COLLEGE RANKINGS College rankings is a term generally used to encompass efforts to rank order individual academic programs, professional schools, and entire institutions of higher education on the basis of academic quality or productivity. Rankings of undergraduate education generally cover entire institutions, while graduate and professional education rankings focus on schools or individual programs. Organizations that conduct rankings include news periodicals, such as U.S. News & World Report and Bloomberg Businessweek; academic organizations, such as the National Research Council and the Center for World-Class Universities at Shanghai Jiaotong University; and individual academic researchers. Accrediting bodies and government agencies historically do not rank institutions or programs. This entry provides an overview of the methodologies employed by ranking organizations, the audiences they seek to reach, a brief history that illustrates how these methodologies and audiences have evolved over time, and a summary of contemporary criticisms.

Underlying Methodology and Target Audiences Although they vary considerably in terms of specific methodology, rankings are usually based on some combination of reputation, resource, and outcome data. Reputation data are frequently derived through the polling of putative experts on the organizations under consideration; those polled may include university presidents, school deans, department chairs, or heads of organizations that hire graduates from the school or program under review. Resource data may include admissions selectivity measures (e.g., grade point averages and standardized test scores), operating budget, endowments, the quality and extent of facilities, grant funding levels, and faculty qualifications. Resource data are usually provided by the schools included in the survey, or obtained through publicly available sources, such as the National Center for Education Statistics Integrated Postsecondary Education Data System database. Outcome data include jobs attained by graduates and research productivity as measured by publications and citation references. Outcome data are typically provided by schools and through surveys of students and graduates.

Rankings also vary in terms of target audience. Those featured in news periodicals most often are intended to provide information for prospective college and university students among the publications’ readers. Rankings sponsored by academic organizations generally target higher education stakeholders, such as faculty, administrators, regulatory bodies, and funding agencies. The ranked institutions themselves are often keenly attuned to their positions within the rankings, which are used as part of their own marketing to prospective students and faculty. In addition, rankings can be used as a source of benchmarking data for comparisons with peers or competitors, which in turn may be used in resource allocation decisions or to provide incentives to academic programs.

The History of Rankings The antecedents of current rankings extend back to the early 20th century, although most were limited in scope and impact until the early 1980s. At that time, two developments increased rankings’ visibility and impact. First, greater calls for accountability within research-focused institutions of higher education created a need for credible ways to benchmark faculty productivity. At roughly the same time, news periodicals seized on the commercial potential of rankings oriented toward the interests of prospective students. Prior to the 1980s, the Gourman Report, which ranked more than 100 institutions and more than 1,200 academic departments worldwide, was virtually the only widely available published ranking. Although cited frequently in academic journals through the 1990s, the Gourman Report was nevertheless criticized heavily for providing minimal explanation of its methodology and treated with skepticism regarding its data collection. In 1982, the National Research Council published the Assessment of Research Doctorates, a reputational study that ranked research doctoral programs in arts and sciences; disclosed its methodology; and was received more credibly. Subsequent iterations of Assessment of Research Doctorates conducted in 1995 and 2010 added resource and outcome data, and it was more widely accepted as an authoritative source. The two most widely circulated rankings that appear in periodicals were also established in the 1980s. In 1982, Business Week (now Bloomberg Businessweek) began publishing a ranking of master

College Rankings

of business administration (MBA) programs, while in 1985, U.S. News & World Report became the first globally circulated periodical to rank academic institutions, covering undergraduate programs and law, medicine, and management graduate programs. Both magazines’ rankings began as reputational surveys but added resource and outcome data in subsequent iterations. Other periodical-based rankings have differentiated themselves in terms of methodology, especially those that ranked MBA programs. The Wall Street Journal, for example, ranked programs by surveying campus recruiters about student attributes. In contrast, Forbes calculates return on investment by relating program costs to postdegree earnings. More recently, global rankings have emerged, most notably the Academic Ranking of World Universities associated with Shanghai Jiaotong University, which was originally developed to benchmark Chinese universities against international counterparts and focuses almost exclusively on research productivity, and the Times Higher Education Supplement, which strongly weighs institutional reputation. Both were established in 2004.

lists. Rankings that result in a heavy concentration of American and British universities at the top of their lists have raised the suggestion of bias toward English-speaking countries. The importance of rankings in maintaining stakeholders’ impressions of institutional quality has also prompted institutions to manipulate the ranking formulas themselves, resorting to means such as making standardized tests optional (since students with higher scores are more likely to submit them), increasing the number of courses with small enrollments, and intentionally undervaluing competitors’ quality on reputational polls. In more extreme situations, staff at some highly competitive institutions misreported standardized test results to ranking organizations. In 2012, Richard Vos, director of admissions at Claremont McKenna College, admitted to falsifying data submitted to U.S. News & World Report between 2004 and 2010; a spokesperson for the college attributed Vos’s motives to the pressure to compete with other top liberal arts colleges. Other institutions, including Bucknell University, Emory University, George Washington University, and Iona College, also self-reported that their staff had provided inaccurate data to U.S. News between 2002 and 2012.

Conclusion Rankings have been faulted frequently on a number of grounds, but while critics argue that rankings play too powerful a role in shaping prospective students’ understanding of institutions and programs, detractors also acknowledge that the influence of rankings is now firmly established within the higher education landscape. A 2003 study by Ronald G. Ehrenberg correlated changes in the ranking positions of private college and universities to the size of the applicant pool, admissions decision yield, SAT scores, and the amount of financial aid required to secure enrollments. Another criticism offered by, among others, Alexander Astin and the former U.S. News rankings editor John Byrne holds that rankings provide little or no insight into the quality of instruction, and they attempt instead to impute quality of teaching effectiveness from the most easily obtainable data. Other criticisms include false precision in delineating between institutions whose quality rankings are almost identical; unclear or nonsensical methodologies; reputational data that rely on unqualified, underqualified, or biased respondents; and the presumption of a zero-sum game among institutions competing for inclusion on

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Alex Duke See also Accountability, Types of; Benefits of Higher Education; College Choice; Credential Effect; Enrollment Management in Higher Education

Further Readings Altbach, P. (2010, November 10). The state of the rankings. Inside Higher Education. Retrieved from http://www .insidehighered.com/views/2010/11/11/altbach Ehrenberg, R. (2003). Method or madness? Inside the USNWR college rankings. Cornell University ILR School Digital Commons@ILR. Retrieved from http:// digitalcommons.ilr.cornell.edu/cgi/viewcontent.cgi?article =1043&context=workingpapers Myers, L., & Robe, J. (2009). College rankings: History, criticism and reform. Washington, DC: Center for College Affordability and Productivity. Tobolowsky, B. F. (2002). College rankings. In J. W. Guthrie (Ed.), The encyclopedia of education (Vol. 2). New York, NY: Macmillan Reference USA. Webster, D. (1986). Academic quality rankings of American colleges and universities. Springfield, IL: Charles C Thomas.

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College Savings Plan Mechanisms

COLLEGE SAVINGS PLAN MECHANISMS When individuals pay for college, few are able to pay the full cost out of pocket, so they rely on a combination of financial aid (grants, scholarships, loans, and/or work-study), earnings, and savings to help defray the costs. College savings plans (CSPs) are federal and state policy instruments designed to encourage individuals to set aside money for paying their children’s future educational expenses. There are two basic types of CSPs: (1) college savings accounts and (2) prepaid tuition plans. These two savings mechanisms are informed by the economic principle of time value of money. In addition, behavioral economics suggests that savings have nonfinancial benefits that promote financial responsibility, teach financial literacy, and encourage children to prepare for college. This entry describes the different types of CSPs, explains key design features of the programs, and briefly highlights some of the ongoing challenges and opportunities with this college financing mechanism.

Types of CSPs The first state CSP was introduced in 1986 when Michigan created the Michigan Education Trust. Under this program, the state utilized a prepaid tuition plan where families could purchase tuition credits while their child was in elementary or secondary school. When the child enrolled in college, they could use these credits to pay current tuition rates. Since college tuition levels have outpaced inflation and median family incomes for at least two decades, the ability to “lock in” to a known tuition rate today is less risky and more affordable than paying an unknown tuition rate in the future. Today, 15 states operate prepaid tuition plans. The more common way states encourage educational investments is via college savings accounts. Ohio introduced the first statewide CSP in 1989, where earnings and withdrawals were tax exempt as long as they were used for qualified educational expenses. The savings account idea spread to other states in the early 1990s, and today, all but two states operate statewide savings accounts. In 1996, Congress codified into Section 529 of the Internal Revenue Code a set of requirements that state prepaid tuition and savings accounts plans must follow to retain federal tax-exempt status. In reference to the section of the

tax code, both programs are commonly referred to as “529 Plans” (or Qualified Tuition Plans). Today, all states operate at least one (and in many cases both) of these types of savings programs. In 1997, the federal government introduced its own college savings account program called Education IRA, which was later renamed to Coverdell Education Savings Accounts (ESA) in 2001. Section 530 of the Internal Revenue Code outlines the eligibility criteria and benefits of the Coverdell ESA program. While Coverdell ESAs are used to help pay for college expenses, beneficiaries can use their accounts to pay for private educational expenses at elementary and secondary schools.

Key Design Features The basic economic principles of college savings accounts are similar to traditional savings accounts and other investment vehicles. That is, if a family invests $100 in a savings account earning 3% annual interest, the family’s initial investment will be worth $103 in 1 year. Interest accrues over time, earning additional money for the beneficiary the longer the account is active. Because of these financial returns, state and federal policymakers encourage families to invest in these accounts when young—even before a child enters elementary school. Had an individual invested in a traditional savings account or other investment vehicles (e.g., Individual Development Account or retirement account), he or she may face additional fees when making withdrawals and/or have restrictions on how funds can be spent. Similarly, when an individual spends funds from traditional savings accounts, he or she pays taxes on whatever good is consumed. CSPs avoid these penalties by offering beneficiaries tax-exempt status, low (or sometimes no) fees, and interest rates on par with traditional savings vehicles. By providing these tax incentives, state and federal policymakers expect more families to participate in and benefit from choosing CSPs over traditional savings accounts. There are some key design features of CSPs that outline who is eligible to participate, how funds can be used, and the types of tax incentives available to beneficiaries. Eligibility

State 529 Plans and Coverdell ESAs are voluntary programs where individuals opt in prior to enrolling in college. Individuals can open 529 Plans or Coverdell ESAs on behalf of themselves or for beneficiaries who are less than 18 years old.

College Savings Plan Mechanisms

State 529 Plans are not restricted to state residents, though individuals with out-of-state accounts may not receive the same benefits as state residents. State 529 Plans do not restrict eligibility according to family income, but higher income families are ineligible for Coverdell ESAs. For Coverdell accounts, annual contributions are limited to $2,000 per year; 529 Plans have no limits, although contributions beyond the allowable federal gift tax allowance may be taxed. Use of Funds

When a beneficiary is ready to use the savings account, he or she must spend the funds on qualified tuition expenses as outlined by state or federal rules. Generally, these include tuition, fees, books, supplies, equipment, and room and board. Account holders must document these expenses and report them when they file taxes to retain the benefits. Tax Incentives

The federal government and states treat contributions to CSPs differently. For state 529 Plans, contributions are not deductible from federal taxes; however, most states allow contributions to be exempt from state taxes. Once an account is opened and begins accruing interest, those earnings are not taxed. And once a student uses his or her funds to pay for qualified educational expenses, those purchases are tax-exempt. These tax incentives give CSPs financial advantages over traditional savings plans. In addition to these tax incentives, some states (e.g., Maine) offer special matching contributions to encourage participation, while others charge no or low annual fees. There are also tax penalties for participants who do not spend their funds. For example, Coverdell accounts tax unused account balances if a beneficiary does not spend all of his or her funds by age 30. Both Coverdell ESAs and state 529 Plans allow beneficiaries to transfer their remaining balances to family members, though specific eligibility criteria differ from state to state.

Nongovernmental CSP Mechanisms In recent years, community-based organizations and philanthropists have been actively promoting and expanding participation in CSPs. Notably, the Corporation for Enterprise Development and partner organizations implemented two large-scale demonstration projects designed to encourage savings and asset building for education. The American

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Dream Demonstration was a 6-year demonstration project (from 1997 to 2002) that opened Individual Development Accounts to encourage asset building for education and other long-term investments (e.g., homeownership, emergencies, etc.). In 2003, the Corporation for Enterprise Development began the Savings for Education, Entrepreneurship, and Downpayment program that opens CSPs for children at birth and encourages family members, account holders, and philanthropic organizations or other donors to contribute to these plans as a way to encourage asset building. Several cities and community foundations are also actively involved in opening CSPs for local school children, often opting to invest in state 529 Plans.

Challenges and Opportunities The tax incentives provided in CSPs are designed to encourage more families to invest in their child’s future; however, less than 3% of families in the United States save in state or federal CSPs. In 2011, 529 Plans and Coverdell accounts held nearly $170 billion in assets and more than $1 billion in forgone federal tax revenue due to their tax-exempt status. On average, account holders are upper income, have tax liability, and are investing in other assets (i.e., retirement and homeownership). Higher income families participate in CSPs at higher rates than lowand moderate-income families because they have the financial capacity to do so and because of these tax incentives. To encourage more low- and moderateincome families to participate in CSPs, communitybased organizations and philanthropists are actively involved in efforts to increase participation rates. Their success has been met with mixed results. In some cases, these savings efforts may be encouraging students to raise their educational expectations, to plan for their future, and to enroll in college. But some Savings for Education, Entrepreneurship, and Downpayment evaluations have found mixed results and only weak evidence that savings accounts (as opposed to other unobservable factors) are improving students’ educational outcomes. Furthermore, even when lower income families use CSP mechanisms, they save small amounts that will unlikely cover the costs of attending college. Further research is necessary to understand the extent to which financial and nonfinancial features of CPSs affect students’ educational pathways and to assess how savings behaviors influence other life choices. Nicholas W. Hillman

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College Selectivity

See also Education Finance; Student Financial Aid; Tuition and Fees, Higher Education; Tuition Tax Credits

Further Readings Baird, K. (2006). The political economy of college prepaid tuition plans. Review of Higher Education, 29(2), 141–166. Beverly, S., Sherraden, M., Zhan, M., Shanks, T., Nam, Y., & Cramer, R. (2008). Determinants of asset building. Washington, DC: Urban Institute Press. Doyle, W., McLendon, M., & Hearn, J. (2010). The adoption of prepaid tuition and savings plans in the American states: An event history analysis. Research in Higher Education, 51(7), 659–686. Dynarski, S. (2004). Who benefits from the education saving incentives? Income, educational expectations, and the value of the 529 and Coverdell (Working Paper No. 10470). Cambridge, MA: National Bureau of Economic Research. Elliott, W., & Sherraden, M. (2013). Assets and educational achievement: Theory and evidence. Economics of Education Review, 33, 1–7. Schreiner, M., & Sherraden, M. (2007). Can the poor save? Savings and asset building in individual development accounts. New Brunswick, NJ: Transaction Books. U.S. Government Accountability Office. (2012). A small percentage of families save in 529 Plans (Report to the Chairman, Committee on Finance, U.S. Senate, GAO13-64). Washington, DC: Author.

COLLEGE SELECTIVITY When making application decisions, prospective college students consider college characteristics such as cost, financial aid, distance from home, strength of programs, and the likelihood of admission. Most students and parents believe that to some extent attending more selective colleges leads to higher paying jobs and a greater chance of being admitted to strong graduate programs. To gain information about colleges, students often turn to publications such as U.S. News & World Report’s Best Colleges Rankings or Barron’s Profiles of American Colleges. These sources rank colleges based on selectivity measures such as the average SAT and high school rank of incoming freshmen, the percentage of faculty with PhDs, average class size, and financial resources. This entry describes how college selectivity is measured and used in economic analyses and explains how college selectivity is related to student outcomes.

Background Economists have long been interested in the relation between educational attainment and student outcomes. Research on college selectivity refines this area of inquiry by examining whether college students who attend schools deemed as more selective have better outcomes (typically measured as earnings) than their peers who attend less selective institutions. Why might students who attend the more selective colleges have more favorable outcomes? Among the possible reasons are that these selective colleges typically have greater financial and educational resources, more academically able peers, more prominent faculty, and stronger alumni networks. The education production function model posits that school resources are inputs into the educational process that produces student outputs. In this framework, increases in selectivity (inputs) will lead to increases in student outcomes such as higher earnings and better graduate school admissions (outputs). Recent conventional wisdom is that U.S. colleges are becoming increasingly selective because colleges have not expanded enough to absorb an increasing population of college-going students. Caroline Hoxby investigated this issue and found that although the top 10% of colleges are substantially more selective now than they were five decades ago, most colleges are not more selective. She found that in the past students attended local colleges regardless of their ability and the characteristics of the college, but today, their college choices are driven less by distance and more by a college’s resources and student body. This result is especially true for the most academically able and prepared students.

Methodological Approaches To conceptualize how economists estimate the relation between college selectivity and student outcomes, consider Equation 1: Yi = β0 + β1Ci + β2Xi + ui , i = 1, 2, . . . , N.

(1) In this regression model, Y represents the outcome of interest (e.g., log earnings), C is a measure of college selectivity, X measures other observable factors that influence Y (e.g., family background), and u is an error term. The primary objective of estimating Equation 1 is to obtain a causal estimate of β1, which gives the effect of college selectivity on the student outcome (holding X constant).

College Selectivity

If a researcher sets out to study how college selectivity influences student outcomes, two obvious questions need to be answered: (1) How does one measure college selectivity? and (2) What outcome does one use? Measuring Selectivity

The simplest way to measure college selectivity is to use a single variable, such as the average SAT score of entering students. Although this measure is straightforward, it is one dimensional and based just on the academic achievement of the student body, and therefore, it misses some nuances of what makes a college selective. The U.S. News & World Report index is designed to reflect numerous selectivity characteristics of colleges. It is a weighted sum of undergraduate academic reputation, student retention, faculty resources (including class size), student selectivity (including SAT/ACT and acceptance rate), financial resources, graduation rate, and alumni giving. Barron’s Profiles of American Colleges places schools into one of six selectivity categories: (1) most competitive, (2) highly competitive, (3) very competitive, (4) competitive, (5) less competitive, and (6) noncompetitive (there is also a “special” category for schools with specialized programs of study). Barron’s ratings are based on selectivity of admissions decisions, such as students’ class rank, high school grade point average, average SAT scores, and the percentage of applicants admitted. Studies that use Barron’s rankings as a college selectivity measure typically use a set of dummy variables associated with the various categories. A new method for ranking U.S. undergraduate colleges has recently been developed by the economists Christopher Avery, Mark Glickman, Caroline Hoxby, and Andrew Metrick. Their method is based on revealed preference—that is, where high school students choose to enroll in college when they are choosing among different schools. This approach treats college selection as a tournament. When a student chooses a college from among those that have admitted him or her, that college “wins” the student’s matriculation “tournament,” while the other colleges record a “loss.” The researchers state that their ranking system is an improvement over existing rankings because it removes the incentives for colleges to engage in strategies that improve their rankings without improving quality, such as encouraging weaker students with low probability of admission to apply, which then reduces the college’s acceptance rate.

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The college selectivity measures described here are not an exhaustive set of methods, but they are representative of the types of measures used by researchers. Outcomes

The majority of studies that estimate how student outcomes vary with college selectivity use wages as the outcome. Some studies focus on entry-level earnings, while others also include earnings several years after graduation. This allows researchers to compare how earnings growth differs according to college selectivity. Researchers have also considered how college selectivity influences nonpecuniary outcomes such as the probability of completing a college degree, the probability of attending graduate school at different types of institutions, and health behaviors such as smoking, marijuana use, and binge drinking.

Statistical Issues While the estimation of Equation 1 using ordinary least squares is conceptually straightforward, there are serious statistical issues that arise. It is clear that students are not randomly distributed across college selectivity types, but rather, there are factors that lead some students to one selectivity type and other students to other types. The concern is that there are unobserved characteristics of the students (e.g., innate ability or motivation to study) that are correlated both with college selectivity and with the outcome (e.g., earnings), and failing to account for these latent attributes can lead researchers to erroneous conclusions about how college selectivity affects student outcomes. For example, highly motivated individuals would have both higher than average earnings and be more likely to attend a highly selective college. Because motivation is unobserved by the researcher, it is part of the error term. The estimated college selectivity coefficient, β1, would reflect both the effect of college selectivity on earnings and the effect of student motivation on earnings; hence, ordinary least squares would yield biased estimates of the influence of college selectivity on earnings. To obtain a causal effect, researchers must employ an estimation approach that breaks the correlation of the college selectivity measure with the error term. Researchers have tried a number of strategies to overcome this statistical barrier. A first step could be to supplement the right-hand side of Equation 1

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with as many relevant individual and family background variables as the dataset permits in an attempt to account for as many unobservables as possible. The coefficient β1 will then be interpreted as the change in earnings, for example, given a change in the college selectivity measure, holding constant other factors such as family background, gender, and race/ethnicity. This is a reasonable first step, but by itself, it is not generally sufficient. Another approach is a “matching” strategy whereby the researcher compares the outcomes of students who are similar to each other but where they choose to attend colleges of different selectivity. The matching may be a function of observable characteristics (race, gender, family background, etc.), or it may even be a comparison among siblings or twins (same socioeconomic and genetic background) who attended different selectivity types of college. A similar approach is to compare students who were admitted to approximately the same set of colleges but who make different choices within that set of schools. Researchers also account for nonrandom sorting into college selectivity through an “instrumental variables” approach by trying to find a variable that is correlated with college selectivity but that does not independently affect the outcome (i.e., the instrument only affects the outcome through its effect on college selectivity). One instrument used in this context is that of variables related to the price of attending college. As a practical matter, the lack of availability of convincing instruments makes this approach challenging. Recent work has employed a “regression discontinuity” methodology to obtain a causal effect of college selectivity on earnings. A recent study by Mark Hoekstra used an admissions policy to a state’s flagship university that is based on a strict cutoff on an admissions exam score. Students who were just above the cutoff are admitted and likely to attend, while students who were just below the cutoff are not admitted and so will not attend. Because these two groups of students are just above and just below the exam score cutoff, they should be very similar in terms of their observed and unobserved characteristics, and so a comparison of their postschooling earnings should provide a credible estimate for the effect of the more selective state university relative to other state universities. This study finds that attending the most selective state university causes earnings to be about 20% higher for White men.

Conclusion College selectivity garners intense interest from parents, students, and education policymakers. On balance, research on college selectivity has shown that students who attend more selective colleges tend to have higher earnings and better nonpecuniary outcomes than students who attend less selective colleges. A notable exception is a widely cited study by Stacy Berg Dale and Alan Krueger that finds negligible effects on earnings for students attending more selective colleges, with the possible exception of a positive premium for low-income students. Because of nonrandom sorting of students across selectivity types, obtaining causal estimates of the effect of college selectivity on student outcomes is challenging and will continue to occupy researchers’ attention in the future. Eric R. Eide See also College Choice; College Rankings; Human Capital; Instrumental Variables; Selection Bias

Further Readings Avery, C. N., Glickman, M. E., Hoxby, C. M., & Metrick, A. (2013). A revealed preference ranking of U.S. colleges and universities. Quarterly Journal of Economics, 128(1), 425–467. Black, D. A., & Smith, J. A. (2004). How robust is the evidence on the effects of college quality? Evidence from matching. Journal of Econometrics, 121(1), 99–124. Brewer, D. J., Eide, E. R., & Ehrenberg, R. G. (1999). Does it pay to attend an elite private college? Evidence on the effects of college type on earnings. Journal of Human Resources, 34(1), 104–123. Dale, S. B., & Krueger, A. B. (2002). Estimating the payoff to attending a more selective college: An application of selection on observables and unobservables. Quarterly Journal of Economics, 177(4), 1491–1527. Fletcher, J. M., & Frisvold, D. E. (2011). College selectivity and young adult health behaviors. Economics of Education Review, 30(5), 826–837. Hoekstra, M. (2009). The effect of attending the flagship state university on earnings: A discontinuity-based approach. Review of Economics and Statistics, 91(4), 717–724. Hoxby, C. M. (2009). The changing selectivity of American colleges. Journal of Economic Perspectives, 23(4), 95–118. Long, M. C. (2010). Changes in the returns to education and college quality. Economics of Education Review, 29(3), 338–347.

Common Core State Standards

COMMON CORE STATE STANDARDS The Common Core State Standards (CCSS) were created by state governors and state education commissioners to ensure that students have access to high-quality educational standards that are consistent across the United States. In 2009, 48 states and the District of Columbia signed a memorandum of agreement with the National Governors Association (NGA) and the Council of Chief State School Officers (CCSSO) to draft the standards. Building on the standards that were established by states to align with their respective accountability policies, the NGA and CCSSO sought to include teachers, parents, administrators, and experts in the development of mathematics and English language arts standards. The focus of the standards was to ensure that students graduating from high schools would be college ready or prepared to enter the workforce. As of January 2014, 45 states, the District of Columbia, four territories, and the Department of Defense Schools have adopted the CCSS. As of that time, Alaska, Texas, Virginia, and Nebraska had not adopted the CCSS, and Minnesota had adopted only the English language arts portion of the CCSS. It is estimated that 85% of all American students attend schools in states that have adopted the CCSS. This entry will cover a brief history of the standards movement, the development of the CCSS, assessments associated with the CCSS, costs of the CCSS, and a summary of the political discourse surrounding the adoption and implementation of the standards.

Brief History of the Standards Movement Standards-based education reform and the subsequent adoption of criterion-referenced exams by each of the states has been part of the educational policy discourse since the 1980s. Most scholars have identified the 1983 publication of A Nation at Risk as the catalyst for the current wave of education reform. Multiple studies have been conducted that have examined the implementation of these standards and the degree to which students have made progress toward mastering the standards, with scholars concluding that the level of rigor of each test, or the definition of proficiency, is the greatest predictor of the percentage of students testing at the proficient level. These studies have relied on the alignment of state criterion-referenced exams

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with a norm-referenced test, typically the National Assessment of Educational Progress. Because each state has a different set of standards and a different definition of proficiency, comparisons across states are not possible. The CCSS represents an initiative to standardize both content standards and assessments across the states. Data from criterion-referenced exams would then be available to measure performance in all states and to enable comparisons across states. Educational standards provide teachers with a clear understanding of the content and skills that students must master to successfully matriculate through the educational pipeline. Proponents of the CCSS have said that the standards promote equity because they are aligned to expectations in college and careers. Whereas unevenness of quality in standards exists on a state-by-state basis, the NGA and CCSSO maintain that under the CCSS, students in each grade level would learn the same evidencebased, rigorous content and skills regardless of their location in the United States.

Development of the CCSS The CCSS for English language arts and math were released on June 2, 2010. Several guiding principles were considered in the creation of the standards. The first goal was to create a set of fewer standards that were more rigorous, clearer, and interpretable for teachers to guide their instruction. Next, standards were only included when evidence suggested that mastery of that standard supported college and career readiness. According to the NGA and CCSSO, the CCSS were internationally benchmarked, meaning that international assessments and other nations’ curriculum standards were analyzed as part of the creation of the CCSS. The workgroups that drafted the standards differentiated between standards, or the expectations for what students should know and be able to do, and the curriculum, or the actual content of courses. The CCSS initiative was about creating common standards to be used across states, while localities and states are responsible for the development and adoption of curriculum. Last, the standards were focused on knowledge and skills across the curriculum that would be required for success in the 21st century. The standards development process was completed in two steps by two different groups. The first group developed college and career readiness standards that described what students were expected

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to know and will be able to do on graduation from high school. The second group drafted K-12 content standards that focused on academic expectations in both elementary and secondary school. The college and career readiness standards were merged with the K-12 standards during the development process. The English language arts standards increase in complexity over the course of students’ academic careers and are expressed through ability levels in terms of lexiles. Teachers use students’ lexile scores to choose appropriate reading materials and guide instruction. The standards are broken into five subelements: (1) reading, (2) writing, (3) speaking and listening, (4) language, and (5) media and technology. Reading standards are focused on what and how students read from a variety of texts, including literature and nonfiction writing in science, social studies, and other disciplines. Writing standards emphasize the capabilities of students to make clear arguments based on evidence, sound reasoning, and substantive claims. The speaking and listening standards require students to present and evaluate increasingly difficult concepts and information in one-on-one, smallgroup, and whole-class settings. Language standards are focused on students’ acquisition and growth of vocabulary, ability to use formal English in writing and speaking, and ability to express themselves informally in a variety of settings. Media and technology are integrated throughout the standards. The math standards also increase in complexity throughout students’ academic careers; the rigor and ability level of the standards are expressed using quantiles. Teachers can use students’ quantile scores to gauge students’ understanding of core concepts and procedures; this helps guide instruction based on students’ readiness to learn new content. The standards for Grades K-5 emphasize the need for students to gain a foundation in whole numbers, addition, subtraction, multiplication, division, fractions, and decimals. For the middle school grades, students build on that knowledge to apply it toward hands-on learning in geometry, algebra, probability, and statistics. Last, the high school standards are focused on students’ abilities to apply the mathematical concepts and procedures to real-life situations.

Assessments Associated With the CCSS In 2010, the U.S. Department of Education awarded $330 million from the Race to the Top grant competition to two different consortia to develop assessments aligned to the CCSS. The Partnership for

Assessment of Readiness for College and Careers (PARCC) was awarded $186 million, while the Smarter Balanced Assessment Consortium (SBAC) was awarded $176 million. To ensure that assessments match individual state needs, states were asked to participate in the consortia either as a governing state or as a participating/advisory state. Governing states participated in the development of proposals to the Department of Education to obtain funding for the creation of assessments aligned to the standards. In addition, these states are members of the consortia advisory boards and have overseen the development of the assessments. Participating/advisory states have pledged to make use of the assessments developed by the governing states. When states joined either of the consortia, they signed a memorandum of understanding to use the consortia assessments to satisfy federal requirements for assessment. Full implementation of the PARCC and SBAC testing is expected in the 2014–2015 school year.

Costs of the CCSS Estimates of costs associated with the CCSS vary widely and focus on the costs of the tests, the readiness for states to implement a system of computerbased assessments, the need for new textbooks and instructional materials, and professional development for teachers and educational leaders. As researchers and policy analysts begin to consider costs, they have categorized costs in two categories: (1) one-time transition costs and (2) ongoing costs and investments. Examples of one-time costs include new instructional materials aligned to the standards, professional development for educators, and new assessments to test student progress. Included in the cost estimates for new assessments are technological upgrades needed to administer the tests. Ongoing investments include maintenance of technology required to administer the assessments, replacement of obsolete equipment, updating instructional materials, and ongoing professional development. PARCC and SBAC have estimated the cost of their tests at $29.50 and $22.50 per student, respectively, which is not substantially different from what states currently pay for testing. Despite this fact, states have been concerned about the costs associated with testing the CCSS because neither PARCC nor SBAC have announced final prices. Several states have withdrawn from the testing consortia, leading to some concern that costs would increase. However, a study from the Brown Center on Education Policy at

Common Core State Standards

Brookings suggests that defections from either group would cause minimal changes in cost.

Political Discourse Surrounding the CCSS As states implement the CCSS and public awareness of the standards has grown, arguments about the standards have become widespread. Proponents of the standards argue, The standards are internationally benchmarked, which means that standards in the United States will compare favorably with other standards in terms of rigor. It is believed that this will improve the international ranking of American students over time. The CCSS will allow for comparisons of student performance across states. Because states no longer have to pay for their own test development, the costs for test development will decrease. The CCSS will better prepare students for success in college and the workforce due to the increase in the rigor of the standards. Optional pretest and progress monitoring tools that are associated with CCSS assessments will allow teachers to monitor student progress throughout the year. The standards will benefit students with high mobility. Because states will now share the same standards, students who frequently move will be learning the same material even if they cross state lines.

Opponents argue, The transition to the new standards will be difficult for teachers and students since most classrooms do not currently include this level of rigor. The CCSS may cause outstanding teachers and administrators to leave the field due to the stress caused by the transition. The CCSS will require younger children to learn more at a quicker pace, causing tremendous changes to early childhood education programs. There is no equivalency test currently in development for students with special needs, which means that all children will have to take the newly developed CCSS assessments.

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The CCSS are only focused on English language arts and mathematics; there are currently no science or social studies standards associated with the CCSS. (However, a similar multistate effort is under way to encourage states to adopt common science standards known as the Next Generation Science Standards.) The CCSS will place even more value on highstakes testing.

Some have concluded that the debate over the CCSS is mostly over whether the standards represent a loss of local control and a federal intrusion on education. In addition, opponents claim that the federal government would use assessment data in such a way that it would infringe on student and family privacy. Proponents have countered that the standards are not a national set of curricula and that they have not been funded by the federal government. They argue that the purpose of the standards is to create a single set of expectations of what students should know to better prepare them for college and the workforce. Robert C. Knoeppel and Matthew R. Della Sala See also Accountability, Standards-Based; Economic Development and Education; U.S. Department of Education

Further Readings Achieve3000. (n.d.). 10 steps for migrating your curriculum to the Common Core (White Paper). Retrieved from http://www.achieve3000.com/resources/white-papers/ gated/31 Chingos, M. M. (2013). Standardized testing and the Common Core standards: You get what you pay for? Washington, DC: Brookings Institution Press, Brown Center on Education Policy. National Governors Association Center for Best Practices & Council of Chief State School Officers. (2010). Common Core State Standards. Washington, DC: Authors.

Websites Common Core State Standards Initiative: http://www .corestandards.org National Conference of State Legislatures, Costs Associated with the Common Core State Standard: http://www.ncsl .org/research/education/common-core-state-standardscosts.aspx

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Community Colleges Finance

Partnership for Assessment of Readiness for College and Careers: http://www.parcconline.org/ Smarter Balanced Assessment Consortium: http://www .smarterbalanced.org

COMMUNITY COLLEGES FINANCE Community colleges constitute a major sector of postsecondary education in the United States, annually enrolling approximately 7 million students who study on a full-time or a part-time basis. This number accounts for approximately 44% of all college students. The topic of community college finances refers to the sources and uses of financial resources at community colleges, which are public institutions. They receive the largest share of their funding from government sources, including the state in which they are located, the federal government, and (in about half of the states) local governments. This contrasts with colleges and universities in the private sector, whether they are established to earn a profit or as not-for-profit institutions, which typically receive a smaller proportion of their funds from direct government subsidies. Payments of tuition and fees by students and their families are an important source of revenue for both public and private institutions. This entry explains the proportion of funding received by community colleges from these and other sources and how funds from the different sources tend to be used. It concludes with a summary of trends and issues affecting community college finances. In this discussion, the term community college refers to those postsecondary institutions that primarily award an associate’s degree or technical certificate to students who successfully complete a full course of study at the college. Community colleges are sometimes referred to as 2-year colleges, but that is no longer an accurate description as the majority of students do not have a clear enrollment pattern in which they enroll and earn a degree in 2 years. The 2-year college label is only accurate in the sense that for the majority of degree programs offered at community colleges, a student who studied full-time for 2 years and successfully earned all appropriate credits would be awarded an associate’s degree. As a sector, community colleges are also distinguished, as are many bachelor’s-degree-granting colleges and universities, by direct governmental oversight of their operations. Private sector institutions are governed by their own independently appointed boards.

Today, there are 1,081 community colleges in the 50 states and 31 tribal colleges that play a similar educational role in American Indian communities. This count excludes the several dozen associate’sdegree-granting colleges that have recently received permission from their state to also award bachelor’s degrees and the 115 private sector associate’s-degreegranting colleges that count themselves among the nation’s “community colleges.” Some states rely more heavily on community colleges than others to educate postsecondary students. California, Florida, New York, and Texas all have large community college systems as well as a growing population of Latino students, who enroll in community colleges more often than do other ethnic groups. These four states enroll approximately one third of all community college students in the country, with California alone enrolling approximately 20% of the total.

State and Local Funding The majority of funds community colleges receive from state and local governments are typically referred to as appropriations, which means funds are provided on a regular (e.g., annual) basis and allocated to allow colleges to meet their operating costs. Colleges typically receive appropriations based on a per-student or per “full-time equivalent” funding allocation formula. In some states, a share of appropriations is tied to the number of students earning degrees or certificates. A recently developed funding mechanism referred to as performance-based funding is intended to promote more effective performance on the part of colleges in producing graduates. State and local appropriations are the largest funding source of community college revenues, contributing about 40% of the total. In fiscal year 2011, states invested nearly $14.4 billion and local governments about $9.6 billion in community colleges. However, community colleges in about half the states received no local appropriations. In the 27 states with local government funding, local contributions contributed slightly more than 20% of the overall college revenues. This local funding role and relationship between localities and their community colleges is unique among the many types of colleges and universities; hence the name “community college.” Consequently, community colleges are typically more oriented to local community needs and concerns, such as for workforce development programs, than other types of institutions.

Community Colleges Finance

The annual appropriations community colleges receive from state and local governments are largely spent on human resources in the form of salaries and benefits for personnel, which take up 80% to 85% of a community college budget. The biggest expenditure is on instructional personnel, followed by those who provide student services and institutional support, which includes administrative and academic services. Community colleges do not typically expend funds on research. Community college students have greater than average financial need, which is reflected in expenditures of $7.5 billion by the colleges in fiscal year 2011 for tuition subsidies in the form of payment waivers and financial aid provided as grants. State and local governments also provide the funds community colleges need to build new buildings and maintain their existing buildings and other physical plant infrastructure. This type of investment is referred to as capital funds. Capital funds are typically budgeted separately. They are sometimes raised through a bond act, where voters have a direct say in whether tax revenues should be collected to make infrastructure improvements. Capital investments are cyclical. Nationwide, the overall contribution to community college finances of capital funds in fiscal year 2011 was less than 4%, perhaps reflecting the nationwide economic recession at that time, which diminished public willingness to fund public infrastructure improvements.

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provide some flexibility for program implementation and are often used to stimulate innovation. Federal financial aid contributes a much larger slice of overall community college funding, more than 20% in fiscal year 2011. The federal financial aid programs include loans and grants, the latter of which do not need to be repaid. Federal grants are typically means-tested, which means that the eligibility and the size of the grant depend on the financial means of a student or a student’s family (if the student is considered financially dependent on family members). The largest is the Pell grant, named after Senator Claiborne Pell, a six-term Rhode Island Democrat who was instrumental in passing the original legislation establishing means-tested grant aid. There are a variety of federal loan programs. Some are means-tested and others are not. Both types must be repaid with interest, but the terms and level of federal subsidy for means-tested loans are typically more generous and provide students with a greater subsidy. Borrowing is very common among college students today. However, the majority of community college graduates earned their degrees without taking loans of any type, whether federal, private, or from family members. The need for borrowing is offset by relatively low tuition charges in this sector of higher education. Also, students often work and pay their college expenses using their wages or savings. That said, approximately one of five full-time, first-time community college students does take a federal college loan.

Federal Funding The federal role in financing community colleges is quite different from the state and local role. The federal government does not have direct oversight of community colleges and does not provide appropriations for routine college operations. Revenues are distributed primarily through federal student financial aid and through special programs that the federal government funds to promote special initiatives. For example, dollars distributed through these types of initiatives are targeted on improving institutional effectiveness; improving rates at which students earn credits that transfer with students to other colleges and universities where they can earn bachelor’s degrees in science, technology, engineering, and mathematics fields; and assisting unemployed adults in obtaining workforce training. Federal dollars for special programs contribute a relatively small share of overall community college finances (less than 7%). Nevertheless, they are important because they

Tuition and Fee Revenues Tuition and fees paid by students are an important source of revenue for community colleges. The amount charged for tuition and fees varies across the 50 states by a factor of 7, with California charging a full-time equivalent student $1,000 in 2011–2012 and New Hampshire charging more than $7,000 that same year. At the middle of this range, tuition and fee charges are approximately $3,000. The amount charged by half of all community colleges nationwide was between $2,000 and $3,600 annually. This means that community college tuition charges are still low relative to other sectors of postsecondary education. For example, the average charge of about $3,400 in 2011–2012 was less than half that charged at public 4-year bachelor’sdegree-granting institutions. Nationally, across all community colleges, student tuition and fee payments contributed an average of 16% of overall

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revenues. Tuition and fees increased in most states from 2007–2008 to 2012–2013, placing a greater share of the college financing role on students themselves. Other Revenues From Students and Community Constituents

Colleges also provide services and sell goods to students, such as books and meals served in dining halls, and contract with employers and other organizations for educational or training programs, which are referred to as contract training programs. Some alumni or community members give gifts or donations. These types of funds are categorized as auxiliary sales and services, educational activities and services, and gifts. Together, these resources amounted to about 16% of community college revenues. Philanthropic revenues are quite small at community colleges compared with other public and not-for-profit colleges and universities, averaging little more than 1% of community college funding.

Trends and Issues Finance Equity

Community colleges are open-access institutions. This means that as long as a potential student meets prerequisites such as a high school diploma or equivalent (e.g., a GED® credential), enrollment is permitted. Open access contrasts with selective admissions at other types of institutions that admit only a proportion of applicants, admitting those who meet higher levels of academic qualifications. Selective institutions have greater resources per student than do open-access institutions, which include community colleges and some public bachelor’s degree (4-year) universities. Students who attend open-access institutions have a lower likelihood of earning a degree than students who attend selective institutions, even when this comparison is made among students with equivalent academic preparation. The students who attend community college are, on average, from lower income households compared with students who attend selective institutions. Community college students are also more likely to be African American or Latino. Therefore, resource disparities among the different types of postsecondary institutions are a source of educational inequity among income and racial/ethnic groups. Resource inequities were exacerbated during the first decade of the 21st century due to fiscal pressures on public colleges and universities. Enrollment demand grew at community colleges,

particularly among African American and Latino students, but resources remained flat or decreased, leading to rationing of access to classes, associate’s degrees, and technical certifications, and restricting opportunities to transfer to bachelor’s-degree-granting colleges or universities to earn higher degrees. Quality of Education and Institutional Effectiveness

Many students who attend community colleges and other open-access institutions, even those who earned high school diplomas, require remedial education, which is also referred to as developmental education, before being permitted to enroll in courses that award credits that count toward a postsecondary degree or credential. Students in developmental education curricula therefore face greater opportunity costs, or lost wages during time spent to pursue a degree, than students who begin college in degree-credit courses. This lengthy curriculum pathway is associated with high rates of student departure from college. Students who begin studies at community colleges with full academic preparation are also more likely to stop or drop out than equivalently prepared peers who start at selective universities. Whether these low rates of institutional effectiveness in producing graduates are due to insufficient resources or ineffective pedagogical and administrative practices is uncertain. Policymakers who govern community colleges are implementing finance reforms intended to improve the efficiency and effectiveness of community colleges. One such reform is performance-based funding, which has been proposed or implemented in approximately half of the states since the 1980s. However, the impact of these funding strategies is also uncertain, and many states have abandoned or modified their performance-based funding policies. Alicia C. Dowd See also College Completion; College Dropout; College Enrollment; Economic Efficiency; Educational Equity; Tuition and Fees, Higher Education

Further Readings Baum, S., & Kurose, C. (2013). Community colleges in context: Exploring financing of two- and four-year institutions. In Bridging the higher education divide: Strengthening community colleges and restoring the American dream (pp. 67–72). New York, NY: Century Foundation.

Comparative Wage Index Bragg, D. D., & Durham, B. (2012). Perspectives on access and equity in the era of (community) college completion. Community College Review, 40(2), 93–116. Breneman, D. W., & Nelson, S. C. (1981). Financing community colleges: An economic perspective. Washington, DC: Brookings Institution Press. Dowd, A. C., & Shieh, L. T. (2013). Community college financing: Equity, efficiency, and accountability. In The NEA 2013 almanac of higher education (pp. 37–65). Washington, DC: National Education Association. Goldrick-Rab, S., & Kinsley, P. (2013). School integration and the open door philosophy: Rethinking the economic and racial composition of community colleges. In Bridging the higher education divide: Strengthening community colleges and restoring the American dream (pp. 109–136). New York, NY: Century Foundation. Melguizo, T., & Kosiewicz, H. (2013). The role of race, income, and funding on student success: An institutional level analysis of California community colleges. In Bridging the higher education divide: Strengthening community colleges and restoring the American dream (pp. 137–156). New York, NY: Century Foundation.

COMPARATIVE WAGE INDEX All types of workers—including teachers and other school district personnel—demand higher wages in areas with a high cost of living or a lack of desirable local amenities (e.g., good climate, low crime rates, high-quality schools, or access to shopping and medical facilities). As a result, the cost of hiring school district personnel varies geographically, and so does the cost of hiring other types of workers. The basic premise of a comparative wage index (CWI) is that one should be able to measure regional variations in the cost of hiring educators by observing variations in the earnings of comparable workers who are not educators. Intuitively, if accountants in the Chicago area are paid 5% more than the national average accounting wage, Chicago engineers are paid 5% more than the national average engineering wage, Chicago nurses are paid 5% more than the national average nursing wage, and so on, then a good estimate of the cost of hiring teachers in Chicago is also 5% more than the national average. This entry describes the data used to calculate a particular CWI and the advantages and disadvantages of using a CWI for regional cost adjustment. A prominent example of a CWI was constructed for the National Center for Education Statistics (NCES) by Lori L. Taylor and William J. Fowler Jr.

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Taylor and Fowler used regression analysis to measure regional differences in the wages of college graduates who are not educators. Their baseline model estimated local wage levels using data from the 2000 U.S. Census on the wage and salary earnings of more than 1 million college-educated workers. The predicted wage levels from their labor market analysis captured systematic variations in labor earnings while controlling for differences in worker characteristics and the local mix of industries and occupations. Dividing each local wage prediction by the national average yielded the baseline NCES CWI. Taylor and Fowler extended the baseline NCES CWI to noncensus years using a regression analysis of annual data from the U.S. Occupational Employment Statistics (OES) survey. If their analysis of OES data indicated that wages in a location increased by 3% between 1999 and 2001, then the baseline CWI for that location was revised upward by 3% to generate an estimate of the CWI in 2001. Combining the Census data with the OES data made it possible to produce NCES CWI estimates for states and local labor markets for each year from 1997 through 2005. The NCES CWI indicates that geographic differences in the cost of education are large, and the gains from cost adjustment could be substantial. For example, in 2005, the NCES CWI for Washington, D.C., was 65% higher than the NCES CWI for Montana, while the NCES CWI for San Francisco was 67% higher than the NCES CWI for El Centro, California. Given such large differences in the prevailing wage for college graduates, cost adjustment is crucial for a complete understanding of school finance equity and adequacy issues both across states and within states. Differences in spending across states are much smaller, and differences within states are much larger, after cost differentials are taken into account using the NCES CWI.

The Advantages of Using a CWI for Regional Cost Adjustment There are a number of advantages to using a CWI to measure regional differences in the costs of education. The greatest advantage is that a CWI measures costs that are clearly beyond the control of school administrators. Unlike cost adjustment strategies that rely on historical patterns of school district expenditure (e.g., the hedonic wage indices of teacher compensation used for geographic cost adjustments in the Texas and Wyoming school funding formulas), there is no risk that a CWI confuses

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high-spending school districts with high-cost school districts. A CWI is also appropriate regardless of the competitiveness of teacher labor markets. If a lack of competition in the teacher market distorts teacher compensation patterns, then cost indexes based on teacher compensation will be biased, but a CWI will not be biased because it does not incorporate data about teacher compensation. Because it can be generated from existing data, a CWI is much less expensive to construct than a cost-of-living index like those used in the Colorado, Wyoming, and Florida funding models. In addition, cost-of-living indexes may overestimate labor cost in an area with many amenities that make it a desirable place to work; a CWI offers a more reliable estimate of labor cost because it can reflect not only differences in the price of groceries and housing but also any influence on wages coming from differences in important community characteristics such as climate, crime rates, or cultural amenities. Another advantage of a CWI approach is its general applicability. Because a CWI is based on systematic differences in the general wage level, it can also be used to measure labor cost for private schools, job training programs, and postsecondary institutions.

The Disadvantages of Using a CWI for Regional Cost Adjustment There are also a number of disadvantages to using a CWI to measure variations in school district costs. First, although labor is a large part of the total cost of education, it is not the only part. Other prices (e.g., energy cost) and other district characteristics (e.g., economies of scale or variations in student need) also influence the cost of education. Any labor cost index such as a CWI or hedonic wage index represents only one dimension of the complete cost of education. Second, a CWI is most useful when the noneducator population under analysis is truly comparable with the educator population in terms of their tastes for local amenities and their sensitivity to differences in the cost of living. If comparability breaks down, then a CWI becomes a poor proxy for the cost of educator labor. The NCES CWI was deliberately estimated using only data on workers with at least a bachelor’s degree to increase the similarity between the educator and noneducator populations. Third, a CWI only reflects labor cost differentials when workers can move easily from one location

to another. If moving costs are high, then wage levels in some locations may temporarily diverge from what would be expected given local amenities and the local cost of living. Employers in fast-growing industries and school districts in fast-growing areas may need to pay a temporary premium to attract workers. A CWI cannot capture this effect. In addition, because a CWI relies on existing data, it is bound by the limitations of that data. The NCES CWI divides the United States into 800 labor markets, based on “place-of-work areas” as defined by the Census Bureau. Those place-of-work areas are geographic regions designed to contain at least 100,000 persons and do not necessarily correspond with labor markets in the traditional sense, or with the urban-centric district locale codes published by NCES. Furthermore, changes over time in the labor market definitions used by OES have undoubtedly introduced error into the most recent estimates. Finally, a CWI will not capture all of the uncontrollable variations in labor cost. By design, a CWI measures cost in a broad labor market like a metropolitan area. It does not capture variations in cost across school districts within a labor market. In particular, it does not reflect any variations in cost attributable to working conditions in specific school districts. All school districts in a given labor market are assigned the same CWI. Lori L. Taylor See also Adequacy: Cost Function Approach; Cost of Education; Hedonic Wage Models; School Finance Equity Statistics; Teacher Compensation; Teachers’ Unions and Collective Bargaining

Further Readings Odden, A., Picus, L., & Goetz, M. (2010). A 50-state strategy to achieve school finance adequacy. Educational Policy, 24, 628–654. Stoddard, C. (2005). Adjusting teacher salaries for the cost of living: The effect on salary comparisons and policy conclusions. Economics of Education Review, 24, 323–339. Taylor, L. L. (2006). Comparable wages, inflation, and school finance equity. Education Finance and Policy, 1, 349–371. Taylor, L. L., & Fowler, W. J., Jr. (2006). A comparable wage approach to geographic cost adjustment (NCES Report No. 2006321). Washington, DC: National Center for Education Statistics.

Compound Annual Growth Rate

COMPENSATING DIFFERENTIALS See Hedonic Wage Models

COMPOUND ANNUAL GROWTH RATE The compound annual growth rate (CAGR), also known as the cumulative annual growth rate, is a statistic used to express trends in expenditures, revenues, or other data over time by providing the annualized rate of change between the base year amount and the final year amount. More specifically, the CAGR is the annual percentage change that when applied to the base year amount and compounded over the number of years between the base year and the final year yields the final year amount. This entry discusses how the CAGR is used in education and how it is calculated. In education finance, the CAGR is expressed in statements such as the following: • In fiscal year (FY) 2007, New Jersey appropriated $10.3 billion for aid to school districts. • In FY2013, the appropriation was $11.7 billion, an increase of 2.15% on average each year.

In other words, $10.3 billion compounded at the rate of 2.15% over 6 years yields $11.7 billion, allowing for rounding. The CAGR smoothes any volatility that may have occurred during the time period covered. In the example above, it does not matter whether the appropriations increased steadily, had small increases in some years and large increases in others (which in this case did happen), or even declined between two of the intervening years (which also happened in this case). The CAGR may vary significantly depending on the base year and final year chosen for analysis. An analyst may therefore want to examine the values for the years adjacent to those chosen to see whether the choices have some external validity or whether different choices would make a major difference. Examples of factors that can lead to substantial changes in the CAGR’s external validity include major changes in law or policy or other external events.

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Although the CAGR is primarily used to describe long-term trends, such as the behavior of the stock market over decades or fiscal policy over administrations, there are, nevertheless, additional uses for it. First, a comparison of CAGRs for different items during the same time period may suggest further lines of inquiry. One might, for example, compare the CAGRs for expenditures and enrollments to see whether there is an apparent correlation. The CAGR may also be used as a forecasting tool, although its value as such a tool will depend, in part, on the time horizon for the forecast. To take the stock market as an example, given the market’s volatility, the CAGR for the market’s growth over a period of decades may not be a good predictor of change during the short term, but the same CAGR may be useful for long-term planning. Conversely, if the growth (or decline) is relatively steady, with yearto-year changes approximating the CAGR changes, the CAGR may be useful for short-term planning, such as for an annual budget, particularly in the absence of other tools. Determining the CAGR between two amounts may also help with investment decisions, by describing the rate of return that would be necessary to reach a desired outcome. If, for example, one required $13,000 in 3 years and had $10,000 available for investment today, the required rate of return would be approximately 9.1%, the CAGR for the two amounts. The CAGR will not, of course, help in evaluating the possible investments.

For Technical Consideration Calculation

It may be helpful to think of the CAGR as the inverse of the formula for calculating compound interest. To elaborate, the compound interest formula calculates a final amount when the base amount and interest (or growth) rates are known. When calculating the CAGR, the base and final year amounts are known; it is the rate that must be calculated. The compound interest formula calculates the final amount by multiplying the base amount by the annual interest rate, expressed as 1 plus the rate’s decimal equivalent, to the nth power, where n is the number of years over which the change will occur. Conversely, the CAGR is the nth root of the final year amount divided by the base year amount, expressed as a quotient, where n is the number of years over which the change occurred. (In the example being

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used so far, the increase is 113.6%, or 1.136.) The CAGR formula may be written as follows: CAGR = n

Final year amount − 1. Base year amount

For the purpose of calculating CAGR in a spreadsheet, the following specification can be used: (Final year amount/Base year amount) ^ (1/(Final year − Base year)) − 1.

This ensures that one does not include the base year as one of the years of change. If the CAGR is to be calculated over a number of years equivalent to one of the powers of 2 (2, 4, 8, 16, . . .), a calculator or spreadsheet square root function can be applied to the quotient and the subsequent results. The number of iterations will depend on the number of years (and thus powers of 2) for which the CAGR is being calculated. For example, an 8-year CAGR will require three iterations because 8 = 23 (to the third power). Although it is less commonly seen, the CAGR calculation can also be used to determine a rate of decline. In such a case, the quotient will be less than 1, and the resulting rate will be a negative number. For example, if fourth-grade enrollments in a school fell from 100 to 90 over a 3-year period, the result of the equation above would be approximately −0.0345, or a 3.45% annual decline.

Additional Considerations The CAGR is not always expressed explicitly as such in the literature. Rather, one often encounters the word average, as in the example in the first paragraph. The word average has several meanings, however, and it is thus incumbent on a reader or analyst to ensure that the “average annual growth” is, in fact, the CAGR. A common mistake is to divide the overall growth by the number of intervals (e.g., years), yielding the arithmetic mean. Using New Jersey’s appropriations to illustrate, the overall increase between FY2007 and FY2013 was 13.6%, and the arithmetic mean would be approximately 2.27%. But compounding the base year appropriation, $10.3 billion, by 2.27% per year for 6 years results in a final year amount of $11.8 billion, not the actual amount of $11.7 billion. Thus, if 2.27% were cited as “the average annual increase,” it would overstate the rate.

If the base and final year amounts are available, one can determine whether or not the so-called average is in fact the CAGR by compounding the base year amount by the “average annual growth rate” cited over the number of years and checking to see whether the result is, in fact, the known final year amount (or close to it, to account for rounding). Robert K. Goertz See also Budgeting Approaches; Education Spending

Further Reading Mikesell, J. (2014). Fiscal administration: Analysis and applications for the public sector (9th ed.). Boston, MA: Wadsworth.

COMPREHENSIVE SCHOOL REFORM Comprehensive school reform (CSR) took hold across the United States during the 1990s. Its aim is to improve a school—the primary unit of change— by applying effective practices in every area, including curriculum and instruction, assessments, parent and community involvement, professional development, and school and classroom management. Key to the success of this effort is the support of school design teams, also known as CSR model providers, whose purpose is to help existing K-12 schools transform themselves into high-performing organizations through “whole-school designs.” This entry describes the early development of CSR designs with the support of the New American Schools Development Corporation (NAS), the research on the implementation and effects of CSR designs, the support of CSR by the federal government, and the future of CSR designs within public education.

Early Development of CSR In July 1991, the NAS was established as a privately funded nonprofit company to create and support CSR. NAS’s premise, taken from prior effective schools research, was that all high-quality schools share a de facto design that allows staff members to function to the best of their abilities and provides students with a consistent and coherent education instructional program. On this basis, NAS posited that school-specific designs could be created that, if adopted, would help schools improve student performance at scale. The best way to create such

Comprehensive School Reform

designs was to invest in talented teams of innovators. Believing that schools across the country would adopt the teams’ designs, NAS devoted more than $130 million to these teams—a significant amount at the time. The NAS effort was dramatically different from previous efforts at school improvement interventions. From the outset, NAS’s vision was to transform thousands of schools, not just a handful. It also involved the private sector, a unique approach in K-12 education, and it made use of the venture capital concept, in that NAS would invest in school designs, which would be tested at each stage, and funding would be continued only for those that demonstrated significant potential. Prior to this, private sector contributions to educational reform were often relatively small (and the same situation holds today as well), either in terms of funding or as materials in partnership programs with individual schools that promote specific activities such as reading or science. In contrast, NAS was an exception because of its investment in comprehensive, wholeschool designs. To make its goal of improving student achievement a reality, NAS organized its work into several phases): 1. A competition phase to solicit proposals and select designs 2. A 1-year development phase to convert proposal ideas into concrete plans 3. A 2-year demonstration phase to pilot the designs in real school settings 4. A scale-up phase in which the designs would be widely diffused in some as yet unspecified fashion

Research on CSR Implementation From its founding, NAS asked an independent think tank, the RAND Corporation, to provide analytic support for its school reform efforts. This support took many forms, but primarily, it documented and analyzed how NAS progressed toward its goals of widespread implementation and improved student performance. Specifically, RAND applied a program of research to record and assess NAS’s interventions, to describe the designs and analyze changes in them over time, to assess the level of implementation in schools during the demonstration and scaleup phases, to identify the factors that impeded or encouraged that implementation, and to determine

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whether the adoption of school designs resulted in the student outcomes desired. Each piece of this program of research became an important building block toward a full understanding of the NAS initiative. Overall, the RAND program offered useful and timely information to those considering or already engaging in whole-school reform—one of the several precursors to what today are more commonly called research-practitioner partnerships. RAND studied the implementation of designs in both the demonstration and the scale-up phases, measuring the level of design use in schools and determining the conditions that promoted it. During the scale-up of seven school improvement designs, NAS and its teams partnered with schools and districts that were characterized by a host of problems related to poverty, low achievement, and other concerns. Achieving high levels of implementation within those schools across all teachers proved to be challenging. RAND’s case study analyses found that 2 years into implementation, about half of the sample sites implemented designs at a level consistent with NAS expectations and half implemented designs at a rate below that. All the sites reported many barriers to further implementation. The longitudinal analysis based on school and teacher surveys supported these findings, showing weak implementation and stagnation. While implementation across schools increased modestly from 1997 to 1999, there was a much greater variance in implementation within schools, suggesting that the designs had not become schoolwide. There were also large differences in implementation by jurisdiction, by CSR design, and across schools. Various RAND analyses identified which conditions fostered higher levels of implementation, including quality assistance from CSR model providers, school leadership support, school and district capacities, and resource support. Analyses also identified barriers to implementation, many of which were related to district and union policies. When teachers viewed district leadership as consistent and effective, they were more likely to commit themselves to implementation. However, they could be easily overwhelmed at adopting a new school design. Most disruptive to their efforts was the district’s need to prepare students for highstakes tests required by the state, which conflicted with implementing the NAS designs. This conflict resulted in lower levels of implementation. Sometimes, schools dropped the designs altogether. The single most important reason principals

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reported for dropping designs was lack of funding. School costs associated with the reform were far greater than the fees they paid to the design teams. (In fact, those fees made up less than one third of the schools’ implementation costs.) Teachers also bore significant costs in terms of the time and effort required to implement reforms. In the end, the RAND analyses illuminated three important areas about the NAS effort. First, the analyses confirmed that an external agent like NAS could create and promote design teams. Second, the analyses indicated that some of the theory of action behind the NAS efforts was underdeveloped, given the conditions needed to implement a design and improve student outcomes. Third, the analyses offered important lessons on how to carry out future efforts at reform. Several studies undertaken since the RAND research have pointed to similar patterns of implementation challenges of CSR reforms in local schools and districts.

CSR Effects on Student Achievement CSR’s goal of improving the academic achievement of students consistently proves elusive. Findings in this regard have been consistent with those from the studies on implementation. For example, in their meta-analysis of CSR achievement effects, Geoffrey Borman and colleagues examined 29 widely implemented CSR designs and found that the overall effects were quite small (effect size [ES] of 0.12 in comparison group studies). Moreover, there was variability across CSR designs, providing evidence that some CSR models worked much better than others in improving student achievement. Borman and colleagues also found that the longer a design was implemented, the greater its effect on student achievement. On average, they found that effect sizes were small in the early years of implementation (ES = −0.17 to 0.15) and it was not until the fifth year that effect sizes increased in meaningful ways (ES = 0.25).

Federal Support for CSR A major contribution of NAS was its advocacy for establishing additional sources of funding for CSR designs and design teams. The federal government, always seeking ways to improve the historically beleaguered Title I program, which provides funding for students who are disadvantaged in some way, was receptive to supporting CSR. In 1997,

NAS presented its views and lessons learned to the U.S. Department of Education and Congress as those decision makers were considering funding for schools attempting whole-school reform. NAS’s efforts to obtain funding were successful. In 1998, new legislation establishing the Comprehensive School Reform Demonstration Program was passed to fund and expand effective designs and strategies for schoolwide reform. The congressional language and the supporting documents clearly recognized NAS’s contributions to reforming America’s schools. In 2002, the reauthorization of the Elementary and Secondary Education Act as the No Child Left Behind Act incorporated the Comprehensive School Reform Demonstration Program and further institutionalized research-based CSR models. Together, the Comprehensive School Reform Demonstration Program and the No Child Left Behind Act supported the adoption of CSR models in nearly 7,000 schools supported by more than 600 different CSR organizations. This came to an end in 2008, when funding was not appropriated for the support of further CSR implementation.

The Future of CSR Amid the large number of CSR designs and models, a few have emerged as being very successful at scale, such as Success for All (SFA) and America’s Choice. Developed by Robert Slavin and Nancy Madden in the mid-1980s, SFA is grounded in research-based practices, ongoing evaluation, cooperative learning, and a coordinated schoolwide program to support instruction, particularly for students in high-poverty schools. SFA received several large scale-up grants in 2010 and 2011 as part of the federal Investing in Innovation Fund (i3) program. America’s Choice was taken over by the Pearson publishing company in 2010, and it now serves as Pearson’s Schoolwide Improvement Model, providing services to schools focusing on the implementation of the Common Core State Standards. Because many other CSR models and designs faltered in traditional public schools and districts, the future is likely to see such reforms in the charter public school sector, where autonomy and independent functioning encourage the implementation of innovative instructional designs, such as CSR models. For example, charter school reform efforts such as the Knowledge Is Power Program—a CSR type of design—have been implemented in several schools across the country (but not at the scale of SFA),

Compulsory Schooling Laws

with studies showing that the Knowledge Is Power Program has significant effects on student achievement. Additional research (apart from the number of studies that have been done over the past decade) is needed to examine whether the development and further scale-up of CSR designs will result in more robust findings in implementation and student performance. Mark Berends See also Charter Schools; Educational Innovation; Investing in Innovation Fund (i3)

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Rowan, B., Correnti, R., Miller, R. J., & Camburn, E. (2009). School improvement by design: Lessons from a study of comprehensive school reform designs. In B. Schnieder & G. Sykes (Eds.), Handbook of education policy research. London, UK: Taylor & Francis. Tuttle, C. C., Gill, B., Gleason, P., Knechtel, V., Nichols-Barrer, I., & Resch, A. (2013). KIPP middle schools: Impacts on achievement and other outcomes. Princeton, NJ: Mathematica Policy Research. Vernez, G., Karam, R., Mariano, L. T., & DeMartini, C. (2006). Evaluating comprehensive school reform models at scale: Focus on implementation. Santa Monica, CA: RAND Corporation.

Further Readings Aladjem, D. K., & Borman, K. M. (2006). Examining comprehensive school reform. Washington, DC: Urban Institute Press. Angrist, J. D., Dynarski, S. M., Kane, T. J., Pathak, P. A., & Walters, C. R. (2012). Who benefits from KIPP? Journal of Policy Analysis and Management, 31(4), 837–860. Berends, M., Bodilly, S., & Kirby, S. (2002). Facing the challenges of whole school reform: New American Schools after a decade. Santa Monica, CA: RAND Corporation. Berends, M., Chun, J., Schuyler, G., Stockly, S., & Briggs, R. J. (2002). Challenges of conflicting reforms: Effects of New American Schools in a high-poverty district. Santa Monica, CA: RAND Corporation. Borman, G. D., Hewes, G. M., Overman, L. T., & Brown, S. (2003). Comprehensive school reform and achievement: A meta-analysis. Review of Educational Research, 73, 125–230. Cohen, D. K., Peurach, D. J., Glazer, J. L., Gates, K. E., & Goldin, S. (2014). Improvement by design: The promise of better schools. Chicago, IL: University of Chicago Press. Desimone, L. (2002). How can comprehensive school reform models be implemented? Review of Educational Research, 72(3), 433–480. Glennan, T. K., Jr. (1998). New American Schools after six years (MR-945-NASDC). Santa Monica, CA: RAND Corporation. Glennan, T. K., Jr., Bodilly, S. J., Galegher, J., & Kerr, K. A. (2004). Expanding the reach of education reforms: Perspectives from leaders in the scale-up of educational interventions. Santa Monica, CA: RAND Corporation. Kirby, S. N., Berends, M., & Naftel, S. (2001). Implementation in a longitudinal sample of New American Schools: Four years into scale-up. Santa Monica, CA: RAND Corporation. Mirel, J. (2001). The evolution of the New American Schools: From revolution to mainstream. Washington, DC: Thomas B. Fordham Foundation.

COMPULSORY SCHOOLING LAWS Compulsory schooling refers to a government policy that requires children to start school at a particular age and remain in school until they reach a certain age. Over the past 150 years, nearly all industrial nations (often through state-level legislation) have adopted compulsory schooling laws. These laws are typically accompanied by child labor laws that limit or prohibit employment of young workers. The labor restrictions reduce labor market prospects for young workers, so the opportunity cost of remaining in school is reduced. This entry describes the rationale for compulsory schooling laws and examines the effects of these laws historically in the United States, Canada, and Europe. The premise of compulsory schooling laws is that without them many youths would leave school too early, whereas further schooling would substantively improve their lifetime earnings and contribution to their community. Several reasons could explain why students might make poor choices about completing school. First, students may underestimate the value of staying in school. Second, they may have a high preference for their immediate well-being, so they may prefer even meager earnings in the short term over waiting for higher earnings in the future. Third, imperfect capital markets may preclude students from borrowing against future income. If so, some students might recognize the value of additional schooling but may have insufficient financial resources to cover current expenses for themselves or their families. Fourth, some students may dislike school or not find the classroom setting conducive to their learning requirements. Finally, education may have social benefits over and above the private benefits accruing to individual students. For

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example, education may have a positive effect on voter participation, community involvement, and crime reduction. Several studies have examined whether compulsory education has increased educational attainment. Some of these studies have also examined the individual economic returns to additional education. Although most studies find that compulsory education laws do increase educational attainment and that the incremental education is associated with a 5% to 10% increase in earnings, some studies find no increase in attainment or earnings. The results differ across time periods and countries, perhaps depending on alternative educational and workplace settings. The next section examines the history of compulsory education laws in the United States. The following section looks at the effects of compulsory education laws in other industrial countries.

Compulsory Schooling in the United States The 19th Century

Most states adopted compulsory education legislation in the second half of the 19th century. Massachusetts, in 1852, was the first state to pass legislation, and 33 of 46 states had passed laws by 1900. Legislation was not enacted in the southern states until early in the 20th century. Mississippi became the last state to require mandatory school attendance—in 1918. Of the states with compulsory education laws, most laws required children to attend school from age 7 or 8 years until the age of 14 or 15 years. Attendance was only required for about 12 to 20 weeks per year, and exemptions were allowed for poverty and for mental or physical deficiencies. School enrollment rates and attendance increased during the late 1800s. Enrollment rates rose from 65% of the 5- to 17-year-old population in 1870 to 72% in 1900. Even with the short school year, the average daily attendance rate in 1870 was only 59%. By 1900, the attendance rate had risen to 69%. William Landes, Lewis Solmon, and Stephen Salsbury used state-to-state differences in the timing and extent of compulsory schooling legislation to measure the effects of these laws on enrollment rates and attendance. Although educational outcomes improved during this era, researchers found that increases in the level of schooling were not caused by the compulsory education laws. Rather, the states that passed the laws had higher enrollment and

attendance rates than other states even before the laws were passed. Enrollment and attendance rates increased from 1870 to 1900 by about the same percentage irrespective of whether the states enacted compulsory schooling legislation. Some researchers have argued that these initial laws were ineffective because states and school districts did not have the infrastructure to enforce the laws. The laws were largely aspirational goals without binding requirements on children or their parents. The 20th Century

The scope and effectiveness of compulsory schooling changed in the early 20th century. The infrastructure to enforce the laws also changed as schools established attendance offices and hired truant officers to ensure that children enrolled and regularly attended school. At the same time, child labor laws limited and often precluded the employment of school-age children. By 1920, 78% of school-age children were enrolled in school, with a 91% attendance rate. In 1920, the average student attended school for 121 days per year as compared with 99 days in 1900 and only 78 days in 1870. In most states, the mandatory age for leaving school rose from 14 to 16 by 1920. Secondary enrollment rates increased substantially as more students remained in school beyond the seventh or eighth grade. Only 3% of students were enrolled in high school in 1900 as compared with 10% in 1920. Population and workplace changes contributed to the changes in education. The country went from 26% urban in 1870 to 51% urban in 1920. Rural children may have needed less formal education than their counterparts in cities because they learned appropriate skills through on-the-job training on the family farm. In contrast, urban students may have required more formal and structured education to prepare for manufacturing or white-collar jobs in cities. Researchers found that changes in the compulsory school laws contributed to the increase in schooling in the early part of the 20th century. Adriana Lleras-Muney focused on the educational attainment of individuals who were 14 years old between 1915 and 1939. The study found that increasing the schooling requirement by a year increased attainment by about 5%. This study showed that, unlike during the 19th century, compulsory schooling laws and more stringent enforcement of those laws caused some increase in schooling attainment.

Compulsory Schooling Laws

Joshua Angrist and Alan Krueger studied the same time period and used differences in individual birth quarter to isolate the effects of compulsory schooling on attainment. Most school districts did not allow students to start first grade unless they would be 6 years old by January 1 of the academic year and required school attendance until their 16th birthday (some recent laws require attendance up to 17 or 18 years). These rules meant that children born in the first quarter of the year started school at an older age than students born in the fourth quarter of the year. This difference meant that firstquarter births complete more school before age 16 than do fourth-quarter births. The researchers exploited these rule differences to examine whether compulsory schooling caused students to stay in school for more years. In addition, the analysts examined whether any extra or marginal differences in schooling associated with the laws affected earnings. The researchers found that compulsory schooling laws kept students in school for an extra 10th of a school year and reduced the high school dropout rate by about 2 percentage points. Each 1-year increase in attainment because of the laws led to a 10% increase in annual earnings. The results provided substantial evidence that compulsory school regulation in the first half of the 20th century increased attainment and provided substantial benefits for many of the affected students. Daron Acemoglu and Angrist used compulsory education laws to disentangle the private and social returns to education. They found little evidence of social returns, or an increase in total earnings, to education between 1960 and 1980. The analysis is dependent on the range of variability in compulsory education laws across states. These laws primarily affect secondary education, so the researchers argue that their finding does not necessarily reflect returns to other programs aimed at the postsecondary school population. The 21st Century

Rebecca Landis and Amy Reschly examined the effects of compulsory schooling laws using data on high school dropouts and graduates from 2000 through 2006. During this period, many states were raising the legal age for leaving school from 16 to 17 or 18. The new laws reflected a growing concern that students would not be adequately prepared for the modern workforce without completing high

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school. In contrast, the laws in the first part of the 20th century only required students to start high school. The researchers found that compulsory school laws had little if any effect on the timing of high school dropouts. The laws had no effect on high school graduation rates, and they argued that small delays in dropout timing are unlikely to benefit students. The authors concluded that compulsory school laws are ineffective and that more emphasis should be placed on understanding academic and behavior factors that are instrumental for high school completion. Other research suggests that recent increases in the school leaving age have been relatively ineffective because these laws have not been adequately enforced. Many school districts have sharply curtailed or eliminated their enforcement of attendance laws for budgetary reasons. For example, Chicago Public Schools eliminated its system of truancy officers in 1991. Given these enforcement issues, the current de facto leaving age is 16 years irrespective of laws “requiring” attendance until 17 or 18 years. Philip Oreopoulos found that more than 95% of 16-yearolds are enrolled irrespective of whether the state sets the leaving age at 16, 17, or 18 years. About 92% of 17-year-olds are enrolled in school both when the leaving age is 16 and when it is 17. About 75% of 18-year-olds are enrolled when the legal age is 18 as compared with 73% when the legal age is 17. This evidence suggests that the new higher requirements are having little or no effect on the stay-or-leave decisions of high school students.

Compulsory Schooling in Canada and Europe Several studies have examined the effects of compulsory schooling laws outside the United States. As in the United States, the effects of compulsory school laws in Canada and Europe are mixed. Oreopoulos looked at the effects of laws in Canada between 1920 and 1990. The study examined the effects of provincial laws on school attainment using Census data on individual background, educational attainment, providence (both birthplace and residence), and labor market income. A 1-year increase in compulsory schooling requirement is associated with a 13% increase in educational attainment. Students who stay an extra year for legal reasons earn an average increase in income of 12%.

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Oreopoulos also examined the variation in schooling laws across the United Kingdom for individuals who were 14 years of age between 1935 and 1965. When the minimum age for leaving school was raised from 14 to 15 after World War II, the proportion leaving school early fell from 57% to less than 10% in 3 years. He found that the change increased attainment by a half-year and annual earnings of early learners by about 5%. West German states passed compulsory ninthgrade attendance over the period from 1948 to 1970. Jörn-Steffen Pischke and Till von Wachter used the differences in the timing of the legislation across states to determine how the laws affected years of schooling and earnings. They found that the laws increased average attainment by 0.17 years and had zero effect on earnings. The authors argued that compulsory school has a much weaker effect in West Germany than in other countries because by eighth grade, German students received much stronger preparation in basic skills than students in other countries. Young German workers may also benefit from a widespread apprenticeship system that is not available in other counties.

Conclusion Compulsory schooling laws are not an all-purpose tool for improving educational attainment and lifetime earnings. The research evidence shows that compulsory schooling laws are effective in some places and over some time periods. The laws have positive effects on attainment and earnings in some situations, but similar research approaches in other places show little or no effect of the laws. In some cases, the laws have little effect due to inadequate enforcement, but the public may be unwilling to support adequate expenditures to enforce the law. In other cases, the laws may simply mirror the decisions of students and their parents to obtain more formal schooling. Finally, the effectiveness of the laws depends on the educational and workplace situation. More formal education may be important to succeed in a complex technological setting, whereas informal on-the-job learning may be adequate preparation in less sophisticated workplace settings. Richard Buddin and Michelle Croft See also Benefits of Primary and Secondary Education; Credential Effect; Demand for Education; Dropout Rates; Student Incentives

Further Readings Acemoglu, D., & Angrist, J. (2001). How large are humancapital externalities? Evidence from compulsoryschooling laws. In B. S. Bernanke & K. Rogoff (Eds.), NBER macroeconomics annual 2000 (Vol. 15, pp. 9–59). Cambridge: MIT Press. Angrist, J. D., & Krueger, A. B. (1991). Does compulsory school attendance affect schooling and earnings? Quarterly Journal of Economics, 106(4), 979–1014. Katz, M. (1972). A history of compulsory education laws. Bloomington, IN: Phi Delta Kappa Educational Foundation. Landes, W., Solmon, L. C., & Salsbury, S. (1972). Compulsory schooling legislation: An economic analysis of law and social change in the nineteenth century. Journal of Economic History, 22(1), 54–91. Landis, R., & Reschly, A. (2011). An examination of compulsory school attendance ages and high school dropout and completion. Educational Policy, 25(5), 719–761. Lleras-Muney, A. (2002). Were compulsory attendance and child labor laws effective: An analysis from 1915 to 1939. Journal of Law and Economics, 45, 401–435. National Center for Education Statistics. (2011). Digest of education statistics 2010 (NCES 2011-015). Washington, DC: U.S. Department of Education. Oreopoulos, P. (2005). Stay in school: New lessons on the benefits of raising the legal school-leaving age. Commentary, 223, 1–24. Oreopoulos, P. (2006). The compelling effects of compulsory schooling: Evidence from Canada. Canadian Journal of Economics, 39(1), 22–52. Oreopoulos, P. (2007). Do dropouts drop out too soon? Wealth, health and happiness from compulsory schooling. Journal of Public Economics, 91(11–12), 2213–2229. Pischke, J., & von Wachter, T. (2008). Zero returns to compulsory schooling in Germany: Evidence and interpretation. Review of Economics and Statistics, 90, 592–598. Roderick, M., Arney, M., Axelman, M., DaCosta, K., Steiger, C., Stone, S., . . . Waxman, E. (1997). Habits hard to break: A new look at truancy in Chicago’s public high schools. Chicago, IL: Consortium on Chicago School Research.

CONTINUING EDUCATION Continuing education is a panoptic term typically used to describe any type of formal education provided outside the parameters of traditional elementary,

Continuing Education

secondary, and postsecondary sources. Due to the rather broad definition of continuing education, it is difficult to clearly define what it is because that determination is often made by the educational institution offering the continuing education programming. Quite often, continuing education is defined not by a clear set of criteria relating to the characteristics of the educational opportunity being offered but in terms of its relation to other offerings in the unit of an educational institution that happens to have the title of the Division of Continuing Education or Continuing Education Department. Thus, these units often control the definition of what is considered continuing education both by the institution and by the community that it serves. This entry provides an overview of the economics of continuing education, including organization, funding, delivery, offerings, and instructional approaches to continuing education. Despite the various views of the definition of continuing education, there are some elements and characteristics of continuing education that are generally accepted by those in the field. First, continuing education is not traditional postsecondary education. A 19-year-old sophomore at a university who is taking for-credit classes is not a continuing education student. Continuing education is typically focused on adult learners, meaning those who are older than traditional students; however, there are even exceptions to this rule. Many continuing education units offer summer camps for children and teens, preparation classes for college entrance exams such as the SAT and ACT, or reading development programs for students, from kindergarteners to high school seniors. Continuing education comprises programs on a wide variety of topics in many shapes and sizes. These offerings can range from short, lunchtime lectures to full degree programs offered in a nontraditional format such as online learning.

Organizational Models Given the wide scope of continuing education and its definition, it is not surprising that continuing education also varies in the way it is organized at institutions of higher education. There are two primary models. The centralized model of continuing education attempts to consolidate all continuing education activity within a particular institution into one central unit. The continuing education unit may collaborate with other educational units at a school, but the continuing education unit is generally responsible for

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the development and delivery of all continuing education–related learning for the school. The decentralized model of continuing education tends to spread continuing education across several academic units. Each academic unit is responsible for the development and delivery of continuing education opportunities that relate to its particular focus. For instance, a college of business may offer continuing professional education seminars for certified public accountants, while a college of health may offer continuing professional education seminars for nurses and other medical professionals.

Funding The funding of continuing education activities also takes many forms and often depends on the types of programs being offered by that particular continuing education unit. At state-supported schools, if continuing education units offer non-credit-seeking or non-degree-seeking programming alone, then more often than not the unit is treated as an auxiliary unit within its home institution, and it does not receive any of the general education funds allocated by the state for the operation of the school. Each year, the continuing education unit is expected to generate enough enrollment revenue to cover all of its own costs, including staff salaries. In units that provide both credit and noncredit programming, the model of funding is usually a little more mixed. In these instances, general education funds will often be used to partially cover continuing education staff salaries and various other operating expenses. In almost every case, continuing education units are expected at a minimum to pay their own way, and ideally they are expected to generate income for their home institution. It is quite common for continuing education units to be charged an overhead assessment by their home institution. The way in which this assessment is calculated varies from school to school; the most common form of assessment is a charge based on a percentage of the revenue collected in a fiscal year by the continuing education unit. Percentage revenue assessments traditionally vary from 5% to 10%. Also, it is common for continuing education units to generate and grow a fund balance that, once it is large enough, can be utilized by the home institution for other strategic purposes.

Delivery Continuing education programming is delivered in several formats. These consist of face-to-face,

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online, hybrid, and contract or customized training. Continuing education programs have a lot of flexibility when it comes to the way they deliver courses. This is especially true in the case of noncredit programming. The most common and most traditional delivery mechanism is through faceto-face, open-enrollment programs. Most face-toface programs are delivered on campus in a more traditional format, with an instructor or instructors teaching a class of a varied group of enrollees. Although face-to-face delivery methods are still the most common, online and hybrid programming formats have been gaining in popularity over the past several years. Another growing delivery mechanism for continuing education is often referred to as contract or customized training. Contract or customized training is typically delivered to employees or members of one particular business or other organization at that organization’s location. Contract training is very often developed in conjunction with the sponsoring organization to help ensure that the training is meeting the needs of the members of the specific organization.

Variety of Offerings Apart from its involvement in the delivery of degree programs to nontraditional students, one of the hallmarks of continuing education is the variety of offerings that fall under its umbrella. The general categories of continuing education offerings include continuing professional education, professional development, workforce development, and personal enrichment. Continuing professional education, still a mainstay of the continuing education industry, is continuing education required by certain licensing bodies or professional organizations as a condition for maintaining a professional credential. Programs for physicians, accountants, lawyers, and engineers are among the most common, but there are myriad other professions that require a certain number of continuing education hours per year or every other year to maintain a professional license. Such professional education has traditionally been regarded as constituting continuing education more generally, but that is only because other types of programming are often overlooked. Professional development programming is a large category of programming that encompasses most other professional continuing education courses that are not being taken specifically for the continuation of a professional license. Some common examples include supervisor and

leadership courses as well as courses designed to teach a specific skill, such as time management or organizational skills. Workforce development programs are often called vocational programs. These are programs designed to give students a certain set of skills enabling them to work in a new profession or advance in their current field. Often these programs are referred to as certificate programs. Some typical examples of workforce development programs are culinary, paralegal, and welding programs. Personal enrichment programs are programs offered to appeal to a student’s personal interests or hobbies. They typically are not taken to achieve a professional goal. General examples of personal enrichment programs include courses in languages, photography, art, travel, and cooking.

Instructional Sources Continuing education units use instructional support from a variety of sources. These sources include the faculty at the unit’s home institution, adjunct faculty, contract consultants, and volunteers. In many continuing education settings, instructors are referred to as subject matter experts. The term subject matter expert indicates that the individuals taking classes have a certain level of expertise in the area they are teaching but may not have the formal credentials that would be expected of teachers in a credit program. Compensation for instruction also varies from institution to institution. The two most popular compensation models are the percentage and flat-rate models. The percentage model offers the instructor a percentage of the revenue collected for the class. It is one of the preferred compensation models because it helps turn instructional cost into a variable cost rather than a fixed cost. The percentage models also incentivize the instructor to promote the program through his or her own networks to increase enrollment and thus increase his or her compensation. The flat-rate model pays an instructor a set hourly or daily rate for instruction. Many instructors prefer this model of compensation because it reduces some of the variability in compensation from their perspective.

Flexibility One of the hallmarks of continuing education is the flexibility that most continuing education providers have in the development and delivery of programming. Flexibility is a key ingredient in the success of continuing education units because

Contracting for Services

programs need to be developed in response to the educational needs of the community that a particular unit serves. Much of the flexibility built into the continuing education system is due to the noncredit nature of the program offerings. In almost every case, there is less formality involved at the institutional level in developing and delivering noncredit continuing education programs versus for-credit courses. The speed to market allows the continuing education unit to be more responsive to the immediate training and educational needs of the community.

Future Trends The most obvious trend in continuing education continues to be the shift to online programs. This trend is most strongly seen in the professional development category of programming. Organizations and individuals engaged in professional development through a continuing education provider seem to prefer the convenience and efficiency of online programming versus more traditional brick-and-mortar offerings. Another significant trend in continuing education is the growth of customized or contract training. There has been a significant increase in the rise of company-sponsored training. Organizations are beginning to view continuing education units as a more fundamental part of their overall training and development strategy. Luke M. Cornelius and Timothy W. Giles See also Higher Education Finance; Job Training; Privatization and Marketization; Professional Development

CONTRACTING

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SERVICES

From an economic perspective, specialization occurs to allow a nation or organization to focus on what it does best. In so doing, the nation or organization can maximize output and outsource all other aspects of production. This concept came to education in the 1970s, when school districts decided to focus on what they did best: educate children. Consequently, educational leaders looked to contracts with outside vendors to provide some of the goods and services that are staples in the governance of public education but beyond its primary scope of educating children. The concept of contracting of services also applies to higher education. This entry is written from the perspective of PreK-12 public education, but the concepts are applicable to higher education. The National Center for Education Statistics reported that a total of $638 billion was spent during the 2009–2010 school year on public elementary and secondary schools. As internal and external forces encourage school districts to consider the merits of contracting for services, or outsourcing different responsibilities, a significant portion of public revenues will be made available to private vendors. This entry provides a definition of contracting for services, discusses the advantages and disadvantages of this practice in public education, provides examples of how school districts have contracted for services in the past, projects future trends related to this practice, and reviews the empirical findings related to outsourcing.

Definition Further Readings Donaldson, J. F. (1991). New opportunities or a new marginality: Strategic issues in continuing higher education. Continuing Higher Education Review, 55(3), 120–128. Kasworm, C. (2011). The influence of the knowledge society: Trends in adult higher education. Journal of Continuing Higher Education, 59(2), 104–107. doi:10.1080/07377363.2011.568830 Stephenson, S. S. (2010). “Faces” and complexities of continuing higher education units: A postmodern approach. Journal of Continuing Higher Education, 58(2), 62–72. UNESCO Institute for Lifelong Learning. (2010). Global report on adult learning and education. Hamburg, DE: United Nations Educational, Scientific, and Cultural Organization.

Public educators are entrusted with limited public funds and are expected to maximize the potential of each dollar through effective and efficient spending decisions. In theory, contracting for services is a mechanism available to educational leaders to increase the effectiveness and efficiency of expenditures. When school districts enter into a contract with outside vendors, either not-for-profit or for-profit organizations, the educational leaders articulate specific public demands for either goods or services and then make payments to the outside vendors when the goods or services are provided. Contracting for services is the process through which school districts enter into an agreement with an outside vendor to provide a specific good or service. For a contract for services to benefit a school district, it must meet one of the following criteria:

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Quality improves at the same cost: The school district is able to receive a greater degree of quality for the same cost. Quality remains the same at a lesser cost: The school district spends less money and continues to receive the same degree of services. Quality improves at a lesser cost: The school district receives more quality at a less expensive rate than it could on its own.

There are two additional conditions that could result in greater benefit for school districts utilizing outside vendors, but when these conditions exist, there is no guarantee that the contract for services is beneficial. Quality decreases, and cost decreases: This may represent an improvement in efficiency depending on the amount of lost services and the savings accrued. Quality increases, and cost increases: This too may represent an improvement in efficiency.

Finally, there are three conditions that would indicate that the contract for services is harming the school district: Quality decreases at the same cost: The school district pays the same for less quality. Quality remains the same at a greater cost: The school district pays more for the same quality. Quality decreases, and cost increases: The school district ends up paying more for less quality.

Advantages of Contracting for Services Much of the research literature surrounding contracting for services lacks empirical data to support the various claims, including those referenced below, and is heavily influenced by political ideology. As a result, claims about contracting for services should be examined carefully. The following two sections present the advantages and disadvantages of contracting for services. Much of what is presented in these two sections are theoretical views that, ultimately, are influenced by political opinions. Ideally, contracting for services should result in lower spending, higher efficiency, and superior services for students due to the influence of market competition. The concept behind market competition states that as multiple entities vie for the same

contract, the costs will decrease while services will increase. However, market competition is predicated on multiple vendors being able to provide the same good or service to a school district. Proponents of contracting for services see it as a panacea to many of the problems within public education. Specifically, contracting for services is believed to offer the following advantages: Efficiency: Government is viewed by proponents of contracting for services as being too large and, as a result, inefficient. Outside vendors are better positioned to provide efficient services to school districts due to economies of scale and an ability to extract a greater amount of productivity from their employees. Legal constraints: Outside vendors do not appear to be encumbered by as many legal constraints as public school districts. The lack of legal constraints placed on outside vendors directly contributes to their ability to be more efficient than public school districts. Cost savings: Proponents of contracting for services claim that outside vendors are able to provide school districts with cost savings as a result of their ability to produce more efficiently even with limited public revenues. Higher quality of services: In addition to being more efficient, proponents of contracting for services claim that outside vendors are able to provide school districts with higher quality of goods and services. Many of these, and other, perceived advantages of contracting for services are presented as absolutes. In reality, school district personnel must look into the particulars of any possible agreement to ensure that the outside vendor can truly benefit the school district. Still, it is important to highlight the potentially vital role played by contracting for services. Contracting for services provides a significant advantage for school districts in that it allows professional educators to focus a greater percentage of their time and resources on educating children.

Disadvantages of Contracting for Services Once again, many of the identified disadvantages in this section may be influenced by ideology and, too often, lack empirical data to support the different

Contracting for Services

claims. The first potential disadvantage of contracting for services lies in the fact that both the school district and the outside vendor have the same goal— namely, to maximize benefit or utility. If both parties in a contract are working to maximize their own benefit, then it stands to reason that there will be a winner and a loser as a result of the contract. In addition, if both sides are working toward maximum utility, then there is bound to be conflict between the two sides. Both sides are seeking the highest possible return on investment, and for the outside vendor to maximize its return, the school district must receive less, and this polarization of purposes is the root of the potential conflict. In addition, critics of contracting for services argue that the claim that contracting is more efficient and effective for school districts is debatable. For contracting for services to be more effective and efficient, school district personnel must compare prior practices before the contract with the outside vendor with current practices. This analysis must examine cost, quality of services, and overall impact on the school district. Rarely do cost analyses of contracting practices take all three factors into consideration. Contracting for services can also result in a lack of accountability. For example, suppose a charter school has to contract with an outside vendor to provide lunch for its students due to the lack of food preparation space. What happens if the outside vendor refuses to provide students who qualify for a free lunch the option of receiving the free lunch? Who is responsible for ensuring that students who qualify for free lunch receive this right—the charter school, the outside vendor, or the chartering school district? The danger with contracting for services is that individuals entitled to specific services may not receive those services due to a lack of accountability. Finally, contracting for services does not always result in increased services and/or cost reductions. For contracting for services to result in cost savings for a school district, school district personnel must be able to monitor the outside vendor regularly. Failure to regularly monitor the outside vendor by school district personnel could result in a decrease in efficiency and/or cost savings for the school district. However, when school district personnel monitor the outside vendors, they are being taken away from other responsibilities, thus defeating the potential advantage of contracting for services—to allow educators to focus on providing children appropriate instruction.

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External Forces Influencing Contracting for Services Over the years, there have been numerous external attempts to improve public education. These efforts include the following: voucher programs, charter school initiatives, school of choice statutes, the federal No Child Left Behind Act, and the federal Race to the Top grant initiative. These efforts are aimed at either reforming public education or, possibly, privatizing it. Those who advocate for the privatization of public education contend that such a change would result in improved quality of services, greater accountability, and increased student performance. The same benefits associated with the privatization of public education have repeatedly been claimed for contracting for services.

Examples of Contracting for Services When discussing contracting for services in public education, the specific examples are typically divided into two categories: traditional and specialty-service contracts. Each category will be discussed in detail below. However, this list is not meant to be exhaustive. There are countless examples of school districts contracting for services that will not be covered in this section. Instead, this section reviews many of the most common examples of school districts contracting for services. The traditional examples of school districts contracting for services include aspects of public education that have been turned over, in their entirety, to an outside vendor. For example, some school districts find it more cost-efficient to contract with a transportation company to provide all transportation services for their students. The transportation company bills the school district for each mile driven and is completely responsible for all aspects associated with running a bus garage (hiring drivers, maintaining the buses, etc.). School districts have also entered into similar agreements with outside vendors in the areas of food services, custodial services, sports facility maintenance, and security services. In each of these cases, the school district pays a flat rate, typically based on the number of students being served, and the outside vendor is responsible for ensuring that the service is provided throughout the school year. Specialty-service providers are a recent phenomenon to surface in the arena of contracting for services. Specialty-service providers are, as the name implies, outside vendors that support school districts in specific manners as the school district strives to

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assess and, ultimately, educate children. Specialtyservice providers have been shown to support school districts in the following areas: assessment preparation, assessment development, data management, data analysis, software development, at-risk and remedial services, and professional development. It should be noted that specialty-service providers are beginning to account for a majority of the total dollars allocated by school districts to outside vendors, eclipsing traditional contractual arrangements. One other effort deserves comment in this section. The charter school movement began in the early 1990s and has constantly grown since that time. As the popularity of the charter school movement increases, there are more public funds available for charter schools. As a result, private, either for-profit or not-for-profit, organizations have begun to manage charter schools, and some, commonly referred to as education management organizations, oversee multiple charter schools simultaneously. These education management organizations can also fit within the definition of contracting for services since the chartering school district is, in effect, entering into a contract with the education management organization to run the charter schools.

Empirical Evidence Related to the Impact of Contracting for Services Although a majority of the literature on contracting for services may be influenced by political ideology, there have been empirical studies with the purpose of determining the impact outsourcing has on a school district’s budget, the quality of services, and student achievement. The findings in four studies are presented in this section as a sampling of the limited empirical findings related to contracting for services. Patricia Burch analyzed financial data from 11 for-profit national vendors that annually enter into contracts with schools and school districts across the United States. Her analysis examined the operational trends of these vendors from 2001 to 2010 and relied on quarterly and annual shareholder reports as well as 10-K tax forms. Burch found that these vendors seemed to be providing additional services to more districts but failed to find substantive evidence of cost savings or improved service quality. Burch claimed that school districts are dedicating more funds to contractual arrangements with outside vendors despite the fact that the data do not support the claims that contracting for services results in cost savings and better quality of services.

Laurence O’Toole and Kenneth Meier analyzed data from more than 1,000 school districts in Texas during the 1997–1998 and 1998–1999 school years. Their analysis of these data focused on the impact that contracting for services had on the quality of services, and they found that the quality of those services remained the same or went down. In addition, they concluded that Texas school districts that contracted for services with outside vendors had less core instructional spending, and they did not find any evidence of improved educational achievement. Eunju Rho replicated the design of the O’Toole and Meier study and expanded the duration of analysis from 2 school years to 11 school years. Rho’s analysis found better performance on Texas standardized tests when there was a greater commitment to contracting over the study period. Rho also reported that budget and enrollment shocks, or significant drops in annual budgets or student populations, proved the greatest catalyst for school districts to contract for services with outside vendors. Based on 12 years of data, Rho concluded that contracting does not free up resources to be used on instruction by school districts. George A. Boyne conducted a meta-analysis of 10 empirical studies that examined the impact contracting for services had on various municipalities. The original studies were conducted between 1976 and 1988. It should be noted that none of the studies included in Boyne’s meta-analysis focused on public school districts. The studies included in Boyne’s meta-analysis concluded that contracting for services led to higher efficiency, but Boyne identified “major deficiencies” in a majority of the studies. If the practice of contracting for services is to continue in public education, then additional research on this possible cost-savings alternative is needed. In a 1998 journal article, Graeme Hodge offered a set of criteria and performance measures for assessing the impact of contracting for services. They include the following: • Economic performance: The outsourced service should provide the school district or government agency with a cost savings. • Social performance: The outsourced service should provide the school district or the government agency with, at the very least, comparable quality of service. • Democratic performance: The outsourced service should provide the school district or the government agency with safeguards against possible corruption.

Cost Accounting

• Legal performance: The outsourced service should provide the school district or the government agency with accountability measures.

Too many studies to date have only examined the cost savings and the impact on quality of services that contracting for services has had on school districts. Future research studies must not only continue to measure these two components of contracting but should also include the other three performances that Hodge identified. A more thorough analysis of contracting for services will ensure that school districts are maximizing the efficiency of each dollar by either utilizing or avoiding this practice, depending on the findings from empirical studies.

Conclusion Myron Lieberman posed a question when the issue of contracting for services was still in its infancy, and that question is still relevant today: Are school districts and school boards in the business of making education or buying it? There are benefits to both approaches. If an outside vendor is selling an educational program that is ideal for a school district, then it makes little sense, assuming the price is reasonable, to re-create that program. However, school districts know the students they serve far better than outside vendors and, as a result, are better positioned to produce education that results in higher student achievement. Contracting for services is a way for school districts to buy education. If school districts opt to buy education through the use of contracts with outside vendors, then greater internal and external accountability measures will likely be required to be put in place to ensure that public dollars are benefitting students. Spencer C. Weiler See also Education Management Organizations; National Center for Education Statistics; No Child Left Behind Act; Race to the Top; Supplemental Educational Services

Further Readings Boyne, G. A. (1998). Bureaucratic theory meets reality: Public choice and service contracting in U.S. local government. Public Administration Review, 58(6), 474–484. Burch, P. (2012). After the fall: Education contracting in the USA and the global financial crisis. Journal of Education

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Policy, 25(6), 757–766. doi:10.1080/02680939.2010.50 8182 Hodge, G. A. (1998). Contracting public sector services: A meta-analytic perspective of the international evidence. Australian Journal of Public Administration, 57(4), 98–110. Lamdin, D. J. (2001). Can P.S. 27 turn a profit? Provision of public education by for-profit suppliers. Contemporary Economic Policy, 19(3), 280–290. Lieberman, M. (1988). Efficiency issues in educational contracting. Government Union Review, 9(1), 1–24. National Center for Education Statistics. (2010). Digest of education statistics, 2009. Alexandria, VA: U.S. Department of Education. Rho, E. (2013). Contracting revisited: Determinants and consequences of contracting out for public education services. Public Administration Review, 73(2), 327–337. doi:10.111/j.1540-6210.2012.02682.x

COST ACCOUNTING Cost accounting in education refers to the methods of accounting for all direct and indirect costs associated with delivering specific educational services. It includes separating out costs, typically indirect or administrative costs, at the state and district levels that are attributable to providing services at the school level to determine a more complete estimate of the cost of services to students. This entry explains how costs are coded and estimated in school accounting systems, defines how cost accounting is applied using economic methods in education, outlines the steps to implement a cost analysis, and concludes with a discussion comparing cost accounting with expenditure and budget analysis in education.

Cost in School Accounting Systems Like other government accounting systems, school financial reporting is based on the classification of monetary expenditures using fund, function, and object codes. Fund codes describe the funding source used for the education service—for example, various state funds such as general fund, federal projects, and school lunch. Function codes refer to the function performed using the money or funds provided and include things like transportation, instruction, and equipment. Object codes are subcategories of function codes. For example, within the instruction function code, the object codes may include objects such as salaries, benefits, or classroom materials

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and supplies. To estimate the cost of services at the classroom or school level, it is important to understand the school accounting system and codes and the underlying definitions contained in these. This is a good starting point to estimate the cost of educational services. Often the educational costs that are of interest to policymakers, families, or others require analysis beyond traditional school accounting classification systems and descriptions. As the next section shows, this entails a more detailed description of the monetary costs of the services that are the focus of the analysis. It also entails a rich description of the services provided, including the number of students and staff involved in various aspects of the services that are the focus of the evaluation.

Cost Accounting Using Economic Methods in Education Accounting for the cost of education programs requires that the education intervention that is the focus of the cost study is described in terms of the resources that lead to the outcome or benefit that will be measured. The ingredients or resource method of cost analysis is designed to measure the actual resources and prices of resources used in the delivery of specific education interventions. The ingredients method is a systematic and well-tested approach to determine the economic value of the resource requirements of any education program. This method includes accounting for the opportunity cost of the resources used for the program. In the ingredients approach, there are the resources that are used to provide services (e.g., the amount of direct service staff time, such as teacher and educational assistant time; classroom equipment; mileages for travel, etc.). Second, the prices of each of those resources is estimated and combined with the quantity of each resource that was used. A detailed description of the resources and their associated prices is used to complete the cost accounting for the education study.

Steps to Implement a Cost Analysis in Education The first step in cost analysis is to identify the research question or cost estimate that is the focus of the analysis. For example, the goal may be to obtain a complete estimate of the cost of educating special education students at a particular school.

Such a cost study will include descriptions of special and regular education services provided to special education students and the staff and other resources used. It also means attributing a portion of the cost at the district and state levels to the special education students at this particular school. Therefore, the second step is often to design a protocol or survey instrument that captures the resources and costs that are the subject of the evaluation. Completing this data collection process will provide a rich description of the education program that is the focus of the cost accounting. It should also provide a detailed description of the staff who deliver those services and the students and families that receive them. The information that is collected includes personnel, and this category is particularly important since it often constitutes 60% or more of all the resources used to implement an education program. Personnel descriptions as a part of cost analysis include fulland part-time staff as well as consultants who are not school or district employees. Cost instruments should describe personnel according to their roles, qualifications, and time commitments for the intervention. It is critical in accurate cost accounting to clearly define direct and indirect services at all levels of the educational system. These definitions are applied in the collection of cost data to determine the total and percentage of cost attributable to direct versus administrative or indirect service costs. The extent and nature of the protocols depend on the research questions and cost data that are the focus of the study. A time diary protocol may be used as part of a cost study if individual student–level cost data are desired. A time diary is a protocol or log that is completed by individual school staff that records how much time is spent on individual activities and may include time spent with particular students in either a group or an individual setting. Typically, time diaries record a time sampling or series of time samplings during the intervention that is the focus of the cost study. Other resources or ingredients, in addition to personnel, typically included in a cost accounting are facilities. Facilities refer to the physical space including classroom space, offices, storage areas, play or recreational space, and other building requirements for the intervention, including space used by the administrators of the program, although the latter is often quite small. Other resources to be accounted include equipment and materials such as furnishings, instructional equipment, computers, books, curriculum, office machines,

Cost of Education

tests, and so on, and other materials whether donated or purchased from specific educational budgets. Other inputs may include extra liability and theft insurance, cost of training, telephone services, or fees for Internet access. The cost to clients or their families may also be an important part of a complete cost accounting in education. Some education programs specifically require parent participation, for example, and there is an opportunity cost to this participation that is important to measure, quantify, and include in a complete cost analysis. Parent costs may include time, mileage, or other contributions they make to the intervention, such as food or materials and supplies. The educational cost and time diary (if applicable) protocols are then administered to the relevant staff. Educational staff who can complete the data in the cost protocol typically include key school and district staff, including administrators and finance specialists. The data are then analyzed so that the cost of the intervention that is the focus of the evaluation can be determined. The cost can then be analyzed and reported. Analysis typically includes average cost per student, total cost of direct and indirect services, and percentage of cost broken out by the object or functions, such as transportation, instruction, and so on.

Cost Accounting Versus Expenditure or Budget Analysis in Education Cost accounting using the ingredients or resource method is different from expenditure or budget analysis since budgets may not include the cost of all ingredients used in the intervention. There are several areas where expenditures or budgets may differ from cost estimates using the ingredients or resource method of cost estimation. First, expenditures and budgets do not measure the contribution of volunteers, donated equipment and services, or other unpaid inputs. Second, resources to support an intervention may come from another budget, such as space or facilities cost for a program that is colocated in a school building. Third, the methods of allocating budgets may not accurately reflect the resources actually used by a given intervention in the time being evaluated. An example of the latter is capital improvements or capital equipment purchases, where the full amount may be charged in one year, the year it was incurred, rather than over the time of the life of the capital purchased. Cost accounting in education focuses on the resources incurred to implement an intervention over the time

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that is the focus of the analysis. If the education intervention that is the subject of the cost analysis is an expansion or addition to an already existing education program, then the appropriate method is to estimate the marginal cost of adding the intervention to the existing education program. This means that only the additional resources and costs used to add the intervention are estimated and compared with the marginal or additional benefits or effects of the intervention. Linda Goetze See also Cost Accounting; Economics of Education; Opportunity Costs

Further Readings Levin, H. M. (1983). Cost-effectiveness: A primer. Beverly Hills, CA: Sage. Levin, H. M., & McEwan, P. J. (2001). Cost-effectiveness analysis: Methods and applications. Thousand Oaks, CA: Sage.

COST

OF

EDUCATION

Cost of education is defined as the minimum amount of money school districts are required to spend to meet educational goals. One simple way to measure the cost of education is to quantify the amount spent by a government to educate its citizens. The National Center for Education Statistics (NCES) reports that during the 2008–2009 school year, government spending on primary and secondary students averaged $10,591 per pupil within the United States, not including expenditures on capital or interest on school debt. Approximately 80% of this amount went to pay for the salaries and benefits of teachers, administrators, and other individuals directly employed by school districts. Of this amount, the substantial majority reflected the salaries and benefits of teachers. This current level of spending reflects a dramatic increase over time. In the Digest of Education Statistics 2011, the NCES reports that between 1945 and 2008, in the United States as a whole, inflationadjusted per-pupil spending increased nearly eightfold. In addition to this increase over time, there exists substantial variation in the spending level across states and districts, with the highest spending state, New York, spending nearly three times as much to educate a student as the lowest spending state, Utah.

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In most contexts, the cost of a good can be defined as how much one must spend in order to obtain it. In the context of education, however, this definition is complicated by the fact that expectations for academic achievement may vary over time and across locations. Some school districts may have a disproportionate share of students who face disadvantages that make it difficult to learn. Also, it is possible that some school districts may utilize financial resources inefficiently and hence spend more than necessary to achieve their academic objectives. These complications suggest an alternative definition of the cost of education as the minimum amount of money required to achieve a particular set of educational objectives for a given population of students. For example, policymakers may wish to know the cost for a school district to teach a child from a lowincome household to read at grade level. Researchers have used a variety of methods to inform courts and legislatures regarding the minimum cost of ensuring that children are academically proficient. These methods, however, are sensitive to the details of how they are executed. Consequently, they can be subject to the particular biases of the group undertaking the study. This entry will focus on the costs of elementary and secondary schooling in the United States. It will

move on to describe how the costs of schooling have changed over time and how costs vary across states within the United States; it will then outline methods of determining the minimal costs of an adequate education. It concludes with an overview of the findings of prior research on the determinants of school costs and the challenges associated with any estimation of school costs.

Costs Over Time The NCES reports that per-pupil current expenditures for primary and secondary students, measured in 2009 dollars, rose from $1,381 in 1945 to $10,694 in 2008. This reflects an average annual increase in real per-pupil spending of 3.2% and excludes capital spending and interest on school debt. This postwar rise in spending reflects increases in both the real wages of instructional staff as well as the number of instructional and administrative staff per student. Figure 1 shows the natural logarithm of average per-pupil expenditures, measured in 2009 dollars, over this period. The slope of the figure shows the percentage change in expenditures over the period. The figure shows a steady growth in school expenditures, with a possible slowing in the

Natural Log of Per-Pupil Expenditures

10

9.5

9

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8

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7 1945

1955

1965

1975

1985

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2005

Year

Figure 1

Natural Log of Per-Pupil Expenditures (in 2009 Dollars) Over Time

Source: From data in U.S. Department of Education, National Center for Education Statistics. Digest of Education Statistics 2011.

Cost of Education

most recent period. From 1990 to 2008, the total increase in inflation-adjusted education spending has been 34%, or an average annual increase of 1.6%, slightly higher than the growth of real per-capita gross domestic product, 1.4%, over the same period. For this later period, the NCES reports statistics on the functional areas on which the spending was targeted. Since 1990, the inflation-adjusted average salary for teachers was virtually stagnant, increasing by only 1.5% total between 1989 and 1998. However, the number of public school employees increased substantially. Administrative and instructional staff increased by 18% over the period, accounting for much of the increase in expenditures. The number of teachers per pupil increased by 12% over the period, causing the pupil-teacher ratio to fall from 17.2 to 15.3. The number of instructional aides increased 55%, though from a much smaller base. Expenditures on the construction and maintenance of schools along with the interest on school debt, which is not included in this figure, rose more than 70% (on a per-pupil basis) over the same period. Collectively, the evidence suggests that educational expenditures in recent years have risen primarily due to a steady increase in the amount of resources used to educate each child.

Expenditure Differences Across States While the average per-pupil expenditure, not including capital expenditure or the cost of school debt, in the 2008–2009 school year was $10,591, there is substantial variation across states. Figure 2 shows the per-pupil spending by state sorted from the highest spending state to the lowest. We see that the 10 highest spending states, which include New York, New Jersey, Alaska, Connecticut, and Vermont, are mostly in the Northeast, with two exceptions in the West. The 10 lowest spending states, which include Utah, Idaho, Oklahoma, Arizona, and Tennessee, are primarily located in the Southeast and Intermountain West. Average per-pupil expenditure of states in the top quintile of education spending (not including the District of Columbia) was $15,083, compared with $7,954 for states in the bottom quintile of the distribution. This variation in spending reflects differences in both how much school staff are paid as well as the number of staff per student. Schools in the top quintile of education spending paid their teachers an average annual salary of $61,010 in 2009. Schools in the bottom quartile paid only $46,422. States in the top quarter of the spending distribution

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had pupil-teacher ratios of 12.9 on average, compared with 16.6 in states in the bottom quartile. The increased cost of staff and the higher faculty sizes contribute in roughly equal measure to the differences in costs across high- and low-spending states.

Cost of an Adequate Education So far, this entry has discussed how much the U.S. public spends on education. However, the variation in costs over time and across jurisdictions begs the question of how much school districts need to spend in order to achieve a particular set of educational objectives and thus provide an adequate education. Academics have proposed different approaches to answer this question and determine an appropriate level of funding to ensure that schools are able to meet their objectives given their student body. There are a few frequently used approaches to determine the minimum cost of education. Professional Judgment Approach

Under the professional judgment approach, experienced educators determine what specific programs and resources would be required for a district with a particular set of student characteristics to achieve a predetermined level of educational achievement. For example, the experts would suggest a size for the teacher faculty, the number of teacher aides, and a set of remedial programs. The proposed programs and resources in conjunction with local wage and price information form the basis for the estimated costs of achieving district or state academic objectives. Ideally, the suggestions should be based on research and experience that allow the experts to make proposals that are cost-effective. In the United States, the professional judgment approach has frequently been used to argue that the existing funding formulas provide inadequate resources to school districts with disadvantaged populations. Matthew Springer and James Guthrie report that at least 18 professional judgment studies were used as evidence in funding adequacy lawsuits between 1996 and 2003. According to the Consortium for Policy Research in Education, this approach has also been used as a basis for making resource allocation decisions in Maine, Oregon, and Wyoming. Some researchers are skeptical of cost estimates generated by the professional judgment method. In particular, the expenditure recommendations may reflect a wish list of desired programs as opposed to the minimum set of programs required to meet

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New York New Jersey Alaska Connecticut Vermont Rhode Island Wyoming Massachusetts Maryland New Hampshire Hawaii Pennsylvania Maine Delaware Illinois Wisconsin Minnesota Virginia Ohio Nebraska West Virginia Louisiana Michigan Kansas Montana Iowa Missouri North Dakota Washington Georgia New Mexico Oregon California Indiana South Carolina Alabama Kentucky Florida Arkansas Colorado Texas South Dakota North Carolina Nevada Mississippi Tennessee Arizona Oklahoma Idaho Utah 0

Figure 2

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

Per-Pupil Expenditures in the 2008–2009 School Year by State

Source: From data in U.S. Department of Education, National Center for Education Statistics. Digest of Education Statistics 2011.

district objectives. Heather Rose, Jon Sonstelie, and Peter Richardson describe how professionals make different recommendations regarding how to meet a predetermined standard depending on whether they are faced with a specified budget. As a consequence

of the inherent subjectivity regarding which programs are required to meet an academic objective, professional judgment approaches can be subject to the preexisting biases of the researchers performing the study.

Cost of Education

Successful District Approach

A second method for determining the cost of education is to identify similar comparison districts that are already achieving the desired academic objectives. One can presume that it is possible to be academically successful by spending no more than the comparison districts. To take into account unusual outliers, practitioners generally omit from the analysis some fraction of very high- and very low-spending districts. The cost of education is then calculated as the mean or median of the costs of education in the remaining comparison districts, perhaps with some adjustment for deviations in the composition of the student body between districts. This approach assumes that unobserved factors do not cause significant variation in the cost of achieving an education objective across otherwise comparable schools and districts. Related to the successful district approach, some researchers, such as Allan Odden and Carolyn Busch, have argued that one can identify successful school reform initiatives and, based on the costs of these programs, determine how much funding is required to achieve an appropriate academic standard. However, to the extent that these school reform initiatives are conducted in unusual settings, the cost estimates generated may not bring about the desired results in a broader set of schools and districts. The successful school district approach has been used in a number of states, including Ohio, Mississippi, Kansas, Louisiana, Colorado, Missouri, New York, and Washington. One example of this type of study was conducted by the firm Lawrence O. Picus and Associates for the state of Washington. This study provides information regarding the amount successful districts spend, which varies depending on the specific standards that are established for defining a successful district. The study also provides specific guidance regarding the strategies successful districts used to perform well academically. Cost Function Approach

A third approach is to statistically estimate the relationship between expenditures and achievement, holding other factors, such as the characteristics of the student body, size of the school district, and wages and prices of other academic inputs, constant. The resulting estimated relationship between expenditures, achievement, and other factors is

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often referred to as a cost function. In other words, a statistically estimated cost function shows the average cost of achieving a particular level of academic achievement for a student body with a given set of characteristics. One can increase the desired level of achievement, and the cost function will reflect the expense of achieving this higher goal. Ideally, this function also identifies cost differentials associated with having a more disadvantaged student population. Generally, estimation of the cost function is done using a method such as ordinary least squares or two-stage least squares. Estimation of a valid cost function relies on a number of assumptions. First, central to this approach is the assumption that schools are efficient in the sense that they produce achievement at the lowest possible cost. Second, the researcher assumes that the unobserved characteristics that drive costs are uncorrelated to academic achievement, once one controls for the observable characteristics of the district. Third, the researcher must assume that wages and prices are outside the control of the district. Fourth, the researcher must use the correct functional form for modeling the statistical relationship between costs and achievement. It is helpful to think about each of these assumptions in more detail. When school districts operate efficiently, estimated cost functions may show the expected minimum cost of reaching a particular level of academic achievement. When school districts do not operate efficiently, achievement will appear more costly than it need be since a substantial fraction of the expenditures are not allocated to activities that increase achievement. Often, researchers will try to overcome this limitation by estimating and controlling for a school district’s efficiency. If achievement is correlated to the unobserved factors that determine school costs, the relationship between costs and achievement will be biased. For instance, suppose that high-achieving school districts have abundant financial resources and choose to fund extracurricular activities that don’t increase achievement; then, spending will be positively correlated to achievement. Furthermore, it will appear that raising achievement costs more than it actually does. Alternatively, if districts that face disadvantages spend extra money to try to overcome these disadvantages, spending will be negatively correlated to achievement. In this case, the cost of achievement will be biased downward. Researchers seek to address this possibility by using instrumental

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variables, which have the potential to overcome this source of bias. When estimating cost functions, researchers generally assume that the wages paid to teachers and other prices are outside the control of the school district. This assumption may be valid in many settings. Empirically, however, David Sims reports that when school districts obtain a funding windfall, teacher salaries tend to rise as well. This suggests that wages may not be completely competitively determined and rather are the result of a bargaining process. In this case, the cost of achievement will tend to be understated because funding increases are in part offset by a rise in teacher wages. One method to deal with this concern is by controlling for cost of living, which is outside the control of the school district, as opposed to teacher wages. Finally, researchers must decide on a functional form for the relationship between costs and achievement. Functional form determines the precise way in which achievement and other factors affect costs. For example, costs may be specified as being linearly related to achievement. Alternatively, the relationship may be linear in the log of achievement. Many functional forms may seem equally plausible but may have very different implications for the cost of meeting educational objectives. Thomas Downes and Leanna Stiefel provide an example from Texas in which the researchers obtained substantively different results despite using the same data due to differences in assumptions regarding functional form. Julie Golebiewski presents an overview of many education cost function studies. The results from the cost functions vary from study to study. The studies suggest that increasing achievement is expensive, though the results tend to be difficult to compare due to differences in how achievement is measured across studies. In areas with higher costs of living, the costs of schooling are higher. Similarly, students living in poverty and students with disabilities are substantially more expensive to educate than other students. Results using other methods, such as the professional judgment method, are also qualitatively similar. Robert Costrell, Eric Hanushek, and Susanna Loeb provide a cogent discussion of the difficulties associated with estimating cost functions. Underlying this difficulty is the fact that educational expenditures and achievement are only weakly correlated. Consequently, there is considerable variation in expenditures across schools

with similar demographics and levels of academic achievement. This could be due to the fact that some schools have unobserved advantages or disadvantages that allow them to produce achievement at a lower or higher cost, respectively. It could be that the objectives of some school districts are broader than simply raising measured achievement. Finally, there may be substantial variation in whether resources are used in the most efficient manner to increase student achievement. For these reasons, it is possible that a school district could increase performance with no additional increase in expenditures simply by reallocating the resources already at its disposal. It is also possible that an increase in funding might not increase performance as much as hoped for if the resources are used inefficiently or the school district faces unobserved disadvantages. Lars Lefgren See also Adequacy; Adequacy: Cost Function Approach; Adequacy: Evidence-Based Approach; Adequacy: Professional Judgment Approach; Adequacy: Successful School District Approach; Baumol’s Cost Disease; Budgeting Approaches; Capital Budget; Cost Accounting; Cost-Effectiveness Analysis; Economic Cost; Education Spending; Equalization Models; Expenditures and Revenues, Current Trends of; Factor Prices; Fiscal Disparity; School District Budgets; School Finance Equity Statistics; School Finance Litigation

Further Readings Costrell, R., Hanushek, E., & Loeb, S. (2008). What do cost functions tell us about the cost of an adequate education? Peabody Journal of Education, 83, 198–223. Downes, T. A., & Stiefel, L. (2008). Measuring equity and adequacy in school finance. In H. F. Ladd & E. B. Fiske (Eds.), Handbook of research in education finance and policy (pp. 222–237). New York, NY: Routledge. Duncombe, W., & Yinger, J. (1997). Why is it so hard to help central city schools? Journal of Policy Analysis and Management, 16, 85–113. Duncombe, W., & Yinger, J. (2008). Measuring equity and adequacy in school finance. In H. F. Ladd & E. B. Fiske (Eds.), Handbook of research in education finance and policy (pp. 238–256). New York, NY: Routledge. Fermanich, M., Mangan, M. T., Odden, A., Picus, L. O., Gross, B., & Rudo, Z. (2006). Washington learns: Successful district study final report. North Hollywood, CA: Lawrence O. Picus.

Cost-Benefit Analysis Golebiewski, J. A. (2011). An overview of the literature measuring education cost differentials. Peabody Journal of Education, 86, 84–112. Odden, A. R., & Busch, C. (1998). Financing schools for high performance. Alexandria, VA: Jossey-Bass. Reschovsky, A., & Imazeki, J. (2001). Achieving educational adequacy through school finance reform. Journal of Education Finance, 26, 373–396. Rose, H., Sonstelie, J., & Harris, P. (2004). School budgets and student achievement in California: The principal’s perspective. San Francisco: Public Policy Institute of California. Sims, D. P. (2011). Suing for your supper? Resource allocation, teacher compensation and finance lawsuits. Economics of Education Review, 30, 1034–1044. Snyder, T. D., & Dillow, S. A. (2012). Digest of education statistics 2011 (NCES 2012–001). Washington, DC: U.S. Department of Education, National Center for Education Statistics, Institute of Education Sciences. Retrieved from http://nces.ed.gov/pubs2012/2012001_0 .pdf Springer, M. G., & Guthrie, J. (2007). Courtroom alchemy. Education Next, 7, 20–27.

COST-BENEFIT ANALYSIS Many education researchers have lamented that too often decisions on the implementation of education policies are made without careful consideration of their benefits and costs. When careful analyses are done of the benefits of a policy intervention, these benefits are rarely considered alongside the costs. This is partially because the authors’ primary concern is to establish what the benefits of a policy intervention are. Other times, costs are not easily accessible to the researcher, either because there are important off-budget items or because they are not able to separate particular policy interventions from a budget of a particular school or district. Nevertheless, this causes many interventions not to be evaluated once implemented, and rarely are interventions evaluated ex ante vis-à-vis a cost-benefit analysis. This entry provides a brief background on cost-benefit analysis, sometimes called benefit-cost analysis, and then discusses the elements of a strong cost-benefit analysis.

Mechanics and Use of Cost-Benefit Analysis The good news for policymakers and research teams interested in evaluating education policies is that the

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machinery of cost-benefit analysis is well established and cost-benefit analyses have been conducted on a number of education policy interventions. Costbenefit analyses were proposed by Otto Eckstein as a way to evaluate whether the benefits exceed the costs of water resource projects. Cost-benefit analysis was first required by the National Environmental Policy Act of 1969 to evaluate the new regulations. The use of cost-benefit analysis became much more widespread in the 1980s. The methodology of costbenefit analysis entails assigning market values to projected benefits and costs, and discounting over the appropriate time frame of the project. The process of discounting adjusts for the fact that money spent today is more valuable than benefits accrued in the future because there may be alternative investments for which that money could be used. While straightforward in theory, challenges often exist in assigning values to nontraded goods, selecting an appropriate discount rate when a public project may displace private investment, and determining an appropriate time frame from which to discount the benefits and costs. The mechanics of cost-benefit analysis are based on the following formula: N

NPV =

(Bt − Ct ) / (1 + r)t . ∑ t =0

It is important to discount by the interest rate, r, to account for the fact that money today is worth more than money in the future.

Example of Cost-Benefit Analysis: Evaluation of Perry Preschool Program The framework of cost-benefit analysis has been used effectively in evaluating education policies for decades. The most convincing examples of cost-benefit analysis have been conducted on early childhood programs. The comprehensive evaluation of the benefits and costs of the Perry Preschool program conducted by Steven Barnett and his colleagues provides an example of the type of cost-benefit analysis that can be conducted for an education policy. The Perry Preschool program provided “intensive services” to the child and family. The costs of the program were fairly straightforward to calculate and involved paying the teachers, renting facilities, and providing family support services. The benefits of the program illustrate the broad set of benefits that might exist in an education intervention. Critical to the assessment of the benefits is the ability to determine the

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incremental benefits that were received by program participants and their families. In an experimental design such as Perry Preschool, one can compare the differences in outcomes between program participants and the control group that did not receive the intervention. In nonexperimental settings, the researcher is forced to calculate the additional benefits that participants would have gained from the program, beyond what they would have gained in the educational system without the intervention. In the evaluation conducted by Barnett and his colleagues, they calculate the benefits and costs from a societal perspective rather than simply the benefits and costs accruing to government programs. This allows the consideration of benefits to the program participant as a member of society, as well as other benefits to members of society that result from the changed behaviors of program participants. The resulting set of benefits is then a combination of reduced costs to the government and increased benefits to program participants and other members of society who are affected by the program participants. Primary Benefits of Perry Preschool Program

The primary benefits that a participant in the Perry Preschool program accrues are derived from increased educational attainment. This entry outlines the three primary categories of benefits and provides discussion of others that have been noted in the literature. It does not discuss the valuation of the costs since most of them are within the program budget. Often, the biggest issues in determining costs are disentangling the budget for an intervention from the overall budget of the school or district and the valuation of volunteer time, if present, in the intervention. Increased Wages. The increased wages of program participants are a societal benefit because they measure the increased productivity in the workforce. While we do not observe the increase in productivity directly, we do observe the increased wages of the participants. This market measure is appropriate if one assumes that wages are paid their marginal product, as is assumed in the competitive market paradigm. Reductions in Criminal Activity. Students with higher levels of education are less likely to engage in criminal activity. The costs of crime fall into two categories: (1) costs to the criminal justice system

and (2) the victim costs of crime. Reductions in Transfer Payments. Similarly, adults with higher levels of education are less likely to need transfer payments. This reduces government expenditure on program participants. Other Benefits of the Perry Preschool Program

More Rapid Movement Through School. One benefit of better performance throughout school is that a student is less likely to be retained. Most analyses ignore the fact that a certain subset of students will enter the labor force at a younger age than their peers with similar educational attainment and therefore would have an additional year of lifetime earnings. An additional benefit is reduced expenditures resulting from fewer students having to repeat a grade. Barnett found that these savings resulted in a savings of $16,594 for male participants and $7,239 for female participants in the Perry Preschool program. Health Benefits. The link between higher rates of education and better health has long been established. These benefits are derived from lower obesity, better health management, and reduction in other maladies. Civic Participation. It is worth noting that adults with higher levels of education are more likely to vote and become involved in many community activities. This benefit is usually left in a category of nonquantifiable benefits.

Assigning Values to Benefits As noted earlier, data drawn from an experimental setting are much easier to evaluate in a cost-benefit analysis framework than data drawn from a nonexperimental setting. In the Perry Preschool setting, Barnett and his colleagues were able to evaluate the benefits of the program by comparing the outcomes for treatment and control groups. Usually, evaluations of educational interventions are needed long before program participants enter the labor force. Pilot programs often need evaluation in short order after the intervention has been tried to determine whether these programs should be scaled up. Often, the only outcomes that are available are increased test scores, reduced retention, and/or changes in behavioral outcomes. To assign benefits to these programs, the researcher will have to develop a model based on

Cost-Benefit Analysis

the research literature that can link changes in early outcomes to later changes in labor market outcomes, criminality, and public assistance spending. Below are two illustrative examples that can guide a researcher in determining the benefits of an intervention. An Intervention That Lowers the Dropout Rate. It is well known that the earnings of high school graduates are higher than the earnings of those who have dropped out. While there are a number of reports that list the differential in lifetime earnings between graduates and dropouts, the researcher needs to be very careful to make sure that these estimates have been discounted appropriately. Alternatively, a researcher can develop his or her own estimates of the differences in earnings by using tables from the U.S. Census Bureau. The annual difference between the earnings of those without a high school diploma and those with a high school diploma can then be projected over the lifetime of the individual and discounted back to the present. Similarly, the probability that an individual engages in criminal activity and receives public assistance is much higher if he or she does not graduate from high school. A researcher must find the difference in this probability in the literature and then must project cost savings over the relevant expected criminal life. There are two categories of cost savings: savings to the criminal justice system and reduced victim costs. Savings to the criminal justice system will depend on the relevant jurisdiction of the educational intervention. A number of articles and monographs have compiled victim costs. Savings from reduced public assistance are based on a similar methodology. The researcher must first find relevant literature that delineates the difference in the annual costs of public assistance received for high school dropouts and high school graduates at each age over their lifetime. He or she can then calculate the net present value of these savings and attribute it to the value of the program. An Intervention That Increases Third-Grade Test Scores. Many education interventions are aimed at students who are in primary or middle school. In these cases, there is no direct connection between dropping out and the intervention, so to estimate the effect of the intervention on the dropout rate, it is necessary for the researcher to build a causal chain of impacts from the measurement of the effect of the intervention to the rate of dropping out.

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A way to connect an increase in third-grade test scores to eventual changes in the dropout rate is to link studies that correlate increases in third-grade test scores to increases in eighth-grade test scores. Then one can link changes in eighth-grade test scores to graduation rates. Once these links have been made, one can proceed according to the previous example.

Sensitivity Analysis In those cases where one does not have experimental data and many years of follow-up research available, there is tremendous uncertainty with respect to many of the variables of interest. The sources of uncertainty are derived from the estimated impact of the policy, any links that the researcher has made between the timing of the policy intervention and the eventual educational trajectory of the participants, and the variation in cost savings that would be derived from increased educational attainment. Carefully addressing these uncertainties is critical to conducting a thorough cost-benefit analysis. The textbook by Anthony Boardman and his colleagues, Cost-Benefit Analysis, provides a good framework for incorporating sensitivity analysis into the analysis. The purpose of the sensitivity analysis is to determine how much the net present value will change under alternative assumptions and scenarios. These alternatives may be considered by investigating the changes in individual variables or scenarios or by conducting simulations. The researcher can then judge the efficacy of the analysis based on how the conclusion might change as a result of various sensitivity analyses. The most common sensitivity analyses are conducted on a variable-by-variable basis. The estimated benefit of a policy comes with bounds of uncertainty. If the effect is estimated in a regression framework, the analysis may test for the sensitivity of the results to the bounds of a 95% confidence interval. Other times, the success of the intervention would have varied from year to year or place to place, and these bounds can be used. Sensitivity analysis on individual variables can also be conducted when there are various estimates for the costs or benefits of the program from different sources. Other times, it is more appropriate to connect sets of variables in scenarios. For example, a researcher may use different assumptions about the path of labor earnings and the reduction in crime and public assistance spending. Rather than assume a constant differential in earnings over a person’s working life or a constant reduction in crime, an

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alternative may be to consider the typical path of labor earnings and the arc of a criminal career. A further nuanced scenario might not only consider the differential between dropouts and high school graduates but also include a scenario that takes into account the differential in earnings and criminality between high school and college graduates. Another popular scenario approach is to estimate the best and worst case scenarios to determine whether a worst case scenario would threaten the results and how likely such a scenario might be. Finally, simulations are appropriate when there are multiple sources of uncertainty. For example, if an analyst needs to build connections between increases in third-grade test scores and adult outcomes, he or she will often need to rely on the estimates from multiple studies because variables may be measured imprecisely in each. Monte Carlo simulations could then be constructed to determine the range of possible values for the net impact of the intervention.

Concluding Comments This entry has emphasized the fact that cost-benefit analysis should be done and has noted that it is relatively straightforward to do so; however, the biggest challenge in evaluating educational interventions is determining their causal impact. If a researcher has confidence in the causal estimates of the impact of the intervention, then a cost-benefit analysis can be conducted to determine whether the policy’s benefits exceed the costs. The best cost-benefit analyses can inform policymakers of the likely impacts if an educational intervention is scaled up, transferred to a different location, or implemented in a different context. This can be done by designing sensitivity analysis to address these concerns. These issues can also be addressed by placing the evaluation in the proper context. Ultimately, careful cost-benefit analyses rely on careful causal analyses. The use of both allows education practitioners to make better decisions and implement policies that improve the educational system. Gary Painter See also Cost-Effectiveness Analysis; Early Childhood Education; External Social Benefits and Costs; Internal Rate of Return; Opportunity Costs

Further Readings Barnett, S. (1996). Lives in the balance: Age-27 benefit-cost analysis of the High/Scope Perry Preschool Program

(High/Scope Educational Research Foundation Monograph No. 11). Ypsilanti, MI: HighScope Press. Belfield, C., Nores, M., Barnett, W. S., & Schweinhart, L. (2006). The High/Scope Perry Preschool Program: Costbenefit analysis using data from the age 40 follow-up. Journal of Human Resources, 16(1), 162–190. Boardman, A. E., Greenberg, D., Vining, A., & Weimer, D. (2010). Cost-benefit analysis: Concepts and practice (4th ed.). Upper Saddle River, NJ: Prentice Hall. Currie, J. (2001). Early childhood education programs. Journal of Economic Perspectives, 15(2), 213–238. Eckstein, O. (1958). Water resource development: The economics of project evaluation. Cambridge, MA: Harvard University Press. Goldman, D. P., & Smith, J. P. (2002). Can patient selfmanagement help explain the SES health gradient? Proceedings of the National Academy of Sciences, 99(16), 10929–10934. Levin, H. M., & McEwan, P. J. (Eds.). (2002). Costeffectiveness and educational policy. Larchmont, NY: Eye on Education. Masse, L. N., & Barnett, W. S. (2002). A benefit-cost analysis of the Abecedarian early childhood intervention. In H. M. Levin & P. J. McEwan (Eds.), Costeffectiveness and educational policy (pp. 157–173). Larchmont, NY: Eye on Education. Miller, T. R., Cohen, M. A., & Wiersema, B. (1996). Victim costs and consequences: A new look. Washington, DC: U.S. Department of Justice, Office of Justice Programs, National Institute of Justice. Reynolds, A. J., Temple, J. A., Robertson, D. L., & Mann, E. A. (2002). Age 21 cost-benefit analysis of the Title I Chicago child-parent centers. Educational Evaluation and Policy Analysis, 24(4), 267–303. Stroup, A. L., & Robins, L. N. (1972). Elementary school predictors of high school dropout among Black males. Sociology of Education, 45(2), 212–222.

COST-EFFECTIVENESS ANALYSIS Cost-effectiveness analysis (CEA) is a method for evaluating interventions, reforms, and policies. CEA supplements commonly used evaluation methods related to effectiveness or efficacy by adding an economic component to the evaluation. CEA indicates which intervention is the most efficient, that is, which intervention generates the biggest improvement per dollar spent. CEA can be readily applied in education research—notably for interventions that necessitate significant changes in organization or input use—and it is especially useful in the public sector, where there is no obvious driver (e.g., the profit motive) to ensure

Cost-Effectiveness Analysis

efficiency. At present, however, education researchers have made only limited use of CEA. This entry describes the general form of CEA, discusses how to estimate costs, and shows how to integrate costs with measures of effectiveness to yield the key metric—the cost-effectiveness ratio. It then identifies some important methodological challenges to performing CEA. Finally, it concludes with a summary of current practice, with some case studies as examples for future reference, and with examples of how education policy might be improved by more extensive use of CEA.

General Method The general method of CEA, at least in its application for education research, was developed by Henry Levin in his seminal 1983 textbook (a second edition was published in 2001 with coauthor Patrick McEwan). In terms of basic principles, this method has not changed since. Fundamentally, CEA is an application of the basic economic concept of opportunity cost to educational interventions. All interventions require resources, and the appropriate way to evaluate them is by comparing them with the next best use of equivalent resources. For example, if a school is choosing across a range of curricular interventions to improve outcomes for struggling readers, it should look at the resources required to implement each one in conjunction with the intervention’s effect on reading. Then, the school should choose the most cost-effective intervention, that is, the one that improves reading outcomes by a given amount at the lowest cost (or by the largest amount for a given cost). The chosen intervention might not be the most effective: The most effective intervention might be the most expensive one and so not necessarily the most cost-effective. Educational decision makers need information on which interventions are the most effective per dollar spent. By applying the concept of opportunity cost, the evaluation incorporates the idea that resources are scarce and educational budgets are constrained. Information on cost-effectiveness is typically presented as a cost-effectiveness ratio where the total costs are divided by the resulting outcomes. The intervention with the lowest ratio is the most costeffective, that is, it is “buying” outcomes at the lowest price. These ratios do not compel decision makers to choose the most cost-effective intervention: Other factors, such as political considerations or budget constraints, may be influential. Nevertheless, the presumption is that the most cost-effective intervention is otherwise preferred.

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CEA is distinct from cost-benefit analysis (CBA). CEA relates costs to existing measures of effectiveness and is informative as to which intervention is the most cost-effective. CBA requires an additional step, which is to translate effectiveness into money values. After this translation, a cost-benefit ratio can be calculated that is expressed entirely in dollars. The advantage of CBA is that it determines whether an intervention is worth implementing at all (and not simply which intervention is the most cost-effective). The disadvantage is that CBA requires considerably more data and analysis. If a decision maker has already specified his or her educational objective and is motivated to determine which way to achieve this objective, CEA is sufficient.

Costs Analysis For CEA, costs data should be collected using the ingredients method. This method requires the specification of all the ingredients or inputs that are used to implement the intervention regardless of who pays for them. For education, the largest ingredient in any reform is likely to be personnel, and especially teacher time, although other ingredients (e.g., computing hardware) may also be important; for some educational programs, parental time may be significant, as might the time commitment by the student. All these ingredients should be tabulated in a spreadsheet, which should also identify the financing for each ingredient. Many education interventions are financed through multiple sources (e.g., federal, state, or local government funds; parents; nonprofit agencies); potentially, cost-effectiveness analysis can be calculated separately for each funding source. Once tabulated, each ingredient should be priced out using independent prices from appropriate datasets of agencies such as the Bureau of Labor Statistics or the National Center for Educational Statistics. Prices should capture what it would cost to purchase these ingredients in a perfectly competitive market. Many ingredients in education are purchased in distorted markets (e.g., ones with significant government regulations), and so shadow prices should be applied. For parental time and student time, for example, it may be appropriate to use the market wage as the opportunity cost of time. These prices can be adjusted for inflation and local price indices applied as appropriate. The cost of the intervention is then reported as the total sum of all ingredients multiplied by their unit prices. It is important to follow the ingredients method. Typically, this requires semistructured instruments

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administered to key personnel responsible for implementing the intervention. These instruments should be framed to determine what ingredients are needed, not what amounts were spent. Crucially, cost estimates should not be derived from budgetary information. Agency budgets are rarely comprehensive, covering all relevant ingredients, and are almost never itemized in a way that clearly identifies how much is spent on a given intervention. Also, budget statements reflect local prices and not what an intervention would cost if another agency decided to implement it in its local context. A clear example of a costs analysis of reading programs using the ingredients method is reported in a 2007 article by Levin and colleagues. This study found substantial variation across sites in the costs of implementing programs that were supposed to be uniform in delivery.

Effectiveness Analysis It is possible to use any domain of effectiveness for CEA. That is, one can perform CEA for interventions that improve phonics outcomes for struggling readers, high school graduation rates for seniors, or credits accumulated by college students. As long as the outcomes can be validly attributed to the intervention, they are legitimate measures of effectiveness for CEA. However, it is necessary that effectiveness be enumerated into a singular measure. If an intervention has multiple outcomes, then these outcomes must be weighted to yield a singular measure of overall effectiveness. Weights may be determined by reference to theory or according to the preferences of the decision maker. A more formal approach is to base the weights on an explicit cost-utility analysis. This type of analysis requires information on how valuable each outcome is, typically based on responses by education professionals or expert panels.

Cost-Effectiveness Ratios In principle, the cost-effectiveness ratio is straightforward to calculate. It is an expression of the total costs of an intervention divided by the effects of that intervention. Its interpretation is also straightforward. Interventions with the lowest cost per unit of effectiveness (the smallest ratios) are the most cost-effective. In some cases, cost-effectiveness may be reported as the increase in effectiveness per dollar spent. However, it is important to be very clear about how this ratio is actually derived. CEA is a comparative evaluation method. There must be at least

two scenarios for comparison. At a basic level, the scenarios must be comparable in several important respects. As noted above, they must be intended to improve the same outcomes, and these must be enumerated in a singular form. Also, the scenarios must be of similar scale, covering equivalent numbers of students. Large interventions cannot be easily compared with small interventions because cost estimates cannot be easily extrapolated up or down. The comparative aspect of CEA also has implications for the interpretation of the results. For a field trial, the scenarios for comparison are the treatment and control groups. Both groups probably receive some educational resources (in regular classroom settings), but the treatment group receives more resources, for example, through extra tutoring or in an after-school program. The costs of the intervention are therefore incremental costs, that is, they are extra resources beyond those all children receive. The cost-effectiveness ratio is therefore the incremental cost-effectiveness ratio. It expresses how the extra resources for the treatment generate gains in outcomes relative to the control group. The treatment is not “buying” outcomes but increases in outcomes.

Empirical and Methodological Challenges CEA has been applied in only the most limited fashion in education research. Many studies that claim to have performed such analysis have typically made only a cursory or partial attempt. In part, this situation has arisen, as noted by Douglas Harris and by Levin, because of the empirical and methodological challenges to performing CEA. Obtaining costs data is challenging. Potentially, it requires access to many personnel, including those in charge of managing an intervention, those delivering it, and the students experiencing it. These personnel will not typically have collected the information needed on costs, and so multiple interview and survey instruments must be designed and administered. Costs data should be calculated during the intervention and not after the evaluation of effectiveness. When collected retrospectively, costs data are much less accurate: Personnel cannot easily account for their time on an intervention, many of the personnel have moved to alternative roles, and there are limited archival data on how an intervention was implemented. Finally, as with effects, costs will vary across participants or sites. However, because costs are bounded at zero, this variation is unlikely to follow a normal distribution. More generally, little

Cost-Effectiveness Analysis

information on this variation may exist, such that sampling sites for costs analysis is challenging. Integrating costs and effectiveness data is also challenging. This is the case even when effectiveness has been rigorously identified, for example, through a field trial. Very few educational interventions have only one single outcome, so it may be necessary to apply cost-utility analysis (weighting each outcome) or to apportion costs to particular outcomes (separating out the costs into those designed to improve each outcome). Effect size gains (differences in means divided by the standard deviation) are also difficult to interpret in terms of incremental costeffectiveness ratios. These effect size gains depend on the variance in the populations being studied, so comparisons should only be made across interventions with the same variance in effects. Also, studies tend to report effects either for the intent-to-treat or for the treatment-on-the-treated samples. This choice has implications for how resources should be calculated. The intent-to-treat sample is often larger than the sample of those who actually receive the treatment, and so the total costs should be higher. Finally, cost-effectiveness results should be tested for robustness, that is, to see how the ratios change with variations in the estimates of costs or effects. The variance in costs may not follow a normal distribution and so may preclude the use of standard statistical tests for differences in means. Given variance in costs and variance in effects, there may be significant variance in cost-effectiveness. This variance may be manifested in imprecise estimates of cost-effectiveness or in wide, site-specific variation in cost-effectiveness ratios. These issues should be addressed by extensive sensitivity testing.

Examples and Findings There are few exemplary cost-effectiveness analyses in education, and the paucity of evidence means that generalizations about what practices might be costeffective are speculative. One example of CEA is the investigation of computer-assisted instruction by Levin, Gene Glass, and Gail Meister (1987). The authors compared the effectiveness of four interventions in improving mathematics and reading performance of elementary school children. These interventions were computer-assisted instruction, cross-age tutoring, reducing class size, and increasing instructional time. Each intervention has been found to be effective at improving achievement, but there is substantial

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variation in the costs of implementing each intervention. By comparing cost-effectiveness ratios, crossage tutoring, in which older students tutor younger ones, appears significantly more efficient than computer-assisted instruction, which in turn is more efficient than either reducing class size or increasing instructional time. Cross-age tutoring appears to be the most efficient for two reasons: (1) the younger students and their tutors both improve their achievement levels and (2) the time of the older students is costless as these students would be studying in class anyway. Another example is a cost-effectiveness study of dropout prevention programs by Levin and colleagues (2012). Using the ingredients method and archival data on spending, the study linked costs data to effectiveness data taken from published studies with high-quality research designs. The incremental cost-effectiveness ratio for increasing high school completion varied significantly across interventions and across sites for each intervention. The most cost-effective way to increase high school completion rates was through the Talent Search program. However, this program is targeted at students who have not yet dropped out of school, and who are therefore easier to reach and encourage to continue their schooling; other programs appear less costeffective because they are delivered to students who have dropped out but can be motivated to return and complete their high school education. CEA is intended to help decision makers allocate resources to educational programs as efficiently as possible. Although the conditions under which CEA is appropriate are limited, costs analysis can be helpful in clarifying how an intervention is actually delivered on the ground and how much it costs when all resources are accounted for. It is possible that some reforms fail to be effective because they do not involve much change (e.g., curricular reforms). It is also possible that some reforms cannot be implemented because they are just too expensive (e.g., reducing class size or whole-school reform). Moreover, given the substantial investments of government funds and student time in education, increasing efficiency is an important goal. For this reason alone, it is likely that the application of CEA will increase in the future. Clive Belfield See also Allocative Efficiency; Cost Accounting; Cost of Education; Cost-Benefit Analysis; Economic Efficiency; Economics of Education

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Credential Effect

Further Readings Harris, D. N. (2009). Toward policy-relevant benchmarks for interpreting effect sizes combining effects with costs. Educational Evaluation and Policy Analysis, 31(1), 3–29. Hummel-Rossi, B., & Ashdown, J. (2002). The state of cost-benefit and cost-effectiveness analyses in education. Review of Educational Research, 72(1), 1–30. Levin, H. M. (2001). Waiting for Godot: Cost-effectiveness analysis in education. In R. Light (Ed.), Evaluations that surprise. San Francisco, CA: Jossey-Bass. Levin, H. M., Belfield, C. R., Hollands, F., Bowden, A. B., Cheng, H., Shand, R., . . . Hanisch-Cerda, B. (2012). Cost-effectiveness analysis of interventions that improve high school completion. New York, NY: Columbia University, Teacher’s College, Center for Benefit-Cost Studies of Education. Levin, H. M., Catlin, D., & Elson, A. (2007). Costs of implementing adolescent literacy programs. In D. Deshler, A. S. Palincsar, G. Biancarosa, & M. Nair (Eds.), Informed choices for struggling adolescent readers: A research-based guide to instructional programs and practices (pp. 61–91). Newark, DE: International Reading Association. Levin, H. M., Glass, G. V., & Meister, G. R. (1987). Cost-effectiveness of computer-assisted instruction. Evaluation Review, 11, 50–72. Levin, H. M., & McEwan, P. J. (2001). Cost-effectiveness analysis: Methods and applications (2nd ed.). Thousand Oaks, CA: Sage.

CREDENTIAL EFFECT The term credential effect is used to describe the increase in wages or earnings associated with earning an educational credential. This phenomenon is also described using the term sheepskin effect, which refers to the historical use of sheepskin leather for educational diplomas prior to the use of parchment paper. This entry describes the different uses of these terms, the significance of credential effects for distinguishing between human capital and signaling theories of the wage effects of schooling investments, and the empirical evidence on the existence of credential effects.

Wage Differences by Credential Status Versus Causal Effects of Credentials It is useful to distinguish between two different uses of the terms sheepskin effect and credential effect. The first is the observed difference in wages between workers who hold a particular credential and those

who complete a similar amount of schooling or training but do not have a credential. This quantity has been calculated for many different countries, time periods, and educational credentials. The second use of the term sheepskin effect refers to the causal effect of holding a given credential on wages. The following thought experiment helps clarify what is meant by the causal effect of a credential on wages. Consider a worker who has completed some course of study and fulfilled the obligations to earn a degree. Denote the wages this worker would earn if he or she received the credential by w1. Now imagine instead that he or she did not receive the diploma, and denote the wages in this counterfactual world by w0. The causal credential effect is simply the difference in the wages the worker would receive with the diploma and without it: w0 − w1.

Credential Effects and the Signaling Versus Human Capital Debate The significance of the causal effect of credentials on wages has to do with its implications for theories of schooling investments. According to the theory of human capital (most closely associated with Gary Becker and Jacob Mincer), workers make investments in schooling to acquire skills for which employers pay a higher wage. An alternative view emphasizes the signaling role of education. Put forth initially by Michael Spence and Kenneth Arrow, this perspective states that employers have incomplete information about workers’ productivity, and workers make investments in schooling to signal the possession of skills that are at least partially unobserved by employers. For instance, in Spence’s original formulation, the disutility of schooling is lower for higher skilled workers, so that schooling investments act as a signal to employers that disutility costs are low and hence skills are high. In this way, schooling investments can result in higher wages irrespective of whether schooling actually improves skills. The question of whether a significant part of the return to schooling reflects unproductive signaling rather than productive skill acquisition has considerable importance for policy issues such as the optimal level of public investments in schooling. The existence of economically important causal credential effects would be strong evidence in favor of the view that employers have incomplete information about productivity and that some of the return to schooling reflects unproductive signaling. This is because credentials themselves have no productive value; they are merely pieces of paper. Thus, it is

Credential Effect

difficult to see how there could be any effect of a credential on wages that does not arise from employers inferring some information about unobserved productivity. The interpretation of the absence of causal credential effects, on the other hand, is less clear. While it would be consistent with signaling not playing an important role in the labor market, the absence of causal effects for one credential does not imply that there are no causal effects of other credentials. The absence of effects of credentials on wages could also be consistent with employers using other educational indicators to infer information about skills that are harder to observe than the skills conveyed by the credential. That said, credential effects on wages have received so much attention from researchers in large part because it seems plausible that, to the extent that education-based signaling occurs in the labor market, credentials might be an especially important signal of unobserved productivity. One reason is that credentials might be easier for firms to verify and observe than other educational indicators, such as courses taken, grades and test scores, and years of schooling. A second reason put forth by Richard Layard and George Psacharopoulos in an early paper on this topic is that earning a credential should be more informative about a worker’s skill than is attendance in a course of study for some period of time.

Empirical Evidence on Credential Effects Obtaining credible estimates of the causal effect of educational credentials has proven to be a very difficult empirical problem. The ideal research design to address this question would be an experiment with a sample of workers, some of whom are randomly selected to receive the credential, with the remainder not receiving the credential. Because the credentials would be allocated randomly in this sample, there would be no systematic difference between workers with and without the credential (apart from credential status). In that case, any differences in wages between the two groups could be attributed to the credential rather than to productivity differences observed by the employer. Many of the studies that have attempted to estimate credential effects have tried to approximate this experimental ideal by comparing the wages of workers with and without a credential and using statistical methods to control for other factors that may differ between these two groups. A common approach is to estimate regression models that

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include controls for how much time a worker has spent in school and other factors such as demographic characteristics. Studies using this approach generally find sizable credential effects. For instance, estimates from these types of wage regressions suggest a wage premium associated with a high school diploma of 8% to 12%. A limitation of this strategy is that the estimated credential effect might reflect productivity differences that employers observe but the researcher does not. Recent research has attempted to address these concerns about selection bias by identifying quasiexperiments where the variation in credential status mimics the random variation in an actual experiment. One setting in which a quasi-experimental approach has been used to estimate credential effects is when credential status is determined by whether one passes a test. Provided that workers with similar test scores are also similar in terms of other wage determinants, it would be possible to isolate the causal effect of the credential by comparing the wages of workers with similar scores but who face different passing cutoffs. A well-known study by John Tyler, Richard Murnane, and John Willet using this type of research design found that the return to earning a GED® credential is sizable for White high school dropouts. Recent studies employing a research design that compares workers with scores just above and just below the score necessary to receive the credential, however, find small signaling values of GED or high school degrees. Given the importance of determining whether credentials have a causal effect on wages, the search for compelling quasi-experimental designs to estimate credential effects ought to continue being an active research area. Paco Martorell See also Human Capital; Quasi-Experimental Methods; Regression-Discontinuity Design; Selection Bias

Further Readings Clark, D., & Martorell, P. (in press). The signaling value of a high school diploma. Journal of Political Economy. Jaeger, D., & Page, M. (1996). Degrees matter: New evidence on sheepskin effects in the returns to education. Review of Economics and Statistics, 78(4), 733–740. Layard, R., & Psacharopoulos, G. (1974). The screening hypothesis and the returns to education. Journal of Political Economy, 82(5), 985–998. Spence, M. (1973). Job market signaling. Quarterly Journal of Economics, 87(3), 355–374.

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Cultural Capital

Tyler, J., Murnane, R., & Willett, J. (2000). Estimating the labor market signaling value of the GED. Quarterly Journal of Economics, 115, 431–468.

CULTURAL CAPITAL The notion of cultural capital originated from Karl Marx’s concept of social relations as a form of capital (i.e., a relation between human beings where knowledge is fostered and exchanged). Pierre Bourdieu was the first scholar to clearly identify and articulate this sociological concept by defining “cultural capital” as associations used in reference to nonfinancial assets. According to Bourdieu, cultural capital manifests itself in three different forms: (1) embodied (e.g., knowledge consciously acquired through family and affiliations—a disposition of the mind and body), (2) objectified (e.g., possession of cultural goods such as books, instruments, and pictures, which can be used both for monetary gain and to convey possession of the cultural capital to others), and (3) institutionalized (e.g., education and institutional recognition). In addition to the summary of information on the concept of cultural capital, this entry includes implications of the concept as it pertains to the field of education. Also included is a critique of the concept and a description of how the concept of cultural capital has evolved given the increasing importance of information technology in the contemporary landscape. According to the theory of cultural capital, these assets encompass a nonmonetary “cultural” wealth of exchange obtained through affiliations, connections, and networking that constitutes a wealth of knowledge, which is established by and valued within the norms of the mainstream dominant group in power in any given society (macro) or entity (micro). On the macro level, the term is used to refer to standards established by members of the dominant groups in power, who impose meanings and define their own culture as worthy of being sought after and acquired. These standards are often prompted by factors such as affluence, societal status, race, and class. They are learned and then applied—consciously, subconsciously, or simply by affiliation—by persons who have been favored to have access to this knowledge through their upbringing (embodied), access to valued tools (objectified), and/or institutional gain (institutionalized). These

persons can then use this knowledge to gain status or individual advancement within the society as well as to drive evaluative perceptions (general or specific) of persons or groups. Similarly, on the micro level, the term is applied in a narrower sense where the dominant group in control of an entity or system (e.g., community, organization) influences and shapes the norms of what is meaningful and legitimate. These recognized norms are then applied by individuals who are privileged enough to obtain or have access to this knowledge in order to gain personal advancement and help promote a belief system, which facilitates organizational perceptions and judgments across various sectors within that community or entity. Once attained, cultural capital plays an essential and, at times, critical role in individual advancement toward achieving social status as well as other types of advancement—professional, financial, or of any other educational or material nature guaranteeing upward social mobility within a society or entity.

Implications of Cultural Capital in Education Cultural Reproduction

In relation to the field of education, the concept of cultural capital has been widely associated with the economic concept of cultural reproduction. Cultural reproduction within the educational system is the way in which individuals of the dominant mainstream society impose their definitions of what is significant in terms of knowledge—creating, facilitating, and imposing a value system in respect to what is worthy and legitimate. Thus, they reproduce the culture of dominant mainstream society and ensure ongoing advancement of those who are in power. This can all then be translated into wealth and power. That said, by association (often of a socioeconomic nature), cultural capital is not equally distributed among all students within the educational system, which naturally leads to differences both in how pupils are taught and in educational attainment (what is taught). In other words, those who are privileged by having been socialized into or associated with the dominant group in power have a natural advantage within society and/or the community. As a result, the educational attainment of various societal groups is directly correlated with the amount of cultural capital they possess, resulting in higher success rates among those pupils who are

Cultural Capital

either socialized into or associated with the dominant mainstream culture in power. The concept of cultural capital’s being used to reproduce the economic status quo of a society by means of the public education system has been well researched and documented in the empirical scholarly literature. In addition to Bourdieu’s work in this area, Samuel Bowles and Herbert Gintis as well as Jean Anyon have documented patterns of a close association between possession of cultural capital (i.e., pupils’ socioeconomic background and family affiliations) and cultural reproduction in schools (i.e., access to knowledge and how the capital of certain knowledge is distributed within educational entities). Bowles and Gintis assert that education agencies (i.e., schools) reproduce the social relations of a society at large to sustain the capitalistic system, mimicking the existing relationships within various class structures of that society. Similarly, Anyon has documented the disparate treatment of students by teachers based on pupils’ socioeconomic standing and background. These scholars argue that schools establish and promote a hierarchical structure, which has been firmly embedded into the education system, by designating pupils to their respective positions in society. Consequently, the education system inherently promotes as well as supports the reproduction of pupils’ economic status by categorizing students into the hierarchical system by means of the message conveyed through their relationships with educators within educational settings.

Critique of and Contemporary Evolution of the Concept Over the years, the concept of cultural capital has been applied in diverse ways, in part due to lack of conceptual clarity. Nan Dirk De Graaf and Gerbert Kraaykamp as well as Alice Sullivan assert in their work that this lack of clarity (mainly due to lack of a clear distinction between different kinds of cultural capital and their effects) leads to varied conclusions when the concept of cultural capital is used as a lens. Additionally, Paul DiMaggio calls for a continued investigation of the concept in relation to merit or “measured ability” as it relates to pupils’ educational attainment and, thus, academic performance, as compared with having predetermined notions associated with the concept of cultural capital’s being used as a vehicle for evaluating social reproduction in educational settings.

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The Age of Technology

With the rapid expansion of, and ongoing changes in, information technology as well as the critical role it plays in contemporary society, access to skills and knowledge associated with information technology could conceivably be considered as a form of cultural capital. In their work, Michael Emmison and John Frow extend the concept of cultural capital to include the system of knowledge encompassing information technology and its use. They argue that the possession of technology and technological tools (i.e., machines) and the ability to use these tools constitute an advantage in any given society and, thus, fall under Bourdieu’s objectified (i.e., having ownership of technology/machines) and embodied (i.e., the existing value system by association stressing the importance of having possession of technological tools) forms of cultural capital. Consideration of this new application and theorization of the concept suggests that Bourdieu’s original formulation of cultural capital and its forms is malleable enough to accommodate new systems of knowledge connecting socioeconomic status, academic achievement, and social advantage. Irina S. Okhremtchouk See also Access to Education; Behavioral Economics; Capacity Building of Organizations; Economics of Education; Social Capital

Further Readings Anyon, G. (1980). Social class and school knowledge. Curriculum Inquiry, 11, 3–43. Bourdieu, P., & Passeron, J. (1977). Reproduction in education and society. London, UK: Sage. Bowles, S., & Gintis, H. (1976). Schooling in capitalist America: Educational reform and contradictions of economic life. New York, NY: Basic Books/Harper. Emmison, M., & Frow, J. (1998). Information technology as cultural capital. Australian Universities’ Review, 41(1), 41–45. Sullivan, A. (2001). Cultural capital and educational attainment. Sociology, 35, 893–912.

CUMULATIVE ANNUAL GROWTH RATE See Compound Annual Growth Rate

D of inputs for producing a given level of outputs (output-oriented perspective). One main problem in measuring the efficiency of educational institutions is that they typically produce multiple outputs by employing multiple inputs, so that simple ratios between a single output and a single input (e.g., unit cost on a per-student base) cannot capture the complexity of the school or college’s production function. For measuring and calculating efficiency in these cases, DEA is proposed as a method that, on the basis of the observed data from a group of (homogeneous) organizations, estimates a production possibility frontier (which represents the different combinations of inputs to produce efficiently a given amount of output) and assesses the efficiency of each organization relative to the frontier. A graphical illustration can provide an intuitive idea of the method, considering five institutions (schools or universities), operating in the same context, named A, B, C, D, and E. All of them produce a single output y (e.g., measured through the number of degrees awarded) using two inputs X1 and X2 (e.g., the number of academic and nonacademic staff, respectively). In a certain period of time t, the relative performance of each organization can be observed graphically, by plotting the ratio X1/y against X2/y (see Figure 1). The line that connects the institutions A, B, D, and E acts as a boundary and is defined as the efficiency frontier, because none of these organizations can use less of both inputs than others on the frontier to produce the observed level of output; thus, they are all evaluated as (technically) efficient. Organization C, instead, is inefficient, because it is

DATA ENVELOPMENT ANALYSIS Data envelopment analysis (DEA) is a quantitative method, widely used in education as well as in other social sciences, to empirically measure the efficiency of organizations in producing their goods and services (outputs). In this entry, the first part illustrates the concept of production function, from which the definition of efficiency stems; then, the practical approach for the measurement of efficiency is described, and an overview of methodological problems is subsequently provided. A snapshot of recent developments concludes the entry. The production of an output is modeled through the use of a production function, which is a mathematical tool showing the amount of input that is necessary for producing a given amount of output. In education, output can be thought of as students’ achievement measured by test scores, graduation rate, numbers of degrees awarded, or other means; inputs, alternatively, can be assumed to be the human, physical, and financial resources used by schools or universities, such as teachers, academic or support staff, expenditures for materials and facilities, and so on. If an institution is not using the optimal amount of inputs for producing the corresponding level of output (in other words, if it is employing too many resources for this purpose), then, it is defined as inefficient. In this entry, the core concept is that of technical efficiency, or the ability to produce the maximum amount of output, given the available inputs (input-oriented perspective), or conversely the ability to minimize the use 191

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not on the frontier but outside it; economically, it could produce the same level of outputs by using fewer inputs, and the point C* is a hypothetical organization (obtained as a linear combination of B and D) that can produce the same output of C by using less of both inputs. The point C* is operationally used to calculate the efficiency score of the unit C, and particularly, this score is equal to the ratio between the length of the two segments OC*/OC. This measure of efficiency also gives information about how much input usage can be proportionally reduced by C without diminishing its output level. The reality is that many inputs and many outputs can be used and produced by educational institutions, so the estimation of the efficiency frontier is obtained through (linear) programming techniques. The contributions in the Further Readings provide details about the technical aspects of DEA mathematical formulation and use. It is nevertheless important to give a synthetic idea that the efficiency score of each organization is calculated as the ratio between a weighted sum of outputs and a weighted sum of inputs. Considering a generic educational institution j, which uses w inputs (x) to produce k outputs (y), the basic mathematical formulation of its (technical) efficiency score TEj is as follows: TE j

∑ = ∑

k

ay r =1 r rj w

q =1

,

bq xqj

X1/y

(1)

A

C B

C* D E O

Figure 1

X2/y

A Graph Illustrating Data Envelopment Analysis Using the Input-Oriented Framework

where a and b are the weights of each output yr and bq, respectively, to be calculated. When using DEA for efficiency analysis, the researcher must make some critical choices, which have an impact on the results. First, it should be decided whether to conduct the analysis in an output- or input-oriented setting; this decision is usually affected by assumptions about the incentives of educational institutions to increase output or to reduce inputs, respectively. Second, DEA models can assume constant or variable returns to scale, that is, if efficiency is affected or not affected by the institution’s size; in the variable returns to scale scenario, the existence of these potential scale effects is considered, so the frontier of efficient units is built by taking the different institutions’ scale of operations into account. Last, the choice of outputs and inputs is critical; there are no statistical tests to define if those selected are the most important ones, so the quality of the empirical work strongly depends on the researcher’s knowledge of the specific reality under analysis, that is, the ability to choose important outputs and relevant inputs related to them. DEA has been extensively used in educational research, since the first developments of the technique in the 1980s. Typically, DEA is employed to calculate the efficiency of groups (samples or populations) of educational institutions of the same level (primary or secondary schools, higher education institutions) in a single country or region. Most of the research literature on education using DEAs up until the early 2000s is reviewed in works by Andrew Worthington and Jill Johnes. Two recent developments are worth mentioning in this context. The first is methodological, as the models proposed by Leopold Simar and Paul Wilson, by means of the so-called bootstrapping procedure, which allows statistically checking the efficiency scores calculated through DEA, thus solving a major drawback of this technique. The second development is empirical, as some authors have started to compare the efficiency of educational institutions across countries (see, e.g., the work of Aleksandra Parteka and Joanna Wolszczak-Derlacz), introducing an international perspective in efficiency analyses that looks promising. Tommaso Agasisti See also Allocative Efficiency; Cost of Education; Technical Efficiency

Demand for Education

Further Readings Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-solver software (2nd ed.). New York, NY: Springer. Johnes, J. (2004). Efficiency measurement. In G. Johnes & J. Johnes (Eds.), International handbook on the economics of education (pp. 613–742). Chelthenam, UK: Edward Elgar. Parteka, A., & Wolszczak-Derlacz, J. (2013). Dynamics of productivity in higher education: Cross-European evidence based on bootstrapped Malmquist indices. Journal of Productivity Analysis, 40(1), 67–82. Simar, L., & Wilson, P. W. (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science, 44(1), 49–61. Worthington, A. C. (2001). An empirical survey of frontier efficiency measurement techniques in education. Education Economics, 9(3), 245–268.

DEMAND

FOR

EDUCATION

Demand for education refers to a family’s willingness to pay for children to attend school and, in the aggregate, the number of school slots that would be purchased at any price. In many cases, demand determines whether children have access to education, and demand-side obstacles are often blamed for low enrollment. This entry describes theories of the demand for schooling, factors that influence demand, demand-side obstacles to access to education, and selected policy remedies.

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alternative uses of resources—including consumption of goods or alternative investments. Investments will continue as long as the value of the future benefits exceeds the current costs. Each individual family will weigh the costs and benefits of schooling and determine how to consume based on the price. The aggregate demand curve for education is reflected in the total number of school slots that would be purchased at any price. The theoretical relationship between price and quantity demanded is negative. As prices decrease, more slots will be purchased, and as prices increase, fewer slots will be purchased. Measurement of the demand for education is quite different if we also consider the many social benefits of education. Mass education has many public benefits, such as reduced fertility, improved public health, and enhanced participatory democracy. These benefits might have no value for an individual family and, therefore, will not influence the private demand for schooling. From a social perspective, the private demand for education is too low, because it excludes the many social benefits that are external to parents’ investment decisions. Economists expect parents to underinvest in children’s education compared with the optimal outcome if all social benefits were considered. Compulsory attendance laws are a common strategy to set expectations for enrollment and attendance, but these laws are often inconsequential if families cannot afford to send children to school. Typically, compulsory attendance is coupled with full or partial education subsidies to increase social benefits without increasing costs to parents.

Factors That Influence Demand for Education Theories of Demand for Education Human capital economists model investments in schooling as a function of supply and demand, similar to consumption of other goods and services. Supply depends on how much it costs to build and run a school. Demand depends on consumers’ willingness and ability to pay. In the case of education, where parents typically make investment decisions on behalf of children, demand is a function of preferences of parents, their ability to pay, and their beliefs about the benefits of schooling. From an economic perspective, parents view education as an investment. They must put in money and effort today to produce benefits in the future, and these benefits may be uncertain or unknown. In a basic economic model, parents invest in schooling if it has the greatest payoff compared with

A family’s individual demand for education is derived from parents’ preferences and resources. Faced with an array of consumption and investment options, each individual family will choose a level of schooling that maximizes the family’s well-being. Factors that influence this choice are personal taste, alternative uses of children’s time, indirect costs of schooling, perceptions of future benefits, and financial resources. These factors combine to determine a family’s investment choice, and each factor can increase or decrease the likelihood that a child will attend school. Tastes are determined by a family’s experiences and beliefs regarding the intrinsic value of education. Educated parents who have experienced the benefits of school may have stronger preferences for education and a greater demand. Tastes may also

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determine if the available supply of schools conforms to parents’ values. For example, demand will be low if parents prefer single-sex schooling when only coeducational schooling is available, or if parents prefer secular education when only religious schools are available. A misalignment of school characteristics with local values and preferences will reduce demand and access. Alternative uses of children’s time create opportunity costs of attending school, as families must give up existing benefits to access the benefits of education. Opportunity costs can arise from children’s participation in paid labor during school hours. This effectively adds to the costs of school, as parents pay for school in both direct costs and lost child wages. The true cost of schooling in the presence of child labor is often difficult to observe. Many children provide unpaid labor in the form of domestic chores or work on a family farm. Although this work is not transparently valued with a wage, the value to the family will influence demand for schooling, and where the need for child labor is high, demand for education will be low. In addition to opportunity costs of work, there are many indirect costs of attending school that can influence demand. These are costs of goods or services that facilitate participation such as transportation, clothing, food, books, and supplies. Although public schools in developed countries typically support most indirect costs, children in developing countries often pay many of these costs on top of tuition. It is also common for students to bear the costs for required exams and tutoring to advance from primary to secondary school and from secondary school to tertiary education. Because these costs add to the total costs of school, increases in indirect costs will reduce the demand for education. Because education is an investment rather than a good that is consumed, expectations for future returns also influence demand. Education is expected to have both monetary and nonmonetary benefits for children in the future. Monetary benefits include increased job opportunities with higher wages and increased productivity in family production. Nonmonetary benefits include opportunities for further schooling, social mobility, prestige, and personal enrichment. Future benefits of education are high when labor markets and social structures reward education with high returns, and in these contexts, demand for education will be high. Demand for schooling will be lower when there are fewer jobs or social opportunities that reward education.

When individuals make investments for themselves, consideration of future benefits is fairly straightforward, and the individual will consider the full present value of future benefits. The role of future returns is more complicated when parents make investments on behalf of children. In this case, self-interested parents may consider both the value of future benefits and the likelihood that these benefits will be transferred to parents. Norms for how adult children transfer wages to parents vary with the social context. While it is likely that altruistic parents have strong preferences for their children’s future success regardless of monetary gain, economists expect demand for education to be higher when parents tangibly benefit from children’s future wages. The final factor in demand for education is family resources. A family with no financial resources cannot purchase education even in the face of strong preferences and high returns. In theory, parents could borrow to purchase schooling if the returns are sufficient to cover the cost of borrowing. This is a common practice for higher education in the United States through a vibrant student loan market. Poor families in developing countries rarely have access to credit markets, and the future benefit of schooling may be too distant and uncertain to support borrowing. This can leave parents with virtually no demand for education due to inability to pay, even if parents recognize the benefits of education and want their children to attend school.

Demand-Side Obstacles to Education Access to education begins with an adequate supply of schools with a seat for every child. Once schools are provided, enrollment can remain low due to demand-side obstacles. Attention to the demand side helps identify why some countries maintain low enrollment despite adequate supply. According to the United Nations Educational, Scientific and Cultural Organization, 57 million primary schoolage children do not attend school, and UNICEF’s (United Nations Children’s Fund) State of the World’s Children report estimates that 9% of children worldwide are out of school. In the least developed countries, 39% of primary school-age children do not attend school. It is difficult to determine the relative importance of supply and demand in a lack of access to education, but many demand-side obstacles to schooling are prevalent in developing countries.

Demand for Education

The first and most pervasive demand-side obstacle to schooling is poverty. Globally, UNICEF reports that 22% of children live in poverty, and in developing countries, more than half of the children are poor. A lack of access to resources and credit can prevent parents from enrolling children in school even if returns are high, slots are available, and parent preferences strongly support sending children to school. A second pervasive demand-side obstacle is child labor. Outlawing child labor does not remove the underlying economic need, so children continue to work even in countries where child labor is banned. UNICEF reports that globally 15% of children of ages 5 to 14 are involved in child labor, with 23% involved in child labor in the least developed countries. A third obstacle is direct and indirect school costs. According to household responses from U.S. Agency for International Development surveys in four sub-Saharan African countries, the total cost of primary schooling is approximately 3% to 4% of per capita income. Many countries have implemented subsidies to reduce costs of tuition and fees, but the largest costs reported in U.S. Agency for International Development’s EdData are for complementary goods such as school uniforms, supplies, and textbooks, and services such as tutoring and transportation. A final important demand-side obstacle is low returns to schooling. While there are documented social benefits of education, financial returns to students depend on local economic conditions. Studies by the World Bank economist George Psacharopoulos identify large differences in returns to schooling across regions and income groups. Individual returns will depend on access to labor markets that reward schooling with increased wages. Importantly, obstacles to schooling do not affect all children equally. The factors that influence demand often vary by individual and family characteristics. For example, it is common to observe gender differences in many of these factors. In countries with a history of gender discrimination and low female enrollments, parents may begin with different preferences for education for daughters and sons. It is also common for opportunity costs to vary by gender as girls and boys typically provide different types of labor. Future returns in the context of gendered labor markets will also contribute to unequal demand, as will different gender norms in the expectation of future transfers of wages to parents.

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In theory, any gender differences in the factors underlying demand will lead to gender differences in access to education. Thus, demand-side factors can often explain both low enrollment and gaps or inequalities in enrollment.

Demand-Side Education Policies Awareness of demand-side obstacles to schooling has led to the design and implementation of demandside interventions as a strategy to improve access when enrollments are low despite an adequate supply. To respond to poverty as an obstacle to schooling, countries can reduce the costs to parents using subsidies or provide cash assistance to increase family resources. To respond more directly to opportunity costs of children’s time, many countries have experimented with targeted scholarships and conditional transfers, which provide assistance conditioned on school attendance or performance. Adding conditions to cash assistance is intended to alleviate poverty while also directly incentivizing education investment over child labor or consumption. Other strategies provide in-kind assistance to overcome indirect costs of schooling such as food assistance, transportation, or free uniforms or textbooks. Jane Arnold Lincove See also Access to Education; Benefits of Primary and Secondary Education; Economic Development and Education; Educational Equity; Labor Market Rate of Return to Education in Developing Countries; Opportunity Costs

Further Readings Filmer, D. (2004). If you build it, will they come? School availability and school enrollment in 24 poor countries (World Bank Working Paper No. 3340). Washington, DC: World Bank. Herz, B., & Sperling, G. (2004). What works in girls’ education? Evidence and policies from the developing world. New York, NY: Council on Foreign Relations. Lincove, J. A. (2006). Equity, efficiency, and girls’ education. Public Administration and Development, 26, 339–357. Psacharopoulos, G., & Patrinos, H. A. (2004). Returns to investment in education: A further update. Education Economics, 12(2), 111–134. United Nations Children’s Fund. (2013). The state of the world’s children 2013: Children with disabilities. Retrieved from http://www.unicef.org/sowc2013/files/ SWCR2013_ENG_Lo_res_24_Apr_2013.pdf

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Department of Defense Schools

DEPARTMENT SCHOOLS

OF

DEFENSE

Department of Defense Education Activity (DoDEA) is a U.S. government agency that administers a PreK-12 curriculum serving school-age children of military personnel stationed on bases in the United States and overseas. It is the functional equivalent of a large American public school district. This entry provides a descriptive overview of DoDEA, with a specific focus on nine system components: (1) history, (2) organization, (3) enrollment, (4) location, (5) demographics, (6) financial resources, (7) assessment systems, (8) performance, and (9) key components.

History Shortly after World War II, the U.S. military established elementary, middle, and high schools for the children of service men and women stationed overseas and expanded the number of schools for military dependents in the United States. Notably, a smaller set of schools located principally on military bases in the southeastern United States was well established at this time. Both the newly created and more established schools were originally governed by the military service branches they served (e.g., Army and Navy); responsibility was transferred to civilian management soon after the expansion and divided into two distinct but parallel systems: The Department of Defense Dependents Schools (known as DoDDS, for military families based overseas) and the Department of Defense Domestic Dependent Elementary and Secondary Schools (known as DDESS, for military families based in the United Sates). The two systems were unified under the DoDEA in 1994. Military personnel must live on the military installation (e.g., base or post) to enroll their dependents in the DDESS system.

Organizational Structure DoDEA is a federal agency in the Office of the Secretary of Defense. DoDEA operates with direction, control, and oversight from the Office of the Under Secretary of Defense for Personnel and Readiness and the Deputy Under Secretary of Defense for Military Community and Family Policy. This coordinated organizational structure provides the focal point within the U.S. Department of

Defense for policies designed to support and enhance quality-of-life programs for service members and their families worldwide. The agency’s headquarters are located in Alexandria, Virginia. The Director of DoDEA oversees three geographically distinct regions: DoDDS-Europe, DoDDS-Pacific (includes DDESS-Guam), and DDESS (U.S.-based schools). Each region is managed by an area director; within each of these three areas, schools are organized into districts headed by superintendents. District- and school-level councils comprising locally elected parents and DoDEA school employees advise superintendents and principals on curricular, budget, and support-related functions.

Enrollment In 2012–2013, DoDEA enrolled approximately 81,000 students in 191 schools across a total of 14 districts in the United States, Europe, and Pacific areas. This is about the same number of students enrolled in the Orleans Parish Schools in New Orleans, Louisiana, which is ranked 38th nationally in size of enrollment. The current enrollment levels in DoDEA reflect a steady decline over the past decade, from the system’s total enrollment of approximately 112,000 students across 227 schools in 2000–2001. Over this period, DoDEA has merged some DDESS (U.S.) districts within states and eliminated a portion of DoDDS (overseas) schools in conjunction with a “draw down” of military personnel and closure of U.S. military installations across Europe and the Pacific, specifically. Today, the DoDEA system includes 12,580 full-time employees. Almost 31,000 students are enrolled in the largest of the three regional systems—DoDDSEurope, with 75 schools. DDESS (U.S.) enrollment ranks second within the system, with 26,700 students in 65 schools; enrollment in the Pacific area’s 50 schools reached just more than 23,400 in 2012– 2013. In sum, less than 7% of the 1.2 million (total) school-age children of active U.S. military personnel attend DoDEA schools. Notably, the vast majority of military children attend a public school in one of the more than 600 civilian public school districts located near military installations in the continental United States. The Military Child Education Coalition and homeschooling advocacy groups estimate that between 5% and 9% of all military children are homeschooled compared with a national rate of 3% (see Table 1).

Department of Defense Schools

Table 1

School Locations

DDESS

DoDEA-Europe

DoDEA-Pacific

Alabama

Bahrain

Guam

Georgia

Belgium

Japan

Kentucky

England

South Korea

New York

Germany

Okinawa

North Carolina Italy South Carolina The Netherlands Virginia

Portugal

Puerto Rico

Spain

Cuba

Turkey

Note: DDESS, Defense Domestic Dependent Elementary and Secondary Schools; DoDEA, Department of Defense Education Activity.

Demographics The student mobility rate among military dependents is 31%. Military families move from their home military installation to another installation every 3 years, on average; this includes DoDEA families. The non-White or “minority” student population in DoDEA includes families who self-identify as African American, Asian, Hispanic, Pacific Islander, or Native American. The minority student population in DoDEA is about 40% (similar to the Alabama public school percentage and the national average) and is reflective of the demographic portrait of the enlisted ranks across the branches of the U.S. military. Approximately, 45% of all DoDEA students qualify for free and reduced-price meals. This is reflective of the generally low pay scales for junior- and midlevel enlisted ranks. Enlisted personnel include about 80% of all DoDEA military families, most of whom have completed high school only. Single-parent households account for about 6% of all military families contrasted with a national rate of about 28%. In the DDESS system, more than 60% of pupils enrolled in the schools are affiliated with the U.S. Army, followed by the U.S. Marine Corps at about 16%. In the DoDDS system, the distribution is different; approximately one third of all students enrolled in the overseas system are affiliated with the Army, with another one third linked to the Air Force.

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average but on par with similarly large, urban school districts. The DoDDS (overseas) system has slightly higher operating costs and is funded at a slightly higher (5%) rate. DoDEA schools do not receive federal grant programs (e.g., Title I), in-state department of education support, or private sector donations. Although DoDEA schools provide certain federally mandated services, such as special education, DoDEA schools do not receive additional funding for these programs; the system utilizes per-pupil funding to cover all student and personnel operating expenses. DoDEA headquarters in Alexandria, Virginia, function as a state department of education for DoDEA districts in allocating services and funds. The U.S. Congress approves all federal appropriations for DoDEA as part of routine congressionally mandated budgeting matters for the U.S. Department of Defense. Teacher salaries and benefits consume the largest proportion of DoDEA (and U.S. public school systems) operating budget expenditures. Maintaining competitive teacher salaries is a top priority for DoDEA, and this is reflected in slightly higher (5%–10%) average starting teacher salaries, and even higher (10%–15%) teacher compensation levels for the most experienced teachers, as compared with public school systems in close proximity to DoDEA school districts.

Assessment DoDEA monitors and measures student progress with performance-based, standardized assessments. DoDEA students in Grades 3 through 11 complete the Terra Nova standardized achievement tests. The Terra Nova test is a norm-referenced achievement test and provides DoDEA with results that can be compared with a nationwide sample. DoDEA students also participate in the National Assessment of Educational Progress (NAEP), sometimes called the “Nation’s Report Card.” NAEP provides a national, 50-state snapshot comparison of student achievement (and DoDEA) in reading, mathematics, science, and writing. DoDEA schools are accredited by the North Central Association Commission on Accreditation and School Improvement or the Southern Association of Colleges and Schools Council on Accreditation and School Improvement.

Financial Resources

Performance

DoDEA operates with approximately 18% higher average per-pupil expenditures than the national

The overall average performance of DoDEA students on the NAEP is impressively high. For example, the

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DoDEA average scores on the fourth- and eighthgrade NAEP reading assessment was higher than the average scores in 48 states/jurisdictions and not significantly different from those in 3 states/jurisdictions. The results in the fourth- and eighth-grade mathematics were slightly lower, though DoDEA continued to outperform most states in this subject area. In addition to high overall scores over successive years of NAEP testing across all subject areas (math, science, reading, and writing), the scores for African American and Hispanic students in DoDEA are among the highest in the nation; the achievement gap between White students and African American students in DoDEA, and that between White students and Hispanic students in DoDEA, is significantly below the national average. In the 2013 NAEP results, for example, DoDEA’s African American eighth-grade students scored an average of 16 points lower than White students’ average score of 282—the national achievement gap for their public school peers on NAEP is 25 points. Hispanic fourth-grade students in DoDEA scored an average of 9 points lower on the mathematics assessment than the White students’ average score; the national achievement gap for White and Hispanic students on this assessment is 20 points. DoDEA employs a standards-driven, continuous improvement plan throughout the system, known as the Community Strategic Plan (CSP). The CSP establishes systemwide goals, action plans, and performance measures that define DoDEA’s organizational and academic components, while allowing individual districts and schools to develop implementation frameworks linked to the CSP. DoDEA is in the process of implementing the Common Core State Standards in mathematics and English language arts.

3. Sufficient financial resources linked to instructional-focused strategic goals 4. Intensive staff development anchored to school improvement goals and student performance, sustained over time 5. Small school size, conducive to strong communication and continuous socioemotional support for families and children 6. Academic rigor and high expectations for all students 7. Nationally recognized, high-quality preschool and afterschool programs 8. Strong organizational culture among military leaders and teachers pegged to educating all children at the highest levels of quality and performance in response to the unique demands of military life

Conclusion DoDEA schools are embedded in a system characterized by strong structural alignment, ample resources, productive professional development, and high-quality teaching. DoDEA has a record of sustained and strong performance on the NAEP and Terra Nova assessments. Further research is needed to determine (a) whether these aspects and outcomes are unique to the military culture and military tradition of structure, discipline, and uniformity and (b) how student achievement in DoDEA is associated with both a supportive military culture and a highly effective school context. Answers to these policy questions are needed to fully understand how the success of DoDEA may point to adaptable performance-based frameworks for state and local education leaders in the United States. Claire Smrekar

Key System Components In 2001, the National Education Goals Panel commissioned a report by researchers at Vanderbilt University to explore reasons for the high academic performance of DoDEA students. The report pointed to the in-school and out-of-school factors that define the organizational profile and drive the strong performance of this system: 1. Centralized direction setting and sustained policy coherence 2. Localized data-driven decision making linked to the CSP

See also Accountability, Standards-Based; Capacity Building of Organizations; Comprehensive School Reform; Evolution in Authority Over U.S. Schools

Further Readings Department of Defense Education Activity. (2013). About DoDEA. Retrieved from http://www.dodea.edu/home/ index.cfm Esqueda, M. C., Astor, R. A., & Tunac De Pedro, K. M. (2012). A call to duty: Educational policy and school reform. Educational Researcher, 41(2), 65–70. Smrekar, C., Guthrie, J. W., Owens, D., & Sims, P. (2001). March toward excellence: School success and minority

Deregulation student achievement in Department of Defense schools. Washington, DC: National Education Goals Panel.

DEREGULATION Deregulation occurs when rules, regulations, and/or oversight are eliminated or reduced in an organization or a system. In the educational context, deregulation generally refers to reducing or eliminating the federal, state, or local constraints that limit the choices of educational decision makers. Although distinct, deregulation may be related to and overlap with other concepts, including decentralization, privatization, and (in the opposite direction) standardization. Applying the taxonomy developed by Joseph Stiglitz, there are four types of educational regulations: (1) disclosure requirements, (2) restrictions, (3) mandates, and (4) ownership restrictions. Disclosure requirements force school districts to reveal information about the educational process and its outcomes. Restrictions prohibit school districts from engaging in particular behaviors (e.g., increasing class sizes or hiring uncertified teachers). Mandates require school districts to take certain actions (e.g., delivering a designated curriculum or providing special education services). Ownership restrictions prohibit certain entities (e.g., for-profit corporations) from operating schools or districts. Although deregulation can involve any of these types of regulations, most recent deregulatory efforts have focused on removing restrictions (including ownership restrictions) on the educational process. Popular initiatives include the establishment of charter schools, alternative teacher certification programs, and block grants in state funding models. This entry includes a brief history of educational regulation in the United States, a review of the expected benefits and costs from deregulation, and a discussion of each of these popular deregulatory initiatives.

A Brief History of Regulation in U.S. Public Education When Europeans first settled in colonies that would eventually become the United States of America, they established educational practices that included private tutoring in their homes, community schools, dame schools, and church-affiliated institutions of higher education. The schools were based on

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European models of education, and each community, family, or faith group decided what type of school would best serve its children. There was little formal regulation of the educational process. Educational regulation remained limited until the common school movement gained traction in the early 19th century. The early leaders Horace Mann and Henry Barnard drew public attention to inconsistencies in the education that students received and in the preparation and payment of teachers. They argued that schools were often in poor condition, and the absence of a school system led to the inefficient duplication of resources. They recommended the establishment of state-funded systems to standardize education for all eligible public school students. All U.S. states eventually adopted at least some of their recommendations. Over time, schools and school systems became increasingly centralized, professional, and bureaucratic. States set broad standards of what education should look like within their borders and introduced restrictions and mandates for certifying and paying teachers, the academic calendar, and safety criteria for school buildings. The federal government made its first comprehensive move into the governance of public schools with the Elementary and Secondary Education Act of 1965. This law extended the efforts of the civil rights movement and the War on Poverty into education and created Title I, a federal program that provides funding for compensatory education services for low-income students. Shortly thereafter, amendments to the Elementary and Secondary Education Act created federal programs for special education (Title VI, which became the Individuals with Disabilities Education Act) and bilingual education (Title VII, which became Title III in the No Child Left Behind Act of 2001, or NCLB). Each program came with regulatory strings attached. Federal legislation for education continued to grow over the ensuing years. The 1994 reauthorization of the Elementary and Secondary Education Act, the Improving America’s Schools Act (IASA), was heavily influenced by the school accountability movement, which emphasized the outputs of the educational process rather than the inputs. The Improving America’s Schools Act introduced federal mandates for standardized testing and required disclosure of those test scores. The NCLB mandated testing in more grades and subjects than the Improving America’s Schools Act. It also expanded disclosure requirements to

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include reporting test scores for subgroups of students (e.g., race/ethnicity and low-socioeconomic status students) and informing parents whenever teachers were not “highly qualified.” States that did not achieve adequate yearly progress on standardized tests risked the loss of Title I funding. Technically, states were not required to follow the NCLB guidelines regarding testing and disclosure, but most complied and imposed the NCLB requirements on school districts because they risked losing Title I funding if they did not. Beginning in 2011, the U.S. Department of Education began granting waivers to many of NCLB’s restrictions, but notably the waivers do not free states or school districts from the mandate for testing or the disclosure requirements.

Expected Benefits of Deregulation Advocates for deregulation in education use a number of arguments to make their case. First, many argue that deregulation will lead to increased efficiency in the education sector. Efficiency in education results when schools produce as much student learning as possible given their limited resources and the educational context in which they operate. Different educational environments require a different mix of resources. Schools that are highly regulated by the state, however, do not have the opportunity to respond to local conditions. They are told how many students each teacher should have, how much money to spend for particular program areas, which materials and technology they can choose from to purchase for their courses, and how curriculum should be delivered. Researchers have demonstrated that many school districts are allocatively inefficient and could produce significantly higher levels of student performance if they were allowed to reallocate their budgets in a deregulated system. Furthermore, there are reasons to suspect that some regulations do not represent educational best practices for any district. The theory of regulatory capture suggests that relatively small groups of individuals (e.g., parent organizations or teachers’ unions) can exert undue influence on the rule-making process through effective and persuasive lobbying. Their efforts can lead to regulations that benefit their groups (or the students they represent) at the expense of other, less influential groups. Removing regulations that arise from regulatory capture can therefore enhance the equity as well as the efficiency of the educational system.

Some proponents of deregulation argue that regulation stunts innovation. When school districts have little or no discretion over their educational practices, they have no opportunity to experiment and discover more effective practices. One of the arguments put forth by charter school advocates is that charter schools can serve as educational laboratories wherein new educational practices can be refined and poor educational practices can be recognized. Removing ownership restrictions—such as has occurred with the charter school movement—can introduce much-needed competition into the public school system. When parents and students have more schools from which to choose, schools and districts have sharper incentives to provide a highquality education for their clients—the students— and the quality and efficiency of the educational system should increase.

Expected Costs of Deregulation Two of the potential benefits of deregulation—efficiency and equity—can also be costs of deregulation. Efficiency concerns arise from the observation that school administrators do not necessarily share the goals and objectives of parents and taxpayers. Because monitoring the behavior of school district administrators is costly and the outputs of the educational process are difficult to measure, there is a classic principal-agent problem in education wherein school district administrators are the agents, and parents and taxpayers are the principals. Well-designed regulations force administrators to advance the interests of the parents and taxpayers instead of their own interests or the interests of school district personnel; deregulation allows school administrators to revert to pursuing their own objectives or those of powerful groups. For example, some argue that absent regulations, administrators will be unable to resist pressures from teachers’ unions and will allocate too many resources toward salary increases for union members. Equity concerns stem from the role of regulations in standardizing educational practices. When binding regulations force school districts to use common resource allocation and instructional practices, there is an expectation that educational outcomes will also be standardized. When schools and districts are left to their own devices, one expects more variation in outputs and correspondingly more potential for inequity across schools and districts. Deregulated school districts might also be tempted to reallocate

Deregulation

resources toward school personnel or squeaky-wheel parents and away from programs that currently serve historically marginalized or politically powerless student groups. As such, deregulation can lead to increased inequity within schools and districts as well as among them. When deregulation takes the form of increased school choice, there is also a concern that deregulation can undermine efforts at desegregation. Evidence suggests that charter schools tend to be more demographically homogeneous than are traditional public schools. Therefore, segregation can be an unintended and undesirable consequence of deregulation.

Selected Deregulatory Initiatives A number of deregulatory efforts in education have taken root in recent years, and many states have changed their regulatory policies as a result of the incentives offered by federal programs, including the U.S. Department of Education’s Race to the Top (RTT) competition. One of the most prominent deregulatory initiatives encouraged by Race to the Top is charter schools. Charter schools are a relatively new class of deregulated public schools that are not required to comply with all of the same restrictions and mandates as traditional public schools. For example, teachers in Texas charter schools do not have to be certified, and charter schools are given greater discretion over their curriculum and instructional methods. On the other hand, Texas charter school students are held to the same standards as other public school students, and charter schools have to meet the same accountability and disclosure requirements through state standardized exams. Since charter schools operate under fewer regulations than traditional public schools and, in most cases, represent a relaxation of ownership restrictions, they are viewed as a move toward deregulation and decentralization in education. Deregulatory initiatives have also extended to teacher preparation programs. In recent years, the perceived need for more teachers has led to the establishment and expansion of alternative certification programs, in which individuals with bachelor’s degrees in any area can take a prescribed series of education courses to prepare them to enter the classroom. Some of the individuals who go through these programs are midcareer switchers who enter teaching with experience in another field, which supporters have viewed as a potentially valuable resource in the educational setting. Detractors, on the other

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hand, have questioned the quality of alternative certification programs, which vary widely in the number and types of courses students have to take as well as the length of the programs overall. Reforms replacing categorical funding with block grants represent yet another type of deregulatory initiative. Categorical grants are financial allocations that are dedicated to a specific purpose; block grants are financial allocations with broader guidelines. In 2009, the California state legislature transformed more than 40 categorical grant programs—including programs specifically for adult education, career and technology education, and teacher professional development—into block grants that could be used for any educational purpose. This transformation was a temporary measure, designed to soften the blow of substantial budget cuts by providing districts with more financial flexibility. In 2013, California introduced a more permanent change with the enactment of the Local Control Funding Formula, which eliminated approximately three quarters of California’s categorical programs. Funding for school districts and charter schools is now based on their average daily attendance, with additional funding for English Language Learners, students receiving free or reduced-price lunches, students in specific grades, and students in foster care. Lori L. Taylor and Paige C. Perez See also Block Grants; Categorical Grants; Centralization Versus Decentralization; Charter Schools; Economic Efficiency; Educational Equity; No Child Left Behind Act; Privatization and Marketization

Further Readings Bifulco, R., & Ladd, H. F. (2006). School choice, racial segregation, and test-score gaps: Evidence from North Carolina’s charter school program. Journal of Policy Analysis and Management, 26(1), 31–56. Brewer, D. J., & McEwan, P. J. (Eds.). (2010). Economics of education. Oxford, UK: Academic Press. Chubb, J. E., & Moe, T. M. (1990). America’s public schools: Choice is a panacea. Brookings Review, 8(3), 4–12. Ferreyra, M. M., & Liang, P. J. (2012). Information asymmetry and equilibrium monitoring in education. Journal of Public Economics, 96(1–2), 237–254. Ferris, J. M. (1992). School-based decision making: A principal-agent perspective. Educational Evaluation and Policy Analysis, 14, 333–346. Grosskopf, S., Hayes, K. J., Taylor, L. L., & Weber, W. L. (1999). Anticipating the consequences of school reform: A new use of DEA. Management Science, 45(4), 608–620.

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Gutek, G. L. (2013). An historical introduction to American education (3rd ed.). Long Grove, IL: Waveland Press. Legislative Analyst’s Office. (2013). An overview of the Local Control Funding Formula. Retrieved from http:// www.lao.ca.gov/reports/2013/edu/lcff/lcff-072913. aspx#Spending_Restrictions Stiglitz, J. E. (2010). Government failure vs. market failure: Principles of regulation. In E. J. Balleisen & D. A. Moss (Eds.), Government and markets: Toward a new theory of regulation (pp. 11–51). Cambridge, UK: Cambridge University Press. Weiher, G. R., & Tedin, K. L. (2002). Does choice lead to racially distinctive schools? Charter schools and household preferences. Journal of Policy Analysis and Management, 21(1), 79–92. West, M. R., & Woessmann, L. (2009). School choice international: Higher private school share boosts test scores. Education Next, 9(1), 54–61. Weston, M. (2011). California’s new school funding flexibility. San Francisco: Public Policy Institute of California.

DESEGREGATION School desegregation refers to the process of ending or minimizing racial separation of students in schools, or overcoming the effects of residential segregation on local school demographics. The Brown v. Board of Education ruling of 1954, which prohibited de jure segregation and became the cornerstone of the social justice movement of the 1950s and 1960s, led to desegregation programs that substantially reshuffled students across schools. As a result, progress was made toward a more balanced racial composition of schools. This entry discusses the nature of school desegregation policies that followed the Brown v. Board of Education ruling, measures of segregation or desegregation, and the effects of school desegregation policies on school enrollment patterns, as well as on student achievement. As the term school desegregation is usually applied to K-12 education, this is the focus of this entry. However, the entry notes some long-term student outcomes when discussing the effects of school desegregation policies. In 1954, a large proportion of U.S. schools were racially segregated. This was especially the case for 17 states where school segregation was required by law. Four other states permitted segregation but did not require it. This phenomenon was legalized by

the ruling in Plessy v. Ferguson of 1896 that held that segregated public facilities were constitutional so long as African American and White facilities were equal to each other. This was changed by the Brown v. Board of Education ruling, when the Supreme Court unanimously decided that racial segregation of children in public schools violated the Equal Protection Clause of the Fourteenth Amendment, which guarantees equal education to all. Based on evidence from psychological studies of African American children that related low selfesteem to segregated schooling, the Supreme Court concluded that even if African American and White school facilities were equal, racial segregation in schools was “inherently unequal,” and so it was unconstitutional. Despite historical desegregation efforts, the unequal access by minority students to high-quality education and the implications that this has on limiting opportunities for these students is still a big concern. In particular, there is a big concern that isolation of low-income African American students in high-poverty schools with higher proportions of minority students could be increasing. As of 1970, on average, an African American student attended a public school with an enrollment rate of White students of around 32%. This figure increased during the 1980s and fell only slightly during the 1990s, with average White enrollment rates of around 36% and 35%, respectively, in public schools attended by African American students. However, since the 2000s, exposure of African American students to White students has been declining in public schools. As of 2010, African American students attended, on average, public schools with White student enrollment of only around 29%.

Measurement of Segregation and Desegregation There are numerous different indexes that have been proposed in the literature to measure segregation levels. In general, the proposed indexes aim at summarizing aspects such as the differential distribution of ethnic groups (evenness), the level of potential contact and interaction between members of different ethnic groups (exposure), level of concentration relative to the amount of space occupied by a minority group (concentration), or location and clustering of a given ethnic group. However, social scientists often focus only on measures of evenness and exposure in their applied work. In this respect, the most

Desegregation

commonly used segregation measures are the socalled dissimilarity index and isolation measures. Isolation is a measure of exposure and captures the extent to which students of a given ethnic group are exposed to one another as opposed to students from other ethnic groups. The isolation measures described in the section above are an example of such measures. One concern with isolation measures is that their trend can be explained by a natural decline in the number of students of the comparison ethnic group. That is, the previously described decreasing trend on the level of exposure of African American students to White students could be explained by the natural decline of the White student population attending public schools. The dissimilarity index aims at avoiding being driven by demographic trends by measuring evenness. That is, applied to school segregation, the dissimilarity index captures the percentage of students of a given ethnic group who would need to change schools for each school to have the same distribution of students of that particular ethnic group as there are in the whole population of a school district or state. If all schools in the population of study (i.e., school district and state) had the same proportion of students of a given ethnic group, this index would take value 0. If, on the other hand, schools were totally segregated and had only students of one ethnic group, this index would take value 1. BlackWhite dissimilarity levels in U.S. public schools had remained fairly stable since the 1990s until 2009. This leads to the conclusion that observed patterns of changes in African American isolation levels could be driven by changes in the racial composition of students in public schools but not to more unequal distribution among schools. This conclusion changes, however, if we focus on certain states where desegregation efforts were more prominent during the 1970s and 1980s. For instance, if we look at the southern states, we do find slight increases in African American/White school dissimilarity measures during this period.

School Desegregation Policies Following the Brown v. Board of Education ruling, districts around the country have used a number of diverse policies to desegregate schools. It should be stressed, however, that the type and intensity by which different jurisdictions used these desegregation policies varies considerably across the United States.

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Due to patterns of residential segregation, the first and principal tool for racial integration was the use of busing to transport students into schools in a way that a more balanced ethnic composition was achieved. In fact, by the 1970s and 1980s, under federal court supervision, many school districts implemented mandatory busing plans. After the 1980s, busing for desegregation started to decline, with many families opposed to having to send their children to faraway schools in unknown neighborhoods. In the early 1990s, the Supreme Court’s ruling on three cases made it easier for districts to be released from court supervision and reduce desegregation efforts. In 1991, ruling in Board of Education of Oklahoma City v. Dowell, the Supreme Court decided that desegregation orders were intended to be temporary and that a return to local control was preferable when a district had made a good faith effort to desegregate its schools. This was reinforced in 1992, when the Supreme Court ruled in Freeman v. Pitts that a school district could be released from active judicial oversight of a required desegregation plan before the district was declared “unitary,” or free of discrimination, as long as officials observe certain specified equitable principles. Finally, in Missouri v. Jenkins in 1995, the Court overturned a lower court ruling requiring salary increases and remedial education programs to correct de facto racial inequality. As a result of these rulings, opposition from families, and the natural decline in residential segregation, most school districts were released from court supervision and ceased using mandatory busing to try to desegregate schools. Several studies have analyzed the effect of release from court order on segregation trends and found possible increases in segregation levels, especially in the South. As a result, these three court rulings are seen by some as the beginning of resegregation trends across American schools. Another major ruling dealing with school desegregation came in 2007, when the Supreme Court declared unconstitutional the voluntary desegregation plans in Seattle, Washington; and Louisville, Kentucky. In the case Parents Involved in Community Schools v. Seattle School District No. 1, the Court held that the plans using race in student assignment were not narrowly tailored to meet a compelling interest. However, many school districts continue to use voluntary desegregation practices, including the following:

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Open enrollment: It allows students to freely attend any school in a district (intradistrict open enrollment) or in a different district if both sending and receiving districts authorize the move (interdistrict open enrollment). Open enrollment policies can be either mandatory or voluntary, depending on whether the districts in the state are required to participate in the program or can choose whether to participate. In all, 21 states currently have interdistrict mandatory open enrollment policies in place. These include, for example, Washington, Arizona, Arkansas, and Vermont, among others. Magnet schools: In these schools, part of school desegregation plans typically use racial guidelines in their admissions processes, which have been seen by states as a way to encourage voluntary desegregation. By offering a specialized curriculum, they aim to attract interested students from all across their districts. In districts such as Kansas City Public Schools, magnet schools are also seen as a way to keep White students within the district, whose families might send them to another district in reaction to mandatory desegregation. Neighborhood attendance zones and rezoning: These allow students only to attend their neighborhood school and have also been used as a way to achieve higher levels of desegregation. Pairing and clustering: By these processes, students are reassigned among a pair or cluster of schools, usually by restructuring the grades taught in each school.

Effects of School Desegregation Policies on School Enrollment Finis Welch and Audrey Light carried out a seminal study in 1987, based on an analysis of 125 large cities, on the effects of desegregation on school racial integration and school enrollment for different ethnic groups. Their study showed that desegregation policies following the Brown v. Board of Education ruling had resulted in greater racial balance in many school districts. The study also documented the exodus of White students that followed desegregation policies in many cities, reducing the impact of school desegregation policies by limiting interracial contact among students. However, the effects varied considerably across different districts depending on the intensity of desegregation efforts and type of

desegregation policies used. Not surprisingly, mandatory desegregation actions, such as busing or pairing and clustering, produced not only the largest desegregation effects but also the largest average drops in enrollment of White students. Finally, this study also showed that the reduction in White students’ enrollment was smaller for countywide districts than for urban districts, even though countywide districts experienced larger average reductions in school segregation. This result suggests that families perceived that the increased expected costs of moving out of larger countywide districts were higher than the trouble produced by increased average distances that students had to be bused to achieve a given amount of desegregation in these districts. Despite the exodus of White students described in Welch and Light’s work, school desegregation policies succeeded in increasing the exposure of African American students to Whites. In addition, the reallocation of students across schools allowed African American and White students to experience different teachers and schools than the ones assigned in their neighborhood of residence. The hope was that this would help the academic achievement of African American students by increasing the quality of education that they have access to, which in turn would increase their labor and economic opportunities later in life.

Effects of School Desegregation Policies on Student Outcomes Since the Coleman Report in 1966, which claimed that African American students had higher performance when enrolled in racially integrated schools than they did when enrolled in schools with a high proportion of African American students, there has been considerable concern that the isolation of students by race or ethnicity and socioeconomic status has adverse consequences for minority and low-income students. However, although multiple observational studies have examined the effects of desegregation policies on academic and labor market outcomes of both minority and White students, there is not yet consensus in the research literature on the effects of school desegregation. Observational studies have showed mixed results on test scores, with a possible positive effect for African American students who attend racially integrated schools. These studies also suggest possible long-term academic and professional gains for African American adults who attended desegregated

Desegregation

schools. In contrast, more recent research that attempted to better control for relevant unobserved student and school inputs found positive effects on elementary school achievement of African American students but no significant effects of attending racially mixed schools on high school students or graduates. Concerning the effect on White students, the evidence is very limited due to the difficulty of estimating the effects given the exodus of White students, which induces biases in the estimates. The little evidence available shows either no effects or potential negative effects.

Conclusion The Brown v. Board of Education ruling in 1954 deeply changed the racial composition of public schools in the United States. Despite the exodus of White students following policies that school districts implemented after this ruling, desegregation policies succeeded in achieving a more balanced ethnic composition in schools, increasing the interaction among African American and White students, and increasing African American access to higher quality teachers and schools. There is considerable evidence suggesting this improved academic, labor, and economic outcomes for African Americans. Despite the desegregation efforts during the 1970s and the 1980s, U.S. schools are far from being fully integrated. In addition, the composition of U.S. schools has changed dramatically in the past 30 years. Hispanic students currently make up 21% of the student population as compared with 7% in 1979, and they outnumber African American students, who are 15% of the current student population. The share of Hispanic students attending schools with more than half minority students has risen from 65% in 1970 to 77% in 2003. These trends are pronounced in the South and Southwest, particularly in California where more than 50% of public school enrollment is now made up of Hispanic students, compared with 25% in 1979. As a result of these trends, school segregation and unequal access to quality of education for minority students continues to be a big concern. Gema Zamarro See also Achievement Gap; Brown v. Board of Education; Peer Effects; Race Earnings Differentials; School Quality and Earnings; Student Mobility

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Further Readings Angrist, J., & Lang, K. (2004). Does school integration generate peer effects? Evidence from Boston’s Metco Program. American Economic Review, 94, 1613–1634. Clotfelter, C. T. (2002). Interracial contact in high school activities. Urban Review, 34, 25–46. Clotfelter, C. T., Ladd, H. F., & Vigdor, J. L. (2005). Who teaches whom? Race and the distribution of novice teachers. Economics of Education Review, 24(4), 377–392. Clotfelter, C. T., Ladd, H. F., & Vigdor, J. L. (2008). School segregation under color-blind jurisprudence: The case of North Carolina. Virginia Journal of Social Policy and the Law, 16(Fall), 46–86. Coleman, J. S., Campbell, E. Q., Hobson, C. J., MacPartland, J., Mood, A. M., Weinfeld, F. D., & York, R. L. (1966). Equality of educational opportunity. Washington, DC: Government Printing Office. Guryan, J. (2004). Desegregation and Black dropout rates. American Economic Review, 94, 919–943. Hanushek, E., Kain, J., & Rivkin, S. (2004). New evidence about Brown v. Board of Education: The complex effects of school racial composition on achievement. Journal of Labor Economics, 27(3), 349–383. Orfield, G. (1983). Public school desegregation in the United States, 1968–1980. Washington, DC: Joint Center for Political Studies. Orfield, G., Kucsera, J., & Siegel-Hawley, G. (2012). E Pluribus . . . separation: Deepening double segregation for more students (The Civil Rights Project). Retrieved from http://civilrightsproject.ucla.edu/research/k-12-education/ integration-and-diversity/mlk-national/e-pluribus... separation-deepening-double-segregation-for-morestudents/?searchterm=e pluribus...separation Rivkin, S. G. (2000). School desegregation, academic attainment, and earnings. Journal of Human Resources, 35, 333–346. Rivkin, S. G., & Welch, F. (2006). Has school desegregation improved academic and economic outcomes for Blacks? In E. A. Hanushek & F. Welch (Eds.), Handbook of the economics of education (Vol. 2, chap. 17, pp. 1019–1049). Amsterdam, Netherlands: North-Holland. Welch, F., & Light, A. (1987). New evidence on school desegregation. Washington, DC: U.S. Commission on Civil Rights.

Legal Citations Board of Education of Oklahoma City Public Schools v. Dowell, 498 U.S. 237 (1991). Brown v. Board of Education of Topeka, Kansas, 347 U.S. 483 (1954). Freeman v. Pitts, 503 U.S. 467 (1992).

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Difference-in-Differences

Missouri v. Jenkins, 515 U.S. 70 (1995). Parents Involved in Community Schools v. Seattle School District No. 1, 551 U.S. 701 (2007).

DIFFERENCE-IN-DIFFERENCES This entry provides a brief overview of one common program evaluation method: difference-indifferences (DID). This technique can be used when the analyst has information on treated and untreated units (i.e., schools and students) both before and after a program was implemented. Using this information, the analyst can identify a program effect by comparing the difference in results between treated and untreated units after the program, and subtracting the difference in outcomes before the program was implemented, assuming that the treated units would have followed the same outcome trend as the untreated units in the absence of the program. This entry describes the basic DID specification and DID’s key assumptions, then discusses some criticisms and extensions to the DID model. It concludes with a discussion of some recent applications.

Background Impact evaluation seeks to measure the effects of a treatment or policy change on a group of units (i.e., individuals or schools) by comparing outcomes of those who received treatment or were part of the policy change and a control group both before and after treatment. In the absence of random assignment of units to treatment and control groups, simple comparisons of pretreatment and posttreatment outcomes are likely to be contaminated by other events affecting the outcome variable occurring at the same time. This key impact evaluation problem is typically referred to as the selection problem. In its simplest terms, because the outcomes of the treatment group cannot be simultaneously observed in a nontreatment state, it is possible that differences between treatment and control groups are due to factors other than the policy. For example, the outcomes of students participating in a school voucher or reduced class-size program cannot just be compared with those who did not participate in the program, unless the evaluator can make a convincing case that the outcome trends of those two groups were already on a similar preprogram trajectory and that there are no unobserved differences among groups that could bias results.

One way to deal with this key impact evaluation problem is through the use of DID estimation. Early DID work dates from the mid-1980s when Orley Ashenfelter and David Card studied the effects of training programs on earnings. Since that time, DID models have been used widely to estimate program effects using nonexperimental data.

Basic Specification Using the notation developed by Guido Imbens and Jeffrey Wooldridge, the conventional DID estimator is derived using a linear model such as the following: y = β0 + β1dT + δ0d2 + δ1d2 * dT + u,

(1) where y is the outcome variable, d2 is a dummy variable for the second time period (and is usually indicated using a dummy variable for the posttreatment period), dT captures possible differences between treatment and control groups (indicated using a dummy variable for treatment status). The d2 variable captures changes in the outcome that can be attributed to temporal factors, that is, aggregate factors that would cause changes in the outcome even in the absence of treatment. Examples of this would include a change in the testing regime affecting all schools in the sample, or an average increase in student family income due to a positive economic shock. The coefficient of interest is δ1, corresponding to the interaction term. The DID estimate is as follows: δ1 = (yT2 − yT1) − (yC2 − yC1).

(2) The first term in Equation 2 represents the postpre difference in outcomes for the treatment group (T). The second term represents the post-pre difference in outcomes for the control group (C). Thus, δ1 nets out the temporal difference in the control group from the total posttreatment difference across groups. DID models are usually estimated using ordinary least squares regression.

Key Identifying Assumptions The two main threats to validity of DID estimators arise from (1) systematic differences in outcome trends over time between treated and untreated units (i.e., parallel trends assumption is not satisfied) and (2) selection based on unobservable

Difference-in-Differences

time-varying factors. Traditional ordinary least squares assumptions also apply to DID. The parallel trends assumption implies that whatever happened to the control group over time is what would have happened to the treatment group in the absence of the program (see Figure 1). Figure 1 shows the change over time in outcomes (Y) of the treated group (Line A) and the control group (Line B). The total difference in outcomes after the intervention is given by AB|T = 1; however, the DID is represented by AC|T = 1. This is the distance between the treated group outcomes in the postintervention period minus a hypothetical counterfactual of treatment group postperiod outcomes, assuming that they had followed a similar outcome growth trend as the control group (Line C), in the absence of treatment. There are various ways to test whether the parallel trends assumption is likely to hold, but the simplest one is to use data from preprogram periods to redo the analysis. If this placebo DID effect is significant (nonzero), it is less likely that the parallel trends assumption holds. If many years of pretreatment data are available, this analysis can also be done graphically. The second way to test whether the parallel trends assumption holds is to use an alternative control group. The analyst should expect that effects with an alternative control group remain significant, for him or her to be reasonably confident that the DID effect is unbiased.

The second threat to validity to DID models arises from selection into treatment made on unobservables. For example, if teachers who are more hardworking select themselves into a particular training program, then differences in their results relative to teachers who received a different training will be biased. In models using panel data, this issue can be dealt with using individual fixed effects. Here, the assumption is that unobserved differences affecting program participation are fixed and can thus be “differenced-out” of the model using repeated observations over time. In many cases, the assumption of selection into treatment made on time-invariant unobservables is more likely to hold than selection made on unobservables.

Critiques Recent work by economists argues that when applied to variables that are constant within a group (i.e., all members of the group received the same policy treatment), and the number of groups is small (i.e., two states in 2 years), DID will produce downwardbiased standard errors, or estimates that are lower than other models. Several ways have been proposed to deal with this issue, such as permutation tests or simulation experiments.

Extensions A triple difference, or DDD, model, using data from a different unit (i.e., state, region, group of schools)

Y A Treated Group

C

Preprogram difference (AB|T=0)

DID Estimator (AC|T = 1)

Difference after intervention (AB|T = 1)

B Control Group Time Preintervention (T = 0)

Figure 1

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Postintervention (T = 1)

Graphical Depiction of Difference-in-Differences Estimation

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that was not subject to treatment, can also be introduced to control for additional sources of variation. This model takes care of two potentially confounding trends: (1) changes in outcome due to factors related to the targeted population (i.e., a new curriculum affecting students across states) and (2) changes in outcome due to factors affecting all individuals within the unit (i.e., a new funding formula that affected resources for all schools within a district). Lucrecia Santibañez See also Econometric Methods for Research in Education; Omitted Variable Bias; Ordinary Least Squares; Quasi-Experimental Methods; Selection Bias

Further Readings Abadie, A. (2005). Semiparametric difference-in-differences estimators. Review of Economic Studies, 72, 1–19. Ashenfelter, O., & Card, D. (1985). Using the longitudinal structure of earnings to estimate the effect of training programs. Review of Economics and Statistics, 67(4), 648–660. Athey, S., & Imbens, G. W. (2005). Identification and inference in nonlinear difference-in-differences models. Econometrica, 74(2), 431–497. Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How much should we trust differences-in-differences estimates? Quarterly Journal of Economics, 19(1), 249–275. Donald, S., & Lang, K. (2007). Inference with difference in differences and other panel data. Review of Economics and Statistics, 89(2), 221–233. Imbens, G. , & Wooldridge, J. (2007). What’s new in econometrics? Difference-in-differences estimation (NBER Summer Institute, Lecture 10). Cambridge, MA: National Bureau of Economic Research. Meyer, B. (1995). Natural and quasi-natural experiments in economics. Journal of Business and Economic Statistics, 12, 151–162. Moulton, B. (1990). An illustration of a pitfall in estimating the effects of aggregate variables in micro units. Review of Economics and Statistics, 72(2), 334–338. Skoufias, E., & Shapiro, J. (2006). The pitfalls of evaluating a school grants program using non-experimental data (World Bank Impact Evaluation Series No. 8, pp. 1–47). Washington, DC: World Bank.

DIGITAL DIVIDE The term digital divide refers to inequality in access to computer-based technologies by socioeconomic or demographic status. This entry discusses trends

in digital divides in education and at home and the importance of access to technology for economic and educational outcomes. Providing universal access to communication technology has been a goal of the United States since at least 1934, when President Franklin D. Roosevelt signed the Communications Act. Improvements in computer technology and the belief that they would create a revolution in communication, education, and the economy generated concerns about equitable access to digital communication technologies by the late 1960s and use of the term digital divide by the mid-1990s. Although private sector investments are no doubt responsible for many of the improvements in access since then, the federal government has invested substantial resources in an effort to balance the needs for equity and efficiency throughout this period. The Obama administration has called for near-universal Internet access in schools at speeds of at least 100 megabytes per second by 2018 and for the availability of home access at speeds of at least 4 gigabytes per second by 2016. These federal efforts are motivated in part by the belief that increased Internet access can improve the education system, especially for low-income and rural families. Federal initiatives include the direct investment of billions from the American Recovery and Reinvestment Act of 2009 in local and national projects to reduce the digital divide in homes and schools. These funds are also used to provide large tax incentives expected to spur billions more in private investments and to almost double the bandwidth available for wireless Internet use over the next decade.

Trends in the Digital Divide Historically, people with high levels of income and education have been first to obtain many new technologies. This often creates large gaps in access when these technologies are first being used, many of which decrease later as the costs of the technologies fall and the benefits become more apparent. For example, currently more than 98% of households have Internet speeds of at least 3 megabytes per second, while less than 4% have speeds of more than 1 gigabyte per second. Thus, the gaps are small at the high and low ends of broadband speed. However, larger gaps can be seen in the intermediate speeds—for example, more than half of urban households have speeds of at least 100 megabytes per second compared with less than a quarter of rural households. Governments

Digital Divide

can intervene to reduce digital divides in part by providing computers and related technologies to schools and libraries and in part by making it easier for individuals to gain access to and use these technologies in their homes. School Access

In 1994, almost no classrooms in the United States had Internet access, regardless of poverty level. By 1997, access had grown and a gap was created, with about 37% of classrooms in low-poverty schools gaining access compared with only 13% of classrooms in high-poverty schools. (Low-poverty schools are those with less than 35% of students eligible for free or reduced-price lunches. High-poverty schools are those with more than 75% of students eligible for free or reduced-price lunches.) By 2005, the gaps were closed again as access became close to universal, with more than 90% of classrooms being connected regardless of school poverty levels. During this time, the federal government made substantial investments designed to reduce these gaps, especially in schools and libraries. These investments include those made through the E-Rate program (designed to subsidize access for schools and libraries), the Enhancing Education Through Technology program (which also subsidized access in schools and replaced the Technology Literacy Challenge Fund), and Title I of the Elementary and Secondary Education Act (which is designed to help reduce inequality in education more generally and allows funds to be used to gain access to more advanced technologies for educational use). However, there are concerns about how some of these funds were being distributed, and some new federal initiatives are designed to help improve the funding process.

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and faster Internet access significantly improve economic outcomes, although it should be noted that researchers can only look at the impacts of changes that have already occurred. Given how rapidly digital technologies are changing, it is difficult to know whether the impacts of future changes will be larger or smaller. Although the economic benefits of access seem obvious, it is less clear that such access improves standard educational outcomes such as test scores measuring academic skills. Of course, mere access to computers and the Internet in schools and homes is unlikely to matter unless those resources are used well for educational purposes. However, many educational software packages designed to help improve students’ educational outcomes do not appear to be effective. A few rigorous studies with small sample sizes (fewer than 300 students each) show positive impacts of computer access or computer-assisted instruction, but there is also some evidence based on much larger sample sizes (hundreds of thousands of students) that access to home computers can actually reduce test scores, perhaps due to high levels of noneducational use of computers, such as social networking. However, even if computer/Internet use by school-age children does not improve standard educational outcomes, it may improve computer/ Internet skills. One key remaining policy question, then, is the extent to which learning computer skills before entering the labor market improves later educational, labor market, or social outcomes. At present, we know of no studies that address this question. Duncan Chaplin and Michael Puma See also Access to Education; Continuing Education; Homeschooling; Tracking in Education

Home Use

Gaps in access at home, which can also have important implications for educational outcomes, have also grown dramatically over time. For example, among households where the head did not graduate from high school, access rose from 11.7% in 2000 to 36.9% in 2009. However, for those with a bachelor’s degree or more, access was already at 66% in 2000 and had risen to 89.9% by 2011.

Economic and Educational Importance The importance of these digital divides depends on how they affect individuals and society in general. A great deal of evidence suggests that computers

Further Readings Campuzano, L., Dynarski, M., Agodini, R., & Rall, K. (2009). Effectiveness of reading and mathematics software products: Findings from two student cohorts (NCEE 2009-4041). Washington, DC: U.S. Department of Education, National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences. Fairlie, R. W., & London, R. A. (2011). The effects of home computers on educational outcomes: Evidence from a field experiment with community college students. Economic Journal, 122, 727–753. Fairlie, R. W., & Robinson, J. (2013). Experimental evidence on the effects of home computers on academic

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Discount Rate

achievement among schoolchildren. American Economic Journal: Applied Economics, 5(3), 211–240. File, T. (2013). Computer and Internet use in the United States: Population characteristics (P20-569). Washington, DC: U.S. Census Bureau, Economics and Statistics Administration, U.S. Department of Commerce. Retrieved from http://www.census.gov/prod/2013pubs/p20-569.pdf Levy, S. (1984). Hackers: Heroes of the computer revolution. New York, NY: Penguin Books. Pai, A. (2013). Connecting the American classroom: A student-centered E-Rate program. Washington, DC: Presentation at the American Enterprise Institute. Retrieved from http://www.aei.org/files/2013/07/16/pais-powerpoint-presentation_154630177586.pdf Prieger, J. E., & Heil, D. (2009). The microeconomic impacts of e-business on the economy. Retrieved from SSRN website: http://ssrn.com/abstract=1407713 Puma, M. E., Duncan, C., Olson, K., & Pandjiris, A. (2002). Draft: The integrated studies of educational technology: A formative evaluation of the E-Rate program. Washington, DC: Urban Institute Press. Rapaport, R. (2009). A short history of the digital divide: A high-tech “who done it.” Edutopia. Retrieved from http:// www.edutopia.org/digital-generation-divide-connectivity Vigdor, J., & Ladd, H. (2010). Scaling the digital divide: Home computer technology and student achievement (NBER Working Papers 16078). Cambridge, MA: National Bureau of Economic Research. Wells, J., & Lewis, L. (2006). Internet access in U.S. public schools and classrooms: 1994–2005 (NCES 2007-020). Washington, DC: U.S. Department of Education, National Center for Education Statistics. The White House. (2013). Four years of broadband growth. Washington, DC: Office of Science and Technology Policy and the National Economic Council, The White House. Retrieved from http://www.whitehouse.gov/sites/ default/files/broadband_report_final.pdf

DISCOUNT RATE The term discount rate refers to the rate at which future payments or payment streams must be discounted to measure their value in prior time periods or in the current period. The use of a discount rate allows adjustment for the fact that future payments are not as valuable as equal payments received in prior time periods, a concept known as the time value of money. Discount rates are used in the discounted cash flow procedure and as part of net present value analysis. This entry discusses why discount rates are necessary and how they are calculated and covers some more complex usages of discount rates.

Why Is a Discount Rate Needed? The first question that needs to be addressed is why an adjustment on future values is needed at all. The answer to this goes back to the concept of time value of money. Essentially, what time value of money implies is that one dollar at one specific point in time may not be worth exactly one dollar at another point in time. The most obvious reason for this is inflation. If someone asked to borrow $1,000 from an individual today and said that he would repay $1,000 to the lender 20 years from now, this would not be a fair deal from the lender’s perspective. The $1,000 that would be repaid in 20 years would no longer be as valuable as it is today due to inflation. Therefore, the lender would likely require some form of interest to accumulate on the money that is owed to make up for the lost value. The concept of time value of money, and therefore the need for a discount rate, becomes obvious in the above scenario. However, the discount rate should adjust not only for the inflation rate but also for the risk being assumed over the time period. If, in the above scenario, there was a 2% inflation rate and it was not guaranteed that the borrower would repay the loan, then the discount rate should be greater than 2% to adjust for the inflation rate and to compensate the lender for bearing the risk of nonrepayment (default). In addition to inflation and risk, a third component of the discount rate that should not be ignored deals with the concept of utility. Individuals may place greater or less importance on the value of wealth today than its value at some point in the future. Those placing a larger value on immediate consumption are likely to require a larger discount rate to compensate for their utility loss as the wealth is received further into the future.

Calculation of Present Value Using the Discount Rate As previously mentioned, the discount rate allows a monetary amount in the future to be shown as an equivalent value at the present time. For a single payment received in the future, the calculation of the present value utilizing the discount rate is a rather straightforward calculation. The following represents the formula: PV =

FV , (1+ r)t

(1)

Discount Rate

where PV represents the present value of the monetary amount, FV represents the future value of the monetary amount, r represents the discount rate, and t represents the number of periods between the future value and the present value. It is important to realize that r and t must be measured in the same period length. For example, if t is given in number of years, then r should be the discount rate per year. Similarly, if t is given in number of months, then r should be the monthly discount rate, and so on. It is also imperative to understand that the discount rate must be stated in decimalized form (i.e., a 9% discount rate would be entered into the equation as .09). To demonstrate how to use the above equation, consider the example mentioned earlier. In that scenario, someone asked to borrow $1,000 and informed the lender that he would repay the same amount in 20 years. In this case, the discount rate can be calculated by plugging the known values into the equation: $1,000 =

$1,000 1+ r 20

.

(2) By solving for the r variable in Equation 2, we obtain a discount rate of 0.00, or 0%. As previously mentioned, this would not even cover the inflation that would occur during the 20 years. Let’s assume that the borrower is someone the lender completely trusts to repay the amount borrowed. Therefore, the lender assumes no risk and must only cover inflation costs. If the inflation rate over this period is expected to be a steady 2% per year, then the amount of money that must be repaid at the end of the 20 years on a $1,000 loan that would cover any possible value lost due to inflation can be calculated as follows: $1,000 =

FV . 1+ 0.0220

(3) Solving for the remaining variable (FV) can identify the missing piece to the time value of money problem in question. To cover the time value of money shift due to inflation, the individual borrowing the money must pay $1,485.95 after 20 years. The additional $485.95 covers the inflation that would accumulate on the $1,000 over the 20-year period. The generic formula shown in Equation 1 allows us to solve for any one missing piece assuming we know

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the value of the other variables in the equation. However, without the discount rate, it would be impossible to find the future value of an amount received today or the present value of an amount received in the future.

More Complex Uses of Discount Rate There are more advanced uses of the discount rate than simply converting single payments to future or present values. Discount rates play an important role in project analysis decisions as well. Utilizing the same principle, discount rates allow corporations to evaluate the present value of multiple cash inflows and outflows across the life of a project. Just as it would have been naive to ignore inflation in the earlier scenario, it would be naive to ignore inflation and risk adjustments in the valuation of a project where cash flows arrive at different points in time. Firms often use what is known as discounted cash flow analysis (or, similarly, net present value analysis) as a way to evaluate whether or not a project adds value to a corporation and should be undertaken. The concept is similar to that shown in the previous section, but with many moving parts, as can be seen by the following equation: DCF = CF0 +

CF1 1

(1+ r)

+

CF2 2

(1+ r)

+$+

CFn

(1+ r)n

,

(4) where DCF represents the discounted cash flow of the project, CF represents the cash flow expected to occur in the period defined by the subscript, and r represents the discount rate for the project. As can be seen in Equation 4, the discount rate (r) is still an integral part of the calculation, which allows corporations to properly identify the projects it should accept or reject. Therefore, a poor estimation of the discount rate may result in a firm accepting projects that will destroy firm value or rejecting projects that would have created firm value. Brian R. Walkup and Matthew D. Hendricks See also Internal Rate of Return; Present Value of Earnings

Further Readings Brealey, R. A., Myers, S. C., & Marcus, A. J. (2012). The time value of money. In Fundamentals of corporate finance (7th ed., chap. 5, pp. 112–144). New York, NY: McGraw-Hill/Irwin.

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Brigham, E. F., & Houston, J. F. (2013). Time value of money. In Fundamentals of financial management (13th ed., chap. 5, pp. 137–171). Mason, OH: South-Western Cengage Learning. Ross, S. A., Westerfield, R. W., & Jordan, B. D. (2013). Introduction to valuation: The time value of money. In Essentials of corporate finance (8th ed., chap. 4, pp. 97–115). New York, NY: McGraw-Hill/Irwin.

DISTANCE LEARNING Distance education is not a novel concept. As early as the 18th century, people used mail correspondence to deliver lessons on a variety of topics, such as shorthand. However, with the advent of the Internet, the speed with which content can be delivered and accessed has eliminated the delay typical of mail correspondence. For the purposes of this entry, distance learning is defined as schooling where learning takes place via online activity in which students are not bound by time and place, and it is one in which offline activity time with teachers, counselors, or other school officials is limited to phone calls and periodic face-to-face counseling meetings. It is noteworthy that distance education is and has been a prevalent delivery model in both the military and nursing fields, among others. Specifically, given the need to increase access to education in order to mitigate the shortage of qualified staff officers in the military and to eliminate the shortage of nurses in the nursing profession, these two fields have leveraged distance education to resolve these problems. In many cases, developments and innovations in the military setting have then been adapted to other settings, such as business and education. Distance education has become increasingly important over the past decade, as it has become the fastest growing component of higher education, with K-12 settings close behind. As more institutes of learning add distance learning opportunities for their students, the need to recruit, train, and support instructors in this setting is crucial. Furthermore, the way students “do school” is rapidly changing, as they are not always required to be physically present on campus. Instead, the mobility of distance learning continues to afford students the opportunity to learn in a variety of settings. The following sections address the components of distance education and the implications for teaching and learning in a distance learning setting. The entry concludes with a discussion of the economic impacts of distance learning.

Components of Distance Learning Historical Delivery of Distance Learning

To understand the current state of distance learning, it is important to briefly examine the historical delivery of distance education. Distance education was initially conducted and continues, albeit to a minimal degree, to be delivered via mail correspondence. In this model, a student would pay for a correspondence course; complete readings, tasks, and assessments; and then return them by mail to a receiving entity that would evaluate the student’s work and grant the subsequent certification, degree, or credentials. This later expanded to include other mediums of delivery such as radio, television, audiotapes, and video. In the case of audio and video, students would receive audiocassettes, DVDs, videotapes, or CD-ROMs; view or listen to the content; and complete associated tasks. With the advent of the Internet, distance education is most commonly delivered online in a variety of models, discussed in a later section. Other mediums continue to be developed and used in lieu of, or in tandem with, Internet-based distance learning. Learning Management System

The most common way distance learners access course materials is through a learning management system, more commonly called an LMS. An LMS is a website wherein an instructor can post assignments; course materials, such as readings, videos, and presentation slides; a grade book; and other content. As well, students can upload their work, read content, read and respond to messages from the instructor and other students, and perform additional activities. There are several commercially produced LMSs, such as Blackboard, Haiku Learning, and Moodle, among others. The range of customization varies by product. Some are ready-made and template based; the instructor simply uploads the content. This allows for uniformity across courses within a program or school. Alternatively, some systems, such as Moodle, allow for greater modification to individual features of an individual course. However, the more customizable the LMS is, the greater the demand on computer programming skills. Access to a course or program LMS typically requires credentials such as a user name and password. This practice is to maintain privacy, to protect instructor- and studentgenerated content, and to ensure confidentiality of students’ grade information. (The use of LMSs is

Distance Learning

not limited to Internet-based courses. They are commonly used with fully on-ground traditional courses as well.) Asynchronous Learning

The most common aspect of distance learning is its asynchronous nature. As the root words imply, it is learning that is “not in time”: Learners are not bound by time or location. In a traditional synchronous (same-time) setting, a student attends class in a physical or “brick-and-mortar” classroom with other students and a teacher all at the same time. By leveraging the Internet, students can complete the requirements for a course and never set foot on a campus nor meet their classmates and teacher. Once a course is available via an LMS, students typically access the content and complete the assignments at their own pace. This means they are not attending class in a physical location with other students at a designated time, are not listening to an instructor deliver a lecture or other presentation, and are not participating in live class activities such as small group discussions. Instead, students complete work individually, read texts, and may access embedded artifacts such as a video or presentation slides. However, they do this at their own pace and from any place where they have access to a computer and an Internet connection. While asynchronous courses maintain deadlines as a face-to-face class would, students never have to participate at a designated physical space at a designated time. In other words, students have a more flexible schedule to complete assignments, whereas students in a synchronous class can only complete a class activity by being physically present with the instructor and the other students. There are many benefits and limitations to an asynchronous approach to distance learning. Some of the benefits include allowing greater access to course offerings, student-paced progression, elimination of commuting to a campus, and the ability to review prerecorded lecture or instructional components. For example, many on-ground programs offer limited sections of a course. If a student works full-time or part-time, he or she may not be able to take a course, especially if it is a requirement, due to the limited offering. However, because asynchronous courses do not have a face-to-face required class time component, students can enroll in a course and complete the work at their convenience. Additionally, some students do not live near an institute of higher

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learning or may work at a location that is far from the school they attend. Getting to class on time regularly may be a challenge. However, asynchronous settings eliminate the need to commute to a specific location. Last, synchronous learning occurs in real time. If an instructor is lecturing, a student must be able to capture the information in real time, which may be challenging based on learner limitations, the speed of instructor delivery, or features of the physical environment such as lighting or noise that may interfere with the student being able to take notes in an effective manner. This challenge is mitigated in the asynchronous environment because the student can listen to or view an embedded audio or video recording multiple times and control the pace at which the information is perceived. However, asynchronous learning has limitations as well. Among the most significant limitations of asynchronous learning are not having real-time access to the instructor, the high demands of student selfregulation, the lack of social interaction with other students, and the modes of assessing student learning. In traditional settings, students are able to interact with the instructor in real time, ask clarifying questions, and take advantage of one-on-one meetings. As well, the instructor is able to gauge students’ engagement based on their body language, the types of responses they give, or the questions they ask. Such interactions are essentially nonexistent in an asynchronous setting. At best, a student can use e-mail or the course discussion board to ask questions, but instructors are not likely to be monitoring these features ubiquitously, resulting in gaps of time between the posing of the question and a response. In a synchronous setting, students physically attend class each week. This can serve as a motivating influence because students know they will be expected to participate, interact with peers, and be accountable for readings or other projects. Therefore, students are more likely to be prepared for class. The lack of social interaction may impede student learning. In an on-ground class, students have the opportunity to form study groups, collaborate on projects, and interact during class. Because students never meet each other in person in the asynchronous setting, it is difficult to form such relationships. However, with the emergence of social media such as Facebook and Twitter, students in asynchronous programs are finding alternative ways to meet and socialize with classmates. In terms of assessment, asynchronous settings do not allow for authentic or performance-based evaluations of student learning.

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Typically, instructors are limited to assessing student learning through embedded multiple-choice quizzes and tests, contributions to discussion boards, and written papers. While these modes of assessment are common and useful, they are limited to assessing only certain types of knowledge and skills. For example, in a communications class, it would be appropriate to assess a learner’s factual knowledge of the features of a speech through a quiz. However, it would not be an appropriate way to assess the student’s ability to give an actual speech. A synchronous class would allow this in that the student could give the speech in front of the classmates and the teacher. The teacher could then provide feedback to the student. In short, performance-based knowledge and skills are difficult to assess in an asynchronous setting. Blended Learning

Blended learning, as the name implies, is a blend of synchronous and asynchronous instructional and learning components. The proportion of a course allotted to each respective component may vary; commonly, 30% of the class takes place synchronously and 70% takes place asynchronously. In this model and others like it, the synchronous meetings are typically spread over the term and occur on campus or at another physical location. The days of synchronous learning are typically chosen to coincide with the first day of the course, the midterm exam, and the final exam. Other variations of the blended model exist with as much as 40% to 50% of a class occurring synchronously. As compared with the aforementioned benefits and limitations of an asynchronous environment, blended environments do allow for more interaction between students and their instructor, decrease the demands of self-regulation, and facilitate social interactions between students.

Implications for Teaching and Learning While there are usually weekly assignments, midterms, and course finals or projects associated with a blended course, the weekly readings and assignments are completed at a pace the student chooses. However, deadlines for assignments still exist, and learners must meet these deadlines in the same way they would be required to in a face-to-face synchronous course. One key difference, however, is that in a purely face-to-face course, an instructor delivers lectures and utilizes any supporting media in real

time. Unless a learner has the means and permission to record the lecture through audio or video means, the instructor input is essentially a one-time event, requiring learners to take copious notes and retain as much from live delivery as possible. In contrast, an asynchronous delivery model is one in which the instructor prerecords video or audio lectures and may also embed supporting media such as a PowerPoint file or video. This allows a learner at least two key learning assets not afforded in a purely synchronous class. First, the learner is able to access this content 24 hours a day so long as he or she has access to the Internet. Second, it allows the student to review the uploaded content as many times as he or she chooses. A recent version of blended learning has emerged through the use of video conferencing software that allows for the creation of a virtual classroom, wherein the instructor and learners are bound by time and virtual location. In this virtual classroom, video conferencing software is used as the means through which instructors and students can participate in live class discussions. The staple features of these programs are the following: (a) they allow for instructors and students to broadcast themselves, individually, via a webcam, allowing them to see themselves and the other participants; (b) they connect the audio of participants through either an integrated phone bridge or through the use of Voice over Internet Protocol; and (c) they provide a chat box or texting tool where participants can read and type text during a discussion. In this environment, instructors and students exploit the distinct features of the virtual classroom much as in a brick-and-mortar classroom. They discuss readings and case studies, solve problems, collaborate, answer questions, respond to polls, share a desktop to demonstrate a procedure or the use of other software, and engage in other traditional learning activities such as sharing experiences and co-constructing knowledge. Two key differences, among many, between a traditional brick-and-mortar classroom and a virtual classroom influence pedagogical choices. First, students and the instructor are equidistant in the virtual classroom. Whereas a traditional class creates literal space and distance between an instructor and the students at any given time, the fact that the instructor and the students are broadcast from the shoulders to the top of the head eliminates traditional rows of a physical classroom. This requires that the instructor become adept at reading facial reactions

District Power Equalizing

and cues whereas the former setting allowed for the reading of full body language and social cues. Second, the instructor has to learn to manage multiple features of the virtual classroom simultaneously, increasing the demands of attention to multiple stimuli. For example, students often use the chat feature in real time where they may be posing questions to the instructor or other students. The instructor may be using presentation elements such as PowerPoint and lecturing simultaneously while students may be responding to polling questions embedded in the presentation. The demands of real-time cognitive processing are higher since multiple students can participate at the same time, whereas a traditional faceto-face course typically involves one student raising her hand and being called on, and so forth. The impacts on student achievement through distance learning are varied.

Economic Impacts The economic impacts of distance learning are not fully known. Comparisons between distance and traditional education indicate that universities tend to charge the same tuition fees for both but sometimes will offer fully asynchronous online courses at a lower rate. Some research shows, however, that the completion rate of fully asynchronous online courses is lower than that for fully face-to-face courses. This implies that students have to retake courses, pay the same tuition again, and take longer to complete higher education degrees. From a purely overhead perspective, the cost of running a distance learning course may be greater upfront due to production, web hosting, and LMS costs; however, over time, the cost may be less, as the expense of construction and maintenance of a brick-and-mortar class is not required. Brandon Martinez See also Digital Divide; Educational Innovation; Online Learning; Teacher Experience; Teacher Training and Preparation

Further Readings Allen, I., & Seaman, J. (2013). Changing course: Ten years of tracking online education in the United States. Wellesley, MA: Babson Survey Research and Quahog Research Group. Bates, R., & Khasawneh, S. (2007). Self-efficacy and college students’ perceptions and use of online learning systems. Computers in Human Behavior, 23, 175–191.

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Celik, V., & Yesilyurt, E. (2013). Attitudes to technology, perceived computer self-efficacy and computer anxiety as predictors of computer supported education. Computers and Education, 60, 148–158. Ferrario, K., Hyde, C., Martinez, B., & Sundt, M. (2013). An honest account of the humbling experience of learning to teach online. LEARNing Landscapes, 6(2), 85–94. Lee, J. K., & Lee, W. K. (2008). The relationship of e-learner’s self-regulatory efficacy and perception of e-learning environmental quality. Computers in Human Behavior, 24(1), 32–47.

DISTRICT POWER EQUALIZING District power equalizing is a state aid to education distribution system that ensures that the same amount of money is available for the same local tax rate regardless of the relative wealth (or lack thereof) of the local tax base. It differs from a guaranteed yield system only in that it recaptures the excess revenue generated when a district meets the state-set funding level at the state-established tax rate. This entry explains the equity principles in operation, the frequency of use and stability of this type of funding formula, the most common variations, and the advantages and disadvantages of the mechanism.

Principles The amount and proportion of state aid to a local district is generally based on the amount of property wealth behind each student. Some districts will have relatively greater wealth, and others will have relatively lesser wealth per pupil. The general principle of district power equalizing is that if the established statewide tax rate does not generate the set spending level, then the state makes up the difference. If the tax rate generates more than the established amount, then the excess is recaptured by the state and redirected to the districts that could not raise sufficient funds at the established tax rate. In effect, the state’s entire property tax base is put behind every child. While a power equalizing feature could theoretically be used in conjunction with any basic funding formula system (e.g., foundation, percentage equalizing, or guaranteed yield), it is generally applicable to guaranteed yield systems. In such cases, if the local district chooses to spend at a higher level than the state-established figure, it may do so. However, the defining characteristic is that equivalent spending

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District Power Equalizing

generates equivalent tax rates for every district in the state. In Vermont’s case, every percentage increase in local spending results in a corresponding percentage increase in local residential property taxes. Thus, the system is, by definition, perfectly horizontally equitable (i.e., the same tax rate for the same level of spending regardless of the wealth of the community).

Frequency and Stability Since its inception in the 1970s, concurrent with the first wave of school funding lawsuits, few states have used a district power equalization formula, and some states have only used it for brief periods. Maine, Utah, and Wisconsin have had power equalizing systems in the past. The reason for their short life is that the wealthier communities (who also enjoy considerably more political power and media access) view the property tax as being their local source of revenue and guard it with a strong, proprietary interest. Sharing their tax wealth with poorer communities is a political hard sell. In virtually all cases, taxing authority is constitutionally vested in the states, but they have delegated property tax authority to local districts or municipalities for local school taxes. Over the years, this has become proprietary. Despite the perfect horizontal equity, states have found it politically unpalatable. As of this writing, Deborah Verstegen reports that only Vermont, Wisconsin, and Rhode Island use the system. However, Rhode Island changed to a money-follows-the-child system in 2011. The longest standing power equalizing formula is in Vermont. It was initiated in 1997 and still existed as of 2013. Five states use some combination such as a base foundation level with a tiered guaranteed yield or district power equalizing system at higher levels of spending.

Variations Caps and Tiers

In unrestricted form, a district could choose to spend as much as it could get local approval to spend, and the state would be obligated to pay its matching share. However, states tend to cap or insert spending disincentives at higher spending levels to prevent what is sometimes portrayed as a raid on the state coffers. Vermont has an excess spending tier that doubles the local tax effort required for all amounts above the 125% threshold. In 1999, Kentucky had a three-tier system that capped the state support level at 150%.

State-Established Tax Rate and Spending Level

Determining the amount the state will support and at what tax level is a political decision. States can cost shift to local districts by decreasing the support level or increasing the tax level. Although state support is ideally indexed to the cost of living to prevent manipulation, economic conditions and political philosophy can (and do) overwhelm intentions to maintain the state share. Income Sensitivity

Recognizing that property taxes are regressive, Vermont instituted an adjustment to property taxes using a sliding scale based on the income of the taxpayer. Property taxes are capped at 1.8% of income except for the wealthiest taxpayers. Categorical Aid (Vertical Equity)

Funds for special populations (poor students, English Language Learners, etc.) can be adjusted using weighted funding, which provides additional funding for students who require more resources, much as is commonly done in foundation programs.

Advantages and Disadvantages The great appeal of district power equalizing is that in funding systems free of minimums, caps, or other constraints, it guarantees perfect financial equity. Furthermore, within limitations (e.g., state minimum and maximum spending figures), each school district determines its own spending level and does so at tax rates equitable across all school districts. The state also has an obligation to fund at the set levels—if it doesn’t give itself a dispensation. Lower wealth districts are (in theory) incentivized to bring their spending up to state norms as they pay at the same rate per spending dollar as the wealthier districts. The disadvantages are that it is difficult to maintain political support for a “Robin Hood” law. Furthermore, more affluent municipalities can and do elect to spend at a higher level. They have greater discretionary wealth, and the demographics of wealthier communities reflect a higher level of support for education. The converse is also true. Even with the incentive of equal returns for equal tax rates, less wealthy districts continue to spend at relatively lower levels. William J. Mathis

District Size See also Education Spending; Educational Equity; Equalization Models; Fiscal Disparity; Guaranteed Tax Base; Horizontal Equity; Vertical Equity

Further Readings Alexander, K., & Salmon, R. G. (1995). Public school finance. New York, NY: Allyn & Bacon. Baker, B. D., Green, P., & Richards, C. E. (2008). Financing education systems. Upper Saddle River, NJ: Pearson Education. Odden, A. R., & Picus, L. O. (2008). School finance: A policy perspective (4th ed.). New York, NY: McGraw-Hill. Verstegen, D., & Jordan, T. (2009). A fifty-state survey of school finance policies and programs: An overview. Journal of Education Finance, 34(3), 213–230.

DISTRICT SIZE Scholars such as David Strang have documented that, historically, schooling in what is now the United States began as small, local operations. But as the nation developed, these local schools consolidated to form larger schools and districts as part of a trend toward greater professionalization and bureaucratization of the American educational enterprise. This trend continued into the 20th and 21st centuries. School districts can be characterized in many different ways, such as broad categorizations based on district locale (i.e., urban, suburban, rural) and quantitative measures of varying means of calculations and precision. An example of the latter of these is district size, which can vary greatly. Districts in some states can be as small as having one all-encompassing PreK-12 grade facility to county school districts that cover large geographic areas with numerous elementary, middle, and high school facilities. The state of Florida, for example, has school districts that are coterminous (having shared boundaries) with county lines. As a result of its coterminous districts coupled with the presence of number of urban areas, Florida is home to some of the largest school districts in the United States, including Miami-Dade County Public Schools. In this entry, the ways in which district size is operationalized will be discussed as well as the implications of district size for education policy (i.e., funding formula and consolidation of districts). The use of district size in research will also be discussed, specifically, district size in relation to education costs and varying educational outcomes (e.g., student achievement). Also discussed will be

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possible directions for future research of district size, specifically, perceptions of community (or lack thereof) among stakeholder groups in districts of various sizes and/or districts that have undergone consolidation.

Operationalizing District Size District size can be operationalized a number of ways. A common measure of district size is the number of students enrolled in the district in a given period of time. The definition and calculation of a district’s size based on enrollment—referred to as “average daily membership,” “average daily attendance,” or “full-time equivalencies”—varies from state to state, particularly in terms of when and how the student population is counted and reported. However, district size can also be measured in terms of a school district’s geographic characteristics—often represented by a district’s square mileage. When comparing an urban school district with a rural school district, the urban school district’s size will typically be larger if measured by student enrollment but relatively smaller if district size is measured by district square mileage. In contrast, the rural school district may cover more square miles but have a student enrollment and student population density that are much less than that of the urban school district. Student population density is typically measured by students per square mile and is sometimes referred to as district sparsity. Both the urban district and the rural school district may have high educational costs, but for different reasons. An urban district will typically face greater challenges and per-pupil costs due to its student population, characterized by greater overall numbers of students and greater diversity in terms of socioeconomic status and educational needs (i.e., English proficiency, race/ethnicity, and disabilities). The urban district will typically incur higher costs due to challenges in dealing with higher incidence of crime (e.g., drugs, violence, and vandalism) and its prevention. In contrast, rural districts will typically incur higher costs due to smaller class sizes and greater transportation needs. The rural district’s larger geographic size and smaller pupil density could mean more bus routes, longer bus rides, and earlier starts to mornings for parents and students. These, in turn, could have indirect impacts on student academic performance. Rural districts that are large in land area but small in terms of enrollments may also share similar challenges to those of urban districts in attracting and retaining personnel to work in

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District Size

more challenging environments. For urban schools, the diverse student body, higher incidences of crime, and range of academic challenges may be a barrier to recruitment, while the more remote settings of rural districts and lack of amenities may make these districts less attractive to job seekers.

District Size and Education Policy Regardless of how these aforementioned proxies of district size are quantified, they are commonly used in the education policies and funding calculations of many U.S. states, thus having implications for district leaders and the provision of education to students within their districts. Some states, such as Arkansas, have legislated administrative consolidation or annexation of smaller school district with fewer than 350 students. Districts in the Commonwealth of Pennsylvania also have been subject to legislative mandates and federal court orders that forced the consolidation of school districts, notably in the 1960s. These legally mandated consolidations, of course, increased district sizes in terms of enrollments while decreasing the overall number of districts in the states. State governments have largely opted to make recommendations for and incentivize school district consolidation. State governments offering incentives have encouraged stakeholders (district leaders and community members) to pursue consolidation as a means of realizing cost savings, easing tax burdens on property owners, and offering an expanded curriculum to students. These incentives often take the form of state aid programs that provide additional public monies for operational expenses and/or capital projects (school construction or renovation) to districts that reorganize or consolidate. However, district leaders and community members in districts considering reorganization/consolidation often have questions and concerns as to how such changes, which likely include an increase in district size, may affect the community’s identity and school district operations. These include issues such as athletics (e.g., “What will our mascot be if we consolidate with______?”) and travel distances to and from schools. Community members also have questions as to whether consolidation advantages one part of the proposed consolidated district over another and how an increase in district size is going to affect the provision of education and performance under state and federal accountability policies. Interestingly, some state governments that have incentivized reorganization/consolidation of school

districts also have incentivized the creation of charter schools or smaller schools. These incentives appear to be at odds with each other but may simply be the result of party politics in which policymakers pursued a larger political agenda or efforts to mitigate the potential for diseconomies of scale (cost disadvantages associated with being larger) in large urban district settings. The mixture of incentives offered to school districts by state governments is reflective of debate among policymakers and educators over which are better—bigger or smaller districts and schools? The debate is also reflective of disagreement over which districts should be eligible for additional aid through state funding formulas, which typically take into consideration characteristics such as district size. Policymakers are tasked with having to balance multiple policy goals, including the prevention of funding inequities, meeting the educational needs of students, and preventing the potential of disincentives for districts to stay small and inefficient.

District Size: Uses in Research Education scholars have examined the relationship between school district size and two broad lines of inquiry. The first of these is district size and its potential to affect costs. Second, researchers have asked if a district’s size is related not only to costs but also to increased productivity and efficiency. In this line of inquiry, researchers asked if there is an optimal district size in terms of various outcomes—notably, district performance under state and federal accountability policies. A potential avenue of research that may warrant further study is the impact of changes in district size through reorganizations and consolidations on community stakeholders’ perceptions. District Size and Costs

Does district size matter in terms of costs to taxpayers? In answering this question, one must understand the concept of economies of scale. Under economies of scale, it is assumed that the larger the district, the more efficient the district, yielding cost savings to taxpayers in terms of administrative costs, instructional costs, and the potential expansion of curriculum offerings available to students. Indeed, these have been used as arguments in favor of school district consolidation efforts. In a review of the research on economies of scale in districts and schools, scholars Matthew Andrews, William Duncombe, and John Yinger contend that research

District Size

suggests that there are cost savings in instructional and administrative costs when districts increase in size, but there is the potential for diseconomies of scale (cost disadvantages associated with being larger) in large urban district settings. It is these large urban districts that face some of the greatest challenges in the provision of education and meeting accountability mandates. District Size: Costs and Outcomes

It is important to understand that providing public education in U.S. states is a complex and complicated undertaking. The provision of public education in the United States involves many different stakeholders—parents, students, teachers, administrators, support staff, community members, and so on. Public education also involves multiple levels of government—federal, state, and local, each providing varying levels of resources. There is also wide variation in the quality and behaviors of stakeholders in the educational enterprise that can influence educational outcomes. For example, teachers can be skilled, developing, or ineffective in producing student achievement gains among their students. Likewise, students can be highly motivated or unmotivated to learn. Their parents can be supportive, or not supportive, of teachers, schools, and their children’s learning. There are also multiple outcomes that districts, schools, and district personnel are tasked with accomplishing, such as meeting student achievement expectations, operating within budgets, keeping tax burdens as low as possible, and attracting and retaining quality employees to decrease opportunity costs associated with the hiring process and to ensure quality instruction. To make sense of this complex undertaking, researchers use production function models with a wide array of inputs and outputs. District size is one of many possible inputs that may be considered in understanding the relationship between district characteristics and increasing output. Andrews, Duncombe, and Yinger have indicated that the existing research is mixed in terms of the return of district size in production function studies and that the evidence on the return of school size is stronger and more consistent.

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perceptions across districts in varying sizes. Districts that underwent (or are considering) consolidation offer researchers opportunities to explore whether or not there is a loss of support for public education or adverse changes in the perceptions of community and bureaucracy among district stakeholders as a result of consolidation. Research suggests that consolidated districts are likely to realize savings in terms of instructional and administrative costs, but what about the perceptions of community members? Do changes in district size through consolidation/reorganization engender ill will among stakeholders in the process? In districts that rely on voter approval of district budgets (e.g., New York, New Jersey) or additional taxation (e.g., Ohio, Minnesota), consolidation has the potential to erode public support of these proposals as they arise. Scholars such as Andrews, Duncombe, and Yinger suggest that there is a lack of evaluation studies that examine the impacts of changes in district size through consolidations and reorganizations and that further study is needed.

Conclusion District size is typically operationalized in terms of enrollments or geographic size. District size, regardless of how it is measured, has impacts on the provision of education and funding. Indeed, many state governments have offered a variety of incentives (sometimes at cross-purposes) that encourage increasing district size through reorganization/consolidation or creating smaller charter schools. This mixture of incentives is reflective of the larger debate on what size district is optimal. Extant research has suggested that larger districts can take advantage of cost savings that result from economies of scale, but more evaluation studies are needed to understand the impact of district size changes (e.g., consolidation or charter school creation) on perceptions of community and quality of educational services among district stakeholders. W. Kyle Ingle See also Economic Efficiency; Economies of Scale; Enrollment Counts

Further Readings

District Size and Stakeholder Perceptions of Community

A possible line of inquiry for researchers to further develop is determining if there are differences (quantitative and/or qualitative) in stakeholder

Andrews, M., Duncombe, W., & Yinger, J. (2002). Revisiting economies of size in education. Economics of Education Review, 21(3), 245–262. Fox, W. (1981). Reviewing economies of size in education. Journal of Education Finance, 6(3), 273–296.

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Jones, J. T., Toma, E. F., & Zimmer, R. W. (2008). School attendance and district and school size. Economics of Education Review, 27(2), 140–148. Strang, D. (1987). The administrative transformation of American education: School district consolidation, 1938–1980. Administrative Science Quarterly, 32, 352–366.

DROPOUT RATES This entry focuses on high school dropout rates in the United States, though the term could equally well be applied to other levels of education and is also used in other countries. The topics covered include key terms used in defining dropout rates, data sources for determining dropout rates, the policy importance of school dropouts, the causes of dropping out, and relevant interventions. Students who drop out of high school do far worse than graduates on many important outcomes such as earnings, employment, involvement in crime, welfare receipt, and life expectancy. These poor outcomes are not entirely caused by dropping out, which may be as much an indicator of low skills as it is a precipitator of problems. Even if only an indicator, however, it is a powerful predictor of longterm success at the individual level and of economic and social problems at the school, community, state, and national levels. Although dropout rates are universally recognized as important, calculating them is not straightforward. Reliable data on dropouts can be difficult to come by, and there are several methods of calculating dropout rates that often produce different results. The appropriate calculation method depends to some degree on the intended use of the measure. For example, when describing the skills of the labor force, it may be best to use status dropout rates (discussed in the next section), which are often used to describe adults at a single point in time. For accountability or evaluation, on the other hand, it may be best to use event dropout rates, which are generally calculated for high school–age youths tracked across multiple years. But regardless of the measure used, there are various ways of calculating the rates as well as various data sources—producing a potentially bewildering array of results. Much of this entry discusses the variations in these measures and methods of calculation. More historical data are available for graduation rates than for dropout rates, so historical trends in

graduation rates are provided as background. Note that graduation rates are closely tied to dropout rates; for adults, few of whom are in high school, the dropout rate is about equal to 100% minus the graduation rate. In other words, when the graduation rate is 77%, the dropout rate is about 23%. Historical evidence suggests that high school graduation rates in the United States rose dramatically and fairly steadily from around 2% of schoolchildren going on to graduate from high school in 1870 to about 77% in 1970. Between 1970 and 2000, U.S. graduation rates stagnated and may have fallen somewhat, perhaps to as low as 70%. Since then, they appear to have risen again, perhaps back to their historical high. However, graduation rates for African Americans and Hispanics remain far below those of rest of the population, rates for males are somewhat lower than those for females, and a large fraction of dropouts come from a small number of high schools.

Definitions This section discusses key terms used when defining dropout rates. Status Rates. A status rate is equal to the number of dropouts divided by the population at a point in time. Status rates are often used for descriptive purposes—for example, to describe the quality of the labor force—and can be calculated for adults as well as for children in school. Event Rates. An event rate is equal to the number of students who dropped out during a period of time divided by the population of students. Event rates are calculated using longitudinal data. They are often used for accountability and evaluation purposes and, in the United States, are generally only calculated for children in high school at the beginning of the period. Cohort Rates. A cohort rate is a rate calculated for a specific set of years, with years defined in various ways, including by grade in school, year of birth, year of entering high school, and year of expected graduation. All dropout rates can be thought of as cohort rates because they focus on a limited range of years. In practice, the word “cohort” is usually only used for rates based on 10 or fewer years of data. Dropout rates are generally only calculated for populations past the normal age of required school

Dropout Rates

attendance (around 16 in the United States) because few students drop out before then. Graduation Rates. Graduation rates refer to the number of graduates divided by the population being used. Graduation and dropout rates both depend on the numbers of people who are dropouts, graduates, and students. If few people in a given population are still students, then the dropout and graduation rates sum to about 100%. If noticeable fractions of the population are still students, however, then this need not be true. On-time graduation rates are used to estimate the fraction of students who graduate within 4 years of starting high school. Completion rates are similar to graduation rates but are more likely to treat GED® credential recipients (discussed in the next section) as graduates. Dropout Status. A dropout is someone who was enrolled in high school but is no longer enrolled and does not have a high school diploma. The dropout status of an individual can change over time since dropouts can reenroll in school and even graduate.

Definitional Issues There are a number of definitional issues that affect the calculation of dropout rates. These relate to how GED recipients, students who transfer between schools, immigrants, and currently enrolled students are treated. GED Recipients. The GED testing program is the primary alternative to the high school diploma in the United States. To obtain a GED credential, a student must obtain a passing score on the GED test and meet some additional requirements that vary by state. The earnings of GED recipients are far closer to those of dropouts than they are to those of graduates. Hence, many researchers and policymakers argue that GED recipients should not be included with graduates in calculations of graduation rates, although there is still some debate about whether they should be treated as dropouts given the evidence of some benefits of earning a GED certificate for those who dropped out of school. Students Who Transfer. Students who transfer to other schools create a number of problems for state and school accountability measures. First, many states do not include adult education programs in their calculations of graduation and dropout rates. Students who transfer from regular schools into adult

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education are dropped from the final graduation and dropout rate calculations in at least some of those states. Second, most states do not hold schools accountable for students who transfer to another school, regardless of the students’ final outcomes. At the extreme, this means that students who are at one high school for almost 4 years, transfer to another for just a month or two, and then drop out do not affect the dropout rate of the first school. Immigrants. It is often argued that recent immigrants should be excluded from graduation or dropout rate calculations because they did not spend enough time at U.S. schools. This may be particularly important for calculations for certain subgroups, such as young Hispanics in the United States, and especially when the dropout rate will be used for school accountability or program evaluation. Dropping recent immigrants because they did not attend U.S. schools for very long might be viewed as similar to adjusting for the amount of time a student spent with a teacher when using student test scores to measure that teacher’s performance. On the other hand, for descriptive purposes, it may be useful to include recent immigrants in the dropout rate calculations in order to provide a more complete picture of the quality of the labor supply. Enrolled Students. Dropout and graduation event rates can be calculated with or without including students enrolled at the end of the period in the denominator. If these students are excluded, the rates will tend to be higher, especially for school-age subgroups with high enrollment rates. Hence, this issue needs to be kept in mind when comparing rates calculated with and without enrolled students.

Data Sources Dropout rates have been calculated using crosssectional survey data, longitudinal survey data, administrative data, and combinations of these sources. Cross-sectional data cover a single period of time, usually for a representative random subset of a population of interest. Sources include the Current Population Survey (CPS), the decennial U.S. Census, and, more recently, the American Community Survey (ACS). Longitudinal data follow individuals over time, again typically for a random subset of a population of interest. They include a series of datasets collected by the U.S. Department of Education (ED) on different cohorts of students, starting with the National Longitudinal Study of the Class of

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1972 and including High School and Beyond, the National Education Longitudinal Study of 1988, the Education Longitudinal Study of 2002, and the High School Longitudinal Study of 2009. Although it is designed primarily to be a crosssectional survey, the CPS also includes a longitudinal component for nonmobile households. That component of the CPS is used to estimate event dropout rates. Administrative data are collected by entities such as ED, states, school districts, and the GED Testing Service® in support of their various operations and functions; the data usually aim to cover all students served by these entities. Administrative data collected by ED include the Common Core of Data (CCD), which covers enrollment and graduates in all public schools in the United States annually, and the Private School Survey, which covers similar topics for private schools, albeit not every year. Some researchers have also augmented these data with administrative data on GED recipients from the GED Testing Service. States are also being strongly encouraged to create their own student-level data with sufficient information to calculate graduation and dropout rates by school and to follow students as they transition between schools throughout their state. Cross-Sectional Survey Data. These datasets can be used to calculate status rates and are typically based on much larger sample sizes than longitudinal survey data. However, there are some concerns about misreporting related to the fact that the major sources of cross-sectional data (the CPS, the U.S. Census, and the ACS) collect less information about high school graduation and dropout status than the longitudinal data collected by ED. Indeed, the numbers of GED recipients reported in the CPS often differ substantially from the numbers reported by the GED Testing Service, in ways that vary over time. There are a number of reasons it can be difficult to distinguish GEDs from regular high school diplomas in survey data. Each state uses its own name to refer to the GED credential. In many states, the names do not include the term GED, and in some, they sound like regular high school diplomas. The names change over time and some states may decide to use alternatives to the GED test. Longitudinal Survey Data. These data, including the CPS, can be used to calculate event rates without relying on retrospective data, as would be required using cross-sectional data. However, a major concern about longitudinal data is that they often have lower response rates than other types of data. For

example, mobile households are effectively treated as missing when calculating event rates using the CPS. This could be particularly important if dropouts are more likely to have missing data than other students. Administrative Data. States are increasingly calculating graduation and sometimes dropout rates for individual districts and schools using student-level administrative data. One issue with these data is that students who have unknown statuses are often dropped from the calculations instead of being treated as dropouts. In addition, these data are often not available at the student level. One way to avoid these problems is to estimate rates of public school graduation using the ratio of graduates from the CCD divided by the number of ninth graders 4 years earlier. However, this method might underestimate graduation rates because of students held back in ninth grade. An alternative now used by ED and some researchers is to replace ninth-grade enrollment with an average of the enrollment numbers for the relevant cohort in 8th, 9th, and 10th grades. But a weakness of this method is that the average enrollment in 8th, 9th, and 10th grades may not provide an accurate measure of the relevant cohort size. Other researchers use similar methods and CCD data to estimate “on-time” graduation rates. A limitation of these methods is that they rely on the assumption that the students held back in the earlier grades, used in the denominator, correctly offset the late graduates in the numerator. Combinations of Data. Some researchers have combined administrative and cross-sectional data to obtain estimated annual graduation rates for young adults at the national level. More precisely, they use the total number of graduates (from the CCD) divided by an estimate of the population of 17-yearolds (from the U.S. Census or the ACS). This method can be used to calculate graduation rates going back to the late 1800s while avoiding many of the issues discussed above. However, the downside (compared with survey data and administrative data at the student level) is that many individuals in the numerator are not in the denominator. Thus, for example, immigrants who entered the United States after age 17 and graduated from high school in the United States would skew these estimates upward.

Policy Importance Dropout rates matter in part because the benefits of education appear to be substantial and growing,

Dropout Rates

based on increased differences in earnings between individuals with higher and lower levels of education. Indeed, some experts estimate very large economic benefits of a high school diploma. Dropout rates may matter even after controlling for academic skills. Evidence for this comes in part from the fact that, at the individual level, the earnings differential associated with completing high school is much larger than the differential associated with completing earlier years of high school. This might mean that a high school diploma helps improve noncognitive skills. Alternatively, completing high school may serve as a signal of worker productivity to employers. Even if a high school diploma provides little of value beyond this signal, it could still help reduce the cost of an employer’s search for suitable job candidates. Dropout rates may also matter even in the absence of any arguments based on noncognitive skills or signaling, because test-based accountability systems need to be designed to ensure that educators are not encouraged to push students out of school. Pressure to raise test scores could, in theory, encourage schools to exert less effort to keep lower achieving students enrolled. This is offset if schools are also required to raise graduation rates (and thus to reduce dropout rates), as they are under the federal No Child Left Behind Act and other federal accountability policies. Using dropout rates for accountability purposes heightens the importance of measuring rates accurately at the school level. One could estimate graduation and/or dropout rates for individual schools using some of the methods described above—for example, one could calculate a high school graduation rate by taking the ratio of graduates from the CCD divided by the number of ninth graders 4 years earlier. But as discussed earlier, this method would be inaccurate for many schools. High-quality, student-level longitudinal data are therefore important for calculating graduation and dropout rates for individual schools. Some districts have developed systems to collect such data, but even district-level data are far from ideal because many students move across district lines. Even when all states have complete student-level data and are able to calculate graduation and dropout rates accurately, a major policy issue will remain regarding whether the standards for graduation are similar across states, or even across schools within states. Many states have created high school exit exams to help ensure all of their graduates reach a

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minimum level of academic skills. At the national level, there is increasing agreement to adopt the Common Core State Standards, a set of curriculum standards developed by state governors and state education commissioners, which might further align the value of a diploma across states.

Causes of Dropping Out Researchers have identified a number of factors that have strong and consistent associations with dropping out. These include family background characteristics such as race/ethnicity, gender, socioeconomic status, and the number of parents with whom the youth lives. These results do not provide direct evidence regarding what policymakers can do to reduce dropout rates, but they may help researchers identify causal factors correlated with these characteristics. Other research focuses on the association between dropout rates and indicators of poor academic performance or low school engagement—for example, test scores, English language status, absenteeism, or having been retained in a grade. A number of policy issues have been evaluated—for example, there is some evidence that policies such as increasing the minimum wage or requiring an exit exam may reduce graduation rates. Other research investigates economic theories on the monetary and nonmonetary benefits and costs of dropping out. In combination, these latter strands of research may provide more direct evidence regarding how to design interventions to reduce dropout rates.

Interventions In 2008, a group of experts produced a summary of the most rigorous evidence available on reducing dropout rates. Their review suggested that a number of types of interventions were promising. In particular, these experts identified interventions with the following types of components: strong early-warning systems to identify which students are likely to drop out, appropriate student-level interventions for at-risk students, and appropriate schoolwide services for schools with particularly high dropout rates. These results were driven in part by the fact that, according to the research, the factors widely used to identify youths who need services were poor predictors of who actually dropped out. The evidence also suggested that many programs targeting atrisk youths in school had relatively small impacts, perhaps in part because of the problem of targeting the right youths. Developing better targeting

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mechanisms is one way that this issue might be addressed and is the first recommendation of the evidence review discussed above. In line with this recommendation, a great deal of effort is going into designing early-warning systems to identify students at risk of dropping out. Many of the problems associated with creating early-warning systems are similar to those associated with estimating graduation and dropout rates in general. But early-warning systems have their own unique problems too. To create an early-warning system that functions effectively, one needs to be able to identify which students are likely to benefit most from dropout prevention services. Current systems are based on the implicit assumption that the students who benefit most are those most likely to drop out. This may not be the case if some students will drop out regardless of whether they receive the available interventions. More generally, the effectiveness of interventions may vary substantially within the subgroup of students identified as most likely to drop out. Providing services to students based on earlywarning systems or to schools with high dropout rates are two ways of targeting services. Alternatives include providing services directly to students who have already dropped out, at one extreme, or providing services to all youths in a target population, at the other. Providing services to students who have already dropped out poses the risk of encouraging the act of dropping out for students attracted to those services. For example, some researchers have found that offering youths the option to earn a GED certificate leads to higher rates of high school dropout. At the other end of the spectrum, many prominent researchers argue that enhanced early childhood programs are the most cost-effective way to improve later life outcomes, including high school completion, especially for low-income families. This would involve providing services to large numbers of students who ultimately would not have dropped out; however, the imprecise targeting might be justified by the academic benefits all participants would receive, even if they were not at risk of dropping out. Duncan Chaplin and Allison McKie Note: GED® is a registered trademark of the American Council on Education (ACE) and administered exclusively by GED Testing Service® LLC under license. This content is not endorsed or approved by ACE or GED Testing Service. Duncan Chaplin, Allison McKie, and this work are not affiliated with or endorsed by ACE

or GED Testing Service LLC. Any reference to “GED” in the title or body of this work is not intended to imply an affiliation with, or sponsorship by, ACE, GED Testing Service LLC, or any other entity authorized to provide GED® branded goods or services.

See also Benefits of Primary and Secondary Education; Compulsory Schooling Laws; General Educational Development (GED®); Risk Factors, Students; Student Mobility

Further Readings Allensworth, E., & Easton, J. Q. (2007). What matters for staying on-track and graduating in Chicago public high schools: A close look at course grades, failures and attendance in the freshman year. Chicago, IL: Consortium on Chicago School Research. Balfanz, R., Bridgeland, J. M., Moore, L. A., & Fox, J. H. (2010). Building a grad nation: Progress and challenge in ending the high school dropout epidemic. Washington, DC: America’s Promise Alliance. Cameron, S. V., & Heckman, J. J. (1993). The nonequivalence of high school equivalents. Journal of Labor Economics, 11(1), 1–47. Chaplin, D. (2002). Tassels on the cheap. Education Next, 2(3), 24–29. Chapman, C., Laird, J., Ifill, N., & KewalRamani, A. (2011). Trends in high school dropout and completion rates in the United States: 1972–2009 (NCES 2012-006). Washington, DC: U.S. Department of Education, National Center for Education Statistics, Institute of Education Sciences. Dynarski, M., Clarke, L., Cobb, B., Finn, J., Rumberger, R., & Smink, J. (2008). Dropout prevention: A practice guide (NCEE 2008-4025). Washington, DC: U.S. Department of Education, National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences. Goldin, C. D., & Katz, L. F. (2008). The race between education and technology. Cambridge, MA: Belknap Press of Harvard University Press. Greene, J. P., & Winters, M. A. (2006). Leaving boys behind: Public high school graduation rates (Civic Report No. 48). New York, NY: Manhattan Institute. Heckman, J. J., & LaFontaine, P. A. (2010). The American high school graduation rate: Trends and levels. Review of Economics and Statistics, 92(2), 244–262. Meehan, M. (1989, October 23). Florida tops for dropouts? Depends on who’s counting. Orlando Sentinel. Retrieved from http://articles.orlandosentinel.com/1989-10-23/ news/8910233490_1_dropout-rates-florida-school-dropout Murnane, R. J. (2013). U.S. high school graduation rates: Patterns and explanations. Journal of Economic Literature, 51(2), 370–422.

Dual Enrollment Murnane, R. J., & Hoffman, S. (2013). Graduations on the rise. Education Next, 13(4). Retrieved from http:// educationnext.org/graduations-on-the-rise/ Rumberger, R. W. (2011). Dropping out: Why students drop out of high school and what can be done about it. Cambridge, MA: Harvard University Press.

DUAL ENROLLMENT Dual enrollment is a college transition program that allows high school students a unique opportunity to take college courses and earn high school and college credit simultaneously. Dual enrollment programs are an increasingly common approach to encourage students in completing high school, enrolling in college, and attaining a postsecondary degree. High school students enrolled in dual enrollment get a head start on earning college credit and may become better prepared to face the academic rigors as well as the social and emotional components of the college experience. Dual enrollment is also referred to as “concurrent enrollment,” “joint enrollment,” or “dual credit.” States, school districts, and colleges are including dual enrollment into their college readiness strategies, hoping to decrease high school dropout rates, improve academic performance, bridge the high school/college divide, increase college completion rates, and maximize educational resources. This entry covers the format, prevalence, and funding of dual enrollment programs, as well as the policies governing them and some concerns about them.

Format and Prevalence Programs that allow high school students to take college classes have been around several decades and are part of a larger series of accelerated learning opportunities. Dual enrollment, while similar to Advanced Placement and International Baccalaureate programs that offer college-level curriculum, differ in that students take actual college courses mostly taught at the college campus rather than at the high school. With increasing frequency, these courses are also taught through distance education. Students may take college courses as part of a dedicated dual enrollment program set up to strengthen the pathway between the high school and a postsecondary institution, or they may simply enroll in a college course on their own. In both cases, a minimum set of standards typically must be met,

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for example, earning a specific grade point average, taking a college placement test, or submitting a letter of recommendation. Courses may be taught by either high school or college instructors; however, the course content, assignments, and structure are that of a college course. Dual enrollment has increased swiftly over the past decade. A 2013 report by the National Center for Education Statistics found that approximately 1.4 million high school students took at least one college-level course in 2010–2011, a 74% increase over the 2002–2003 school year. Opportunities for dual enrollment can be found at 53% of all postsecondary institutions with the highest incidence found in 2- and 4-year public institutions (at 98% and 84% participation, respectively). Dual enrollment programs have also shifted from being primarily an opportunity for advanced students to one whose goals are to provide access and support for disadvantaged, first-generation, and traditionally middle- or low-achieving students.

Policies and Funding State policies for dual enrollment as a key strategy for strengthening academic preparedness have been expanding. Almost every state has a policy that governs dual enrollment, with a dozen or more states requiring mandatory participation by public higher education institutions. At the highest level, policymakers emphasize ways in which school districts and postsecondary institutions can help students become more college and career ready, accentuating the importance of increasing the percentage of adults with a college degree and thus inspiring economic competitiveness. Dual enrollment funding differs by state. According to the Education Commission of the States, college tuition for students in dual enrollment programs is paid for most commonly by students or parents (22 states), the school district (6 states), the college (3 states), or the state department of education or another state organization (3 states). In 10 states, the source of payment varies, or there is no clear system of funding. Therefore, in some cases, any interested student participates for free, while in other cases, students or parents must pay tuition fees. For the K-12 school district and the community colleges involved, in more than half of the states, dual enrollment students mean both full-time equivalent (FTE) and average daily attendance (ADA) funding for both the high school and the college.

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This full funding model provides an incentive for institutional participation in dual enrollment, but it is also criticized for costing state and local entities twice for the same course. In the end, students who receive dual credit may save time and money, having reduced time in college. Society may benefit overall when tax dollars are used to support students and prevent them from dropping out of school.

Concerns While overall support for dual enrollment is moving forward, some concerns still exist. Proponents argue that dual enrollment has the potential benefit of improving college attendance among students who lack academic preparation for college. However, high school students who choose to enroll in college courses may inherently have the academic ability and motivation that supports their college going— the problem of selection bias. Others share concerns that dual enrollment may be “fast-tracking” students too quickly into college-level courses, and teenagers may lack the maturity and research, academic, and time management skills to be successful on a college campus. When students are inadequately prepared for college classes, they may become discouraged from continuing. Furthermore, as an issue of quality, in some cases, high school teachers are brought in as instructors for the college-level coursework, and they may be unprepared or unqualified to teach such coursework. Last, dual enrollment programs may help strengthen the ties between K-12 and higher education, or they may take additional monies and resources that could be better spent elsewhere. Dual enrollment offers a wide variety of possibilities, including diverse types of students, and research suggests that many dual enrollment programs have shown significant success. Evaluations of the James Irvine Foundation’s Concurrent Course Initiative and the Early College High School Initiative begun by the Bill and Melinda Gates Foundation showed that participants had better academic outcomes than comparison students, such as higher rates of high school graduation. Additional comprehensive research is needed on the impact of dual enrollment participation. Finding out how dual enrollment students perform after high school graduation with regard to persistence in college and degree completion will be especially useful, particularly how it relates to students who are traditionally underrepresented in higher education. Stefanie Stern

See also Access to Education; College Completion; College Enrollment

Further Readings Barnett, E., & Stamm, L. (2010). Dual enrollment: A strategy for educational advancement of all students. Washington, DC: Blackboard Institute. Edwards, L., Hughes, K., & Weisberg, A. (2011). Different approaches to dual enrollment: Understanding program features and their implications. San Francisco, CA: James Irvine Foundation. Hoffman, E. (2012). Why dual enrollment? New Directions for Higher Education, 158, 1–8.

DUAL LABOR MARKETS The analysis of the labor market is a critical topic in economics. From a microeconomic perspective, rational individuals invest in human capital (mainly accumulated in two ways: schooling and experience) to get better jobs and higher wages once they complete their educational attainment and join the labor market. An efficient labor market that properly matches the supply and the demand of jobs by qualifications is thus of salient importance for individuals. This entry describes the term dual labor market and discusses the implications of this market for education.

Definition Optimal functioning of the labor market is prevented by the existence of a number of imperfections. These include persistent divisions among workers based on, for example, sex, ethnicity, educational attainment, and industry. Peter Doringer and Michael J. Piore argue that the labor market appears to be divided into two mutually exclusive sectors. Jobs in the primary sector (good jobs) offer relative high wages and status, employment stability and job security, ladders with internal promotion possibilities, the protection of unions, and advantageous working conditions in general. In contrast, in secondary sector jobs, workers are paid less, hold lower status, and have limited employment security. In the secondary sector, workers receive little training, and their promotion opportunities are minimal. Typically, secondary sector jobs tend to be occupied by women, ethnic minorities, and individuals from disadvantaged backgrounds. There is vast empirical evidence that documents the existence of dual labor

Dual Labor Markets

markets in modern economies and confirms that workers in the secondary sector receive jobs systematically differing from those of workers in the primary sector (see, e.g., the studies of Richard Anker, and William A. Darity and Patrick L. Mason).

Segmentation and Segregation Two closely related concepts are segmentation and segregation of the labor market. The first refers to the division of the labor market into separate submarkets or segments, where different working conditions and different market institutions operate, and where there are few substitutes for workers to do those jobs. This last characteristic suggests that workers in the secondary sector are unable to compete for vacancies in the primary sector on equal terms. In fact, workers holding a job at the lower sector are in general trapped there due to limited mobility between the primary and the secondary sectors. Another advantage to workers in the primary sector, often referred to as the internal labor market, is that firms are more willing to invest in training, which ensures stable working careers for workers in that sector. This leads to greater employment security and often higher profits for the firm. In contrast, workers in the secondary sector suffer from underemployment and are usually permanently confined to unskilled jobs as firms are less likely to offer training opportunities for them. Segregation refers to the imbalance in the distribution of workers between good and poor jobs due to characteristics such as gender or race (as well as other exogenous criteria based on workers’ social background). For example, certain types of jobs have been historically restricted to men, while others have been traditionally accomplished by women. In the same vein, African Americans have often been prevented from occupying particular jobs historically restricted to White workers. As a result, the distribution of men and women (or Blacks and Whites) in these occupations is far from being equal. In this sense, it is an enduring characteristic of labor markets around the world to have overrepresentation of women in certain establishments and occupations where their productive results are not equally regarded to those of their male counterparts. This factor contributes to the gender wage gap. The analysis of concentrations of women into low-wage jobs has indeed attracted a great deal of attention in recent decades. Authors dealing with this topic have

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proposed different explanations for why women are confined to less attractive jobs with findings focused on social, economic, cultural, and historical causes. Thus, differences in preferences by gender, social norms, and stereotypical visions regarding men and women, as well as the structure of the labor market and discrimination in hiring and on the job, determine both the extent and the characteristics of this form of discrimination.

Measurement Segregation is often measured on the basis of the differences between the distributions of two groups (Blacks and Whites, men and women, etc.). Looking at measures of segregation, recent research has suggested exploring measures of segregation in a multigroup context, allowing one to control concurrently for disparities among all these groups (e.g., in ethnicity studies referring to the U.S. economy between Whites, Asians, Hispanics, African Americans, and Native Americans). Nevertheless, both types of indexes are suitable to quantify aggregate segregation but are unable to calculate the segregation of a particular demographic group. In this context, and as a forward contribution, it is worth mentioning the availability of a new set of indexes that focus on the segregation of particular groups, thus providing information about how the target group departs from the occupational distribution of the economy (the group is segregated as long as its distribution deviates from the job structure of the economy). Inés P. Murillo See also Gainful Employment; Job Training; Nonwage Benefits

Further Readings Alonso-Villar, O., & Del Río, C. (2010). Local versus overall segregation measures. Mathematical Social Sciences, 60(1), 30–38. Anker, R. (1998). Gender and jobs: Sex segregation of occupations in the world. Geneva, Switzerland: International Labour Office. Darity, W. A., & Mason, P. L. (1998). Evidence on discrimination on employment: Codes of color, codes of gender. Journal of Economic Perspectives, 12(2), 63–90. Doringer, P., & Piore, M. J. (1971). Internal labor markets and manpower analysis. New York, NY: Wiley. Saint-Paul, G. (1996). Dual labor markets: A macroeconomic perspective. Cambridge: MIT Press.

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Due Process

DUE PROCESS Due process is a formal set of procedures that the government must follow when making decisions to ensure the fair treatment of individuals. Most often, due process is thought of in relation to legal civil and criminal matters, but due process also affects education. In the United States, public K-12 education is a vast system comprising all levels of government. The federal government provides funding and loose oversight, and each state has its own education system, granting authority to thousands of local districts and schools. Numerous decisions are made daily: decisions regarding instruction, student discipline, personnel, and so on. Those decisions affect a large number of people. In 2010, there were approximately 98,000 schools serving 49 million public school students with 3 million public school teachers and 3 million other public school staff. Both public and private schools have general policies and procedures to protect students, teachers, and staff from arbitrary and capricious decision making, but public schools are afforded extra protection through the Due Process Clause of the U.S. Constitution as well as their own state’s constitution. This entry will describe due process: what it is, what is required, and what levels of protection are afforded. The entry will then discuss how due process is important for education research, highlighting issues related to notice and special education.

Due Process: What It Is and What Is Required Due process protects individuals from arbitrary actions by governmental actors and dates back to Chapter 39 of the Magna Carta. On the federal side, the Due Process Clause in the Fifth Amendment to the U.S. Constitution applies to federal actions and states that no one “shall be deprived of life, liberty, or property without due process of law.” States are also required to provide due process through the Fourteenth Amendment. In addition to the U.S. Constitution, states have their own due process requirements contained in their state constitution. For due process protections to apply, there must be a government (federal or state) action. Due process protections only apply to government actions, not actions by private parties (citizens or corporations). All states have education clauses within their

constitutions providing public education to their citizens such that public education is a government activity. Furthermore, when states delegate their authority to local districts and school boards, the local action also becomes a “state action” for the purpose of due process. In addition to a state action, there must be a life, liberty, or property interest at stake for due process to apply. Liberty is a fairly broad concept. For instance, damage to one’s reputation is a liberty interest. Teachers who are dismissed or whose contracts are not renewed for reasons that may affect their reputation, such as incompetence or misconduct, are entitled to due process. Likewise, student disciplinary matters, such as suspensions, also require due process as the infraction is included in the student’s permanent record (Goss v. Lopez, 1975). Liberty also extends to First Amendment rights such as freedom of speech (Morse v. Frederick, 2007). Property rights also apply in public education. Property is not limited to real property such as land or personal items. Property rights extend to education issues such as the right to attend school, right to get a high school diploma, and teacher tenure.

Aspects of Due Process Once the due process requirements have been established, there remains the question of what types of protections apply. There are two aspects of due process: (1) substantive due process and (2) procedural due process. Substantive due process protects against the government infringing on certain fundamental rights through regulation or legislation. As such, the court applies higher levels of scrutiny to government actions involving fundamental rights, often requiring the state to have a compelling interest and to use narrowly tailored means to accomplish its goal. The federal courts have never found that education is a fundamental right (San Antonio Independent School District v. Rodriguez, 1973). Some states, however, have interpreted education as a fundamental right under their state constitution, as California did in Serrano v. Priest, 1976 (Serrano II). Other states, according to research by Brooke Wilkins, have not found a fundamental right to education but have employed higher levels of scrutiny regardless. Procedural due process, on the other hand, seeks to ensure that governmental procedures that must be followed are fair when there is a life, liberty, or

Due Process

property interest at stake. The level of process is flexible, and the procedural protections depend on the situation (Morrissey v. Brewer, 1972). Notice and a hearing by an impartial arbiter are always required, but the amount of notice and the extent of hearing may differ depending on the situation. For instance, minimal process is required for student suspensions shorter than 10 days, as it is a temporary denial of educational benefits (Goss v. Lopez, 1975). The school authority provides short notice, tells the student why he or she may be suspended, and provides the student with an immediate informal hearing where the student has an opportunity to explain herself or himself. Student expulsion, on the other hand, requires a higher level of process. The student is provided notice further in advance of the hearing, typically in writing, and is given a full opportunity to be heard in front of a neutral fact finder. Similarly, teacher dismissal, given the seriousness of the property right, would require a more extensive process, similar to student expulsions. The constitutional due process provisions set the minimum requirements. Additional process requirements may be required because of state or local laws or by policies such as union agreements. Teacher dismissal is an area where additional due process protections above the constitutional requirements are often provided. The additional protections are typically through state law or union agreements. For instance, due process would require administrators to provide notice of a hearing to terminate an ineffective teacher, but state law may also require that administrators provide an ineffective teacher notice of unsatisfactory performance and an opportunity to remediate before moving to the dismissal stage (for an example of this provision in California, see Cal. Educ. Code § 44938). Likewise, state or union contracts may specify when hearings take place. Many state laws specify how soon after notice the hearing must begin, but some also specify when the hearing must be concluded, as in the case of Michigan (see Mich. Comp. Laws § 38.104(3)). The union agreements may then provide additional requirements. For instance, Steven Brill reported that in 2009, the New York City teachers’ union contract specified that hearings be held 5 days a month during the school year and 2 days a month during the summer, which results in lengthy hearings surpassing the length of most criminal trials in the United States. The additional requirements demonstrate that other jurisdictions’ laws or policies can augment the constitutional protections.

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Implications for School Reform As established above, due process is applicable in many aspects of education and, as such, has implications for researchers in the areas of school reform. Notice

The main implication for researchers is the notice requirement, which affects when a policy can start. Because of due process requirements, policies must be delayed from the time a law is passed to when the law goes into effect. The case of Debra P. v. Turlington, a 1981 challenge to Florida’s high school graduation exam, illustrates the rationale for the delay. In 1976, the Florida Legislature enacted the Educational Accountability Act, which included a minimum number of credits for high school graduation, mastery of certain basic skills, and satisfactory performance in functional literacy. In 1978, the Educational Accountability Act was amended to specify that students had to pass a functional literacy examination in order to receive a high school diploma. The graduating class of 1978–1979 was the first subject to the requirement. The Debra P. plaintiffs argued that the tested material was not taught. Furthermore, students did not have adequate opportunity to prepare for the test, and the school district did not have adequate time to adjust its curriculum and prepare a remedial program. The Court of Appeals, without specifying what would be adequate notice, held that students and schools must have reasonable time to prepare. The notice requirement in Debra P. is not limited to only high school graduation cases. Many reforms require a delayed start so that students, educators, and school systems have ample notice, and school systems have time to begin the implementation of the reform. Thus, states and districts cannot quickly start reforms. Instead, there are delayed starts and phased-in implementations to satisfy due process requirements as well as other barriers to implementation such as staff training. For researchers, this delay between the passage of a law and its full enactment makes it difficult to be definitive as to when the reform begins. This becomes a problem when constructing groups of students to evaluate the effects of the reform. Research designs are further complicated as states or districts may have already been engaging in the activity before the law is passed. For instance, the No Child Left Behind Act (NCLB) was passed in 2002, but at

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least half of the states had some test-based accountability system in place at the time. To account for the different start times in a study examining the effectiveness of NCLB, Thomas S. Dee and Brian Jacob (2011) classified states into treatment and nontreatment groups. The treatment group was made up of those states that did not have an NCLB-like school accountability policy during the 1991–1992 school year and the start of NCLB in 2002.

school population (i.e., all students within a school including students with disabilities), there may be confounding factors due to additional services that students with disabilities may receive. For example, if evaluating the effectiveness of a schoolwide reading program, and certain students with disabilities were receiving additional instruction in reading, it would become difficult to interpret whether increases in reading ability were due to the schoolwide program or the additional instruction.

Special Education and Individualization

Due process is also instrumental in special education. Historically, many students with disabilities were denied access to an appropriate public education (Pennsylvania Association for Retarded Children v. Commonwealth of Pennsylvania, 1971; Mills v. Board of Education, 1972). In 1975, Congress found that 1 million students with disabilities were being improperly excluded from public education (Education for All Handicapped Children Act, P.L. 94-142). Because of the prevalence of the denial of education to students with disabilities, Congress (and some states) enacted legislation to ensure that students with disabilities were provided with a free appropriate education, including due process protections for enforcement (20 U.S.C. § 1400 et seq.). The due process requirements contained within the Individuals with Disabilities Education Act (20 U.S.C. § 1415) are much more extensive than would be required under constitutional due process protections. The Individuals with Disabilities Education Act detail the procedures for the complaint process, including the contents of the notice and timelines for the hearing and determination. It also includes additional procedural safeguards to ensure that parents are informed of their due process rights. A component of providing a free appropriate education, which is enforced through the due process requirements, is that the educational programs for students with disabilities are tailored to their specific needs. This individualization has implications for researchers. A researcher interested in evaluating the effectiveness of an intervention specifically designed for students with disabilities would need information on key student characteristics, including information about their specific disability and possibly their education plan. Knowing and identifying the key characteristics would allow for matching or stratified assignment, particularly when random assignment is unavailable. Likewise, when evaluating the effectiveness of a program for the general

Conclusion Decisions in education are made at every governmental level: school, district, state, and federal. The due process clauses of the U.S. Constitution and a state’s due process clause provide additional protection against arbitrary and capricious decisions for all of those involved in the education system when the decisions involve a liberty or property interest. As described in the case of teacher dismissal, due process requirements may also be augmented by state or local law as well as by union agreements. Due process has implications for research design. For instance, the notice requirement influences the design of treatment and nontreatment groups. Likewise, additional protections to ensure that students with disabilities are provided an appropriate education can introduce confounding variables in experimental designs. Michelle Croft and Richard Buddin See also Individuals with Disabilities Education Act; San Antonio Independent School District v. Rodriguez; School Boards, School Districts, and Collective Bargaining; School Finance Litigation; Serrano v. Priest; Teacher Evaluation

Further Readings Bireda, S. (2010). Devil in the details: An analysis of state teacher dismissal laws. Washington, DC: Center for American Progress. Brill, S. (2009, August 31). The rubber room: The battle over New York City’s worst teachers. The New Yorker. Retrieved from http://www.newyorker.com/ reporting/2009/08/31/090831fa_fact_brill Chemerinsky, E. (2002). Constitutional law: Principles and policies (2nd ed.). New York, NY: Aspen. Dee, T. S., & Jacob, B. (2011). The impact of No Child Left Behind on student achievement. Journal of Policy Analysis and Management, 30(3), 418–446. Gersten, R., Fuchs, L. S., Compton, D., Coyne, M., Greenwood, C., & Innocenti, M. S. (2005).

Due Process Experimental and quasi-experimental research in special education. Exceptional Children, 71, 149–164. Wilkins, B. (2005). Should public education be a federal fundamental right? Brigham Young University Education & Law Journal, 2005, 261.

Legal Citations Debra P. v. Turlington, 644 F.2d 397 (5th Cir. 1981). Goss v. Lopez, 419 U.S. 565 (1975).

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Mills v. Board of Education, 348 F. Supp. 866 (D. DC 1972). Morrissey v. Brewer, 408 U.S. 471 (1972). Morse v. Frederick, 551 U.S. 393 (2007). The Pennsylvania Association for Retarded Children v. Commonwealth of Pennsylvania, 334 F. Supp. 1257 (E.D. Pa. 1971). San Antonio Independent School District v. Rodriguez, 411 U.S. 1 (1973). Serrano v. Priest, 557 P.2d 929 (Cal. 1976).

E Mapping the Landscape

EARLY CHILDHOOD EDUCATION

In the United States, ECE services are provided through a complex network of private and public providers, funded through a variety of federal, state, and local streams. Roughly 1.3 million children participate in state-funded prekindergarten programs, and approximately 400,000 additional children are estimated to be in special education programs. The federally funded Head Start program serves more than 800,000 children of ages 3 to 5 years nationwide. In addition, the federal government subsidizes child care in a variety of settings through the Child Care and Development Fund, reaching almost as many children as Head Start and state prekindergarten program combined. Across sectors, programs face starkly different minimum quality thresholds and vary substantially with respect to their quality, duration, and scope.

Nearly 75% of 4-year-olds in the United States are enrolled into early childhood education (ECE) programs, and more than half of those slots are publicly funded. Public investment in ECE has grown rapidly over the past two decades. Between 1990 and 2011, the number of 3- to 5-year-olds enrolled in public preschool programs more than doubled, from 1.2 million to 2.9 million children. Growth in state spending for preschool has been particularly pronounced, rising from 2.4 billion to 5.5 billion between 2001 and 2011. Furthermore, while most public programs target low-income children, as of 2013 three states funded voluntary preschool programs available to all 4-year-old children and eight offered programs to more than half of the 4-year-olds in each state. This entry presents an overview of ECE in the United States, providing a brief description of the current ECE landscape, and offering several explanations for the heightened interest and investment in educational opportunities for children under age 6. The entry summarizes the empirical evidence based on the effects of early childhood interventions. Although the term ECE is used to refer to a broad range of early interventions, from home visitation programs targeted at infants and their mothers to full-day kindergarten classrooms, the entry focuses primarily on preschool programs, defined as classroom-based programs offered through schools or community-based providers, aimed at children ages 3 to 5.

Justifications for Early Investment In recent decades, neurobiological discoveries have vastly expanded our understanding of brain development over the life course and have highlighted the unique importance of early childhood experiences and environments. Scientists have demonstrated, for instance, that the brain’s capacity to change and develop neural pathways is far higher in early childhood than later in life, a concept known as “declining brain plasticity.” The implication for social policy is that interventions that occur earlier in life are likely to have higher returns than those experienced even in elementary school. This is both 233

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Early Childhood Education

because early interventions allow individuals and society more years to reap returns on investment and because the early developmental gains set the stage for greater gains at every later stage. A second key factor driving the push for ECE is heightened policy discourse around narrowing of academic and social achievement gaps. Over the past two decades a variety of accountability policies targeted at K-12 schools have been framed around gap closing, most notably the federal No Child Left Behind Act of 2001 (NCLB). However, by the time children enter kindergarten, large cognitive and social gaps are already present, both across racial groups and by socioeconomic status. In fact, gaps in cognitive measures are already seen among toddlers. Targeted early interventions, such as Head Start, have been justified as strategies to help low-income children begin elementary school on more equal footing with their nonpoor peers. In recent years, ECE is often framed as a necessary component of efforts to meet accountability goals and eliminate achievement gaps. These factors as well as a growing body of social science research suggesting that early educational interventions yield substantial short- and long-term benefits have led the Nobel Prize–winning economist James Heckman to argue that ECE programs are unique among social policies in that they are both equity and efficiency enhancing.

Experimental Evidence on the Long-Term Impact of Targeted Interventions The evidence most commonly cited in support of early childhood interventions comes from two relatively small randomized trials of intensive, targeted preschool programs whose participants have been followed from early childhood well into adulthood. The key strengths of these studies are their experimental design and longitudinal nature, which together allow for credible causal estimates of preschool effects on a host of important longer term life outcomes. The HighScope Perry Preschool project began in 1962 with 123 low-income, 3- and 4-year-old children from Ypsilanti, Michigan, randomly assigned either to a preschool intervention or to a control group. The treatment group participated in a halfday program for 1 to 2 years, taught by certified public school teachers with at least a bachelor’s degree who also conducted weekly home visits. Children in this group completed approximately 1 more year of schooling and were 20 percentage

points more likely to graduate from high school relative to the control group. In addition, individuals in the treatment group were far less likely to experience teen pregnancies, out of wedlock births, arrests for violent crimes, or receipt of government assistance. At age 40, their median monthly income was about 43% higher than the control group. A decade later, the Carolina Abecedarian project randomly assigned 111 infants from high-risk, low-income families to a very intensive early childhood intervention including full-day care with an educational curriculum from infancy through school entry. In this case too, longitudinal comparisons between children in the treatment and control groups favored the treatment group. Children in the treatment group were less likely to repeat grades, be placed in special education, or become teen parents, and by age 30 were almost four times as likely to have earned college degrees. Both demonstration projects are considered costly relative to large-scale state preschool interventions available today. However, both also showed substantial social return on investment, between a $2 and $16 return on every $1 invested. Taken together then, findings from these long-term studies serve as “existence proofs,” compelling case studies showing that early childhood interventions, serving very lowincome children through intensive interventions, provide cost-effective ways to meaningfully impact individuals’ life trajectories. Given that these studies were done several decades ago and focused on small, highly targeted programs, however, the studies may lack generalizability.

Broader and More Recent Interventions Larger studies suggest that the long-term benefits associated with ECE interventions are not limited to small, targeted demonstration projects. The Chicago Longitudinal Study evaluated the impact of ChildParent Centers, which are federally funded comprehensive early childhood services for low-income children starting at age 3. While not a randomized trial, the study has tracked approximately 1,000 participants and a demographically similar control group starting in 1985 when children entered the program and continuing through age 26. Results suggest increases in educational attainment and substantial drops in grade retention, special education classification, felony arrests, and food stamp receipt. The total social return on investment, in 2007 dollars, is estimated to be almost $11 per dollar invested

Early Childhood Education

(Reynolds, Temple, White, Ou, & Robertson, 2011). Quasi-experimental studies measuring the long-term impacts of Head Start also suggest positive longer term outcomes (Ludwig & Miller, 2007). These larger longitudinal studies suggest that early childhood interventions can have large, positive impacts into adulthood. It is worth noting, however, that the bulk of the research evaluates programs that were targeted toward low-income children and implemented several decades ago. Studies that examine the effects of preschool programs in the past two decades have yielded mixed results. A large body of research demonstrates that children who participate in preschool—particularly high-quality programs—start kindergarten outperforming their peers. Effect sizes are typically largest for poor and minority children, though recent studies also suggest meaningful gains for nonpoor children (Bassok, 2010; Gormley, Gayer, Phillips, & Dawson, 2005; Weiland & Yoshikawa, 2013). However, some studies that track children as they progress through elementary school show that the positive effects observed at school entry dissipate, often as early as first grade. The Head Start Impact Study, the first large-scale randomized trial of Head Start, is commonly cited as powerful evidence of this “fade out” in early childhood impacts. In 2002, roughly 5,000 children eligible for Head Start were randomly assigned either to Head Start or to a control condition. While in Head Start, the treatment group outperformed the control on a number of outcomes. However, by third grade, Head Start participants were indistinguishable from their control group peers on all of the developmental domains examined. In fact, many of the gains realized by participants at the end of the program year had eroded by kindergarten or first grade. Researchers and policymakers have tried to understand why the longer term studies find substantial impacts well into adulthood, while more recent studies suggest the benefits of ECE are short-lived. One partial explanation is the substantial rise in preschool participation and its implications for research design. In 1964, only 10% of 3- and 4-year-old children were enrolled in any form of preschool, whereas today preschool participation is the norm. For this reason, control group children in more recent studies, such as the Head Start Impact Study, have early childhood experiences that are relatively similar to those experienced by their treatment group peers. In other words, while early studies estimated the effects of intensive preschool interventions

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relative to no formal care at all, more recent studies often make comparisons among different forms of formalized ECE and are therefore less likely to find effect sizes of the same magnitude. Another key point is that the observed fade-out of benefits in early elementary school does not preclude substantial longer term outcomes. Indeed, a number of the studies that demonstrate large, longterm benefits (notably including the HighScope Perry Preschool study) also show fade-out in test score benefits throughout school. It may therefore be the case that despite the medium-term fade-out observed, preschool participation in more recent programs also yields significant long-term gains.

Conclusion ECE interventions can yield powerful economic benefits both to individuals and to society. As states expand their investments in ECE, new research will be necessary to assess not only whether large-scale present-day preschool interventions yield meaningful benefits but also under which circumstances programs yield the best results. A growing body of research suggests that the quality of both the early childhood intervention and the schooling experience in the years that follow are critical, and research that uncovers the essential components of quality and the policies necessary to ensure such quality is needed. Daphna Bassok See also Achievement Gap; Educational Equity; Human Capital; Public Good

Further Readings Barnett, W. S., Carolan, M. E., Fitzgerald, J., & Squires, J. (2012). The state of preschool 2012: State preschool yearbook (National Institute for Early Education Research). Retrieved from http://nieer.org/publications/ state-preschool-2012 Bassok, D. (2010). Do Black and Hispanic children benefit more from preschool centers? Understanding the differential effects of preschool across racial groups. Child Development, 81(6), 1828–1845. Belfield, C. R., Nores, M., Barnett, W. S., & Schweinhart, L. (2006). The High/Scope Perry Preschool Program: Costbenefit analysis using data from the age-40 follow-up. Journal of Human Resources, 41(1), 162–190. Campbell, F. A., Pungello, E. P., Burchinal, M., Kainz, K., Pan, Y., Wasik, B. H., & Ramey, C. T. (2012). Adult outcomes as a function of an early childhood educational program: An Abecedarian Project follow-up.

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Econometric Methods for Research in Education

Developmental Psychology, 48(4), 1033–1043. doi:10.1037/a0026644 Chetty, R., Friedman, J. N., Hilger, N., Saez, E., Schanzenbach, D. W., & Yagan, D. (2011). How does your kindergarten classroom affect your earnings? Evidence from Project Star. Quarterly Journal of Economics, 126(4), 1593–1660. Deming, D. (2009). Early childhood intervention and life-cycle skill development: Evidence from Head Start. American Economic Journal: Applied Economics, 1(3), 111–134. Gormley, W. T., Jr., Gayer, T., Phillips, D., & Dawson, B. (2005). The effects of universal Pre-K on cognitive development. Developmental Psychology, 41(6), 872–884. Loeb, S., & Bassok, D. (2008). Early childhood and the achievement gap. In H. F. Ladd & E. B. Fiske (Eds.), Handbook of research on education finance and policy (pp. 517–534). New York, NY: Routledge. Puma, M., Bell, S., Cook, R., Heid, C., Broene, P., Jenkins, F., & Downer, J. (2012). Third grade follow-up to the Head Start Impact Study: Final report (OPRE Report 2012-45). Washington, DC: U.S. Department of Health and Human Services, Administration for Children & Families. Reynolds, A. J., Temple, J. A., White, B. A. B., Ou, S.-R., & Robertson, D. L. (2011). Age 26 cost-benefit analysis of the child-parent center early education program. Child Development, 82(1), 379–404. Shonkoff, J. P., & Phillips, D. (2000). From neurons to neighborhoods: The science of early childhood development. Washington, DC: National Academies Press. Weiland, C., & Yoshikawa, H. (2013). Impacts of a prekindergarten program on children’s mathematics, language, literacy, executive function and emotional skills. Child Development, 84(6), 2112–2130.

ECONOMETRIC METHODS RESEARCH IN EDUCATION

FOR

Econometric tools are increasingly being used to study issues in education. The primary focus of econometrics in education research is to establish causal inference—to identify whether some factor, be it a student’s race, teacher quality, or a policy, for example, affects education outcomes. This entry provides background on various econometric methods that are commonly used in education research and provides a basic understanding of the benefits and drawbacks of each strategy. It also

provides examples of particular pieces of research that effectively use each strategy. The entry begins with background on distinguishing between causality and correlation, then it discusses the “gold standard” strategy of the randomized controlled trial (RCT). Since conducting an RCT is usually not feasible for a researcher, the entry then discusses methods that try to pull causal relationships from existing data, starting with basic regression and moving on to natural experiments, difference-in-differences (DD), instrumental variables (IV), and a few other strategies.

Causality and Correlation Before diving into specific econometric strategies, it is important to review the distinctions between correlation and causality. Consider a set of students in a school. One looks at their test scores and finds that high-income students have higher test scores. This is a correlation—a relationship between two variables. It is tempting to say from this that there is an “effect” of income on test scores, that is, that higher income causes test scores to increase. This is the same as saying that income has a causal relationship with test scores. However, as will be described in detail later in this entry, just because two variables have a correlation, their relationship is not necessarily causal. In fact, most of the time, these relationships are not causal. The example given earlier also highlights the importance of distinguishing between correlation and causality for policy. A reasonable policy response to the finding of the income-achievement relationship may be to increase income support and welfare programs. However, such a policy presumes that the lower income causes achievement to fall. Suppose instead it is not income directly but the fact that low-income students attend low-quality schools that leads to low achievement. If this is the case, then the income support policy may help a bit, but it would likely not be as effective as trying to directly improve the schools themselves. Thus, policy decisions based on correlative results are more likely to be ineffective, or potentially even harmful, than policies based on causal evidence. The differences between correlation and causality can be illustrated through the analysis of how class sizes affect student achievement. Economists and education researchers have long theorized that smaller classes lead to improvements in student performance. However, establishing a causal relationship between these variables has proved elusive due

Econometric Methods for Research in Education

to the fact that high-achieving students tend to enroll in schools with smaller classes. Thus, this positive relationship between students and class size may not be causal. This is a general problem in education research called endogenous selection. A closely related problem is omitted variables, where some characteristic of a student that affects both the variable the researcher is interested in and the outcome is unobserved. For example, smaller classes may be an attractive quality of the teaching experience, and thus, high-quality teachers may gravitate toward smaller classes. In this case, the positive relationship between class size and achievement may be due to teacher quality rather than class size. Thus, these omitted variables can also explain the relationship. There are very few questions in education research in which a causal relationship can be established by simple comparisons. Fortunately, economists and education researchers have developed a variety of tools that are able, in some cases, to extract causal relationships from data.

handful of other techniques that are sometimes used will be discussed briefly at the end of this entry. A basic regression model uses ordinary least squares (OLS) techniques where one takes a set of data and tries to draw the “best fit” line through those data by minimizing the sum of the squares of the distances from the line to the observed data points (Figure 1). Consider the problem of evaluating the impact of class size on student achievement. Figure 1 shows some hypothetical data on class size and achievement. Each dot reflects the class size and the average test score for that class. The line shows the results of an OLS regression of test scores on class size as defined by the following equation: Test score ⫽ 3.0 ⫺ 0.06 ⫻ Class size.

(1) This says that at class size equals 0, test scores start at 3.0 and drop by 0.06 points for each one student increase in class size. More generally, this type of OLS model is defined by Yi ⫽ α ⫹ βXi ⫹ εi,

Regression

(2)

Almost all econometric techniques used in education research are based on linear regression, though a

where Y is some outcome measure, X is an educational input we care about, and ε takes into account

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Ordinary Least Squares Estimate of Class Size on Test Scores

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everything that is not included in the model. For example, if Y is a test score and X is class size, then ε includes things like teacher quality, student socioeconomic status, and school quality. The “i” subscript denotes that we are talking about an individual person or thing such as a student or a school. OLS uses data on Y and X to estimate β, which says how much Y changes for a 1-unit change in X, for example, how much does the test score change when increasing class size by 1. The model also estimates α (the “constant”), which tells us how much Y equals when X equals 0. OLS further estimates a standard error term for β. The standard error is a measure of the reliability of the estimate of β, or how much would we expect β to change on average if we took a different random group of people from the population. The estimated standard error of β is σ. These two pieces of information allow one to say whether a result is statistically significantly different from 0. Generally, given a large enough sample, if the following equation holds, then the estimate of β is significant: β β ≥ 1.96 or ≤ −1.96. σ σ

(3) Typically, a regression model has more than the two variables in Equation 2. Thus, a typical OLS regression model is as follows: Yi = α + β1 X1i + β 2 X 2i + $ + β N X Ni + ε,

(4) where there are multiple “X” variables. Typically, a researcher cares most about one variable—called the variable of interest—and then adds others as control variables, which are variables that are included in the model because they may affect both the variable of interest and Y. Indeed, adding controls allows us to remove some variables from ε and include them in the model. So, for example, in our class-size model, one may want to estimate a model like this: Test score ⫽ α ⫹ β1 ⫻ Class size ⫹ β2 ⫻ Student income ⫹ β3 ⫻ Teacher experience ⫹ ε.

(5) The reason adding income and teacher experience to the model is important is because both of these factors can affect both test scores and class size. One might also include higher order terms for the variable of interest (e.g., X2) or interactions between two variables of interest (e.g., X1 ⫻ X2). Doing this is

useful when a researcher suspects that the relationship between the variable(s) of interest and the outcome variable is not linear. Finally, in the case of test scores, if the researcher has multiple years of data on a student, he or she will often include the prior year’s test score as a control as well. This allows one to interpret an estimate as an impact on test score gains rather than on levels. While OLS is commonly used, it suffers from severe problems. Most important of these is statistical bias. That is, the estimate β is systematically different from the true value because it only captures correlation rather than causality. In Equation 1, it is estimated that larger class sizes reduce achievement. But what if low-socioeconomic status leads to larger class size? Then, the estimate on class size may reflect the effect of income. This is called an omitted variable bias—something is left out of the regression that affects both the X and Y variables. In some cases, one can include the omitted variable as a control. This is what is done in Equation 5. Student income and teacher experience are important omitted variables, thus, rather than leave them out of the model, it is better to include them. However, often one cannot include an omitted variable because it is unobserved. For example, teacher and school quality are also likely to affect both test scores and class size, but one cannot observe these factors in most data. Thus, omitted variable bias is only rarely eliminated using OLS. Luckily, econometrics provides a number of tools that remove this type of bias and allow researchers to find the causal impacts of education interventions. The rest of this entry will go through the strategies most commonly used in economics of education research.

Randomized Controlled Trials An RCT, or experiment, is an effective strategy for estimating the causal effects of education interventions since a well-done RCT is free of bias. Hence, recently, experiments in education research have exploded in popularity. An RCT is defined by a few key characteristics. First, within a given set of participants, some people are randomly assigned to receive an intervention—the treated group—while others receive no intervention—the control group. Second, the intervention is implemented with the intention of studying its effects. Thus, an RCT can be fully designed by a research team or by the

Econometric Methods for Research in Education

agency administering the intervention (e.g., school district, state) as a pilot program prior to full implementation. Since treatment in an RCT is based on random assignment, analysis is generally simple and relies on OLS, though complications such as attrition, noncompliance, and problems with the randomization process can arise. Typically, to assess the outcome from an RCT, a researcher will estimate the regression Yi = α + βTreatedi + γ 1X + $ + γ N XNi + ε. 1i

(6) Thus, the researcher regresses an education outcome on whether or not a person, school, or district receives the treatment and estimates the impact of treatment on this outcome (β). As with any OLS model, the researcher can add control variables to improve precision or account for minor problems with randomization. An example of an effective RCT was the Project STAR (Student/Teacher Achievement Ratio) experiment in Tennessee. This experiment, conducted in the mid-1980s, randomly assigned a set of kindergarten students in multiple schools in Tennessee to small or large classes. Researchers have used this experiment to show that there are significant positive causal effects of class size on test scores and the likelihood of taking college entrance exams, as well as reductions in crime.

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was nonrandom; that is, only schools with high free-lunch eligibility rates are chosen. Economists have a number of econometric tools for exploiting these natural experiments to answer important questions in education.

Difference-in-Differences The most common natural experiment technique used in education research is DD. This method can generate causal estimates by relating changes before and after treatment in a treated group to changes in a group that does not receive a treatment. To see this, consider the example in Figure 2. The figure shows two groups that are eligible to receive financial aid to attend college. At year zero, the treated group becomes eligible to receive additional aid, while the comparison group’s aid does not change. The DD approach takes advantage of the fact that, while different in levels, attendance prior to the increase follows similar trends in both groups. Thus, as in work by Susan Dynarski that looks at how the elimination of a Social Security financial aid program affected enrollment, one can calculate the impact of financial aid on college attendance by calculating DD ⫽ ΔTreated ⫺ ΔComparison ⫽ CollegeRateTreatedPostCollegeRateTreatedPre CollegeRateComparisonPostCollegeRate ComparisonPre.

(7)

Natural Experiments While growing in prominence, RCTs are still rarely used in education research as they involve high costs, and not every question is suitable to being answered via an experiment. Thus, as a second-best option, education economists often seek out natural experiments. These are situations where the education system is modified in a way that generates random differences in exposure to a treatment across students, schools, or staff. For these random changes to occur, typically the change in the system—often a policy change—needs to be sudden and unexpected. Furthermore, the policy generally must affect similar groups in different ways. Using our class-size example, a good natural experiment would be a situation where a school district decides to implement class-size reductions for some schools but not for others. Ideally, the choice of these schools would be random, but one can also extract causal estimates in some situations where the choice of schools

Typically, one uses a regression-based equivalent to models like Equation 8 as this allows for the addition of controls. Thus, the general DD model is of the following form: Yit = α + β1Treatedi + β 2 Postt + β3Postt ∗ Treatedi + γ 1X1it + $ + γ N XNit + ε.

(8) In this model, β3 provides the causal estimate. Note that there is now a “t” subscript to allow variables to change over time. For DD to provide an unbiased causal effect, the groups must have similar trends over time in the absence of treatment. If the trends differ, then one may mistakenly attribute differences due to diverging or converging trends to the treatment. Unfortunately, this needs to be assumed as one cannot observe the trend for the treated group in the absence of treatment. Given this, researchers need

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0.4

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Difference-in-Differences Estimates of Impact of Financial Aid on College Attendance

to use the data available to try to rule out potential differential trends.

Lotteries When an open-enrollment school has more applicants than slots available, admission is often determined by a random lottery. The student receives a randomly assigned number, and if that student’s number is chosen before space fills up, he or she is offered admission to the school. From a research perspective, lotteries are very attractive as they closely replicate an RCT at a much lower cost to the researcher. The procedure for analyzing a lottery is very similar to that of an RCT. Julie Berry Cullen, Brian Jacob, and Steven Levitt use lotteries to look at the impact on achievement of attending a higher quality high school. Their basic model assesses the impact of being admitted to a higher quality school using a model like the following: Yi = α + βAdmittedi + γ 1X + $ + γ N X + εi . Ni 1i

(9) Thus, instead of a “treated” variable, one is interested in the effect of being admitted to the school through the lottery. This analysis can be conducted

by OLS as in the RCT framework or, if compliance with admission is not perfect, an IV framework, described in the next section. Nonetheless, there are some drawbacks to lotteries relative to RCTs. First, since researchers have no control over who enters the lottery, there may be concerns that lottery participants are different from other students and thus do not tell us about the impacts of these programs beyond this particular subpopulation. Second, often schools are oversubscribed because they are perceived by parents to be better than alternative options. If this parent perception is accurate, then lotteries may only tell us about the effectiveness of the best schools.

Instrumental Variables The IV method isolates changes in a variable of interest solely due to factors unrelated to the outcome variable. Thus, if one is worried about omitted variable bias, IV ensures that the estimated impact is based only on changes not induced by the omitted variable and thus establishes a causal effect. This is done through the use of an additional variable (the “excluded instrument” or “instrument” for short) that is correlated with the variable of interest

Econometric Methods for Research in Education

but uncorrelated with the outcome variable except through the variable of interest. For example, a number of papers have used changes in minimumdropout-age laws as instruments for the impact of dropping out of school on life outcomes. The idea behind this instrument is that people born in different states and years face different restrictions on dropping out, forcing some to stay in school longer in a way that is unrelated to other factors that could affect life outcomes. In another example, Scott Imberman uses shopping centers as an instrument for charter school penetration to test the impact of charters on public school performance. The worry is that charter schools may choose locations with low-quality public schools. Imberman argues that shopping centers are popular locations for charter schools— thus correlated with charter penetration—but are uncorrelated with student outcomes. To estimate an IV model, the most common strategy is two-stage least squares. Using the charter competition example, one first estimates how the number of nearby shopping centers affects charter penetration: CPit = γ 0 + γ 1ShoppingCentersit + γ 2 X2,it + $ + γ N XN,it + μ it .

(10) Then, one uses the estimates from Equation 10 to create CPit, the predicted values of CPit. The last step uses these predicted values in place of CP to estimate the causal impact of CP on achievement: Achievementit = β0 + β1CPit + β2 X2,it + $

+ βN XN,it + εit . (11) Despite IV's usefulness, it is often difficult for researchers to find instruments. First, the instrument needs to be completely uncorrelated with factors that affect the outcome variable regardless of whether those factors can be observed. This is not testable and thus must be assumed. The requirement for a complete lack of correlation is very strict, and few variables can satisfy it. Second, the correlation between the instrument and the variable of interest needs to be large; otherwise, the standard errors become too big. Nonetheless, instruments have been used in many other contexts in education research. For example, Tom Dee uses IV to estimate the impact of schooling on civic engagement, and

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Caroline Hoxby uses it to estimate the education benefits from school districts competing with each other for students.

Regression Discontinuity The basic idea behind regression discontinuity (RD) is that if an intervention is implemented due to a student or school reaching some threshold, then one can compare those who just barely exceed the threshold with those who just barely fall below, as usually only random factors determine who exceeds the threshold within this small group. While the RD method can be very powerful, there are key limitations. First, one cannot have any manipulation around the cutoff. For example, if students who fail have the opportunity to retake the exam, this can cause problems, as being above the cutoff is no longer random. Second, the RD only provides the causal effect for a very specific group of people—those near the cutoff. Thus, one cannot say, for example, what the impact of attending an elite school is for students at the top of the class. Hence, researchers must be careful about extrapolating results to a more general environment. Finally, the variable that determines the cutoff needs to be quantitative and continuous. That is, one cannot do RD on a categorical variable. For example, suppose an education intervention is provided to special education students and students who have limited English proficiency but not other groups. While one may be able to evaluate such an intervention with another method listed earlier such as DD, one cannot conduct an RD in this situation. Atila Abdulkadiroğlu, Joshua Angrist, and Parag Pathak used RD to estimate the impact of attending elite public high schools in Boston and New York on achievement. These schools admit students based solely on a threshold score on an entrance exam. Thus, the authors are able to use an RD design that compares those who barely exceed with those who barely miss the cutoff. RD designs can be “strict” or “fuzzy.” A strict RD occurs when compliance with the cutoff is perfect— everyone who exceeds it gets treated while everyone below does not. In a fuzzy RD, compliance is imperfect. Figure 3 graphs a hypothetical likelihood of admission to the elite school versus points on the entry test. The panel on the left shows the strict case, while the one on the right shows the fuzzy case when the cutoff is 50. Figure 4 shows that, in either case, what a researcher is looking for is a break in the trend for the outcome variable at the cutoff.

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Fuzzy RD

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To estimate a strict RD, the most common method is to choose a range of observations close to the cutoff and estimate a regression model of the form Achievementit ⫽ β0 ⫹ β1Aboveit ⫹ f(EntryScoreit) ⫹ εit.

(12)

β1 provides the causal impact of being above the admissions threshold on achievement. Note that in the strict model, β1 is also the causal impact of enrolling in the elite school on achievement. A very important part of this model is f(EntryScoreit), which is some polynomial function of the entry score that is allowed to vary above and below the cutoff.

Econometric Methods for Research in Education

Researchers will often also add control variables or use nonparametric methods such as local-linear regression. For a fuzzy RD, one can use a two-stage least squares approach, where first one estimates the impact of the cutoff on enrollment and then the impact of enrollment on achievement. Enrollit ⫽ γ0 ⫹ γ1Aboveit ⫹ f(EntryScoreit) ⫹ μit

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research. The entry also highlights the importance of figuring out the causal impacts of educational interventions. Because econometric models are well suited to establishing causal effects of education policy, it is likely that their use will continue to increase in the future, which makes understanding these methods essential for education policy researchers, policymakers, and anyone else with an interest in education policy.

Achievementit ⫽ β0 ⫹ β1Enrollit ⫹ f(EntryScoreit) ⫹ εit

Scott A. Imberman (13)

Other Methods

See also Instrumental Variables; Omitted Variable Bias; Ordinary Least Squares; Quasi-Experimental Methods; Regression-Discontinuity Design

Fixed Effects

If one has data that follow individuals over time, fixed effects may be used. These models estimate impacts using changes within individuals over time. Their advantage is that they remove bias resulting from omitted variables that do not change over time, such as innate ability. However, they do not account for bias from omitted variables that do change over time; hence, generally, economists prefer natural experiments or randomized trials. Random-effects models that provide smaller standard errors but require stronger assumptions are also used. Propensity Score Matching

This method follows a two-step process, where one first estimates the likelihood of an individual being treated with an education intervention and then generates predicted probabilities of treatment— the “propensity score.” Then, the researcher calculates the differences in outcomes between treated observations and control observations with similar propensity scores. Although this method does not account for unobservable omitted variables, it has advantages over OLS in that it does not make restrictions on the form of the estimating equation and ensures that treated individuals are only compared with similar untreated individuals. However, it typically requires large amounts of data to get sufficiently reliable estimates.

Conclusion This entry provides a broad overview of the econometric tools now used in education research. The use of these tools has been increasing dramatically over time and has had a marked effect on education

Further Readings Abdulkadiroğlu, A., Angrist, J. D., & Pathak, P. A. (2011, March). The elite illusion: Achievement effects at New York and Boston exam schools (Working Paper No. 17264). Cambridge, MA: National Bureau of Economic Research. Retrieved from http://www.nber .org/papers/w17264 Angrist, J. D., & Pischke, J.-S. (2008). Mostly harmless econometrics: An empiricist’s companion. Princeton, NJ: Princeton University Press. Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How much should we trust differences-in-differences estimates? Quarterly Journal of Economics, 119(1), 249–275. Cullen, J. B., Jacob, B. A., & Levitt, S. (2006). The effect of school choice on participants: Evidence from randomized lotteries. Econometrica, 74(5), 1191–1230. Dee, T. S. (2004). Are there civic returns to education? Journal of Public Economics, 88(9–10), 1697–1720. Dynarski, S. M. (2003). Does aid matter? Measuring the effect of student aid on college attendance and completion. American Economic Review, 93(1), 279–288. Hoxby, C. M. (2007). Does competition among public schools benefit students and taxpayers? American Economic Review, 97(5), 2038–2055. Imberman, S. A. (2011). The effect of charter schools on achievement and behavior of public school students. Journal of Public Economics, 95(7), 850–863. Lee, D. S., & Lemieux, T. (2010). Regression discontinuity designs in economics. Journal of Economic Literature, 48(2), 281–355. Murnane, R., & Willett, J. B. (2010). Methods matter: Causal inference in educational and social science research. New York, NY: Oxford University Press. Schanzenbach, D. W. (2006/2007). What have researchers learned from Project STAR? Brookings Papers on Education Policy, 205–228.

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Economic Cost

Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. Cambridge: MIT Press. Wooldridge, J. M. (2012). Introductory econometrics: A modern approach (5th ed.). Mason, OH: South-Western Cengage Learning.

ECONOMIC COST In a world of scarce resources, the pursuit of one opportunity does not leave other opportunities untouched. Due to scarcity, each production and consumption decision is a trade-off and has costs. There are different ways to conceptualize and measure costs. Typically, when lay people think of costs, they are considering accounting costs rather than economic costs. Accounting costs include monetary expenses, depreciation, and other bookkeeping debits and credits. However, economic cost also encompasses the next best use of an input. In other words, the economic cost of an input is the compensation necessary for an input to remain in a productive activity. Economic cost is an important but often ignored concept in education and education finance. Educational policymakers and administrators face trade-offs in the allocation of scarce resources in the provision of schooling. Although the opportunity cost in resource decisions in education is often overlooked, economic cost remains a primary concern and becomes more important as education budgets decrease. This entry includes an overview of economic cost. First, the key differentiation between explicit or accounting cost and implicit or opportunity cost is discussed. Next, the concept of opportunity cost is explained in greater detail before describing the challenges in measuring economic cost and outlining the applications of economic cost.

Explicit Versus Implicit Costs To better understand economic cost, consider the decision to attend college or graduate school. The explicit or accounting costs of college attendance include out-of-pocket expenses such as tuition, books, and food. These dollars could have been devoted to other opportunities such as investing in a business. The implicit costs of attending graduate school are the costs associated with the personal use of time and energy. The time and effort spent pursuing a PhD could have been spent on other incomegenerating activities, such as a job. These forgone

wages represent the implicit or opportunity cost of attending college. The critical difference between accounting and economic costs is the consideration of opportunity cost. The economic cost of attending college takes into account the opportunity cost of the decision, such as the wages that could have been earned while an individual goes to school. Therefore, the economic cost of college attendance is the accounting cost plus the opportunity cost of the best job an individual could hope to get if he or she did not attend college. Stated differently, economic cost is the sum of the explicit and implicit costs associated with the use of a particular resource. Implicit costs matter when making decisions regarding the use of scarce resources. Indeed, a full consideration of implicit costs may lead to different determinations about the allocation and organization of resources. To fully illustrate the differences between using the accounting and economic perspectives, consider a car dealership in a prime location in downtown Los Angeles that makes sufficient revenue to cover the sum of accounting costs such as payroll and electricity. From an accounting perspective, the business may appear profitable. However, when one adopts an economic perspective, that is, considering the next best alternative use of the valuable real estate, the business may not seem as successful due to the opportunity cost of the land. Put another way, the land could be used in different ways, such as leasing to another business that may generate more income than its current use as a car dealership.

Technical Considerations The Next Best Alternative (Opportunity Cost)

Opportunity cost is a fundamental economic concept and can be defined as the value of the best forgone alternative use of a resource. A central tenet of economic cost is the recognition that costs extend beyond monetary payments into forgone alternatives. These sacrifices in the allocation of a resource must be taken into account to gauge its true economic cost. If a resource can be used in more than one way but can only be devoted to one use at a time, the opportunity cost of using that resource is the value of the resource when deployed in its best alternative use. Resources are scarce, and thus, each allocation of resources has trade-offs. Thus, the choice to produce one good means going without another.

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Measuring Economic Costs

Further Reading

The implicit aspect of economic costs (opportunity costs) is less apparent and harder to identify than explicit costs. It is not possible to always quantify opportunity costs. Explicit costs are commonly reflected in accounting documents, such as income statements and balance sheets. It is difficult to accurately estimate the economic costs of using a resource due to the nature of the implicit costs of resources. Many intangibles have no market price and pose a considerable challenge to measure in monetary terms. For example, estimating a monetary value for environmental damage such as polluted air is complicated, especially when there is no direct harm to consumers. For resources without a market price, economists employ several methods to ascertain the economic costs associated with their use. For instance, in numerous branches of economics, housing prices are used to determine the willingness of individuals to pay for local amenities, such as higher quality schools or safer neighborhoods.

Nicholson, W., & Snyder, C. M. (2011). Microeconomic theory: Basic principles and extension (11th ed.). Mason, OH: South-Western Cengage Learning.

Applications of Economic Costs Economic cost is applicable to consumption as well as production decisions. The concept of economic cost has been applied to several fields. In business and finance, economic costs are prevalent given their wide applicability across different types of firms. Economic costs are used in the economic literature to describe profit-maximizing firms. Profit maximization means that firms aim to achieve the most economic profits—the largest possible difference between total revenues and economic costs. Economic costs have also shaped how accountants view costs and have adapted the concept to depreciation accounting. Generally, economic costs are not documented in accounting books but are considered in the strategic decisions of a firm. In decisions such as shutting down operations or subleasing facilities and property, economic costs as well as accounting costs play a major role. As “market-based” reforms such as charter schools and portfolio districts gain prominence in education reforms, the concept of the economic cost becomes even more relevant in education finance and policy. Dominic J. Brewer and Richard O. Welsh See also Education Production Functions and Productivity; Markets, Theory of

ECONOMIC DEVELOPMENT AND EDUCATION The definition of economic development is somewhat unclear in the economic literature. Economic development can be viewed as government action to increase economic output through investing in some form of economic growth (e.g., infrastructure, business, defense, or other types of enterprises affecting the population). This action can involve direct investment of tax revenues or indirect investment such as tax cuts for corporations to bring in industry into an area. Government spending has an impact on the economy, and any area of government spending could be connected to economic development. Economic development can be measured by looking at the growth and development of per-capita income, social inclusion, health and safety, literacy, infrastructure, regional competiveness, and other areas. This entry discusses what economic development entails, how it relates to education, the history of education as economic development, and the ways in which states can use education as an economic development tool. The purpose of economic development is to improve the economic status of all citizens by creating systems that provide for basic human needs while attempting to eliminate or at least reduce social inequality by raising the living standards of the majority through economic growth. It improves a nation’s or state’s economic well-being or strength because it generally increases productivity, job creation, and/or personal income. However, the outcomes of economic development include more than just an increase in jobs, gross domestic product, or income; they also include improvements in social welfare in areas such as health care, housing, and food security. Government spending on medical care or the creation of parks has benefits for citizens that go beyond the financial. Moreover, government spending on infrastructure and services can contribute not only to the good of individuals but also to social goods such as a strong economy and an engaged citizenry. In a society with a strong

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economy and healthy citizens, individuals have more time to pursue social and political endeavors. Economic development should be a collective endeavor incorporating the concerted efforts of citizens and their elected officials, working together to raise the living standards and economic health of communities. Although economic development includes investment (government spending) in other areas such as infrastructure (e.g., roads and bridges), health care, fire safety, and police protection, it also includes investment in human capital—that is, investment in the development of its citizens’ skills through education. Instead of being viewed as a liability in the government’s budget, education should be viewed as a participant, and a strong participant, in the economic development of the municipality, state, and nation.

Relationship Between Economic Development and Education The relationship between economic development and education is often misunderstood. Education is generally viewed as an investment, especially in the development of society’s human capital; the relationship between economic development and education can be clearly realized through observation of the impact of education in increasing the economic strength of citizens and the communities where they reside. Early research in human capital theory demonstrates that characteristics such as aptitudes, abilities, knowledge, skills, and experiences can be viewed as a form of capital parallel to hard capital, because these characteristics bring economic benefits to the individual, business, and community. Investment in education can be seen as an economic development tool because it is an investment in human capital, which in turn can lead to a stronger workforce that can handle higher skilled, higher paid occupations. Higher paid occupations lead to a higher tax base for municipalities, states, and the nation. Also, investment in education programs that lead to a stronger education system helps to create the skilled labor force that businesses desire when seeking to develop new business in communities. Employees spend money in the communities where they live and work, and higher skilled, higher paid employees have more money to spend. This multiplier effect (the ability to increase the money supply in a community, resulting from an increase in the personal income of the community) creates a positive influence on economic growth for the community. An alternative view holds that education

should be viewed as an expenditure rather than an investment, which has led to increasing scrutiny of public education in general. Since the 1983 publication of A Nation at Risk, scrutiny of public education in the United States has grown, and there has been an increasing focus on standardized test results as a measure of educational success. The perceived failure of students to succeed on these tests has led some members of the public and some policymakers to criticize the level of spending on education, despite its economic return. Determining the best level of investment in education is a complex undertaking, and there are many different views as to the appropriate level of overall government spending in a nation’s economy. Education has a direct effect on economic development because of its impact on the social characteristics (i.e., poverty, health, the family structure) and economic (i.e., labor productivity, trade, technology, and income distribution, or per-capita income) characteristics of society. Education is fundamental to the development of a society’s economy because it increases the economic productivity and social steadiness of society by providing those in the labor force with the skills they need to compete for higher paying jobs. As a society’s education improves, its economy will become stronger because its economic strength is related directly to the increased skill sets of the labor force and the economic homogeneity that ensues among the citizenry. Research by Todd Behr, Constantinos Christofides, and Pattabiraman Neelakantan demonstrates that states with relatively high spending on education had less income disparity than other states. Research by Thomas L. Hungerford and Robert W. Wassmer also indicates that increased education spending leads to lower income disparity. In addition, research by Hungerford and Wassmer demonstrates that higher spending on public education by communities correlates to higher housing values and improved economic growth for the community. The development of the workforce through investing in education also enables the nation to become more competitive internationally, because citizens are more integrated with international social and ethnic groups. Thus, an educated workforce has a greater knowledge of its competition.

History of Education as Economic Development Prior to the 19th century, countries did not consider investing in human capital to be an economic priority. Public education and public job training

Economic Development and Education

programs were scarce. In the latter half of the 20th century, investment in these areas increased dramatically after World War II with large numbers of soldiers returning home and needing to acquire skills for jobs. Also, scientific advances such as the launch of the Soviet satellite Sputnik in 1957 and the development of personal computers in the 1970s prompted calls for investment in education. Nations that invested highly in their citizens’ education, skills, and knowledge gained an economic edge during the post–World War II economic expansion. These countries, notably Great Britain, the United States, Japan, France, and Germany, led the world in productivity and economic growth. Some developing countries, such as Turkey, that have invested in education have grown their economies to become major economic influences in the world and future sources of investment by business. Neoclassical growth models from the early 20th century did not consider the impact of investment in education on economic growth. Research in the 1960s began to consider investing in human capital as an economic determinant. This change could be related to Sputnik’s influence on the United States, which resulted in government calls for increasing the scientific and other educational skills of the nation’s youth. Today, a growing segment of economic development research looks at the impact of investing in human capital by investing in education.

Using Education as an Economic Development Tool To make an economy strong, emphasis on higher value rather than lower costs is important. Focusing only on keeping down governmental spending is not how strong societies compete. To remain competitive in the economic marketplace, societies should consider investing in programs that add value to the economic base, because it is that base that provides stability through economic downturns. When such economic downturns occur, the ensuing cuts in education and other governmental programs that add value to the economy can hinder a society’s competitiveness by reducing the strength of its human capital. Research by Hungerford and Wassmer has demonstrated that investing in human capital by investing in areas such as development of workplace skills provides economic benefits through increased productivity, reduced reliance on public services such as medical care, and reduced social problems. Leaders who view education as an economic development tool for society, rather than a

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liability in governments’ budgets, are less likely to reduce investment in education and human capital. Investment in education, as a human capital investment tool for economic development, can improve the economies of local communities, the state, and the nation. Improvement in Local Economies

Some might question the idea that local education investment (or spending) has an impact on the local community. However, research has demonstrated that money spent on local schools has a multiplier effect on the local economy by creating more money for that local economy. Educational activities supported by community investment create income for households and businesses within the community. For example, wages paid to school personnel are used to purchase automobiles, groceries, houses, utilities—all the goods and services that make up consumer spending. This consumer spending stimulates the local economy. In like manner, the expenditures of local educational institutions stimulate the local economy through their direct impact on the revenue of local businesses, the availability of jobs, and the income of individuals who are not employed by the school system but supply the school system with goods and services. Also, regional building contractors, food vendors, office suppliers, and other school service providers earn part of their incomes from the direct spending of the education agency. In addition, investment in education has an impact on the local community by providing a higher quality education and attracting business to the community because of the higher skills of the workforce. Improvement in Statewide Economies

Investing state resources in the state’s education system (whether the investment is at the capital level or at the local level) increases the state’s personal income. Investing in education will have a positive impact on income not only because of the multiplier effect but also because education is labor intensive. Education delivery has traditionally required a high level of personnel to support the education enterprise. In most states, labor costs can take up the majority of the education budget. This increased labor cost means jobs. More jobs mean that there are more people with money to spend in the local economy. Labor expenditures for education in local schools are often the highest expenditure for schools, reaching as high as 80% of the education budget.

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Education expenditures also have a positive impact on property values. Although the relationship between education investment and property values is not direct, higher property values can be correlated to strong school systems. Highly skilled, affluent people are willing to pay more for homes in neighborhoods with strong schools, resulting in a higher level of property taxes paid in these neighborhoods, which in turn can strengthen the local school system. However, higher property values, due to increased spending on education, might at times have a negative impact on a community. Some communities have a large population of citizens with no children or with grown children who may not benefit directly from having their property values increased, and who are also retired and on fixed incomes. Increased costs for those on fixed incomes may have a negative impact on their local spending, which may affect the local economy. Investing in education can also lead to higher graduation rates. High school graduates make more over their lifetime than dropouts, producing more taxes for the state to invest into the state’s economy. Researchers have estimated that some states lose billions of dollars in state income taxes each year as a result of students dropping out of school. States that invest in education contribute to their economic growth while increasing advancement for individuals, because investment in education has the effect of increasing income statewide, decreasing income disparity, increasing social capital, and increasing property values. Higher education attainment also leads to other secondary effects such as improved individual health, increased quality of life, and more efficient economic decision making. Improvement in National Economies

That investing in education is a means of increasing a nation’s economy is a strongly held belief in many countries. Principally, a country that invests in human capital leads the world by creating a highly skilled labor force, which provides for a sustainable growing economy. Countries that invest highly in education generally have a higher gross domestic product and per-capita income. By creating a labor force that has a greater understanding of technology and world affairs and by increasing social stability within the society, education supports a country’s economy and participates as a key factor in its growth. Investing in public education, on-the-job training, and other education programs is considered by

many to be a key factor in a community’s economic development. Stimulating growth of an economy, a primary goal of economic development policy, requires that policymakers consider investing in human capital. As the skills of citizens increase, the impact on the economy is evident. Higher education and skills result in higher income for individuals, resulting in higher spending within the community. This higher spending has a positive multiplier effect on the economy. Educational investment should be considered a key part of a society’s economic development strategy and should be a major focus in economic development research. Michael C. Petko See also Cost of Education; Education Spending; Human Capital; Parcel Tax; Property Taxes; Social Capital

Further Readings Behr, T., Christofides, C., & Neelakantan, P. (2004, April). The effects of state public K-12 education expenditures on income distribution (National Education Association Working Paper). Washington, DC. Retrieved from http:// www.nea.org/assets/docs/HE/expenditures.pdf Bingham, R. D., & Mier, R. (Eds.). (1993). Theories of local economic development: Perspectives from across the disciplines. Newbury Park, CA: Sage. Hungerford, T., & Wassmer, R. W. (2005, April). K-12 education in the U.S. economy: Its impact on economic development, earnings, and housing values (National Education Association, Working Paper). Washington, DC. Retrieved from http://www.nea.org/assets/docs/HE/ economy.pdf Lucas, R. E., Jr. (1988). On the mechanics of economic development. Journal of Monetary Economics, 22(1), 3–42. Ozturk, I. (2001). The role of education in economic development: A theoretical perspective (MPRA Paper No. 9023). Munich, Germany: University Library of Munich. Porter, M. (2007). Colleges and universities and regional economic development: A strategic perspective (Excerpted from Forum Futures 2007). Cambridge, MA: Forum for the Future of Higher Education. Schultz, T. W. (1971). Investment in human capital: The role of education and of research. New York, NY: Free Press. Schumpeter, J. (1961). The theory of economic development: An inquiry into profits, capital, credit, interest, and the business cycle (Vol. 55). New Brunswick, NJ: Transaction Books.

Economic Efficiency Sims, R. (2004, April). School funding, taxes, and economic growth: An analysis of the 50 states (National Education Association Working Paper). Washington, DC: National Education Association. Retrieved from http://www.nea.org/assets/docs/HE/schoolfunding.pdf Todaro, M. P., & Smith, S. C. (1997). Economic development. Reading, MA: Addison-Wesley.

ECONOMIC EFFICIENCY In the private sector, being “more efficient” means one of two things when discussing finance and economic issues: (1) increasing output levels while using the same amounts of input or (2) maintaining output levels while using lesser amounts of input. However, the standard used to measure efficiency in public schools is not the same standard often used to measure efficiency in the business community. When applied to education finance, the concept of efficiency is concerned with how much education or knowledge is delivered to—and acquired by—students, and at what cost. Efficiency can be measured and interpreted in many ways, for example, when no individual can be made better off without another person being worse off (Pareto efficiency) or when the gains to some individuals outweigh the losses to others (Kaldor efficiency). Public school spending typically is conducted Pareto efficiently such that no student’s educational situation is made worse to improve the situation of another student. This entry provides an overview of economic efficiency in education. It briefly describes the origins of the effective schools research literature, then discusses the relationship between educational resources and outcomes.

The Evolution of Economic Efficiency in Public Schools Education is widely considered a public good with positive externalities. Stated differently, individuals may not fully consider the social benefits of education, thus governments typically play a leading role in the provision of education to avoid underproduction. The concept of efficiency is important in the provision of education. Despite the large amount of research and analyses that support the claim that public educational organizations produce outcomes inefficiently, the notion that economically efficient relationships do exist between educational expenditures and outcomes also is well supported. In 1983, the report A Nation at Risk raised the alarm about

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the level of education provided in U.S. schools and prompted what is now known as effective schools research. Written by the National Commission on Excellence in Education, A Nation at Risk claimed the primacy of economic efficiency in educational pursuits. The commission charged that within the equity movement, the traditional measures of academic success (e.g., high school graduation, standardized scores, and college admission) came to be seen as compensation to which all students were entitled regardless of their academic performance rather than as rewards to be earned through persistence and achievement. After the publication of A Nation at Risk, and the resulting focus on raising education standards, education finance researchers began to move away from focusing on issues of fiscal equity, or the fair distribution of funding to schools and students. Their research began to look at the development of stronger curriculum standards and improving levels of economic efficiency.

Effective and Efficient Schools Efficient schools research focuses on the interrelationships of three concepts: (1) educational leadership, (2) effectiveness, and (3) equity. The concepts of educational equity are embedded in seminal education finance research, including works by Ellwood Cubberley, George Strayer and Robert Haig, and Paul Mort that address issues of educational efficiency in terms of the minimum amount of funding necessary to generate desired outcomes. Moreover, it was asserted by these researchers that fiscal equity and economic efficiency research no longer should imply that spending more money on schools will generate increases in educational outcomes. More useful would be research to determine the best ways to spend funding to improve student learning. In economic terms, educational revenues are necessary— but not sufficient—to increase student achievement.

Increased Spending, Increased Student Achievement? There is some disagreement among educational researchers on whether increased spending leads to improvements in student achievement. Eric Hanushek has detailed the relationship between educational resources and student learning outcomes in several meta-analyses. His research found no significant statistical relationships between educational expenditures and student achievement.

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Economic Efficiency

A reanalysis of Hanushek’s research by Larry Hedges, Richard Laine, and Rob Greenwald used an alternative methodology and found that an increase in education spending did improve student achievement. Their research found relationships between increased spending and higher student achievement that were large enough to have statistical and practical significance. The aforementioned research studies that conclude that spending on public education should decrease or remain constant sustain their arguments using complex statistical analyses. The evidence provided by these works shows that statistically significant fiscal equity and economic relationships do not exist between educational expenditures and the outcomes generated. Research studies that conclude that spending on public education should increase also sustain their arguments using similarly complex statistical analyses. The evidence provided by these works shows that statistically significant economic relationships do exist between educational expenditures and the outcomes generated. Regardless of one’s perspective, the fiscal equity and educational efficiency debate highlights the importance of understanding the impact of increased funding on educational outcomes.

Resources and Educational Improvement Despite the lack of consensus on whether money matters in educational outcomes, fiscal equity and economic efficiency analyses have led to four main generalizations about which resources consistently improve educational outcomes: 1. Fiscal and physical capacity: Appropriate levels of expenditures per student, high teacher salaries, and contemporary buildings and facilities 2. Administrative policies: Appropriate levels of collaborative management, low student-teacher ratios, and small class sizes 3. Teacher characteristics: Appropriate levels of teacher training, verbal ability, years of experience, and cultural diversity 4. Classroom and curriculum content: Appropriate preschool preparation, student ability groupings, and instructional interventions for student at risk of failure

Researchers assert that understanding how resources are used equitably within schools is important. Even though traditional economic research methods have indicated that the relationship between

educational resources and students is unclear, there is some type of economically efficient relationship between educational inputs and student outcomes as long as the resources reach schools, classrooms, and students. Therefore, it is necessary to understand the reasons behind resource allocation decisions. Certain education reformers claim that at the center of their proposed fiscal equity and educational efficiency reforms is the need to demand high academic standards for all children and teachers, as well as effectiveness and efficiency from the system as a whole. Research studies should be conducted to investigate the levels of financial and human resources equity, economic efficiency, and educational accountability. And yet, despite the breadth of research available, there has been no concerted empirical effort to examine these relationships. As such, in both a methodological and a practical sense, it is necessary to continue to conduct these types of analyses so that policymakers can decide if the current usage, a reallocation, or an increase in resources is appropriate to achieve the desired educational purposes. Anthony Rolle and Richard O. Welsh See also Allocative Efficiency; Education Production Functions and Productivity; Nation at Risk, A

Further Readings Cubberley, E. P. (1906). School funds and their apportionment. New York, NY: Columbia University, Teachers College. Hanushek, E. A. (1981). The impact of differential expenditures on school performance. Educational Researcher, 18(4), 45–62. Hanushek, E. A. (1989). Throwing money at schools. Journal of Policy Analysis and Management, 1, 19–41. Hedges, L. V., Laine, R. D., & Greenwald, R. (1994). Does money matter? A meta-analysis of studies of the effects of differential school inputs on student outcomes. Educational Researcher, 23, 5–14. Laine, R. D., Greenwald, R., & Hedges, L. V. (1996). Money does matter: A research synthesis of a new universe of education production function studies. In L. Picus & J. Wattenbarger (Eds.), Where does the money go? Resource allocation in elementary and secondary schools (pp. 44–70). Thousand Oaks, CA: Corwin Press. Mort, P. R. (1924). The measurement of educational need: A basis for distributing state aid (Contributions to Education Series, No. 150). New York, NY: Columbia University.

Economics of Education National Commission on Excellence in Education. (1983). A nation at risk: The imperative for educational reform. Washington, DC: U.S. Department of Education. Retrieved from http://www.ed.gov/pubs/NatAtRisk Strayer, G. D., & Haig, R. M. (1923). The financing of education in the state of New York. New York, NY: Macmillan.

ECONOMICS

OF

EDUCATION

Education is a considerable public and private investment in the United States. At the individual and social levels, education produces different types of knowledge and skills through formal schooling. The field of economics of education largely concerns the allocation and organization of resources among various stakeholders in the educational system, such as federal and state governments, schools, parents, and students. It addresses the returns to investments in education, financing education, and improving the quality of education. Economics of education also includes the application of economic concepts and econometrics in educational research. Indeed, economics of education has become primarily an applied field. This entry focuses on the theoretical concepts and empirical applications of the economics of education. It first describes the origin and evolution of economics of education and then three central theoretical concepts: (1) the theory of markets, (2) human capital, and (3) the education production function. Following this, a brief overview of data and empirical evidence in the economics of education is provided. The entry is intended to provide the reader with a basic understanding of the theories underlying the economics of education and their empirical application.

Origin and Evolution Ever since Adam Smith’s groundbreaking work The Wealth of Nations in 1776, the concept of markets or the “invisible hand” through which resources are allocated has been central to economics and thus the economics of education. Early classic works by a host of economists including Alfred Marshall highlighted the importance of studying education. Later, seminal papers by researchers such as Jacob Mincer, Theodore Schultz, and Gary Becker confirmed the economic nature of decisions about schooling. The field of economics of education is surprisingly nascent given the importance of education to

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individuals and societies as well as the magnitude of resources committed to education. Economics of education largely evolved as a principal area of research inquiry in the latter half of the 20th century. The field emerged in the aftermath of World War II as policymakers sought to stimulate economies adversely affected by the war. Consequently, there was an intense focus on ways to improve the productivity and productive capacities of countries. In the 1950s and 1960s, economists utilized economic data to relate the changes in economic growth to increases in inputs such as physical capital and the labor force. Growth accounting, or assessing how an increase in economic inputs, such as physical capital, led to increases in economic outcomes, such as the gross domestic product, resulted in increased attention to the quality of labor. The key emerging insight was that labor productivity could be improved over time through better education, training, and health. Thus, investment in education is viewed as an avenue to improve a nation’s economic prospects by enhancing the productivity of the population. In other words, a society may invest in education to improve the productivity of its citizens, much as it may invest in physical capital such as tools and infrastructure. Given that education factors prominently in the attainment of social and personal goals, the field of economics of education steadily grew in influence and the scope of activities. As the literature on returns to educational investments expanded, new major lines of research began to emerge in the economics of education. Over time, there was a progression in emphasis from estimating the rate of return (ROR) on educational investments (e.g., individual, social, and nonmarket returns to education) to improving the quality of education. More important, in the past decades economists of education have paid increasing attention to the causal relationship between student achievement and a variety of influences such as family, neighborhood, school, and classroom. The ever-increasing cost of education also resulted in an increased focus on improving the quality of education while minimizing costs.

Theoretical Concepts Theory of Markets

In most societies, resources are allocated by the government, the market, or a mixture of both. Historically, K-12 schooling has been allocated at the government level. However, concerns regarding

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efficiency and the effectiveness of the centrality of governments in the provision of education have resulted in the emergence of market-based mechanisms in the allocation of educational resources. A market is a set of buyers and sellers whose interaction leads to the exchange and allocation of goods and services. Consensus on price is the key determinant of transactions. Sellers seek to maximize profits, whereas buyers seek to maximize satisfaction (or “utility”) based on their preferences and budget constraints. The market functions to adjust price to reflect changes in the supply and demand in the most efficient fashion. The dynamics of markets also encourage the “survival of the fittest” and are presumed to increase efficiency and innovation as producers of goods and services compete to improve their offerings to customers. In the event that markets do not efficiently allocate goods and services, a market failure occurs. There are numerous reasons why markets may fail, including the market power of a supplier (e.g., monopolies), incomplete information about products and their quality, and externalities. Externalities are costs borne by, or benefits received by, people other than the individual partaking in the activity. For example, a well-educated individual is less likely to commit a serious crime, thus the individual’s education has positive externalities as society also benefits from lower crime rates and safer environments. Governments are involved in the provision of education mainly because of its social benefits, since left to their own accord, markets would underproduce education. Some recent education reforms have attempted to separate the provision of education from its financing, in an attempt to reduce government’s role in education and lead to greater efficiency. Whereas the theory of markets presents an alternative perspective of the provision of education, human capital theory provides justification of the economic value of education.

These benefits are presumed to outweigh the costs of investment in additional schooling. ROR, or pecuniary returns to investments in education, has been extensively studied in both developed and developing countries. Overall, these studies have shown that better educated workers have better labor market outcomes than those with less schooling, as well as other improved societal outcomes such as lower unemployment, lower crime, better health, and increased civic participation. Education has a strong influence on labor market outcomes such as wages—more educated individuals, on average, tend to earn more over their lifetimes. In fact, differences in educational levels account for a substantial portion of the differences in earnings. An individual with more education is presumed to have higher productivity and thus command higher wages in the long run. There have been several critiques of the human capital theory, but the most noteworthy is perhaps signaling theory, which, instead of focusing on the knowledge and skills gained through education, focuses on how employers interpret a potential employee’s educational choices and qualifications. Private rates of return, or the costs and benefits of an additional year of schooling accrued solely by the individual, are typically higher than social rates of return, which also considers externalities and thus the benefits and costs associated with educational investments from a society’s perspective. ROR analyses strongly indicate that higher education or a college degree is a prudent investment for both individuals and society. Research by the economists George Psacharopoulos and Harry Patrinos found that the estimated average ROR to an extra year of schooling is at least 10% for individuals. Across many countries, both developed and developing, there is a strong positive relationship between education level and earnings. Education production functions have allowed researchers to delve deeper into the relationship between educational inputs and outputs.

Human Capital

Education Production Function

Human capital theory is the cornerstone of the theoretical foundation of economics of education. Beginning in the 1950s and 1960s, economists such as Gary Becker and Theodore Schultz made the compelling argument that individuals’ decisions about schooling were similar to firms’ decisions about physical capital. In other words, individuals make expensive upfront investments to enhance their productivity in the hope of a future stream of benefits.

Education production functions provide one straightforward approach to estimating how combining different inputs may lead to various school outputs. The production of education is modeled similar to the production of other goods and services, with various inputs combined to obtain schooling outputs. Inputs such as student and teacher characteristics are transformed into outputs, or educational outcomes such as standardized test scores or college

Economics of Education

completion. Typically, student achievement, as measured by test scores, is modeled as a function of student characteristics, such as family demographic and socioeconomic features; teacher characteristics, such as teacher certification and experience; school and program characteristics, such as class size, facilities, and pupil-teacher ratio; as well as peer characteristics (the educational performance and socioeconomic status of other students in the school). Beginning in the late 1960s, empirical work by researchers such as the sociologist James Coleman linked schooling inputs and outputs using education production functions. Education production functions are the result of the application of the production function, an important economic concept, to education. The relationship between inputs and outputs is generally estimated using a variety of regression methods. The estimation of education production functions is complicated by difficulties in accurately identifying and measuring the inputs and outputs of schooling. For instance, outputs may be cumulative in nature, and measures of input may not capture its essential features. Regardless of the measurement challenges, education production functions are a valuable conceptual and empirical framework for predicting and explaining the differences in student outcomes. Education is regarded as a technologically dormant sector with very limited labor-saving techniques in its provision. Simply put, it is almost impossible to replace teachers with technology, even as the costs of teachers rise. As such, education is afflicted with the “cost disease,” where economy-wide increases in productivity and wages are absorbed, resulting in higher wages in education rather than substitution into physical capital or cheaper labor. The “cost disease” problem in education is made worse by the fact that personnel costs constitute the majority of costs in education. In the search for more efficient means of provision, economists of education have used education production functions in conjunction with longitudinal data to analyze resource allocation and program effectiveness at the national, state, and school district levels.

Empirical Applications Data

In the past three decades, richer and more sophisticated education data have become available. Data on schools, students, and teachers over time can be found in various national, state, and

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school district databases. The National Center for Education Statistics is the federal body responsible for collecting education-related data in the United States. The National Center for Education Statistics houses a variety of programs that collect and analyze qualitative and quantitative data. For instance, surveys, such as the Schools and Staffing Survey, provide descriptive data on a state-level nationally representative sample of schools including: student, teacher, and principal characteristics, general conditions in schools, and hiring and retention practices in school districts. The Common Code of Data annually collects fiscal and nonfiscal data on all public schools, school districts, and state education agencies in the United States. The National Assessment of Education Progress is the only nationally representative assessment of K-12 education in the United States and allows for comparisons of student test scores across states. International databases, such as the Programme for International Student Assessment and the Trends in International Mathematics and Science Study facilitate comparisons between the United States and other countries. In the late 1980s, national longitudinal data that track the same student over multiple years started to emerge. The National Educational Longitudinal Studies program tracks students beginning with their elementary or high school years and follows them over time into adulthood. These datasets typically include a nationally representative sample of students who were surveyed and resurveyed in multiple follow-ups that span several years. Researchers have used individual datasets from the National Educational Longitudinal Studies to answer specific questions. For example, the National Education Longitudinal Study of 1988 (NELS:88) is generally used in teacher-related research, such as work by the economists Dominic J. Brewer and Dan Goldhaber that analyzed the impact of teacher certification on student achievement. James Coleman used High School and Beyond (HS&B) to investigate the effect of Catholic schools on students’ test scores. Thomas Kane and colleagues used the National Longitudinal Study of the High School Class of 1972 (NLS-72) to examine the effect of attending college on earnings. Beginning in the late 1990s, individual states began to implement education accountability systems that resulted in student-level longitudinal databases. The passage of the No Child Left Behind Act in 2001 accelerated accountability changes already under way in some states, such as New York,

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Florida, and Texas. Consequently, the majority of states have data systems that track the academic progress and characteristics of individual students over time. The successive waves of data collection at the local, state, and national levels have facilitated more meaningful empirical research in the economics of education. The volume and quality of research on the economics of education has grown dramatically in the past three decades, facilitated by the evolution of the quality of data combined with creative empirical methods. The range of topics has also expanded beyond ROR and education production function studies to studies on teacher labor markets and the impact of various school, classroom, and family factors on student achievement. As a result, better ROR studies, a growing understanding of the causal relationship between education inputs and student outcomes, as well as more credible evaluation of several educational policies have emerged. More recent research has increasingly focused on policy evaluation and assessing the effects of market- and incentive-based policies to improve education, such as charter schools. ROR in Education

One of the early lines of research in economics of education was estimating the ROR on educational investments and relating economic growth to education. Stated differently, economists sought to address the question of what portion of economic growth was attributable to education. Related concerns, such as how much society and individuals should invest in education, also preoccupy economists of education. Typically, the earnings associated with incremental investment in education can be compared with costs such as facilities cost and the expenses families incur to obtain education. Research in the United States and other industrialized nations indicates that a significant amount of economic growth is due to investments in education that led to improvements in the labor force. Indeed, increases in educational attainments are also reflected in the national income. The economic benefits can be considered broadly to include not only increased productivity and earnings but also better health and child rearing, family planning, increased mobility, and higher consumption. Moreover, education benefits not only the individual but also the larger society, resulting in private and social rates of return for educational investments.

Private rates of return are typically higher than social rates of return on educational investments. Workers with more education tend to have higher earnings even when one considers other determinants, such as social connections and ability. Different educational levels account for a substantial portion in the differences in earnings. In other words, individuals with more education are presumed to have higher productivity and thus command higher wages in the long run. Better educated workers will experience more favorable labor market outcomes than those with less schooling. In a global, knowledge-based economy, the returns of investment in education are ever more critical as individual and societal economic success becomes largely dependent on knowledge and skills. Empirical evidence has largely confirmed that the substantial resources devoted to education are justified by the numerous private and social benefits, including higher productivity and income as well as greater civic involvement. The costs and benefits of education may vary depending on whether the country is developing or developed. In developing countries, families bear more of the direct costs of education and may face a higher “opportunity cost” (especially at the secondary and tertiary levels). Another issue is the value of investments at different levels of education. For instance, differences in the rates of return can shape a country’s decision about whether to improve quality at one level (e.g., secondary schools), expand access to higher education, or improve vocational education (as opposed to general education). Improving the Quality of Education

Another major line of inquiry in the economics of education is the challenge of improving the quality of education by increasing the effectiveness of teaching while lowering costs. Improving the quality of education encompasses a wide range of issues, including educational productivity, cost-effectiveness, and teacher effectiveness. There is mixed evidence on the value of additional funding in improving the quality of education. In his meta-analysis of several studies on educational production, the education researcher Eric Hanushek concluded that increased allocation of funding to certain inputs, specifically teacher qualifications and reduced class size, does not necessarily improve educational outcomes. Several researchers have challenged this notion and argue that there is a

Economics of Education

positive relationship between resources and educational achievement. For instance, measures of input may not adequately capture the particular attribute that affects student outcomes. Other critiques of Hanushek’s review focused on the methods used to combine the studies and the quality of the data in the individual studies included in Hanushek’s metaanalysis, which led to limited control variables and measures of school and teacher quality that were inadequate. It is difficult to overstate the importance of teachers in educational production. Over time, the focus on education production placed the spotlight on teacher effectiveness. Even though teacher quality is widely presumed to be one the most important determinants of student achievement, the evidence suggests that observable teacher characteristics such as education and experience are barely related to student learning. Other measures of teacher performance, such as the principal’s observations, identify performers at the high and low ends but fail to give meaningful information on average teachers. However, there are significant differences in student performance associated with individual teachers, and economists have attempted to identify effective teachers by their student test score gains using valueadded analysis. Consequently, measuring teacher quality, particularly directly measuring a teacher’s performance through his or her students’ test scores rather than traditional qualifications, and the workings of the teacher labor market have gained prominence in the past decade. There are limitations to the value-added approach, namely, that value-added estimates are only available in tested subjects, as well as concerns about the validity, reliability, and appropriateness of these growth estimates in highstakes decisions. Performance-based salary has also become popular as a means of injecting efficiency into education production. The results of early empirical work on the efficacy of such incentives are inconclusive. Gradually the focus on teacher effectiveness has broadened to include study of teacher labor markets and has incorporated elements such as recruiting and retention of teachers, not just their performance in the classroom. In other words, economists of education became preoccupied with who chooses to become a teacher and the incentives, such as salaries and nonmonetary benefits, that are necessary to attract and retain effective teachers. Insights from this line of research have helped shaped teacher policy. For instance, studies have shown that teacher

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shortages in subjects such as mathematics and sciences are linked to higher salaries and benefits available in professions other than teaching. This has resulted in providing bonuses or additional incentives to compensate teachers who have a high opportunity cost of entering teaching. Along with teacher effectiveness and teacher labor markets, the cost-effectiveness of various programs and initiatives has become a major focus within the economics of education. Causal questions such as whether a reduced class size improves student achievement have been investigated using experimental and quasi-experimental methods. In an era of shrinking education budgets, cost-effectiveness analysis seeks to determine which policies maximize educational outcomes given budgetary constraints. These analyses are often hampered by the unavailability of accurate costs. For instance, measuring the opportunity cost of educational policies is fraught with challenges, and the information needed to quantify certain costs is usually not collected. In recent decades, quasi-experimental methods such as instrumental variables, propensity score matching, difference-in-differences, and regression discontinuity have been increasingly used in the economics of education in attempts to derive causal estimates of a variety of educational policies and programs. For example, the researcher Susan Dynarski used difference-in-differences to examine the causal impact of an offer of college financial aid on decisions about whether or not to attend college. The economists Joshua Angrist and Victor Lavy used instrumental variables to estimate the causal impact of smaller class sizes. Brian Jacob and Lars Lefgren used regression discontinuity to investigate the effects of remedial education on student achievement.

Conclusion In recent decades, economics of education has progressed briskly and evolved as one of the most important fields in applied economics and empirical studies. The perspectives of economics are increasingly shaping the analysis and creation of education policies. The analysis of increasingly available educational data using econometrics has yielded significant breakthroughs in multiple facets of education. Dominic J. Brewer and Richard O. Welsh See also Baumol’s Cost Disease; Cost-Benefit Analysis; Cost-Effectiveness Analysis; Economic Cost;

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Economies of Scale

Education Production Functions and Productivity; Markets, Theory of

Further Readings Becker, G. S. (1962). Investment in human capital: A theoretical analysis. Journal of Political Economy, 70, 9–49. Card, D. (1999). The causal effect of education on earnings. In O. Ashenfelter & D. Card (Eds.), Handbook of labor economics (Vol. 3, pp. 1801–1863). Amsterdam, Netherlands: Elsevier Science. Carnoy, M. (1995). Rates of return to education. In M. Conroy (Ed.), International encyclopedia of economics of education (2nd ed., pp. 364–369). Oxford, UK: Pergamon Press. Hanushek, E. (1997). Assessing the effects of school resources on student performance: An update. Educational Evaluation and Policy Analysis, 19(2), 141–164. Hanushek, E., & Woessmann, L. (2008). The role of cognitive skills in economic development. Journal of Economic Literature, 46(3), 607–668. Hentschke, G. (2007). The role of government in education: Enduring principles, new circumstances and the question of shelf-life. In L. Ealy & R. Endlow (Eds.), Liberty and learning: Friedman’s voucher idea at fifty (pp. 11–23). Washington, DC: Cato Institute. Hoxby, C. (2003). Does competition among public schools benefit students and taxpayers? American Economic Review, 90(5), 1209–1238. Levin, H. (2011). Economics of education. Albany Government Law Review, 4, 394. Psacharopoulos, G., & Patrinos, H. A. (2004). Returns to investment in education: A further update. Education Economics, 12(2), 111–134. Rivkin, S., Hanushek, E., & Kain, J. (2005). Teachers, schools and academic achievement. Econometrica, 73(2), 417–458.

ECONOMIES

OF

SCALE

The question of economies of scale has been explored since the Industrial Revolution. In fact, in his most important work, The Wealth of Nations, Adam Smith wrote about the benefit of increasing the size of a plant, which brings lower per-unit cost through specialization, or the division of labor, resulting in greater worker productivity. This concept later became known as economies of scale, defined as the per-unit decreasing cost advantages that a plant or an enterprise gains by increasing the overall size of

operation. Thus, economies of scale imply decreasing long-run average cost per unit of output. This is because the fixed costs are spread out over more units of output; it is also the result of specialization and technological advances. On the other hand, diseconomies of scale imply increasing average cost as the size of the plant is increased. As the production increases beyond a certain point, the diseconomies may set due to a number of reasons. It may be due to difficulties in managing a large workforce. As the size of operation increases, communication can become poor, resulting in decreased motivation among the workforce. Workers become less productive. This implies an increase in per-unit cost. Diseconomies of scale may also happen due to growing demand for the factors of production, which usually results in increasing the price of inputs. Economies of scale apply to a variety of organizational and business situations, such as a manufacturing unit, a plant, or an entire enterprise, or even an institution of higher education. In the early stage of research on economies of scale, empirical studies were mainly conducted in the manufacturing sector in the United States and the United Kingdom. These studies were for single-output plants and estimated either long-run total cost function or, in some cases, average long-run cost function. The concept of economies of scale is readily applicable in education finance. A review of the research literature indicates that the existence of economies of scale in secondary or higher education began to be studied in the 1920s. However, until about the 1980s, these studies used simple regression methods to estimate the marginal and average costs of higher education for a single output, such as total number of students or total credit hours. For example, let us assume that a higher education institution just offers a BBA (bachelor of business administration) degree and has 500 students. The total annual cost of running this institution is $1 million, and the annual average cost per student is $2,000 ($1 million divided by 500). Now this institution admits 1,000 students and hires more employees. The total cost increases to $1.8 million, thus decreasing the perstudent cost from $2,000 to $1,800. This reduction of per-unit cost is due to economies of scale. Using economies of scale, one can determine the optimum size of a school or higher education institution. The estimation of per-unit cost will help the institution to set tuition and plan its budget. This entry provides an overview of economies of scale and economies of scope. First, the interrelation

Economies of Scale

between economies of scale and economies of scope is discussed. Following this, the technical considerations, specifically the estimation of both economies of scale and economies of scope, are described in further detail.

Economies of Scope Economies of scope are similar to economies of scale. Consider the higher education example described earlier. Normally, secondary schools and institutions of higher learning produce more than one product. For example, universities grant bachelor’s, master’s, and doctoral degrees. These are heterogeneous products since the cost to grant a bachelor’s degree is not the same as the cost to grant a PhD degree. To deal with multiproduct production processes in manufacturing and education, during the 1970s and 1980s, the concept of economies of scope was developed. As mentioned earlier, the concept of economies of scale for an organization producing a single output pertains to reductions in the cost per unit. On the other hand, economies of scope refers to lowering the average cost for an organization in producing multiple products. The concept of economies of scope has recently become popular for strategic planning in business as well as in higher education. With advanced computer technology, it has become possible to apply the technique of economies of scope to business as well as to educational institutions. For example, recently researchers have estimated economies of scope for various types of educational institutions. The benefit of estimating economies of scope is to determine the optimum level of production of each output where the organization produces multiple products. For example, a 2000 study of liberal colleges by Rajindar K. Koshal and Manjulika Koshal suggests that an optimum liberal arts college, in terms of economy of scope, has 2,343 undergraduate full-time equivalent and 88 graduate full-time equivalent.

Estimation of Economies of Scale The next two sections address some of the challenges involved in the empirical estimation of economies of scale and economies of scope. The estimation of economies of scale involves several key variables including total cost, marginal cost, and average cost. Before the 1980s, generally single-output cost functions in higher education have been defined as follows: TC(Q) ⫽ f(Q, Pi, Yi),

257

where TC(Q) is the total cost of producing Q units of output; Pi is the price vector of inputs vector Yi; and f represents the functional relationship relating cost to the level of output. For a single-output cost function, one can estimate the economies of scale by the ratio between marginal cost and average cost. Average total cost (ATC) and marginal cost (MTC) are defined as follows: MTC ⫽ dTC(Q)/dQ

and

ATC ⫽ TC(Q)/Q.

If MTC/ATC is less than 1, then economies of scale are said to exist. On the other hand, if MTC/ATC is greater than 1, then diseconomies of scale are said to exist. This implies that as the operation of production is expanded, the per-unit cost of production increases. The researchers who have calculated average total cost to examine the existence of economies of scale have defined the quadratic average total cost function as follows: ATC(Q) ⫽ f(Q, Q2),

where ATC(Q) is the total long-run average cost and Q is the units of output. This method of estimation can be applied to those organizations producing only a single output. This method also estimates the efficient plant size for an organization producing only a single output.

Estimation of Economies of Scope In the mid-1980s, to explore the question of economies of scale in education, researchers including Emmanuel Jimenez and Elchanan Cohn, Sherrie L. W. Rhine, and Maria C. Santos began estimating multiproduct cost functions for secondary education as well as for higher education. Basically, these studies define multicost function as follows: TC = a0 +

k

k

∑ i =1

aiQi +

k

1 b Q Q + ν, 2 i =1 j =1 ij i j

∑∑

(1)

where TC is the total cost of producing k products, a0 is a constant, and ai and bij are the coefficients associated with various output variables. Qi is the output units of the ith product, and ν is a random error term. A cost function of this form allows researchers to estimate both economies of scale and economies of scope. In the case of multiproduct cost functions, there is no simple analogy to the traditional average cost function. However, one is able to estimate ray economies of scale and product-specific economies

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Economies of Scale

of scale. Generally, in higher education, there are three products: (1) QU, undergraduate students; (2) QG, graduate students; and (3) QR, research activities. The researchers first defined the average incremented cost (AITC) for one of the outputs, say undergraduates, as follows: AITCU =

{

}

{

}

TC QU , QG , QR − TC 0, QG , QR , SU

(2) where TC{QU, QG, QR} is the total cost of producing QU units of undergraduate students, QG units of graduate students, and QR units of research, and TC{0, QG, QR} is the total cost when the output for product QU is 0. Similarly, average incremental costs, AICG and AICR, for products G and R, respectively, are defined. The product-specific economies of scale (EU) for product SU is defined as follows: EU =

AITCU MTCU

,

(3) where MTCU ⫽ ∂TC/∂QU is the marginal cost of producing product QU. If EU is greater (less) than 1, economies (diseconomies) of scale are said to exist for product QU. Ray (overall) economies of scale (ERAY) may exist when the quantities of the product are increased proportionally. Ray economies of scale are defined as follows: ERAY =

{

C QU , QG , QR

}

QU MTCU + QG MTCG + QR MTCR

(4) Ray economies (diseconomies) of scale are said to exist when ERAY is greater (less) than 1. In any multiproduct production process, economies of scope are defined as the cost efficiencies to be gained by joint production of multiple products as opposed to their being produced separately. Economies of scope are divided into global and product-specific economies of scope. The degree of global economies of scope (GES) in the production of all three products, QU, vQG, and QR, is defined as follows:

( {

}

{

}

{

{

− TC QU , QG , QR

} ) / (TC {QU , QG , QR } ).

( {

}

{

PESU = TC QU ,0,0 + TC 0, QG , QR

{

− TC QU , QG , QR

}

} ) / (TC {QU , QG , QR } ). (6)

Product-specific economies (diseconomies) of scope associated with product QU are said to exist if PESU is greater (less) than 0. Most of the multiproduct total cost studies have observed that both marginal and average costs of graduate education are higher than those of undergraduate education. The ray economies of scale for research exists especially for doctoral-granting institutions. Also, the results of many studies imply that there are product-specific economies of scale for all three outputs. The statistical results of a number of studies indicate that global economies of scope exist for the entire output range for research universities. Rajindar K. Koshal See also Demand for Education; Tuition and Fees, Higher Education

Further Readings

.

GES = TC QU ,0,0 + TC 0, QG ,0 + TC 0,0, QR

Global economies (diseconomies) of scope are said to exist if GES is greater (less) than 0. Cost advantages gained from the production of each product jointly with the other outputs are called product-specific economies of scope (PES). For example, for product QU, this is given by the following equation:

} (5)

Brinkman, P. T. (1990). Higher education cost functions. In S. A. Hoenack & E. L. Collins (Eds.), The economics of American universities (pp. 107–128). Albany: State University of New York Press. Cohn, E., & Cooper, S. T. (2007). Multiproduct cost functions for universities: Economies of scale and scope. In G. Johnes & J. Johnes (Eds.), International handbook on the economics of education (pp. 579–612). Cheltenham, UK: Edward Elgar. Cohn, E., Rhine, S. L. W., & Santos, M. C. (1989). Institutions of higher education as multi-product firms: Economies of scale and scope. Review of Economics and Statistics, 71, 284–290. Jimenez, E. (1986). The structure of educational costs: Multiproduct cost functions for primary and secondary schools in Latin America. Economics of Education Review, 5, 25–39.

Education and Civic Engagement Koshal, R. K., & Koshal, M. (1999). Economies of scale and scope in higher education: A case of comprehensive universities. Economics of Education Review, 18, 269–277. Koshal, R. K., & Koshal, M. (2000). Do liberal arts colleges exhibit economies of scale and scope? Education Economics, 8(3), 209–220.

ECONOMIES

OF

SCOPE

See Economies of Scale

EDUCATION AND CIVIC ENGAGEMENT The belief that education increases civic engagement is one of several rationales provided for increased government involvement in education. Civic engagement generally refers to involvement in social and political life, with the goal of improving the quality of life in a society. Signs of civic engagement can include voting and other forms of political engagement, interpersonal trust, institutional trust, tolerance, and political knowledge. Proponents of this view argue that the public benefits of education in the form of the increased civic engagement associated with higher levels of education are generally ignored by individual decision makers, which leads to inefficient personal choices regarding educational attainment. This entry provides a conceptual overview of the potential links between education and civic engagement, summarizes the results of studies investigating the extent of the relationship between education and civic outcomes, and provides suggestions for future research.

Conceptual Links Between Education and Civic Engagement How does education influence civic engagement? Which factors link educational attainment and civic outcomes? Those who argue that education is positively related to civic outcomes mention three primary mechanisms. The first mechanism is concerned with increased cognitive and analytical ability, social skills, and cultural awareness associated with higher levels of education. These acquired skills and traits then reduce the costs and difficulties of civic engagement for individuals by allowing them to

259

understand complicated political information more easily and demonstrate higher levels of tolerance toward other individuals’ political and religious opinions and personal lifestyles. The second mechanism operates through increased opportunities and incentives for civic participation and better realization of the benefits of civic participation associated with higher levels of education. For example, many elementary and secondary schools in the United States provide opportunities for their students to participate in community service activities. In addition, many higher education institutions encourage their students to be engaged with their local communities and participate in volunteer activities. Also, highly educated individuals tend to associate with others with similar education levels. Higher levels of civic engagement demonstrated by even a small number of members of a group tend to increase the overall civic participation of the whole group as individuals tend to participate when requested. Third, education is considered to increase individuals’ knowledge of history, civic and social norms and responsibilities, and the fundamentals of a functioning democratic society. Primary and secondary curricula in many countries include courses on civic and social norms. For instance, in a recent survey, almost 90% of young Americans reported taking at least one civics course in high school. Similarly, many colleges and universities offer courses and interdisciplinary programs on the foundations of civic engagement and participation. Therefore, better educated individuals tend to be more aware of basic democratic values and civic liberties and value the democratic system more, all of which may increase their levels of civic participation. Increased educational attainment may not always result in higher levels of civic engagement. For example, income and earnings tend to increase with education, which in turn increases the opportunity cost of some civic activities that require substantial time commitments. In addition, highly educated individuals may think that their vote or civic service has a very insignificant effect on the political outcomes or the overall welfare of the society, which may cause them to be less active citizens.

Empirical Evidence on the Association Between Education and Civic Engagement Many studies have empirically examined the extent of the relationship between education and civic

260

Education and Civic Engagement

outcomes. Huang Jian and his coauthors conducted a meta-analysis of such studies published since 1990, which included 26 studies that provided 142 estimates for the relationship between education and social trust and 31 studies that provided 268 estimates for the association between education and social participation. The authors concluded that 1 year of additional schooling was associated with 5% and 6% of a standard deviation increase in civic trust and participation, respectively. Researchers recommend caution when interpreting the results of most of these studies because the observed positive associations between education and civic outcomes may be spurious artifacts of confounding factors that are simultaneously related to both education and civic outcomes. For example, innate cognitive ability and certain unobserved personal traits may lead to higher levels of both education and civic participation. Some also argue that intergenerational transmission of educational attainment and civic engagement (i.e., children of highly educated parents with higher levels of civic outcomes tend to be highly educated and more civically engaged) may be responsible for the correlations between education and civic outcomes, which may not necessarily reflect true causal associations. This issue is also described as the conflict between the two views (1) education as cause and (2) education as proxy. A number of recent studies attempt to address these concerns by implementing more rigorous statistical analysis techniques that utilize regional factors and/or policy changes that create exogenous variation in the levels of educational attainment across individuals who are differentially affected by these factors and changes. For instance, researchers compared populations before and after changes in compulsory schooling laws that resulted in more people graduating from high school. These techniques rely on the assumption that such exogenous variation in educational attainment is free of the aforementioned confounders; therefore, the extent of their relation to civic outcomes may have a causal interpretation. For example, Kevin Milligan and his coauthors examined the effect of additional schooling induced by stricter schooling laws on civic involvement in the United States and the United Kingdom. They found strong and persistent effects of education on various measures of civic engagement, such as community service and political involvement, in the United States and the United Kingdom, while a strong and robust effect of education on voting was observed only in the United States. One of their findings was

that graduating from high school may raise voter participation by about 30%. Giorgio Di Pietro and Marcos Delprato utilized two reforms enacted in the Italian educational system that led to additional schooling, which they found to be positively related to interest in politics. Thomas Dee implemented an estimation strategy exploiting the variation in U.S. educational attainment levels created by two factors: (1) availability of junior and community colleges and (2) changes in child labor laws. He found large and significant effects of education on voter participation, support for free speech, and civic knowledge, while education did not seem to affect volunteering. Specifically, he concluded that having a college degree may increase voter participation by between 20% and 30%, while an additional year of high school may increase the likelihood of voting in a presidential election by 7%. Finally, Rachel Sondheimer and Donald Green analyzed two randomized experiments and one quasi-experiment in which the treatment group had higher high school graduation rates than the control group (viz., the HighScope Perry Preschool Study, the “I Have a Dream” Foundation’s scholarship program, and the Project STAR, or Student/Teacher Achievement Ratio, experiment in Tennessee). They found strong suggestive evidence for a causal link between educational attainment and voting. Specifically, their results suggest that a high school dropout with a 16% probability of voting would have a 65% probability of voting if randomly induced to graduate from high school. Two studies implementing similar rigorous estimation strategies yielded contradictory findings to those summarized above. Thomas Siedler concluded that the substantial positive correlations he observed between many civic outcomes and education disappeared with the use of a more rigorous estimation strategy that utilized changes in compulsory schooling laws in Germany. In a similar study, Adam Berinsky and Gabriel Lenz employed changes in the educational levels of American males induced to attend college by the Vietnam War era draft. They did not find any robust evidence for increased political participation corresponding to the increased educational attainment as a result of the draft.

Additional Analyses of Education and Civic Engagement Several researchers investigated additional aspects of the relationship between education and civic

Education and Civic Engagement

outcomes. For example, Jennie Brand examined the potential variation in civic returns to higher education by individuals’ propensity for college enrollment and found suggestive evidence for such heterogeneity: Individuals with low propensity for college graduation tend to demonstrate higher levels of civic engagement. Thomas Dee examined the comparative effects of enrollment in Catholic and public high schools and found that students who attended Catholic high schools had a higher likelihood of voting as adults than their peers who attended public high schools. Huang Jian and colleagues also investigated how the association between education and civic outcomes evolved over the years and noted a coincidence between diminishing civic returns to education and decreases in overall levels of civic participation. Some researchers addressed the reciprocal question of whether civic engagement or participation causes better educational outcomes. For example, Alexander Astin and Linda Sax examined the effect of community service on college students’ academic outcomes and found that community service participation in college led to substantially better academic development and a higher sense of civic responsibility. Alberto Davila and Marie Mora conducted a similar study for secondary education and concluded that high school students with higher levels of civic participation tended to make greater academic progress and were more likely to obtain a college degree.

Recommendations for Future Research There are now a good number of rigorous evaluations that examine whether higher levels of education are causally associated with higher levels of civic engagement. While most of these evaluations (with a few exceptions like the two summarized above) provide empirical evidence pointing to positive returns of education for some civic outcomes, such as political knowledge and voting, none of these evaluations provide information about the potential mechanisms or channels through which education may influence civic outcomes. This shortcoming may be due to the lack of extant databases that include all the data elements that would be needed for such an investigation, namely, measures of educational attainment, civic outcomes, and factors that are hypothesized to mediate the relationship between the two, such as cognitive ability and opportunities and incentives for civic engagement and participation. This constitutes

261

an important consideration for designers of future surveys collecting information on civic outcomes. Fatih Unlu See also Benefits of Higher Education; Benefits of Primary and Secondary Education; College Dropout; Cultural Capital; Educational Equity; Fiscal Disparity; Income Inequality and Educational Inequality

Further Readings Astin, A. W., & Sax, L. J. (1998). How undergraduates are affected by service participation. Journal of College Student Development, 39(3), 251–263. Berinsky, A., & Lenz, G. (2011). Education and political participation: Exploring the causal link. Political Behavior, 33(3), 357–373. Brand, J. E. (2010). Civic returns to higher education: A note on heterogeneous effects. Social Forces, 89(2), 417–433. Campbell, D. E. (2006). What is education’s impact on civic and social engagement? In R. Desjardins & T. Schuller (Eds.), Measuring the effects of education on health and civic/social engagement (pp. 25–126). Paris, France: Centre for Educational Research and Innovation/Organisation for Economic Co-operation and Development. Davila, A., & Mora, M. T. (2007, January). Civic engagement and high school academic progress: An analysis using NELS data (CIRCLE Working Paper No. 52). Edinburg: University of Texas–Pan American. Dee, T. S. (2004). Are there civic returns to education? Journal of Public Economics, 88(9–10), 1697–1720. Dee, T. S. (2005). The effects of Catholic schooling on civic participation. International Tax and Public Finance, 12(5), 605–625. Di Pietro, G., & Delprato, M. (2009). Education and civic outcomes in Italy. Public Finance Review, 37(4), 421–446. Jian, H., van den Brink, H., & Groot, W. (2008). A metaanalysis of the effect of education on social capital. Economics of Education Review, 28(4), 454–464. Milligan, K., Moretti, E., & Oreopoulos, P. (2004). Does education improve citizenship? Evidence from the U.S. and the U.K. Journal of Public Economics, 88, 1667–1695. Siedler, T. (2010). Schooling and citizenship in a young democracy: Evidence from post-war Germany. Scandinavian Journal of Economics, 112(2), 315–338. Sondheimer, R. M., & Green, D. P. (2010). Using experiments to estimate the effects of education on voter turnout. American Journal of Political Science, 54(1), 174–189.

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Education and Crime

EDUCATION

AND

CRIME

Education and crime are correlated societal concerns. As an individual’s educational attainment increases, the likelihood of this individual engaging in criminal behavior decreases. This negative relationship between education attainment and criminal behavior is hardly unexpected. The strength of this correlation, however, is astonishing. This is why few reports from government bureaus have received as much attention as the one by Caroline Harlow of the Bureau of Justice Statistics in 2003. According to that report, in the late 1990s, 65% of the U.S. prison population did not have a high school diploma, and only 13% had taken any postsecondary courses. By way of comparison, almost half of the U.S. general population had some college experience in 1997. Incarceration in the United States has risen substantially over the past three decades, and the nation now has the world’s highest incarceration rate, and presumably the highest public spending on corrections. This has fueled increasing attention on education as a cost-effective strategy for reducing the costs of crime and corrections. Indeed, one national organization, Fight Crime: Invest in Kids, has this idea in its name. More generally, crime reduction is now realized to be one of the important social benefits of education. This entry discusses the various ways in which education and crime are correlated, the data used to measure the correlation, empirical research on the relationship, and the value of reducing crime for society.

Explanations for the Correlation There are several avenues through which education and crime can be related. There could be characteristics that separately influence both education attainment and criminal behavior. Criminal behavior could also affect education attainment. Or education could affect criminal behavior. Education and Crime Are Affected by the Same Things

There are numerous compelling reasons why the observed correlation between education and crime could be reflective of childhood environment and/ or personal characteristics rather than a causal relationship. One can easily imagine that childhood peers could influence both education attainment

and criminal behavior. Additionally, growing up in a poor inner-city neighborhood can clearly affect both average educational attainment and the probability of engaging in criminal activities. Growing up in a household (or households) with adults and siblings having low expectations of academic success for children has similar effects on both educational attainment and criminal behavior. Crime Affects Education

There are compelling reasons why inclination toward criminal behavior could determine education attainment. Crime, like sports, is mostly a young man’s game (actually, it is more male-centric than sports). And, as in sports, careers start with significant participation while still in school. Time spent engaging in criminal activity competes with time spent completing schoolwork, and thus affects the probability of academic success and educational attainment. Moreover, an expectation of continued criminal activity into adulthood undermines the incentive to persevere in education. Also, the stigmatizing effects of juvenile detention further reduce the prospects for academic progress. Education Affects Crime

Policy interest in the link between education and crime generally centers on education’s impact on crime. Education may directly reduce criminal behavior through its influence on ethics and civic values. Education may also indirectly affect crime through its influence on preferences toward avoiding or taking risks and toward discounting future consequences (i.e., placing a lower value on future amounts or events than on current amounts or events). Education also creates human capital (i.e., skills and competencies that increase the productivity of labor), which is likely to significantly affect some of the important expected costs and benefits associated with criminal activities. In creating human capital, education creates incentives that generally discourage criminal behavior. Greater human capital is likely to have two disincentive effects on criminal activity. First, education increases wage rates and thus makes market work more attractive relative to illegal activities. In other words, those with more education are more likely to have better alternatives than careers in crime. It is also possible, however, that education increases productivity in some criminal activities (including the

Education and Crime

ability to avoid getting caught and convicted) in the same manner that it increases productivity in market work. If education increases criminal productivity in the same proportion that it increases market productivity, then this disincentive effect on crime does not occur. This is plausible for white-collar crime, perhaps, but not for violent crime and probably not for property crime. In addition, the higher wages earned from increased human capital also discourage criminal behavior by increasing the cost of being convicted. Those with higher wage rates have more to lose during periods of incarceration. Moreover, criminal convictions likely have a stigmatizing effect on postincarceration earnings. Thus, for both reasons those with more human capital have more to lose from engaging in illegal activities. In other words, human capital can act as a form of surety bond or hostage against illegal behavior.

Data Few individual-level datasets have information on criminal activity, presumably because we cannot expect truthful self-reporting. An important exception is the U.S. National Longitudinal Survey of Youth, although there is concern that self-reported crime is significantly understated. Thus, crime is generally measured as only that which is reported to the police. Reported crime can be matched against education attainment only at a geographical level, such as metropolitan areas. This limits the potential sample sizes for empirical study, and omitted variable bias is a distinct possibility (i.e., potential bias because some unmeasured geographic characteristics may separately affect education attainment and crime). The link between education and crime is usually quantified using data on arrests and incarceration. If education negatively affects the probability of arrest for a given level of criminal activity, then the education-arrest correlation overstates the underlying relationship between education and crime. If education also negatively affects the probabilities of prosecution and/or conviction (and/or length of sentence) for a given level of arrests, then the correlation between education and incarceration further overstates the education-crime relationship.

Empirical Evidence Empirical research on the relationship between education and crime has been limited by both the lack of

263

data and the difficulty in isolating the causal effects. A careful and thorough study by Lance Lochner and Enrico Moretti published in 2004 dealt with these issues effectively. The key element in this study was using changes in state compulsory schooling laws as an instrumental variable for schooling. Individual states changed the number of years of compulsory schooling at different times throughout the past century. This created exogenous increases in education attainment—that is, increases in schooling unrelated to things that could also affect crime independent of the effect from education. Exogenous increases in education were estimated in a first-stage regression equation and then used in a second-stage regression equation explaining the arrests and incarceration. Lochner and Moretti’s results suggest that the observed correlations between education attainment and arrest rates and the probability of incarceration (after controlling for other observable characteristics such as age, race, etc.) are indicative of the causal effects of education on arrests and incarceration. That is, the estimated causal effects from exogenous increases in education attainment are similar to the estimated conditional correlations. To be specific, an additional year of education for men is estimated to reduce their arrest and incarceration rates by approximately 11% to 12%. Lochner and Moretti further show that the effects of education on arrests and imprisonment are comparable with the effect of education on selfreported criminal activity. Thus, the observed negative correlations between education and arrests and imprisonment do not appear to be due to differences in conviction rates. Arrest and incarceration rates are evidently good proxies for criminal behavior in studying the impact of education. The estimated effect of education on crime is similar to the estimated effect of wage rates on crime. Lochner and Moretti contend that most of the causal effect of education on crime works through education creating human capital and increasing market earnings. In separate work, Lochner shows that education discourages property crime and violent crime but not white-collar crime. The evidence also indicates that most of the effect on crime reduction occurs at high school completion. There appears to be limited scope for postsecondary education to further reduce crime. Numerous recent studies have corroborated these general findings. High-quality preschool education for at-risk children has been shown to have

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Education and Crime

important negative effects on crime and incarceration. Indeed, the highly publicized research on the lifetime effects of the HighScope Perry Preschool program and the Chicago Child-Parent Center program indicates that the influence on future crime is one of the most important social benefits of early childhood education. Improving the quality of middle and high schools for disadvantaged youth has also been shown to have a significant negative effect on crime. Recent research by David Deming on this issue is compelling. Education can discourage crime without its necessarily being through greater attainment (although higher quality education generally does lead to greater attainment). A strong negative effect of education attainment on crime has been demonstrated in the United Kingdom by Stephen Machin, Oliver Marie, and Suncica Vujic and in the Netherlands by Wim Groot and Henriëtte van den Brink. Machin and colleagues also summarize corroborating research in Italy.

Value of the Reduction in Crime The large impact of education on crime is clearly an important benefit to society. To illustrate how important, numerous studies have attempted to put a monetary value on this effect. Although some might find it silly, or perhaps even offensive, to put a price tag on the harm to the victims of violent crime (although this is common in civil courts), it facilitates a comparison of the different effects of education. In particular, it allows an apples-to-apples comparison of the effect on crime with the well-known effect of education on earnings. Lochner and Moretti’s estimates of the social value of the crime reduction from education are indicative of this approach. They estimated that a 1-percentage-point increase in the high school graduation rate would lead to about 94,000 fewer crimes per year, which would reduce victim costs, property losses, and prison costs by about $2.1 billion per year (in 2008 dollars). These crime costs fall by more than $3,000 annually for each additional male high school graduate. This social benefit is about 25% as large as the average increase in earnings. Moreover, these estimates do not include reductions in police and court costs, private security expenditures, or incarceration costs for drug offenses. The estimated value of the reduction in crime is even higher for the HighScope Perry Preschool program. Several studies of this controlled experiment

in early childhood education for disadvantaged youth estimated the value of reduced crime to be larger than all other private and social benefits combined. Although the results are sensitive to alternative assumptions about crime victimization costs, the value of reduced crime is clearly a huge benefit to society, particularly for men. James Heckman and colleagues recently estimated the lifetime value of crime reduction per male participant to be well in excess of $100,000, even after discounting back to the value in early childhood using a 3% real interest rate. Philip Trostel See also Benefits of Higher Education; Benefits of Primary and Secondary Education; Compulsory Schooling Laws; Cost-Effectiveness Analysis; Early Childhood Education; External Social Benefits and Costs; Human Capital; Instrumental Variables; Omitted Variable Bias; Opportunity Costs; Spillover Effects

Further Readings Deming, D. J. (2011). Better schools, less crime. Quarterly Journal of Economics, 126, 2063–2115. Ehrlich, I. (1975). On the relation between education and crime. In F. T. Juster (Ed.), Education, income, and human behavior (pp. 313–338). New York, NY: National Bureau of Economic Research. Groot, W., & van den Brink, H. M. (2010). The effects of education on crime. Applied Economics, 42, 279–289. Harlow, C. W. (2003). Education and correctional populations. Washington, DC: U.S. Department of Justice, Bureau of Justice Statistics. Heckman, J. J., Moon, S., Rodrigo, P., Savelyev, P. A., & Yavtiz, A. (2010). The rate of return to the HighScope Perry Preschool program. Journal of Public Economics, 94, 114–128. Lochner, L. (2004). Education, work, and crime: A human capital approach. International Economic Review, 45, 811–843. Lochner, L. (2011). Nonproduction benefits of education: Crime, health, and good citizenship. In E. A. Hanushek, S. Machin, & L. Woessmann (Eds.), Handbook of the economics of education (Vol. 4, pp. 183–282). Amsterdam, Netherlands: North-Holland. Lochner, L., & Moretti, E. (2004). The effect of education on crime: Evidence from prison inmates, arrests, and self-reports. American Economic Review, 94, 155–189. Machin, S., Marie, O., & Vujic, S. (2011). The crime reducing effect of education. Economic Journal, 121, 463–484.

Education Finance

EDUCATION FINANCE Education finance concerns the distribution and use of money for the purpose of providing educational services and producing student achievement. For most of the 20th century, education finance policy focused on equity, or issues related to the widely varying education expenditures per pupil across districts within a state, caused by the uneven distribution of the property tax base used to raise local education dollars. In the 1990s, new attention began to focus on education adequacy and productivity—or the relationship of student achievement to the level of funds and how those funds are used. As the 1990s ended and the 21st century began, policymakers increasingly wanted to know how much money was needed to educate students to high standards; how those dollars should be distributed effectively and fairly among districts, schools, programs, and students; and how both the level and the use of funds affected student performance. In recent years, an emphasis on accountability and the need to reduce the achievement gap, the adoption in most states of the Common Core State Standards, and the lingering effects of the Great Recession all continued to move school finance beyond its traditional emphasis on fiscal equity. This entry traces the development of education finance , including the growing importance of state governments in school funding, the evolution of the state’s role in funding schools, and the shift in focus within the field of education finance from issues of equitable distribution of funds to a focus on adequacy and productivity.

Early Developments The United States has not always had a system of free, tax-supported schools. Free public education was an idea created in the United States during the 19th century, and a large network of public school systems was formed in a relatively short period, primarily during the latter part of the 19th and early part of the 20th centuries. American schools began as local entities, largely private and religious, during the 17th, 18th, and even early 19th centuries. As in England, educating children was considered a private rather than a public matter. Providing for education was a mandate for parents and masters, not governments. Eighteenth-century leaders of the new American republic viewed education as a means to enable

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citizens to participate as equals in affairs of government and thus essential to ensure the liberties guaranteed by the Constitution. Even though Thomas Jefferson proposed the creation of free public elementary schools, his proposal was not adopted until the mid-1800s, largely through the efforts of Horace Mann and Henry Barnard, state superintendents of public instruction. Mann spearheaded the development of public-supported “common schools” in Massachusetts, and Barnard did the same in Connecticut. In the 19th century, as education began to assume significance in economic terms, many compulsory attendance laws were passed. Despite these laws, when school attendance became compulsory beginning in the mid-1800s, government financing of schools was not uniformly required. The Growing Importance of State Governments

Initially, one-room elementary common schools were established in local communities, often fully supported through a small local tax. Each town functioned as an independent school district, since there were no state laws or regulations providing for a statewide public education system. At the same time, several large school systems evolved in the big cities of most states. Even at this early time, these different education systems reflected differences in local ability to support them. Big cities usually were wealthy relative to the smaller, rural, one-room school districts, which typically had greater difficulty financing a school. As the number of these small rural and big city school systems grew, however, and the importance of education as a unifying force for a developing country became increasingly realized by civic and political leaders, new initiatives were undertaken to create statewide education systems. By 1820, 13 of the then 23 states had constitutional provisions, and 17 had statutory provisions pertaining to public education. In the mid-19th century, several states began to completely rewrite the state constitutions, not only calling for creation of statewide systems of public education but also formally establishing government responsibility for financing schools. Today, all states have constitutional provisions related to free public education. The creation of free common schools reflected the importance of education in the United States. It also shifted control over education from individuals and

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the church to the state. Control over schools was a problematic aspect in crafting statewide education systems. The resolution to the control issue was the creation of local lay boards of education that, it was argued, would function in the place of parents and the church. While the local boards essentially controlled public schools during the first century of their existence, the strength of local control has changed substantially in recent years. In the early 20th century, school control was mostly given to the new breed of educational professionals, as the Progressive Era of education sought to take politics out of education. Beginning in the 1960s, both state governments and the federal government began to exert new initiative and control over public schools. States continued this trend by taking the lead in education policy throughout the education reform period of the 1980s. Local boards were for the most part uninvolved in those reforms. In the early 1990s, the president and the nation’s governors established nationwide education goals; these were codified into law in 1994 by the U.S. Congress and continue in spirit if not in detail today. The development of the state-controlled and government-financed “common school” also raised many fundamental issues about education finance. The key issues concerned the level of government (e.g., local or state) that would support public education and whether new constitutional phrases such as general and uniform, thorough and efficient, basic, or adequate meant that an equal amount of dollars would be spent for every student in the state or whether they meant just providing a basic education program for every student, with different amounts of total dollars determined at the local level. Evolution of the State Role in Education Finance

While major differences exist in the specific approaches taken, most states finance public schools primarily through local property taxes and a combination of state income and sales taxes. In the midto late 1800s, most states required local districts to fully finance mandated public schools through local property taxation. In designing locally administered school systems, states generally gave local governments the authority to raise money for schools by levying property taxes. But when states determined school district boundaries, districts ended up with widely varying levels of property wealth per pupil and, thus, large differences in the ability to raise

local dollars to support public education. Districts with above-average property tax bases per pupil traditionally were able to spend at above-average levels with below-average tax rates, while districts with below-average tax bases spent at below-average levels even with above-average tax rates. Elwood Cubberly noted these inequities as early as 1905. School finance policy debates throughout the 20th century focused on these fiscal inequities. While some individuals pointed to spending differences per se, regardless of whether they were related to varying tax bases, and argued that they should be impermissible in a state education system, the bulk of the discussion centered on the links between spending differences and local property wealth per pupil. States began to intervene in school financing, at first through small, per-pupil flat grant programs, in which the state distributed an equal amount of money per pupil to each local school district. The idea was for the state to provide at least some assistance in support of a local basic education program. Over the years, these flat grants came to be recognized as being too small. In the early 1920s, states began to implement minimum foundation programs, which provided a much higher level of base financial support and were financed by a combination of state and local revenues. These programs were the first in which states explicitly recognized the wide variation in the local property tax base and designed a state aid structure to distribute larger amounts to districts with a small property tax base per pupil and smaller amounts to districts with a large property tax base per pupil. These equalization formulas, were designed to “equalize” differences in local fiscal capacity (i.e., the unequal ability to finance education because of the variation in the size of the local property tax base). But over time, the level of the minimum foundation programs also proved to be inadequate, and additional revenues above the foundation program were raised solely through local taxation. As a result, local educational expenditures per pupil varied widely across local districts in most states, with the differences related primarily to the size of the local property tax base. Beginning in the late 1960s, these fiscal disparities caused by unequal distribution of the local tax base and inadequate state general equalization programs led to legal challenges to state school finance systems, in which plaintiffs, usually from low-wealth and low-spending districts, argued that the disparities not only were unfair but also were unconstitutional.

Education Finance

Current Issues in Education Finance Education finance has become more complicated since the 1990s. Though many still define the major school finance problem as differences in spending across school districts caused by varying levels of property wealth per pupil, others argue that linking finance to an adequate education is the core school finance issue. Still others argue that educational productivity—determining how to produce higher levels of educational performance with current education resources—is the primary school finance goal.

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finance, some suggest that any remaining spending differences are a matter of local taxpayer choice and reflect neither an inherent inequity nor a schoolfunding problem. Others argue that since education is a state function, spending differences per se (as a proxy for education quality) are a problem regardless of whether they are caused by the unequal distribution of the property tax base or local taxpayer choice. Still others focus on the spending of the lowest-spending half of school districts, arguing that their level of spending should be higher.

Traditional Fiscal Disparities

Education Finance as a Matter of Fiscal Adequacy

An early example of the traditional school finance problem can be shown with the data presented at one of the first successful school finance cases in the United States, California’s Serrano v. Priest. At the time the case was filed in 1968, California had a typical minimum foundation program, and most districts raised additional funds to spend at a higher level. Across the state, there were vast differences in school district assessed value per pupil, and for the most part, the districts with higher assessed value per pupil had both higher expenditures per pupil and lower tax rates. The wealthy districts (defined by their property value per pupil) enjoyed the advantages of both high expenditures and low tax rates, while the poor were disadvantaged by both low expenditures and high tax rates. The school finance system in California began to change in 1971, when the state supreme court ruled in the Serrano case that these discrepancies meant that the quality of education a child received was a function of where that child lived, which it said violated the Equal Protection clauses of the U.S. Constitution and the state constitution of California. However, the U.S. Supreme Court later ruled in a separate case from Texas that discrepancies in per-pupil spending as a result of variations in district wealth were not a violation of the Equal Protection Clause of the U.S. Constitution. This equity problem was the focus of most education finance lawsuits in the 1980s and 1990s and led to the expansion of the state’s role in funding schools. Today, most states provide a substantial level of support for schools overall, most of which is distributed either in inverse relationship to district property wealth or to provide additional funds for students with greater educational needs. While the continued existence of spending disparities and their relationship to local property wealth, whatever the cause, remains a problem in school

The problem with the concept of equity as discussed earlier in this entry is that it deals only with money, and largely with whether or not base funding is equal. It is not related to any other substantive education goal, such as education quality or student achievement. Making the connection between school funding and education goals has become the key challenge in the field of school finance. The driving education goal is raising the level of student achievement (i.e., setting high and rigorous standards and teaching students to those standards). Research from cognitive science suggests that we know how to produce a much higher level of learning, or at least make substantial progress toward this goal. Given this knowledge, Linda Darling-Hammond argues that learning to high standards should be considered a right for all children. Moreover, school finance litigation in many states now often stresses whether funding is sufficient for an adequate education in all school districts, rather than whether it is distributed equitably among districts. The goals for student achievement created by state standardsbased education reform were reinforced with the federal No Child Left Behind Act of 2001. Given the student achievement goals of the early 21st century, the overarching question about school finance is this: What curriculum, instruction, incentive, capacity development, organization, and management strategies are required to meet these goals, and what level of funding is required for these strategies? Many argue that the main problem in school finance is to link funding to the strategies needed to teach students to higher standards. In new school finance parlance, the challenge is to determine an “adequate” level of spending. The task is to identify for each district or school the level of base spending needed to teach the average student to state

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standards, and then to identify how much extra each district or school requires to teach students with particular needs—the learning disabled, those from poverty and thus educationally deficient backgrounds, and those without English proficiency—to the same high and rigorous achievement standards. As William Clune has argued, this requires a shift in school finance thinking from a focus on equity to focusing on adequacy. In many states, educators and policymakers have begun to raise the issue of school finance adequacy in many ways. Some question whether the spending levels of the bottom half of all districts (i.e., those districts with just average or mostly belowaverage tax rates) are a problem (i.e., these spending levels are too low) or whether those spending levels, even though below average, are adequate to teach students to acceptable standards. Others have attempted to calculate a state-supported spending level that can be linked to a specified level of student performance (e.g., It will cost X dollars for 90% of students to meet or exceed state proficiency standards in core subjects). In a sense, this is a “back to the future” school finance objective, as many foundation programs have sought to make this linkage throughout the 20th century. Still others explore the degree to which any spending level deemed adequate should be supplemented by additional money to provide extra resources to teach students with particular needs to high standards. For many, this focus on adequacy constitutes a shift in defining the basic school finance problem—away from the sole focus on fiscal disparities across districts and toward linking spending to what could be construed as an adequate education program (i.e., a program designed to teach students to high levels of achievement). Productivity as an Education Finance Issue

Despite funding disparities across school districts and other potential shortcomings of current state education finance systems, analysts such as Eric Hanushek argue that the most prominent school finance problem is the low levels of system performance and student achievement attained with the relatively large levels of funding in the system. These analysts are convinced that, on balance, there is a substantial amount of revenues in the U.S. public school system and that the core problem is to determine how best to use those resources, particularly how to use the resources differently to support strategies that dramatically boost student performance.

Although equity will likely remain an important school finance goal, it is clear that as greater demands are made for spending efficiency and for accountability for student performance, issues of adequacy and productivity will continue to dominate education reform discussions. Today, educators need to show how to transform current and new dollars into student achievement results, or the argument that education needs more—or even the current level of—money will be unlikely to attract public or political support. Lawrence O. Picus See also Adequacy; Educational Equity; Equalization Models; Fiscal Disparity; Fiscal Neutrality; Property Taxes; San Antonio Independent School District v. Rodriguez; School District Wealth; School Finance Equity Statistics; School Finance Litigation; Serrano v. Priest; Tax Burden

Further Readings Coons, J., Clune, W., & Sugarman, S. (1970). Private wealth and public education. Cambridge, MA: Belknap Press of Harvard University Press. Odden, A. R., & Picus, L. O. (2013). School finance: A policy perspective (5th ed.). New York, NY: McGraw-Hill. Rebell, M. (2007). Professional rigor, public engagement and judicial review: A proposal for enhancing the validity of education adequacy studies. Teachers College Record, 109(6), 1–72. Retrieved from http://www .tcrecord.org/Content.asp?ContentID⫽12743 Serrano v. Priest (Cal. 3d 584, 487 P.2d 1241, 96 Cal. Rptr. 601, 1971). Tyack, D., & Hansot, E. (1982). Managers of virtue. New York, NY: Basic Books.

EDUCATION MANAGEMENT ORGANIZATIONS The role of private enterprise in publicly funded schools is a prominent and contentious education issue in the United States today. Education management organizations (EMOs) are for-profit or nonprofit private companies that operate publicly funded schools. These organizations can take several different forms. Some EMOs focus solely on founding and operating charter schools (these organizations, e.g., KIPP: Aspire Academy, are often referred to as charter management organizations, or CMOs).

Education Management Organizations

Other EMOs operate district schools as contractors to public agencies or other organizations. EMO-run schools may be newly founded or existing schools previously managed by the district or some other entity (such “takeover” or “turnaround” schools are often those with a record of poor performance). EMOs may operate a single school, a small network of schools within a district or state, or a large, multistate network of schools. The contractual arrangements under which EMOs operate schools also vary widely. They differ in length and payment structure, the types of performance targets set for EMOs, and the degree of operational autonomy enjoyed by the EMO. For example, contracts may or may not give EMOs wide latitude regarding the school curriculum, staffing decisions including the hiring and firing of teachers, and operational matters such as establishing the length of the school day or year. In sum, EMOs provide a private alternative to the direct operation of schools by districts or states, while retaining government funding, maintaining tuitionfree access for students, and operating with varying degrees of oversight and regulation by public authorities. The following section presents a brief summary of the origins of EMOs in the United States and the arguments made for and against their use. Next, the entry reviews trends in EMOs’ characteristics and growth and discusses evidence of their impacts on students’ academic achievement and other outcomes. The entry concludes with a discussion of issues affecting the future growth of EMOs nationwide.

Origins of EMOs Public education in the United States has long used private providers for specific services, including professional development for teachers, curricular materials, and transportation. However, involving private organizations in the direct management of publicly funded schools is a relatively recent development. The first EMOs began operating in the early 1990s, part of a broader trend in which education reformers began piloting initiatives designed to bring market forces into public education. These efforts included voucher programs (which provide public funding for private school tuition), charter schools (publicly funded schools that function outside the direct operational control of elected officials), and the creation of EMOs to introduce private management in public schools more generally.

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Two separate forces contributed to the early optimism about the potential of EMOs. First, education reformers believed that introducing market forces into the public education system could boost the academic performance of public schools. EMOs would compete among themselves to operate as efficiently as possible and maximize students’ academic potential. The competition from EMOs would create incentives for district-run schools to improve their performance as well. During this early period, many state legislatures introduced charter school legislation, and a number of nonprofit CMOs were founded. Second, private investors were optimistic about for-profit EMOs’ growth potential and ability to generate consistent profits. They believed that with some initial success, increasing numbers of districts would turn to EMOs and establish a growing market for their services. A prominent early for-profit EMO was the Edison Project (now Edison Learning), founded in 1992. In its early days, Edison grew rapidly, and for a time its stock was publicly traded on NASDAQ (National Association of Securities Dealers Automated Quotations). The growth of EMO contracting arrangements with public agencies has been slower than many expected, particularly among for-profit EMOs operating district schools. These organizations have encountered challenges both in their funding and in their operations. As EMOs lost contracts in some districts and with the crash of the dot-com bubble in 2000, private investors became less enthusiastic about their growth potential. On the operations side, EMOs ran into challenges related to their level of autonomy and the stability of their contractual arrangements. One prominent illustration of the challenges experienced by EMO contractors occurred in Philadelphia. In 2002, following the state takeover of the city’s struggling school district, six for-profit and nonprofit EMOs won contracts to manage approximately 40 schools (Edison received the largest contract, with 20 schools). Negotiations between the district stakeholders and state authorities were contentious, however, and several aspects of the privatization effort did not function as originally designed. For example, parents were not allowed to choose which EMO school or traditionally managed school their children would attend; because students were centrally assigned, there was little competition among EMO or district schools to attract students. EMO operations were also constrained in important ways. Staff within EMO schools remained

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employees of the school district (so EMOs did not control hiring and dismissal decisions), and EMOs were required to maintain many of the district’s standard operational practices, limiting the scope for innovation at EMO schools. Ultimately, several EMOs lost their contracts at underperforming schools. In 2008, the state commission overseeing Philadelphia’s schools severed EMO contracts at six schools, and subsequently the number of EMOmanaged schools in the district has steadily declined. On the other hand, other forces have encouraged the expansion of EMOs in recent years. In 2002, the federal No Child Left Behind Act introduced rules promoting several possible turnaround strategies for persistently struggling schools, including takeover by private organizations or conversion to charter school status. More broadly, the charter school sector has continued to grow rapidly in recent years as many states and districts have substantially reduced or removed restrictions on the number of new charter schools that can be established. Charter schools are becoming increasingly likely to be operated by CMOs as these organizations professionalize the process of opening new schools and continue to grow in scale.

Potential Benefits and Drawbacks of EMOs EMO proponents argue that these organizations’ flexibility and ability to attract resources, combined with competitive markets and proper incentives, have the potential to improve the efficiency of public education and benefit students. They argue that private sector organizations are better equipped than public school districts to enact school reforms or adapt to changing educational circumstances. If so, EMOs may have opportunities to experiment with innovative operational practices or educational models. Private organizations with the ability to grow beyond district boundaries and recruit nonunion staff may be able to lower costs and realize economies of scale more readily than public entities. In addition, proponents note that EMOs (particularly for-profit organizations) may draw resources not attracted to traditional public school systems, including both financial resources and human capital—talented teachers and administrators who might contribute to school operations or management. EMO supporters argue that market competition among education providers should produce beneficial incentives. Where EMOs operate schools of choice that receive funding on a per-student basis

(e.g., charter schools), proponents hold that competition will improve school quality both at EMO schools and at traditional public schools as providers respond to the market accountability enforced by student enrollment decisions. Competition among EMOs seeking contracts from public agencies may also produce benefits, to the extent that the contractors engage in price competition or seek to demonstrate improved student outcomes. By using contracts with clear and measurable outcome targets, advocates argue, EMOs can provide a more accountable option to administrators through the use of performance bonuses (or sanctions for underperformance) in ways that are not possible in traditional, district-operated schools. In contrast, critics of EMOs identify important potential drawbacks in the private operation of public schools. Most fundamentally, these skeptics argue that private sector management of public schools undermines existing forms of democratic accountability, since elected public officials have less control over EMO schools’ operations, curriculum, and instructional methods. Critics also argue that the advantages of market competition are exaggerated, suggesting that there are significant barriers impeding free and open competition among EMOs. For example, there may be few eligible and qualified organizations participating in a given bidding process for a school operations contract, in which case EMOs would face weaker incentives to hold down costs or perform effectively. Contracts between EMOs and school districts are also complex, requiring legal and administrative expertise that district and state agencies may not possess. As a result, planned accountability measures for EMOs may not be structured or enforced in ways that lead to improved student outcomes. Skeptics have also raised concerns about the impact of private management on enrolled students and surrounding schools. Critics of for-profit EMOs object to the introduction of the profit motive in the public school system, arguing that financial incentives may lead EMOs to make decisions not in students’ best interests. Critics also argue that EMOs are more likely to recruit inexperienced teachers and administrators or seek to apply overly standardized or inflexible pedagogical approaches that do not respond to students’ needs. Finally, they argue that EMOs running schools of choice (and fast-growing CMOs in particular) may draw the most motivated or highest achieving students from traditional public schools through stringent application requirements

Education Management Organizations

or by dismissing struggling students after they enroll. If this is true, it may leave the remaining districtoperated schools with a population of students that are predominantly at risk and more difficult to teach.

Trends in EMO Growth and Enrollment The National Education Policy Center reported that in the 2010–2011 school year, there were 296 EMOs in 33 states, operating 1,928 schools that enrolled just under 800,000 students (roughly 2% of public school students nationally). About one third of EMOs (99) were for-profits, but their 758 schools enrolled more than half of all EMO students. The average size of the 1,170 schools operated by nonprofit EMOs was smaller, so that slightly fewer students attended these schools. EMOs tend to be concentrated largely in a few states, although this geographic concentration differs by EMO type. Among for-profit EMO schools in 2010–2011, 71% were located in four states— Michigan, Florida, Ohio, and Arizona. Among nonprofit EMO schools, 61% were in just three states—Texas, California, and Arizona—and nearly 80% were in six states. Growth in the number of for-profit EMOs was greatest in the 1990s, as the first EMO opened at the beginning of that decade and 57 were operating by 2000–2001. Growth slowed somewhat between that year and 2010–2011, with 42 additional EMOs opening during that decade. However, due to growth in the size of existing EMOs, the number of students served by for-profits increased dramatically over the past decade, from about 71,000 to 394,000. Growth in the number of nonprofit EMOs has been steady over time, with an average of 11 new nonprofits opening per year between 2001–2002 and 2010–2011. The number of students served by schools operated by nonprofits skyrocketed during that period, from just 20,000 to 384,000. As a result, while for-profits dominated the EMO market as of 2001–2002 in terms of the number of students served, the two types of organizations served nearly equal numbers of students as of 2010–2011. The vast majority of EMO schools (94% among both for-profits and nonprofits) are charter schools, and EMO-operated schools made up more than a third of all charter schools in 2010–2011. Forprofit EMOs have become more likely over time to operate charter schools. For example, only 77% of for-profit EMO schools were charter schools as of 2003–2004.

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EMOs operate a growing number of virtual schools (93), which were rare at the turn of the century. Most of these virtual schools are operated by for-profit EMOs. On average, virtual schools are much larger than other EMO schools in terms of student enrollment. Between 2003–2004 and 2010–2011, there was a tenfold increase in enrollment at EMO-run virtual schools, from about 11,500 to 115,000.

Evidence on the Effectiveness of EMOs Although charter schools have been the subject of considerable empirical research over the past decade, the performance of EMO schools has received less attention. Most existing research has focused on organizations operating charter schools (CMOs, which, as noted earlier, make up a large subset of all EMOs) and has primarily examined their effects on student achievement. The research suggests that, on average, EMO schools perform about as well but no better than comparable traditional public schools, with some EMOs having positive impacts on students and others having negative impacts. Two large national studies have examined the effectiveness of EMO-operated charter schools, or CMO schools. A 2012 study by Mathematica Policy Research used both experimental (based on school lotteries) and quasi-experimental methods to study the effects of a set of nonprofit CMOs. This study found that CMO schools did not do significantly better or worse than comparable district schools on average, but this overall nonimpact masked considerable variation across organizations. About half had significant positive impacts on student achievement in reading and/or math, and slightly fewer had significant negative impacts. The CMOs with more positive impacts tended to be larger, serving more students. Among a subsample for which longer term outcomes were available, half had positive impacts on high school graduation and postsecondary enrollment. In 2013, the Center for Research on Education Outcomes cast a broader net and used a quasiexperimental design to study both for-profit and nonprofit CMOs, shedding light on this subset of EMOs. This study also found substantial variation in impacts across CMO schools, with some CMO schools having positive effects on student achievement and others having negative effects. Again, this variation led to average effects across all CMOs that were very close to zero, small and negative in the

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case of math achievement and small and positive in the case of reading achievement. Few rigorous studies have been conducted of EMOs that operate district schools, with the best evidence coming from two studies conducted by RAND Corporation. Their 2005 evaluation of Edison Schools found that student achievement growth during the first 3 years of Edison management was lower than that in comparable district schools, while growth during the fourth and fifth years was higher. The evidence about the cumulative effect of Edison management was mixed. Moreover, the study noted substantial turnover in Edison management schools—among schools that began operating under Edison management in 1995–2000, only 36% remained under Edison management in the spring of 2005. A 2007 RAND study focused on the effects of 40 Philadelphia schools under private management starting in 2002. This study found gains in student achievement from before to after the private takeover, but it also found that these gains were no different from similar gains among comparable district-run schools in Philadelphia or among similarly low-achieving schools throughout the state. The research on EMOs has left a number of questions unanswered. While studies have focused on the effects of EMO schools on the students who attend them, a critical question is whether the introduction of EMOs into a district has any influence on the performance of nearby district schools. As noted above, EMO supporters argue that one benefit of their introduction may be that the heightened competition they provide district schools could lead to improvement among all district schools, but we have little evidence as to whether this is occurring. A key theme of the existing research is that the performance of EMOs varies widely, but less is known about what factors cause some EMOs to be successful and others to struggle. For example, it would be useful if research could shed light on whether some EMO contract types are more effective than others or if there are differences in the performance of forprofit and nonprofit EMOs.

The Future of EMOs It remains to be seen whether EMOs will continue to expand despite the political, regulatory, and operational challenges the sector has encountered thus far. While the number of students served by EMOs continues to increase, in recent years this growth has been largely confined to organizations

operating charter schools (especially nonprofit CMOs). Many obstacles stand in the way of EMOs seeking to assume a more substantial role in existing traditional public schools. Other than for charter schools, district and state administrators have been largely unable or unwilling to provide the combination of operational autonomy and school choice that EMOs would need to establish large-scale, competitive, and dynamic private markets along the lines of what reformers envisioned in the 1990s. However, the diversity of models and approaches currently being applied by EMOs may yet produce transformative changes. For example, the rapid rise in the number of students enrolled in EMO-operated virtual schools could presage new growth models for innovative private education providers. Little is known about whether the virtual school model or other practices developed by EMOs can efficiently provide a beneficial set of public education alternatives for students. If EMOs are to play a more substantial role in the United States, much will depend on the ability of these private organizations to more effectively develop new approaches to education services, to prove that such approaches work, and to demonstrate ongoing success as these new organizations grow in scale. Philip Gleason and Ira Nichols-Barrer See also Charter Management Organizations; Charter Schools; Economies of Scale; Privatization and Marketization

Further Readings Center for Research on Educational Outcomes. (2013). Charter school growth and replication. Stanford, CA: Author. Furgeson, J., Gill, B., Haimson, J., Killewald, A., McCullough, M., Nichols-Barrer, I., . . . Lake, R. (2012). Charter-school management organizations: Diverse strategies and diverse student impacts (Report prepared by Mathematica Policy Research and the University of Washington’s Center on Reinventing Public Education). Princeton, NJ: Mathematica Policy Research. Gill, B. P., Hamilton, L. S., Lockwood, J. R., Marsh, J. A., Zimmer, R. W., Hill, D., & Pribesh, S. (2005). Inspiration, perspiration, and time: Operations and achievement in Edison Schools. Santa Monica, CA: RAND Corporation. Gill, B. P., Zimmer, R., Christman, J., & Blanc, S. (2007). State takeover, school restructuring, private

Education Production Functions and Productivity management, and student achievement in Philadelphia. Santa Monica, CA: RAND Corporation. Miron, G., Urschel, J. L., Yat Aguilar, M. A., & Dailey, B. (2011). Profiles of for-profit and nonprofit education management organizations: Thirteenth annual report—2010–2011. Boulder, CO: National Education Policy Center. Wilson, S. F. (2006). Learning on the job: When business takes on public schools. Cambridge, MA: Harvard University Press.

EDUCATION PRODUCTION FUNCTIONS AND PRODUCTIVITY Many economic analyses of education follow a simple model of production and reflect a desire to understand what determines student outcomes. Economists use the structure of an education production function to organize thinking about the various influences on student achievement and to consider various policy alternatives. A production function relates an outcome to inputs, or factors of production. In education, the inputs are typically school resources, teacher quality, and family attributes, while the outcome is a measure of student achievement, such as test scores. The results of this research can be used in the development of education policy. This entry discusses the shift in education research to studying the relationship between inputs and outcomes and details which inputs are most commonly studied. It then reviews the results of research on the impact of school resources on student achievement, which overall indicate inefficiency in the use of school resources. It then discusses more recent research focusing on teacher quality and how this research relates to policy discussions on teacher evaluation, teacher pay, and overall school spending. Research on the role of school resources in determining student achievement began in earnest after the U.S. government released its 1966 study on educational opportunity, referred to as the Coleman Report after its principal author, James Coleman. Although the report was controversial because it was interpreted as saying that “schools did not matter,” it directed attention to the distribution of student performance—the outputs, rather than school inputs such as spending per pupil or characteristics of teachers. As Eric A. Hanushek (2010) has discussed,

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The underlying model that has evolved as a result of this research is very straightforward. The output of the educational process—the achievement of individual students—is directly related to both inputs—ones that are directly controlled by policymakers (e.g., the characteristics of schools, teachers, and curricula) and others that are not so controlled (such as families and friends and the innate endowments or learning capacities of the students). Further, while achievement may be measured at discrete points in time, the educational process is cumulative; inputs applied sometime in the past affect students’ current levels of achievement. (p. 407)

The general focus of much of the attention is the measurement and the impact of school inputs (although a somewhat separate line of research has investigated the role of peers). Historically, the measures of school inputs typically included teacher background (education level, experience, sex, race, etc.), school organization (class sizes, facilities, administrative expenditures, etc.), and district or community factors (e.g., average expenditure levels). While more recent work has moved away from these measures, it is valuable to understand what the research has said about them.

The Impact of Resources The early analyses of education production functions, or those that appeared before 1995, focus on a relatively small set of resource measures. An analysis of 90 individual publications with 377 separate production function estimates showed these results: For classroom resources, only 9% of estimates for teacher education and 14% for teacher-pupil ratios yielded a positive and statistically significant relationship between these factors and student performance. Moreover, these studies were offset by another set of studies that found a similarly negative correlation between those inputs and student achievement. Twenty-nine percent of the studies found a positive correlation between teacher experience and student performance; however, 71% still provided no support for increasing teacher experience (being either negative or statistically insignificant). Studies on the effect of financial resources provide a similar picture. These indicate that there is very weak support for the notion that simply providing

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higher teacher salaries or greater overall spending will lead to improved student performance. Perpupil expenditure has received the most attention, but only 27% of studies showed a positive and significant effect. In fact, 7% even suggested that adding resources would harm student achievement. (Hanushek, 2010, p. 408)

The studies of per-pupil spending tended to be of low quality, calling into question even the weak support found for the idea that spending improves performance. Moreover, and surprisingly, these results change little when the focus is on education in developing countries, where one might expect added resources to have a greater effect. They also hold in looking across countries. An important issue is whether this analytical approach accurately evaluates the causal relationship between resources and performance. School resources are often directed to students deemed to have greater educational needs, so a higher level of resources in a particular school or school district could simply signal a greater number of lower achieving students. Various regression discontinuity, experimental, or panel data approaches can be used to deal with this.

Modern Approaches In recent years, researchers have begun to use large administrative databases, such as those created by state education agencies, in education production studies. Many of these databases follow all students in a state over time, which allows researchers to relate individual student performance to programs and personnel and to control for a wide range of influences on achievement by introducing fixed effects for schools, individuals, and time.

influence of sample selection and other possible omitted factors. Until some recent studies in developing countries, the only major education study in which this approach has been used to evaluate the impact of schools on student performance was in the Project STAR (for Student/Teacher Achievement Ratio) experiment. This Tennessee experiment in the mid-1980s involved randomly assigning students to either small classes (13–17 students) or large classes (22–25 students). Concerns about the quality of the Project STAR experiment have led to ongoing controversy about the results and their interpretation. The results suggested that smaller class size had a small but significant impact on student achievement for students enrolled in smaller classes in kindergarten or first grade. No additional impact on student achievement was found from being enrolled in smaller classes in later years. Surprisingly, no replications of this controversial study have been undertaken in the three decades since the STAR experiment was conducted. Although education researchers, particularly in developing countries, have been making increased use of randomized experiments in recent years, so far, the use of randomized experiments has not provided much information about the impact of resources on student achievement.

Measuring the Impact of Teachers and Schools on Performance

These fixed effects hold constant any systematic differences that are constant among the category (such as constant differences among the sampled schools in terms of the selection of schools by families and teachers) and obtain estimates of various inputs from their variation within each of the schools. By eliminating systematic selection and sorting of students and school personnel, they can concentrate on specific causal factors that determine individual student outcomes. (Hanushek, 2010, p. 409)

The Coleman Report and subsequent research have led to debates over whether schools matter in terms of student performance, rather than, as some would suggest, that performance is almost entirely influenced only by families and peers. The problem has been the difficulty in measuring school effects. Research estimating education production functions has found no clear, systematic relationship between resources and student outcomes. However, research focusing on the differences among teachers in student achievement growth rates, rather than teachers’ characteristics, has shown that teachers strongly influence student performance. Recent research strongly indicates that differences in teacher quality is the most significant in-school factor for differences among schools in student achievement. This approach to studying teacher quality defines it in terms of students’ performance over time.

Random assignment experimentation has been used instead of statistical analysis to eliminate the

A good teacher would be one who consistently obtained high learning growth from students, while

Education Production Functions and Productivity

a poor teacher would be one who consistently produced low learning growth. The general research design is to estimate models of the growth in individual student achievement that can be attributed to various measured school and family factors and to mean differences in learning across the students with different teachers. The differences in student-achievement growth across classrooms, which can be taken as a measure of teacher quality, appear to be very large. (Hanushek, 2010, p. 409)

These estimates of teacher quality, first introduced in 1971, are now commonly referred to as teacher value-added measures, because they attempt to separate the influence of the teacher from all the other factors affecting student achievement. They have become central to much of the policy discussion about school improvement. Hanushek has estimated that the variation between a good teacher and a bad teacher can be as much as a full year’s worth of learning each academic year. That is, while students of a poor teacher show academic growth equaling 0.5 grade levels during a school year, those of a good teacher achieve the equivalent of 1.5 grade levels of growth. Much larger effects on performance can occur when students are taught by a series of good teachers or by a series of poor teachers. More recent research using a Texas state administrative database by Hanushek, John F. Kain, and Steven G. Rivkin found that one standard deviation in teacher quality implies around a 0.15 standard deviation in the growth of student achievement when comparing teachers within schools. Based on this research, it has been argued that if a typical low-income student has a good teacher for 4 to 5 years, it would be enough to close the average achievement gap between low-income and higher income students. Differences found among teachers in terms of their student performance are not closely correlated with teachers’ experience, degrees earned, credentials, teacher training, or salaries. Teacher quality, when measured this way, also does not appear to be related to teachers’ decisions to leave for other schools or for jobs outside of teaching. These analyses have been controversial because of concerns about the statistical modeling used to determine teacher value-added measures. The research discussions have been heightened considerably by proposals to use value-added measures in the evaluation and compensation of teachers.

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Teacher Quality and School Productivity Teacher salaries have been largely determined by the amount of experience and the amount of education that a teacher has, but these factors are not systematically related to effectiveness in the classroom. (The exception is that teachers become more effective over the first few years of teaching.) Concerns about teacher effectiveness and rewards for teachers have led to various efforts to improve the evaluation of teachers, which would then allow for relating salaries more directly to performance. Part of the controversy related to teacher evaluation reflects disagreements about how much emphasis should be placed on measured student performance. The estimation of teacher value-added measures, which relies on standardized assessments of student achievement, has been the focus of considerable research, and there are differing opinions on how much weight should be placed on such estimates in teacher evaluations. Teacher salaries and pupil-teacher ratios—the main drivers of expenditure per pupil—are largely unrelated to student outcomes, leading to the conclusion that expenditures are higher than necessary to achieve a given level of student achievement (and reinforcing the prior discussions that spending itself is not closely related to achievement). That is, schools are paying for inputs that have little to no impact on outcomes. Nonsalary expenditures make up one third or more of total school spending, but again, there is not sufficient evidence to move schools toward different spending patterns that improve efficiency and performance of schools. Eric A. Hanushek Author’s Note: Portions of this entry were reprinted from Hanushek, E. A. (2010). Education production functions: Developed country evidence. In P. Peterson, E. Baker, & B. McGaw (Eds.), International encyclopedia of education (Vol. 2, pp. 407–411). Oxford, UK: Elsevier. Copyright 2010, with permission from Elsevier.

See also Economic Efficiency; Economics of Education; Education Finance; Teacher Effectiveness; Teacher Evaluation; Teacher Experience; Teacher Value-Added Measures

Further Readings Boyd, D., Grossman, P., Lankford, H., Loeb, S., & Wyckoff, J. (2006). How changes in entry requirements alter the teacher workforce and affect student

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achievement. Education Finance and Policy, 1(2), 176–216. Burtless, G. (Ed.). (1996). Does money matter? The effect of school resources on student achievement and adult success. Washington, DC: Brookings Institution Press. Hanushek, E. A. (1992). The trade-off between child quantity and quality. Journal of Political Economy, 100(1), 84–117. Hanushek, E. A. (1995). Interpreting recent research on schooling in developing countries. World Bank Research Observer, 10(2), 227–246. Hanushek, E. A. (2003). The failure of input-based schooling policies. Economic Journal, 113(485), F64–F98. Hanushek E. A. (2010). Education production functions: Developed country evidence. In P. Peterson, E. Baker, & B. McGaw (Eds.), International encyclopedia of education (Vol. 2, pp. 407–411). Oxford, UK: Elsevier. Hanushek, E. A., Kain, J. F., & Rivkin, S. G. (2005). Teachers, schools, and academic achievement. Econometrica, 73(2), 417–458. Hanushek, E. A., & Rivkin, S. G. (2012). The distribution of teacher quality and implications for policy. Annual Review of Economics, 4, 7.1–7.27. Hanushek, E. A., & Woessmann, L. (2011). The economics of international differences in educational achievement. In E. A. Hanushek, S. Machin, & L. Woessmann (Eds.), Handbook of the economics of education (Vol. 3, pp. 89–200). Amsterdam, Netherlands: North-Holland. Kane, T. J., Rockoff, J. E., & Staiger, D. O. (2008). What does certification tell us about teacher effectiveness? Evidence from New York City. Economics of Education Review, 27(6), 615–631. Krueger, A. B. (1999). Experimental estimates of education production functions. Quarterly Journal of Economics, 114(2), 497–532. Mishel, L., & Rothstein, R. (Eds.). (2002). The class size debate. Washington, DC: Economic Policy Institute.

EDUCATION SPENDING Education spending refers to expenditures on a range of educational materials and activities, including instruction, research, public services, student services, and institutional support. Educational expenditures also include spending on the operation and maintenance of physical plants, vehicles, purchases of land, construction, and other capital. These disbursements are generally for the obtainment or delivery of educational advancement or educational services. Spending occurs in two broad areas: (1) private spending and (2) public spending.

Private spending is that by individuals (e.g., students, teachers, and consultants) or private agencies (e.g., schools and education service providers) for educational services. Public spending is that of public entities, such as the federal, state, and local governments. Expenditures on education are usually for the goal of obtaining or imparting knowledge, skills, or service. According to the U.S. Census Bureau, the United States spends more than $1 trillion per year on education. This amount covers only the officially collected resources and does not include individual spending for supplies and other expenses. This entry describes education spending and then discusses it in terms of private spending and public spending. It briefly addresses issues of measuring spending and determining what level of spending can be considered adequate and equitable.

Private Spending on Education Private agencies and individuals spend resources to provide educational instruction, services, or products. Private agencies may include private schools and colleges and service providers (e.g., consultants, tutors, and early childhood programs). The agencies may be either for-profit enterprises, which can distribute profits to shareholders and investors, or nonprofit entities, which must direct any excess revenues toward organizational goals. They can be either religiously based or secular. The majority of private spending on education is by individuals, and it is usually in the form of tuition and fees for the purchase of knowledge, skills, or services. Private Education Agencies

A private agency that provides educational services has several types of budgetary expenditures that are similar to public agency expenditures. Educational spending includes both expenditures for delivery of educational services (e.g., salaries, maintenance, supplies) and expenditures on auxiliary services or deliverables (e.g., transportation, interest on loans, supplies, diplomas). Individual Education Expenditures

Individuals may include consultants, students, or employees, such as public school teachers, who spend their personal resources on educational supplies, but this is a small component of individual spending (as of 2013, the Internal Revenue Service provided an income tax deduction for teachers of up to $250 per year for money spent on classroom

Education Spending

supplies). Educational consultants spend resources for information and/or networking. Part of their spending is on supplies. Individual college students spend money on tuition and fees, supplies, and living expenses (for private housing or on-campus housing, including utilities and food). Tuition and fee costs are found in all levels of educational service from early childhood education up through graduate education. In higher education, expenditures may also include fees for social activities (e.g., sports, student services, student centers) or services such as parking. Another private cost of education would be debt repayment. Tuition and fees for education have increased more than the average cost of living for several decades and continue to outpace inflation. Most students are unable to afford the full cost of education and require assistance in the form of grants, work-study opportunities, or student loans, with a major portion of spending increasingly coming from loans. Today’s college students are often finishing their 4-year degrees deeper in debt than their parents, with student loans easily exceeding $100,000 for students at more expensive universities. Some schools have begun to offer creative financing programs such as pay-as-you-go plans or limited, interest-free semester loans. Other, more costly schools that are well endowed, such as Harvard, Princeton, and Yale universities, are able to offer more grants to incoming students. This is also a form of spending but would be considered private, institutional spending. Another area of private spending is the personal expenditures that families pay out for educationrelated services or programs. Often, families are charged for attending sporting events or special school performances, such as plays that are hosted by their child’s school. Some schools also charge families for bus transportation to and from school, while others charge families for transportation only when students go on field trips. Other family costs include providing school supplies, such as books, paper, pencils, lunches, uniforms, specific dress attire (e.g., band clothes), and musical instruments.

Public Spending on Education Public spending for education derives from federal, state, and local sources; the majority of public spending on education comes from taxation. Public spending attempts to spread the burden of educating students across all levels of economic status within a governmental region. With local spending (mostly

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from property taxes), state spending, and federal spending on education, the burden of taxation is applied, theoretically, to all citizens, instead of the individual user of the education system. Spending on education by local government agencies, mainly school districts, comes from local, state, and federal sources. Some federal money for education is distributed to states, which in turn distribute it to local agencies. Local agencies also receive funds for education directly from the federal government. Federal Spending for Education

Although federal spending is the smallest level of spending for education on an aggregate scale (until recently, less than 10% of funds distributed by states for K-12 schools came from the federal government), it is extremely complex and diverse, with the intent of assisting states and local agencies in education at all levels of student achievement. Federal funds are expended during the federal fiscal year, which runs from October 1 through September 30 of the following year. With the Elementary and Secondary Education Act of 1965, and subsequent reauthorizations, the federal government became a contributor to education spending in all U.S. states and territories. The Elementary and Secondary Education Act originally provided funding to six areas of education. These were known by their placement in the law, although some of the law sections have subsequently changed. Title I, which is still identified by its original name, is by far the largest expenditure within the law and provides assistance to local education agencies (LEAs) for the education of children of low-income families. Other sections of the law provide resources for libraries, textbooks, and other instructional materials; supplementary educational centers; research and training; state education departments; and general needs. When the federal government distributes funds for education, they are generally referred to as appropriations from the federal budget, which is the budget authority granted through the congressional appropriation process, permitting federal agencies to incur obligations and make payments. Besides Elementary and Secondary Education Act expenditures, the federal government also provides funds for a wide variety of specific programs for elementary and secondary education. These include the Race to the Top grant competition to promote education reform, grants to turn around low-performing schools, funding for magnet schools as a

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means of desegregation, and grants to help schools serve homeless students. In the area of special education, the federal government provides resources for preschool and K-12 programs, early intervention services for infants and toddlers, teacher preparation and training, parent information, technology, and rehabilitation services. It also provides funds for disability research and additional sources for training to handle disabilities in instruction. Federal spending on education also goes toward career and technical education, adult literacy programs, and higher education. Within higher education, financial aid is one of the largest federal expenditures, with the federal government spending on all forms of student aid, including tax benefits, Pell grants, Supplemental Educational Opportunity Grants, Work-Study, Perkins loans, Presidential Teaching Fellows, and Federal Family Education Loans and Direct Loans. Other higher education expenditures include aid for Hispanic institutions, programs for foreign language studies, funds to improve postsecondary education, programs for migrant students, early awareness for undergraduate programs, graduate fellowships for students in fields designated as areas of national need, and assistance to specific universities serving minority students. The federal government also funds the Department of Education’s Institute of Education Sciences for research and development, regional laboratories, special education research, and data development. State Spending for Education

States spend resources for early childhood education, elementary and secondary education, higher education, and other educational services sponsored by various state agencies. Early childhood programs vary the most from state to state. Some states do not provide any resources for early childhood education, while others might provide limited services through school districts. Elementary and secondary education expenditures are usually provided according to a state aid formula. Each state’s formulas vary, and the variations are based more on historical and political practices than econometric or budgetary planning. On average, states provide half of the funding for public elementary and secondary schools, with the remainder coming from federal and local sources (with property taxes the primary local source). School finance reform in many states has shifted funding for public schools away from

local property taxes and toward a greater reliance on state sources. Higher Education Expenditures

These expenditures include spending for community colleges, state colleges, state universities, private universities, and public and private occupational training programs. States also provide scholarships for various educational programs from occupational training to the traditional 4-year baccalaureate programs. Spending for higher education may also include grants for research and development. State Funding Formulas

Analysis of funding formulas has been conducted since the early 1900s, starting with Elwood Cubberly’s publication of his doctoral dissertation, titled School Funds and Their Apportionment. However, continued analysis and measurement of funding formulas and their effects has been difficult due to the variations in the formulas created by historical and political practices in each state. The American Education Finance Association (now the Association for Education Finance and Policy) produced a two-volume study in 2000 titled Public School Finance Programs of the United States and Canada, 1998–99, which analyzed all 50 states’ and Canadian provinces’ finance formulas in great detail. However, the effort took several years to complete, and plans for revising it have not been forthcoming. Types of Funding Formulas

Foundation formulas are based either on some foundation or base appropriated by the legislature every year or on a teacher allocation method. A few states have created formulas based on some per-student calculation. Other states have formulas in their statutes but fail to follow them directly and appropriate funds based on previous years, with adjustments made for inflation. Only one state, Hawaii, allocates funds for education using a full state funding formula, where funds are collected by the state and disbursed directly to individual schools rather than to local education agencies. States may make adjustments to the foundation or base funding formula based on classifications for instructional programs or student needs. States also use tax-effort equalization funding to provide additional funds to school districts with weak local tax bases.

Education Spending

Local Spending for Education

Local spending for education is generally viewed across 14 categories of expenditures. These categories can be classified under two broad categories of current expenditures and noncurrent expenditures. Local spending is generally collected from the LEA and not from individual schools. Current Expenditures for Education

Current expenditures incorporate 10 categories of state expenditures, and these are separated into four areas: (1) instruction, (2) student support services, (3) food services, and (4) enterprise operations. Instruction covers all the activities that directly relate to the interaction between students and teachers, whether in a school building or other facility or venue (e.g., the Internet). These expenditures include salaries, benefits, services, supplies, and tuition to nonpublic schools for certain services. Student support services include expenditures for student support (e.g., health, attendance, and speech pathology), instructional staff (e.g., for curriculum development services, training, libraries, and media or computer centers), general administration, school administration, operation and maintenance, transportation, and any other services required to support instruction. Enterprise operations may include expenditures for operations that are funded by the sales of products or services sponsored through the individual LEAs (e.g., bookstores, computer time, sporting events, and food services). Noncurrent Expenditures for Education

Noncurrent expenditures include other types of expenditures that are not part of the day-to-day operations of K-12 education. These might include expenditures for adult education, private school programs funded by public LEAs for special services and community services, capital outlay (e.g., expenditures for property, building, and alterations), and interest on school debt. (School debt is an expenditure incurred by the LEA for the development of buildings and schools. Debt is generated through bond offerings and repaid with future tax revenues.)

Issues Related to Educational Spending

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collect information on education spending and report these expenditures to the public through databases and factual reports. Various interest groups, research organizations, and individual researchers report on government spending for education. These reports on education spending both support and challenge the quality of the programs for which funds are used, based on the researchers’ or organizations’ political agenda or research purpose. The purpose of measuring federal, state, and local spending for education has a multifaceted justification. First, measuring spending is necessary for good accounting practices. Accounting for spending helps jurisdictions to determine where funds are being spent, whether these funds are sufficient to meet costs, and also whether they are being efficiently used. Second, jurisdictions monitor spending to project future needs for raising resources. Future costs need to be analyzed routinely to ensure that sufficient tax revenues are raised to cover these costs. Third, measuring spending provides accountability to the public. Equity and Adequacy of State Educational Spending

Since the 1960s, many states have been sued by various groups claiming that state funding of education was inadequate or that state funding was distributed in an inequitable way. Equity litigation has had mixed results for plaintiffs, whereas adequacy litigants have had more success in effecting changes in state spending. There are a few states where litigation was avoided by the state by making changes to the state formulas. Some states were able to avoid litigation on a temporary basis, but later increases in cost and the inactivity or unwillingness of political parties to make the needed changes has led to statebased litigation regarding the distribution of spending. State-based litigation continues to be a strategy of reformers in attempting to initiate changes in the distribution of state spending. Michael C. Petko See also Adequacy; Economics of Education; Educational Equity; School District Budgets; School Finance Equity Statistics; School Finance Litigation

Measuring State and Federal Spending

Measuring educational spending has become an important function of both state and federal governments. State and federal departments of education

Further Readings Augenblick, J., Fulton, M., & Pipho, C. (1991). School finance: A practical guide to the structural components

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of, alternative approaches to, and policy questions about state school finance systems (ERIC No. ED342132). Denver, CO: Education Commission of the States. Berne, R., & Stiefel, L. (1984). The measurement of equity in school finance: Conceptual, methodological, and empirical dimensions. Baltimore, MD: Johns Hopkins University Press. Clark, D. L., & Astuto, T. A. (1986). The significance and permanence of changes in federal education policy. Educational Researcher, 15(8), 4–13. Cubberley, E. P. (1972). School funds and their apportionment. New York, NY: AMS Press. (Original work published 1905) Lovell, M. C. (1978). Spending for education: The exercise of public choice. Review of Economics and Statistics, 60(4), 487–495. Sielke, C., Dayton, J., Holmes, C. T., Fowler, W., & Jefferson, A. (Eds.). Public school finance programs of the United States and Canada 1998–99. Washington, DC: National Center for Education Statistics. Retrieved from http://nces.ed.gov/edfin/StateFinancing.asp/ Thompson, D. C., Crampton, F. E., & Wood, R. C. (2012). Money and schools. Chicago, IL: Eye on Education. Thompson, D. C., Wood, R. C., & Honeyman, D. S. (1992). Fiscal leadership for schools: Concepts and practices. White Plains, NY: Longman.

EDUCATION TECHNOLOGY The term education technology refers to the use and implementation of various tools and technologies in educational contexts. For example, technologies for education may be used for an individual’s informal learning, teaching and learning in formal institutions such as K-12 schools and universities, or the administrative purposes of schools. In recent years, the United States has seen rising access to personal and mobile computing in the general population. Some form of Internet access is nearly ubiquitous across the country as individuals can connect via home, schools, libraries, and mobile services. In this context, education technology frequently refers to the implementation of computing, Internet, and other digital technologies, and this entry focuses on these forms of technology. Digital technology has fundamentally altered social interaction, commerce, entertainment, news, and other aspects of society. Its growing adoption in school settings is also rapidly altering aspects of formal education institutions (e.g., K-12 schools and higher education) by influencing school funding systems, configuration of

organizational resources, teaching and learning in the classroom, and uses of student data.

Education Technology Issues Access

Much of the early focus of policymakers and educators focused on increasing access to computing and the Internet in schools. Federal programs such as E-Rate, which began in 1996, funded public schools and libraries to acquire the needed infrastructure to connect to the Internet. Simultaneously, school systems invested in technologies such as computers and other related devices. The result of this investment has been the rising availability of computers and Internet access in K-12 schools. According to 2009 data from the National Center for Education Statistics, computing resources in schools have increased tremendously since 1995. For example, in 1995, there was an average of 72 computers per school in the United States, and only 8% were connected to the Internet. In 2008, there was an average of 189 computers per school with 98% connected to the Internet. The ratio of students to computers dropped substantially in this time period. In 1995, the student to computer ratio was 6.6, with this number falling to 3.1 students per computer in 2008. Despite substantial progress in raising access to computing and the Internet, the digital divide continues to be an issue in education technology. The term digital divide describes gaps in access and skills related to computing that exist between different segments of a population. For example, while overall numbers of computer availability and Internet access suggest that technology is ubiquitous in K-12 schools, levels of this access differ tremendously between schools serving communities of lower and higher socioeconomic status. All schools may have classroom computers, but a digital divide exists if computers are 10 years old in one classroom and current in another classroom. In addition to digital divides in levels of access, researchers also observe gaps between students of different socioeconomic status, gender, and ethnicity in the opportunities to use digital technologies to develop related skills and literacy. Digital technologies evolve rapidly, and divides across levels of access and skills will always remain as certain segments of a population have less opportunity to readily adopt new developments. In general, the cost of computing devices is falling every year, and thus we are seeing rising access in the

Education Technology

general population. However, gradients of digital divide will always persist. For example, surveys of U.S. adults and families by the Pew Internet & American Life Project show that in 2012, approximately 78% of adults report using the Internet. However, stark gaps in Internet usage continue to exist based on income, with individuals in lower household income being substantially less likely to report using the Internet. Approximately 4 in 10 U.S. adults reported not having broadband Internet connection, and racial minorities were more likely to not have broadband access compared with Whites. Individuals who have online access were found to be doing more with their digital tools such as e-mail, information search, social networking, banking, and shopping. Individuals who are not connected do not have the same opportunities to participate in these life activities nor develop the relevant skills and literacy. Finally, a new trend is emerging around mobile devices and connectivity. A rising number of U.S. adults and youths report owning a mobile device such as a smartphone or a mobile tablet. Minority communities appear just as likely to own a smartphone as their White peers. Most interestingly, youths (below 18 years), minorities, and individuals with lower household incomes were more likely to report that their phone was their main source of Internet connectivity. Such individuals are “connected” and have access, under general definitions of the term, but the capabilities of mobile phones are still very different and limited compared with a computer or laptop. These subtle developments of digital divide influence how schools that serve different communities can use technology for teaching and learning. Classroom Use: Changes to Teaching and Learning

Another enduring issue in education technology involves changes in teaching and learning with new digital tools. Prior research in teacher adoption of computers and the Internet highlighted the lack of technology use in K-12 schools and college classrooms. General findings suggested that teachers did not readily adopt computers, the Internet, or software applications in the classroom, and when teachers did adopt technology, they did so to support already existing teaching practices and not to change them. Some of the major challenges associated with lack of teaching with technology included teachers’ prior knowledge of using technology, professional development, and lack of school resources.

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Recent data suggest shifts in teacher practices with technology. A 2013 survey of K-12 teachers by Pew found that the majority of teachers report using computers and the Internet in the classroom. For example, the majority of teachers reported that the Internet played a substantial role in their ability to access educational content, to network with colleagues and share ideas, and to communicate with parents and students. Nearly 75% of the surveyed teachers also reported that they use mobile devices in the classroom. These trends suggest that as computers, mobile devices, and the Internet are increasingly used for everyday communication and information sharing, these behaviors are also blending into teaching practices. However, teachers also reported stark digital divides. Educators in high-income districts were more likely to report having sufficient technology resources at their school, more professional development, and students who had home access to technology. Teachers in lower income schools were less satisfied with school resources, had few professional development experiences, and had students who often had no access to technology at home. New developments in digital technology are also influencing how learning occurs for students in both the formal classroom and in their everyday life. In the formal classroom, the increased availability of information sources on the Internet and other software applications has led to several trends in the classroom. In a 2013 survey of K-12 teachers by Pew, the most common ways in which teachers used technology was to have students conduct research online (e.g., using search engines), in addition to accessing and submitting assignments online. An evolving trend in classroom practice is blended learning or flipped classrooms. In blended learning, students work through curriculum through online platforms and then utilize the classroom face-to-face time for additional learning activities. For example, students may watch a teacher’s lecture online and then work through problem sets in class, as the teacher works with individual students through problem areas or misconceptions. In another example, students might work through a math curriculum at home, through online software. Teachers could receive a report of student progress and then utilize class time to do small-group projects catering to the areas of student need. The idea of blended or flipped classrooms is to reassign the information transfer components of learning (e.g., watching a lecture) to technology and then free classroom time for other learning activities, such as personalized support, group learning,

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or active learning. Blended models of learning are just emerging in K-12 and higher education settings, with few research studies that have explored their impact and potential. Finally, researchers are increasingly observing how students learn with technology in new ways. The majority of learning activities that young people experience with technology are in stark contrast to what occurs in formal classroom settings. There is rising awareness of games and video games as unique and effective learning environments. Researchers and educators are experimenting with designing and using games to teach educational concepts ranging from science, technology, engineering, and math to civics, history, and college readiness. Students also learn tremendously from their participation in online communities. Social media platforms are an everyday tool for today’s youths. Research has shown that young people learn a substantial amount from their participation in social media communities, such as information sharing, development of digital literacy skills, and networking. The specific tools that young people use change rapidly as technology evolves. However, the use of online tools to learn new information and skills will remain a substantial factor in students’ learning experiences. A major trend and opportunity is to better understand how students learn with technology in these informal ways and how they incorporate these insights to improve teaching and learning practice in other settings. Online and Open Education

Online and open education have also begun to mature as more people access and use the Internet. Distance learning programs have long existed in various forms from mail correspondence courses and educational television to current forms that deliver education over the Internet and online platforms. Online learning has not only slowly evolved in the K-12 sector but has also steadily increased in visibility and student enrollment. Numerous versions of fully online schooling exist in the U.S. K-12 system, including state- or district-run virtual schools and cyber charter schools that offer full-time or supplemental programs for students. Estimates in 2012 suggested that more than 250,000 students were enrolled in fully online schools in the K-12 education system. The largest segment of online learning activity appears to be in the area of blended learning where schools and districts create education

programs that combine online education with faceto-face classroom instruction. It was estimated that more than a million U.S. students participated in some form of online or blended learning program in 2012, but exact enrollment numbers are unavailable largely due to the inability to record and log blended learning enrollments in state, public education data systems. Student enrollment in online and blended programs is expected to continually rise in the near future, which underscores several changes and challenges. Numerous questions remain concerning the educational effectiveness and cost-benefit of online and blended options in K-12 school systems. The increased adoption of online and blended schools also introduces new controversies surrounding the following: (a) the design of education policy to best govern these unique organizations, (b) funding policies to effectively and equitably finance online and blended programs, (c) understanding best teaching practices and related issues of teacher preparation, and (d) new issues of digital divide that may arise from widened adoption of online and blended programs. The research literature and available data are lacking, as public education departments currently grapple with how to record and collect systematic data on these new education models. In higher education, online learning and open education brought about substantial new developments and disruption in 2012. Open education refers to a movement to create freely accessible and openly licensed educational content that can be used for teaching and learning. Open education resources have been created and available for several years. High profile examples include the Open Courseware Initiative at the Massachusetts Institute of Technology and similar repositories of open syllabi, textbooks, materials, and courses. The year 2012 saw the introduction and adoption of a particular form of open, online learning: massive open online courses (MOOCs). The idea of MOOCs originated around 2008, as scholars and educators began to theorize about how to leverage the capabilities of the Internet to drive new forms of learning. This original conception focused on how large networks of individual learners could come together in online platforms, share learning resources openly, connect with others, and collaborate to create organic learning communities. In 2011 and 2012, the concept of MOOCs became synonymous with freely available online courses that were offered by colleges and universities. Several

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Stanford University professors in computer science began offering online courses that garnered the enrollment of more than 100,000 students worldwide. The massive adoption of these open courses (as in freely available, but not open as in terms of copyright and licensing) led to the development of several MOOC ventures, including Coursera, edX, and Udacity. These ventures quickly garnered participation from leading universities that began offering their own courses on the platforms. The rapid adoption of freely available, online courses from major universities suggests that online learning will play an increasing role in higher education. These developments have also introduced new questions related to the funding challenges in higher education, the role and compensation of faculty, new models of teaching and learning in college classrooms, the business model of higher education, and the relationship between public and private institutions. Education Data and Learning Analytics

A final issue that is emerging in education technology is the proliferation of data sources due to the widespread use of digital tools and platforms. The rising availability of data (“big data”) allows for new forms of analytics to better understand teaching and learning in classrooms, schools, and education systems. The term learning analytics describes this trend of research that increasingly utilizes digital data to better understand educational processes. For example, universities are beginning to utilize student activity data from learning management systems, which organize and provide access to online learning services, to predict whether students are at risk of failing a course. Potentially, educators could then intervene and provide students with personalized help to improve their course performance. Researchers are also exploring ways to assess student learning through data culled from digital environments such as video games and other online platforms. These initiatives offer several potential implications for education technology in the future. Learning analytics may provide ways to assess student learning in authentic environments by collecting and analyzing data of students acting within learning environments (e.g., in the classroom, an online course, a video game, etc.). These assessments differ from the typical system of assessment currently used in K-12 education, such as end-of-year, standardized testing. Data analytics may be used more efficiently and rapidly to help school systems and teachers

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understand student learning and intervene more efficiently to help struggling students during their learning process. Finally, as abundant data become available from diverse sources online and through software applications, new forms of research and analysis could potentially uncover deeper insights into educational issues and predictive models of student learning.

Education Technology: Impacts on Education Systems Education technology is a rapidly evolving field. The trends and issues identified here will continuously change. However, several foundational themes in education technology will likely remain and continue to affect how education is delivered to students in the future. First, an enduring question will be the relationship between new technologies and student learning. Researchers now firmly recognize that technology alone is not a causal factor for learning. For example, asking a student to read a passage on a computer screen versus a book is not likely to lead to different learning outcomes. The same learning behavior—reading—is the causal factor. Thus, merely introducing a computer into the classroom to reenact the same teaching and learning practices already in place will likely not improve student learning or outcomes. However, technology does change how humans interact with information, their learning environment, and each other. Future developments in technology-enhanced teaching and learning that can alter how students obtain information, interact with peers and teachers, and share their knowledge will likely relate to student learning and teacher practices over time. Research is needed to identify what these new learning behaviors could be when new technologies are introduced to society and schools. Second, the use of education technology alters education systems in various ways. The example of virtual schools and blended learning illuminates the organizational and systemic impact of education technology. Online schools can potentially enroll students across geographical boundaries. In addition, MOOCs could potentially deliver educational content to thousands of students but require less teaching staff. These affordances of online education have an impact on issues of education policy, such as geographically based school boundaries, education funding mechanisms, and teacher labor markets. Developments such as blended learning alter

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the organization of classrooms and configuration of school resources. For example, as students complete curriculum via computers and software, the physical classroom organization can differ in diverse ways. The configuration of teachers, student groupings, and learning activities can be drastically different in blended classrooms, which directly influences the ways that school resources (e.g., classroom space, materials, and teaching staff) are utilized. Third, data will play an increasing role in education systems. Large and diverse data sources will allow for analytics, predictive modeling, and education research. New forms of assessment made possible by digital data could inform the ways in which education systems evaluate students and teachers and will also play a role in accountability systems. Teachers and students can also utilize data, information, and data analyses to inform their own decisions, practices, and learning behaviors. Datadriven practices continue to be a foundational issue in education. Finally, as education researchers, policymakers, and educators make decisions about education technology, cost-benefit and effectiveness of new tools will be enduring issues. Education technologies have the potential to make three types of contributions to these debates. In some cases, new technologies may enhance or improve already existing goals. For example, blended learning models may provide opportunities to improve student achievement (on measures such as standardized exams). In this case, the burden of proof for education technology is to improve student outcomes over and beyond already existing practices. Technology could also introduce cost-efficiencies in the delivery of schooling. For example, MOOCs may put pressure on colleges and universities through the free offering of courses or lower cost degree programs. However, these changes in the cost structure of schooling may introduce other challenges such as quality and access concerns. Finally, new technologies often introduce new ways for people to interact with each other, and provide information and resources. These cultural and social changes alter what skills (e.g., literacy) young people will need from education and change how they go about learning itself. New developments in education technology could also be a mechanism through which to redefine what schools teach, how they educate students, and what we measure and value about learning itself. June Ahn

See also Access to Education; Digital Divide; Distance Learning; Online Learning

Further Readings Borgman, C. L., Abelson, H., Dirks, L., Johnson, R., & Koedinger, K. R., Linn, M. C., . . . Szalay, A. (2008). Fostering learning in the networked world: The cyber learning opportunity and challenge. Washington, DC: National Science Foundation. Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2010). Evaluation of evidence-based practices in online learning: A meta-analysis and review of online learning studies. Washington, DC: U.S. Department of Education. Purcell, K., Heaps, A., Buchanan, J., & Friedrich, L. (2013). How teachers are using technology at home and in their classrooms. Washington, DC: Pew Internet & American Life Project. Retrieved from www.pewinternet.org/ Reports/2013/Teachers-and-technology.aspx U.S. Department of Education. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. Washington, DC: Author.

EDUCATIONAL EQUITY Equity is synonymous with fairness and is a contested concept. It outlines the parameters of both our moral obligations and ethical responsibilities as a community. The essence of equity is a focus on the distribution of valued resources, both tangible and intangible. In education finance discussions, the concept of equity goes beyond the allocation of dollars to include what those dollars buy, how those resources are used, and who benefits from the resources. When systemic gaps emerge in school funding, educational processes, or student achievement, we want to know why. Ideally, gaps are for legitimate reasons rather than a systemic flaw that results in discrimination against particular groups. For example, we typically consider funding gaps to be legitimate if those groups who have fewer family resources get more public educational dollars. We recognize that children are unique, so we understand if they receive different educational programs that fit their varied educational needs. It is not the differences in student achievement that are troubling, per se; it is those differences in performance that are a result of privilege or lack thereof. Consequently, in the face of

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disparities, policymakers, researchers, practitioners, and the community at large face critical questions: If funding disparities exist, do they compensate for different student needs, program costs, or district expenses? If there are differentiated curricula, do the distinctions reflect student ability or student demographics? Are achievement gaps tied to ethnicity, first language, or socioeconomic status? In other words, can we predict negative or positive outcomes for individual children once we know their ethnicity, the language at home, where they live, and how much money their parents earn? If we can correctly predict these outcomes based simply on knowledge of student background, then the system is inequitable. Scholars have found that school districts and schools in the United States are often not equitable places for children. Wealthier communities tend to spend more on their youngsters than less affluent areas. Children of color and children from lowincome households tend to be tracked into less rigorous courses. Achievement gaps between ethnic and racial groups prevail and persist throughout the United States. This entry primarily examines the concept of equity as it relates to the economics and finance of education. It provides an overview of equity in a broad policy context, then describes the principles of equity, providing examples relevant to the field of education finance. Next, it discusses the emergence of adequacy as a concept in school finance evolving from the concept of equity. It concludes with a discussion of equity and its treatment in current research.

Equity in Policy Contexts Equity has long been part of policy discussions, but stakeholders have different philosophies of how equity should be applied to school finance. For example, policymakers sometimes use equality and equity interchangeably. However, providing equal amounts of resources to individuals or groups with different needs would not satisfy commonly held notions of equity. Similarly, some activists and scholars use equity and social justice synonymously, but the two concepts are different. That is, social justice infuses equity with calls for action. State officials typically create finance policy to modify existing discrepancies in the school system. They develop rules and formulas to collect and distribute revenues with an eye on ensuring equity among both taxpayers and targeted groups. These

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formulas are often shaped by the legislative process or in response to court decrees. When using school finance formulas to pursue equitable education systems, policymakers need to address whether their actions will cause certain groups or individuals to experience a disproportionate share of the burden or to receive windfall benefits. They must consider fairness both in terms of who has to pay for a policy and who enjoys, or suffers, its impact. Consequently, when policymakers think about equity, they must examine how the benefits and costs of policy are distributed. They must target the consumption side (who benefits?), the production side (who pays?), and the processes that connect them. Because equity is about distribution, standard indicators of dispersion are important for all three aspects of the education production function—inputs, processes, and outputs. Dispersion measures such as range, standard deviation, the McLoone index, and coefficient of variation are commonly accepted in the field.

Principles of Equity It is possible to identify five basic principles of equity: (1) horizontal equity, (2) vertical equity, (3) transitional equity, (4) ability to pay, and (5) benefit principle. In addition, John E. Coons, William H. Clune, and Stephen D. Sugarman coined the term fiscal (wealth) neutrality, an equity measure often used in school finance litigation. All these concepts relate to equal and nondiscriminatory treatment; that is, children should be treated similarly unless there is good reason for the differentiation. It is often difficult to tell what constitutes a good reason, and there is still wide disagreement on the appropriate role of states, districts, and schools in the matter of achieving education equity. Horizontal Equity. This definition of fairness calls for the equal treatment of equals but does not go beyond the basic concept to provide a working definition of what that looks like for the practitioner. It may be applied both to the impact and the cost of policy options. One of the problems with the simple application of horizontal equity is to be able to know what makes for equally situated entities. Because each child is unique, it is difficult to tell which characteristic is a legitimate distinction vis-àvis policy options. In education finance formulas, states typically have a general formula that applies to all students. To address “legitimate” differences among students, state school finance formulas add

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special factors that account for different student types (e.g., English Language Learners) and educational contexts (e.g., high per-pupil educational costs in sparsely populated districts). Vertical Equity. This conceptualization of fairness refers to the distribution of goods and services to those in unequal circumstances. This begs the question of what constitutes unequal circumstances. For example, should the population be subdivided by region, city, or neighborhood? Should subgroups be categorized by their ethnicity, income level, gender, noncognitive skills, or abilities? Conceptualizing equity in this way assumes that differential treatment would result in those who need more resources getting that support and those who need less getting less. Vertical equity not only requires appropriate grouping but also appropriate differentiation in the distribution of resources among groups. It is assumed that if policymakers were accurately able to account for these differences, there would be no systemic achievement gaps among student groups. As policymakers consider different ways of allocating resources, the political and policy landscape could experience major changes, and there may be a need to help individuals in transition. Transitional Equity. Fairness in this context means that individuals are held harmless for the changes made in policy. Policymakers would note if the rules of the game changed to the detriment of certain groups. They would also consider the possibility of compensating the losers for the change in rules. Many hold that the harmless clauses that exist in present school finance laws are attempts to ensure that groups are able to transition smoothly when the rules of the game have changed. However, by doing this, the costs of implementing policy are increased. Increased costs turn our attention to who pays for policy implementation. Ability to Pay. While the above concepts of fairness may be applied both to the impact and costs of policies, ability to pay focuses on the equitable distribution of costs. Under this definition, it is essential to consider the ability of individuals to make the required payments. This definition of fairness links one’s ability to pay to one’s income. School finance systems that collect more revenues from higher priced properties (with an assumed higher ability to pay) are an example of this notion of fairness. How-

ever, property wealth is not always highly correlated with income (especially for retired people). To address equity considerations under those circumstances, states sometimes employ ”circuit breakers,“ which limit the portion of one’s income that would go toward paying property taxes. Benefit Principle. This concept of fairness is predicated on there being a link between those who pay for a particular policy option and those who receive its benefits. Because of the nature of many publicly provided services, establishing this linkage may be difficult. For schools, this may mean charging those students who participate in extracurricular activities for the cost of those programs. Since this may inhibit students with less means from participating, there is a potential cause for concern in terms of vertical equity. Many school leaders grapple with the appropriate funding for noncore programs, especially in an era of shrinking budgets. Many policymakers have chosen to balance both the benefit principle and the ability-to-pay principle. They have a sliding scale for participation in afterschool activities, where only those who participate have to pay, but the school subsidizes those students who need the financial support. Fiscal (Wealth) Neutrality. This concept of fairness dominates equity-based court challenges to the financing of schools. It requires that there be no systematic association between where a child lives in a state and the amount of money spent on that child’s public schooling. Robert Berne and Leanna Stiefel note that in school finance, policymakers consider a system fair for taxpayers if communities making the same tax effort raise the same amount of revenue per student. This differs from the definition of equity used by public finance scholars, who consider taxpayer equity to be achieved when taxpayers are equally treated based on their income or benefits received. Fiscal neutrality also speaks to the provision of equal educational opportunities and assumes a link between the money spent and performance achieved.

Adequacy Equal educational opportunity is often couched in terms of equal access to an adequate education. As educators move beyond the common understandings of equity described above, they are increasingly drawn to notions of adequacy. It shifts the focus

Educational Equity

from the distribution of education funds or other resources to the question of sufficiency. Is the public spending enough to allow schools to meet standards set by the state? In school finance discourse, discussions of adequacy have often been framed as the level of funding that allows all children, or at least a suitable portion of them, to meet the education standards set by both federal and state guidelines. Determining whether education funds are adequate is a growing concern among many educators, and for the past two decades, it has been the center of discourse on developing appropriate school finance mechanisms and formulas. Currently, four key strategies have emerged as leading ways to determine adequacy: (1) professional judgment, (2) successful schools, (3) cost functions, and (4) evidence-based approaches. Professional judgment calls on a panel of experts in the field to identify the educational resources needed for students to achieve at particular standards. In a successful schools approach, a school that is successful at meeting or exceeding established targets acts as a prototype for other schools in the state. The cost function approach calls for the use of econometric analyses where existing budgets provide the grounding for estimating the costs of achieving particular educational outputs. Evidence-based approaches rely heavily on research evidence and best practices to frame recommendations regarding the cost of an adequate education. More recently, Nicola Alexander and Dennis Schapiro have argued for the creation of an “adequacy condition index” in the same way that we have indicators of fiscal condition. The search for adequacy is not only a quest for greater effectiveness but also a pursuit of achieving greater equity in the production of education. Adequacy of Inputs

A focus on adequacy of inputs is aligned most closely with past research on equity of resource allocation, where fiscal neutrality, horizontal equity, and legitimate differences serve as important guideposts for policymakers who seek to be on the “right” equity path. Providing equity in access characterizes this focus. When policymakers emphasize an input approach to adequacy, they may target the curriculum, governance, or teaching. While resources spent on teachers are the largest part of the school budget, researchers have explored the variation in the quality of the schooling inputs in different ways. Some have looked at the impact of time, the differential effect of interaction between teacher and student

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characteristics, and the consequences of student groupings. Emphasizing the equitable distribution of resources assumes linkages between inputs and outputs. Some scholars have questioned this approach, noting that higher amounts spent on education do not always lead to better results. As with other transactions, the cost of inputs will influence the number and quality that may be purchased. Consequently, it is important for policymakers to distinguish between “basic” and “luxury” brands of schooling components. The work on cost indices is crucial as policymakers assess how much they need to spend to obtain an adequate level of resources. However, a limitation with the cost estimation approach is the assumption that differences in costs are largely reflective of differences in efficiencies rather than simply differences in tastes. Therefore, when policymakers allocate budgets to reflect “efficiency-defined” cost differentials, they may unwittingly be rewarding a particular taste in education. Adequacy of Schooling Processes

Research on adequacy of schooling processes typically examines student tracking, effective schools, and transformative approaches. Work in this area frequently involves descriptions of how schools work and the interactions among individuals within them. This research provides an important foundation for discussions on the ways in which money does matter and how leaders can provide equitable schooling contexts for all students. For example, Gloria Rodriguez argues that funding should focus on the responsiveness of schools to different student groups, perhaps through the use of targeted professional development. In contrast, Eric A. Hanushek argues that focusing on inputs is not the appropriate strategy for pursuing equitable student outputs, since the nature of the education process is unknown. Hanushek asserts that school funding should focus instead on achieving adequate educational outputs through an incentivized policy strategy. Adequacy of Outputs

Discussions of adequacy explicitly link schooling resources, pedagogic practices, and the attainment of particular standards. While fiscal neutrality marks a focus on inputs, “results neutrality” characterizes a focus on outputs. That is, there should be no systematic associations between the demographic

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characteristics ascribed to students and student achievement if the appropriate funding formula for an adequate system is derived. Plaintiffs may contend that the underperformance of students is prima facie evidence that the state has not fulfilled its responsibility to all its students. The argument is that a high level of illiteracy (or some other standard of education) and gaps among student groups illustrate an inadequately and inequitably funded system. As justices increasingly embrace adequacy as the standard of fairness for school systems, “results neutrality” may serve as the new standard of equity.

Conclusion Abstract notions of fairness play a big role in how we evaluate resource allocation within and between school systems. There are a variety of perspectives on how society can achieve more equity in its schools. However, policymakers need concrete guidance in assessing whether policies are more or less fair. Berne and Stiefel added much to the early school finance debate by grounding the abstract concept of equity into measurable economic terms. Recent research foreshadows a fork in the road among school finance scholars, dividing them between those who see efficiency and equity as increasingly competitive values and those who see them as increasingly complementary. Research that considers equity and efficiency as complements often favors incentives as a means of achieving a more equitable distribution of student achievement. This focus has resulted in an increased emphasis on value-added models as a means of isolating and rewarding the individual contributions of teachers. The logic is that if achievement is rewarded (and lack thereof, punished), educational actors will do what it takes to achieve improved performance for all students. This desire to assess the performance of individual teachers has resulted in many states relying increasingly on value-added methodology. In utilizing value-added methods, researchers use statistical approaches to isolate the contribution of individual teachers to student learning in a particular subject in a particular year. Both supporters and detractors of this approach recognize that the method may not be very reliable. Teachers who are ranked at the top in any one year may find themselves at the bottom in the subsequent year. In addition, the biggest difference between value-added estimates is between models that ignore possible school effects, such as size and the demographics of the student body, and

models that explicitly recognize them. These considerations have substantive implications for equity. Scholars who consider equity and efficiency competing values often call for an explicit recognition of the institutional and social contexts that influence student outcomes regardless of funding. Rather than inducing teachers to improve educational outputs for all children, these scholars typically support capacity-building strategies such as professional development and enhancement of cultural competency. This path to a more equitable educational system calls for education organizations to develop institutional structures that are better able to meet the diverse needs of their students. Ultimately, there is a long-established focus in the school finance literature on the adequacy and equity of educational resources and outputs. Researchers and policymakers consistently question whether the system produces results that are “high enough” or “equal enough.” As long as persistent and predictable gaps in educational achievement remain between student groups, it is clear that more needs to be done to address issues of adequacy and equity in education. Nicola A. Alexander See also Ability-to-Pay and Benefit Principles; Achievement Gap; Adequacy; Fiscal Disparity; Fiscal Neutrality; Horizontal Equity; School Finance Equity Statistics

Further Readings Alexander, N. A. (2013). Policy analysis for educational leaders: A step-by-step approach (pp. 84–87). New York, NY: Pearson Education. Alexander, N. A., & Schapiro, D. (2009). Seeking educational adequacy beyond the school walls: Public expenditures on children in urban communities. Paper presented at the annual conference of the American Education Finance Association, Nashville, TN. Baker, B. D., Sciarra, D. G., & Farrie, D. (2010). Is school funding fair? A national report card. Newark, NJ: Education Law Center. Berne, R., & Stiefel, L. (1984). The measurement of equity in school finance: Conceptual, methodological, and empirical dimensions. Baltimore, MD: Johns Hopkins University Press. Berne, R., & Stiefel, L. (1999). Concept of school finance equity: 1970 to the present. In H. F. Ladd, R. Chalk, & J. S. Hansen (Eds.), Equity and adequacy in education finance: Issues and perspectives (p. 19). Washington, DC: National Academies Press.

Educational Innovation Coons, J., Clune, W., & Sugarman, S. (1970). Private wealth and public education. Cambridge, MA: Belknap Press of Harvard University Press. Espinoza, O. (2007). Solving the equity-equality conceptual dilemma: A new model for analysis of the educational process. Educational Research, 49(4), 343–363. Hanushek, E. A. (2003). The failure of input-based schooling policies. The Economic Journal, 113(485), F64–F98. Retrieved from http://www.jstor.org/ stable/3590139 Rice, J. K. (2004). Equity and efficiency in school finance reform: Competing or complementary goods? Peabody Journal of Education, 79(3), 134–151. Rodriguez, G. M. (2004). Vertical equity in school finance and the potential for increasing school responsiveness to student and staff needs. Peabody Journal of Education, 79(3), 7–30. Rodriguez, G. M., & Rolle, R. A. (Eds.). (2007). To what ends and by what means? Social justice implications of contemporary school finance theory and policy. New York, NY: Routledge, Taylor & Francis Group. Roellke, C., Green, P., & Zielewski, E. (2004). School finance litigation: The promises and limitations of the third wave. Peabody Journal of Education, 79(3), 104–133.

EDUCATIONAL INNOVATION Educational innovation refers to the creation of new educational tools, the development of new methods to provide education, and the reorganization of inputs in the educational process. The education sector has acquired a bad reputation for being impervious to innovation. This is an argument with face validity: While information technology has revolutionized many parts of the economy, most classrooms from kindergarten through graduate school look much like they did more than 100 years ago and remain centered on the interaction between teachers and students in the classroom. While traditional classrooms are still the norm in education, computer technology is giving rise to innovations that may significantly alter the way education is administered at every level. This entry discusses some implications of limited innovation in the education sector and some of the ways technological innovations are beginning to change the educational landscape.

Limited Innovation in Education The reluctance of many educators to embrace innovation may help explain why educational costs have

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increased dramatically in recent years, while student academic performance has only changed modestly. The economist William Baumol has argued that certain economic sectors, such as education, do not benefit from technological innovations and have slow productivity growth because labor is essentially the only input of production. Furthermore, Baumol proposed that declining relative productivity of such sectors makes their products increasingly expensive over time, a phenomenon subsequently dubbed “Baumol’s cost disease.” Of course, the increasing costs of education production have made the idea of incorporating additional technology into the educational process ever more attractive. From when Sidney Pressey first designed automatic multiple-choice testing machines in the 1920s to today, educators and researchers have looked toward technological innovation to reduce costs and improve student academic outcomes. While the testing machines Pressey developed in the 1920s never saw widespread use, the use of new technological tools in education has exploded over the past two decades. Recent technological innovations in education can be broadly categorized as increasing teachers' and students’ access to educational information, providing novel methods to instruct and assess students, restructuring the educational process to improve student outcomes, and reducing costs of delivering education.

Innovation in Access to Information A primary purpose of technological innovation in education has been to increase teacher and student access to educational material. Unlike with other forms of technological innovation, educators have been among the early adopters of information and communication technology. Current data suggest that information communication technology (ICT) is being used regularly to complement instruction in the classroom: When U.S. public school teachers were asked in 2009 how often they used computers during instruction, the most common response was “often” (40%), followed by “sometimes” (29%), “rarely” (19%), and “never” (10%). Additionally, 98% of U.S. public schools have Internet-connected instructional computers, and the ratio of students to instructional computer has dropped precipitously in recent years, from 6.6 in 2000 to just 3.1 in 2008. A natural question is whether increasing teacher and student access to computer-based ICT has improved student academic outcomes. In general, high-quality research in economics suggests

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that providing students increased access to ICT has little impact on student performance. In the United States, Austan Goolsbee and Jonathan Guryan utilized a quasi-experimental design to examine the impact of Internet access on academic performance for K-12 students. In their study, the authors found no evidence that increased Internet access improved test scores in math, reading, or science at any grade level. International studies have found similar results. For example, Felipe BarreraOsorio and Leigh Linden analyzed the impact of the Columbian Computadores para Educar (Computers for Education) program, which randomly assigned computers to 49 (out of 97 eligible) schools in 2006. The authors found no significant impact of the computer program on either math or Spanish language test scores for any students in Grades 2 through 8. Additionally, small or insignificant impacts of ICT on test scores have been estimated in the Netherlands, Israel, and the United Kingdom. While increasing access to information technology does not appear to improve academic performance, these results should be considered in a broader context as ICT may improve student and teacher outcomes that are not captured by math, language, or science testing.

Innovation in Instructional Methods One exciting development in educational innovation is the emergence of computer-based instruction (CBI). Many types of CBI have been developed to assess student ability and adapt instruction to appropriately challenge students. Given its adaptability, CBI could be a significant improvement over traditional classroom instruction, where instructors may be unable to assess or address the unique strengths and weaknesses of each student. High-quality studies that investigate the impact of CBI have typically found significant positive impacts on math scores but no impact on language test scores. In the United States, Lisa Barrow, Lisa Markman, and Cecilia Rouse analyzed the impact of randomly assigning pre-algebra and algebra students to the computer-based I Can Learn program instead of traditional instruction. The authors found that students assigned to use the software scored between 0.17 and 0.25 standard deviations higher than students in traditional classrooms on mathematics achievement tests. To put this effect size in context, this is about twice the impact Alan Krueger and Diane Whitmore measured for reducing Grades K-3 class size from 23 to 15 students (0.10 standard

deviations). The estimated effects of CBI on math scores are even larger for poor students in countries such as India and Ecuador. For example, when Abhijit Banerjee and colleagues examined the impact of randomly assigning impoverished elementary school children in India to spend 2 hours a week playing computerized math games, they found that the software improved test scores by 0.47 standard deviations. High-quality research investigating the impact of CBI for learning language has been less promising. For example, Paul Carrillo, Mercedes Onofa, and Juan Ponce conducted a randomized controlled trial in Ecuador where impoverished elementary students participated in a program called Más Tecnología (More Technology), which randomly assigned students to spend 3 hours a week using an adaptive math and language software. Like Banerjee and his coauthors, the authors found a large positive impact of software on math test scores (0.30 standard deviations) but found an insignificant effect of the software on language test scores. Several studies in the United States have found similarly disheartening results. Rouse and Krueger conducted a study in the United States where third to sixth graders in the bottom fifth of language test distribution were randomly assigned to use language development software called Fast ForWord for 1.5 hours a day, 5 days a week, for 6 to 8 weeks, but they saw no improvements in language test scores. While it is common to find a greater impact of interventions on math test scores than on reading test scores, it is unclear why math CBI has generated such large test score improvements while reading CBI has led to no significant improvements.

Innovation in the Educational Process Recently, educators have been experimenting with novel ways to incorporate technological innovation into the educational process. For example, a number of schools have implemented 1:1 computer programs that provide mobile computing devices for every student. These programs can allow for teachers to seamlessly incorporate individual computerbased activities into instruction. Other schools have made even more drastic changes to the educational process and are experimenting with “flipped” classrooms, where students watch lecture content such as Khan Academy videos on computing devices at home and then come to class to work on assignments with the instructor’s help. While some preliminary evidence suggests that using computer technology to

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restructure the educational process in these ways has a positive impact on student outcomes, further research is still needed.

Innovation in Cost Reduction While each of the educational interventions discussed thus far have the potential to reduce educational costs, perhaps the most straightforward way educators have used technology to reduce costs has been to move courses from the classroom to computers. Moving courses online can significantly reduce or eliminate educational costs associated with infrastructure, instructor time, and student travel. Likely due to cost savings, online courses have become commonplace in higher education. More than 85% of universities offer online courses and more than 30% of college students take at least one of their courses online. Although online courses appear to generate significant cost savings, there has been limited research that estimates the cost savings associated with moving courses online or the impact that online courses have on student academic outcomes.

Conclusion While evidence of the impact of computer technology is mixed, computer innovations will continue to play a prominent role in education. Evidence from math CBI programs suggests that there are at least some applications in which computer innovations have the potential to improve educational outcomes. Furthermore, there are a number of innovations that are currently being introduced that could alter the way computers are used in producing academic outcomes. Although incorporating technology into the classroom has not yet been shown to consistently improve academic outcomes, it is possible that one or more computer innovations will prove to be effective in reducing educational costs and in improving academic performance for a variety of students. More high-quality evaluations are critically needed to help educators select from the increasingly dizzying array of technological programs and methods, since the evidence is clear that some highly regarded technological solutions have not produced the results expected. Jordan Matsudaira and Richard Patterson See also Access to Education; Baumol’s Cost Disease; Education Technology; Online Learning; QuasiExperimental Methods; Randomized Control Trials

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Further Readings Allen, I. E., & Seaman, J. (2013). Changing course: Ten years of tracking online education in the United States. Oakland, CA: Babson Survey Research Group and Quahog Research Group. Retrieved from http://www.onlinelearningsurvey.com/reports/ changingcourse.pdf Angrist, J., & Lavy, V. (2002). New evidence on classroom computers and pupil learning. Economic Journal, 112(482), 735–765. Banerjee, A. V., Cole, S., Duflo, E., & Linden, L. (2007). Remedying education: Evidence from two randomized experiments in India. Quarterly Journal of Economics, 122(3), 1235–1264. Barrera-Osorio, F., & Linden, L. (2009). The use and misuse of computers in education: Evidence from a randomized experiment in Colombia (Policy Research Working Paper Series No. 4836). Washington, DC: World Bank. Barrow, L., Markman, L., & Rouse, C. E. (2009). Technology’s edge: The educational benefits of computer-aided instruction. American Economic Journal: Economic Policy, 1(1), 52–74. Baumol, W. J. (1967, June). Macroeconomics of unbalanced growth: The anatomy of urban crisis. American Economic Review, 57(3), 415–426. Borman, G. D., Benson, J. G., & Overman, L. (2009). A randomized field trial of the Fast ForWord Language computer-based training program. Educational Evaluation and Policy Analysis, 31(1), 82–106. Carrillo, P., Onofa, M., & Ponce, J. (2011). Information technology and student achievement: Evidence from a randomized experiment in Ecuador (Research Department No. 4698). Washington, DC: InterAmerican Development Bank. Goolsbee, A., & Guryan, J. (2006). The impact of internet subsidies in public schools. Review of Economics and Statistics, 88(2), 336–347. Gray, L., Thomas, N., & Lewis, L. (2010). Teachers’ use of educational technology in US public schools: 2009. First look (NCES 2010-040). Washington, DC: National Center for Education Statistics. Kline, S. J., & Rosenberg, N. (1986). An overview of innovation. In R. Landau & N. Rosenberg (Eds.), The positive sum strategy: Harnessing technology for economic growth (pp. 275–306). Washington, DC: National Academy Press. Leuven, E., Lindahl, M., Oosterbeek, H., & Webbink, D. (2007). The effect of extra funding for disadvantaged pupils on achievement. Review of Economics and Statistics, 89(4), 721–736. Machin, S., McNally, S., & Silva, O. (2007). New technology in schools: Is there a payoff? Economic Journal, 117(522), 1145–1167.

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Rouse, C. E., & Krueger, A. B. (2004, August). Putting computerized instruction to the test: A randomized evaluation of a scientifically based reading program. Economics of Education Review, 23(4), 323–338.

EDUCATIONAL VOUCHERS The subject of educational vouchers has emerged as a vibrant topic of educational finance and policy. An educational voucher represents a government commitment to families to pay a specified amount of tuition at any approved school, whether public or private, that the family chooses for its offspring. Schools must attract sufficient numbers of students to prosper. The voucher system of finance is viewed by its supporters as improving efficiency of resource use and equity in education through market competition and better matching of students to school strengths as well as providing access to schools outside of residential neighborhoods. Detractors assert that the system leads to greater segregation of students by race and social class and lacks evidence of promised educational benefits. This entry provides a brief introduction to educational vouchers followed by an analysis of issues and dynamics. Particular attention is devoted to the public and private goals of education as a context for understanding controversies about vouchers. This is followed by a presentation of how the design of specific voucher plans affects their operation and outcomes. Finally, there is a discussion of the impacts of educational voucher approaches and the issue of trade-offs among different educational goals.

Background In their current form, educational vouchers were first proposed by the economist Milton Friedman in 1955, but they are best known from his classic 1962 article on “The Role of Government in Education.” Friedman posed two fundamental questions: Who should pay for basic education? and How should that education be provided? In response to the first question, Friedman asserted that government has both public and private purposes. Private educational goals of families should be embodied in their free choice of schools that match their family preferences. In contrast, he defined the public purpose as a “neighborhood effect” in which the impact of the overall educational system provides benefits not just

to the educated individual but also to all of society as embodied in his famous quote in the 1962 article: A stable and democratic society is impossible without widespread acceptance of some common set of values and without a minimum degree of literacy and knowledge on the part of most citizens. Education contributes to both. In consequence, the gain from the education of a child accrues not only to the child or to his parents but to other members of the society; the education of my child contributes to other people’s welfare by promoting a stable and democratic society. (Friedman, 1962, p. 86)

According to Friedman, this public purpose is so important that government should fund basic education. That is, the public benefits justify government funding because of their key role in promoting a functioning society that supports freedom, stability, and democracy. But Friedman did not view public funding as necessitating public provision of education. Instead, he asked whether public operation of schools was as efficient in meeting family educational goals as a market system of competing schools. He concluded summarily that a competitive market of schools would provide superior results, limiting the role of government to funding basic education and setting minimal requirements for schools in terms of meeting democratic goals. Schools meeting these requirements could then compete to attract students on the basis of their attractiveness to families. The joint goals of promoting public, democratic values and private, consumer choice would be financed by Friedman’s voucher plan. Private goals would be achieved by the government provision of a basic voucher to families to use for tuition at eligible schools. Families could add private funding to the voucher to pay for a costlier school. Schools would enter the marketplace to compete for students by meeting the democratic, eligibility criteria, probably some basic curriculum requirement in social studies. Friedman was vague about the substance of this requirement, but recent empirical work indicates that curriculum and teacher pedagogical practices and student interactions must be intertwined in complex ways to induce and deepen democratic participation beyond a simple curriculum requirement. Schools would compete for students, and government vouchers would be redeemed with the state treasury and supplemented by parental fees. Those schools that were able to succeed in the marketplace would be sustained by vouchers and private fees

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but not those that were unable to attract sufficient students. Friedman argued that such a competitive arrangement would use resources more efficiently than government provision of schooling and that it would also provide greater choice and equity than school attendance based on neighborhood schools. Although the use of educational vouchers is not widespread, there have been some notable national adoptions as well as state and local adoptions and experimental studies. In 1980, the Chilean government decided to establish a national voucher plan that was inspired by Friedman, but it differed in some of its features. Sweden followed with its own national voucher plan in 1992. Colombia has used educational vouchers to increase opportunities for secondary students. And since 1917, the Dutch adopted an educational system based on choice principles with public funding similar to a voucher system. In the United States, the state of Wisconsin established a voucher mechanism for low-income students in Milwaukee in 1990, and in 1995, Ohio followed with vouchers for low-income students from Cleveland. Vouchers for low-income students or ones in failing public schools have been adopted in Louisiana, Ohio, and Indiana, and a voucher demonstration has accommodated students from failing schools in Washington, D.C. There have also been voucher experiments in several cities. These voucher approaches differ from Friedman’s original proposal in many details but not in their underlying principles. The educational choice movement in the United States has gone in a somewhat different direction with its much larger movement of charter schools, of which some 5,000 have been established since 1992. Of the approximately 55 million elementary and secondary students in public and private schools in the United States, more than 2 million are in charter schools, but fewer than 100,000 are in schools funded by educational vouchers. However, educational vouchers continue to be actively proposed in many states and local jurisdictions in a concerted movement to expand their presence.

Importance of Design It is important to emphasize that there is not a single voucher plan but many different ones that vary according to their finance, regulations, and support services. Henry M. Levin has argued that the specific details are crucial for determining the different shapes and outcomes of specific voucher plans.

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There are two main features of voucher finance, both of which can have profound effects. The first is the basic size of the voucher and whether it is differentiated by level and type of education, the latter including special education, gifted and talented education, and the educationally disadvantaged. Friedman suggested a flat voucher of modest cost provision without differentiation for student need. The second is whether families can add their private funding to the voucher to obtain better schools for their children. Friedman argued for parental “add-ons” according to the rationale that families should be able to purchase more education just as they can acquire more of other goods in the marketplace according to their priorities. In contrast, The Netherlands has provided larger vouchers for the educationally disadvantaged and the educationally handicapped, as has Chile. The size of the voucher will have a large effect on the numbers of schools seeking voucher funding, as well as their variety. A larger voucher will induce more schools and a greater diversity of them to enter the market to compete for students. The level of the voucher for serving more costly educational needs, such as students with severe disabilities, also influences the supply of schools serving those purposes. The ability of families to add to the voucher would not only reflect relative desire by families for education as Friedman asserted, but the capability to augment the voucher depends clearly on the financial resources of families, directly creating an educational marketplace with enrollment tiers dependent on family income. Regulation

Among the more common regulations governing voucher participation are standards for admission, curriculum, testing, personnel requirements, and religious sponsorship of schools. With the exception of some provision for exposing students to democratic values and minimal literacy and knowledge, Friedman argued against regulatory restrictions. Friedman advocated that not only should families have choice of schools, but schools should also select their students from their applicants rather than accepting all who wished to enroll, limited only by school capacity. Chile allows private schools to choose their students, but not public schools. The Netherlands permits private schools to set admission criteria according to religion or ideology of the

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school, and secondary schools can refuse a student based on primary school achievement or recommendation. U.S. voucher plans are mixed with some requiring lotteries when applications exceed enrollments and others allowing the school to choose, particularly in the case of religion. Friedman implied that a minimal curriculum requirement to address the needs for democratic content should be required for voucher eligibility of schools, but he was not specific on details. Chile and the Netherlands require voucher schools to adopt national curriculum and tests for accountability of student results. Sweden also requires a national curriculum. U.S. voucher plans generally require compliance with state standards, but they allow religious schools considerable liberty. Friedman did not mention testing, apparently because it would place pressure on schools to have similar goals when his view was that a highly diverse market with many different emphases would be most effective. But for most voucher plans, governments have required academic achievement tests for students as public accountability indicators. Personnel requirements for teachers and other school staff are common requirements for educational voucher plans in Chile, the Netherlands, and Sweden, but they are more variable among states and local governments in the United States. In most cases, the government sets standards for teacher licensure and allows schools to select from those who are officially qualified. In some cases, they interpret the personnel criteria liberally. Friedman did not see a need for certifying personnel because he viewed parental satisfaction as the most important criterion for choice and considered personnel licensing as a device to restrict teacher supply. The U.S. Constitution has traditionally been interpreted as prohibiting public funding for religiously affiliated schools under the First Amendment. Considering that about 80% of students enrolled in private schools attend religious institutions, this could be a major obstacle to market utilization in education. A number of recent legal challenges have succeeded in loosening this strict interpretation, but all states have their own constitutions, some with strict limits to funding religious schools. Most recent applications of vouchers in the United States have permitted religiously affiliated schools to participate in voucher plans, but the future is unclear. For example, Florida’s voucher plan was rejected by its state courts as violating the state constitution. Overall, the shape of the educational marketplace

and its diversity and competitiveness are likely to be affected by regulatory requirements. Support Services

Some educational voucher plans provide support services, particularly transportation and information. Access to a wide range of schools may require transportation arrangements. Also, market choice presumes excellent information on alternatives. Many studies suggest that parents are not well informed on educational options and that families with the lowest levels of income and education are least informed, though they have the most to gain from better schools. Friedman did not address support services, but many voucher plans provide minimal information on choices, and a few provide transportation assistance. Other support services include arbitration for parents who are dissatisfied and wish to shift the voucher to another school and technical support for schools that are having difficulties. A decade ago, Chile provided a national program of technical assistance for its bottom 900 schools, whether public or private, that had a positive effect on their performance.

Evaluating Voucher Outcomes The details provided by the design tools of finance, regulation, and support services can create dramatically different voucher plans with different likely outcomes. Four broad criteria have been used to evaluate the results: (1) freedom of choice, (2) productive efficiency, (3) equity, and (4) social cohesion. Freedom of Choice

This goal refers to the ability of families to choose the educational experience that they desire for their child. Presumably, parents will choose schools according to their values, educational philosophies, religious beliefs, and political leanings, as well as perceived effectiveness in learning results. Freedom of choice can be viewed as a means for meeting the private educational goals that families seek when not considering the aggregate social purpose of education. This dimension can be assessed in terms of the numbers and diversity of options, their accessibility, and their quality. There is no question that implementation of vouchers expands the freedom to choose schools. Such plans abandon school assignment and provide increased numbers and diversity of choices. Shorter term voucher demonstrations are more limited in

Educational Vouchers

their expansion of choice because of uncertainty regarding future prospects. But it is universally agreed that vouchers represent an effective mechanism for increasing choice. Productive Efficiency

An important goal for any educational system is the degree to which it uses both family and public resources in a parsimonious fashion. A system with high productive efficiency maximizes educational outcomes for any given resource constraint for both families and society. Privatization and quasimarket advocates believe that more choice promotes competition, which creates incentives that improve productive efficiency. Although according to the freedom-of-choice criterion, there are many different aspects of schools that could be used to assess their effectiveness relative to their resource requirements, most of the literature is limited to student test scores. For Friedman, expansion of consumer choice would be expected to automatically increase productive efficiency because consumer choice promotes competition among schools and more closely matches schools with student needs. But this gain is assumed rather than measured, and the diversity of parent motives makes it difficult to assess. Most studies on the productivity of educational voucher plans rely exclusively on student achievement gains in comparison with those of traditional systems, after attempting to control for differences in student selection. Clearly, student achievement as measured by test scores is not the only outcome of schooling, but it is an important one and often available from existing data sources. An important survey of the research evidence by Cecilia Rouse and Lisa Barrow provides no consistent pattern of evidence that voucher systems have achievement advantages over other schooling approaches with comparable students. The official evaluations for voucher plans in Cleveland, Milwaukee, and Washington, D.C., also do not support such advantages. In the case of the most notable national voucher plan, Chile has shown no concrete evidence of the impact of vouchers on overall educational achievement, and Sweden has experienced large declines in student achievement on international achievement studies such as the Programme for International Student Assessment and the Trends in International Mathematics and Science Study as its voucher system has expanded.

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However, there is some evidence that vouchers and choice increase high school completion based on studies in Milwaukee and Washington, D.C. This finding is a bit puzzling because neither study shows an advantage in academic results for voucher students. One interpretation is that high schools chosen by voucher students have nonacademic features that improve high school completion rates, irrespective of their effects on test scores. It is important to note that all these studies are limited by narrow measures of school outcomes, and they do not account for differences in the noncognitive educational results that have been linked increasingly to economic and social success. Equity

The goal of equity is commonly used to evaluate education. It typically refers to the quest for and achievement of fairness in access to educational opportunities, resources, and outcomes by gender, social class, race, language origins, disability, and geographical location of students. Of particular concern are the distribution of educational quality of schools, the availability and efficacy of resources they provide to overcome disabilities or disadvantages, the degree to which they are segregated racially and socially, and the differential educational outcomes in achievement and attainments. The equity impact of a voucher plan is heavily influenced by design. Most of the U.S. voucher plans limit eligibility for vouchers to low-income families, favoring those with educational disadvantage. In contrast, voucher policies allowing schools to choose their students and charge extra fees to parents are likely to increase the separation and stratification of students and schools by social class. This is a major criticism of the Friedman plan. Such stratification can undermine educational equity by providing better educational resources and more talented peer influences for advantaged students. Data from the Programme for International Student Assessment of the Organisation for Economic Co-operation and Development show rising gaps among schools in achievement in Sweden as its voucher system has expanded. Programme for International Student Assessment data for 2009 also showed that Chile was 64th out of 65 countries in socioeconomic equality of achievement because of the performance chasm between students of low-socioeconomic status and those of high-socioeconomic status. This result is supported by separate studies of the impact

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of vouchers on student stratification in that country by Gregory Elacqua and by Chang-Tai Hsieh and Miguel Urquiola. Studies on the Netherlands choice system by Helen Ladd and her associates also show higher segregation in urban areas by immigrant status than in the United States. Contrary to Friedman’s assertion of predicted improvement in equity from vouchers, the evidence on a general voucher plan does not support his conclusion, even though conventional schooling arrangements also stratify students by income and ethnicity. Social Cohesion

A universal goal of schooling in a democratic society, as stipulated by Friedman, is the provision of a common educational experience that will orient all students to grow to adulthood as full participants in the social, political, and economic institutions of society. This goal requires common elements of schooling with regard to curriculum, values espoused, content, language, and political understanding. A democracy requires that its members master the skills and knowledge necessary for civic and economic participation, including citizen rights and responsibilities under the law, principles of democratic government, and an understanding of economic institutions and preparation for productive roles in that system. Social cohesion is the criterion that has been least measured and evaluated in voucher studies. This is somewhat of a paradox, given that Friedman’s support for government finance of education is predicated on social cohesion and equity. Part of the problem is the difficulty of identifying and measuring outcomes that typically lag the schooling experience by occurring in adulthood rather than being concurrent with education. Insights on civic effects of schooling can be found in a study by Rebecca Callahan and Chandra Muller that examined the impact of social studies experiences in high school among children of immigrants. At the time of writing, most students in private schools in the United States are in Catholic schools that share similar civics goals with public schools, so the threat to social cohesion would likely arise only if a significant number of schools diverged from the mainstream in these aspects. Thomas Dee found that Catholic high school students were more likely to vote as young adults than were comparable public school students, but they were less likely to engage in voluntary service.

Goals and Trade-Offs

There are many ways that the policy design tools of finance, regulation, and support services can be used to address the four types of benefits outlined above. Some detailed examples are found in an application by Levin of this framework to the design of educational vouchers. In theory, it is possible to design approaches to school choice that provide a balance among the four criteria, but there are conflicts among some goals that cannot be fully attained without sacrificing others. That is, greater achievement of one goal through design can reduce the attainment of competing goals. For example, freedom of choice can be expanded by allowing families to add their own financial resources to whatever the government provides and encouraging schools to charge supplementary fees as Milton Friedman has suggested. But such a plan would undermine equity and social cohesion by stratifying schools according to family income. To the degree that income overlaps with race, as it does in the United States, the Friedman plan would also promote racial segregation. In contrast, larger vouchers for the poor and the prohibition of parental “add-ons” for participating schools could reduce student segregation, but they also reduce freedom of choice for those with financial means. Alternatively, as a nation we could consider a plan to increase social cohesion by requiring a common curriculum, teacher credentialing standards, testing, and admissions by lottery or affirmative action. Such a plan will tend to make schools more uniform in their offerings, admission policies, and instructional approaches, thus raising social cohesion and equity. But it would reduce freedom of choice, as all schools would be bound with greater uniformity. Trade-offs in satisfying one goal may drastically reduce the achievement of a competing one. Drawing firm conclusions on the impacts of educational vouchers depends heavily on the specific goals of concern. An unsettled question is the degree to which the educational consequences of vouchers are generic, and to what degree they depend on the specific details of design. Certainly, the present evidence is not adequate to draw any final conclusions on this topic. As Clive Belfield and Levin have asserted, 50 years after the publication of Friedman’s provocative analysis, most claims on educational vouchers are still based more on ideology than on evidence. Henry M. Levin

Effect Size See also Charter Schools; Economic Efficiency; Educational Equity; Privatization and Marketization; Public Good

Further Readings Altonji, J., Elder, T., & Taber, C. (2005). Selection on observed and unobserved variables assessing the effect of Catholic schools. Journal of Political Economy, 113(1), 151–184. Belfield, C., & Levin, H. M. (2005). Vouchers and public policy: When ideology trumps evidence. American Journal of Education, 11(4), 548–567. Callahan, R., & Muller, C. (2013). Coming of political age. New York, NY: Russell Sage Foundation. Dee, T. (2005). The effects of Catholic schooling on civic participation. International Taxation and Public Finance, 12, 605–625. Elacqua, G. (2012). The impact of school choice and public policy on segregation: Evidence from Chile. International Journal of Educational Development, 32, 444–453. Epple, D., & Romano, R. E. (1998). Competition between private and public schools, vouchers, and peer effects. American Economic Review, 88(1), 33–62. Forman, J., Jr. (2007). The rise and fall of school vouchers: A story of religion, race, and politics. UCLA Law Review, 54, 547–604. Friedman, M. (1962). The role of government in education. In Capitalism and freedom (chap. 6, pp. 85–107). Chicago, IL: University of Chicago Press. Hanushek, E., Kain, J. F., Markman, J. M., & Rivkin, S. G. (2003). Does peer ability affect student achievement? Journal of Applied Economics, 18, 33–62. Hsieh, C.-T., & Urquiola, M. (2006). The effects of generalized school choice on achievement & stratification: Evidence from Chile’s voucher program. Journal of Public Economics, 90, 1477–1503. Ladd, H. F., Fiske, E. B., & Ruijs, N. (2009). Parental choice in the Netherlands: Growing concerns about segregation (Occasional Paper 182). New York, NY: Columbia University, National Center for the Study of Privatization in Education, Teachers College. Retrieved from http://ncspe.org/publications_files/ OP%20182.pdf Levin, H. M. (1998). Educational vouchers: Effectiveness, choice, and costs. Journal of Policy Analysis and Management, 17(3), 373–392. Levin, H. M. (2002). A comprehensive framework for evaluating educational vouchers. Educational Evaluation and Policy Analysis, 24(3), 159–174. Levin, H. M. (2012). More than just test scores. Prospects: Quarterly Review of Comparative Education, 42(3), 269–284.

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Rouse, C. E., & Barrow, L. (2009). School vouchers and student achievement: Recent evidence and remaining questions. Annual Review of Economics, 1, 17–42. Wolf, P., Gutmann, B., Puma, M., Kisida, B., Rizzo, L., & Eissa, N. (2010). Evaluation of the DC Opportunity Scholarship Program, Executive Summary (NCEE 20104019). Washington, DC: U.S. Department of Education, Institute of Education Sciences.

EFFECT SIZE Effect size is a statistical index that refers to the degree of relationship between two variables or the magnitude of an effect. It can be calculated as a generic statistic for change in the absence of treatment from pretest to posttest for a single group, or include treatment effects based on two or more groups. Effect size is a standard part of much statistical reporting in education economics and finance. Whether researchers develop indicators of teacher effectiveness or analyze impacts of educational resources on student outcomes, effect size is an important tool in interpreting study findings. Because different studies employ different outcome measures with different metrics, effect size estimates are used to convert diverse measures into the common metric, allowing studies to be compared, combined, or otherwise analyzed despite the variety of measures and measurement scales. This entry summarizes basic choices for the most common effect size statistic, known as a standardized mean difference. The following sections briefly describe types of effects sizes, the relationship of effect size with tests of statistical significance, as well as the conventions used in the calculation, interpretation, and application of effect sizes.

Types of Effect Size There are several types of effect size, generally based on either correlations or difference scores. The best known are Pearson correlation coefficients (r). Effect sizes such as a squared correlation coefficient (r2) account for variance shared by two variables and indicate how much variability in one variable (e.g., student performance) is associated with variation in a second variable (e.g., cognitive ability). Effect sizes based on dichotomous data (frequencies, proportions, or percentages) include risk ratio, odds ratio, and risk difference. For continuous outcomes, standardized mean difference (Cohen’s d) is commonly used.

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Because the different effect sizes can be algebraically converted from one to the other, the choice of a particular effect size metric can vary depending on the problem and even on customs in different fields of research. Although correlation coefficients can be computed for effectiveness studies, they are not typically used to demonstrate a causal relationship between a program and its impact. For effectiveness studies of treatments, interventions, and programs, where impact is the most significant outcome, standardized mean difference is one of the most widely used effect size estimates.

Relationship Between Statistical Significance and Effect Size Although researchers generally seek to draw conclusions about large numbers of individuals, they rarely have access to the entire population of interest, and a sample of this population is studied instead. When generalizing from the sample to a population, a test of significance is used to evaluate the probability that a relationship found between variables is not due to chance. Researchers conventionally present the treatment and control group means and a p value (probability value) for the significance test of their difference to indicate the estimated probability that a difference of a given size can be detected in the population from which the samples were selected. An effect size, though, emphasizes a different type of result of an individual study—the size of the difference between the group means. Effect size is a measure of the practical (educational, clinical, etc.) significance that indicates whether the difference observed is a difference that matters. These two types of results, the test of significance and the effect size, are directly related to each other. The p value depends essentially on two factors: (1) effect size and (2) sample size. Thus, statistically significant findings do not necessarily indicate substantive effects. One could get a significant result when the actual effect size is small but the samples are very large. On the other hand, a statistically insignificant finding can be obtained when the actual effect size is large but the samples are very small. The judgment about statistical significance is important for generalizing sample results to the population. However, because statistical significance conflates effect size and sample size, a separate judgment regarding the practical significance or the magnitude of the effect should also be made.

Calculation of Effect Sizes as Indices of Effectiveness The standardized mean difference statistic (Cohen’s d) is especially useful for measuring effect size in treatment outcome studies such as randomized control trials, where two or more groups may be compared on a continuous variable after administering a treatment to one or more groups. The actual calculation of an effect size depends on both the design of the study and the type of statistics being reported. The ideal research design would employ a large number of subjects randomly assigned to either an experimental treatment (e.g., the group that was given the new mathematical curriculum being tested) or to a control condition (the group given the standard mathematical curriculum—or no math curriculum—for comparison). In that case, one could rely on the preexisting attributes, including baseline pretest scores, to be equally represented in experimental and control groups and, therefore, not confounded with outcomes. A treatment’s effectiveness can be quantified through effect size statistics calculated as the standardized difference between the treatment group mean and the control group mean: x −x d= T C. Sp

Here, x represents the mean of the treatment and control samples, indicated by subscripts T and C, respectively, and S is the pooled standard deviation that measures the spread of a set of values “pooled” from both samples. For example, if a treatment group and a control group differed by an average of six points on a mathematical achievement test with a pooled standard deviation of 30 for those two samples, then d would be 0.20 (i.e., the groups differed by 0.20 standard deviation units). The purpose of the denominator of the formula is to standardize the difference between the outcome means in the numerator into scale-independent standard deviation units—in other words, to present the effect in the context of the variability observed in a study. When different studies use different instruments (e.g., different mathematical tests) to assess the outcome, the scale of measurement differs from study to study. Studies for which the difference in means is the same proportion of the standard deviation will have the same effect size, regardless of the actual scales used to make the measurements. The What Works Clearinghouse (WWC), a U.S. Department of Education initiative that provides systematic

Effect Size

reviews of the effectiveness of educational interventions, uses raw (unadjusted) posttest standard deviations to standardize the effect. As such, the standard deviations are not adjusted by any covariates (i.e., variables predictive of the outcome measure) that happened to be used in the design or analysis of the specific study. Combining the sample standard deviations for the experimental and control groups is intended to provide the best possible estimate of standard deviation for the respective population by using all available data. If the intervention does not alter the variance, the pooled estimate has a smaller probable error. If the variance of the outcome is affected by the intervention (e.g., when effective programs shrink the variance more than less effective ones do), then the pooled estimate will be biased. In this case, the comparison group standard deviation is used in the denominator of the effect size formula, resulting in an effect size known as Glass’s delta: x −x T C. ∆= S

C

The numerator should be the best estimate available of the mean intervention effect estimated in the units of the metric used in a particular study. If there are initial differences between groups on variables of interest (e.g., mathematical test scores), an effect size from posttest data needs to be adjusted for these initial differences. When adjusted group means from an analysis of covariance are available for both the treatment and the comparison groups (or coefficient for the intervention’s effect from regression models), they are used in the formula instead of the difference between the unadjusted posttest means. Besides means and standard deviations, effect sizes can also be estimated from many different kinds of statistics generated in intervention studies (e.g., t-tests or F-tests). Many statistical software packages compute effect sizes, and there are numerous books on meta-analysis that present formulas for computing effect sizes and converting indices from one to the other.

Interpretation of Effect Size An effect size of 0.50 indicates that the experimental group performed half of a standard deviation higher than the comparison group. The effect size is typically reported to two decimal places, and by convention, a positive value indicates that the experimental group does better than the control

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group. Effect size direction (positive or negative) is derived from a desirable condition in the outcome measure. For example, a smaller mean is desirable for the experimental group in research investigating school dropout rates; therefore, a positive effect size estimate would indicate a better performance in the experimental group, even though the arithmetic sign that results from subtracting the means would be negative. The effect size can be converted into a percentage of improvement using the standard normal curve, and be interpreted as percentile scores in terms of the amount of overlap between the two groups. A measure known as Cohen’s U3 index is used to show the expected change in percentile rank for an average control group student if the student had received the intervention. The WWC employs an “improvement index” (defined as U3 − 50%), which represents the difference between the percentile rank corresponding to the experimental group mean and the percentile rank corresponding to the control group mean (i.e., the 50th percentile) in the control group distribution. As an example, if an intervention produced a positive impact on students’ reading achievement with an effect size of 0.80, the effect size would be translated into a Cohen’s U3 index of 29 percentile points for an average student in the control group. To put it another way, the score of the average person in the experimental group exceeded the scores of 79% of the control group (29% ⫹ 50% ⫽ 79%). To interpret the magnitude of an effect size relative to other effect sizes, Jacob Cohen drew on a range of effect sizes found in social and behavioral research and suggested general rules, which have become broadly accepted. For standardized mean difference, an effect size of about 0.20 is considered “small,” about 0.50 is considered “medium,” and about 0.80 is considered “large.” However, as Cohen argued, many interesting educational effects are of “small” magnitude, and researchers must use caution when using his benchmarks to interpret relationship magnitudes within particular social science disciplines. To contextualize benchmarks, researchers develop standards based on the empirical distribution of effect sizes in studies of a specific field of research. The WWC, for example, defines an effect size of 0.25 as “substantively important” and uses it for rating the effectiveness of educational interventions. The WWC benchmark is standardized on variation between individuals—that is, regardless of the unit of assignment or the unit of intervention

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(schools, classrooms, etc.), student-level standard deviations are used.

Applications of Effect Sizes Effect sizes are now widely used to represent the impact of treatment, program, or intervention across a broad range of social research and topic areas. Researchers also need to compute effect sizes for power analysis when a study is first being proposed (i.e., before collection of any data). The power of a study (i.e., the probability that it will yield statistically significant results) is determined by three factors: (1) sample size, (2) significance level of the test, and (3) effect size. Use of effect sizes also can be combined with other data—for example, cost—to provide a measure of cost-effectiveness. When different interventions are expected to produce similar effects, the consequences of the options need to be assessed. The routine use of effect sizes, however, has often been limited to meta-analysis, the quantitative synthesis of multiple studies on the same topic. Metaanalysis focuses on the direction and magnitude of the effects across studies and further reduces reliance on the significance test of finding as a measure of its value. In the landmark meta-analysis that influenced public debate about financial resources and education, Larry Hedges and his colleagues reanalyzed results from a large set of studies that initially demonstrated inconsistent relationship between student performance and school resources and found a positive effect of practical importance. By putting all outcomes onto the same standardized scale, effect size provides a prerequisite for meta-analysis. The basic synthesis of individual effects is accomplished with the (weighted) average effect size. As a rule, some studies of a particular phenomenon will overestimate, and others will underestimate the size of the true effect. Averaging findings across studies produces a more reliable estimate of the effect than that of any individual study. Meta-analysis also allows for investigation of differences in effect sizes along any dimension of interest (e.g., participant characteristics, program features, etc.) that varies across studies. Andrei Streke See also Cost-Effectiveness Analysis; Econometric Methods for Research in Education; Randomized Control Trials; Teacher Effectiveness; U.S. Department of Education

Further Readings Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2009). Introduction to meta-analysis. Hoboken, NJ: Wiley. Cohen, J. (1988). Statistical power for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum. Greenwald, R., Hedges, L., & Laine, R. (1996). The effect of school resources on student achievement. Review of Educational Research, 66(3), 361–396. Hedges, L. V. (1981). Distribution theory for Glass’s estimator of effect size and related estimators. Journal of Educational Statistics, 6, 107–128. Hedges, L. V., Laine, R. D., & Greenwald, R. (1994). Does money matter? A meta-analysis of studies of the effects of differential school inputs on student outcomes. Educational Researcher, 23(3), 5–14. Lipsey, M. W., & Wilson, D. B. (2001). Practical metaanalysis. Thousand Oaks, CA: Sage. U.S. Department of Education. (2011). What Works Clearinghouse: Procedures and standards handbook (Version 2.1) [Online]. Retrieved from http://www.ies .ed.gov/ncee/wwc/pdf/reference_resources/wwc_ procedures_v2_1_standards_handbook.pdf

ELASTICITY Economists are often interested in how changes in one variable affect other related variables. A summary quantitative measure of this relationship is provided by elasticity, which refers to the degree of responsiveness of an entity (e.g., quantity of a good demanded by consumers) to a change in specific determinants (e.g., price, income, etc.). Elasticity varies among different types of goods. Some commodities have a greater magnitude of responsiveness to changes in elasticity’s determinants than do others. For instance, a reduction in the price of alcohol may result in a minuscule increase in sales, whereas a price cut in smartphones may lead to a substantial rise in purchases. With the increasing prevalence of market-based reforms and educational marketplaces where parents choose from among various schooling and online course options, the concept of elasticity has taken on greater importance in education and education finance. Elasticity is crucial for understanding market behavior (in this case, the actions and decisions of providers, e.g., charter schools and parents). This entry presents an overview of the concept of elasticity. First, some technical considerations are discussed, including the calculation of elasticity.

Elasticity

Next, the four main elasticities are outlined before concluding with the applications of elasticity.

Technical Considerations Calculating Elasticity

Elasticity captures how the changes in one variable are manifested in another variable—for instance, how the change in the price of a good affects the quantity demanded, or how a change in income affects total expenditures. Suppose there are two variables, A and B, which are related is such a way that B is a function of A and other variables (B ⫽ f [A . . . ]). The elasticity of B with respect to A (EB,A) is the percentage change in one variable (B) divided by the percentage change in another variable (A). The following equation demonstrates the general formula for calculating elasticity. EB,A ⫽ Percentage change in B/Percentage change in A ⫽ ([Change in B]/B)/([Change in A]/A).

Ceteris Paribus

The concept of elasticity illustrates how variable B responds to a 1% change in variable A, assuming all other things are equal (ceteris paribus). Stated differently, all other influences on the determination of variable B are held constant to isolate the effect of a 1% change in variable A. Commodities are typically measured in different units, but the concept of elasticity allows for comparisons of the responsiveness of different entities regardless of unit. Units are “dropped” in the calculation, and elasticity is expressed in terms of percentages. In essence, elasticity is “unitless” and captures relative percentage changes. The independence from units makes elasticity a convenient measure for economists to calculate and aids, for example, in the assessment of welfare distribution.

Supply and Demand In economics, the concept of elasticity is related most frequently to the slope of the demand and supply curves (see the entry “Markets, Theory of”). The preferences of buyers or consumers are represented in a downward-sloping demand curve. The demand curve illustrates the willingness to buy different quantities at various prices, assuming all other things are constant. The upward-sloping supply curve demonstrates the preferences of sellers and

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the willingness to supply different quantity of goods at different prices. The intersection of the demand and supply curves results in equilibrium price and quantity. The flatter the demand or supply curve, the more elastic the demand or supply is; conversely, the steeper the slopes, the more inelastic the demand or supply.

Types of Elasticities There are four major elasticities that are often used by economists: (1) price elasticity of demand, (2) income elasticity of demand, (3) cross-price elasticity of demand, and (4) price elasticity of supply. Price Elasticity of Demand

The price elasticity of demand (EQ,P) measures the sensitivity of quantity demanded (Q) to changes in the price of a product (P). Stated differently, EQ,P captures how quantity demanded or consumption changes in response to a 1% change in prices. EQ,P is defined as the percentage change in Q divided by the percentage change in P. For example, if a 10% increase in prices of textbooks (prices rise from $100 to $110) results in a 20% decline in quantity demanded (consumption falls from 2,000 to 1,600 textbooks), then EQ,P is equal to 2 [(-0)/(⫹10)]. Though technically EQ,P is always a negative value due to the downward-sloping nature of the demand curve, EQ,P is always expressed in absolute terms and thus is typically positive. A price elasticity of demand of 2 can be interpreted as a 1% rise in prices causes quantity demanded to decline by 2%. The higher the value of the price of elasticity of demand, the bigger the effect of a change in price on quantity demanded. If EQ,P is greater than 1, the demand is considered “elastic.” In this case, the percentage change in quantity demanded is greater than the percentage change in prices, suggesting that consumption is fairly responsive to price changes. When EQ,P equals infinity, demand is classified as “perfectly elastic.” If EQ,P is less than 1, the demand is considered “inelastic.” In this scenario, the percentage change in quantity demanded is less than the percentage change in prices, implying that quantity demanded is somewhat unresponsive to price changes. When EQ,P is equal to 0, demand is categorized as perfectly inelastic, meaning an increase in prices results in no change in quantity demanded. If EQ,P is equal to 1, the demand is considered unit elastic. In this case, the percentage change in quantity demanded is the same as the percentage change

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in prices. Generally speaking, for elastic goods, price affects quantity substantially; for inelastic goods, price has a lesser effect on quantity demanded. EQ,P is determined by various factors but mainly by the availability and closeness of substitutes and the time horizon over which buyers adjust to a change in price.

Price Elasticity of Supply

Price elasticity of supply is the degree of responsiveness of quantity supplied to a change in the price of a good. The calculation and interpretation of the price elasticity of supply is similar to EQ,P. The price elasticity of supply tends to be more elastic in the long run than in the short run.

Price Elasticity of Demand and Total Revenue

Total revenue (TR) is equal to price multiplied by quantity sold. TR represents the total amount paid by buyers and collected by sellers of a product. When demand is inelastic, an increase in prices results in an increase in TR. When demand is elastic, an increase in prices leads to a decrease in TR. Income Elasticity of Demand

The income elasticity of demand (EQ,I) is another type of elasticity frequently used in economics. EQ,I is how sensitive consumption of a good is to changes in income, assuming the price of the good itself does not change. In other words, EQ,I measures the relationship between income changes and changes in quantity demanded. It is calculated by the percentage change in quantity divided by the percentage change in income. When EQ,I is positive, the quantity demanded of a good increases with income and the product is called a normal good. When EQ,I is negative, the consumption of a good decreases as income increases, and the product is called an inferior good. Products considered as necessities such as food are typically income inelastic, whereas luxury goods such as sports cars are generally income elastic. Cross-Price Elasticity of Demand

Cross-price elasticity of demand measures the response of the quantity demanded of one good to changes in the price of another good. The crossprice elasticity of demand captures the magnitude of the responsiveness of the consumption of good A to a change in price of a related good B. Crossprice elasticity is positive if the two goods are substitutes and negative when the goods are complements. For instance, consider two private schools A and B in a neighborhood. If the price (school fees) of school A rises by 10% and the quantity demanded of school B increases by 5%, the cross-price elasticity of school B with respect to the price of school A is 0.5.

Applications Elasticity provides useful and meaningful information for researchers, policymakers, and businesspeople. Elasticity measures a key quantitative relationship between the determinants of demand and supply and the quantity demanded and the quantity supplied. Even though there are several applications of the concept of elasticity, the most prominent is the price elasticity of demand. In essence, elasticity measures how buyers and sellers respond to changes in market conditions, namely, the price of products and the income of consumers. These insights are especially valuable in competitive industries and are crucial in analyzing supply and demand. Cross-price elasticity of demand is commonly used in antitrust cases to measure the strength of the relationship between two goods. Dominic J. Brewer and Richard O. Welsh See also Markets, Theory of

Further Readings Browning, E., & Zapan, M. (2009). Microeconomics: Theory and applications (10th ed.). Hoboken, NJ: Wiley. Nicholson, W., & Snyder, C. M. (2011). Microeconomic theory: Basic principles and extension (11th ed.). Mason, OH: South-Western Cengage Learning.

ELEMENTARY AND SECONDARY EDUCATION ACT The Elementary and Secondary Education Act (ESEA) is a law passed by Congress in April 1965 that provides federal aid to PreK-12 schools and essentially sets federal PreK-12 education policy. Enacted as part of President Lyndon B. Johnson’s War on Poverty, the primary aim of the ESEA was to improve the educational experience of disadvantaged students. Although the ESEA has remained the single largest source of federal aid for K-12 education,

Elementary and Secondary Education Act

there have been several amendments to ESEA since 1965 that have shifted the focus of federal education policy and increased restrictions on the use of federal education aid. This entry provides background information about the passage and development of the ESEA, outlines the components of the ESEA and revisions to it, and discusses the policy debates and controversies surrounding the ESEA.

Background Prior to 1965, concerns about the role of the federal government in education prevented the passage of large-scale federal education aid to K-12 schools. However, in 1964, two political shifts occurred— Lyndon B. Johnson was elected president and the Democratic Party gained majority control over Congress—that played important roles in the passage of the ESEA. In 1965, with a vote of 263–153 in the House and 73–18 in the Senate, the ESEA passed through Congress quickly with no amendments and little deliberation and was later signed into law by President Johnson. The goal of the ESEA was to provide federal aid to local school districts to promote equitable educational opportunities for disadvantaged students. In the same year of passage, the ESEA provided $1 billion in federal funds to K-12 schools, and by the 1970s, 94% of all school districts received some type of ESEA aid. The passage of the ESEA marked the beginning of the expansion of the federal role in education. Since 1965, federal legislation and aid have supported (a) higher education, (b) bilingual education, (c) special education, and (d) Head Start programs. Following the election of President Ronald Reagan in 1980, domestic spending was reduced, including in education. Compared with the 1970s, the number of disadvantaged children served under ESEA in the 1980s declined. While roughly 5.1 million students benefited from Title I funding in 1979, this number dropped to as low as 4.4 million during Reagan’s presidency. The Reagan administration’s focus on the poor performance of American schools coupled with the release of A Nation at Risk in 1983 steered many states to center education reforms on improving student achievement and teacher quality.

Stakeholders and the Development of the ESEA The ESEA was developed with the assistance of recommendations provided by the Gardner Commission, led by John W. Gardner, and the U.S. commissioner of education, Francis Keppel. The

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Gardner Commission, whose primary aim was to formulate innovative legislation on federal education aid, recommended that federal education aid be categorical, or targeted at key stakeholders. Meanwhile, Keppel focused on balancing the competing interests of states’ rights conservatives, Catholics, and the National Education Association. While the National Education Association opposed the distribution of federal education aid to private schools, Catholics wanted some federal funds to be directed toward parochial schools. Moreover, states’ rights conservatives were concerned that the provision of federal aid would weaken the role of states in the delivery of education. Thus, the commission recommended targeted federal aid for poor children regardless of whether they attended private or public school; public school districts would be responsible for taking care of their own students and also purchasing books and hiring teachers for public schools.

Components of the ESEA The original ESEA consisted of six sections: Title 1, which is the largest financial component of ESEA, provided financial assistance for the education of children of low-income families; Title II provided funds for school library resources and instructional materials; Title III provided aid to supplementary educational centers and services; Title IV provided funds for educational research and training; Title V provided grants to strengthen state departments of education; and Title VI outlined general provisions. Subsequent revisions to ESEA have led to the addition of several sections that cover topics such as bilingual education, children with special needs, Native American students, standardized state testing, and national academic standards.

Revisions to the ESEA In 1988, Title I of the ESEA was amended to require states to document and define levels of academic achievement for disadvantaged students. This amendment further required school districts receiving Title I federal aid to annually assess student academic progress using standardized test scores. The 1994 reauthorization of ESEA, known as the Improving America’s Schools Act, required all school districts to identify schools not making “adequate yearly progress” and to devise a plan to improve the academic performance of these schools. Also, to receive Title I funds, states had to demonstrate that learning goals, academic expectations,

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and curricular opportunities were equal for disadvantaged students and nondisadvantaged students. The 2001 reauthorization of the ESEA is more commonly known as the No Child Left Behind Act (NCLB). Signed into law on January 8, 2002, NCLB changed the U.S. educational landscape by mandating standardized testing of students, focusing on research-based practices, expanding on the standards-based education reform of the 1990s, and increasing accountability. Although both NCLB and the original ESEA were intended to aid economically disadvantaged children, NCLB also holds state and local education agencies accountable for student achievement. Based on the notion that high standards, measurable goals, and incentives can improve educational outcomes, NCLB links federal funding to student performance outcomes and sanctions schools with low student performance. Congress has not reauthorized NCLB; instead, in 2011, the U.S. Department of Education invited each state to apply for a waiver of NCLB requirements. As of January 2014, 42 states and the District of Columbia have received waivers that grant them flexibility to create their own state plans to improve educational outcomes.

Policy Debates and Controversies Shortly after the passage of the ESEA in 1965, legislative ambiguities in the act along with minimal congressional oversight led to the misuse of ESEA funds such as distributing Title I funds to all students instead of restricting funds to poor children only. In 1969, a report outlining abuses of ESEA funds was written by Ruby Martin of the Washington Research Project and Phyllis McClure of the National Association for the Advancement of Colored People. Martin and McClure found that more than 15% of Title I funds had been misused and subsequently published their findings in a report titled Title I of ESEA: Is It Helping Poor Children? National attention on the misappropriation of federal education money led to several amendments to the ESEA to more effectively assist disadvantaged students. Recent reauthorizations of ESEA, such as NCLB, have been challenged by education and states’ rights advocates for a number of reasons. One reason is that it restricts the autonomy of local districts by increasing restrictions for the use of federal aid. A second reason is the increased linkage of federal funding to standardized assessments of student performance and teacher quality. Thus, the effectiveness of NCLB is the topic of great debate. Leading

education researchers such as Caroline Hoxby and Eric Hanushek have conducted empirical studies on the effectiveness of NCLB on students, teachers, and schools. Another current policy debate is the appropriation of Title I funds. Empirical studies give conflicting assessments of the effectiveness of Title I funds on student achievement. A longitudinal study and a meta-analysis conducted by Geoffrey Borman and Jerome D’Agostino show that Title I students had greater achievement gains than similar non–Title I students; however, the authors cite limitations. The positive academic gains were restricted to the more advantaged portion of the Title I population. The role of the federal government in education has changed since the passing of ESEA in 1965. Currently, the federal government is incentivizing states to create their own education reforms by inviting them to compete for federal funding, as with the Race to the Top grant competition that began in 2009. Further research can help scholars and policymakers determine the effectiveness of federal education funding. Dominic J. Brewer, Tenice Hardaway, and Quynh Tien Le See also Accountability, Standards-Based; Accountability, Types of; Adequate Yearly Progress; No Child Left Behind Act; Title I

Further Readings Bailey, S. K., & Mosher, E. K. (1968). ESEA: The Office of Education administers a law. Syracuse, NY: Syracuse University Press. Borman, G. D., & D’Agostino, J. V. (1996). Title I and student achievement: A meta-analysis of federal evaluation results. Educational Evaluation and Policy Analysis, 18(4), 309–326. Borman, G. D., D’Agostino, J. V., Wong, K. K., & Hedges, L. V. (1998). The longitudinal achievement of chapter 1 students: Preliminary evidence from the Prospects study. Journal of Education for Students Placed At Risk, 3(4), 363–399. Kaestle, C. F., & Smith, M. S. (1982). The federal role in elementary and secondary education, 1940–1980. Harvard Educational Review, 52(4), 384–408. Martin, R., & McClure, P. (1969). Title I of ESEA: Is it helping poor children? Washington, DC: Washington Research Project. Murphy, J. T. (1971). Title I of ESEA: The politics of implementing federal education reform. Harvard Educational Review, 41(1), 35–63.

Enrollment Counts Thomas, J. Y., & Brady, K. P. (2005). The Elementary and Secondary Education Act at 40: Equity, accountability, and the evolving federal role in public education. Review of Research in Education, 29, 51–67.

ENROLLMENT COUNTS Enrollment refers to the number of students associated with an organization. These counts are one of the primary data points by which revenue and expenditures are created, used, and understood in the education arena. Calculations of enrollment are not as straightforward as determining a headcount. Instead, organizations have a clearly defined method by which to determine these counts for reporting to local, state, and federal entities for funding and other purposes. The way in which organizations relay enrollment figures has the utmost importance to the level and access of resources available to them, and therefore the economics of education, as funding systems rely heavily on these data. This entry includes information on three of the methods by which to count students: (1) average daily attendance (ADA), (2) average daily membership (ADM), and (3) average number belonging (ANB).

Average Daily Attendance ADA is the average number of attending students over the course of a school year. Usually using attendance records for every day of the school year, ADA gives an accurate assessment of the average number of students served over time. For example, if students are in attendance 95% of the time, a 400-student school has 380 ADA, or 400 multiplied by 0.95. At a more granular level, if a student attends school 171 days of a 180-day school year, that student is considered a 0.95 ADA, or 171 divided by 180. One of several methods by which students may be counted for revenue or expenditure calculations, ADA does not include students absent the days of the counts, therefore providing an incentive for both attendance and maintaining membership in a school.

Average Daily Membership ADM is a count of students taking into consideration the varying school membership over the course of a year. Typically determined by the average of counts performed throughout the school year, ADM gives a more refined measure than a single enrollment count. For example, a 400-student school’s

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enrollment will fluctuate over the course of the year as students enroll or move from the school. If the average enrollment count is 405 students, the school has 405 ADM. Some organizations will include less than a count every day or include only the highest of several counts. Unlike ADA, ADM counts students who are enrolled but absent the days that the school determines membership. Therefore, ADM provides an incentive for an organization to maintain its membership and does not positively influence attendance policies.

Average Number Belonging ANB is a count of students using a method nearly identical to ADM, except for the quantity of times a count is made. ANB typically represents the average of enrolled students (present or absent) on 2 days during the school year. For example, if the number of students enrolled in a school on October 1 is 400 and on April 1 is 450, the count is 425, or the sum of 400 and 450 divided by 2. Similar to ADM, an ANB method encourages an organization to maintain or increase membership, at least on the days a count is performed, and does not reflect attendance patterns.

How Enrollment Counts Are Used For each of these enrollment counts, ADA, ADM, and ANB, policies usually instruct agencies to use the prior years’ counts for funding purposes, barring a significant increase or decrease in counts for the current funding year, although current year counts at times are used for funding purposes. An additional policy gaining popularity is the use of a system to soften the decrease in funding an organization would receive for declining enrollment. This formula uses the greater of the prior year’s count and the prior 3-year average count for funding purposes. Additional factors may influence the calculation of an enrollment count. Organizations may take into consideration that some students only participate in an educational program for part of the day. For example, a kindergarten student may only be counted as half a student if only attending half of the day, or a high school student may only be counted as three fourths of a student if concurrently attending a college one fourth of the time. In these situations, policies may dictate a decrease in the ADA, ADM, or ABN applicable to these students. Furthermore, for the purposes of determining a student count, organizations may apply “weights”

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to enrollment counts to increase funding to students who require additional resources to meet educational goals. This method may be used in allocating funds from districts to schools, or from states to districts. A weight may be applied for classifications such as special education, economically disadvantaged, or English Language Learners. These students may need to participate in extra help strategies such as tutoring, summer school, or speech therapy, which increase the cost of educating them to achieve the level of peers. For example, an English Language Learner may have a weighted status of 1.2 students, so an increase in English Language Learners at a school would increase the per-pupil funding the school would receive. In this case, if the school receives $10,000 per year for each student in no special categories, it would receive $12,000 a year for each English Language Learner, or $10,000 multiplied by 1.2. Federal, state, and district agencies decide on which enrollment count should be used in reporting student participation in the education system. The primary reason multiple time points of enrollment counts are mandated is to more precisely measure the number of students who receive services in schools. The additional step to limit counts to attending students (vs. counting all students enrolled) is recognized as a way to promote student’s physical presence in school. Using attendance measures also has the effect of reducing counts and therefore reducing funding to and/or from the district. While multiple counts are commonplace across the United States, policies of enrollment versus attendance counts differ by state, meaning significant differences in funding occur across states simply by how, and if, a student is counted. Mike Goetz See also Enrollment Management in Higher Education; National Datasets in Education; Pupil Weights; Weighted Student Funding

Further Readings Colorado Children’s Campaign. (2010, August). Student enrollment count mechanisms for school funding: A survey of state policies. Denver: Colorado Children’s Campaign. Retrieved from http://www.coloradokids.org/ data/publications/pastpublications.html Keaton, P. (2012). Public elementary and secondary school student enrollment and staff counts from the Common Core of data: School year 2010–11 (NCES 2012-327).

Washington, DC: U.S. Department of Education, National Center for Education Statistics. Retrieved from http://nces.ed.gov/pubsearch National Education Association. (2012, December). Rankings and estimates: Rankings of the states 2012 and estimates of school statistics 2013. Washington, DC: Author. Retrieved from http://www.nea.org/assets/ img/content/NEA_Rankings_And_Estimates-2013_ (2).pdf North Carolina Department of Public Instruction. (2012). School attendance and student accounting manuals, 2012–13. Raleigh, NC: Author. Retrieved from http:// www.ncpublicschools.org/docs/fbs/accounting/manuals/ sasa.pdf

ENROLLMENT MANAGEMENT HIGHER EDUCATION

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In higher education, the term enrollment management refers to how institutions use various means to meet enrollment goals. Enrollment management involves a number of economic and social concerns in both public and private colleges and universities. These concerns include factors influencing consumer demand, such as tuition and fee rates, effectiveness of financial aid strategies, market positioning, and decisions regarding enrollments and student access on main and other campus locations. There are also factors dealing with supply, such as program offerings, student services, and physical plant limits. Because the enrollment management function is closely related to concerns surrounding access, rising costs, and the public service mission of most institutions, professionals in the field are required to balance the need to generate sufficient net revenues with notions of traditional academic selectivity and broad student diversity in the structure and formation of the student body. These responsibilities are also closely tied to institutional strategic planning and budgeting efforts as well as the public service mission of higher education. Moreover, in its central role in institutional affairs, the enrollment management function intersects with the duties of many offices and programs, including financial aid, admissions, academic and career counseling, marketing, communications, orientation, retention, institutional research, and a broad array of student services. Finally, the growing importance of enrollment management has led to increasingly professionalized practice in the field.

Enrollment Management in Higher Education

This entry discusses the history of enrollment management; current enrollment management strategies in colleges and universities; the use of marketing, pricing, and institutional aid in enrollment management; how pricing strategies affect enrollment and net revenues; and how enrollment management differs based on the type of institution.

History and Current Practice Early attempts at comprehensive enrollment management were primarily a response to changing student demographics brought about by the baby boom generation. It is typically associated with the efforts of Jack Maguire, a physics professor at Boston College, who coined the term enrollment management in 1976 to reflect a more holistic approach to the management of enrollments across the student population. This definition moved what was a narrow understanding of responsibilities among a number of offices to form what was essentially a new synergistic and systematic means for managing enrollments. This early definition sought to include not only admissions offices but also retention efforts, financial aid strategies, research-based decisions, and enrollment forecasting as components of this larger approach. The field garnered significant attention through the 1980s, which served as a basis for its increasing professionalization and utilization on campuses across the country. During this time, many scholars began to consider the role played by enrollment management in institutional strategic planning. Specifically, they concluded that enrollment management plans, including marketing and diversity initiatives, should be supported both by institutional research and as a means for supporting larger planning goals on campus. It was over this period that the field took on a distinctly strategic focus resulting in a rebranding termed strategic enrollment management. It is important to note that while enrollment management is currently understood more precisely as strategic enrollment management, the former term is used for this entry. While the field still remains closely tied to the admissions office in many institutions, the broader importance of enrollment management remains apparent. Across the higher education landscape, enrollment management offices are ubiquitous. While it might be argued that some institutions are not necessarily practicing enrollment management but rather admissions marketing aimed at selling

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the image of the institution rather than managing enrollments, the underlying notion that coordinated and collaborative efforts across a number of offices are the best way to manage enrollments continues to guide most institutions. For enrollment management, the overarching purpose should be to align program offerings, financial aid, marketing plans, student demand, retention efforts, and institutional planning in a comprehensive manner. This approach facilitates an effective institutional response to social, economic, and political factors that affect college and university finances as well as enrollments.

Marketing: Choice, Selectivity, and Forecasting At both private and public colleges and universities, marketing has become a central component of enrollment management and strategic plans. Even those who suggest that enrollment management is not reducible to admissions marketing can agree that this function is the primary means by which institutions communicate and implement recruitment, selection, and retention activities. College and university marketing is directly related to the market perception students and families maintain about an institution, thus serving an important role in the college search and choice stages. It is intended to communicate the value of degree programs offered by the institution; the prestige, ranking, and selectivity of an institution; and how students attending a particular institution are affected by these factors. Additionally, the marketing message commonly includes information regarding campus diversity/ commitments to social justice, campus life and amenities, and the wide range of available student services. Another facet of higher education marketing is the extent to which an institution can determine the ability and willingness of students to pay for particular programs or enrollment. Marketing research can provide data for enrollment forecasting and analysis of various pricing strategies. Because pricing is a key factor in the college choice process, understanding the demand for enrollments is a key part of marketing research in enrollment management.

Pricing and Institutional Aid Pricing can be one of the most important and contentious topics for enrollment managers. The often political nature of rising tuition and fees is of perennial interest to stakeholders across public and private higher education. Pricing concerns in enrollment management are commonly related to the ability

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Enrollment Management in Higher Education

of higher education institutions to promote access and affordability. Pricing strategies can have a major impact on the size and makeup of the student body. On the one hand, pricing can deter some students from applying or attending particular institutions. On the other, it may serve as a signaling device for those seeking out more prestigious, selective institutions. Still, tuition and fees can be set at such a level that some students simply cannot afford to attend a particular institution. In these instances, the willingness of some students to meet a certain price point is outstripped by their ability to do so. To lower the price students actually pay, many colleges and universities provide institutional financial aid (known as tuition discounts). The goal of these awards is often twofold. First, merit-based financial aid is intended to attract students who might not otherwise attend the institution. This type of aid is aimed mostly at attracting students with high grade point averages and scores on standardized tests who would likely attend more prestigious institutions but will heighten the academic profile of the institution making the offer. Second, an institution can offer need-based aid for students who have been offered admission but who do not qualify for merit-based funding. Awards provided in this category are primarily offered to defer some of the upfront costs faced by students and can serve as a means for easing the financial burden faced by pricesensitive students, especially those who might not otherwise attend college due to price concerns. For enrollment management, tuition discounts of both types generally share similar purposes: help diversify the student population, support student access to college, and compete effectively for high-performing students. Finally, pricing and institutional aid are directly related to revenue-generating capacity. Pricing strategies and tuition discounts affect an institution’s financial positioning. Therefore, they can offer a means for examining how these decisions affect net revenues and student responsiveness.

Net Revenues and Responsiveness Enrollment management is fundamentally related to the revenue-generating capabilities of an institution in at least three ways. First, student tuition and fees add to total revenues and generally serve as the primary income source for private institutions. When more students attend a college or university, depending on institutional physical plant limits (available enrollment spaces), faculty size, and program

offerings, total revenues increase. Second, institutional aid in the form of tuition discounts lowers total revenues. By convention, institutional aid is treated as a revenue discount. Third, for public institutions, state funding is often tied to total enrollments. Therefore, enrollment management is tasked with balancing enrollments and tuition discounts so as to reach a point where net revenues increase. It is important to note that tuition discounts can be an effective technique for managing net revenues. Net revenues are determined by aggregating total operating revenues from all sources and subtracting revenue discounts, typically institutional aid. Net revenues can increase, decrease, or remain stable based on the responsiveness of students to changes in price (demand elasticity). If an institution can raise prices without a commensurate or significant decrease in enrollments, student responsiveness to price is less of a concern. Still, reaching enrollment numbers in aggregate is not the same as reaching enrollment goals that balance selectivity, access, diversity, and academic excellence. Conversely, if an institution raises its price and enrollments decrease significantly, this could imply that the student demographic being served is more price sensitive. Using tuition discounts has also been shown to be an effective means of generating increased net revenues. For some students, especially those who are very responsive to price increases, even a small change in net price can swing their enrollment decision to favor a particular institution. In this instance, the institution can garner increased net revenues for only a small financial aid outlay. Moreover, institutional aid can also help enrollment managers meet enrollment goals and diversity objectives. Again, these goals can often be reached by employing appropriate tuition discounts to “make” a class or increase retention rates.

Institutional Mission and Type: Some Considerations How enrollment management is practiced can vary depending on the mission and type of institution. Variances in the function and responsibilities of the enrollment management office may exist based on the demographic the institution serves, its level of selectivity, competitive positioning, recruitment goals, and research portfolio. For example, 4-year institutions might employ a centralized enrollment management system at the undergraduate level so that admissions decisions, recruitment strategies,

Enrollment Management in Higher Education

and marketing messages are cohesive. At the graduate level, however, it is more likely that a decentralized system will be preferable, given the direct role played by faculty and departmental staff in admissions, recruitment, and marketing. While a few of these operations might be directed through the graduate school, departments and programs are regularly responsible for handling these activities. Community colleges on the other hand, tend to employ enrollment management strategies that are more reflective of their admissions’ policies and multitude of missions. For the most part, community colleges adhere to open-enrollment policies. They seek to serve and enroll students within the local community. They are normally charged with the provision of adult basic education, remedial education, undergraduate liberal arts training, and workforce and vocational preparation, among other roles. Based on open-admissions policies, which allow all students wishing to enroll to do so, a centralized structure may make more sense at these types of institutions, given their already intricate missions and longer histories with strong administrations. It is also possible that fewer resources need to be expended on marketing, recruitment, selection of students, and even aid at community colleges. It is not uncommon at these institutions for enrollment management to deploy resources to improve retention efforts and support services instead of marketing or recruitment activities. Moreover, it is important to note that community colleges do not normally provide aid (tuition discounts) beyond what is offered by the state and federal governments. Finally, community colleges are now considered a pipeline for students wishing to transfer to 4-year institutions. As a result, part of the enrollment management process must now focus on articulation and transfer agreements to help smooth student transitions across sectors. An additional particular concern for both public and private 2-year colleges is the retention of students. Because community colleges frequently serve underprepared students and students from marginalized backgrounds, their enrollment management is often more closely linked to retention efforts than might be the case at 4-year institutions.

Conclusion The duties of those charged with enrollment management intersect with a variety of institutional offices, programs, and departments, as well as external constituencies. This means that enrollment

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management as a function must consider myriad internal and external factors. Enrollment management provides marketing information and analysis, develops and implements pricing and aid strategies, carries out recruitment and admissions activities, and plays a special role in institutional financial planning. It has also become much more professionalized and strategic in focus. It now employs sophisticated forecasting and pricing models and integrated strategic planning processes and often serves as a hub for carrying out the academic, public service, and social justice missions of colleges and universities. Enrollment management also serves as the primary outlet for communicating the market position, selectivity, and diversity of an institution. Gabriel R. Serna See also College Choice; College Enrollment; College Selectivity; Demand for Education; Higher Education Finance; Student Financial Aid; Tuition and Fees, Higher Education

Further Readings Black, J. (2001). The strategic enrollment management revolution. Washington, DC: American Association of Collegiate Registrars and Admissions Officers. Bontrager, B. (2007). The brave new world of strategic enrollment management. College & University, 82(2), 3–6. Bontrager, B. (2008). SEM and institutional success: Integrating enrollment, finance, and student access. Washington, DC: American Association of Collegiate Registrars and Admissions Officers. Cheslock, J., & Kroc, R. (2012). Managing college enrollments. In R. Howard, G. McLaughlin, W. Knight, & Associates (Eds.), The handbook of institutional research (pp. 221–236). San Francisco, CA: Jossey-Bass. Henderson, S. (2001). On the brink of a profession. In J. Black (Ed.), The strategic enrollment management revolution (pp. 3–36). Washington, DC: American Association of Collegiate Registrars and Admissions Officers. Hillman, N. (2012). Tuition discounting for revenue management. Research in Higher Education, 53, 263–281. Hossler, D. (1986). Creating effective enrollment management systems. New York, NY: College Entrance Examination Board. Hossler, D. (2000). The role of financial aid in enrollment management. New Directions for Student Services, 89, 77–90. Hossler, D. (2004). Refinancing public universities: Student enrollments, incentive-based budgeting, and incremental

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Equalization Models

revenue. In E. St. John & M. Parsons (Eds.), Public funding of higher education: Changing contexts and new rationales (pp. 145–163). Baltimore, MD: Johns Hopkins University Press. Hossler, D., Bean, J., & Associates. (1990). The strategic management of college enrollments. San Francisco, CA: Jossey-Bass. Hossler, D., & Gallagher, K. (1987). Studying college choice: A three-phase model and the implication for policy makers. College & University, 2, 207–221. Hossler, D., Schmit, J., & Vesper, N. (1999). Going to college: How social, economic, and educational factors influence the decisions students make. Baltimore, MD: Johns Hopkins University Press. McPherson, M., & Schapiro, M. (1998). The student aid game: Meeting need and rewarding talent in american higher education. Princeton, NJ: Princeton University Press. Whiteside, R. (2004). Marketing for colleges and universities. Washington, DC: American Association of Collegiate Registrars and Admissions Officers.

EQUALIZATION MODELS School finance equalization originated as a tax reform, and it empowered state governments to address the problem of interdistrict funding disparities. States finance and administer a tax effort equalization policy so that high-need local school districts with weak tax bases and high tax rates can operate at levels similar to those of more affluent school districts. Such a scheme equalizes per-pupil expenditure levels. The goal of equalizing tax capacity differences across school districts is known as equalization, and it is frequently used to establish horizontal equity across school districts. Along with tax effort equalization, school finance equalization can sometimes allocate state resources to school districts to meet the needs of groups of students with conditions that can impair learning, such as limited English proficiency or physical and mental disabilities. This equalization, which provides varying levels of resources based on need, is designed to create vertical equity. Unlike horizontal equity that assumes equal conditions among students, vertical equity recognizes unequal conditions among students. Using vertical equity formulations, Table 1

students with greater needs will receive higher funding allocations than regular students. Cost equalization recognizes that there are cost differences between regions within a given state due to market and size variations. Education potentially costs more to local taxpayers in vibrant cities and in remote rural areas than in suburban areas. Adjustments in state financing are made for these cost variations for all districts to meet a set of state expected performance outcomes with a goal of achieving regional equity. This entry provides an overview of equalization models by describing and explaining several state policy remedies for fundamental school finance problems. It briefly defines three types of equalization approaches in state school finance policy: (1) tax effort equalization, (2) need equalization, and (3) cost equalization. It will also explicate several equalization models, namely, the flat grant, the foundation program, district power equalizing, and full state funding.

Equalization Models Table 1 exhibits the spectrum of equalization models. The flat grant represents one extreme of equalization typifying a scheme that is less centralized, whereas the other extreme exhibits full state funding as the most centralized plan. Flat Grant

The flat grant, which was instituted as early as the mid-19th century, called for state distribution of equal per-pupil revenue to each school district. However, this funding measure did not mitigate the vast interdistrict spending differences created by disparate tax bases, disparate interschool community attendance counts, and widespread and contrasting average family income differences. The flat grant’s equal state aid allocation did not consider any elements of tax effort equalization, need equalization, or cost equalization. Foundation Program

The foundation program responded to the shortcomings of the flat grant in the early 20th century. It rewarded the tax effort of school districts with

Postsecondary Students of Ages 25 and Older in 2011, by Subgroup and Type of College or University Least Centralized

Most Centralized

Flat Grant—Foundation Program—District Power Equalization—Full State Funding

Equalization Models

state aid inversely related to local capacity up to a base level of support. This base level of support was known as the foundation level, which ideally corresponded to a state’s unofficial minimum level of educational expectations. In addition to the established foundation level, each district levied the same required tax rate. The state allocated more aid for schools that generated less levied revenue up to the foundation level, and the state allocated less aid for schools that generated more levied revenue up to the foundation level. While single-tier foundation programs instituted more equalization than the flat grant, wealthy school districts in many states outspent the official equalizing level of the foundation plan. Some states simply allow wealthy districts to have lower tax rates or higher revenues. Other states recapture “excess” revenue raised by wealthy districts and redistribute it to lower wealth districts. A number of states have instituted a second tier in their funding formula that equalizes local decisions to raise revenue, up to a point. This is often in the form of a guaranteed tax base or percentage power equalizing. The architects of the foundation program were George Strayer and Robert Haig. Paul Mort first operationalized the Strayer-Haig equalization model when it became New York State’s school finance formula in 1925. Since then, most states have instituted some form of a foundation program in their respective school finance systems. Guaranteed Tax Base

Guaranteed tax bases (GTB) or district power equalizing emphasize the full intent behind the principle of tax effort equalization in that this equalization model empowers low-wealth school districts to generate tax revenue at the level of high-wealth school districts. District power equalizing evolved through two distinct equalization models, namely, (1) percentage equalizing and (2) the guaranteed tax base. The GTB formula mirrors an important component of the foundation program in that this scheme allocates state aid inversely in relation to local capacity. Although the GTB and percentage equalizing formulas provide state aid inversely in relation to local capacity as does the foundation program, these two approaches differ from the foundation program in that they establish a pupil expenditure level that exceeds the foundation program’s minimum per-pupil expenditure concept. These two district power equalizing schemes also

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differ from each other in that the GTB empowers school communities to a state-guaranteed tax base, whereas the percentage equalizing formula raises low-wealth districts’ per-pupil spending capacity. Rather than use a specific foundation level, the percentage equalizing formula institutes an aid ratio to determine the actual state aid allocation. The aid ratio calculates the relationship of a school district’s wealth over the state’s average of school district wealth. If this ratio is less than 1 and closer to 0, then the school district will be deemed needy, and state aid will be more generous. Conversely, state aid will diminish proportionately if this aid ratio is 1 or exceeds 1. Although it was designed in the 1920s, states did not begin using the percentage equalizing model until the 1960s, and some states that once used it no longer do. Responding to a series of school finance lawsuits, states adopted a more aggressive equalization model through GTBs. The GTB incentivizes school districts to increase school taxes to an official guaranteed yield by providing state matching funds for these additional local school contributions. As with the foundation program, the state allocates aid in inverse relation to local capacity by providing matching aid to poor school districts that raise their tax rates and less or no state aid to those school districts with tax rates higher than the guaranteed tax level. But unlike the foundation program, the state allocates matching revenue based on a school district’s local tax effort, which can far exceed the required minimum local effort under a foundation program. The subsequent effect is that low-wealth school districts have a greater potential to generate as many taxable resources as high-wealth school districts. This also means that a similar base of funding is established for low-wealth districts when compared with the funding base for high-wealth school districts. Many states use a GTB approach. For example, until recently, Missouri used this approach as its sole method of distributing funds to school districts. Combined Equalization Models

Some states use a two-tier approach combining the foundation program and GTB in ways that incorporate tax effort equalization, need equalization, and cost equalization. Kentucky goes so far as to have a three-tier approach. The first is a traditional foundation program, the second a GTB that allows local districts equalized choice in funding

312

Equalization Models

levels, while the third tier is unequalized. Despite the potential for substantial revenue disparities with the third tier, Lawrence O. Picus, Mark Fermanich, and Allan Odden found that from 1990 to 2000, the distribution of revenues to Kentucky school districts became more equal using traditional school finance equity statistics. Full State Funding

In describing full state funding, it is important to note the contribution of Henry Morrison. During the 1930s, Morrison argued that the statelocal partnership contribution was fraught with instability since local communities lacked the competency to administer the state equalization policy effectively. He insisted that local control of the tax base would invariably perpetuate interdistrict disparities. Morrison, therefore, called for a state tax, complete state centralization, and a fully stateadministered equalization program. Morrison conceived the equalization model of full state, arguing that only the state through a completely centralized taxation policy could ensure that an equalization initiative stabilized tax rates for all school communities and distributed revenue equitably to the same. A full state program is an equalization model that has the highest level of state centralization because it includes no local partnership in school funding. The state levies a state tax to generate revenue for all local schools. The only state that uses a full state approach is Hawaii. Hawaii is unique because its public schools are under the auspices of one state school district, and the state endeavors to achieve equity in spending with no need of a tax effort component in its equalization approach. This means that nearly all public school funding comes from the state, apart from the share from the federal government (which in 2013 was about 13% of all direct spending on K-12 schools nationally). Local property taxes are used to finance county and municipal services that do not include public school education. Hawaii’s full state program has need equalization components including pupil weights for students with special needs, students with limited English proficiency, gifted and talented students, gradelevel differences, and student demographic changes. Hawaii’s formula also has cost equalization weights for regional density or population sparsity and geographical isolation. Apart from the classic top-down approach of state responsibility for school funding, a select number of

school finance policy analysts also maintain that full state funding occurs when the state oversees a partnership with local districts, in which they continue to raise a share of district funds, to ensure that spending differences do not prevail across the state. These policy analysts use classical equity measures of the range, federal range ratio, coefficient of variation, Gini coefficient, McLoone Index, and Vertsegen Index to determine whether the distribution of perpupil expenditures by the state reveals a measure of interdistrict variation. If these interdistrict spending variations are small or nonexistent under this highly state-supervised partnership with local contribution, then these analysts conclude that full state funding prevails.

Conclusion Taxation policy options have been used with sophistication to achieve equitable delivery of public school education starting with the early equalization models of the flat grant and minimum foundation program. The complexity of remedies for interdistrict and interstudent differences, while always present, has nonetheless increased over time in an effort to perfect the attainment of equitable school expenditures. The perfection of state aid has also addressed the unique challenges of regional cost differences, especially for small rural school communities. This explains why states have adopted aggressive tax effort equalization models of district power equalization, especially through a transition from percentage equalizing to the guaranteed tax base, and why some states have begun to use a combined equalization model. The simplest method to guarantee equity might be the classical notion of full state funding, but only one state uses this model. Still, other state-sponsored funding equity programs have noticeably reduced interdistrict spending disparities. Whether through combined equalization models or full state funding, contemporary school finance programs contain aspects of tax effort equalization, need equalization, and cost equalization. Tyrone Bynoe See also Guaranteed Tax Base; Horizontal Equity; Vertical Equity

Further Readings Baker, B., Green, P., & Richards, C. (2008). Financing educational systems. Upper Saddle River, NJ: Merrill Prentice Hall.

Evolution in Authority Over U.S. Schools Berne, R., & Stiefel, L. (1984). A measurement of equity in school finance: Conceptual, methodological, and empirical dimensions. Baltimore, MD: Johns Hopkins University Press. Coons, J., Clune, W., & Sugarman, S. (1970). Private wealth and public education. Boston, MA: Harvard University Press. Johns, R. L. (1972). The coming revolution in school finance. Phi Delta Kappan, 54(1), 18–22. Mort, P. (1926). Equalization of educational opportunity. Journal of Educational Research, 13(2), 90–103. Odden, A., & Picus, L. (2008). School finance: A policy perspective (4th ed.). New York, NY: McGraw-Hill. Strayer, G. (1938). Financial implications of the philosophy underlying American education. Peabody Journal of Education, 15(4), 204–210. Works, G. (1927). The relation of the state to the support of education. Elementary School Journal, 27(5), 335–343.

EVOLUTION IN AUTHORITY OVER U.S. SCHOOLS This entry discusses the role of the three levels of government in the United States in influencing education policy and production. Essential to understanding any particular element of education policy, whether it be expenditures per pupil, productivity of the schools, teacher quality, accountability issues, or broader education reform, is a basic knowledge of the level of government in which decision making over these elements is concentrated. This entry describes the evolution of authority and responsibility for the provision and financing of K-12 schooling in the United States. The description illustrates that the initial concentration of control over education policy in local governments experienced at least two dramatic shifts toward state control during the 20th century. The most important change over the past 15 years has been a shift in control away from state and local governments to the federal government.

State-Local Relationships Early History and Local Control

The Founding Fathers of the United States did not explicitly mention education in the Constitution. As a result, the Tenth Amendment implicitly granted the rights regarding education to the states. These states’ rights were evident in state constitutions, but control and financing for public education initially belonged to local government, whether because

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of geographic necessity, political mores, or simple efficiency. Although local governments had access to limited resources, by 1870, approximately 65% of the K-12 school-age population was enrolled in some type of school—either locally funded public schools or private schools. The public funding for schooling remained small until well into the 20th century. In 1920, for example, the amount of revenues collected per pupil to fund public schools was just over $500 (in 2010 dollars). More than 83% of these revenues for elementary and secondary education came from localities with virtually all the remaining contributed by state governments. The federal government’s contribution to education spending was less than one half of 1% during this period. Enrollment growth and funding growth began to accelerate at the beginning of the 20th century as a result of the so-called high school movement that took place between 1910 and 1940. The extension of schooling to include high school reflected actions largely of local communities but expanded across the country at a relatively fast rate. While in 1910, only 9% of school children earned a high school diploma, by 1935, that grew to 40%. Expanding schooling from primary level to secondary level required that governments contribute more financial support to schooling, and states began contributing a greater share of the costs of schooling. The revenues collected almost tripled over this time period, so that by 1940, government revenues for primary and secondary schooling had grown to almost $1,500 per pupil (in 2010 dollars). From an organizational perspective, not only did the revenues grow over this time period but a shift in authority for the provision of schooling occurred as well. As high schools expanded, states became more involved in funding schools. Indeed, the states’ contribution to revenues for schools almost doubled during the high school movement. As illustrated in Table 1, by 1940, the states were providing approximately 30% of the total revenues of schools. The role of the states continued to grow gradually throughout World War II and the postwar period into the 1960s. Over this entire time, the federal government played little role in directly financing schooling or in exerting direct authority over the provision of schooling. Increasing State Control

A significant change in state-local organizational roles began in 1971 with the Serrano v. Priest court decision by the California Supreme Court. Local

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Evolution in Authority Over U.S. Schools

Table 1

Revenues for Public Elementary and Secondary Schools, by Source of Funds by Decade: 1919–1920 Through 2009–2010

School Year

Revenues Per Pupil (Percent Distribution)

Table 2

Revenues for Public Elementary and Secondary Schools, by Source and State: 2009–2010 Percentage of Total Revenues

State

Federal

State

Local

Federal

State

Local

California

13.8

54.2

32.0

1919–1920

0.3

16.5

83.2

Kentucky

16.6

52.1

31.3

1929–1930

0.4

16.9

82.7

New Hampshire

12.5

32.1

55.4

1939–1940

1.8

30.3

68.0

New York

9.0

41.0

50.0

1949–1950

2.9

39.8

57.3

Nevada

8.5

32.6

58.8

1959–1960

4.4

39.1

56.5

1969–1970

8.0

39.9

52.1

1979–1980

9.8

46.8

43.4

1989–1990

6.1

47.1

46.8

1999–2000

7.3

49.5

43.2

2009–2010

12.7

43.5

43.8

Source: Data from Snyder and Dillow (2013, table 202, p. 281).

revenues for funding of public schools both in California and nationally were historically derived predominantly from property taxes. This reliance on local property taxes meant that the revenue capacity for funding schools varied across localities within a given state. Critics of the property tax funding argued that such reliance was inherently unfair to children residing in lower property wealth areas. Others argued that the variance of revenues across localities was a means by which residents could express differences in desire to support public schools. The California Supreme Court found that local funding plans were unconstitutional and ordered the legislature to develop a new funding plan for schools that would break the link between per-pupil revenues and local wealth. Legislators responded by passing Senate Bill 90 in 1972, creating the revenue limit system that put a ceiling on the amount of generalpurpose money each district could receive. The California decision led to a wave of challenges to state funding systems across the United States. Over the next 25 years, courts in 43 states would hear cases regarding the constitutionality of the funding system for public schooling, and these court challenges continue today. Many states have followed the California lead and overturned their funding systems, while others have not overturned their systems but have modified existing sources of revenues for schools. But even in states without a

Source: Data drawn from Cornman, Young, and Herrell (2012, table 1, p. 7).

court challenge, states generally have been proactive in moving toward funding formulae that shift more responsibility to the state and away from local tax bases. The result of these changes in funding sources has been to reduce the average variance in funding across local districts within a state. By 1980, the absolute dollars stemming from state revenues on average across the states exceeded that from local sources, and the percentage of revenues had shifted such that the state-level governments represented the single largest source of funding for public schools. The trend has reversed slightly with state and local governments contributing roughly equal proportions of school revenues as of 2009–2010 (see Table 1). Averages across states, however, disguise strong differences in revenue sources that remain today. As illustrated in Table 2, some states, such as California and Kentucky, rely heavily on revenues from state rather than local tax bases. Other states, such as New Hampshire and Nevada, have continued to tie funding more strongly to local tax bases under the assumption that local financing more closely ties voter and taxpayer preferences to the level of expenditures of the local districts. In addition to the continuing differences in sources of funding across states, there remain differences in absolute per-pupil expenditures across states. The five highest spending states spend more than double per pupil than do the five lowest spending states. Viewed from this perspective, education provision continues to reflect state and local preferences.

The Federal Government’s Role Until recently, the federal government has played a relatively small role in K-12 education. To the extent the federal government was involved, it was sparked

Evolution in Authority Over U.S. Schools

by specific events such as the launch of the Russian Sputnik satellite in 1957. Following Sputnik, Congress was concerned about U.S. competitiveness and appropriated funds for math and science education and foreign language to elementary and secondary schooling through the defense budget. In the 1960s, funding from the federal government expanded for two reasons. One was the passage of the Civil Rights Act in 1964. The second was the Elementary and Secondary Education Act (ESEA) passed in 1965 and its subsequent amendments, which were part of President Lyndon B. Johnson’s War on Poverty and provided direct funding of local education agencies to support low-income students. Title I of the ESEA targeted financial assistance to educationally deprived children. Although the assistance was aimed at local education agencies, all applications for assistance were subject to approval by the relevant state educational agency. Title II of the ESEA in 1967 made state agencies responsible for the development of local programs that would receive federal assistance. Finally, Title V of the ESEA was explicitly designed to strengthen state departments of education and appropriated funds among the states to be spent as state agencies deemed appropriate. Continuing growth and influence of the federal government in elementary and secondary schooling culminated with the establishment of a U.S. Department of Education in 1979 under President Jimmy Carter. Over the past decade, the most significant organizational change in K-12 public education has related to the enhanced role of the federal government in influencing the relationship between the state-local government authority and finance. This growth in federal government responsibility and financing of K-12 education has taken place through its effort to promote “accountability” within public schools as a stated means of improving the quality of U.S. schools and increasing the competitiveness of U.S. students with those worldwide. The accountability movement began with the states and, in some cases, was related to the changing funding formulae prompted by the court challenges described earlier. For example, in response to Rose v. Council, the Kentucky Supreme Court ordered the legislature to establish a new system of financing schools. In response, the Kentucky legislature enacted a broad-based school reform in 1991, the Kentucky Education Reform Act, and provided additional funding to local school districts but also required the schools be held accountable

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for their students’ performance as measured by state standardized exams. In Kentucky, the exams have changed over the almost 25-year history, and the state recently moved to include a national exam— the ACT—as part of the accountability index. While originating with the states, outcomes-based accountability has been adopted by the federal government in such a way that it continues to alter the relationship between state and local governments. Under the George W. Bush administration, the accountability emphasis resulted in bipartisan federal legislation known as the No Child Left Behind Act of 2001. This law replaced the earlier ESEA. Under the No Child Left Behind Act, states were required to develop their own systems of accountability. Rather than the “school-level” accountability required under the Kentucky legislation, which focused on the average performance of students within a school, the federal legislation required states to track grade-level student performance via standardized test scores as a condition of receiving continuing Title I funding. Failure to enact some type of performance measures would result in a variety of sanctions. Critics called the law an unfunded mandate as no additional funds were provided to implement the testing systems or develop the necessary data systems for compliance purposes. The federal government’s growing influence over states’ education policy has continued under the Obama administration; the most well-known example has been the grant competition known as Race to the Top. As was the case with the legislation directing federal funding to schools during the 1960s, the funding to implement Race to the Top was part of a larger economic issue. Following the recession of 2008, Congress passed the American Recovery and Reinvestment Act of 2009, also known as the stimulus act. The law authorized nearly $100 billion for education, most of it to fill state funding gaps and keep existing programs, but $5 billion of the money went to the U.S. Department of Education to distribute on a competitive basis; most of the competitive funding was used for Race to the Top. The purpose was to spur innovation and reforms in K-12 education in states and local districts. The Race to the Top program gave points for various components of the proposals from states. For example, points were given for satisfying performance-based standards, for building data systems to track performance of students and teachers, for easing barriers against the creation of new charter schools, and for adopting

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the English and math curriculum standards known as the Common Core State Standards. The cumulative effects of the education policies of the past two presidential administrations have been a substantive increase in the role of the federal government in U.S. education. To a large degree, the role of the federal government has increased beyond what is reflected in the budget per se, although the per-pupil revenues for education more than doubled in real terms over the decade from 2000 to 2010. But as described above, the shift is not reflected simply in the increase in federal dollars but in changes in the way in which dollars are allocated. As federal spending has grown, the restrictions necessary for receiving the funding have grown, and compliance costs for the states to garner the funding have grown. Opponents of the increased federal role argue that the administrative costs imposed on state and local governments erode or exceed the benefits to states in the aggregate. There appears to be significant momentum toward further increasing centralization of education policy at the federal level. And relative to much of the world, the U.S. degree of centralization remains small. Shared responsibility between levels of government for education, as with many other types of goods and services, has been common throughout American history. However, the move by most states to adopt the Common Core curriculum standards may represent a shift toward federal policy control. Although the standards are voluntary, the federal government has strongly encouraged their adoption through efforts such as Race to the Top. Whether the United States will move to national exit exams, as commonly practiced in some countries, remains unknown at this point. Certain interest groups and some states are challenging the increasing role of the federal government in an issue that has historically been under the purview of state and local governments. Eugenia F. Toma See also Centralization Versus Decentralization; Elementary and Secondary Education Act; Intergovernmental Fiscal Relationships; Local Control; School Finance Litigation; Serrano v. Priest

Further Readings Cornman, S. Q., Young, J., & Herrell, K. C. (2012). Revenues and expenditures for public elementary and secondary education: School year 2009–10 (fiscal year

2010) (NCES 2013-305). Washington, DC: U.S. Department of Education, National Center for Education Statistics. http://nces.ed.gov/pubsearch/ pubsinfo.asp?pubid⫽2013305 Goldin, C., & Katz, L. F. (1999). Human capital and social capital: The rise of secondary schooling in America, 1910 to 1940. Journal of Interdisciplinary History, 29(4), 683–723. Gordon, N. E. (2008). The changing federal role in education finance and governance. In H. F. Ladd & E. B. Fiske (Eds.), Handbook of research in education finance and policy (pp. 295–313). New York, NY: Routledge. Murray, S. E., Evans, W. N., & Schwab, R. M. (1998). Education-finance reform and the distribution of education resources. American Economic Review, 88(4), 789–812. Snyder, T. D., & Dillow, S. A. (2013). Digest of education statistics 2012 (NCES 2014-015). Washington, DC: U.S. Department of Education, National Center for Education Statistics, Institute of Education Sciences. Retrieved from http://nces.ed.gov/pubsearch/pubsinfo. asp?pubid⫽2014015 Toma, E. F. (1980). Education. In E. J. McAllister (Ed.), Agenda for progress: Examining federal spending (pp. 197–215). Washington, DC: Heritage Foundation.

EXPENDITURES AND REVENUES, CURRENT TRENDS OF Many factors, including changes in enrollment and in state and federal policy, have caused shifts in recent years in the revenues (income) and the expenditures (disbursement of revenues) in public education in the United States. This entry covers assessments of current trends in student enrollment, expenditures, and revenues in education relative to K-12 public schools and institutions of higher education in the United States and strategies that schools are using to constrain spending and improve operational efficiency. Student enrollment is one of the multiple factors that schools consider, in conjunction with expenditure and revenue trends, when making funding decisions for educational programs. Unless otherwise indicated, the expenditure and revenue figures in this entry are presented using constant dollars per pupil for K-12 and per full-time-equivalent (FTE) student for higher education. Constant dollars, or dollars adjusted for inflation, are dollars adjusted to account for changes, over time, in the value of a dollar or what a dollar can purchase.

Expenditures and Revenues, Current Trends of

Public Elementary and Secondary Education Current Trends in K-12 Education

The National Center for Education Statistics (NCES) reported that in the fall of 2010, there were a total of 49.5 million students enrolled in public elementary and secondary schools in the United States. Of this total, 70% (34.6 million) attended elementary schools and 30% (14.9 million) attended secondary schools. The number of students enrolled in elementary schools increased by 1% a year from 2007 to 2010: by 81,000 students between 2007 and 2008, by 123,000 between 2008 and 2009, and by 216,000 between 2009 and 2010. The number of students enrolled in secondary schools during the same period decreased by 1% with the largest decrease, 107,000, in the fall of 2008. Enrollment in public elementary schools is expected to increase through 2020. The NCES enrollment data for fall 2012 was not available as of January 2014, but enrollment in secondary schools was expected to decrease through that year, after which enrollment is expected to increase through 2020. K-12 Expenditures

Public elementary and secondary school total expenditures were approximately $12,743 per pupil in 2009–2010 or $638 billion, a decrease of 0.1% ($214 per pupil) from 2008–2009. Schools report total expenditures in three main categories: (1) current expenditures, (2) capital outlay, and (3) interest on school debt. Current expenditures as reported by NCES in 2009–2010 were $11,184 per pupil, an increase of 1% ($112 per pupil) over 2007–2008 current expenditures for students. Current expenditures include programs and services that are part of the formula for calculating per-pupil expenditures: instruction, student support, instructional staff services, operation and maintenance, administration, transportation, and food services. Programs and services to private schools, adult education, and other similar expenditures are not included. The calculation and reporting of perpupil expenditures vary across states and school districts and are determined by the entity preparing and reporting the expenditures. State-level per-pupil expenditures for the 2009–2010 school year ranged from a low of $6,676 in Utah to a high of $19,889 in the District of Columbia. Instruction, the largest expenditure reported to NCES by public elementary and secondary schools

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in 2009–2010, comprised 61% ($6,852 per pupil) of total current expenditures. Instruction captures expenditures for salaries, benefits, services, materials, equipment, and contractual programs and services that relate to or support the interaction between teachers and students. Salaries and benefits can account for the greatest amount of instructional expenditures. Operation and maintenance, which includes activities for the maintenance and safety of school facilities, was the second largest expenditure category in 2009–2010 at $1,063 per pupil, or 0.095% of total current expenditures. K-12 Revenues

Just as expenditures vary across states as well as districts within the same state, revenue sources vary as well. Traditionally, public schools are funded through three main sources: local revenues and state and federal governments. Local governments use the property tax to fund education, while states use individual and corporate income taxes and the sales tax. The federal government uses individual and corporate income taxes and payroll taxes. The levels of local, state, and federal funding allocated to public elementary and secondary public schools reached historic levels in recent years, with the highest level of state funding in 2007–2008 ($304 billion, or 48% of total revenues), the highest level of local funding in 2008–2009 ($275 billion, or 44% of total revenues), and the highest level of federal funding in 2009–2010 ($80 billion or 13% of total revenues). However, when total revenues are considered for the 2008–2009 school year through the 2009–2010 school year, there was actually a 0.01% decrease in constant 2011–2012 dollars. Half or more of public elementary and secondary school revenues for the 2009–2010 school year came from either state or local sources in 64% (32) of the nation’s states. State governments provided at least half of the revenues for education in 56% (18) of those states; local revenues provided at least half of the revenues in 44% (14) of those states. Strategies to Constrain Spending and Improve Efficiency in Operations

Decreased revenues, increased expenditures, and expected increases in student enrollment through 2020 present public elementary and secondary schools with unprecedented opportunities for funding educational programs in nontraditional, creative ways. Schools have depleted the short-term stimulus

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funds the American Recovery and Reinvestment Act of 2009 provided at the beginning of the recession. The economy has shown some evidence of recovery in the areas of job growth and the housing market, but less so in education. The extent and nature of job loss has been novel and staggering in some areas. Schools will continue to look for creative ways to constrain spending in the largest category of current expenditures, instruction. In an effort to better position them to qualify for federally funded initiatives as a new revenue stream, states have enacted mandates that require schools to redesign programs and implement personnel evaluation models tied to performance pay and student achievement. State mandates of this nature, in conjunction with pension reforms that require educators to remain in the workforce longer, may make it increasingly difficult for teachers and administrators to enter and stay in the profession. While this may unintentionally help reduce salaries and benefits, one of the largest areas in the category of instruction, it will not accommodate staffing for the expected increase in student enrollment. School districts are adding consideration of financial impact as a decision-making factor in discussions exploring the feasibility of reducing class size. Others are basing staffing allocation decisions on student enrollment. These and other decisionmaking strategies that include the use of schoolspecific data will help schools control spending and improve operations in a more efficient manner.

Higher Education Higher Education Enrollment

The NCES reported that in the fall of 2010, there were a total of 18.1 million students enrolled in undergraduate institutions. Of this total, 58% (10.4 million) attended 4-year institutions and 42% (7,680,875) attended 2-year institutions. Enrollment also varied by type of institution. Approximately 76% (13.7 million) of students attended public institutions, some 15% (2.7 million) attended private nonprofit institutions, and about 9% (1.7 million) attended private for-profit institutions. Although the fall 2010 enrollment of 18 million students is a decline from the fall 2009 student enrollment of 20.4 million, the total number of students seeking postsecondary education has increased overall. The U.S. Government Accountability Office reports that since the 1998–1999 school year, both public and nonprofit institutions have experienced a 31% increase in student enrollment. As the

economy slowly recovers from the past recession, more students may seek a postsecondary degree at an institution of higher education as a key to a better economic future. Higher Education Expenditures

As the demand for a postsecondary degree has grown, so has the cost of paying for a college education. Higher education institutions report per-student expenditures, which are the total amount of monies spent per FTE student for instructional activities and noninstructional activities, including student services, conducting research, academic support, scholarships and fellowships, net grant aid to students, and medical expenditures. The NCES reported the total and per-FTE student expenses for public institutions, private nonprofit institutions, and private for-profit institutions for the academic years 2004–2005 and 2009–2010. According to NCES, in 2004–2005, instruction was the largest expense category receiving $7,358 per FTE student (in 2010–2011 constant dollars) in public institutions and $7,239 in 2009–2010. Private nonprofit institutions’ expense per FTE student was $14,569 in 2004–2005 and increased to $15,321 in 2009–2010. Private for-profit institutions’ expense per FTE student was $3,389 in 2004–2005 and slightly less in 2009–2010 ($3,017). Instruction was the largest expense category for both public and private nonprofit institutions during this time period. The second-largest expense category in public institutions was research. The expense was $2,781 per FTE student in 2004–2005 and $2,664 in 2009–2010. Research was the third-largest category in private nonprofit institutions, costing $5,148 per FTE in 2004–2005 and $5,212 in 2009–2010, while institutional support was the second-largest category at a cost of $5,903 per FTE student in 2004–2005 and $6,270 in 2009–2010. Instruction, however, was the second-largest category in private for-profit institutions ($3,385 per FTE in 2004–2005 and $3,017 in 2009–2010). But student services, academic services, and instructional support were the most expensive, with a cost of $8,329 per FTE student in 2004–2005 and $8,310 in 2009–2010. Overall, the aforementioned figures indicate that the receipt of revenues to provide these education services has declined over time. Higher Education Revenues

The NCES also reported the total and per-FTE student revenues of postsecondary degree-granting

Expenditures and Revenues, Current Trends of

institutions by control of institutions and source of funds for academic years 2004–2005 and 2009–2010. Revenues received and reported by public institutions, private nonprofit institutions, and private for-profit institutions include tuition and fees, grants and contracts, appropriations, and other revenues (e.g., capital appropriations, gifts, etc.). Specifically focusing on the source of funds for public institutions, the NCES reported that in the academic year 2009–2010, total revenue received by public institutions was $309 billion (in constant 2010–2011 dollars) at public postsecondary degree-granting institutions. Revenue received per FTE student in 2009–2010 was $28,781, a decrease from the $28,966 reported in 2004–2005. Although revenues were higher in 2004–2005, FTE enrollment in 2009–2010 was 15% higher (9.3 million in 2004–2005 and 10.8 million in 2009–2010). Private nonprofit institutions received total revenues of about $172 billion (in constant 2010–2011 dollars). The revenue received in the academic year 2004– 2005 was $56,315 per FTE student. This amount decreased to $54,425 in 2009–2010. Private forprofit institutions received about $25 billion in total revenues (in constant 2010–2011 dollars). The revenue for 2004–2005 was $16,063 per FTE student. This amount decreased in 2009–2010 to $15,675 per FTE student. The NCES also reported the total revenue received per FTE student by institutional level, institutional control, and source of funds. Focusing only on public institutions as an example, the NCES reports total revenue received per FTE student at 2-year public postsecondary institutions was $13,107 (in 2010–2011 constant dollars) for the academic year 2010–2011. This amount is higher than the 2004– 2005 amount of $12,765 received. The amount, however, decreased in 4-year institutions. The total revenue received per FTE student in 2009–2010 (in constant 2010–2011 dollars) went from $39,614 in 2004–2005 to $39,221 in 2009–2010. The revenue per FTE student that was generated from tuition and fees in 2-year institutions increased only marginally from $2,132 in 2004–2005 to $2,133 in 2009–2010. In 4-year institutions, tuition and fees also increased from $6,473 per FTE student in 2004–2005 to $7,421 in 2009–2010. Grants and contracts declined, however, across all institutions. In 2009–2010, 2-year institutions received $1,017 per FTE in contrast to the $2,195 received in 2004–2005. The 4-year institutions also experienced a decline, receiving revenue of $6,424 per FTE

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student in 2009–2010 and $7,628 in 2004–2005. The receipt of appropriations also declined from 2004–2005 to 2009–2010. The federal appropriations for 2-year institutions, for example, went from $1,474 per FTE student (in 2004–2005) to $504 (in 2009–2010). The pattern was similar in 4-year institutions, which received $5,178 per FTE student in 2004–2005 and $4,153 in 2009–2010. Strategies to Constrain Spending and Improve Efficiency in Operations

Overall, student enrollment in higher education has increased over the past decade for public and private institutions; most public and private nonprofit institutions, however, saw a decrease in revenues to cover increasing expenses between 2004–2005 and 2009–2010. The recent recession, coupled with revenue shortfalls, has been devastating for higher education. Even though the economic outlook is brightening, more budget cuts are expected in the future. Institutions of higher education are trying to compensate for the decline in appropriations by relying less on state government funding and more on direct student contributions to finance higher education. The researchers John Lee and Sue Clery, in American Academic, report that higher education institutions have also diversified their funding sources by generating own-source revenues, which include increased private fundraising; renting out campus facilities; developing or expanding corporate, online, and contract training; offering alumni programs; and claiming intellectual property rights for research done on campus. Other adopted strategies reported by the U.S. Government Accountability Office to contain spending and improve the efficiency of their operations include delaying capital projects, deferring maintenance, and cutting classes and support services that will not affect core academic areas. Judith A. Green and LaShonda M. Stewart See also Administrative Spending; Education Spending; Higher Education Finance

Further Readings Barr, A., & Turner, S. E. (2013). Expanding enrollments and contracting state budgets: The effect of the Great Recession on higher education. Annals of the American Academy of Political and Social Science, 650, 168–193.

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Extended Day

Brimley, F., Verstegen, D. A., & Garfield, R. R. (2011). Financing education in a climate of change (11th ed.). Boston, MA: Pearson Education. Crampton, F. E., & Vesely, R. S. (2006). Allocating human, financial, and physical resources. In S. C. Smith & P. K. Piele (Eds.), School leadership: Handbook for excellence in student learning (pp. 401–427). Thousand Oaks, CA: Corwin Press. Larry, R. S. (2013). Higher education: Recent trends, emerging issues and future outlook. New York, NY: Nova Science. Levenson, N. (2012). Smarter budgets, smarter schools: How to survive and thrive in tight times. Cambridge, MA: Harvard Education Press. Petrilli, M. J., & Roza, M. (2011). Stretching the school dollar: A brief for state policy makers. Washington, DC: Thomas B. Fordham Institute.

EXTENDED DAY At both elementary and secondary school levels, some struggling students are likely to benefit from afterschool or extended day programs, even if they receive other instructional interventions during the regular school day. Extended day programs are often used by school districts to provide academic support as well as to provide a safe environment for children and adolescents to spend time after the school day ends. This entry describes the research surrounding the value of extended day programs in schools and offers some ideas of how such programs might be successfully implemented to help struggling students in school. Research by Deborah Lowe Vandell, Kim Pierce, and Kim Dadisman found that well-designed and administered afterschool programs yield numerous improvements in academic and behavioral outcomes. On the other hand, the evaluation of the 21st Century Community Learning Centers Program, though hotly debated, indicated that for elementary students, extended day programs did not appear to produce measurable academic improvement. The critics of this study argued that the control groups had higher preexisting achievement, which reduced the potential for finding program impact. They also argued that the small impacts that were identified had more to do with lack of full program implementation during the initial years than with the strength of the program. Overall, studies have documented positive effects of extended day programs on the academic performance of students in select afterschool programs.

However, the evidence is mixed because of the research methods used (there have been few randomized trials), poor program quality, and imperfect implementation of the programs studied. The researchers have identified several structural and institutional supports necessary to make afterschool programs effective: Staff qualifications and support (staff training in child or adolescent development, afterschool programming, elementary or secondary education, and content areas offered in the program) Staff stability/turnover Adequate compensation Small enrollment size with children grouped in similar ages, a low child-staff ratio, and a program culture of mastery Dedicated space and facilities that support skill development and mastery, in a location that is accessible to youth and families Equipment and materials to promote skill development and mastery Curricular resources in relevant content areas Program partnerships and connections among administrators, teachers, and programs; connections with larger networks of programs; and connections with parents and community Program sustainability strategies (institutional partners, networks, and linkages; community linkages that support enhanced services; and long-term alliances to ensure long-term funding)

Overall, the research suggests that resources for extended day programs are productive in improving student achievement—and the more rigorous academic experiences along with regular attendance improves the likelihood of extended day programs leading to improvements. The challenge that school districts face in developing extended day programs is finding resources to provide these programs as well as encouraging students to attend. If this can be achieved, the extent to which the extended day program focuses on academic support for students seems to correlate with how well such programs help improve student achievement. Thus, staffing extended day programs with certificated teachers and maintaining relative pupil-teacher ratios is important despite the considerably higher expense associated with such programs. Lawrence O. Picus

External Social Benefits and Costs See also Adequacy: Evidence-Based Approach

Further Readings Dynarski, M., Moore, M., Mullens, J., Gleason, P., JamesBurdumy, S., Rosenberg, L., . . . Levy, D. (2003). When schools stay open late: The national evaluation of the 21st Century Community Learning Centers Program. Princeton, NJ: Mathematica Policy Research. Fashola, O. S. (1998). Review of extended-day and afterschool programs and their effectiveness (24). Washington, DC: Howard University, Center for Research on the Education of Students Placed at Risk (CRESPAR). Kleiner, B., Nolin, M. J., & Chapman, C. (2004). Before and after school care programs, and activities through eighth grade: 2001. Washington, DC: U.S. Department of Education, National Center for Education Statistics. Vandell, D. L., Pierce, K. M., & Dadisman, K. (2005). Outof-school settings as a developmental context for children and youth. In R. V. Kail (Ed.), Advances in child development and behavior (Vol. 33, pp. 45–78). Oxford, UK: Elsevier.

EXTERNAL SOCIAL BENEFITS AND COSTS The external social benefits of education are education outcomes that benefit others, including those in future generations. They include contributions of education to the operation and improvement of civic institutions essential to democracy, the rule of law, human rights, and political stability. They also include contributions to longevity; reduced poverty; lower crime rates; lower public health, prison, and welfare costs; environmental sustainability; social capital (or trust); and the creation of new ideas for product and process innovation and well-being of the society. Some externality effects are included in earnings such as taxes paid, earnings benefits of living in an educated community, and growth effects over time. External social benefits are in addition to private job market benefits and private nonmarket benefits. The latter include benefits to one’s health, longevity, and quality of life that are private benefits that individuals are willing to pay for (if they are aware of them), although they are sometimes confused with social benefits. The external social benefits of education are important to measure not only because they are education’s contribution to making the world a better place and creating a better future but also because

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they are the main reason for public support of education through tax revenues. This entry discusses how the external social benefits and external direct benefits of education are measured and valued. Individuals, families, and firms have insufficient incentive to invest enough in education to generate external social benefits, since they are benefits to others that cannot be captured privately by those who do the investing. So education requires a public subsidy to encourage larger private investment and counteract underinvestment if social efficiency, or the optimal use of resources in society, is to be achieved. These external social benefits are generated as human capital increases the productivity of time spent in household production, during the 72 or so waking hours each week typically spent at home and in the community. There are some negative externalities or costs, such as white-collar crime, that result from a society having higher education levels. That is, education may contribute to the reduction of street crime but add to the capacities for educated embezzlers or computer hackers to commit crimes. But most prisoners have less education than average in a society, so these negative externalities net out, leaving much larger positive external benefits. Other costs are the tax costs of education, although most tax costs provide private benefits. The value of external social benefits as a percentage of total private plus social benefits is the main criterion for the degree of public support that would achieve social efficiency (equity aside, the other reason for public support) required for optimal social welfare and optimal growth. If, for example, the estimated value of external social benefits is 50% of the total benefits, then 50% of the investment costs including private foregone earnings costs should be covered publicly for efficiency and optimal growth.

Methods of Measurement and Valuation There are currently four basic methods of measuring and valuing the external social benefits of education. Some are more comprehensive than the others, and each measure has different aspects of total social benefits, so extreme care must be used in making comparisons. These methods are as follows: 1. The contribution of education to each social benefit listed above is measured quantitatively by regression methods and then a monetary value is estimated for each based on how much it would cost to produce the same outcome by other means. This is the most comprehensive method. But care

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External Social Benefits and Costs

must be used to avoid overlap. It measures only the direct external benefits. 2. Indirect social benefits are education externalities that operate through other variables. These then feed effects back on growth in ways that build up over time. They are measured by estimating a system of difference equations within which the effect of education on each outcome enters after a lag, then solving this system recursively to estimate the total indirect impacts on growth as a function of time. These are valued by expressing them as a percentage of market-based growth outcomes. Indirect benefits need to be added to the direct social benefits, but again, care must be used to avoid overlap. 3. Aggregate single-equation methods estimate a standard macro growth equation using cross-country (interstate or intercity) data that seek to capture the (market-measured) social return to education and then a. subtract a private return estimated by regressions such as those specified by Jacob Mincer based on individual earnings data or b. include a term for the “average level of education in the community.”

Both are said to reflect aggregate education externality benefits. This method is simple and has been the most widely used. But it requires very strong assumptions and also measures only direct education externalities that affect market outcomes. It typically omits indirect effects by using control variables that ignore the effects of technology embodied in human capital by education and other effects that cumulate and affect future generations. 4. The “willingness to pay” approach asks respondents about their willingness to pay taxes (e.g., for community colleges) and then subtracts a private return estimated from microearnings data. This approach is more comparable to the first approach in that both are comprehensive in including direct nonmarket outcomes. For example, both include education’s contributions to democratization beyond earnings, whereas the aggregate method includes only effects on earnings.

Measuring and Valuing Each External Direct Benefit Using the first method, individual external social benefits have been measured quantitatively in studies

in many refereed publications in the United States, United Kingdom, Sweden, Germany, and elsewhere. These have been collected systematically in ways that avoid overlap among education outcomes, with scientific standards requiring appropriate controls and significant coefficients, and controls for per capita income, so that the effects measured are those beyond income. Education and income coefficients are standardized so that they are in comparable units and can be averaged to reduce the influence of outliers. There are 45 studies cited in Higher Learning, Greater Good by Walter McMahon (2009) where the controls and statistical properties are discussed. The monetary value of each education outcome is estimated as a separate step based on how much it would cost to produce each outcome by other means (e.g., in the Haveman-Wolfe method). The external social benefits from a typical U.S. bachelor’s degree are shown in the second part of Table 1. These comprehensively estimate the value of the annual direct contribution of education to democratic institutions; judicial human rights institutions; institutions conducive to political stability; life expectancy (net of the growth costs of retirement systems); reduced inequality (community college and Pell grant support); reduced poverty; lower homicide and property crime rates; reduced state welfare, public health, and prison costs; improved environmental sustainability; increased social capital; and new ideas. (Education indirectly benefits environmental sustainability through lower population growth, reductions in poverty, and higher levels of democracy.) The total value of these external benefits is estimated to be $27,726. A precise error margin cannot be estimated because a number of education coefficients from underlying studies (all of which reach the 0.05 level or better) are averaged and because an additional assumption is required to estimate the monetary value of each quantitative outcome. The estimate is conservative, however, because no monetary value can be placed currently on the value of education’s contributions to reduced inequality, social capital, or new ideas. The estimated value of these external social benefits above and beyond earnings is 106% of the annual earnings increments due to college of $26,204, which, in turn, are above the earnings of the typical high school graduate in 2010 of $30,719 on average. In other words, the best available estimate of the direct external social benefits above and beyond earnings suggests that they are about equal to or slightly larger than the earnings benefits.

External Social Benefits and Costs

Table 1

Estimated Value of Benefits From Bachelor’s Degree Above Earnings in the United States

Private Nonmarket Benefits Own health

Value Per Year $16,800

Better child health

$1,333

Better spousal health

$1,917

Greater longevity

$2,179

Child education and cognitive development

$7,892

Smaller family size (less poverty)

$1,551

Increased happiness Efficient purchasing and saving management

⫹, $NA $3,401

Work and location amenities

⫹, $NA

Lifelong learning

⫹, $NA

Total private nonmarket benefits

$38,080a

Direct External Social Benefits

Value Per Year

Democracy (better civic institutions)

$1,830

Human rights (judicial institutions)

$2,865

Political stability

$5,813

Longer life expectancy

$2,308

Less inequality (associate degrees and Pell Grants) Poverty reduction Lower homicide rates Less property crime Lower public welfare, prison costs Water, air, forest, wildlife sustainability Increased social capital More new ideas and adaptation of research and development Total social nonmarket benefits

NA $3,100 $719 $4,928 $544 $5,609 NA ⫹⫹, $NA $27,726a

a. Total private is 145% of U.S. male and female earnings increment (156% in the United Kingdom). Total social is 106% of U.S. male and female earnings increment (114% in the United Kingdom). NA, not applicable.

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The private nonmarket outcomes are included in Table 1 because all of these except own health and longevity also become external social benefits if the decision unit is regarded as the individual and not the family. The family typically is the decision unit for K-12 students since parents cover room, board, and tax costs. Parents also contribute heavily for most undergraduates at college through help with foregone earnings costs, tuition, and fees, although some of these are covered by others through tuition waivers and student financial aid. But as undergraduate students increasingly bear the costs through student loans, they and postgraduate students become independent decision units with benefits to other members of the family valued at $19,101 (i.e., they also become external social benefits to others).

Measuring the Indirect Benefits of Education The indirect benefits of education are those that operate through other variables. For example, to the extent that education improves democracy and political stability, and the latter aid growth, there is an indirect effect from education on earnings and growth. Indirect benefits are externalities because they diffuse to the society at large, benefiting others including future generations. Individuals do not notice these small effects or take them into account as they make their investment decisions. These indirect effects are measured by estimating a difference equation for each outcome within which education operates after a lag and then using the resulting system of difference equations for simulating the effects of education over time. Coefficients for the indirect effects can be suppressed and the remaining direct effects subtracted from the total effects. These indirect effects are valued by expressing them as a percentage of the total effects of education on growth. Shorter term impacts set the stage for successive rounds of growth and development typically converging after about 40 years. An important conclusion is that the size of the external social benefits increases and is a function of time. This is one important reason why point estimates of the value of education externalities found in the literature are not the same. The best current estimate of the indirect effects puts them at about 42% of the total effects of education on growth after 40 years. That is, the initial effects of education’s indirect social benefit externalities are small, but as the various effects of education

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External Social Benefits and Costs

interact causally over time, the size of the external social benefits in the form of indirect effects build up and can be valued at about 42% of the marketbased growth effects and need to be added to the direct social benefit externalities that were valued at 106% of the growth effects as shown in Table 1.

Aggregate Methods The main approach used previously for measuring the aggregate external social benefits of education is simple but less comprehensive. It involves a single growth equation where the education coefficient estimated from cross-country, interstate, or intercity data net of the micro (private) returns to education is said to be the external social benefit. Alternatively, the growth equation contains a significant term for the average level of education in the community. This approach is limited to measuring market impacts. Studies using this single-equation approach have thus far been inconclusive. For example, using interstate and intercity data within the United States (or any other country), the Lange-Topel spatial equilibrium model based on the work of Fabian Lange and Robert Topel suggests that as the interstate or intercity education coefficient is larger, or the “average education level in a community” coefficient is significant, either of these could merely be reflecting demand as educated workers are induced to migrate to high-demand areas. Since it is not the presence of educated workers in an area contributing to greater productivity in this case, there is a major problem of endogeneity and hence inconclusive results. But the problem is less severe with cross-country data where migration is restricted. However, in the cross-country studies, Jess Benhabib and Mark M. Spiegel in 1994, and later Daron Acemoglu and Lant Pritchett, find no effects from education on per capita growth, and hence no externalities. Benhabib and Spiegel in a 2006 revised study then found positive effects. They got this result by introducing additional controls such as state variables and time dummies that eliminate the effects from technology embodied in the human capital formed by education. New ideas and new technologies are embodied in each new generation of students, and as these students enter the labor force, they then not only contribute to a surge of new productivity growth but also earn a premium in the labor market in an effect well known at least since Ann Bartel and Frank Lichtenberg’s study in 1987. Another aspect of the

specifications that minimizes the effects of embodied technology is their use of school attainment. The latter is a stock of human capital that adds the net new accumulation of human capital each year and nets out the replacement investment, which is the new human capital replacing those who die and retire, about 68% of the total investment each year. Using educational attainment, the number of persons in the stock of human capital, therefore, ignores all of the effects on productivity and income growth from the newer technologies that replacement investment carries with it. Longer run education effects are also eliminated by using short time frames, and/ or a lagged dependent variable on the right, and/or by using controls for democratization and political stability that exclude the indirect effects of education (as in Benhabib and Spiegel’s earlier study). These studies that find no effects are not typical, however, because most studies find positive education effects on growth that allow for externalities. These include studies by Enrico Moretti, Katrina Keller, Maria-Angels Oliva, Luis A. Rivera-Batiz, Xavier Sala-i-Martin, Robert J. Barro, Walter W. McMahon, Dean Jamison, Theodore Breton, Philip Oreopoulos and Kjell G. Salvanes, and Moses Oketch, and others. One estimate that a 1% increase in graduates in a community raises wages by 1% is regarded by Lange and Topel as “simply huge,” which implies that they think it is unrealistically large. However, Topel, and James J. Heckman and Peter J. Klenow estimate the purely external social rate of return to be 14.7% and 21.7%, respectively, which is also large. These are biased upward because they do not control for other things that affect growth. But then they introduce life expectancy and time dummies that are dubious controls. Overall, it is easy to agree with Moretti’s conclusion that there is little consensus using this aggregate method about the size of education externalities and that the single-equation method of estimating market-based education externalities is inconclusive. Furthermore, all of these aggregate approaches underestimate externalities because they omit externalities included in nonmarket outcomes and omit most indirect effects.

The “Willingness to Pay” Method The “willingness to pay” method is illustrated in a study in the Journal of Benefit-Cost Analysis that uses a large sample of respondents in one state, asking them about the amount they were willing to pay in taxes for a 10% expansion in enrollment in

External Social Benefits and Costs

community colleges. From this, the microearnings (private) benefits are subtracted. The best point estimate of the external social benefits is 74% of the private earnings benefits. This is not all that much different from the 106% of the earnings benefits obtained by the first method above for the direct benefits. Both include nonmarket outcomes and both focus on the direct benefits. To fine-tune this comparison, private own-health and longevity benefits must be removed from the “willingness to pay” estimate of social benefits, and the social benefits included in earnings such as taxes paid and earnings increments arising from an educated community must be added. Finally, to both the 74% and 106% of earnings estimates must be added the unknown value of the increased flow of new ideas. “Willingness to pay” omits most of this because of its focus on community colleges, and the itemization of outcomes omits it because it considers only direct effects.

Conclusion Omissions and valuation assumptions mean that no precise estimate of the value of external social benefits of education is yet possible. But the methods are improving, why the approaches using the single-equation methods differ is becoming more clear, and more is now known. Based on a systematic averaging of 45 studies of individual outcomes that avoids overlap, and also on independent studies by totally different methods of overall “willingness to pay,” the best estimate is that the external social benefits are about 134% above and beyond the earnings benefits. This sum consists of the average of the two methods that yield 74% to 106% of direct social benefits (both of which omit new ideas for different reasons), plus 42% for indirect benefits after they have compounded about 40 years into the future. The latter does include effects from increased new ideas. This estimate omits intrafamily benefits regarded here as private that would add 60% if students were to be regarded as independent decision makers. It also omits external benefits included in earnings due to taxes paid and any earnings benefits from living in an educated community on which there is insufficient agreement. Although external social benefits may be greater than earnings, they are not greater than total benefits (earnings plus nonmarket outcomes), and at 41% to 60% of total benefits (depending on how intrafamily spillovers are treated), they may be closer to about 50% of total education benefits.

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These findings have implications for the public financing of education. If external social benefits are about half of the total benefits of education, then about 50% of the total investment costs (including foregone earnings costs) should be from public support if social efficiency and optimal growth are to be achieved. If social benefits are underestimated by public policy, total private and public investment in human capital formation is too low to meet the efficiency conditions in any nation in which this occurs. Evidence of underinvestment in human capital in the United States consists of social rates of return that are significantly higher at the 2- and 4-year college levels than the longer run average of a benchmark of opportunity costs such as the return on S&P 500 index funds. The symptoms of this underinvestment are national skill shortages; stagnant earnings in the middle class; higher poverty rates; high public health, prison, and welfare costs for states; higher birth rates among the undereducated than those with more education; less participation in voting at lower education levels; less participation in civic institutions at lower education levels; vocationalization of university curricula; and increasing privatization of university research increasingly favoring fields offering private financing and patents. The study of the external benefits of education is a young and seriously underresearched field, but, as suggested, with major policy implications. This overview has also sought to isolate omissions where work is needed. But even without fully comprehensive and precise measurement, many external social benefits of education are known to exist, have been measured, and are clearly substantial. Walter W. McMahon See also Economic Development and Education; Education and Civic Engagement; Education and Crime; Human Capital; Spillover Effects

Further Readings Keller, K. R. I. (2006). Investment in primary, secondary, and higher education and the effects on economic growth. Contemporary Economic Policy, 24(1), 18–34. Lange, F., & Topel, R. (2006). The social value of education and human capital. In E. Hanushek & F. Welch (Eds.), Handbook of the economics of education (pp. 459–510). Amsterdam, Netherlands: Elsevier. Lochner, L. (2011). Non-production benefits of crime, health, and good citizenship. In E. Hanushek,

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S. Machin, & L. Woessmann (Eds.), The handbook of the economics of education (Vol. 4, pp. 183–282). Amsterdam, Netherlands: North-Holland. McMahon, W. W. (2002). Education and development: Measuring the social benefits. Oxford, UK: Oxford University Press. McMahon, W. W. (2007). An analysis of education externalities with applications to development in the Deep South. Contemporary Economic Policy, 23(3), 459–482. McMahon, W. W. (2009). Higher learning, greater good: The private and social benefits of higher education.

Baltimore, MD: Johns Hopkins University Press. McMahon, W. W., & Oketch, M. (2013). Education’s effects on individual life chances and on development: An overview. British Journal of Education Studies, 61(1), 79–107. doi:10.1080/00071005.2012.756170. Retrieved from http://www.tandfonline.com/toc/rbje20/61/1# .Uyvzpc7K3Fx Moretti, E. (2004). Estimating the social returns to higher education: Evidence from longitudinal and repeated cross-sectional data. Journal of Econometrics, 121(1–2), 175–212.

F Factor Markets

FACTOR PRICES

Factor markets are parallel to the market for finished goods. In a market setting, factor prices are determined by demand and supply. For factor demand, factor price is inversely related to the quantity demanded of a factor. For factor supply, the quantity of factor supplied increases as factor price increases. Equilibrium factor price and quantity are identified when the downward-sloping factor demand curve intersects the upward-sloping factor supply curve. Under ideal market conditions, the behavior of producers (in this case, workers) and consumers (firms) converges toward the equilibrium factor price and quantity. Thus, there is a tendency for surpluses and shortages to lead back to equilibrium. However, there are also market failures in factor markets. For instance, a monopsony is the sole employer of an input and, thus, has market power and can set factor prices.

An important component of microeconomics is the way in which goods and services are produced. The process of production transforms inputs into outputs or into final goods. These inputs are also referred to as factors of production and include labor, capital, land, technology, and entrepreneurship. Similar to the markets of finished products, there is a factor market through which the factors of production are exchanged. Factor prices are the prices of the factors of production when factor supply equals factor demand. The main factor prices are as follows: wage rates (factor price of labor), interest rates (factor price of capital), rents (factor price of land), and profits (factor price of technology and entrepreneurship). Factor prices play a prominent role in education. Labor is important in production—both the quantity and the quality. Education affects the quality of labor and partly contributes to the entrepreneurial and technological capacity of a country. Moreover, the main cost of education, and thus a matter of concern in education finance, is the cost of labor. The growing importance of teacher labor markets in educational research is testimony to the significance of factor markets in the consumption and production of education. This entry provides a brief explanation of factor prices, beginning with a description of factor markets and the determinants of factor prices. Next, the theory of factor price equalization is outlined. The entry concludes with a discussion of the applications of factor prices.

Determinants of Factor Prices There is some disagreement among economists about the determinants of factor prices. A lack of consensus exists on whether demand for the finished product is a determinant of factor prices. On the one hand, some scholars argue that demand for the finished product is important in determining retail prices but does not influence factor prices. In essence, factor prices are the intrinsic value of inputs or factors of production. On the other hand, demand may indirectly affect factor prices through the volume of production precipitated by consumer demand. Thus, factor prices are influenced to some extent by the demand for finished goods. 327

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Factor Prices

Factor prices may depend not only on the demand and price of finished products but also on factor endowments, or the amount of inputs (e.g., land, labor, capital, entrepreneurship, and technology) that a country or firm possesses that can be employed in production. For instance, a society may possess factor endowments, such as favorable climate conditions; a significant amount of natural resources, such as oil; or a well-educated population that readily innovates and adapts in multiple industries. These resources are valuable to different production processes. Research by the economists Kenneth Sokoloff and Stanley Engerman found that the nature of factor endowments partly accounts for the differences in the economic development of North America and the rest of the Western Hemisphere. Generally, factor endowments have a positive relationship with economic development; hence, countries with greater factor endowments tend to be more prosperous. The concept of factor endowments is readily applicable in education. For instance, many scholars note that schools with better teachers tend to be higher performing and have a greater impact on student achievement. Factor substitution or the availability of viable alternatives of an input may also affect factor prices. Factors of production with many possible substitutes tend to have lower factor prices. The process of factor substitution varies across industries and countries. Education is afflicted with the “cost disease,” where economy-wide increases in productivity and wages are absorbed, resulting in higher wages in education rather than substitution into physical capital or cheaper labor. Factor substitution is challenging in education as there are few viable alternatives to replacing teachers, the main input in the production of schooling. Indeed, education policy and the discourse on reform centers more on assessing teachers’ impact on student achievement and identifying effective teachers than the substitution of teachers in the education production function. An example of factor substitution in education may be a hybrid education model where teaching time is partially replaced by interaction time with learning technologies, resulting in fewer teachers in a school and greater use of technology (which itself requires a capital investment).

Factor Price Equalization In the mid-20th century, the economist Paul Samuelson posited that as a result of international

trade in goods and services, the factor prices of similar inputs in production would converge across countries. This phenomenon is known as factor price equalization. The prices of the factors of production in different countries such as wages will equalize over time as countries specialize in producing goods whose factors of production are abundant. However, this is based on several assumptions: (a) there are only two goods and two factors of production (capital and labor); (b) each country produces both goods and has equivalent productive technologies; (c) there are no barriers to trade; and (d) there is factor mobility. For example, there are schools and teachers in the United States and Japan. If the wages of teachers are higher in the United States than in Japan, wages in the United States will decrease and wages in Japan will increase until both wages are equal. In this case, factor price equalization will occur if free trade in education exists between the United States and Japan, and teachers can easily transfer between schools in each country.

Applications Factor prices play a considerable role in production economics and determining the viability of producing a good. In other words, considering factor prices answers the question of whether production of a good is worth the costs of the production process. If cost of production is greater than the market price of the finished good, production of that good would be ill-advised. Factor prices are applied in microeconomics as well as in macroeconomics and thus are useful to analyze the production decisions of firms and countries. Factor prices are also important price-setting tools: They link sale prices to manufacturing or production expenses and partly explain why the price of a good and the costs of producing that good approach each other. The flexibility of factor prices is also a crucial component of macroeconomics, specifically the analysis of aggregate market in the short run. Finally, factor prices are increasingly important in key educational debates in which insights about the determinants and characteristics of factor prices, such as the wages of teachers, are crucial. Dominic J. Brewer and Richard O. Welsh See also Baumol’s Cost Disease; Education Production Functions and Productivity; Markets, Theory of

Faculty in American Higher Education

Further Readings Samuelson, P. A. (1948, June). International trade and the equalisation of factor prices. Economic Journal, 163– 184. Sokoloff, K., & Engerman, S. (2000). History lessons: Institutions, factor endowments, and paths of development in the New World. Journal of Economic Perspectives, 14(3), 217–232.

FACULTY CONTRACTS See Faculty in American Higher Education

FACULTY IN AMERICAN HIGHER EDUCATION The typical model of faculty employment that existed in the mid-20th century was of full-time faculty being employed in an environment in which faculty were hired into tenure-track positions as assistant professors and then in their sixth year faced an “up or out” decision in which their suitability for a tenured appointment would be considered. If granted tenure, a faculty member had considerable protection from dismissal. While the financial situation of the institution as a whole could lead to the loss of a tenured faculty member’s position, tenured faculty members’ positions were secure employment positions up until the age of mandatory retirement, which in many states and at most private academic institutions was 65 years. Tenured faculty members could be dismissed for cause if they were convicted of felonies or if they were judged to no longer be performing up to expected academic and moral standards. However, a decision to dismiss tenured faculty members for academic performance reasons could occur only through a process that, at the first level, included a decision by their faculty peers that they were no longer competently performing their jobs. This rarely occurred. A tenure system has obvious costs to academic institutions. Having a high proportion of faculty with tenure limits an institution’s ability to transfer faculty resources to new and growing areas of academic importance from areas that may have declining student demand and are no longer of high academic importance. It limits the ability of the

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institution to hire younger faculty members who are at the cutting edge of their disciplines and to make the faculty more diverse by hiring women and members of racial and ethnic minorities. The fact that many academic institutions voluntarily adopted such a system of faculty employment suggests that the benefits of a tenure system outweighed its costs. In addition to traditional arguments that a tenure system provides support for professors’ academic freedom (but only after they achieve tenure), the economist George Stigler argued that it facilitates the intergenerational transmission of knowledge and the sharing of knowledge among colleagues. Other economists have pointed out that an “up or out” tenure system is like a type of rankorder tournament that provides an incentive for all faculty members to work harder than they otherwise might in the years before the tenure decision and that this leads to a greater production of knowledge. Still others have argued that tenure is a desirable job characteristic, and other factors held constant, the cost of hiring equally qualified faculty would be higher if a tenure system was not present. Indeed, research that Ronald G. Ehrenberg conducted with Paul Pieper and Rachel Willis confirmed that academic economics departments that offered lower probabilities of obtaining tenure to young faculty members had to pay higher starting salaries. Finally, some argue that if it is desirable for academics to specialize their research in narrow subject matter areas, students contemplating doctorate-level study need to believe that they have a reasonable probability of obtaining a tenured position, because otherwise their specialization might put them at the risk of having no alternative career options.

The End of Mandatory Retirement In 1978, a federal legislation was passed that, with few exceptions, prevented anyone from being forced to retire prior to age 70. In 1987, the legislation was modified, again with few exceptions, to prevent anyone from ever being forced to retire. Academic institutions pleaded for an exemption from this new law but were granted only a delay in its application to them until 1994. So as of 1994, being awarded tenure truly was close to receiving a lifetime employment contract. Tenure was not a guarantee of a fixed real salary level; if prices went up by more than salary increases, a faculty member’s standard of living could decline (and this occurred for many faculty members during

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the 1970s when inflation was rampant). At many institutions, however, tenure was thought to be a guarantee that nominal salary levels would not be cut (in truth tenured faculty members’ salaries can be reduced for performance reasons). Hence, the elimination of mandatory retirement increased the costs of a tenure system relative to its benefits. As academic institutions began to face financial problems during the last quarter of the 20th century, they began to rethink the conditions under which they employed faculty. The increase in the mandatory retirement age to 70 in 1978, and then, the elimination of mandatory retirement for tenured faculty in 1994 also dramatically contributed to the institutions’ rethinking of the usefulness of tenure systems.

Changes in Faculty Types and Their Impact Table 1 presents data on the trends in the types of faculty teaching in American higher education institutions between 1975 and 2011. In 1975, 56.8% of faculty either were full-time faculty with tenure or were full-time faculty on tenure-track appointments. Another 13% of faculty members were full-time employees who were not on tenure tracks, and the remaining 30.2% were part-time faculty. Over time, the number of faculty who are full-time tenured or on tenure tracks has declined and, by 2011, only 29.8% of faculty were in these categories. In contrast, the proportion of faculty who are part-time had increased to 51.1% by 2011, and the proportion of faculty who are full-time non-tenure track had increased to 19.1%.

Table 2 presents data on trends in faculty types, broken down by institutional category and form of control for a shorter period of time, 1995 to 2007. The proportion of full-time faculty who are not on tenure-track appointments increased at all types of institutions and is highest at the private for-profit institutions and the private not-for-profit associate’s degree institutions. Very few full-time faculty members at private for-profit institutions have tenure or are on tenure-track appointments. The proportion of faculty who are part-time increased at all categories of institutions, and by 2007, the proportion of faculty who are part-time exceeded 75% at the private for-profit bachelor’s, master’s, and doctoral-level institutions and exceeded 55% at all categories of associate (2-year) degree institutions and at the private not-for-profit master’s-level institutions. Clearly, there is a “new normal” in the type of employment situations under which most faculty members find themselves employed. Salaries and benefit levels of full-time faculty members vary widely across faculty types and within faculty types across institutional types. Table 3 presents average full-time faculty salary data for 2012–2013, by institution types and category of control (public or private) for professors, assistant professors, and lecturers. In the main, lecturers are non-tenure-track faculty, while most full-time assistant professors are on tenure tracks and most fulltime professors (the rank to which tenured associate professors are typically promoted after 7 to 10 years if the quality of their teaching and research continues to remain at a high level) have tenure.

Table 1 Trends in Faculty Status, 1975–2011

Year

Full-Time and Tenured as Percentage of Faculty

Full-Time and Tenure Track as Percentage of Faculty

Full-Time Tenured or Tenure Track as Percentage of Faculty

Full-Time NonTenure Track as Percentage of Faculty

Part-Time as Percentage of Faculty

1975

36.5

20.3

56.8

13.0

30.2

1989

33.1

13.7

46.8

16.9

36.4

1995

30.6

11.8

42.4

16.7

40.9

2007

21.3

9.9

31.2

18.5

50.3

2011

20.7

9.1

29.8

19.1

51.1

Source: John Curtis, director of research and public policy, American Association of University Professors (AAUP) Research Office, Washington DC, from the U.S. Department of Education’s Integrated Postsecondary Education Data System (IPEDS) fall staff survey data. Used with permission.

Faculty in American Higher Education

Table 2

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Faculty Types by Institutional Category and Control in 1995 and 2007 Full-Time Non-Tenure-Track Faculty as a Percentage of All Full-Time Faculty, 1995–2007

Part-Time Faculty as a Percentage of All Faculty, 1995–2007

38.4%–43.1%

64.7%–68.9%

Private not-for-profit

74.3–82.5

52.3–56.1

Private for-profit (101)

98.7–97.7

49.0–57.7

Public (139)

17.1–23.4

39.6–43.7

Private not-for-profit (497)

22.2–30.8

33.1–41.7

Private for-profit

79.6–90.6

57.9–78.6

Public (261)

12.7–20.6

29.3–40.3

Private not-for-profit (332)

25.1–33.6

50.8–59.5

Private for-profit (17)

71.6–93.7

62.2–89.7

Public (166)

24.4–35.2

19.7–24.0

Private not-for-profit (106)

18.2–46.2

32.2–31.7

98.3–100.0

44.8–84.0

Category (Sample Size) Associate’s Public (899)

Bachelor’s

Master’s

Doctoral

Private for-profit (4)

Source: Calculations based on data from 2,606 institutions that reported information to the Integrated Postsecondary Education System (IPEDS) fall staff surveys in 1995, 2001, and 2007.

Table 3

Average Full-Time Faculty Salary, by Rank and Institution Type in 2012–2013

Institution

Professor

Assistant Professor

Lecturer

Private doctoral

$167,118

$90,622

$66,519

Public doctoral

123,393

73,212

54,382

Private master’s

104,186

66,050

58,312

Public master’s

88,988

61,041

48,086

Private bachelor’s

104,335

62,763

60,939

Public bachelor’s

86,427

58,591

49,064

Public associate’s

74,845

52,754

45,819

Source: From data in Curtis and Thornton (2013, table 4). Note: The private institution categories exclude religiously affiliated institutions and for-profits.

Over time, with cutbacks in state support for public higher education, average faculty salaries at each institution type have fallen behind the salaries of comparable faculty at private institutions. For example, at the doctoral institutions, which are the

highest paying institutions, the average professor salary level was about 35% higher at private institutions than it was at public institutions in 2012–2013. The comparable difference at the assistant professor level was 24%. Growing private-public salary

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Faculty in American Higher Education

differences led to concerns that it is increasingly difficult for public higher education institutions to attract and retain high-quality faculty. This, along with financial pressures causing student-faculty ratios to increase at the public institutions relative to the student-faculty ratios at their private sector counterparts, has led to concerns about the ability of the public institutions to maintain the quality of their teaching and research programs. Because of the “up or out” nature of tenure systems, very few assistant professors remain in that rank for more than 7 years. In contrast, there is no limit on the length of time that non-tenure-track faculty, such as lecturers, can remain in their rank. While some faculty members employed in full-time non-tenure-track positions move to tenure-track positions at the same or other institutions, many do not. At each institution type, the average salary of lecturers is lower than that of assistant professors, and absent the possibility of advancement to higher ranked associate professor and professor positions and higher paid positions, career earnings for nontenure-track faculty will be much lower than for their tenure-track counterparts. As Adriana Kezar notes, full-time non-tenure-track faculty members also usually have higher teaching loads than their tenure-track colleagues, receive limited institutional support, and often play little role in faculty governance at the department and institutional levels. All these things may limit their effectiveness as teachers and also the willingness of college graduates to go on for doctoral-level study in the future. Even more problematic are the salaries, benefits, and working conditions of part-time faculty, who are referred to as adjuncts. A fall 2010 survey of contingent academic work conducted by the Coalition on the Academic Workforce, whose results were summarized in the 2012–2013 American Association of University Professors annual salary report, showed that the median salary per course for part-time faculty varied at public institutions across institutional categories from $2,250 to $3,200 and at private nonprofit institutions from $2,238 to $3,800. Data for a much smaller sample of private for-profit institutions suggested that median per-course salaries were even lower at them, typically less than $2,000 per course. To keep these numbers in perspective, if a part-time faculty member cobbled together a fulltime teaching load (eight courses per year) by working at multiple institutions, he or she would have a median total academic year salary of only slightly more than $30,000 a year. Moreover, the vast

majority of part-time faculty members receive no employer health insurance benefits and no employer contributions to retirement benefits. Finally, many part-time faculty members do not have offices at the campuses in which they are teaching where they can prepare lectures and meet with students. Some do not know far in advance if they will be employed in a given semester and what classes they will be teaching. With few exceptions, after controlling for other factors expected to influence student success, a large body of research suggests that at both 2-year and 4-year institutions, holding constant other factors, both first-year student persistence rates to the sophomore year and graduation rates are lower as the proportion of part-time faculty or full-time nontenure-track faculty increases. Put simply, employing lower paid faculty, under conditions of employment that limit their ability to perform at high levels, limits their effectiveness in the classroom. Some part-time faculty members are employed full-time in nonacademic positions and teach parttime primarily for professional satisfaction. Other part-time faculty members do so voluntarily to accommodate family responsibilities. However, many part-time faculty members are involuntarily employed in such positions and would prefer to find full-time tenure or tenure-track employment. While there is some possibility of moving from an adjunct position to a full-time tenure-track position, the odds of this happening are not very high. The low compensation levels and poor conditions of employment faced by part-time faculty have led to increased campaigns, with some success, to achieve collective bargaining rights for them in both public and private higher education. Discussion is also occurring among higher education institutions and scholars about how to restructure these positions.

Changing Faculty Salaries by Field Another important change that is occurring that relates to the faculty is the growing differentiation of faculty salaries by field of study at institutions. To illustrate this, Table 4 presents data on the average salaries of faculty in four relatively high-paying fields—business, computer science, economics, and law and legal studies—relative to the average salaries of faculty in English language and literature for faculty in a set of large, predominately public land grant universities during academic years 1980–1981 and 2009–2010. In 1980–1981, average professor

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Table 4

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Percentage Advantages in Terms of Average Salaries of Professors and Assistant Professors in Selected Disciplines Relative to Comparable Faculty in English Language and Literature in 1980–1981 and 2009–2010 Professor 1980–1981/ 2009–2010

Assistant Professor 1980–1981/2009–2010

Business

11.4%/50.9%

31.8%/114.6%

Computer/information science

13.4%/28.4%

26.9%/ 53.2%

Economics

13.9%/41.2%

16.1%/ 59.7%

Law and legal studies

33.2%/59.5%

56.7%/ 71.6%

Discipline

Source: From data in tables G and H in It’s not over yet: The annual report on the economic status of the profession, 2010–11 (a report from the American Association of University Professors). Retrieved from http://www.aaup.org/ reports-publications/2010-11salarysurvey Note: These data are for a set of primarily land grant universities.

salaries in business were 11.4% higher than average professor salaries in English; by 2009–2010, the difference had risen to 50.9%. The average assistant professor of business earned 31.8% more than the average assistant professor of English in 1980–1981; by 2009–2010, the difference had risen to 114.6%. Changes for the other (currently) high-paying fields are similar. Growing salary differences across fields at academic institutions, which tend to be larger at the larger institutions, derive largely from changing nonacademic employment opportunities that faculty members in some fields face. As faculty in different disciplines at an institution increasingly learn about these growing differences, a sense of concern for the institution as a whole and collegiality may be reduced. Ronald G. Ehrenberg See also Cost of Education; For-Profit Higher Education; Hedonic Wage Models; Teacher Effectiveness; Tuition and Fees, Higher Education

Further Readings Curtis, J. W., & Thornton, S. (2013). Here’s the news: The annual report on the economic status of the profession 2012–2013. Academe, 99(2), 4–19. Retrieved from http:// www.aaup.org/file/2012-13Economic-Status-Report.pdf Ehrenberg, R. G. (2012). American higher education in transition. Journal of Economic Perspectives, 26(1), 193–216. Ehrenberg, R. G., Pieper, P. J., & Willis, R. A. (1999). Do economics departments with lower tenure probabilities pay higher faculty salaries? Review of Economics and Statistics, 80(4), 503–512.

Ehrenberg, R. G., & Zhang, L. (2005). Do tenured and tenure-track faculty matter? Journal of Human Resources, 40(3), 647–659. Kezar, A. (2012). Spanning the great divide between tenuretrack and non-tenure-track faculty. Change, 44(6), 6–13. Stigler, G. (1984). An academic episode. In G. Stigler (Ed.), The intellectual and the marketplace (pp. 1–9). Cambridge, MA: Harvard University Press.

FACULTY TENURE See Faculty in American Higher Education

FAMILY EDUCATIONAL RIGHTS PRIVACY ACT

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The Family Educational Rights and Privacy Act (FERPA; 20 U.S.C. § 1232g; 34 CFR Pt. 99) is a federal law that protects the privacy of student education records. It was signed into law on August 21, 1974, as an amendment to the Federal Privacy Act and is commonly referred to as the Buckley Amendment after its main sponsor, Senator James Buckley of New York. The law applies to all schools that receive funds under an applicable program of the U.S. Department of Education. FERPA gives parents certain rights with respect to their children’s education records. These rights transfer to the student when he or she reaches the age of 18 or attends a school beyond the high school level. Students to whom the rights have transferred are “eligible students.” This entry discusses the records that are

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protected from public disclosure under FERPA and describes circumstances under which certain parties can obtain these records without a parent’s consent. It also explains changes to the law since it was first enacted and gives information on how to file a complaint about a suspected violation of the law. A parent or eligible student has the right to inspect and review the student’s education records maintained by the school. A school is defined as “any public or private agency or institution” that is the recipient of funds under any applicable program. In amendments to FERPA enacted on December 31, 1974, Congress clarified that when a record or data pertain to more than one child, parents “have the right to inspect and review only such part of such material or document as relates to such student or to be informed of the specific information contained in such part of such material.” Schools are not required to provide copies of records unless, for reasons such as great distance, it is impossible for parents or eligible students to review the records in person. Schools may charge a fee for copies. “Education records” are defined in the 1974 amendments to FERPA as “those records, files, documents, and other materials which contain information directly related to a student; and are maintained by an educational agency or institution or by a person acting for such agency or institution.” The 1974 amendments limited the right to inspect and review records so that postsecondary students do not have access to (a) financial records of their parents and (b) confidential letters of recommendation placed in records before January 1, 1975, or letters placed in records after that date if the student has voluntarily waived access to these letters, provided that the waiver cannot be required as a precondition for admission, employment, or receipt of awards. Parents and eligible students also have the right to request that a school correct records that they believe to be inaccurate or misleading. If the school decides not to amend the record, the parent or eligible student then has the right to a formal hearing. After the hearing, if the school still decides not to amend the record, the parent or eligible student has the right to place a statement with the record setting forth his or her view about the contested information. Generally, schools must have written permission from the parent or eligible student to release any information from a student’s education record. However, FERPA allows schools to disclose those

records, without consent, to the following parties or under the following conditions (34 CFR § 99.31): • School officials with legitimate educational interest (teachers, administrative personnel, etc.) • Other schools to which a student is transferring • Specified officials for audit or evaluation purposes • Appropriate parties in connection with financial aid to a student • Organizations conducting certain studies for or on behalf of the school if such studies are conducted in such a manner as will not permit the personal identification of students and their parents by persons other than representatives of such organizations and such information will be destroyed when no longer needed for the purpose for which it is conducted • Accrediting organizations • To comply with a judicial order or lawfully issued subpoena if the knowledge of such information is necessary to protect the health or safety of the students or other persons • Appropriate officials in cases of health (e.g., physician, psychiatrist, or psychologist treatment records for eligible students) and safety emergencies • State and local authorities, within a juvenile justice system, pursuant to specific state law

Schools may disclose, without consent, “directory” information, such as a student’s name, address, telephone number, date and place of birth, honors and awards, and dates of attendance. However, schools must tell parents and eligible students about directory information and allow parents and eligible students a reasonable amount of time to request that the school not disclose directory information about them. Schools must notify parents and eligible students annually of their rights under FERPA. The actual means of notification (e.g., special letter, inclusion in a Parent-Teacher Association bulletin, student handbook, or newspaper article) is left to the discretion of each school. The No Child Left Behind Act of 2001 (20 U.S.C. §§ 6301 et seq.) addresses the disclosure of directory-type information (e.g., students’ names, addresses, and telephone listings) to military recruiters. Congress included similar language in the National Defense Authorization Act for Fiscal Year 2002. Both laws, with some exceptions, require schools to provide directory-type information to

Federal Perkins Loan Program

military recruiters who request it. Typically, recruiters request information on junior and senior high school students that will be used for recruiting purposes and college scholarships offered by the military. The USA PATRIOT Act of 2001 added a new subsection (j) that allows the U.S. attorney general to apply for an ex parte court order requiring an educational agency or institution, such as local educational agencies, elementary schools, or secondary schools, to allow the attorney general to collect and use education records relevant to investigations and prosecutions of specified crimes or acts of terrorism (domestic or international). As first enacted, FERPA required those desiring access to education records to sign a written form, kept permanently with the student’s file, indicating specifically the “legitimate educational or other interest” the person had in seeking the information. The 1974 amendments to FERPA modified this provision so that each educational agency or institution is required to maintain a record, kept with the education records of each student, indicating all individuals, agencies, or organizations that have requested or obtained access to a student’s education records and indicating specifically the legitimate interest that each has in obtaining the information. School officials with legitimate educational interests were excluded. The record of access for an individual student is available only to that student’s parents and to school officials responsible for custody of records and auditing the system. In January 2013, Congress passed the Uninterrupted Scholars Act, which amended FERPA to permit educational agencies and institutions to disclose education records of students in foster care to state and county social service agencies or child welfare agencies. In addition, FERPA allows educational agencies and institutions to disclose student records without parental consent to the following parties under the following conditions (34 CFR § 99.31): • School officials with legitimate educational interest • Other schools to which a student is transferring • Specified officials for audit or evaluation purposes • Appropriate parties in connection with financial aid to a student • Organizations conducting certain studies for or on behalf of the school

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• Accrediting organizations • To comply with a judicial order or lawfully issued subpoena • Appropriate officials in cases of health and safety emergencies • State and local authorities, within a juvenile justice system, pursuant to specific state law

If a parent believes that a school has violated FERPA by improperly disclosing personally identifiable information from his or her child’s education records, the parent may file a FERPA complaint form. The form should include the following information: the date the alleged improper disclosure occurred or the date the parent learned of the disclosure; the name of the school official who made the disclosure, if that is known; the third party to whom the education records were disclosed; and the specific nature of the information disclosed. The form should be mailed to the following address: Family Policy Compliance Office, U.S. Department of Education 400, Maryland Avenue, SW, Washington, DC 20202-8520. As institutions and research organizations in the United States increasingly rely on personally identifiable information from students’ education records to determine educational, health, and safety policies, it is essential that federal laws exist that protect the communication and release of these data between numerous potential institutions. FERPA provides that protection and guidelines for families. Angela Hasan See also Due Process; Parental Involvement; U.S. Department of Education

Websites Family Educational Rights and Privacy Act: http://www2 .ed.gov/policy/gen/guid/fpco/ferpa/index.html Family Policy Compliance Office: http://www2.ed.gov/ policy/gen/guid/fpco/index.html

FEDERAL PERKINS LOAN PROGRAM The Federal Perkins Loan Program has its origins in the 1958 National Defense Education Act, which created the federal government’s first student loan program. The act established the National Defense

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Student Loan Program, a pool of federal funds to be loaned to students with financial need attending participating colleges and universities. These institutions were responsible for administering the program and contributing $1 of their own funds for every $9 in federal contributions. The name of the program changed to the National Direct Student Loan Program in 1972 and the Perkins Loan Program in 1987, after former Congressman Carl D. Perkins (D-KY). Later, the word “federal” was added to the name. Colleges and universities have become more responsible for funding the Perkins program over time. To participate in the program, a college must now match every $3 provided by the federal government with $1 of its own. The federal government stopped contributing additional money (capital) toward the program in 2004, and no new funding for loan forgiveness (where part or all of a student’s loan may be canceled if he or she enters into a public service career or is on disability) has been provided since 2009. Colleges are also responsible for contacting their students with Perkins loans to ensure that students are repaying their debt whenever possible. Other student loan programs (e.g., the subsidized and unsubsidized Stafford loan programs) do not have the same institutional requirements as the Perkins loan program; as a result, not all colleges whose students are eligible to receive federal financial aid participate in the Perkins program. Perkins disbursements were approximately $950 million in the 2011–2012 academic year to 485,000 students across 1,521 colleges and universities. This is compared with more than 8 million students at over 6,000 institutions who received other federal student loans in the 2011–2012 academic year. After adjusting for inflation, the value of newly issued Perkins loans is less than any time since the 1960s and less than half of the 2003–2004 academic year. This entry discusses the eligibility criteria for receiving Perkins loans and terms of the loans (e.g., interest rate, repayment terms, and loan forgiveness) as well as the possible future for the program.

Eligibility For students to be eligible to receive Perkins loans, they must attend a college or university that participates in the program as an undergraduate, graduate, or professional student. Students must also be enrolled at least half-time to receive the loan in a given year; less than half-time enrollment requires a student to enter the repayment timeline. Perkins

loans are designated for students with “exceptional financial need (as defined by the school),” and students must have completed the Free Application for Federal Student Aid to be considered for the grant. Institutions then disburse available funds based on demonstrated financial need. No loan award can cause a student’s financial aid package to be greater than the institution’s posted cost of attendance.

Terms of the Loans Since the 1981–1982 academic year, the interest rate for Perkins loans has been fixed at 5%, which is between the recent interest rate for subsidized Stafford loans (3.4%) and unsubsidized Stafford loans for undergraduate students (6.8%). Students receiving Perkins loans do not have to pay any loan origination fees, unlike students receiving Stafford loans. As of the 2012–2013 academic year, students can borrow $5,500 per year for their undergraduate education and $8,000 per year for graduate education, subject to loan caps of $27,500 for undergraduate education and $60,000 for graduate education (which includes Perkins loans received as an undergraduate). Students have a 9-month grace period after leaving school or enrolling less than half-time to begin repaying their loan, and interest is not charged during this period. Loan payments may be deferred in the case of economic hardship, public service, or military service. After the grace or deferment period expires, interest is capitalized on a simple basis and students have 10 years to repay their loans. Colleges are allowed to create small repayment incentives using their own funds to encourage lower default rates. Loans may be forgiven if a student enters certain public service careers (e.g., special education teacher, nurse, police officer, or public defender) or is on total and permanent disability. Colleges are subject to penalties if too many students default on their Perkins loans. If a college’s cohort default rate (calculated among campuses with 30 or more borrowers entering repayment in a given year) is more than 25%, it cannot receive federal matching funds. If it is 50% or higher for the three most recent years, the school is ineligible to participate in the program and must liquidate all Perkins loans.

The Program’s Future The future of the Perkins loan program is uncertain. With no new federal funds for the program, loan volume has steadily declined over the past decade. More than $2 billion in Perkins loans were issued

Federal Work-Study Program

in the 2003–2004 academic year, but that amount fell to $1.5 billion in 2007–2008 and below $1 billion by the 2008–2009 academic year. Since October 2012, all federal funds resulting from student loan repayments are being sent back to the federal government instead of being used for new loans. The program’s authority to operate (through the Higher Education Act of 1965) expires at the end of fiscal year 2014, although it may be extended through September 30, 2015, if Congress does not reauthorize the Higher Education Act. Proposals have been advanced in recent years to extend and expand the Perkins loan program, most notably through President Obama’s budget proposals. Under these proposals, new loan dollars would be based on whether a college provides a good value and effectively serves students with financial need, and interest rates would be the same as unsubsidized Stafford loans. While the recent change to marketbased interest rates in federal student loans does not currently affect the Perkins loan program, any changes to the program may alter the terms for future loans. Robert Kelchen See also Higher Education Finance; Stafford Loans; Student Financial Aid; Student Loans

Further Readings Baum, S., & Payea, K. (2012). Trends in student aid. Washington, DC: College Board. Federal Student Aid. (2013). The Federal Perkins Loan Program. Retrieved from http://studentaid.ed.gov/types/ loans/perkins Federal Student Aid. (2013). Federal Perkins loan status of defaults as of June 30, 2012. Washington, DC: U.S. Department of Education. Flemming, A. S. (1960). The philosophy and objectives of the National Defense Education Act. Annals of the American Academy of Political and Social Science, 327, 132–138. U.S. Department of Education. (2013). Department of Education student loans overview: Fiscal year 2014 budget proposal. Washington, DC: Author.

FEDERAL WORK-STUDY PROGRAM The Federal Work-Study (FWS or Work-Study) program is a federally funded program that provides funds for employment opportunities to assist students with the cost of higher education. First enacted

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by the Economic Opportunity Act (EOA) of 1964 and then expanded in the Higher Education Act of 1965, the FWS program’s stated purpose consists of two parts: (1) to promote employment among students with financial need and (2) to encourage students to participate in community service activities in order to help others and to develop a sense of social responsibility (42 U.S.C. §§ 2751–2757). According to the National Postsecondary Student Aid Study, there were approximately 1.4 million FWS recipients out of 26 million enrolled undergraduate students (5.3%) in the 2011–2012 academic year—accounting for a total of $3 billion in FWS funds awarded. While the amount awarded per student varies by the institution and the individual’s financial need, the average amount received was $2,200 for the 2011–2012 academic year. This amount, however, does not include the share of FWS funds contributed by the university, state, or other sources, which can be as much as the federal amount. This entry discusses the origins and structure of FWS program, followed by an overview of what is known about the impacts of FWS program on student outcomes.

Background and Organization The original FWS program was one component of the EOA of 1964, a legislative cornerstone of President Lyndon B. Johnson’s War on Poverty. The programs under the EOA—including Volunteers in Service to America, Jobs Corps, and Head Start— were intended to empower Americans, particularly young Americans, to lift themselves out of poverty. Hence, FWS emphasizes community service jobs such as tutoring school children in math, reading, and literacy; working in neighborhood improvement; community development; and assisting in emergency preparedness and response. The FWS program places explicit priority on working with students at public schools. For a student to receive FWS funds, both the institution and student must be deemed eligible. The U.S. Department of Education allocates funds to approximately 3,400 participating postsecondary institutions, which in turn administer FWS programs for students. The participating institutions include public and private as well as 4- and 2-year institutions. To be allowed to administer FWS, an institution must have national accreditation and maintain certain admissions criteria, such as ensuring that the proportion of incarcerated students not

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exceed 25%, the percentage of correspondence (distance learning) courses not be more than 50%, and the proportion of students without a high school diploma or equivalent must not exceed 50%. Students apply for FWS by indicating that they would like to be considered for it on their Free Application for Federal Student Aid. Institutions use the Student Aid Report based on the Free Application for Federal Student Aid to determine eligibility for FWS, along with other types of financial aid, given the cost of attendance at the particular school. After financial eligibility is determined, the institution offers an FWS award to the student as part of his or her financial aid package for the coming academic year. It is then up to the student to find a job where the FWS funds can be applied, and the student receives the FWS funds as compensation for work through regular paychecks. A student who is awarded an FWS will not receive the funds without working in an FWS job. The institution decides which jobs are FWS jobs, but there are guidelines set forth by the government that place certain restrictions on the work performed and the employer. The FWS amount awarded to each student is a subsidy on the compensation that employers pay students, and the share of federal funding allowable for a particular job depends on both of these. The federal subsidy rate is up to 100% for students working in civic education and participation activities or literacy and tutoring, 90% for jobs at nonprofit private organizations or government agencies, 25% for private for-profit organizations, and 75% for nearly all other cases. Apart from the more generous subsidy, the program encourages community service work by requiring that institutions spend a minimum of 7% of total FWS allocated funds to support students working in community service jobs. According to the National Postsecondary Student Aid Study, the total funds disbursed for FWS have grown from approximately $34.1 billion in 1995–1996 to $57.2 billion in 2011–2012. While the average amount of FWS grew by 21% between 2000 and 2008, it declined by 14% from 2008 to 2012. In the 2011–2012 academic year, there were 1.4 million participants receiving an average of $2,200 in the FWS program. By comparison, in 2007–2008, 1.5 million students participated with an average award of $2,460 (all amounts are in real 2012 dollars).

Research Evidence The academic literature regarding the impact of the FWS program is limited. This section reviews

research findings on the effects of the FWS on enrollment, persistence, and academic performance. One note is that these studies generally examine the total FWS award amount offered to a student rather than distinguishing between the share that is from the federal government and the share that comes from the institution or other sources. Thus, the term work-study will be used to refer to the total amount awarded regardless of the source. Enrollment and College Choice

FWS affects the choice of college a student chooses to attend. There is evidence that the more FWS offered, the more likely an applicant will choose to enroll at the school. Christopher Avery and Caroline Hoxby examined the determinants of college choice for a sample of high-achieving high school seniors and found that an additional $1,000 in FWS funds increases the probability of matriculation to a college by 13%, which is more than for loans (7%) but not statistically significantly different from the effect of an extra $1,000 in grant money. This suggests that students do not view FWS as being much less desirable than grants and that they are willing to give up leisure time to help pay for the cost of schooling. There is also evidence of complementarity of FWS with other types of aid. Andrew Braunstein, Michael McGrath, and Donn Pescatrice found that the 1999 FWS program did not have a positive impact unless it was combined with grants or loans. When examining differences in the enrollment impact of FWS by family income, Avery and Hoxby found that they cannot reject the hypothesis that the effect is the same across income groups, but they note that there are difficulties in interpreting the results for FWS because of the substantial heterogeneity in job types. One college may have a greater proportion of FWS students in research or office positions, whereas another may have more working in maintenance and other service jobs. Persistence and Completion

Student financial aid, including FWS, generally promotes graduation among recipients. Stephen L. DesJardins, Dennis A. Ahlburg, and Brian P. McCall, using data from a large public university, found evidence that FWS decreases the likelihood of dropping out and increases the likelihood of graduating within 6 years. One reason for this could be that a student who works on or close to campus promotes acclimatization to the school community.

Federal Work-Study Program

For poor students, FWS has a positive effect on persistence, whereas for working-class students (lower middle income), FWS negatively affects persistence, according to Michael B. Paulsen and Edward P. St. John, who suggested that higher levels of offcampus employment may be a possible reason. In a sample of first-generation college students, Terry T. Ishitani reported that FWS students have higher rates of retention compared with those without an FWS job and that students with an FWS job in the first year of college were 81% more likely to graduate in 4 years. There is also evidence that shows that a student at a 4-year college is more likely to receive FWS than a student at a 2-year institution. Academic Performance

Ronald G. Ehrenberg and Daniel R. Sherman, using data from a national sample of male college students, found that hours worked on campus do not affect grade point averages, but in contrast, off-campus hours worked tended to lower them. Unfortunately, they do not have information about whether the jobs are FWS jobs, but FWS jobs tend to be near or on campus. Examining these differences by FWS job versus non-FWS job could be a productive topic for future research. Perhaps the most credible evidence on the impact of FWS on academic outcomes comes from Ralph Stinebrickner and Todd R. Stinebrickner. They analyzed a unique sample of students attending a college where FWS is mandatory, and the students are all from low-income backgrounds. Each student is assigned one of six jobs and must work a minimum of 10 hours per week, but some jobs allow students to work more hours if they desire. They conclude that working during the first semester (the only time when students are assigned to jobs rather than selecting jobs themselves) negatively affects a student’s grade point average. Appropriately, the authors caution that their results should be taken in the context of the particular college but suggest that there may be similarities in other situations where the endogeneity of hours worked may be an issue. Postcollege Outcomes

Ehrenberg and Sherman found no relationship between hours (for both on- and off-campus jobs) worked while in college and expected future earnings. In addition, they found that working on campus was associated with a higher probability of going to graduate school. Apart from this study, there is

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scant research studying the relationship between FWS participation and postcollege outcomes. Future research is needed on such longer term outcomes to better inform policy making in regard to student financial aid.

Conclusion While existing research has helped elucidate some enrollment effects of the FWS program, little evidence is available on how FWS affects student academic impacts or postgraduation outcomes. Part of this relates to the inherent difficulty in estimating causal affects of the program. FWS recipients tend to be poorer than the average student but may have above-average motivation. Moreover, jobs may differ greatly in how complementary they are to a student’s course of study—some are directly related to a student’s intended degree, while others have little or no value in terms of the student’s future career. These differences are likely related to the effects of hours of work on academic performance or persistence in college. In summary, the effects of FWS on college choices are difficult to identify because of the complex and varied nature of FWS and the influence of other factors involved. As FWS continues to support individuals investing in their education, it would be useful to verify whether it is achieving its goals by submitting it to a high-quality, experimental evaluation. Jordan Matsudaira and Maricar Mabutas See also Access to Education; Higher Education Finance; Pell Grants; Student Financial Aid; Tuition and Fees, Higher Education

Further Readings Avery, C., & Hoxby, C. M. (2004). Do and should financial aid packages affect students’ college choices? In C. M. Hoxby (Ed.), College choices: The economics of where to go, when to go, and how to pay for it (pp. 239–302). Chicago, IL: University of Chicago Press. Braunstein, A., McGrath, M., & Pescatrice, D. (2001). Measuring the impact of financial factors on college persistence. Journal of College Student Retention, 2(3), 191–203. DesJardins, S. L., Ahlburg, A. A., & McCall, B. P. (2002). A temporal investigation of factors related to timely degree completion. Journal of Higher Education, 73(5), 555–581. Ehrenberg, R. G., & Sherman, D. R. (1987). Employment while in college, academic achievement and post-college

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outcomes: A summary of results (NBER Working Paper No. 1742). Cambridge, MA: National Bureau of Economic Research. Ishitani, T. (2006). Studying attrition and college completion behavior among first-generation college students in the United States. Journal of Higher Education, 77(5), 861–885. Paulsen, M. B., & St. John, E. P. (2002). Social class and college costs: Examining the financial nexus between college choice and persistence. Journal of Higher Education, 73(2), 189–236. Stinebrickner, R., & Stinebrickner, T. R. (2003). Working during school and academic performance. Journal of Labor Economics, 21(2), 473–491. U.S. Department of Education. (2011). Federal Work-Study (FWS) program. Retrieved from http://www2.ed.gov/ programs/fws/index.html Velez, W. (1985). Finishing college: The effects of college type. Sociology of Education, 58, 191–200.

FINANCIAL LITERACY AND COGNITIVE SKILLS Cognitive skills in economics and finance can be considered aspects of an individual’s financial literacy. Similar to reading or mathematics, the level of literacy within the domain of economics and finance determines the performance of individuals in handling financial problems. Financial literacy is considered a desirable focus of educational efforts, and it has been identified as a 21st-century skill. Within the Organisation for Economic Co-operation and Development (2013) financial literacy program, it has been defined as follows: Financial literacy is knowledge and understanding of financial concepts and risks, and the skills, motivation and confidence to apply such knowledge and understanding in order to make effective decisions across a range of financial contexts, to improve the financial well-being of individuals and society, and to enable participation in economic life. (p. 144)

Many students and adults lack the knowledge, skills, and attributes to be financially literate. For example, federal student loan debt for college is approximately a trillion dollars. Many of these college students were in majors leading to occupations where the likelihood of adequate income to repay these loans is low. Thus, the return on investment is very low and often leads to personal bankruptcy. Likewise many adults are facing a bleak financial

retirement as they were unaware of the financial consequences of their earlier economic decisions, such as not investing in their retirement plans. Reducing the risks of such poor economic decision making defines one outcome of education for financial literacy. A second outcome can be defined as support for achievement of personal financial well-being. Societal benefits and costs define a third outcome of education for financial literacy. We are living within monetary societies in which individuals and society are interlinked by financial processes. The 2008 real estate crisis serves as an outstanding example of such financial processes. In the implementation of financial literacy instruction in educational systems, there are several issues. For example, What are the knowledge, cognitive skills, and attributes we wish to teach? How do we measure such knowledge, skills, and attributes? We find it useful to define these issues as either knowledge (the what of learning), skills (the how of learning) or attributes (the characteristics of people that facilitate learning). Attributes are sometimes characterized as dispositions. Examples of dispositions are expertise; creative thinking; metacognition, which includes planning and self-monitoring; and beliefs, which include selfefficacy. Such dispositions are both cognitive (e.g., expertise) and noncognitive (e.g., self-efficacy or attitudes). These dispositions are usually difficult to train. It takes long-term interventions to foster dispositions and repeated measurement approaches for empirical confirmation of respective changes. Although the term cognitive skills is not well defined or used in a uniform way in the research literature, most authors would include in their definition existing or learnable thought processes that enable people to solve problems in both economic and financial situations. We define cognitive skills as the ability to learn and make sense of new information in a specific domain. This includes the reference to internal as well as external information available for solving a specific task or problem. In problem solving, one’s thought processes can be described in terms of operations consisting of inferential and causal relations, which are based on one's knowledge structures (economic and financial) and are applied to solve economic and financial problems. For problem solving, there is domain knowledge (one cannot solve problems in the abstract), problem-solving strategies (one needs domain-specific procedures to solve problems), and self-regulation (metacognition [planning and self-monitoring] and motivation [self-efficacy and effort]).

Financial Literacy and Cognitive Skills

Cognitive skills and knowledge can be analyzed via mental representations or mental models of the knowledge. For example, knowledge in the field of economics and finance can be assessed via the mental representations of the domain-specific knowledge structures in their fields. In the literature, there are various representations from which we can draw inferences about the underlying types of knowledge, such as declarative, procedural, and strategic knowledge. Declarative knowledge (knowing what) is represented in rules, principles, and concepts, whereas procedural knowledge (knowing how) applies to selected concepts, rules, or principles. One understanding of contextual knowledge (knowing why, where, and when) is the domain-specific application of cognitive skills—that is, a cognitive skill strategy tied to a specific context. In its use, the term representation follows the assumptions that cognitive information processing is based on mentally represented information and that human behavior during the processing of cognitive tasks results from processes running with those representations. Thus, mental representations are generated by individuals when they are required to process a (field-specific) cognitive task, such as an economic or financial question, and when they apply their existing knowledge structures to try to answer the question. These representations usually differ between individuals, since the individuals have different knowledge structures and a different understanding of the meaning of the question on the text level. Daniela Barry and coauthors have provided such evidence using knowledge maps (graphical representations of knowledge). When learners attempt to answer an economic or a financial question, their mental representation of the specific knowledge also determines how they process information while responding. Hence, we can draw inferences from the response, first about the mental representations and, after subsequent analyses, also about the underlying knowledge structures. Consequently, a knowledge test, for example, can provide indications only about the existing mental representations. Only after an analysis of the associated thought processes that take place during the answering of economic and financial questions can we also draw inferences about the subjects’ knowledge structures as well as their response strategies. Therefore, designers of economic and financial assessments must be acutely aware that correct answers may also accidentally result from wrong representations that are applied

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inconsistently. As indicated in American Educational Research Association test standards, only a broadly tested and strong validity argument can ensure that the responses are based on representations that are also consistent and can function as sufficiently reliable and valid indicators for the underlying knowledge. Such indicators should be addressed in the modeling and measuring of cognitive skills in economics and finance. Overall, the models found in the literature are characterized by a focus on knowledge structures consisting of interrelated field-specific concepts connected to one another through mental operations or knowledge relations. In the literature, knowledge structures are regarded as a fundamental component of understanding in a specific field. Robert Glaser suggests that the literature on expert versus novice would indicate that experts have an elaborate knowledge structure with regard to their field. This means that, with increasing knowledge in a field, the knowledge elements are increasingly integrated. The elaborateness and the degree of connectivity of the knowledge structure are assumed to be meaningful measures for assessing the understanding in a specific field. Olga Zlatkin-Troitschanskaia and her colleagues have suggested that the methods of modeling and measuring mental representations of subject-specific knowledge are an adequate means for obtaining a valid assessment of the knowledge structure in a given subject of study. Furthermore, the different areas of knowledge research show that the theoretical construct of knowledge consists of dimensions of content and structure and that it is multidimensional in terms of both content and structure. With regard to structure, we find the distinction between declarative, procedural, and strategic knowledge useful. This distinction is different from the concept of “structural or conceptual knowledge,” which was found in some studies and which describes how (declarative) knowledge in a specific field, such as business and economics, is interconnected. The assessment of case-related knowledge, in particular, requires an adequate and meaningful case-related context geared toward a specific situation and action. Furthermore, items embedded in a situational context also evoke patterns of thought and argument that are based on an expanded logical understanding of the adequacy of the argument structure. While the assessment of declarative knowledge in the fields of economics and finance has come to rely on the proven format of conventional multiple-choice items, the assessment

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of other types of knowledge or cognitive process would require different formats—for example, essays, knowledge maps, computer simulations, or computer games. Various authors recommend the use of different test formats for the examination of different aspects of economic and financial knowledge, especially procedural and strategic knowledge. Overall, the theoretical framework model for the assessment of learners’ field-specific knowledge can be described as a three-dimensional matrix. The first dimension includes assumptions about the structure of field-specific knowledge with regard to the cognitive requirements—for example, declarative, procedural, and/or strategic knowledge. The second dimension includes assumptions about the level of field-specific knowledge that Lorin W. Anderson and his colleagues identify as remembering, understanding, applying, analyzing, creating, and evaluating. The third dimension includes the distinction of different content subfields, such as micro- and macroeconomics, accounting, and so on. In this framework, first, content-related requirements are modeled, from which we can draw conclusions about the content-related structure or dimension of knowledge, such as the respective dimensions of economics and finance. Second, cognitive requirements are modeled, which provides not only a model of the cognitive knowledge structure, including factual knowledge, propositions, and procedures, but also a model of knowledge levels. To achieve an adequate operationalization of cognitive requirements with regard to their structure and levels, we need to make sure that, if possible, all cells of this framework are sufficiently represented through item formats that are suitable for the assessment of different dimensions, such as the ones briefly discussed above, including not only conventional multiple choice items but also simulation and so on. Thus, the theoretical framework model can be understood to serve as a general orientation in the multistep process of modeling and measuring cognitive skills in economics and finance. One example of this framework can be found by Manuel Förster and his colleagues in the WiWiKom project. The WiWiKom model for students in business and economics is based on the study content and curricula and on an understanding of cognitive skills as the latent cognitive underpinning of performance. In the first instance, this model narrows down the object of study in line with research pragmatism and comprises basic contents of the curricular subdomains of business and economics

(e.g., accounting, marketing, and management). Furthermore, the domain-specific cognitive skills are taken into account by identifying both job-related and study-curricular aspects that play a role in specific situations of decision making in the business and economics domains. The WiWiKom goal consists of adapting international assessment instruments so that the theoretically postulated cognitive skills structures and levels become empirically measurable as incremental occurrences of professional business and economic performance. Assessment is needed as education systems are currently subject to a harmonization trend toward, for example, the bachelor’s or the master’s degree study model, which is also associated with an increase in student mobility between education locations in different countries, particularly in the domains of business and economics. In this context, a vital question is whether there is a valid assessment of cognitive skills such as economic knowledge available that can be used with different groups of students from different countries. An additional dimension of measuring cognitive skills is addressed from the perspective of development. The notion of teaching is inseparably linked to the assumption of progression of knowledge, of refinement of skills, or of elaboration of knowledge structures. Beyond the natural logic of the idea, there is some complexity to be mastered in the assessment of developmental changes. As addressed above, the refinement of cognitive structures implies the ability to solve more complex or more refined problems within a domain. However, up to now, it is hard to affirm the correspondence between traditional psychometric properties (e.g., test item difficulty) and properties of mental structures. This correspondence becomes an additional aspect of test and item validity. Up to now, progress of learning has been mostly based on repeated measurement approaches. The difference in scores between test results before and after teaching or interventions is considered to represent learning progress. From a measurement approach, such conceptualization forms the basis of value-added computation. However, there is controversy whether their psychometric properties are adequate. With respect to progress in learning, there are, however, disillusioning results from recent studies. For example, Barry and colleagues have studied aspects of progression of learning with young adults aged 18 to 25 years. Based on quasi-experimental approaches, they have tested for advancement of

Financial Literacy and Cognitive Skills

knowledge comparing test outcomes from novices and graduates in bachelor’s degree programs and participants in apprenticeship programs in the financial and the technical (information technology and mechatronics) domains. The quasi-experimental rationale in both studies is that students-apprentices within finance-oriented programs will have both an inclination to the domain as well as active teachinglearning experiences enhancing the levels of knowledge available to novices. This is different for the students-apprentices within the technical domain, who experience no direct inputs on financial topics within their study-training programs. Both groups, however, actively experience managing the financial needs of their daily living. This should be an additional or second source of knowledge acquisition, affecting prerequisite knowledge over time. In both studies, there is a (minor) effect associated with the formal teaching experiences. There is, however, in these studies little effect that can be associated with informal economic learning. These results do not meet our expectations. We assume by common sense that active practice within a domain over a span of time of at least 2½ years will enhance knowledge and skills of individuals. We suspect that the assessment of learning did not measure the appropriate declarative, procedural, and strategic knowledge. Furthermore, the teaching and training in the financial domain are based on the concepts in academic textbooks; this is different for informal learning activities that are part of economic real-life demands. Although the test used has some level of expert validity for the financial domain, it did not directly cover the requirements needed for coping with everyday economic situations. The objective to measure cognitive skills within a domain requires a multifaceted, theoretically based test design to achieve validity within the financial domain beyond the mere academic curriculum. There are foundations within theory to meet such demands. They have not, however, been applied yet at an appropriate level. The alternative approach to the measurement of cognitive skills starts from the notion that economic decision making can have a long-term character. Funding a suitable pension plan is not an issue of a single moment, such as purchasing some form of goods, but instead, it is embedded in long-term processes over years, having phases of activity and of relative inaction. Sometimes, there is no defined single result but rather a stream of situational states with different levels of quality. To represent such

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processes and the embedded procedural activities, simulation-based measurement should be developed. For example, the person can “intervene” in the process by means of a sequence of decisions. In business and economics fields, this approach has a long tradition represented by business games. Eva L. Baker and her colleagues have reported example games and simulation that may be useful in finance and economic areas. Klaus Breuer, Olga Zlatkin-Troitschanskaia, and Harold F. O’Neil Authors’ Note: The work herein was supported in part by the Office of Naval Research under Award No. N0001409-C-0813, in part by the Defense Advance Research Project Agency under Award No. N00014-11-10089, and in part under the Educational Research and Development Centers Program under Award No. R305C080015, as administered by the Institute of Education Sciences, U.S. Department of Education. The opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the Office of Naval Research, Defense Advance Research Project Agency, or the positions or policies of the National Center for Education Research, the Institute of Education Sciences, or the U.S. Department of Education.

See also Economics of Education; International Assessments; Stafford Loans; Validity; Vocational Education

Further Readings American Education Research Association, American Psychological Association, & National Council on Measurement in Education. (2004). Standards for educational and psychological testing. Washington, DC: American Educational Research Association. Anderson, L. W., & Krathwohl, D. R. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. New York, NY: Longman. Baker, E. L., Dickieson, J., Wulfeck, W., & O’Neil, H. F. (Eds.). (2008). Assessment of problem solving using simulations. New York, NY: Routledge. Barry, D., Bender, N., Breuer, K., & Ifenthaler, D. (2014). Shared cognitions in a field of informal learning: Knowledge maps towards money management of young adults. In D. Ifenthaler & R. Hanewald (Eds.), Digital knowledge maps in education: Technology enhanced support for teachers and learners (pp. 355–370). New York, NY: Springer.

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Fiscal Disparity

Blömeke, S., Zlatkin-Troitschanskaia, O., Kuhn, C., & Fege, J. (Eds.). (2013). Modeling and measuring competencies in higher education. Rotterdam, Netherland: Sense. Förster, M., Zlatkin-Troitschanskaia, O., Bruckner, S., & Hansen, M. (2013). WiWiKom: Modeling and measuring competencies in business and economics among students and graduates by adapting and further developing existing American and Mexican measuring instruments (TUCE/EGEL). In S. Blomeke & O. Zlatkin-Troitschanskaia (Eds.), The German funding initiative “Modeling and measuring competencies in higher education”: 23 research projects on engineering, economics and social sciences, education and generic skills of higher education students (KoKoHs Working Paper No. 3, pp. 19–22). Berlin, Germany: Humboldt University. Glaser, R. (1991). Expertise and assessment. In M. C. Wittrock & E. L. Baker (Eds.), Testing and cognition (pp. 17–33). Englewood Cliffs, NJ: Prentice Hall. O’Neil, H. F., & Perez, R. S. (Eds.). (2008). Computer games and team and individual learning. Oxford, UK: Elsevier. Organisation for Economic Co-operation and Development. (2013). Financial literacy framework. In PISA 2012 assessment and analytical framework: Mathematics, reading, science, problem solving and financial literacy (pp. 139–166). Paris, France: Author. doi:10.1787/9789264190511-7-en Walstad, W. B., Watts, M., & Rebeck, K. (2007). Test of understanding in college economics: Examiner’s manual (4th ed.). New York, NY: National Council on Economic Education.

FISCAL DISPARITY A fiscal disparity is an enduring mismatch between necessary expenditures and a government’s capacity to raise sufficient revenue. A needs-resources discrepancy emerges when economic and societal conditions that call for public spending are not consistent with circumstances that support resource acquisition. The resulting gap may be either positive or negative. A positive disparity occurs when a government’s ability to pay exceeds expenses. For example, revenues tend to be plentiful in a community in which income, employment levels, and property values are high. At the same time, prosperity reduces the prevalence of societal problems and eases expenditure demands. The resulting fiscal advantage permits budget increases for schools and

amenities, and acceptable tax burdens. A negative disparity exists when obtainable financing falls short of spending requirements. For example, when most residents have low incomes, a jurisdiction’s capacity to secure tax revenues is lessened. To make matters worse, the very conditions that reduce tax collections signal greater need for public programs such as housing assistance and supportive school services. Fiscal disadvantage forces chronic service cutbacks, deferred maintenance, and intolerable tax burdens. Needs-resources disparities affect individual jurisdictions but become visible to observers when fiscal environments of governments that perform similar functions are contrasted. For example, school districts’ respective fiscal situations might be evaluated by comparing districts’ percentages of children eligible for the federally subsidized lunch program, household incomes, educational levels, and other variables known to affect service demands and financing ability. To analyze the prevalence and severity of fiscal disparities, researchers use placespecific data to estimate spending requirements and potential revenues and then to compute and compare needs-resources differentials. The remainder of this entry distinguishes a fiscal disparity from a common budget shortfall, discusses the development and intensification of disparities, describes how intergovernmental aid programs may address needs-resources inequities, and considers policy implications of increasing resource scarcity.

Evolution of Fiscal Disparities Fiscal disparities emerge naturally within a federalist system of government. Federalism refers to a constitutional division of authority between a central government and states or provinces. In the United States, state governments assign responsibility for some functions, such as education, police, and fire services, to school districts, cities, towns, and counties. States also grant to local governments some taxation authority to be used within geopolitical boundaries. Comparatively small sizes of governments and vastly differing fiscal situations promote divergences between jurisdictions’ expenditure needs and capabilities to collect revenue. A fiscal disparity is distinct from a short-term revenue shortage. Most governments regularly face spending requests that exceed obtainable revenues. These ordinary budget variances are addressed by delaying or denying some budget requests and by increasing tax rates. In contrast, fiscal disparities

Fiscal Disparity

reflect the inverse relationship between available funding and what ought to be spent to respond effectively to human needs and address other public priorities. Needs-resources imbalances endure and may be exacerbated by fiscal conditions and public policies. Two important influences on the progression of disparities are taxpayer’s willingness to pay for government programs and service production conditions. Willingness to Pay

Willingness to pay for services is an increasingly crucial aspect of resource capacity. With less demand for services that benefit businesses or address societal problems, fiscally advantaged communities are free to cater to the tastes of privileged taxpayers. Affluent residents of disadvantaged places may relocate to communities where services align with their preferences, thereby reducing the resource capacity of the place left behind. Research informs us that higher income, well-educated households are not only more able but also more amenable to paying for preferred public services, especially highquality schools. Sought-after locales benefit from in-migration through increases in tax base value and enhanced taxpayer willingness to approve spending proposals. Consequently, differences in ability to pay and public services preferences encourage household location decisions that foster socioeconomic clustering and intensify needs-resources mismatches. Service Production Conditions

Weather, geography, topography, population density, and crime rates are but a few of the service production features that can make it more or less expensive to deliver public programs in a given jurisdiction. Cities and rural communities often have older buildings that are expensive to heat and keep usable. The small scale of rural facilities, low utilization rates, and great distances over which children must be transported increase spending, sometimes considerably. In these settings, the extra costs incurred in producing services do not obtain added quality but, rather, crowd out hoped-for budgetary outlays and add to tax burdens. In contrast, suburban schools frequently are newer, larger, and well utilized, which permits a greater portion of total spending to be devoted to objects that enrich programs, for example, expanding curricular offerings in schools. As a result, differences in service production costs may aggravate fiscal disparities.

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Intergovernmental Dimensions In a federalist system, intergovernmental aid is an indispensible remedy for fiscal disparities. The federal government reimburses states for a large portion of costs incurred for medical care payments made on behalf of needy residents. Federal aid to lowincome school districts helps close the gap between heightened need and reduced ability to finance programs. States frequently share a portion of tax collections with local governments to underwrite some costs associated with offering nonschool functions. When targeted to cities and towns with greater service needs, inadequate resource capacity, and unusually expensive service production settings, state aid to local government helps mitigate fiscal disparities. States also allocate substantial financial support to school districts to supplement available resources and thereby ensure adequate educational opportunities for all pupils. Many states additionally supplement general-purpose education aid with subsidies for the higher costs associated with geographic isolation and educating pupils who have special needs or face learning barriers, for example, not speaking English at home. State aid allocations targeted to places with low resource capacity and greater spending requirements decrease fiscal disparities.

Conclusion In the aftermath of the Great Recession, many states have reduced local aid and foresee level financial assistance or further retrenchment. As legislators work to balance their own budgets, they would do well to keep in mind the fiscal disparity problem that troubles local governments and school districts. The frequency with which court challenges have been brought against states’ education finance systems suggests that unacceptable differences in learning opportunities persist. Cities, towns, and school districts troubled by substantial spending demands, heavy tax burdens, and depleted resources are the most dependent on state assistance, and therefore, the most vulnerable to retrenchment. Managing scarce resources in a way that imposes the same percentage reduction on all recipients hurts the neediest communities and schools unduly, because they have little or no capacity to increase local revenues or retrench budgets without doing real harm. Conversely, directing aid dollars to places with the greatest needs and the lowest revenue capacities will diminish fiscal disparities. Josephine M. LaPlante

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Fiscal Environment

See also Demand for Education; Education Spending; Equalization Models; Fiscal Environment; School Finance Litigation; Tax Burden

Further Readings Bradbury, K. L., Ladd, H. F., Perrault, M., Reschovsky, A., & Yinger, J. (1984). State aid to offset fiscal disparities across communities. National Tax Journal, 37(2), 151– 170. Ladd, H. F. (1999). The challenge of fiscal disparities for state and local governments: The selected essays of Helen F. Ladd. Northampton, MA: Edward Elgar. LaPlante, J. (2013). A framework for deciphering and managing the fiscal environment. In J. Justice, H. Levine, & E. Scorsone (Eds.), Handbook of local fiscal health (pp. 285–320). Burlington, MA: Jones & Bartlett Learning. Yinger, J. (2004). Helping children left behind: State aid and the pursuit of educational equity. Cambridge: MIT Press.

FISCAL ENVIRONMENT Whether comparing governments or school districts, dissimilarities in expenditures and revenues are extensive. Understanding why differences are commonplace and evaluating the appropriateness of variations require knowledge of the fiscal environments within which public expenditures are budgeted and revenues acquired. A jurisdiction’s fiscal environment is a place-specific mixture of circumstances that establishes service production costs, spending pressures, and resource availability. This entry describes key components of fiscal environments and how a government’s budgetary circumstance is shaped by, and sets the stage for, both spending choices and tax burdens. This entry then explains how the combination of disparities in fiscal environments and distinctive policy responses to conditions produces public service diversity and helps explain differences in learning opportunities and tax burdens across American school districts.

Service Production Costs The setting within which public services are produced may increase or decrease spending requirements relative to the costs facing similar governments. Wage levels, prices, heating expenses, and cooling requirements are common sources of cost differences. The ages, conditions, and sufficiency of facilities and infrastructure may make it more or less

expensive to offer services. Geography and population density influence expenses for transportation, road maintenance, fire fighting, and schools. The cost structure for a service is a fundamental source of cost differences. Cost structure consists of the fixed costs associated with the framework for service delivery, such as buildings and school buses, and variable costs associated with serving an individual, for example, textbooks and science laboratory supplies for students. Many public services have dominance of a fixed costs plus a variant known as semifixed costs. Semifixed costs are expenses for teaching staff and activities that vary only when there is an appreciable change in the number of participants. Fixed and semifixed costs establish a threshold expense that remains constant over a broad range of users. For example, a new school that is partially filled will have high per-pupil costs because the threshold expenditure is divided by an initially small number of students. However, per-pupil expenditures will decline as enrollment grows and additional students are accommodated within existing capacity. When school enrollment declines, per-pupil costs tend to rise because fixed and semifixed costs cannot be reduced appreciably until classrooms or buildings are able to be closed. Cost structure is affected by scale, which refers to the sizes of facilities and programs. Larger facilities and organizations may obtain discounts and achieve other economies unavailable to smaller scale operations. Small schools often have larger-than-expected per-pupil costs, without commensurately high levels of learning resources, because they suffer from diseconomies caused by high fixed costs and inability to attain efficiencies. Diseconomies also may affect very large-scale operations as a consequence of added administrative levels.

Sources of Spending Pressure Expenditure demands derive in part from prior spending commitments, as state and local pension financing difficulties underscore. Population growth increases demand for programs and infrastructure. Federal and state government mandates often broaden the range of activities performed and necessitate service adaptations in local governments and school districts. In cities, older suburbs, and tourist destinations, both residents and nonresidents use services, which increases expenditure requirements for nonschool functions. Citizens’ income and

Fiscal Environment

educational levels affect needs and tastes for services, viewpoints about acceptable quality, and preferences among programs. Needs for services are distinguished from tastes by the compensatory nature of programs designed to ameliorate disadvantages. For example, overcoming the effects of economic disadvantage and language barriers requires specialized programming, which increases educational expenses. In districts serving affluent families, expenditure needs may be lower, but residents often have a higher taste for education, which prompts requests for quality improvements. Because resource availability establishes an upper bound on expenditures, school districts with greater needs may spend less per pupil than more prosperous neighboring districts do.

Determinants of Resource Availability Resource availability begins with taxation authority, which specifies the tax types that may be used to acquire financing. The values of tax bases are affected by the composition of the economic base, income levels, and economic conditions and trends. The economy affects tax collections, especially from responsive taxes on income and sales. Federal spending for salaries, procurement, grants, and income support injects resources into state and local economies. State aid to municipalities and school districts is a crucial component of local revenue capacity. Taxpayers’ willingness to support public services is a crucial variable that affects resource availability. Many states and local governments have faced citizen-initiated tax and expenditure limitation ballot measures. The local property tax, on which school districts depend for the majority of their financing, has been a frequent target. Research reveals that above-average income and education levels, satisfaction with service levels and the balance between spending for schools and other functions, and reasonable tax burdens are linked with greater willingness to permit access to revenue bases. In contrast, lower income and educational levels and dissatisfaction with public services and tax levels tend to constrain access to taxing authority.

Unique Features Affecting School Districts The fiscal environment of schools is complicated by several circumstances unique to education. On the one hand, education budgets benefit from an acknowledged link between home values and school quality. Policymakers concerned about property values may be reluctant to enact spending cutbacks

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that imperil a district’s comparative standing. On the other hand, the dominance of education spending within both state and local budgets makes this function highly visible and, therefore, vulnerable to cutbacks. The challenging budget climate is exacerbated by financial dependence on property taxes, with little opportunity to acquire alternative financing from user charges and variable state aid allocations. Even in the absence of a legal limitation on spending or taxing, districts may need to plan budgets as though a limit is in place.

Conclusion The duality of key spending and revenue determinants produces mismatches between spending needs and accessible resources. When the fiscal environment is favorable, governments may provide suitable public service levels while maintaining reasonable tax prices. When available funds consistently fall short of spending obligations, governments cannot respond satisfactorily without inflicting unreasonable tax burdens that risk fiscal sustainability. Robust state equalizing aid programs are crucial for alleviating fiscal disparities and for ensuring adequate and equitable educational resources. Fiscally disadvantaged places and school districts are most dependent on state aid dollars and are least able to replace retracted funding. Josephine M. LaPlante See also Ability-to-Pay and Benefit Principles; Demand for Education; Education Spending; Fiscal Disparity; School Finance Litigation; Tax Burden; Tax Limits

Further Readings Baker, B. D., & Welner, K. G. (2010). Premature celebrations: The persistence of interdistrict funding disparities. Educational Policy Analysis Archives, 18(9). Retrieved from http://epaa.asu.edu/ojs/article/view/718 LaPlante, J. (2013). A framework for deciphering and managing the fiscal environment. In J. Justice, H. Levine, & E. Scorsone (Eds.), Handbook of local fiscal health (pp. 285–320). Burlington, MA: Jones & Bartlett Learning. Rebell, M. A. (2002). Education adequacy, democracy and the courts. In T. Ready, C. Edley, & C. Snow (Eds.), Achieving high educational standards for all (pp. 218–268). Washington, DC: National Academies Press. Yinger, J. (2004). Helping children left behind: State aid and the pursuit of educational equity. Cambridge: MIT Press.

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Fiscal Neutrality

FISCAL NEUTRALITY The concept of fiscal neutrality entails that no relationship should exist between the wealth of a community and the revenues or expenditures of the schools in the community. The notion is that the resources available to a child at school should not be a function of the wealth of the community in which he or she lives. In reality, communities with more wealth generally raise more revenues for their schools and have schools that spend more on education than communities with less wealth do. This entry places fiscal neutrality in the context of traditional school finance issues, discusses how to measure fiscal neutrality, and touches on the standards used to determine whether a given level of fiscal neutrality is regarded as “acceptable” by school finance scholars.

Importance of Fiscal Neutrality Fiscal neutrality plays a key role in school finance analyses precisely because most school funding

systems lack fiscal neutrality. Traditionally, localities funded schools for students in their communities. One can understand readily how districts in wealthier communities possess a greater capacity to fund their schools at higher levels than districts in poorer communities, often being able to raise more money with lower tax rates. The addition of state and federal funding to the system alleviated this issue to a certain extent. Nevertheless, local funding plays a major role in most school finance systems, so the issue of fiscal neutrality remains important. The relationship between wealth and spending can be visualized graphically. Figure 1 shows a traditional relationship between wealth and revenues. As can be seen, wealthier districts tend to have greater revenues than poorer districts, though the correlation between wealth and revenues is not perfect. Students in poorer communities would be attending schools with less per-pupil funding and correspondingly less educational resources than students in wealthier districts. A fiscally neutral funding system is depicted in Figure 2. As can be seen, not all districts have equal

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Property Wealth and Revenues: Traditional

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Property Wealth and Revenues: Fiscally Neutral

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per-pupil funding, but essentially, no relationship exists between wealth and funding. Some wealthy districts have low funding levels and some have high funding levels, while the same holds true for poorer districts.

Measuring Fiscal Neutrality School finance scholars measure fiscal neutrality in two ways. The first is the Pearson’s correlation coefficient between per-pupil wealth and per-pupil revenues (or expenditures), which follows from the fact that fiscal neutrality examines the relationship between these variables. The correlation coefficient ranges from −1 to 1 (though negative values rarely are seen in school finance) and effectively shows the relationship between the variables, but it possesses the drawback of not indicating the magnitude of the relationship. This drawback occurs because the correlation coefficient shows how consistently revenues vary with wealth changes, regardless of the magnitude of those changes. For example, the same correlation can exist between wealth and spending in a state in which districts spend $1 more per pupil for every $100,000 difference in wealth per pupil as in a state in which districts spend $1,000 more per pupil for every $100,000 difference in wealth per pupil. Due to this drawback, school finance scholars also use elasticity as a measure of fiscal neutrality. The elasticity indicates the percentage of change in revenues (or expenditures) per pupil relative to the same percentage of change in the measure of wealth, usually property value per pupil. The elasticity involves regressing revenues (or expenditures) on wealth, so it also depends on the correlation between the two variables. The elasticity can be any number, but in school finance, it is almost always positive and less than 1.0. A value near 0 for the elasticity shows that revenues increase very slowly as wealth increases, while higher values indicate more rapid increases in revenues as wealth increases. A high elasticity indicates that the relationship between the variables has policy importance.

Standards for the Fiscal Neutrality Measures The ideal value of the correlation coefficient, for purposes of fiscal neutrality, is 0.0, which implies that no relationship exists between wealth and school funding. By definition, the same value is ideal for the elasticity. However, the values of these measures tend to be positive in real life, often relatively far from the ideal value. Therefore, school finance

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scholars have developed standards used to determine whether school districts have met an “acceptable” level of fiscal neutrality. The standards for fiscal neutrality are arbitrary to some extent, but they reflect the professional judgment of experts in the field regarding standards that are far better than what most school systems achieve in the absence of funding structures designed to reduce the impact of wealth differences. The standard for the correlation coefficient is .50, while the standard for elasticity is .10. To provide the reader with a sense of what these numbers mean, the values for the data in the above two figures have been calculated. For Figure 1, the correlation is .656 and the elasticity is .197, while for Figure 2, the correlation is .060 and the elasticity is .023. The fiscal neutrality values can be interpreted best in relation to the standards. A situation like the one depicted in Figure 1 has fiscal neutrality issues because the correlation is strong and the elasticity is of a magnitude that indicates policy importance. A situation in which the correlation is below .50 but the elasticity is high means that the relationship between the variables is not particularly strong, but is of policy importance. In this relatively rare case, policymakers will want to investigate the funding system to determine whether adjustments need to be made. The situation in which the correlation is high but the elasticity is low means a strong relationship exists between the variables, but the relationship is not of policy significance. Finally, a situation like the one depicted in Figure 2 in which both values are low shows an acceptable degree of fiscal neutrality due to the weak relationship between the variables and the low magnitude of the elasticity. In summary, fiscal neutrality measures the relationship between wealth and school funding. A fiscally neutral system, in contrast to the traditional school funding system, has (essentially) no relationship between these variables. William Glenn See also Education Spending; Educational Equity; Elasticity; Fiscal Disparity; School District Wealth; Tax Burden; Tax Yield

Further Readings Consortium for Policy Research in Education at the University of Wisconsin, Madison. (2012). School finance equity statistics. Retrieved from http://cpre .wceruw.org/finance/equitystats.php

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Fixed-Effects Models

Coons, J. E. (1974). Introduction: “Fiscal neutrality” after Rodriguez. Journal of Law and Contemporary Problems, 38(3), 299–308. Odden, A. R., & Picus, L. O. (2014). School finance: A policy perspective (5th ed.). New York, NY: McGraw-Hill.

FIXED-EFFECTS MODELS Much research on education examines whether an intervention or treatment causally affects educational outcomes. Experimental studies with random assignment to treatment or control groups are rare in education research but are more likely to demonstrate causality. More commonly used in education research are quasi-experimental studies without random assignment. These studies must overcome several threats to the internal validity of their conclusions. One of the major threats to internal validity of quasi-experimental designs is selection bias, defined as estimation error as a result of systematic differences in observed and unobserved characteristics between treatment and comparison units. Selection bias, if unaddressed, leads to the possibility that the difference in outcomes between treatment and comparison groups is caused by unobserved differences between the two groups rather than by the treatment. Under certain assumptions, fixed-effects models can control for these unobserved differences and can come closer to achieving unbiased estimates of causal effects than cross-sectional ordinary least squares regression. Fixed-effects models are widely used in various fields of education (e.g., psychology, finance, and policy evaluation) and at different levels of analysis, namely, students, classrooms, schools, or districts. This entry provides background on fixed-effects models, discusses different units of fixed effects, presents an example of a study using fixed-effects models, and discusses the limitations of fixed-effects models.

Background Causality is at the heart of evaluation research in education finance and policy, for example, the effects on student outcomes of school spending, class size, school choice, teacher quality, and ability grouping. The gold standard in program evaluation research involves the use of randomized control trials or “true” experiments. These experiments have the strong ability to demonstrate causality between treatment and effect because they, by design, create

treatment and control groups that are virtually equivalent on both observed and unobserved factors (except for the treatment) that affect the dependent variable, eliminating the problem of selection bias. However, experimental designs are not without their weaknesses. They tend to be expensive and difficult to implement, can suffer from the problem of “unhappy randomization” (when those randomly assigned to treatment are systematically different from the control group as a result of small sample sizes), and have ethical concerns (when random assignment prevents those in the control group from receiving a potentially helpful program) and limited generalizations to other settings, times, or circumstances. Given these weaknesses, most evaluations of educational policies are not experimental and can thus suffer from selection bias. To evaluate these policies, researchers have developed quasi-experimental empirical methods to address selection bias. One of these quasi-experimental methods is the use of multivariate ordinary least squares regression with unit fixed effects, mostly for panel data. Unit fixed effects control for unobserved time-invariant (i.e., unchanged over time) factors that are correlated with the likelihood of receiving both the treatment and the outcome. Examples of these factors include gender, race, intelligence, or genetic makeup at the individual level or management practice at the school or district level. Including unit fixed effects is equivalent to regressing the mean dependent variable on mean values of all control variables. This specification provides unbiased estimates of the treatment effect if we assume that all of the potential explanatory variables including time-varying (i.e., variables included in the regression) and time-invariant (by fixed effects) factors are controlled for. The underlying idea for fixed effects is simple: Use each unit as its own control. For instance, if we are interested in whether charter schools have an effect on student achievement, we can compare a student’s achievement during the years he or she is in a charter school with his or her achievement during the years when he or she is in a public school. The difference in achievement between the two schools is the estimated effect of charter schools on achievement for that student, assuming that nothing else has changed. Averaging out this effect for all individuals in a sample gives us the average treatment effect of charter schools. Fixed-effects models based on the ability to control for within-unit time-invariant factors have helped researchers make substantial progress toward valid causal inferences.

Fixed-Effects Models

Units Unit fixed effects can be used at different levels of analysis—that is, individuals, teachers, families, schools, districts, cities, and states. Student fixed effects can be used when a particular student is observed at multiple times during a sample period. Researchers have long understood the importance of family environments (e.g., sibling effects) to children’s educational outcomes. To prevent family environments from biasing the estimate of other variables, one can control for sibling fixed effects if data are available on each child in a family. Studies with multiple years of data can also use year fixed effects to control for common factors affecting all units in the sample. Fixed-effects models do not require pooled time-series cross-sectional data for multiple observations. For instance, all the students in a single classroom in a single year share a teacher or classroom fixed effect. However, this use of fixed effects is less common than fixed effects with panel data.

An Example A recent study by Ryan Yeung and associates provides an example of fixed-effects models. Yeung and his colleagues examined the effect of the State Children’s Health Insurance Program (SCHIP) on school absenteeism. The treatment in this study was the share of children (individuals 19 years of age or less) who were enrolled in the SCHIP program in each year in each state. The dependent variable was states’ annual average daily school attendance rates. The concern was that unobserved state characteristics that did not change over time may have been associated both with the share of children in SCHIP and with school attendance. Perhaps, states with high rates of children enrolled in SCHIP were also associated with certain institutions such as instructional curricula and testing regimes that have changed little over time and were related to differences in attendance rates between states. Failing to control for these state-unobserved institutions would hence bias the estimated effect of SCHIP share, a classic case of selection bias. It is the presence of these state-specific curricula and testing regimes that result in differences in attendance between states and not SCHIP. The authors’ solution to this problem was to control for state and year fixed effects. The state fixed effects controlled for all the variables that remained constant over time within a state, such as political and educational institutions, thereby relying on

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within-state variation in SCHIP and attendance rates to identify the coefficient on SCHIP share. The state fixed effects did not control for systematic differences across years that affect all states related to SCHIP. These were controlled for through the use of the year fixed effects.

Limitations of Fixed-Effects Models Fixed-effects models are not without their limitations. First, fixed-effects models do not address all the threats to internal validity, including bias from omitted variables that change over time. Fixed-effects models do not control for variables that do change over time (time-variant) that may be correlated with the treatment variable and the dependent variable. This is the concern studies by Michael Gottfried and by Phuong Nguyen-Hoang and colleagues address through the use of instrumental variables regression in addition to their use of fixed effects. The omission of these time-variant variables may bias the estimated effects of student attendance and military base closures on student achievement. Second, substantial data are needed to successfully estimate the most commonly used fixed-effects models using multiple observations per unit. Finally, fixed-effects models do not estimate variables of interest that do not change across observations. Fixed-effects models also require sufficient variation in the treatment and dependent variable within a unit for precise estimation.

Conclusion In recent decades, there has been an increasing emphasis on the use of empirical data in education research to determine whether government policies and interventions can improve student outcomes. Fixed-effects methods, under certain strong conditions, are a technique that can aid in this type of causal research. Ryan Yeung and Phuong Nguyen-Hoang See also Instrumental Variables; Propensity Score Matching; Quantile Regression; School Quality and Earnings; Social Capital

Further Readings Farkas, G. (2005). Fixed effects models. In K. KempfLeonard (Ed.), Encyclopedia of social measurement. Oxford, UK: Elsevier.

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Foregone Earnings

Gottfried, M. A. (2010). Evaluating the relationship between student attendance and achievement in urban elementary and middle schools: An instrumental variables approach. American Educational Research Journal, 47(2), 434–465. Nguyen-Hoang, P., Yeung, R., & Bogin, A. (2013). No base left behind: The impact of military base closures on educational expenditures and outcomes. Public Finance Review. doi:10.1177/1091142113482570 Yeung, R., Gunton, B., Kalbacher, D., Seltzer, J., & Wesolowski, H. (2011). Can health insurance reduce school absenteeism? Education and Urban Society, 43(6), 696–721.

FOREGONE EARNINGS Discussions of the cost of education typically focus on expenditures, both public (e.g., personnel, facilities, and supplies) and private (e.g., tuition, books, and fees). However, by most estimates, the largest cost of education is associated with the earnings that students forego while they are pursuing their education. Elchanan Cohn defines foregone earnings as the difference between a student’s potential and actual income. Stated differently, foregone earnings refer to the earnings that a student would have received if he or she had opted to enter the workforce rather than pursue education. The concept of foregone earnings is relevant to individuals as they assess the costs and benefits of continued education and to public decision makers who consider policies related to compulsory schooling and social investments to promote ongoing education. Decisions about public and private investments in human capital through education must recognize the costs associated with foregone earnings. This entry describes approaches to measuring foregone earnings, sources of variation in foregone earnings, evidence from empirical studies of foregone earnings, and the impact of foregone earnings in individuals’ decision making regarding education.

Approaches to Measuring Foregone Earnings While research has documented the significance of foregone earnings in the total cost of education, debate regarding how best to estimate foregone earnings has resulted in a range of values. In his pioneering 1960 work on human capital, Theodore Schultz estimated the foregone earnings of high school and college students. He used median annual wages and number of weeks worked by age and gender to determine a weekly wage, discounted

for unemployment rate, and then multiplied by the number of weeks in a typical school year. This approach assumes that students’ earnings would resemble those of their age and gender cohort and that they earn on par with their cohort during the summer. Alternative methodologies for estimating foregone earnings challenge these assumptions and capture nuances that affect the earning potential of students.

Sources of Variation in Foregone Earnings Potential earnings vary greatly across countries and across regions within the same country. Generally speaking, the cost of foregone earnings is lower in developing countries than in developed countries. Several factors affect foregone earnings, such as education level, gender, and economic conditions. These are described in further detail below. Education Level

The level of education has implications for foregone earnings in at least two ways. First, because of their age, students participating in primary education in the United States and many other countries are not generally considered to forego earnings as a result of their school attendance, although this is not true in all economies. Second, prior educational attainment affects earning potential. For example, a high school senior would earn less, on average, than a college senior if the two were to leave school and enter the labor force. Neither is likely to earn as much as a college graduate. Generally speaking, the value of foregone earnings increases with level of education. Gender

Because average salaries for men tend to be higher than those for women, gender is a relevant factor in estimating foregone earnings. Additionally, labor force participation rates vary by gender. Women are more likely to engage in unpaid domestic labor, but few estimates of foregone earnings include a substitution value for unpaid labor. For these reasons, foregone earnings are typically higher for men than for women. Economic Conditions: Unemployment Rates and Demand for Labor

High unemployment rates result in comparatively low foregone wage valuations because some students would be unemployed were they not in school.

Foregone Earnings

Some research suggests that many students give up leisure time rather than income-generating activities, particularly when unemployment rates are high. As a result, unemployment rates are inversely related to foregone earnings. Likewise, economies vary in terms of the relative salaries for unskilled versus skilled labor. In economies with relatively good labor market prospects for young, unskilled workers, the cost of forgone wages is greater than in labor markets that place a premium on skilled work and educational credentials.

Similarly, demand for child labor increases costs of forgone wages for young students.

Evidence From Empirical Studies Different methodological approaches have generated a range of estimates of foregone earnings. Despite this variation, foregone earnings are widely recognized as the single largest cost of education. Figures 1 and 2 show Organisation for Economic Co-operation and Development estimates for total

$60,000 $50,000

Foregone earnings Direct cost

$40,000 $30,000 $20,000 $10,000 Slovak Republic Turkey Estonia Hungary Spain Poland Czech Republic Slovenia Portugal Australia OECD average Sweden United States Finland Israel Korea Canada France Ireland Denmark New Zealand United Kingdom Germany Italy Austria Norway

$0

Figure 1

Cost of Male to Obtain an Upper Secondary or Postsecondary, Nontertiary Education (2008 or Latest Available Year)

Source: Laura Egan, using data from Education at a Glance 2012: OECD Indicators.

$1,40,000 $1,20,000

Foregone earnings Direct cost

$1,00,000 $80,000 $60,000 $40,000 $20,000 Turkey Slovak Republic Hungary Poland Slovenia Czech Republic Estonia Portugal Belgium Spain Israel Canada Australia Denmark OECD average New Zealand Italy Finland France Sweden Austria Ireland Norway Germany Korea Japan Netherlands United States United Kingdom

$0

Figure 2

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Cost of Male Obtaining Tertiary Education (2008 or Latest Available Year)

Source: Laura Egan, using data from Education at a Glance 2012: OECD Indicators.

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private investment (direct costs and foregone earnings) required for a male to complete secondary (Figure 1) and tertiary (Figure 2) education in different countries. (Figure 1 uses cost estimates for upper secondary or postsecondary, nontertiary education, i.e., predegree foundation courses and short vocational programs.) These data confirm that foregone earnings are higher for tertiary than for secondary education and higher in developed countries than in less developed countries. Foregone earnings exceed direct costs in all cases except tertiary education in the United States, though these data exclude public investments that cover most or all direct costs at the secondary level.

Impact of Foregone Earnings Research suggests that potential earnings lost as a result of engaging in schooling rather than entering the workforce affect individuals’ decisions. As low-skilled wages rise and unemployment rates shrink, participation in higher education decreases. Countries that offer grants to offset individuals’ forgone wages tend to have higher levels of participation in postsecondary education. Research also has identified factors that affect individual decision making about the trade-offs between potential earnings and additional education. For example, individuals in low-income households are more sensitive to foregone earnings than their wealthier peers, and individuals with access to inexpensive credit are less sensitive to foregone earnings than individuals without that access. Expectations for future earnings have a strong impact on willingness to forego potential income at present. Although there is a growing body of research on foregone earnings, including estimations of the costs of foregone earnings and their impact on decisions regarding education, researchers have not reached a consensus on these issues. Some researchers have attempted to provide more precise estimates involving extensive local and contextual information and actual earnings of paired samples. However, additional work is needed to inform public and private decisions about investments in education. Jennifer King Rice and Laura Egan See also Cost of Education; Cost-Benefit Analysis; Human Capital; Opportunity Costs; Tuition and Fees, Higher Education

Further Readings Becker, G. S. (1964). Human capital: A theoretical and empirical analysis, with special reference to education. Chicago, IL: University of Chicago Press. Cohn, E. (1989). Foregone earnings of college students in the U.S., 1970 and 1979: A microanalytic approach. Higher Education, 18, 681–695. Organisation for Economic Co-operation and Development. (2012). Education at a glance 2012: OECD indicators. Retrieved from http://dx.doi. org/10.1787/eag-2012-en Schultz, T. W. (1960). Capital formation by education. Journal of Political Economy, 68, 571–583. Schultz, T. W. (1971). Investment in human capital: The role of education and of research. New York, NY: Free Press.

FOR-PROFIT HIGHER EDUCATION For-profit higher education is a term used largely within the United States to refer to higher education institutions (HEIs), incorporated as private profitseeking businesses, which provide educational services and grant certificates and degrees. They differ from traditional colleges and universities that are incorporated as private nonprofit institutions or as public institutions. With the increase in demand for postsecondary education, for-profit colleges and universities (FPCUs) have seen an unprecedented growth in both the numbers of institutions and enrollments, from 2% of all higher education enrollments two decades ago to now accounting for approximately 13%, according to the latest figures from the U.S. Department of Education. FPCUs generally serve nontraditional students who are older (more than 25 years of age), from lower income households, financially independent, and minorities. Other administrative and financial aspects such as distribution of profits, funding sources, governance, regulatory oversight, faculty structure, and types of programs offered, which will be addressed in the sections below, also differ in varying degrees from traditional HEIs. This entry gives a background of FPCUs in the United States, lists the types of for-profit institutions operating in the higher education sector, and discusses the main distinguishing features separating FPCUs from their traditional counterparts. It then highlights some recent developments in U.S. higher education that see the traditional HEIs partnering with FPCUs in delivering high-volume online and

For-Profit Higher Education

classroom courses to meet the demand of students adversely affected by budget cuts in public HEIs.

Background The definitions of private and public HEIs internationally, as determined by the Organisation for Economic Co-operation and Development, distinguish between the institutions’ governance and funding to determine their independence. Public institutions are managed and controlled by a public education authority, government agency, or a governing body appointed by a public authority, whereas a private institution is controlled and managed by a nongovernmental organization, and its governing board is not selected by a public agency. In the United States, private institutions are further divided into for-profit and nonprofit entities. The public versus private distinction has less to do with the amount of funding than with governance and taxation. Private for-profit HEIs sell and buy shares of their organization and distribute their profits to shareholders and investors. Their nonprofit counterparts plow back whatever they have in excess of operating expenses into their universities’ balance sheets. Each American college or university is thus classified as public, private nonprofit, or private forprofit. Students in the United States have three primary choices for higher education: (1) public universities and community colleges (e.g., the University of California and the Los Angeles community college district), (2) private nonprofit universities (e.g., Harvard University and University of Southern California), and (3) for-profit colleges (e.g., University of Phoenix and Corinthian Colleges). The first universities established in the United States during the 17th and 18th centuries were private and nonprofit in nature. It was not until the late 1700s that the first public universities were established under charter. Public HEIs began to proliferate after the Morrill Land-Grant Acts of 1862 and 1890 under which the federal government supplied land to states for them to create comprehensive public universities for agriculture and mechanic arts. Most of these earlier private colleges and universities existed as charitable institutions and any surplus funds were used toward future university expenses. The Revenue Act of 1954 codified the 501(c)(3) taxexempt status for nonprofit educational organizations, which allows donors to make tax-deductible contributions to these institutions and provides an attractive incentive for gift giving during fundraising campaigns.

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They are also eligible for government and private grants. Although they are allowed to run profitably, these profits cannot be distributed to any individuals. The other type of private colleges in the United States are profit seeking. Running similarly to other forms of private businesses and corporations, profits from for-profit HEIs are subject to taxation and may be distributed to the shareholders, whether they are publicly listed or privately owned. Of the more than 7,000 institutions of higher education that are eligible for federal funding, almost 50% are FPCUs, compared with 25% each of public and private nonprofit HEIs. The vast majority of these FPCUs is privately owned and operated, although there are around 15 publicly traded corporate entities operating hundreds of campuses around the country.

Types of FPCUs The U.S. Department of Education classifies FPCUs into three main types: (1) private for-profit, 4 years and above; (2) private for-profit, 2 years; and (3) private for-profit, less than 2 years. Of the more than 3,500 FPCUs currently reporting institutional data to the U.S. Department of Education, about 20% offer 4-year (bachelor) degrees and above, 30% offer 2-year (associate) degrees, and the remainder offer certificate programs lasting less than 2 years (typically between 9 and 18 months). Two-Year and Less Than 2-Year Career Colleges

These institutions offer courses leading to career or vocational certificates and/or associate degrees that are comparable with similar programs at community colleges. Many FPCUs concentrate their courses in a specific field. For example, Corinthian Colleges are focused on health, and ITT Educational Services are largely focused on information technology programs. Courses are designed to teach students the skills required to enter a particular field of employment on graduation or to prepare them to pass specific licensure exams (e.g., surgical technicians, nursing, etc.). To save time and money, there are no general requirements or elective classes such as those found in traditional 2- and 4-year colleges. Classes are on local campuses or are in storefronts located along freeway exits that are easily accessible by car or public transport. Four-Year and Above Universities and Colleges

These institutions offer associate, bachelor’s, and master’s degrees in areas such as psychology,

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business administration, computer systems analysis, criminal justice, and so on. Many of these classes are offered online for working individuals. Students enroll in classes and progress at their own pace. Communication with the instructor generally is via e-mail or through scheduled class time over the Internet. In contrast, the model for traditional colleges is primarily based on seat time with classes scheduled during the day from 8 a.m. to around 5 p.m. Students have to attend classes at the assigned times.

Distinguishing Features of FPCUs Funding Sources

Typically, sources of revenue at traditional HEIs depend on a combination of tuition, state and local appropriations, federal grants, auxiliary enterprises (e.g., hospitals), endowments, and investment returns. FPCUs, on the other hand, rely heavily on tuition fees (with fees amounting to about 85% of total revenues). In comparison, tuition only accounts for about 20% to 25% of total revenues at public nonprofit institutions and 35% of total revenues at private nonprofit institutions. Tuition is the responsibility of the student, who has the option of paying for it through federal government grants, federal loans, or private means such as family support or private bank loans. Since student demographics of FPCUs consist of a higher proportion of low-income individuals who are eligible for federal grants and loans, as much as 90% of an FPCU’s revenue is derived from tuition paid for by federal financial aid (Title IV funding). This is also in stark contrast to federal funding, comprising less than 20% of revenues at public and 15% at private nonprofit universities. Title IV funding refers to the Title IV legislation of the Higher Education Act of 1965, as amended through the years, which establishes general rules that apply to the student financial assistance programs. To be eligible to participate in Title IV programs, FPCUs must be accredited by one of the national or regional accreditation agencies that are recognized by the Department of Education. Accreditation

Although accreditation for HEIs is not mandatory in the United States, in practicality, any institution (public or private) desiring access to Title IV funding needs to be accredited by one of 6 regional

or 52 national accreditation agencies recognized by the U.S. Department of Education. Each accrediting agency has its own set of requirements for accrediting its members. Regionally accredited HEIs are predominantly public and private nonprofit institutions (i.e., the traditional colleges and universities) that cover diverse areas of study, and nationally accredited institutions are primarily forprofit career and vocational schools that focus on a specific field. Some of the largest FPCUs (e.g., University of Phoenix) gained regional accreditation status through the acquisitions of HEIs with existing regional accreditation or direct application for regionally accredited status. Governance

Governance lies along a spectrum with central decision making at one end and shared governance at the other and many forms of variation within. Governance in FPCUs is very different from their traditional HEI counterparts. While all three forms of HEIs have governing boards (boards of regents or boards of trustees) to vote on the major strategic and planning decisions, in traditional HEIs, administrators, faculty, and sometimes the student body participate in shared governance where they have a role to play to shape and implement decisions prior and subsequent to a vote by the governing board. Elected individuals at student and academic (tenured teaching staff) senates have the authority to make decisions in the given areas. Instructors, staff, and students at FPCUs, however, are rarely accorded this level of autonomy, and they generally follow the corporate plans handed down from corporate headquarters, resulting in very little say in what courses are taught and how the budget should be allocated. The main governance tendency for for-profits (especially the larger corporations with multiple campuses) tends to lean toward central planning directives from headquarters, while public and private nonprofit HEIs have a tradition of governance by a committee that allows more autonomy to respective departments and academia. Governance also reflects the different goals of for-profits and the public and private nonprofits. Since the primary goal of for-profits is to deliver consistent income and investment growth to investors and owners, this often entails streamlining operations to achieve high productivity, efficiency, and returns, often with a shorter quarterly or annual timeline. Decisions made by the board and

For-Profit Higher Education

the CEO are passed down to the corporate senior management team and then disseminated to the different campuses in a top-down hierarchical structure. Instructors, staff, and students are employees and customers who have limited input in how the institutions are run. Enrollment and Growth

With the passage of the Higher Education Act of 1965 that made federal grants and low-interest loans available to students as well as increased federal spending to universities, a college degree that once was an elusive achievement reserved for the privileged few has now come within reach of millions more Americans. College enrollment grew from about 6 million students in 1965 to more than 21 million in 2010. The predominantly needs-based federal grant and loan programs succeeded in offering many more low-income and minority students the means to pursue postsecondary qualifications at eligible institutions. FPCUs stand to benefit most from federal student aid programs, since they admit a disproportionate number of minorities and low-income students. Whereas the percentage of minorities in all HEIs is around 28%, at FPCUs, the percentage is above 50%. Enrollment in for-profit higher education has increased dramatically. Enrollment numbers at FPCUs rose from around 350,000 in 1989 to 3.2 million in 2009. Since enrollment in higher education tends to be countercyclical, enrollment tends to rise more rapidly when the economy is weak and unemployment is higher than when the economy is stronger and unemployment is lower. Unemployed or underemployed adults return to college to upgrade their education with certificate courses and associate degrees and exit the education sector when the economy improves and hiring increases. For these individuals, FPCUs offer easy access to short-term courses and the flexibility they need to acquire certifications without the time investment needed at traditional HEIs. The National Center for Education Statistics reported that only 15% of enrollment in fall 2011 was made up of traditional students living on campus, while more than 30% were nontraditional students. FPCUs have the advantage over traditional HEIs in attracting this large and increasing demographic with course offerings that are flexible enough to accommodate nontraditional students’ work schedules and other responsibilities.

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A third reason for the growth of FPCUs is their ability to access federal Title IV funding through accreditation. As discussed earlier, as long as their programs are properly accredited by one of a list of accredited agencies recognized by the U.S. Department of Education, FPCUs can admit students who are eligible for federal financial aid. Fourth, the rapid growth of FPCUs stems from employers requiring more formal certification of employment-related training. As machinery and operating processes become more computer based and technologically complex, employers require workers to function at a higher level of expertise. FPCUs capitalize on this need by tailoring their programs to meet the specific requirements of the job market. For example, if the local community has a shortage of surgical technicians, the FPCUs in that area will design a 9- to 12-month curriculum that will graduate students with the requisite skills to address this need. Instruction at FPCUs

Instructors are generally contractually hired on a course-by-course, part-time basis. There is no tenure-track faculty, whereas in traditional HEIs, both research and teaching faculty belong to the academic senate. The main implication for this difference lies in faculty members’ ability to design their own classes. Instructors at FPCUs follow a set of instructional guidelines and do not veer away from the course materials that have already been printed and distributed to the students. Instructors are hired for their expertise in the field of study and are often still employed in the industry. Although most of the instructors do not have advanced degrees, they nevertheless have in-depth knowledge of their field and can impart valuable experience-related know-how to students. Admission Criteria

Admission is by open enrollment, which means any student with a high school diploma or GED® credential can apply and be admitted into the program of his or her choice. To reduce the incidence of dropouts, many FPCUs now monitor the students for the first 30 days to ensure that they are motivated and on track and will likely persist. Most courses are offered year-round, and enrollment is ongoing (rolling enrollment) as opposed to the semester or quarter systems at traditional HEIs where the students typically take the summer months off. The aim is to

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accommodate the nontraditional students who wish to finish the course as expediently as possible. Regulations That Pertain Exclusively or Primarily to FPCUs

In response to outcries about unscrupulous FPCU operators recruiting unqualified students who would eventually drop out and default on their loans, an 85/15 rule was put in place with the reauthorization of the Higher Education Act in 1992, which stipulated that no more than 85% of institutional revenue at FPCUs could come from federal Title IV funding. An amendment in 1998 changed the threshold to 90/10 and has been in effect ever since. In 2011, in an attempt to ensure that FPCUs did not overcharge for programs and burden their students with debts they could not service, the Education Department announced gainful employment regulations that required that at least 35% of former students at all institutions providing vocational and career programs must be repaying their debt, that the former students’ debt-to-income ratio be no more than 12%, and that the debt-todiscretionary income ratio be no more than 30%. However, the court vacated this ruling on a technicality the day before it was to go into effect. Although it is put on hold, the notion of holding FPCUs and other HEIs accountable for student outcomes in the workplace through gainful employment continued to be debated during the latest round of reauthorization of the Higher Education Act.

Future of For-Profit HEIs and Service Providers With the recent popularity of massive open online courses, academics-turned-entrepreneurs have established several for-profit ventures such as Udacity and Coursera. Along with the nonprofit EdX set up by Harvard and Massachusetts Institute of Technology, these educational service outfits are exploring ways in which to monetize the massive open online courses model. This newest innovation is disrupting the traditional college format of semesters and quarters, classes during the daytime with summers off. It is also challenging the notion of physically being on campus for instruction and instead designing courses with curricula currently being offered on campuses nationwide but with technologically advanced online delivery systems that are interactive and facilitate discussion and collaboration online with other enrolled students to simulate classroom interactions.

These new education providers are also offering bundled services to traditional nonprofit HEIs wishing to make courses available online. These services help institutions establish and grow online programs. While they can help with course development and conversion, platform and IT needs, and compliance and reporting, their real added value is in marketing and student recruitment. At the same time, bundled services can also help ease overcrowded classrooms at public HEIs by moving some of the lower level general requirement courses online. Starting in the spring semester of 2013, San Jose State University in California was the first to offer college credit for entry-level online classes in this format. Plans to expand the courses, which were offered in partnership with Udacity, stalled in part due to faculty concerns. Within months, higher education systems in New York, Tennessee, Colorado, and the University of Houston also unveiled plans to offer modified versions of massive open online courses as well. For-profit educational services in higher education, rather than operating separately to absorb excess demand spilled over from traditional HEIs, are moving to partner directly with nonprofit HEIs. Once marginal and insignificant, for-profit higher education has grown and developed into a relevant and indispensable participant in the U.S. higher education sector. Guilbert C. Hentschke and Shirley C. Parry See also Accreditation; Gainful Employment; Organisation for Economic Co-operation and Development; U.S. Department of Education; Vocational Education

Further Readings Bennett, D. L., Lucchesi, A. R., & Vedder, R. K. (2010). For-profit higher education: Growth, innovation and regulation. Washington, DC: Center for College Affordability and Productivity. Breneman, D. W., Pusser, B., & Turner S. E. (Eds.). (2006). Earnings from learning: The rise of for-profit universities. Albany: State University of New York Press. Deming, D. J., Goldin, C., & Katz, L. F. (2011). The forprofit postsecondary school sector: Nimble critters or agile predators? (NBER Working Paper 17710). Cambridge, MA: National Bureau of Economic Research. Rosenbaum, J. E., Deil-Amen, R., & Person, A. E. (2006). After admission: From college access to college success. New York, NY: Russell Sage Foundation.

Fund Accounting Tierney, W. G., & Hentschke, G. C. (2007). New players, different game: Understanding the rise of for-profit colleges and universities. Baltimore, MD: Johns Hopkins University Press. U.S. Senate Committee on Health, Education, Labor and Pensions. (2012). Committee print—for profit higher education: The failure to safeguard the federal investment and ensure student success. Retrieved from http://www .gpo.gov/fdsys/browse/committeecong.action?collection= CPRT&committee=health&chamber=senate&congresspl us=112&ycord=0

FUND ACCOUNTING The principal mechanism used by public agencies (including school districts) to indicate how monetary resources will be translated into educational practice is the budget. William T. Hartman says that the budget defines the planned expenditures and anticipated revenues of a school district along with information relating the fiscal elements to the educational philosophy, programs, and needs of the district. Once the budget has been developed and approved, it is important to account for all revenues and expenditures so that school district managers can be held accountable for using the resources in the manner planned. School districts—like other government agencies—use what is commonly called fund accounting to track revenues and expenditures. A fund is a self-balancing set of accounts related to a common topic. This entry describes how fund accounting is used in public school districts and identifies the primary reasons for using this approach to develop, balance, and monitor school budgets.

Accounting for Expenditures and Revenues After a school district’s budget is developed, districts need mechanisms for tracking expenditures. They do this through fiscal accounting systems that have various elements, including funds, objects, and functions. Fund Accounting

Like other public agencies, school district budgets rely on fund accounting. A fund is a self-balancing set of accounts related to a common topic. Although the number of allowable funds varies across the 50 states, all school districts have a general fund. The general fund sometimes has a different name such as the operations and maintenance fund or the operating

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fund, but its purpose is the same: to account for the general revenues and expenditures of a school system. As such, the general fund accounts for 75% to 90% of total resources in the average school district, and expenditures made through the general fund include those for instructional services (salaries, benefits, and supplies), general administration, maintenance of school buildings, utilities, and other expenses associated with day-to-day operations. Additional funds are used by school districts as needed. Table 1 lists potential account funds as identified by the National Center for Education Statistics.

Objects of Expenditure The basic unit of accounting for a budget relies on objects of expenditure. Objects represent actual items that can be purchased. Object-oriented budget systems can be very basic or very specific. Table 2 provides a simplified hypothetical accounting code for expenditures by object for a school district. The table is not based on the actual accounting system of any particular state or school district; rather, it is representative of what an object-level accounting system might look like. In a large, complex organization, all four of the digits in the code column might be used to fully identify the objects of expenditure in more detail. Again, the particular code combinations displayed here are only an example and not necessarily reflective of how codes are actually assigned to expenditures and revenues. Most school systems would have a more sophisticated set of object codes to fully track their expenditures.

Table 1

Federal Fund Classification

Fund

Description

01

General fund

02

Special revenue funds (i.e., special education or federal projects)

03

Capital project funds

04

Debt service funds

05

Permanent funds

06

Enterprise funds

07

Internal service funds

08

Trust funds

09

Agency funds

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Fund Accounting

Table 2

Hypothetical Object-Level Accounting Classifications

Code Objects 1000 1100 1200 1300 1400 1900 2000 2100 2200 2300 2400 2500 2600 2900 3000 3100 3200 3300 3400 3900 4000 4100 4200 4300 4400 4600 4700 5000 5100 5200 5300 5400 5500 5900 6000

Classification Professional certificated salaries Teachers’ salaries School administrators’ salaries Supervisors’ salaries Central office salaries Other certificated salaries Classified salaries Instructional aides’ salaries Administrators’ salaries Clerical and office worker salaries Maintenance and operations salaries Food services salaries Transportation salaries Other classified salaries Employee benefits Retirement Health Workers’ compensation Unemployment insurance Other benefits Books and supplies Textbooks Books other than textbooks Instructional materials and supplies Other supplies Pupil transportation supplies Food services supplies Services and other operating expenditures Consultants Travel Insurance Utilities Rentals and leases Other services Capital outlay

Code

Classification

6100 6200 6300

Sites and improvement of sites Buildings and improvement of buildings Books and media for new school libraries or major expansions 6400 Equipment 6500 Equipment replacement 7000 Other outgo 7100 Tuition 7200 Transfers out 7300 Interfund transfers 8000 Revenue 8100 Local revenue 8200 State revenue 8300 Federal revenue Functions 1000 Instruction 2000 Support services 2100 Students 2200 Instructional staff 2300 General administration 2400 School administration 2500 Business 2600 Operations and maintenance of plant services 2700 Student transportation services 2800 Central 2900 Other support services 3000 Operation of noninstructional services 3100 Food services operations 3200 Other enterprise operations 3300 Community support operations 4000 Facilities acquisition and construction services 5000 Other uses 5100 Debt services 5200 Fund transfers

Source: Based on Allison, Honegger, and Johnson (2009). Note: Classifications displayed here are examples of codes that are typically found in object-level accounting systems for public schools. Individual states and districts may use different definitions and provide greater detail through the use of the third and fourth digits in each code.

Fund Accounting

The problem with budgets that provide only object-level data is that they do not give the reviewer any sense of the purposes for which the resources are being used. For example, an object-level budget might contain a line item summarizing total certificated salaries for teachers (e.g., the 1100 category in Table 2). This would give the reviewer a concept of how much is spent on teacher salaries by the school district, but no sense of how those teachers are allocated among elementary, middle, or high schools, or of how much is paid to substitute teachers when compared with regular teachers. Moreover, a line item for teacher salaries at an individual school provides little information as to how those teacher resources are used by educational programs. To answer these questions, budgets can also be aggregated by function or program. Function Classifications

Functions describe general areas of expenditure such as instruction, administration, operations and maintenance, pupil transportation, and instructional support. Functional definitions vary across state accounting systems, but the federal government has attempted to standardize them. Table 2 also displays sample school district function classifications as developed by the federal government in Financial Accounting for Local and State School Systems: 2009 Edition.

Program Classifications Expenditures can also be classified by programs. The more detailed the programmatic distinctions, the more complex the budget process becomes, and the bigger the budget document. The advantage of a program budget is that it gives managers, as well as school board members and the general public, a better picture of what the funds are actually being used to purchase. One of the difficulties with program budgets is that many services are hard to assign to a single program. Custodial services, for example, serve all programs at the school. If classified separately as a function, a great deal of information can be obtained about custodial services. But how those costs could— or should—be allocated to individual programs is not always a simple task since some programs, such as science labs and home economics, require larger spaces and more cleaning resources than do, say, traditional language arts and math classes. Today, many states require (or at least encourage) districts to report expenditures at the school

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level, which can help show how resources are used to produce student learning. The goal of such systems should be how such a reporting system can be developed so that policymakers and the public know not only how resources are budgeted by educational strategy at the school level but also the degree to which they are spent as intended. Accounting Codes

In practice, it is helpful to be able to track an individual expenditure by all three of these methods— object, function, and program—and in the future, perhaps, by educational strategy at the school level. In addition, it is often helpful to track expenditures by the location where the expenditure is made. Locations usually include the schools in the district, the central office, and other areas and subareas as determined by the district. Additional sets of digits could be added to this account code structure to provide additional detail about every expenditure. The important factor to keep in mind in designing accounting code structures is that the greater the level of detail (i.e., the more digits in the code), the greater the potential for error in coding expenditures. Thus, there is always a trade-off between the complexity of the code and the potential accuracy of the data entered into the code. In Oregon, when a new accounting system was instituted, the system designers realized early on that if decision makers were to have useful data at the school level as promised, a significant portion of the resources for implementation of the system would need to go to training staff at all of the schools and school districts.

Conclusion Managing the fiscal resources of a school district is crucial to the operation of public schools. To account for all of the revenues and expenditures of a school system, a process of fund accounting is used. Each fund is a self-balancing set of accounts that include information on object, function, and program and can be used to understand the allocation and use of resources. Accounting codes are used to identify each expenditure or revenue. Most resources are accounted for through the general fund, but special funds are often used for activities that must be tracked separately, for example, capital construction, federal categorical programs and some state categorical programs, and other types of enterprise (or self-funding) funds. Lawrence O. Picus

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See also Capital Budget; Categorical Grants; Education Spending

Further Readings Allison, G. S., Honegger, S. D., & Johnson, F. (2009). Financial accounting for local and state school systems:

2009 edition (NCES 2009-325). Washington, DC: U.S. Department of Education, National Center for Education Statistics, Institute of Education Sciences. Retrieved from http://nces.ed.gov/pubs2009/2009325.pdf Hartman, W. T. (2003). School district budgeting. Lanham, MD: Scarecrow/Education in partnership with Association of School Business Officials International.

G employment. This entry discusses the various ways in which gainful employment has been defined and its relevance to education and economics and then discusses its use in public policy, particularly in higher education regulations.

GAINFUL EMPLOYMENT Gainful employment generally refers to work done for pay (vs. work done without pay, e.g., volunteer work, an unpaid internship, or cooperative education experience, known as a “co-op”). This work done for pay may or may not result in a profit, meaning the costs of doing the work may be greater than the pay received. For example, a worker’s costs for transportation to and from work and child care while working may be greater than his or her takehome income. In this situation, the worker would still be considered to have gainful employment, even though he or she may not actually make money after he or she accounts for the costs of working. While gainful employment is usually defined as any work that would typically be paid, psychology (specifically, positive psychology) offers a wider definition of gainful employment. In positive psychology, gainful employment is characterized by the quality of the job, which includes more than just income. Psychology’s definition of gainful employment focuses on aspects of a job that enrich one’s life and make it more fulfilling. Characteristics of gainful employment include satisfaction with one’s job, social relationships at work, a safe work environment, varied work tasks, and a respect for diversity in the work environment. Positive psychology focuses on the impact of gainful employment on other life outcomes, such as a sense of fulfillment or recovery from illness, and stresses an interest in increasing gainful employment over decreasing unemployment without regard to for quality of

Relevance to Education and Economics Education has many benefits—accruing both to the individual (private) and to the society at large (public). One of the more commonly discussed public benefits of education, particularly higher education, is an improved economy. Education can improve the economy by building students’ human capital, or skills, which allows them to better contribute to the economy through gainful employment. Gainful employment has other benefits, too. The gainfully employed individual benefits from having a career that is more satisfying—which can mean a higher income, better working conditions, and so on. Society benefits by increased economic productivity and reduced dependence on public supports such as welfare.

Use of the Term Gainful Employment in Public Policy The term gainful employment is widely known for its use in federal policy. The Higher Education Act of 1965 states that vocational colleges must offer a “program of training to prepare students for gainful employment in a recognized occupation” (20 U.S.C. § 1002), but the act does not define what it means by gainful employment. The Social Security Act references gainful activity and the Social Security 363

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Administration regulations (20 C.F.R. § 416.972) defines gainful work activity as “work activity that the claimant does for pay or profit. Work activity is gainful if it is the kind of work usually done for pay or profit, whether or not a profit is realized.” In 2011, the U.S. Department of Education (ED) announced gainful employment regulations that explicitly defined how the agency would measure gainful employment for vocational higher education programs. A program would be considered to not be adequately preparing its students for gainful employment (and would be denied federal financial aid) if it fails one of these three conditions of gainful employment three times over 4 years: 1. Student loan repayment is not more than 30% of his or her discretionary income. 2. At least 35% of former students are repaying their loans.

for students by reducing the number of programs available and by making colleges less willing to take on low-income students who would need to take out student loans. Opponents also take issue with ED’s ability to effectively implement the regulations and claim that the gainful employment regulations fail to consider local and regional labor markets that have an impact on a student’s ability to get gainful employment, especially for students living in depressed inner-city and rural communities. The contested gainful employment regulations have given the term gainful employment new life. Its meaning of work for pay, or positive psychology’s wider definition of enriching and fulfilling work, have been overtaken by ED’s gainful employment regulations’ definition, which goes beyond simply work for pay but implies that a certain amount of profit is required—specifically, enough to pay back student loans without too much of a burden.

3. Student loan repayment is less than 13% of his or her income.

This definition is more specific than the general definition of gainful employment. ED’s gainful employment regulations are specific about how much of an income should be earned to be considered gainful—conceptually, enough of an income to repay student loans incurred while procuring a vocational education. These regulations have been controversial. Forprofit colleges and universities, who would have been greatly affected by these regulations, filed suit to block the gainful employment regulations. This suit led to the gainful employment regulations being struck down in 2012 by the U.S. District Court for the District of Columbia. The court cited lack of a basis for the 35% threshold in the second condition. In November 2013, ED released draft regulatory language to address the court’s concern. Supporters of the regulations have cited the high tuition cost and student loan default rate for many vocational programs, particularly vocational programs at for-profit institutions. They are concerned that the programs do not prepare students for gainful employment, leaving the students unable to pay back their high student loans, and they question whether taxpayers, who subsidize many of the targeted programs through students’ use of state and federal student financial aid to pay tuition, fees, and other educational expenses, are getting a good return on their investment. Opponents of the regulations claim that the regulations would limit access

Su Jin Jez See also Benefits of Higher Education; For-Profit Higher Education; Human Capital; Nonwage Benefits; Student Financial Aid; Student Loans; Tuition and Fees, Higher Education; U.S. Department of Education; Vocational Education

Further Readings Higher Education Act of 1965, 20 U.S.C. § 1001 et seq. Snyder, C. R., Lopez, S. J., & Pedrotti, J. T. (2011). Positive psychology: The scientific and practical explorations of human strengths. Thousand Oaks, CA: Sage. Social Security Administration, 20 C.F.R. § 416.972 (2012). U.S. Department of Education. (2013, April 16). Negotiated Rulemaking Committee: Public hearings. Retrieved from https://ifap.ed.gov/fregisters/FR041613. html

GENERAL EDUCATIONAL DEVELOPMENT (GED®) GED® is an abbreviation for the General Educational Development examination used in the United States and Canada to assess the academic skills of high school dropouts. The exam is administered by the GED Testing Service®, a for-profit company formed in partnership between the American Council on Education and Pearson, an educational publishing company. The GED test is designed to measure

General Educational Development (GED®)

whether an individual has academic proficiency comparable to individuals who earn a high school diploma. Individuals who pass all levels of the exam are awarded a GED certificate. According to the GED Testing Service, a GED certificate is equivalent to a high school diploma in the United States and Canada. More than 98% of U.S. colleges and universities accept GED certificates as an alternative for high school transcripts. This entry will include a history of the GED exam, a profile of GED credential recipients, a description of the postsecondary and labor market success of GED credential recipients, and a short discussion of the GED exam.

History The GED exam was developed by the American Council on Education in 1942. The exam initially targeted military members who dropped out of high school and served in World War II. The exam was administered to about 30,000 individuals in the 1940s, while more than 700,000 individuals now take the GED exam. In the early 1950s, GED credentials accounted for less than 1% of all high school credentials; they currently constitute 12% of those credentials. The GED Testing Service revised the initial examination in 1978, 1988, 2002, and 2014 to keep pace with changes in high school curriculum. The GED Testing Service sets a minimum passing standard or cut score for the examination and has raised the requirements over the years. To set a minimum passing standard, the exam was given to a representative group of high school seniors. Based on the test scores in this norm group, the 2002 GED exam pass rate was set and reflected the cognitive skills of 60% of graduating high school seniors. Examinees were also given percentile scores that reflected how they compared with the typical graduating high school seniors in the 2002 norm group. Some states set standards higher than the GED Testing Service minimum, and some postsecondary institutions require GED scores higher than this minimum for admission. In January 2014, American Council on Education and Pearson introduced a new edition of the GED examination. The subject matter sections are aligned with the Common Core State Standards that have been adopted by 45 states and the District of Columbia. The new assessment is divided into four sections—literacy, social studies, science, and math—that emphasize problem solving and higher order skills that are often important

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for postsecondary education and the workforce. The 2014 GED exam not only assesses high school–level skills but also includes a postsecondary education and workforce competency assessment. The transition to the latest version of the test has been controversial. The 2014 GED exam is only offered online and costs about twice as much as the earlier version. Several states have abandoned the GED exam and shifted toward alternative assessments. One alternative, the Test Assessing Secondary Completion, has been developed by educational assessment company CTB/McGraw-Hill and offers a less expensive alternative to the GED exam. The Educational Testing Service and the Iowa Testing Programs have also developed the High School Equivalency Test that measures the academic skills of high school dropouts.

Profile of GED Credential Recipients Recent research shows that the academic background and parental education levels (an indication of family wealth and socioeconomic status) of GED credential recipients are closer to those of the average high school dropout than to those of the average high school graduate. Nathaniel Malkus and Anindita Sen (2011) examined data from the Education Longitudinal Study of 2002 and tracked the educational progress of a nationally representative sample of 10th graders. They compared the characteristics of three groups at the time of a 2006 follow-up survey: (1) high school dropouts who were GED credential recipients, (2) high school dropouts without a GED credential, and (3) students who successfully earned a traditional high school diploma. About 40% of graduates had a parent with a college degree as compared with 27% of GED credential recipients and 18% of high school dropouts. Only 18% of the high school graduates had a 2.0 grade point average or below in ninth grade as compared with 58% and 68% of GED credential recipients and other dropouts, respectively.

Postsecondary Success About 60% of GED examinees take the assessment to pursue further education, but many of these students find little success in postsecondary education. Malkus and Sen (2011) found that only 31% of GED credential recipients enroll in postsecondary schools within 5 years of taking the examination. Margaret Becker Patterson, Wei Song, and Jizhi Zhang (2009)

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General Educational Development (GED®)

found that 71% of these enrollees completed only one semester of postsecondary schooling. John Tyler and Magnus Lofstrom (2010) examined postsecondary enrollments for a cohort of atrisk eighth-grade students. They found that 46% of the at-risk students who graduated from high school enrolled in postsecondary education within 3 years of graduation. In contrast, only 22% of comparable GED credential recipients enrolled in postsecondary education within 3 years of earning their GED credential. The evidence suggests that GED credential recipients are on a much different postsecondary path than similar students who graduate from high school. GED credential recipients have cognitive skills comparable with 60% of graduates, but they are much less likely to pursue postsecondary training and much less likely to succeed in these programs.

Labor Market Success Stephen Cameron and James Heckman (1993) used longitudinal student-level data to measure the labor market success of GED credential recipients relative to high school graduates and to dropouts who did not earn GED credentials. They found that GED credential recipients had better labor market outcomes than dropouts but much worse than those for high school graduates. GED credential recipients completed more years of high school than other dropouts, but GED credential recipients were indistinguishable from uncredentialed dropouts after controlling for differences in student background for the two groups. Cameron and Heckman concluded that GED credential recipients were “nonequivalent” with high school graduates. Richard Murnane, John Willett, and John Tyler (2000) examined the labor market outcomes for a cohort of 27-year-olds who did not attend college. They found that GED credential recipients had lower earnings at the age of 27 than high school graduates even after controlling for differences in 10th-grade test scores and family backgrounds of the individuals. GED credential recipients did somewhat better than other high school dropouts, but most of the gains were for students with low 10th-grade test scores. They argued that dropouts with low cognitive skills might reap an earnings gain from sharpening their cognitive skills through GED exam preparation classes. Song and Patterson (2011) examined the wages and hours of uncredentialed dropouts, GED

credential recipients, and high school graduates who did not enroll in postsecondary education. The study included detailed information on individual demographics, early high school ability, and experience. They found that GED credential recipients have higher wages and hours than comparable dropouts but worse labor market outcomes than comparable high school graduates. In another study, Song looked at the long-term labor market outcomes for GED credential recipients relative to uncredentialed dropouts. This research showed that GED credential recipients had higher wages and worked more hours than other dropouts. For each year after earning a GED credential, wages grew at about 2% more for GED credential recipients than for dropouts who did not earn a GED credential. The overall research evidence is mixed on whether GED credential recipients have better labor market outcomes than dropouts, but the studies consistently find that GED credential recipients have “nonequivalent” labor market outcomes when compared with high school graduates.

Discussion For high school dropouts, the GED exam provides an opportunity to demonstrate their knowledge and skills relative to high school graduates. Doing so is important not only for many jobs that require a high school diploma or the equivalent but also for access to postsecondary training that would otherwise be unavailable. However, despite the ability of GED credential recipients to demonstrate the academic content knowledge required on the exam, the weak performance of GED credential recipients in the labor market and postsecondary institutions suggests that high school completion may be important over and above the cognitive skills acquired in the coursework. Heckman, John Humphries, and Nicholas Mader (2011) argue that high school graduates have better noncognitive skills, such as persistence, motivation, and reliability, than GED credential recipients. High school graduates demonstrate these skills by completing their formal classroom learning, and these noncognitive skills are crucial for success in college and careers. One significant policy concern is that the GED exam and similar alternatives induce some students to leave high school early with the expectation that GED credentials are sufficient to succeed

General Obligation Bonds

in postsecondary education and the workforce. The evidence shows that high school graduates have much better academic and labor market outcomes than students who leave high school early. On average, the GED credential recipients do not catch up with classmates who do complete high school. Will those who earn a GED credential by passing the 2014 version of the GED exam have better postsecondary and workplace success than GED credential recipients under earlier versions of the test? The answer to this question will not be determined for several years until the new Common Core State Standards are fully implemented, and students receive instruction under the new standards. The effectiveness of the new test will depend on several factors. First, the success of the new test will depend on its alignment with curriculum taught in the schools as well as its alignment with the demands of postsecondary education and the workforce. Second, high school graduates may gain some cognitive advantage through intensive high school instruction that is not fully measured by the test and not offset by less intensive but time-consuming GED exam preparation. If so, high school graduates will still have an edge over GED credential recipients. Finally, if high school graduates have greater motivation, persistence, and reliability than students who leave school early, then the new test may be little improvement over the previous versions of the exam. Richard Buddin and Michelle Croft Note: GED® is a registered trademark of the American Council on Education (ACE) and is administered exclusively by GED Testing Service LLC under license. This content is not endorsed or approved by ACE or GED Testing Service. Richard Buddin, Michelle Croft, and this work are not affiliated with or endorsed by ACE or GED Testing Service LLC. Any reference to “GED” in the title or body of this work is not intended to imply an affiliation with, or sponsorship by, ACE, GED Testing Service LLC, or any other entity authorized to provide GED® branded goods or services.

See also Adult Education; Credential Effect; Demand for Education; Dropout Rates

Further Readings Cameron, S., & Heckman, J. (1993). The nonequivalence of high school equivalents. Journal of Labor Economics, 11(1), 1–47.

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GED Testing Service. (2009). Technical manual: 2002 series GED test. Washington, DC: American Council on Education. Heckman, J. J., Humphries, J. E., & Mader, N. S. (2011). The GED. In E. A. Hanushek, S. Machin, & L. Woessmann (Eds.), Handbook of the economics of education (Vol. 3, pp. 423–483). Amsterdam, Netherlands: Elsevier. Malkus, N., & Sen, A. (2011). Characteristics of GED credential recipients in high school: 2002–06. Washington, DC: National Center for Education Statistics. Murnane, R., Willett, J., & Tyler, J. (2000). Who benefits from obtaining a GED? Evidence from high school and beyond. Review of Economics and Statistics, 82(1), 23–37. Patterson, M., Song, W., & Zhang, J. (2009). GED candidates and their postsecondary educational outcomes: A pilot study. Washington, DC: Educational Testing Service. Song, W. (2011). Labor market impacts of the GED® test credential on high school dropouts: Longitudinal evidence from NLSY97. Washington, DC: Educational Testing Service. Song, W., & Patterson, M. (2011). Young GED® credential recipients in the 21st century: A snapshot from NLSY97. Washington, DC: Educational Testing Service. Tyler, J., & Lofstrom, M. (2010). Is the GED an effective route to postsecondary education for school dropouts? Economics of Education Review, 29(5), 813–835.

GENERAL OBLIGATION BONDS General obligation (GO) bonds are debt instruments issued by public agencies to raise funds for capital construction and other public works projects. GO bonds are backed by the full faith and credit of the issuing municipality, and generally the interest earnings that accrue to bond holders are exempt from personal income taxes. GO bonds are backed by the issuing agency’s taxation capability, making them different from revenue bonds, which are repaid using revenue from the project they were used to fund. This entry discusses GO bonds and how they are used to help finance public works projects, in particular school construction and renovation. It also describes the relative advantages and disadvantages of these instruments. While the bulk of expenditures made by school districts are for ongoing or annual needs, there are instances where school districts need to spend funds for projects with substantially longer life spans—most

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GI Bill

obviously for school buildings. As with one’s home mortgage, most school districts cannot afford to simply appropriate the funds necessary to build a school over 1 or 2 fiscal years and instead must borrow the money for the construction and then pay that money back with interest over time. A common approach for funding capital construction and other long-term resources is the use of GO bonds. In effect, the school district borrows enough money to build a school and then levies a property tax on each parcel of land in the district to repay the bond over a period of 20 to 30 years. Although state requirements for issuance of GO bonds differ, in general, public agencies (including school districts) cannot issue GO bonds without voter approval. In some states, any increase in local property taxes requires voter approval, so in these states, voters must also approve the issuance of GO bonds. The tax revenues raised to repay the bond must be used for that purpose, making GO bonds a relatively low-risk investment for investors. This low risk translates into relatively low interest rates paid by the bonds, which reduces the borrowing costs to school districts. Conversely, the low interest rate may be an impediment to finding enough investors. While it is likely that market mechanisms would cause interest rates to rise so the bonds could be sold, the income from most municipal bonds are not subject to federal income taxation—which effectively raises the rate of return on the investment. Returns on GO bonds issued by school districts are often not subject to state or local income taxation either, although this varies from state to state depending on state income tax laws. The amount of debt a local entity can incur generally is limited by state law, and for GO bonds that are repaid through property taxes, the amount of allowable debt is typically expressed as a percentage of total assessed value of the taxing jurisdiction or school district. GO bonds represent a large source of funding for long-term capital projects for school districts and give those districts access to long-term borrowing at low interest rates. The advantage of such bonds for investors is their relative security and the fact that despite low interest rates, earnings are generally not taxed. Lawrence O. Picus See also Capital Budget; Capital Financing for Education; Infrastructure Financing and Student Achievement; School District Budgets; School District Cash Flow

Further Readings General obligation bonds. (n.d.). Retrieved from http:// news.morningstar.com/classroom2/course.asp?docId= 5384&page=1&CN=sample Guthrie, J. W., Hart, C., Ray, J. R., Candoli, C., & Hack, W. G. (2008). Modern school business administration: A planning approach. New York, NY: Pearson Education. Hartman, W. (2002). School district budgeting (2nd ed.). Reston, VA: Association of School Business Officers International.

GI BILL The GI Bill refers to a package of benefits for U.S. veterans financed by the federal government and dating back to World War II. The marquee component of the GI Bill is a generous amount of financial aid for veterans enrolled in college. This entry discusses the history of the GI Bill and researches on the effect of the GI Bill on education and earnings.

Postsecondary Benefits for Veterans: World War II to Present World War II was a long and taxing war for participants; approximately 672,000 U.S. troops were wounded and more than 405,000 were killed in deployments that lasted up to 4 years. The U.S. Congress passed the Servicemen’s Readjustment Act of 1944, otherwise known as the GI Bill of Rights, to further compensate World War II veterans for their service abroad, to help distressed soldiers return to civilian life, and to prevent the civilian job market from becoming overwhelmed with returning troops. Benefits outlined by the original GI Bill included low-interest business loans and mortgages, unemployment compensation, and grants for veterans attending college. Postsecondary benefits woven into the GI Bill were unprecedented in their generosity. Veterans received in-kind aid that typically covered tuition, fees, books, and supplies at U.S. colleges and universities for as many as 48 months. In addition, veterans were given a cash allowance, which researchers estimate to be worth at least half the opportunity cost of not working. The bill helped 2.2 million World War II veterans from a vast range of socioeconomic backgrounds attend college. In 1947, veterans accounted for 49% of college admissions.

GI Bill

The structure and scope of GI Bill provisions shifted in the years following World War II, but the centerpiece of the program continued to be a generous set of incentives to pursue postsecondary education. Under the Veterans Readjustment Assistance Act of 1952 (known as the Korean Conflict GI Bill), the maximum length of benefits was reduced from 48 months to 36 months, and vouchers for college expenses were paid directly to veterans rather than their higher education institutions. The Vietnam-era GI Bill extended benefits to at most 45 months of schooling or training and expanded eligibility to include active-duty personnel, high school dropouts, and peacetime soldiers. The U.S. reliance on military conscription ended in 1973 with the introduction of the all-volunteer military. At this point, the military used benefits such as postsecondary financial aid as an incentive to join the armed forces and not merely as a bonus for mandatory service. Eligibility criteria changed as well. The Post–Vietnam War Veterans Education Assistance Program offered active-duty soldiers the opportunity to contribute to an education fund with a $2 match by the government for every $1 contributed by the veteran. The Veterans Education Assistance Program was replaced in 1985 by the Montgomery GI Bill, which offered benefits to both active-duty (MGIB-AD) and selected reserves (MGIB-SR) personnel. Eligibility entailed a small up-front contribution or 6-year agreement as an active reservist. In return, veterans received up to $300 per month for 36 months of tuition assistance based on the number of years of service and the type of postmilitary training solicited. Generally, benefits were to be used within 10 years of discharge. The current iteration of the GI Bill is the Post9/11 GI Bill, the result of the Post-9/11 Veterans Education Assistance Act of 2008 and the Post-9/11 Veterans Education Assistance Improvements Act of 2010. The Post-9/11 GI Bill increased postsecondary benefits beyond the level codified by the Montgomery GI Bill. Individuals are eligible to receive benefits if they have served 90 days of active duty after September 10, 2001. Benefits increase proportionally with the amount of time served and are available for 36 months of education within 15 years of the last period of qualifying active duty. Veterans are covered for the entire cost of in-state public tuition and fees or up to about $18,000 for private school costs. On top of the direct costs of attendance, beneficiaries receive a monthly housing allowance and stipends for books and supplies.

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Research Researchers credit the GI Bill with significantly increasing the college attainment of young men who were more apt to be drafted into service during World War II. In a widely cited study, the economists John Bound and Sarah Turner find that eligibility for the World War II GI Bill increased years spent in college by anywhere from 32% to 38%. Later, Joshua Angrist and Stacey Chen showed that eligibility for the Korean GI Bill increased years of postsecondary education for affected White men by about 15%. Interestingly, a similar effect was not found for eligible non-White men. Drawing on extensive surveys and interviews of veterans, Suzanne Mettler finds evidence that the GI Bill enhanced the civic and democratic engagement of men who took advantage of its postsecondary financial aid provisions. Supporting evidence comes from studies showing that GI Bill beneficiaries were more likely to work in the public sector. In more recent years, higher education benefits have been shown to increase the likelihood that soldiers separate from the military following the completion of their contracted enlistments. Although the GI Bill certainly raised the college attainment of participating veterans, the effect of the GI Bill on earnings—via increased education—is less clear. Economists using data from the 2000 U.S. Census attribute a 7% increase in earnings to the Vietnam-era GI Bill, which was largely offset by veterans’ lower experience levels. Research has also examined the World War II–era GI Bill provisions for home loan benefits and unemployment compensation. Utilization of unemployment compensation was low, in large part because take-up of postsecondary support was high. Home ownership incentives, however, had a significant impact on the lives of World War II veterans. The GI Bill explains perhaps 40% increase in home ownership between 1940 and 1960; favorable mortgage terms motivated veterans to purchase homes earlier than they would have in the absence of the GI Bill. Celeste K. Carruthers and Justin Roush See also Adult Education; College Enrollment; Higher Education Finance; Student Financial Aid

Further Readings Angrist, J. D., & Chen, S. H. (2011). Schooling and the Vietnam-era GI Bill: Evidence from the draft lottery. American Economic Journal: Applied Economics, 3(2), 96–118.

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Globalization

Bound, J., & Turner, S. (2002). Going to war and going to college: Did World War II and the G.I. Bill increase educational attainment for returning veterans? Journal of Labor Economics, 20(4), 784–815. Fetter, D. K. (2013). How do mortgage subsidies affect home ownership? Evidence from the mid-century GI Bills. American Economic Journal: Economic Policy, 5(2), 111–147. Mettler, S. (2005). Soldiers to citizens: The G.I. Bill and the making of the greatest generation. New York, NY: Oxford University Press. Simon, C. J., Negruas, S., & Warner, J. T. (2010). Educational benefits and military service: An analysis of enlistment, reenlistment, and veterans’ benefit usage 1991–1995. Economic Inquiry, 48(4), 1008–1031. Stanley, M. (2003). College education and the midcentury GI Bills. Quarterly Journal of Economics, 118(2), 671–708.

GLOBALIZATION Globalization is a process marked by increasing interaction among individuals, organizations, and governments from different parts of the world. Various trends in the modern world are described as both causes and manifestations of globalization. For example, rapid increases in global Internet use and other recent technological advances contribute to the accelerated pace of international trade and finance, resulting in global demand and supply of Internet-based communication technologies. Such trends lead to greater standardization across national trade laws, tax systems, and monetary and fiscal policies. Globalization is most commonly associated with trends pertaining to global economics, many of which affect education, both directly and indirectly. “Western” aid organizations may, for example, offer financial assistance to developing countries conditioned on implementation of “Western” educational policies, thus affecting the revenues of schools in developing countries. At the same time, flows of higher education students from developing to developed nations are affecting college revenues in developed countries. This entry is organized in three parts. It first defines globalization and offers a history of its key features. Second, it examines the four primary dimensions of globalization: technological, economic, sociocultural, and political. Finally, it describes globalization from an educational perspective, noting the implications of increasing global convergence for education.

History and Defining Features Globalization is a term popularized by the economist Theodore Levitt in 1983 to describe changes in global economics affecting production, consumption, and investment. Today, globalization is a technology-facilitated process by which ideas, practices, and people converge—bridging geographic, economic, political, and cultural boundaries, resulting in greater global interdependence. Among the many interpretations of globalization, the most common themes are connection and integration of world economics through social, transnational, and technological networks. David Held and Anthony McGrew argued that it is the intensification of worldwide social relations that link distant localities such that local happenings are shaped by events occurring many miles away. In addition to changing social processes, as the journalist Thomas Friedman observed, globalization integrates markets, nation-states, and technologies in a way that enables individuals, corporations, and nation-states to reach around the world farther, faster, deeper, and cheaper than ever before. It is a process of increasing cross-border interaction and interdependence among nationstates in a variety of contexts, including trade, travel, and financial transactions. As a result, globalization is blurring national boundaries, shifting solidarities within and between nation-states, and affecting the constitution of national and interest group identities. Owing to its impact on alliances and competition, globalization both affects and is affected by these technological, economic, political, and cultural trends. Over time, these trends tend to foster various forms of convergence, wherein idiosyncrasies of individual countries begin to appear more similar to those in other countries over time. The trend toward convergence occurs despite major, fundamental differences that continue to exist among nations.

Dimensions of Globalization The following sections describe four broad categories of phenomena that characterize globalization, namely, the technological, economic, cultural/linguistic, and political dimensions. These dimensions make up the major components of daily life in which global integration is most apparent. Technological

One primary catalyst affecting the pace of global convergence is the proliferation of high-speed,

Globalization

low-cost transportation and information and communication technologies. Information exchanges occur instantaneously across vast distances, compressing time and space, at continually decreasing levels of cost, effectively accelerating globalization processes across a wide range of economic and social transactions, such as finance, trade, immigration, employment, education, and politics. The exchange of goods and services across national borders is not a new practice. Advancements in ship building and cartography accelerated the pace of European exploration, facilitating trade and commerce beginning in the 15th century. The speed and volume of international trade gained momentum over the past 200 years with the rise of steamships and railroads and later airplanes and automobiles. Telephones expedited cross-border communication, and the 21st-century proliferation of digital technologies has allowed individuals to manipulate, store, and transmit data instantly at minimal cost. Though technologies are in place to allow communication from all corners of the world, globalization has not uniformly affected the world’s population. Global videoconferencing, online education, and politically targeted social media are examples that depict activity in heavily technologically integrated societies. However, these societies still constitute a minority of the world’s population. A nation’s ability to keep pace with the global economy depends on its ability to process and absorb new knowledge through technology. Specialized expertise is required for societies to process and effectively utilize increased information flow. With the rise of the knowledge economy, information skills—data handling expertise, systems innovation, and so on—have become highly valued economic resources; and increasingly, knowledge-intensive business environments result in greater consumer demand for a new kind of education—one capable of preparing students for knowledge management and for global citizenship. Economic

Although globalization affects nearly every aspect of social life, economic globalization plays a foundational role. The recent acceleration of economic globalization can be attributed to a number of factors, including advances in science and technology, finance, and investment banking, which facilitate business development and the cross-border movement of goods, services, technology, people, and

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information. These in turn enhance the impact of cross-border specialization and division of labor, growth-pursuing activities of multinational corporations (MNCs), and production for global supply chains by domestic businesses, all justified by national-level neoliberal economic and trade policies. Neoliberalism describes economic and political policies premised on the deregulation of markets and the lowering of trade barriers to promote increased global flow of economic activity. The dominance of neoliberal trade policies over the past three decades is reflected in the growing influence of three international organizations—the International Monetary Fund (IMF), the World Bank, and the World Trade Organization. The IMF and the World Bank were two of the organizations at the forefront of the Washington Consensus of the 1990s, a series of neoliberal policies typically recommended for nations in financial crisis. IMF loans were commonly conditioned on implementation of the policies derived from the Washington Consensus. The current role of the IMF is threefold: (1) reviewing national economic policies with an eye toward stabilizing global economic activity, (2) short-term lending to member nations experiencing economic difficulties, and (3) providing technical assistance and training for member nations. In contrast, the World Bank is concerned primarily with long-term economic development and the eradication of poverty in developing nations. The World Trade Organization, as the principal governing body overseeing transnational trade relations and agreements, promotes trade through regulations based on fairness in exchange and provides dispute resolution for aggrieved members. Global economic activity is carried out largely through MNCs, corporations that are registered or maintain operations in more than one country. These corporations account for approximately 80% of the $20 trillion in trade every year. MNCs facilitate globalization through cross-border divisions of labor and “outsourcing” of business functions from originating countries. Outsourcing contracts out internal business operations to third-party organizations, which opens up domestic labor markets to international competition. Examples of outsourced U.S. operations include call centers in India and MexicoU.S. border factories popularly known as maquiladoras. As outsourcing requires the familiarization of employees with international work products, policies, and management structures, it can be perceived as both a cause and an effect of globalization.

372

Globalization

Though economic globalization has brought benefits and opportunities to many nations, many economists argue that neoliberal policies like those of the Washington Consensus have done more harm than good in developing economies. Joseph Stiglitz, for example, found no empirical evidence to support the belief that trade liberalization makes markets more efficient. To the contrary, he argues that such policies may have played a causal role in the Asian and Argentine economic crises. Furthermore, evidence suggests that economic inequality between and within many nations is increasing. The potential for developing nations to keep pace with the global economy may depend on their capacity to absorb knowledge through technology, but bridging the gap between the rich and the poor requires strategic policies and institutional and multilateral arrangements that are often difficult to identify and enforce. Cultural/Linguistic

Increasing transnational cultural and political interactions result in part from economic globalization. The spread of capitalism facilitates the production of material goods, which are placed in an increasingly open market. Reductions in national trade protections, coupled with increased transnational migration in pursuit of enhanced employment and schooling opportunities, foster the free flow of cultural and political ideas, generating greater economic and cultural integration. A nation’s cultural identity is challenged as its citizens interact with people in other countries and absorb outside cultural references and customs through business connections, social media, and entertainment. Although cultural exchanges can be two-way, scholars such as Roland Robertson and Robert Rhoads have argued that economically developed countries assert their cultural dominance on less developed countries, weakening their cultural and national identities. According to this view, Western and American ideals are spread to the rest of the world through conditions tied to foreign aid or through goods and services sold by Western corporate conglomerates, resulting in “cultural hegemony” or “cultural imperialism.” This hegemony is problematic in that it overshadows local customs and cultures, thereby diminishing cultural diversity. Some argue that “receiving nations” are mindful of their own political and economic circumstances and self-interests and intentionally pursue international relations with this danger in mind.

The net impact is often a hybrid of globalization and localization. In these instances, a foreign cultural concept that is introduced into the local context may be interpreted in a way different from its original intent, or where it is blended into the local customs and develops a whole new meaning specific to the adoptive community. This trend is sometimes referred to as “glocalization,” wherein hybrid cultures arise at the local level after the foreign culture is introduced. Political

Owing to the pervasive spread of economic and cultural influences discussed above, globalization is often believed to diminish a nation’s political autonomy. Participation in international trade fosters interdependency, leaving nations vulnerable to the influences of global financial markets, international organizations, and MNCs. Though governments of nation-states can direct policies in the interests of their own citizens, they cannot do so without careful consideration of how legislation will affect the actions of its corporate (especially multinational) citizens and reactions from other trading partners. Political globalization is therefore characterized by increased multilateral arrangements, such as the European Union and the North American Free Trade Agreement, to address a growing array of issues that were once strictly domestic in nature. The impetus behind these arrangements may be political, economic, based on military power, or a combination of the three. International business arrangements also affect domestic politics. MNCs may lobby individual nations for favorable tax treatment, pressure governments to negotiate bilateral or multilateral agreements with nations with which they intend to do business, or push for changes in international trade and treaties to their advantage. Conversely, the political process in a country can also advance or impede globalization within its own borders, as when trade unions lobby against outsourcing production overseas or when business leaders lobby for domestic subsidies to keep prices competitive against foreign competition. As nations become more culturally and economically integrated, they gain some economic advantages but also risk becoming vulnerable to transnational issues, including terrorism, climate change, and international crime, as well as competition from abroad. As regulation of these issues is

Globalization

often beyond a single nation’s control, coordination of international efforts is frequently placed in the hands of global political institutions like the United Nations, the IMF, and transnational nongovernmental organizations as well as multilateral negotiations among nations.

Globalization and Education Similar to many other segments of society, education is emerging as both a vehicle and a product of globalization. It can be metered in increased crossborder interaction, interdependencies, and integration, and it is illustrative of the interactive effects of technology, economics, sociolinguistics, and politics discussed above. The following sections describe five current issues at the nexus of globalization and education.

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education), (3) commercial presence of a supplier in a consumer country (e.g., offshore foreign universities), and (4) the presence of natural persons from a supplying country in a consuming country (e.g., professors, researchers working abroad). The liberalization of trade in education reflects the global trend toward commercialization of education, subject to cross-border trade agreements, as with other goods and services. Varying Rates of Return to Education

In recent years, societies in the developing and the developed world alike have begun to place greater emphasis on education. The worldwide growth of education can be readily explained by its growing contribution to the changing nature of work and economic development. Nations that wish to thrive in the global knowledge economy increasingly recognize the value of extending formal education for their citizens. As nations shift from manufacturing to service-based economies, the demand for highly skilled workers is rising, and student enrollment has grown steadily, in line with the increasing demand for knowledge workers across all levels (primary, secondary, and tertiary) and most nations, regardless of their stage of development. As a result of these economic changes, the benefits of education are measured in terms of their impact on a nation’s workforce.

Nations seek easier and cheaper methods of accessing knowledge to prepare their citizens to enter a workforce that is increasingly reliant on knowledge-based, postindustrial, and serviceoriented skills. Education plays an important role in accommodating modern shifts in human capital needs. Students living in high per capita GDP (gross domestic product) countries tend to earn more from an added year of schooling than those in low per capita GDP countries, a fact which fosters net migration from the latter to the former. However, empirical evidence suggests that returns on investment in education are dependent on a variety of additional factors. For example, rates of return for one added year of primary school are higher in developing countries than in developed countries, while one additional year of tertiary schooling yields greater returns in developed countries. Furthermore, investment in women’s education tends to generate greater returns across the developing world. Finally, research suggests that rates of return in developed countries may be greater for those individuals at the top of the income distribution. If true, the latter phenomenon has significant consequences for globalization, as many researchers theorize that the income gap within and between nations is widening.

Education as a Service

Institutional Trends in Higher Education

Given the increased emphasis on credentials for employment, higher education is often treated as a service or commodity that can be traded, subject to the laws of supply and demand. Trade in education services is regulated in many countries through the World Trade Organization’s General Agreement on Trade in Services in one of four ways: (1) consumption abroad of service by consumers traveling to a supplier country (e.g., students studying abroad), (2) cross-border supply of a service to a consumer country without the supplier (e.g., open and distance

Another consequence of globalization is the multinationalization of educational institutions— institutions from one nation offering programs in another nation. Multinational educational provision takes a variety of organizational forms. These may be prestigious, state-recognized, or accredited schools and colleges; commercial and corporate enterprises, training their own employees or selling their training programs on the open market; or organizations that represent partnerships among various organizations, domestic and foreign. All incorporate

Changing Economic Climate

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Globalization

some combination of virtual and in-class education services. Some institutions even offer transnational tuition-free courses and learning resources through massive open online courses and Open Educational Resources, enabling participants to reuse, repurpose, customize, and reoffer content based on domestic needs. Language of Instruction

No issue of cross-border standardization is more fundamental than the language of instruction. English is presently the language of science and scholarship, the most widely studied foreign language worldwide, and often the required second language in non-English-speaking countries. Among the many consequences of the dominance of the English language to higher education is the resulting dominance of ideas from the major English-speaking academic systems. Many non-English-speaking scholars must publish in English to join a particular academic discussion, thereby sacrificing some portion of their intellectual and cultural autonomy. Additionally, some non-English-speaking nations have changed the medium of instruction at their universities entirely to English. English language instruction in developing countries may benefit local populations by preparing citizens to migrate to Western nations. However, four fifths of international students from China and India—the two largest student-sending countries—do not return to their home countries on degree completion but find work and remain in their host country. Largely referred to as “brain drain,” the emigration of highly skilled citizens from their countries of origin is an increasingly common occurrence, and one with considerable global implications—positive and negative—for both the sending and the receiving countries. Though relieved of their human capital, sending countries often benefit from remittances sent back from their citizens abroad. The reverse phenomenon is known as “brain gain,” wherein receiving countries obtain a highly skilled workforce, resulting in greater competition for their own citizens.

Conclusion The dimensions of globalization discussed in this entry highlight the ways in which ideas, practices, and values converge to increase global interactions and interdependence. The Great Recession in 2008 and the ensuing years illustrates the potential

for widespread impact of globalizing processes. The decrease in U.S. trading activity in December 2007 affected its trading partners and resulted in extreme global repercussions. Financial institutions across the world cut back on lending across their international portfolios, creating a credit crunch that forced many more businesses to lay off workers or fail outright. Consequently, some European nations were unable to refinance their sovereign debt and were forced to accept unpopular bailouts with stiff austerity measures from the IMF, European Central Bank, and European Commission, resulting in political power shifts in those countries. Public distress over the financial turmoil gave rise to the Occupy Movement, a social/cultural phenomenon that spread to all corners of the globe through traditional and new media. Globalization has irrevocably altered the nature of the nation-state, but global governance still relies heavily on the behavior of nations and the participation of individuals of varying circumstances. Globalization thus drives interaction and, in some cases, integration, within technological, economic, cultural, and political dimensions. At the same time, the speeds and levels of penetration at which globalization occurs are predicated on local conditions of these same dimensions, thus fostering the simultaneous recognition of both increased interdependency and difference among nation-states. Guilbert C. Hentschke, Shirley C. Parry, and Samantha Bernstein See also Capitalist Economy; Evolution in Authority Over U.S. Schools; Human Capital; International Organizations; Organisation for Economic Co-operation and Development

Further Readings Carnoy, M., & Rhoten, D. (2002). What does globalization mean for educational change? A comparative approach. Comparative Education Review, 46(1), 1–9. Dewey, J. (1927). The public and its problems. Athens, OH: Swallow Press. Friedman, T. L. (2005). The world is flat: A brief history of the twenty-first century. New York, NY: Picador. Held, D., & McGrew, A. (2007). Globalization/antiglobalisation: Beyond the great divide (2nd ed.). Malden, MA: Polity Press. Knight, J. (2005). New typologies for cross border higher education. International Higher Education, 38(Winter), 2–4.

Governmental Accounting Standards Board Levitt, T. (1983, May/June). The globalization of markets. Harvard Business Review, 61(3), 92–102. Marx, K., & Engels, F. (1888). Manifesto of the Communist Party (F. Engels, Ed., E. Moore, Trans.). London, UK: William Reeves. (Original work published 1848) Rodrik, D. (2011). The globalization paradox: Democracy and the future of the world economy. New York, NY: W. W. Norton. Spring, J. (2009). Globalization of education: An introduction. New York, NY: Routledge. Stiglitz, J. (1998). Globalization and its discontents. London, UK: Allen Lane. Tomlinson, J. (2002). Cultural imperialism: A critical introduction. Baltimore, MD: Johns Hopkins University Press. Wildavsky, B. (2010). The great brain race: How global universities are reshaping the world. Princeton, NJ: Princeton University Press.

an established set of accounting standards. As a result, the random accounting practices of state and local governments led to a high degree of confusion and misunderstanding, which allowed for corruption. By 1934, the National Committee on Municipal Accounting was established, and this organization developed the first accounting standards for state and local governments. This initial effort at standardizing accounting practices focused on developing standard classifications, establishing common terminology, and recognizing the unique accounting needs of state and local governments. By the late 1960s, the National Committee on Municipal Accounting had become the National Committee on Governmental Accounting. The National Committee on Governmental

Table 1

GOVERNMENTAL ACCOUNTING STANDARDS BOARD The Governmental Accounting Standards Board (GASB) is an independent organization that was created by the Financial Accounting Foundation in 1984. The primary role of GASB is to create and update accounting standards and financial reporting mechanisms for state and local governments, including local school districts and higher education. GASB is recognized as the authoritative source of generally accepted accounting principles for state and local governments. GASB is not a government entity. Rather, it is a part of the Financial Accounting Foundation, a not-for-profit organization, and as such, GASB lacks any authority to enforce the accounting standards it establishes. However, given the near universal acceptance of the GASB standards, the requirement on state and local governments to adhere to these standards is codified in some state statutes. In addition, most auditors of state and local accounts follow the GASB standards. This entry includes a brief history of the governmental accounting standards and explains how the standards are applied to public education.

Governmental Accounting Standards: A History Prior to the early 1900s, state and local governments were not required or encouraged to follow

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Fund Categories

Classification

Type of Fund

Explanation

1

General

2

Special revenue

3

Capital projects

4

Debt service

5

Permanent

6

Enterprise

7

Internal service

8

Trust

Accounts for a majority of all funds Accounts from specific revenue sources Accounts for major capital projects Accounts for paying long-term debts Accounts that limit spending to interest and not principle Accounts that charge outside users a fee Accounts for internal transfer of goods or services Accounts for assets generated from trust funds

9

Agency

Accounts for student activities or taxes

376

Governmental Accounting Standards Board

Table 2

Program Number Categories

Digit

Category

Explanation

100

Regular education

200

Special education

300

Vocational and technical education

400

Other instructional

500

Nonpublic school

600

Adult/ continuing education Community/ junior college education

800

Community services

Includes both elementary and secondary Includes all programs supporting special education Includes all programs associated with career and technical education Includes bilingual, alternative, and programs for at-risk students Includes funds required to allow students to attend a nonpublic school Includes programs aimed at supporting adults Includes programs that allow students to enroll in institutions of higher education Includes programs that benefit the community

900

Cocurricular and extracurricular activities

Includes programs that enhance a student’s overall learning experience

700

Accounting issued a book that pushed the evolution of governmental accounting standards to include combined financial statements. However, all of these efforts to standardize accounting practices met with criticism and calls for government reports to become more businesslike. In 1984, the Financial Accounting Foundation established the GASB to build on the completed work by both the National Committee on Municipal Accounting and the National Committee on Governmental Accounting and to address the concerns raised by critics. The GASB continues to monitor and update its accounting standards as needs arise. The GASB mission, according to its website, is to “establish

and improve standards of state and local governmental accounting and financial reporting.” One cannot overstate the importance of effectively managing public funds. The GASB standards empower state and local government officials to more fully maximize the potential of the public funds entrusted to their stewardship.

GASB Standards and Public Education Codes are assigned to each line of a school district’s budget. These codes offer a detailed explanation concerning the purpose of the allocated funds within each category of the budget and are established by the GASB standards. Codes serve to break down revenues and expenditures and to ensure that dollars are being spent properly. These codes are typically broken down into four categories: funds, programs, functions, and objects, which are detailed in Table 1. According to GASB, there are nine categories for funds. Table 3

Function Numbers

Function Range

Category

Explanation

1000–1999

Instruction

2000–2999

Support

3000–3999

Operation of noninstruction

4000–4999

Facility acquisition and construction

Includes all activities supporting the interaction between the teacher and the student Includes administration, guidance, health, and logistical support Includes all noninstructional and nonsupportive services Funds allocated for the purchase of land, remodeling, and other support

5000–5999

Debt

Includes the managing of long-term debt

Governmental Accounting Standards Board

The program numbers are three digits and offer a general explanation of where the dollars are being allocated. Table 2 summarizes the categories for the program numbers. The second and third digits of the program number offer additional insight concerning grade level, term, type of service, or student population. For example, 110 would represent regular education (100) and elementary school (10), and 111 would represent regular education (100) and prekindergarten (11). The function is a four-digit number that offers additional explanation concerning the program number. Table 3 identifies the five function ranges.

Table 4

FUND PROGRAM FUNCTION Category

Explanation

100–199

Salaries

200–299

Employee benefits

300–399

Professional and technical services

Funds for permanent and temporary employees Funds paid by the school district for employees’ benefits Funds for outsourced work (e.g., professional development speaker) Funds for the upkeep of properties owned by the school district Funds for personnel not on school district’s payroll Funds for consumable goods Funds for the purchase of land, buildings, and equipment Funds for goods and services not mentioned above

500–599

As with program numbers, the function range offers additional insight into the fund allocation. The function ranges allow school districts sufficient ability to clearly classify funds. There are nine major object numbers, and each category can be subdivided. The object number is a way of offering further definition and explanation concerning the budget allocation. Table 4 identifies the nine ranges for the object numbers. Each of the budget codes is designed to provide specific and detailed explanation concerning a school district’s revenues and expenditures. The GASB accounting standards present a uniform coding system for designating the purpose of public funds. The 11-digit accounting code consists of four groups of numbers:

Object Number

Object Range

400–499

377

Property services

600–699

Other purchased services Supplies

700–799

Property

800–899

Miscellaneous and debt services

900–999

Other items

Used to identify unique transactions that require reporting

X

XXX

XXXX

OBJECT XXX

An accounting code that reads 1-110-1000-600 becomes a bit easier to decipher. This allocation would be for general (fund), regular elementary education (program), instruction (function), and for supplies (object). Spencer C. Weiler See also Budgeting Approaches; Cost of Education; Education Finance; Fund Accounting; School District Budgets; School District Cash Flow

Further Readings Allison, G. S., Honegger, S. D., & Johnson, F. (2009). Financial accounting for local and state school systems: 2009 edition. Washington, DC: U.S. Department of Education, National Center for Education Statistics, Institute of Education Sciences. Cox, B., Weiler, S. C., & Cornelius, L. M. (2013). The costs of education: Revenue and spending in public, private and charter schools. Lancaster, PA: ProActive. GASB mission, vision, and core values. (n.d.). Retrieved from http://www.gasb.org/jsp/GASB/Page/GASBSectionPage&cid=1175804850352 Odden, A. R., & Picus, L. O. (2008). School finance: A policy perspective (4th ed.). Boston, MA: McGraw-Hill. Owings, W. A., & Kaplan, L. S. (2006). American public school finance. Belmont, CA: Thomson Wadsworth. Poston, W. K., Jr. (2011). School budgeting for hard times: Confronting cutbacks and critics. Thousand Oaks, CA: Corwin Press.

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Guaranteed Tax Base

Thompson, D. C., Wood, R. C., & Crampton, F. E. (2008). Money and schools (4th ed.). Larchmont, NY: Eye on Education.

GUARANTEED TAX BASE In most states, local property taxes provide a large share of the funding available to public schools. Thus, differences between the resources available to school districts in wealthy communities compared with those in poor ones may contribute to inequities in educational opportunity. The guaranteed tax base is a type of school finance equalization grant intended to account for disparities among local tax bases, allowing all school districts in a state to operate with seemingly equal per-pupil tax bases. To compensate for local tax disparities, the state provides school districts with additional aid that allows districts where property values are below average to choose higher tax rates in order to raise a minimally “guaranteed” amount of local tax dollars. Conversely, it allows school districts with access to above-average amounts of property value to tax at lower than average tax rates and still raise that minimum amount of local funds. This entry provides a short history of the rise of guaranteed tax base and percentage power equalizing programs, explains the relationship of such programs to the legal obligation to provide all students with equal opportunity, and discusses problems in implementation and possible solutions. Under a guaranteed tax base program, the proportion of state revenues provided to school districts will increase as the district property value per pupil decreases. If a guaranteed tax base program is set at $500,000 of assessed property value per pupil, a 10-mill levy will raise $5,000 in revenues per pupil. (A mill is one thousandth of a dollar and is used in reference to the property tax rate percentage that is to be assessed for individual homeowners.) Districts with property value less than $500,000 per pupil will receive state aid to make up the remainder of the revenue needed after local revenues are raised. The wealthier a district’s property value per pupil, the more local revenues will be used to reach the $5,000 per pupil, and the less state revenue will be raised. Those districts with more than $500,000 property value per pupil will not receive any state aid. Since districts have different values of property to tax, the guaranteed tax base was created to equalize the funds available to each school district within a state.

The guaranteed tax base program has its roots in the percentage power equalizing programs of the 1920s. These programs were crafted to address the growing reliance and corresponding disparity of local add-ons to the foundation programs and to increase the state’s fiscal role in funding school districts. It was designed as an equalization grant to address statewide inequities in overall spending. Most state constitutions include some sort of wording for an equal educational opportunity. As a result of school finance litigation in the 1970s to meet that definition of equal educational opportunity, state legislators enacted guaranteed tax base programs to address the differing local property tax bases among the school districts and bring their state funding formulas in line with their constitutional requirements. These school finance cases were argued and won based on John Coons, William Clune, and Stephen Sugarman’s theory of fiscal neutrality and the corresponding concept of district power equalization. The Serrano v. Priest case in California was the first major case to establish fiscal neutrality as a way to measure the constitutionality for a state funding system. In Serrano, the state supreme court held in 1971 that the quality of education received by a pupil in California was a function of property wealth where the child lived, in violation of the Equal Protection clauses of the U.S. Constitution and of the state constitution of California. In its second ruling in the case in 1976, the state supreme court ruled against the school finance reform legislation passed in response to the Serrano I decision and mandated a new system to equalize school funding. In the only school finance case to go to the U.S. Supreme Court, plaintiffs in San Antonio Independent School District v. Rodriguez (1973) argued that per-pupil spending could not be a function of district wealth. Although they won in district court, they lost at the Supreme Court because they did not prove any relevant violations under the Equal Protection Clause of the U.S. Constitution. This set the stage for all subsequent cases to focus on the language of education clauses in state constitutions, including the Edgewood I, II, and III lawsuits that eventually ruled Texas school funding unconstitutional under its education clause. According to Deborah A. Verstegen and Teresa S. Jordan, only three states utilize the so-called district power equalizing approaches. Rhode Island uses a percentage equalizing approach. Vermont uses a guaranteed yield approach, which means districts

Guaranteed Tax Base

receive a block grant from the state plus an additional guaranteed yield that depends on property wealth in the district. Wisconsin uses a three-tiered guaranteed tax base approach. The intended equalizing impact is undermined by the adjustments in the implementation of these guaranteed tax base formulas. Gilbert J. Reilly believes that the two main implementation problems are caps on reimbursable expenditures and inaccurate portrayal of districts’ fiscal capacity. He argues that caps help high-wealth districts exploit their tax base and that unmeasured fiscal capacity will lead to greater inequities. The key element of a pure guaranteed tax base program is a recapture clause. This means that the state recaptures any funds above the “guaranteed” amount of local tax dollars. Those recaptured funds are then distributed among the districts that have below-average property values on which to raise local funds. For example, Wisconsin had a recapture clause, wherein any local money raised above a specified amount was recaptured by the state; it was eventually struck down in the courts when highwealth districts sued and won (Buse v. Smith, 1976). This recapture clause is not often politically viable, and it can serve as the demise of equalization programs because wealthier districts can spend above the guaranteed level of funds, negating the intention to equalize access to revenues. Often a political compromise is to provide more educational dollars from the state for all districts, in effect raising the guaranteed base. Without total recapture, a guaranteed tax base program will not achieve the equal tax base it is trying to create for each district. Allan R. Odden and Lawrence O. Picus identify two policy issues related to guaranteed tax base programs. One is whether to set a minimum local tax rate. This could have the effect of raising the floor and eliminating all the lowest spending districts. The second policy issue they identify is whether to cap the guaranteed tax base at a specific tax rate (wherein any funds raised with a higher tax rate than that specified would not receive additional matching funds) or to place an absolute cap on the local tax rate that would act as an expenditure cap. Overall, the guaranteed tax base programs have not achieved their equalization goal. The low-wealth districts have not been able to pass high enough tax rates to raise their spending levels by any substantial amount. On the contrary, the high-wealth districts had the political will and fiscal capacity to raise their own tax rates, which even at modest increases

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resulted in much higher spending levels. This actually served to widen the spending gap between low- and high-wealth districts, strengthening the relationship between per-pupil property wealth and per-pupil spending. Bruce D. Baker and Preston C. Green III point out that in states such as Wisconsin, Kansas, and Alabama, this spending gap is also tied to racial disparities where de facto segregation has led to the inability of low-wealth districts to raise sufficient revenues compared with higher wealth districts (with a majority of nonminorities) that have the fiscal capacity and support of the taxpayers to raise local dollars above the guaranteed tax base. These disparities are further exacerbated by tax limits, leaving the states’ minority populations to suffer from unequal revenue pools. Andrew Reschovsky argues that because there are no required tax rates for equal per-pupil spending, these district power equalization programs are in fact achieving taxpayer equity instead of equalizing revenues for students. He suggests solving the inequities inherent in a guaranteed tax base program by implementing a costadjusted foundation formula (indexed for inflation) coupled with a required district levy of a minimum tax rate. Whatever the approach, the general consensus is that guaranteed tax base programs have not worked and will not work because of political tinkering with the original intent of the approach. Michelle Turner Mangan See also Adequacy; Educational Equity; Equalization Models; Fiscal Disparity; Fiscal Neutrality; Property Taxes; San Antonio Independent School District v. Rodriguez; School District Wealth; School Finance Equity Statistics; School Finance Litigation; Serrano v. Priest; Tax Burden

Further Readings Baker, B. D., & Green, P. C., III. (2005). Tricks of the trade: State legislative actions in school finance policy that perpetuate racial disparities in the post-Brown era. American Journal of Education, 11(3), 372–413. Coons, J., Clune, W., & Sugarman, S. (1970). Private wealth and public education. Cambridge, MA: Belknap Press of Harvard University Press. Odden, A. R., & Picus, L. O. (2007). School finance: A policy perspective (4th ed.). New York, NY: McGraw-Hill. Owings, W. A., & Kaplan, L. S. (2006). American public school finance. Belmont, CA: Thomson Wadsworth. Reilly, G. J. (1982). Guaranteed tax base formulas: Why equalization doesn’t work. Journal of Education Finance, 7(3), 336–347.

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Guaranteed Tax Base

Reschovsky, A. (1994). Fiscal equalization and school finance. National Tax Journal, 47(1), 185–197. Verstegen, D. A., & Jordan, T. S. (2009). A fifty-state survey of school finance policies and programs: An overview. Journal of Education Finance, 34(3), 213–230.

Legal Citations San Antonio Independent School District v. Rodriguez, 411 U.S. 1 (1973). Serrano v. Priest, 96 Cal. Rptr. 601 (Cal. 1971), 135 Cal. Rptr. 345 (Cal. 1976) cert. denied, 432 U.S. 907 (1977).

H their application to education finance using teacher labor markets as the main example.

HEDONIC WAGE MODELS The quality of a product, a job offer, or working environment plays a major role in the utility, satisfaction, or pleasure that is derived from consumption and production. In the hedonic model, the key explanatory variables are related to quality. When this concept is applied to the labor market, the hedonic wage model posits that there are compensating wage differentials. In other words, jobs that are more demanding, unpleasant, or have less desirable working conditions will command higher wages. Thus, jobs with dangerous circumstances or lackluster on-the-job amenities will have a higher wage. Different workers have varying preferences and appetites for risk. In a competitive labor market, workers will select jobs that provide them with the most satisfaction or utility. There are numerous wage-risk combinations in the labor market. Workers will maximize their utility by selecting a wage-risk combination that best matches their preferences. The hedonic wage function captures the observed relationship between wages and job characteristics. Simply put, jobs with greater risk will have a higher wage, all other things being equal. Hedonic wage models are readily applied in education finance, especially in teacher labor markets where variations between schools in terms of resources, location, student socioeconomic status, student achievement, and student demographics make some sites difficult to staff. This entry provides an overview of compensating wage differentials and

Compensating Wage Differentials Labor markets are characterized by heterogeneity on multiple dimensions. Labor markets are diverse with a variety of workers with varying skill levels and experience seeking different jobs with varying wages and job characteristics. Thus, the labor market equilibrium is affected by several factors including wage as well as other nonwage characteristics of a job. For example, a teacher considering employment at competing schools may consider not only the salary but also other working conditions such as the student population and the quality of the school facilities. The concept of compensating wage differentials addresses the workers’ compensation for the nonwage attributes of a job with a focus on working conditions and job characteristics. In a competitive labor market, workers will consider a broad range of factors outside of wages. Wage has two major components: (1) the worker’s value of marginal product and (2) the price paid for job characteristics. Compensating wage differentials represent the latter component that compensates for job attributes. In other words, the differences in wages that are driven by differences in job characteristics are known as compensating wage differentials. In circumstances of less than desirable working conditions, firms are compelled to make a higher offer of wages to attract and compensate 381

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Hedonic Wage Models

workers. Conversely, in situations of in-demand working conditions, firms may be able to offer lower wages. When labor market equilibrium is reached, the heterogeneous preferences of workers will be aligned with the diverse working conditions offered by employers. Hence, jobs with more risky work environments may need to offer higher wage rates to attract workers. For instance, in theory, schools in high-poverty neighborhoods, serving large proportions of difficult-to-educate students with limited resources, will experience greater difficulty in attracting teachers and should offer higher salaries than their counterparts to reach equilibrium. However, empirical evidence of teacher sorting in urban districts raises questions about compensating differentials in practice.

Empirical Evidence in Education Economics and Finance Hedonic wage models have been applied to the study of many industries. The majority of the analyses in economics have employed the hedonic framework in explaining observed wage differences. Some studies have used particular job characteristics to explain the variation in wages across individuals and jobs. Overall, research on the factors associated with wage differences has not exhibited a consistent pattern. With the exception of job risks, many job and worker characteristics that were expected to lead to equilibrium have exhibited opposite or statistically insignificant correlations with wages. Empirical evidence on compensating wage differentials in teacher labor markets paints a similarly mixed and nuanced picture that differs by K-12 and higher education settings. In higher education, theoretical models imply hedonic wages that depend on faculty productivity, departmental amenities, and locational amenities. Empirical studies, on the other hand, have indicated that professors’ salaries are associated with several factors, including teaching loads, secretaries, climate, and whether a department has a doctoral program. In the K-12 education sector, researchers have found that some urban school districts offer higher teacher salaries than neighboring suburban school districts or private schools. In doing so, the urban districts are theoretically able to attract higher quality teachers than they would be able to with lower wages. Such teachers consider a higher wage, in effect, making up for urban schools’ less appealing work environments. Thus, compensating wage

differentials are present in interdistrict teacher labor movement. Such differences, however, are not found within school districts. In most K-12 school districts, teacher compensation is determined by collective bargaining agreements or by district policymakers. Compensation varies according to a limited set of teacher attributes but not by job characteristics. In other words, apart from teacher education and experience, all teachers within a school district are compensated with the same amount regardless of their school placement or working conditions. In theory, difficult-to-staff schools are less appealing to teachers due to school resources, student populations, or location. However, these schools are unable to offer higher wages to reach equilibrium in the labor market. Research by Hamilton Lankford and colleagues has indicated the sorting of teachers within districts, with low-income, lowachieving, and non-White students, especially in urban schools, disproportionately taught by the least effective teachers. Furthermore, certain teaching positions within schools, including those for English Language Learners and special education students, are often more demanding and less desirable than regular education positions, yet are compensated at the same levels. In an attempt to ameliorate this disparity, policymakers have offered incentives such as loan forgiveness and signing bonuses to teachers who are willing to work in difficult-to-staff schools. There is yet little evidence on the effectiveness of such incentive programs. In essence, compensating wage differentials are found less often within school districts than between school districts. Richard O. Welsh and Matthew Duque See also Economics of Education; Markets, Theory of; Teacher Compensation

Further Readings Boyd, D., Lankford, H., Loeb, S., & Wyckoff, J. (2003). Analyzing the determinants of the matching of public school teachers to jobs: Disentangling the preferences of teachers and employers. Journal of Labor Economics, 31(1), 83–117. Graves, P., Marchand, J., & Sexton, R. (2002). Hedonic wage equations for higher education faculty. Economics of Education Review, 21(5), 491–496. Lankford, H., Loeb, S., & Wyckoff, J. (2002). Teacher sorting and the plight of urban schools. Educational Evaluation and Policy Analysis, 24(1), 37–62.

Higher Education Finance

HIGHER EDUCATION FINANCE Federal data show that in 2011 (the most recent year for which data are available) postsecondary degree-granting institutions in the United States spent $483 billion to provide educational programs and services to some 21 million students in 4,726 different institutions. These colleges and universities are very diverse, ranging from highly prestigious private research universities such as Yale University and Stanford University to large public research institutions including the University of California, to 4-year public institutions whose mission is primarily teaching, to small private liberal arts colleges and hundreds of locally governed 2-year community colleges. The way these institutions are financed varies as much as the different types of institutions. Yet today, despite this variation, there is a general sense that the costs of college education are growing too fast and have outpaced the ability of average-income families to send their children to college. Families take on an increasing debt and rely on available financial aid to pay for their children’s college, but few understand the complexities of how these institutions are funded. This entry describes the mechanisms used to fund public and private institutions of higher education in the United States, focusing on the way various types of institutions generate resources for their operations. The entry begins by distinguishing several categories of institutions of higher education (IHEs) and the general funding mechanisms for each. This is followed by discussions of the sources of public and private funding for IHEs.

Categorization of IHEs IHEs come in many shapes and sizes. For the purpose of this entry, the focus is on four general categories: (1) research universities, (2) 4-year public institutions, (3) 4-year private liberal arts colleges, and (4) 2-year community colleges. While there are many other possible distinctions, and a vast array of finance mechanisms within each of these categories, these four provide an overarching categorization for a general understanding of higher education finance.

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substantial numbers of both public and private institutions. Public research universities receive funding from general state support, student tuition, and research funding, while private universities generate funding through student tuition, private fundraising for operations and endowment, and research grants. In recent years, public universities have become more aggressive in seeking private donations from alumni and supporters, generating substantial new sources of revenues for operations and endowment. The level of public support for these institutions has in many cases dropped to between 15% and 20% of total operating expenditures. Four-Year Institutions

Four-year institutions are generally large public colleges and universities that offer baccalaureate and master’s degrees, but less often doctorates. The focus of these institutions is more on teaching than research (although there has been a growing focus on research in recent years). Funding for these institutions comes from student tuition—which is generally lower than the tuition paid at public research universities—and public-operating funds. One result of this approach to funding is that the resources available to these institutions are affected more by fluctuations in the economy and variations in state revenues than are research universities. While relatively limited in number, there are several medium-sized private colleges and universities that also fit into this category. Institutions such as Hofstra University, Chapman University, and the University of Redlands fit into this niche. Their funding is largely through student tuition payments and private fundraising. Four-Year Liberal Arts Colleges

Small liberal arts colleges represent some of the best known and most prestigious colleges in the United States. These are almost always private institutions, with generally high tuition rates and considerable reliance on private fundraising. Liberal arts colleges generally do not offer advanced degrees, although many have one or two specialized master’s degree programs.

Research Universities

There are relatively few 4-year research universities across the United States. These institutions are among the most prestigious and well-known universities, and this is one category where there are

Two-Year Community Colleges

Two-year colleges enroll more than one third of all postsecondary students, and are almost exclusively public institutions. Most receive state funding

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Higher Education Finance

support, and in many states, they are governed and financed through locally elected boards with the authority to raise property or other taxes to support the institutions. Tuition charges are generally low, and these institutions provide access to students who otherwise might not have access to college.

Funding Mechanisms There is a range of ways in which funds reach IHEs. The most common are student paid tuition and government support. Other sources of funds for IHEs include private giving and endowments as well as research funding. These are described below. Student Tuition

Virtually every college and university charges some level of tuition to attend its programs. While overall tuition rates have increased substantially over time, there are considerable differences in the level of tuition charged by different types of institutions. Regardless of the level, since 1970, the growth in tuition has exceeded the rate of inflation by a factor of two in most cases. Table 1 shows the growth in tuition by type of institution in inflation-adjusted dollars. As Table 1 shows, tuition has increased at twice the rate of inflation for all but 2-year colleges, and even at that level, tuition increases have been more

Table 1

Changes for Tuition, Room and Board, and Fees by Type of Institution, 1969–1970 to 2012–2013 in 2012–2013 Adjusted Dollars Average Tuition Charges

Type of Institution All institutions Public 4-year Private 4-year 2-year

2012–2013

Percentage Change

$9,660

$20,234

109%

$7,576

$17,474

131%

$15,584

$34,483

121%

$5,842

$8,928

53%

1969–1970

Source: National Center for Education Statistics, Digest of Education Statistics (2013, table 330.10). Retrieved from http://nces.ed.gov/programs/digest/d13/tables/ dt13_330.10.asp

than 50% since 1970. Of course, the actual rates of tuition have varied dramatically by institution. As of 2014, some of the most expensive private universities and liberal arts colleges had tuition rates nearing $50,000 a year, and full tuition, room and board, and fees at these colleges cost well over $60,000 a year. There are several ways colleges have attempted to mitigate the impact of these high tuition charges. Some of the wealthiest institutions offer need-blind admission and provide financial aid to admitted students based on identified family ability to pay. Others offer considerable assistance to students based on either need or merit. This financial aid is often funded through endowments and/or operating revenues. While this offers many more students the opportunity to attend the most prestigious colleges and universities, many low-income, high-ability students never apply to these institutions because of the apparent costs of attending. While tuition charges are lower at public institutions, in many cases, they too have increased dramatically in recent years, limiting access for lowincome students. As Table 1 shows, tuition in the 4-year public sector has grown faster than any other sector, increasing by 131% between 1970 and 2013 after adjusting for inflation. Even 2-year schools are increasing tuition at alarming rates, with the inflation-adjusted rate of growth over the same period amounting to 53%. A result of this rapid growth in tuition has been recent calls for reductions in the cost of higher education, and in some instances, there has been a call for a $10,000 bachelor’s degree. Whether any institution can provide a quality education over 4 years for that price without a subsidy is yet to be seen, but it appears unlikely. Growing pressures may, however, lead more institutions to consider reducing costs through technological options such as online courses, larger class sizes, and use of fewer tenured full-time faculty. In the meantime, public funding, both through direct assistance of IHEs and through assistance to students, has been the dominant approach to helping students cope with high tuition. Government Assistance

Government assistance for IHEs falls into three categories: (1) direct support for operations of public institutions, (2) loans and loan guarantees for students, and (3) support for research at universities.

Higher Education Finance

Of the 3,178 not-for-profit IHEs in the United States, just over half or 1,623 are public institutions (934 are 2-year institutions, the balance are 4-year institutions). All of these receive some form of public funding for their operations. Funding levels and approaches vary by state. Some states provide funding on a per-pupil basis, others cap enrollments and fund some portion of that level of enrollment. In some states, IHEs receive block grants, which are lump sums that can be spent at IHEs’ discretion, and in many community college districts, local governing boards have the authority to raise local property taxes to support their institutions. In most states, tuition rates—particularly for undergraduate students—are set by the state, although many institutions have some local ability to charge fees for certain services (i.e., lab fees, activity fees, health fees, and similar charges). The federal government provides direct loans and loan guarantees for college students. The Federal Perkins Loan Program provides direct loans to students with exceptional need up to $5,500 a year or a maximum of $27,500 for undergraduates and up to $8,000 a year and a maximum of $60,000 for graduate students. The federal government also guarantees low-interest Stafford loans through private lenders. Loans, whether guaranteed or not by the government, are increasingly used by families to fund college education, and by some estimates, there is more than $1 trillion in outstanding student debt today. Loans are available to attend public or private institutions, and in many instances, the loan may be used to fund programs in for-profit institutions as well. Loans are also available through states, and many families are able to take advantage of equity in their homes to borrow money for college. Private Giving

Private colleges have long relied heavily on private giving to support capital construction, endowment growth, and even annual operations of the institution. In recent years, public institutions have found ways to tap into their vast networks of alumni and increased the level of private giving to those institutions as well. While an important component of higher education finance, private giving is subject to considerable variation depending on the condition of the economy and other factors and is therefore risky to use for funding continuing operations. Private giving is often used to increase an institution’s endowment and for student financial

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aid. Another use of private funds is for capital construction across a campus. These uses of one-time gifts make strategic sense as the institution does not become dependent on a source of funding that may vary considerably from year to year. Endowments

Most IHEs have an endowment or an amount of money invested such that the proceeds can be used to fund operations of the institution. Few IHEs have large endowments, and the resources generated by endowments represent relatively small shares of total operating costs for most institutions. Some private universities such as Harvard University have very large endowments and are able to use the proceeds to finance a wide range of activities. Staff members at the university professionally manage most endowments with an eye toward both growth and security. Proceeds from endowments are generally conservative, and generally, a specific rate is used, such as 5% of the endowment. The goal is to maximize available funding at a level that can remain stable, and not lower the value of the endowment. Research Funding

Large research universities often have substantial levels of research funding provided by the federal government and charitable foundations. The federal government has a long history of funding scientific research, much of it at universities, and charitable foundations similarly rely on the research expertise of faculty at universities to better understand problems of interest to the foundation. In recent years, there has been more corporate sponsorship of university research; the challenge with this funding is finding ways to avoid conflicts of interest or the appearance of such conflicts of interest. This funding is found most frequently and in the largest amounts in research universities.

Conclusion Higher education is a big business. Expenditures in 2011 represented some 3.2% of gross domestic product in the United States. These resources are used by more than 4,700 public, private nonprofit, and private for-profit institutions to provide postsecondary education to more than 21 million students. Today’s students are increasingly burdened by rising tuition and greater debt burdens. This leads to pressure to reduce costs at IHEs and creates equity issues

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Homeschooling

between wealthy and less well-off families. Finding a way to finance high-quality college education for all who seek it remains an elusive goal. Lawrence O. Picus See also Student Financial Aid; Tuition and Fees, Higher Education

Further Readings Ehrenberg, R. G. (2002). Tuition rising: Why college costs so much. Cambridge, MA: Harvard Education Press. Zumeta, W., Breneman, D. W., Callan, P. M., & Finney, J. E. (2012). Financing American higher education in the era of globalizaiton. Cambridge, MA: Harvard Education Press.

HOMESCHOOLING Homeschooling, the oldest form of schooling, was the norm until the introduction of the common school movement in the 1840s. The modern homeschooling movement began in the 1970s but was then considered a fringe activity. Since then, its movement from the fringe to the mainstream has been remarkable. In the late 1970s, only about 10,000 children were homeschooled. By 2010, more than 2 million children, or 3.8% of the school-age population, were homeschooled. This is approximately equal to the number of students enrolled in charter schools. In principle, homeschooling is similar to other educational choice options, but it strictly confines the choice to a primarily home-based, family-funded, and parent-led education program. It is often regarded as the ultimate form of school choice because (a) parents act as the sole provider of a child’s formal education and (b) contrary to public and private schooling, a child’s home is the epicenter of educational activity. This entry discusses homeschooling and its implications for research on economics of education. It begins by describing the characteristics of homeschooling in the United States, then it evaluates homeschooling using criteria of freedom of choice, equity, productive efficiency, and social cohesion that were set out by Henry M. Levin. It concludes with a discussion of research developments and directions in homeschooling. Homeschooling is legal in all 50 states. In many states, families must sign a release exempting their

children from compulsory public school attendance. The release also frees the local public school from any obligation to educate a homeschooled child, but it may allow, at the discretion of the school, partial access to classes, activities, and sports. Although homeschooled children are not eligible for local, state, or federal funds, a handful of states have regulations and policies in place to ensure access to education-related resources as well as compliance with academic content standards, standardized tests, attendance, and record keeping. The study of homeschooling presents several issues that warrant investigation as part of the rapidly growing scholarly research in the economics of school choice and the privatization movement in the United States. Despite four decades of steady growth, methodological constraints and the paucity of reliable datasets have limited the empirical knowledge on the impact of homeschooling on students and conventional schools. Levin’s framework for evaluating school choice policies offers comprehensive guidance on the implications of homeschooling for research, policy, and practice.

Issues Freedom of Choice

That families are consumers is a basic assumption underlying the economic theory of freedom of choice. Economists assume that parents choose schools that maximize their children’s well-being, or “utility,” considering the costs, benefits, and probabilities of success. Like any maximizer of school choice, the demand for homeschooling depends on parental preferences and values about education. For example, in the Parent and Family Involvement in Education Survey of the 2012 National Household Education Surveys Program, parents who homeschooled their children were asked for their reasons for doing so. Concerns about school environmental factors such as safety, drugs, or negative peer pressure ranked highest at 91% for the respondents. A desire to provide religious or moral instruction (64% and 77%) and dissatisfaction with academic instruction in other schools (74%) were of lesser importance. On the whole, the characteristics of the typical households that are overrepresented in homeschooling are White, middle-income, and well-educated parents and married two-parent families. Research scholars such as Joseph F. Murphy describe the demographics of homeschooled families as larger

Homeschooling

than average and likely to reside in small towns and rural areas rather than in large, urban centers. The religious affiliation of homeschoolers is predominately Protestant and often fundamentalist. Muslim Americans are a growing segment of homeschoolers. Homeschooled families are also more likely to hold conservative social and political views. The bulk of the evidence suggests that families who choose homeschooling are different in many ways from families who do not. As homeschooled families make choices, it is important to consider the impact of those choices on conventional schools. Homeschooling could exacerbate segregation in an already highly stratified American public school system because the preferences that serve as motivation for homeschooling lead to homeschooling families being a relatively homogeneous group. Equity

One of the most important equity concerns about homeschooling is whether or not it has accentuated or perpetuated differences in educational opportunity between homeschoolers and those in conventional schools. A major challenge is the social inequality in homeschooling choice, with some research suggesting that minorities, low-income parents, and less-educated parents are less likely to choose homeschooling and may lack informationrich resources (e.g., libraries, broadband connection, and computers) and social networks. Critics have also questioned whether homeschooling families have the ability to determine which homeschool curriculum, programs, and materials are appropriate for their children. Homeschooling parents may not have the specific capabilities to teach their children with moderate to severe disabilities. A relevant concern is the scope and depth of knowledge required in core subjects as homeschooled children transition into higher grades. To date, little is known about homeschool instructional methods, qualifications/credentials of homeschool teachers and resource use, and homeschool instructional time and activity. This issue arises largely as a consequence of the complete transfer of responsibility for a child’s education from the public sphere to the homeschooling family. Homeschooling may reduce the state’s ability to ensure that children receive an equitable and adequate education. Another equity issue of homeschooling pertains to the impact on students in conventional schools.

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Because homeschooling is more common in isolated small towns and rural areas and given the rapid growth of the homeschooling sector, one of the prevalent concerns is that homeschooling may aggravate social problems such as income inequality, racial and ethnic isolation, stereotyping, and profiling. Such concerns are heightened in the age of increased immigration, a global economy, and greater stratification of the population by race, gender, religion, politics, economic circumstances, and backgrounds. Unfortunately, there is little current research to help us understand the extent of the impact of homeschooling on equity issues concerning both choosers and nonchoosers. Productive Efficiency

Determining productive efficiency involves estimating the highest educational effectiveness for a given cost or the lowest cost for any given level of academic performance. For many policy analysts and economists, student achievement is the most important bottom line in education. This means that one of the most critical aspects of productive efficiency involves the degree to which homeschooling stimulates the academic achievement of homeschooled children and children in conventional schools. One argument is that since homeschooled parents are empowered to make their own decisions, they may become more involved in their children’s education, and their children may have better educational outcomes as a result. While some descriptive studies have suggested that a tenuous link exists between homeschooling and academic performance, more rigorous research designs must be employed to draw any firm conclusions. In addition, current comparisons are mostly based on standardized test scores, while other desirable outcomes such as child health, employment and earnings, participation in college preparatory or advanced work, and college attendance have largely been ignored. Another important aspect of productive efficiency relates to cost. From an economic point of view, families are likely to homeschool their children only if they believe that the benefits of homeschooling outweigh the price differential. Determining the full cost of running a home-based education (using Levin’s ingredients method) will have to take into account family expenditure patterns, time allocation patterns and the opportunity costs of time, use of public facilities and extracurricular activities to supplement home-based education plans, home school

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Homeschooling

tax credits or tax deductions, and public funding from cyber or virtual charter schools and online or blended learning. To date, limited information is available on the breakdown of these expenditures or costs. Social Cohesion

The role of social cohesion in homeschooling operates at the nexus of public and private interests. A key challenge facing society today is striking a balance between individual choice of pedagogical philosophy and methods that reflect individual values and priorities, on the one hand, and the public requirements of education for democratic knowledge and values, on the other. The balance achieved between public and individual interests in education has implications for achievement in college and adult life (e.g., appropriate socialization, civic obligation, and tolerance). Multiple studies examining the influence of homeschooling on social cohesion are based on qualitative descriptions and seek to influence the debate by constructing arguments that challenge or endorse homeschooling. Typically, such arguments fall into one of two camps. On the one hand, there is the worry that homeschooled children will become socially isolated and will not learn essential social norms and will not develop the needed social interaction skills. These studies show that parents who select homeschooling are much more likely to emphasize a particular set of traditions and value orientations than are other parents. This raises challenging concerns about the social benefits, or civic socialization, of education. On the other hand, homeschoolers may participate in many recreational and educational group activities and have flexible opportunities for multiage socializing, real-world learning, and participating in civic life. Descriptive studies assert that homeschooled children obtain socialization skills and practices of engaged citizenship through their religious institutions and regional and national associations. Homeschooled students who participated in these organizations went on to volunteer, vote, and engage with community groups and their peers at rates higher than national averages. Although there is more research on the impact of homeschooling on social cohesion or civic socialization than on other homeschool outcomes, the research on this is thin, and its quality is contested. Limitations in research designs restrain their conclusions about causation. There are serious drawbacks

in relying on conclusions about civic socialization outcomes derived from a self-selected sample or an imprecisely defined population.

Directions for Research Despite the widespread growth of homeschooling and the length of time over which it has evolved, there are few rigorous quantitative studies that combine the results of empirical research with analyses of the economics of school choice issues underlying homeschooling. The limited number of datasets currently available on homeschooling is primarily due to the methodological and practical problems in data collection. Homeschooled students are often not included in many important federally funded education databases (e.g., Common Core of Data). Unlike with students in traditional school settings, there is a paucity of databases that provide exhaustive information on homeschooled students. Records from individual families are more limited due to concerns about privacy and security. Murphy claims that the most reliable estimates of homeschooling in the nation are drawn from the National Household Education Survey, which has been conducted every 4 years since 1999. Today, the impact of homeschooling is largely unknown. Much of what we know about the impact of homeschooling along many dimensions comes from other forms of school choice such as charter schools and school vouchers. Homeschooling research shares common conceptual, theoretical, and methodological challenges with other forms of school choice. More rigorous empirical work is needed regarding what happens inside the “black box” of homeschooling before definitive conclusions are drawn. At issue are several limitations for the study of homeschooler outcomes. First, there has been no empirical study thus far based on data obtained from a random sample of all homeschoolers. This means that the findings from the available studies cannot be generalized from the study samples to the entire homeschooling population. Second, existing studies rarely control for the confounding influence of background variables such as family structure, race, socioeconomic status, political or religious orientation, or parent education level. Since these variables are often highly related to student outcomes, it is impossible to establish any causal relationship between the homeschooling effects and student learning or other outcomes if a study does not control for these variables. Third, self-selection bias remains an

Horizontal Equity

endemic weakness that affects most studies of school choice: Because homeschooled families are widely presumed to have stronger than average commitment to education, their children might well have had higher than average achievement even if they had remained in conventional schools. Finally, any effort to use measures of student achievement (e.g., interim or benchmark assessments, SAT or ACT college admission exams, and Advanced Placement exams) will need to address the variation in content, format, difficulty, and other characteristics of the tests. In addition, reliance on self-reporting of scores from tests administered by the parent weakens the validity, the reliability, and the potential to generalize about student achievement. The same considerations apply to other measures and outcomes of homeschooling, such as civic socialization. In the best of circumstances, it takes sophisticated econometric analysis on representative samples to separate homeschooling effects from other confounding factors such as self-selection bias, sampling error, and measurement error. Key to future research on homeschooling effects is the need to collect better data and develop better econometric models that not only control for a rich set of observable characteristics of students and their families but also account for unobserved variables such as motivation and commitment.

New Developments With the advent of publicly funded virtual schools, homeschooled students can now access a blended or hybrid model of homeschooling, combining elements of both homeschooling and traditional schooling. This hybrid model is a means of pursuing education from home while simultaneously being matriculated in a public school. While the outcomes of other forms of school choice such as charter schools and vouchers have been extensively researched, homeschooling remains largely understudied despite its rapid growth. The descriptive results of homeschooling, however plausible, cannot substitute for the more rigorous empirical work needed to quantify the scale of homeschooling’s theorized effects. This is an important area for future school choice research. Charisse Gulosino and Yongmei Ni See also Charter Schools; Econometric Methods for Research in Education; Educational Equity; Parental Involvement

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Further Readings Belfield, C. R. (2004). Democratic education across school types: Evidence for the U.S. from NHES99. Education Policy Analysis Archives, 12(43). Retrieved from http:// epaa.asu.edu/ojs/article/view/198/324 Belfield, C. R. (2004). Modeling school choice: A comparison of public, private-independent, privatereligious and home-schooled students. Education Policy Analysis Archives, 12(30). Retrieved from http://epaa. asu.edu/ojs/article/view/185/311 Kunzman, R., & Gaither, M. (2013). Homeschooling: A comprehensive survey of the research. Journal of Educational Alternatives, 2(1), 4–59. Levin, H. M. (2002). A comprehensive framework for the evaluation of educational vouchers. Educational Evaluation and Policy Analysis, 24(3), 159–174. Murphy, J. F. (2012). Homeschooling in America: Capturing and assessing the movement. Thousand Oaks, CA: Corwin Press. Noel, A., Stark, P., & Redford, J. (2013). Parent and family involvement in education, from the National Household Education Surveys Program of 2012 (NCES 2013-028). Washington, DC: U.S. Department of Education, National Center for Education Statistics, Institute of Education Sciences. Retrieved from http://nces.ed.gov/ pubs2013/2013028.pdf Planty, M., Hussar, W., Snyder, T., Kena, G., KewalRamani, A., Kemp, J., . . . Dinkes, R. (2009). The condition of education 2009 (NCES 2009-081). Washington, DC: U.S. Department of Education, National Center for Education Statistics, Institute of Education Sciences. Ray, B. D. (2000). Home schooling for individuals’ gain and society’s common good. Peabody Journal of Education, 75(1–2), 272–293. Romanowski, M. H. (2006). Revisiting the common myths about homeschooling. Clearing House, 79(3), 125–129.

HORIZONTAL EQUITY The term horizontal equity is defined as an “equal treatment of equals.” In the school finance context, this typically refers to the distribution of expenditures for public education. Perfect equity exists only when every individual in a distribution receives equal treatment. In other words, school districts with similar characteristics (e.g., size, wealth) should have similar funding levels. This entry describes the origins and evolution of horizontal equity as a conceptual and methodological tool applied to school finance. It discusses horizontal equity in public education funding

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Horizontal Equity

at the state, district, and school levels and outlines the limitations of horizontal equity.

Origins of Horizontal Equity Horizontal equity as a concept in education economics and finance began to develop in 1978 with two manuscripts published by Robert Berne and Leanna Stiefel. Horizontal equity conceptually is categorized into four elements: (1) What is the perspective for assessment of equity? (2) What is to be equitably distributed? (3) What is the determining factor in equitability? (4) How is the empirical measure of equity formulated? Through this framework, schooling inputs are examined as expenditures per child. As equity in funding improves, the likelihood that each student receives an equal portion of resources distributed should increase. Horizontal equity examines the validity of specific units of measure when exploring dependent and independent variables, pupil measures, two units of analysis, or a specific set of equality and wealth neutrality associated with school finance. A number of univariate dispersion measures, including Theil indices, Gini coefficients, coefficients of variation, and McLoone indices, were originally used to determine the presence of horizontal equity in school finance systems before the data become available to allow for multivariate statistical analysis.

Evolution of Horizontal Equity Horizontal equity has experienced two significant evolutions: (1) moving from state-level to within-district-level analysis and (2) moving from univariate to multivariate analysis. Horizontal equity began as a methodological tool to examine state-level public school funding equity associated with fiscal policy challenges. Due to a lack of district- and schoollevel data, analyzing the equity of large or extremely large districts with diverse schools and student demographics was challenging. However, as states began to collect district- and school-level data, they allowed horizontal equity research to progress, and it became feasible to measure equal funding streams, tax revenue, and the equitability of public education funding. As data collection systems at the state level began to include school-level data, horizontal equity grew to include an analysis of equal distribution of operating revenue (funding used for school operations) that is separate from categorical revenue

directed mostly at specific programs for specific student groups. Statistical measures of horizontal equity have evolved as well. Initially, statistical analysis of horizontal equity was constrained to univariate measures (e.g., Theil indexes). Once it became possible to access data at the district and school level, multivariate statistical analysis (analysis of more than one outcome variable) could be used to assess equity between and within districts. This type of statistical analysis includes residual regression analysis, a way to analyze the difference between observed data values and estimated values.

Limitations of Horizontal Equity Horizontal equity is limited conceptually and methodologically due to its inability to measure funding at the district and school level without the use of data associated with measuring vertical equity. Horizontal equity provides an accurate account of equitability in public school funding at the base level but cannot provide an accurate reflection of equity as a whole and in special populations (e.g., English Language Learners, special education students). In other words, horizontal equity assumes that two or more districts or schools are the same, whereas in reality, each district or school has some degree of variation. Research has shown horizontal equity’s limited ability to use multivariate measures and its reliance on residual regression analysis of vertical measures to accurately depict equitability in school- and district-level funding. Moreover, while horizontal equity can measure the amount of dispersion at the base funding level, it does not assess equity due to multiplier levels (e.g., student weights). Funding multipliers are percentages of funding that are intended to increase the amount spent on special populations (e.g., English Language Learners, special education students). These marginal funds are often measured through vertical equity and are thus outside the scope of horizontal equity.

Conclusion In conclusion, horizontal equity is a foundational concept in education finance. It provides a framework for the statistical measure of “equal treatment of equals” at the base funding level. However, given that school populations have become more diverse and accountability systems are focused on studentlevel performance, education finance equity research

Human Capital

has recently shifted from examining horizontal equity toward examining vertical equity in an effort to account for the marginal costs associated with different student populations. Oscar Jimenez-Castellanos and David Martinez See also Brown v. Board of Education; Education Finance; Educational Equity; Progressive Tax and Regressive Tax; Property Taxes; Tax Burden; Vertical Equity

Further Readings Berne, R., & Stiefel, L. (1984). The measurement of equity in school finance: Conceptual, methodological, and empirical dimensions. Baltimore, MD: Johns Hopkins University Press. Crampton, F. E. (1991). The measurement of efficiency and equity in Oregon school finance: The beginning stages. Journal of Education Finance, 16(3), 348–359. Stiefel, L., Berne, R., Iatarola, P., & Fruchter, N. (2000). High school size: Effects on budgets and performance in New York City. Educational Evaluation and Policy Analysis, 22(1), 27–39. Toutkoushian, R. K., & Michael, R. S. (2007). An alternative approach to measuring horizontal and vertical equity in school funding. Journal of Education Finance, 32(4), 395–421. Wyckoff, J. H. (1992). The intrastate equality of public primary and secondary education resources in the US, 1980–1987. Economics of Education Review, 11(1), 19–30.

HUMAN CAPITAL The human capital model was first developed in the 1960s by Gary Becker and Theodore Schultz and is used to explain both the amount of schooling an individual receives as well as the amount of onthe-job training an individual receives after leaving school. The theory of human capital involves individuals investing in education and training. Such investments lead to an increase in individuals’ skills and their productivity in the labor market. This in turn leads to higher wages and earnings. This entry first discusses the basic model of human capital. Next, reinterpretations of the model are examined, after which more recent elaborations of the theory are touched on. Finally, some limitations of the theory are discussed.

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This entry focuses only on human capital theory as applied to the schooling decisions of individuals. One key insight of human capital theory is its treatment of the educational investment decision in a manner similar to investments in physical capital. Like physical capital, investments in human capital involve costs that are typically incurred by individuals early on, while benefits are usually received later on. For example, a student must pay tuition and fees while attending university to develop a set of skills, while any increase in earnings or wages due to the increased skills occurs only after college is completed. Because costs and benefits occur at these different times, one key element in human capital theory is the notion of present value of future earnings that adjusts for these time differences using a specified discount rate. All else being equal, benefits received later have a lower value than benefits received earlier. How rapidly this present value of a fixed nominal benefit declines with time depends on the discount. The higher the discount rate, the more rapidly the present value of the fixed nominal benefit declines with time. Human capital theory predicts that an investment in human capital will be made when the sum of the present value of benefits exceeds the sum of the present value of costs, or in other words, when the net present value exceeds zero. In the simplest model of investment in human capital, costs are borne by an individual in the initial period and are compared with a set of known benefits or net returns that are received over the remainder of the individual’s career. The initial period can be thought of as the college-going period. Here, there are two types of costs. Direct costs are the tuition, fees, and book costs that an individual pays to attend college. The indirect costs are the lower earnings that an individual receives while attending college, since every hour spent in class could have been spent working. The benefit of attending college is the higher yearly earnings that an individual will get when he or she is a college graduate as opposed to a high school graduate. The human capital model predicts that an individual will attend college if the present value of benefits exceeds the present value of costs. The technical details of this model are described below.

Technical Details Let C0 be the costs borne by an individual in period 0. One can think of C0 as the tuition, books, and fee

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Human Capital

costs of going to college. This simple model assumes that if an individual decides to attend college, he or she will earn a degree, so C0 could be thought of as the present discounted value of 4 years of tuition, books, and fee costs. The net returns in period t of getting a college education are denoted by Bt = EtC − EtH where EtC represents the earnings received by an individual in period t if he or she receives a college degree and EtH represents the earnings an individual earns in period t if he or she has only a high school diploma. Assume that an individual discounts future earnings by the discount rate r. Then the net present value (NPV) of obtaining a college degree equals NPV =

T

∑ t =0

C H Et − Et

(1 + r )

t

− C0 =

T

∑ t =0

Bt

(1 + r )

t

− C0 ,

where the variable T equals the last period of a person’s career, which we assume is fixed and known by the individual at the time of the decision (from T + 1 onward the individual is retired, which is assumed in this simple model and does not depend on whether or not an individual attends college). Human capital theory states that a person will choose to obtain a college degree if NPV > 0.

Empirical Predictions Several empirical predictions emerge from this simple economic model of the college-going decision: 1. The lower the cost of college, the more likely a person is to attend college. 2. The greater the net returns to a college education, as measured by the earnings difference between having a college degree versus only having a high school diploma, the more likely a person is to attend college. 3. The longer a person’s remaining career (as measured by T), the more likely the person is to attend college. 4. The more a person discounts future earnings (as measured by r), the less likely the person is to attend college.

Prediction 3 is typically stated in terms of younger persons being more likely to attend college. The intuition is that younger people have a longer period of time to realize the net returns of a college education, making it more likely that they

will attend college. Prediction 4 is alternatively stated as individuals who are more “present oriented” are less likely to attend college.

Reinterpretations Some have interpreted the simple human capital model literally and have criticized it for ignoring the nonmonetary benefits of a college education. However, a simple reinterpretation of the model is possible that captures all types of benefits (both monetary and nonmonetary). Instead of using monetary values, one can think of net returns in terms of the increased well-being, or utility, a person gets when completing a college education. Another assumption that is unnecessary is the assumption that the individuals know exactly what their future earnings streams are when they decide whether or not to go to college. In reality, this is never the case. However, if it is assumed that individuals are risk neutral, then all they need to know is what an average earnings gain will be if they go to college. On the other hand, if individuals are risk averse, then not only does the expected net return to a college education matter but the extent to which the realized net return deviates from the expected return also matters. The larger the variance of the future net return is, all else being equal, the less desirable the investment. One possible way to incorporate such risk aversion is to adjust the interest rate to reflect the riskiness of the investment and/or risk aversion of an individual. So, all else being equal, individuals who either think the earnings gain from going to college is more risky or who are more risk averse would discount the expected returns using a larger discount rate. The technical details of adjusting the interest rate to reflect this are discussed below.

Technical Details C t

Let U be the (maximum or indirect) utility (or well-being) that an individual in period t would enjoy if he or she received a college education and UtH the utility the individual would enjoy if he or she received only a high school education. Utility would depend on the consumption of goods purchased in the market, which, in turn, would depend on an individual’s earnings. However, going to college may also change a person’s preferences, and so the types of goods that he or she consumes may differ (i.e., college may change the utility function of

Human Capital

an individual). Under these circumstances, we will define Bt = UtC − UtH instead of Bt = EtC − EtH. Then, T

NPV =

C

H

Ut − Ut

∑ (1 + r )t t =0

− C0 =

T

∑ t =0

Bt

(1 + r )

t

− C0 ,

Suppose future earnings streams are uncertain and individuals are risk neutral. Let E(EtC) denote the expected earnings at time t if college educated and E(EtH) represent the expected earnings at time t if high school educated. So E(Bt) = E(EtC) − E(EtH) and T

E ( NPV) =

∑ t =0

( ) − (E ) − C C

E Et

(1 + r )

t

H t

( ) −C , t 0 =∑ 0 t =0 (1 + r ) T

noncognitive skill levels where the production function shows how these skills depend on various inputs (investments) such as parental time, teacher time, and so on. Dynamic complementarity means that an increase in a current input (e.g., time spent at school) will lead to a larger increase in a child’s skill level the greater the investments early in the child’s life. So large amounts of investment in skills early in life make later skill acquisition easier. One consequence of this theory is that it is more cost-effective to make skill investments earlier in a child’s life. Brian P. McCall

E Bt

Individuals will choose to go to college if E(NPV) > 0. If individuals are risk averse, a risky earnings stream with the same expected benefit stream— E(Bt), where t = 1, . . . , T—would be discounted with a higher discount rate r.

Recent Extensions The simple human capital model can also be extended to allow for capital market constraints as well as alternative ways of funding college such as working while in school. Other elaborations would allow individuals to update costs and benefits while attending college so that, although an individual may initially think that it is optimal to attend college, information received while in college may cause him or her to revise these net benefits downward. If this downward revision is sufficiently severe, then the individual may find it optimal to drop out of college before completing a degree. Individuals of course would be aware of this possibility when starting college, which gives rise to an option value for college. In a more general formulation, one can think of an individual maximizing expected discounted utility subject to budget constraints where information is revealed over time. Such a model could also explain why some individuals delay attending college directly after high school. Another avenue of research is to allow for dynamic complementarity in the production function that determines both cognitive and

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See also Discount Rate; Economic Cost; Economic Development and Education; Internal Rate of Return; Present Value of Earnings; Tuition and Fees, Higher Education

Further Readings Altonji, J. G. (1993). The demand for and return to education when education outcomes are uncertain. Journal of Labor Economics, 11, 48–83. Becker, G. S. (1962). Investment in human capital: A theoretical analysis. Journal of Political Economy, 70, 9–49. Cunha, F., & Heckman, J. J. (2007). The technology of skill formation. American Economic Review, 97, 31–47. Cunha, F., Heckman, J. J., & Schennach, S. M. (2010). Estimating the technology of cognitive and noncognitive skill formation. Econometrica, 78, 883–931. Keane, M. P., & Wolpin, K. I. (2000). Eliminating race differences in school attainment and labor market success. Journal of Labor Economics, 18, 614–652. Keane, M. P., & Wolpin, K. I. (2001). The effect of parental transfers and borrowing constraints on educational attainment. International Economic Review, 42, 1051–1103. Manski, C. (1989). Schooling as experimentation: A re-appraisal of the postsecondary dropout phenomenon. Economics of Education Review, 8, 305–312. Schultz, T. W. (1961). Investment in human capital. American Economic Review, 51, 1–17. Stange, K. (2012). An empirical investigation of the option value of college enrollment. American Economic Journal: Applied Economics, 4, 49–84.

I Income Inequality

INCOME INEQUALITY AND EDUCATIONAL INEQUALITY

Causes

Wealth and income inequality, although related, do not represent the same concept. Wealth refers to the total worth of an individual’s assets, which includes homes, investments, and savings. Income is the money an individual receives regularly such as wages/salaries, interest, and dividends. Although wealth inequality is an important topic, for simplicity, the discussion in this entry is limited to income inequality. Income inequality exists in all economies due to occupational wage differential, socioeconomic status (SES), and educational level. Rising income inequality is a concern owing to the negative social and economic consequences associated with it. Income inequality has been increasing in most OECD (Organisation for Economic Co-operation and Development) countries between 1985 and 2008 (see Figure 1) and, markedly so, in the United States, Israel, New Zealand, Germany, Finland, and Sweden. The United States ranks second among OECD countries in income inequality, behind Mexico. Only in the past 30 years has the income gap grown in the United States. From the 1950s through the 1970s, incomes rose similarly for all earners. Research on rising income inequality focuses on four dimensions. First, the distribution of income has become increasingly skewed in the United States, as the top 1% of earners take home an ever larger share of the economic pie. This so-called Great

Income inequality is the unequal distribution of household or individual income across the various participants in an economy. Income inequality exists in every country, although some countries are relatively more unequal than others. One way to measure income inequality in a country is to compare the average income of the top 10% of earners with that of the lowest 10% of earners. Using this metric, the top earners in the United States make almost 16 times as much as the lowest earners. By comparison, the lowest ratios of income between the highest and the lowest earners is found in other developed nations such as Japan, Finland, Sweden, and Norway with ratios from 4.5 to 6.2, while the highest ratio is found in Bolivia at 93.9. While some degree of income inequality may be inevitable, there are concerns that high rates of inequality are detrimental to a country’s economy as well as its social and political order. To narrow the income gap, many governments adopt policies that address social welfare, health care, investment in education, and provision of educational opportunity, which are generally viewed as viable means to increase earnings mobility between generations and to mitigate income inequality over time. This entry discusses the factors associated with income inequality and educational inequality, the growth of each, interactions between the two, and legislative and social attempts to attenuate the negative consequences of both. 395

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Income Inequality and Educational Inequality

0.50 0.45

increasing inequality

0.40

Little change in inequality

0.35

2008( )

0.30

-1985

0.25

Decreasing inequality

0.20

ke re y ec e

Tu r

G

U

H

Fr an un ce Be gar lg y iu m

ni Me te x d ic St o U at ni e te d Is s Ki ra ng el do m N Au Ital ew s y t Ze rali al a a Ja nd p C an a G nad N er a et m h Lu er any xe lan m ds bo Fi urg n C ze S lan ch w d R ede ep n u N blic o D rw en ay m ar k

0.15

Figure 1

Income Inequality Measured by Gini Coefficients in Organisation for Economic Co-operation and Development Countries, 1985–2008

Source: Organisation for Economic Co-operation and Development (2011, figure 1, p. 6).

Measures of Income Inequality

Several measures of income inequality have been developed by social scientists. Some common measures include the Gini index, the 20:20 ratio, the Palma ratio, and the Hoover index. Of these, the Gini index is by far the most popular because of its ease of use and comprehensibility. The Gini index produces a coefficient between 0 and 1 in which values closer to 1 indicate greater inequality. The first step in the calculation of the coefficient is to construct a Lorenz curve, which ranks all earners in a country in terms of income on the horizontal axis of

100 90

Perfect equality

80

Actual

70 % of income

Divergence is brought about in part by widening economic returns between and among levels of educational attainment, as well as fields of study. Second, on the other end of the distribution scale, real wages (adjusted for inflation) for the bottom 10% of the workforce have remained stagnant compared with other groups over the past two decades. Third, income mobility, or the ability of people to move from one income level to another, has decreased slightly, which implies that income inequality is becoming more entrenched. Fourth, despite a progressive tax system where individuals with higher incomes are taxed at higher rates, tax policy changes, such as reductions in individual, capital gains, inheritance, and estate taxes, have disproportionately benefited the top income earners because only the wealthiest taxpayers have the assets to take advantage of them.

60 50 40 30 20 10 0 0

20

40

60

80

100

% of families

Figure 2

Lorenz Curve

a graph with their cumulative income on the vertical axis (see Figure 2). Perfect equality in income would be represented by the diagonal line as the earner in the middle of the distribution would have earned an income at the mean. The area between the Lorenz curve and the diagonal line indicates the degree of inequality; the Gini coefficient is defined as twice the area between the Lorenz curve and the diagonal line and ranges from 0 ⫽ perfect equality to 1 ⫽ perfect inequality. The Gini index of the United States, for example, has hovered around 0.35 from 1950 to 1980, but it has since climbed steadily to 0.45 in 2010, which indicates a widening of income inequality.

Income Inequality and Educational Inequality

Consequences of Income Inequality

A widening income inequality gap makes inequality more entrenched and less easy to reverse, because the distance between income levels is, by definition, greater. Research has linked income inequality with negative outcomes in health indicators, such as physical and mental health, obesity, teenage pregnancies, and child well-being. Income inequality also negatively affects not only social indicators such as civic participation, crime rates, social mobility, and education but also economic indicators such as gross domestic product and competitiveness with other countries.

Educational Inequality Causes

Left to their own devices, individuals who will benefit from more education are, all else being equal, more likely to proactively seek more education, leading to greater inequality of education outcomes. Because levels of education are so closely linked to levels of income in the workplace, greater inequality in education outcomes fosters greater economic and social inequality. There is a variety of factors associated with educational inequality that can be grouped into three main categories: (1) family background, (2) race, and (3) institutional resources. Of the three factors, parental education level predicts a student’s likelihood of going to college. In other words, a collegeeducated parent is more likely than a parent with a high school diploma to have a child enrolled in college. Combined with the effects of income inequality and income immobility, students from a lower SES with less educated parents have a diminishing chance of attaining the education necessary to move out of the lower income groups. Even during early childhood, children from higher income backgrounds have the advantage of resources that prepare them for academic success—greater access to books, more intellectual stimulation, and greater parental familiarity with a school’s academic expectations. Moreover, families with higher income levels live in neighborhoods that have schools with better resources—both in terms of teacher quality as well as financial provisions—which, again, leads to better opportunities of attending and succeeding in college. Race is another predictor of inequality. Black, Latino, and Native American students have historically received discriminatory treatment in education.

397

Even in the present-day K-12 compulsory education system, there is de facto residential segregation in schools for most students because home address determines the school district and school districts with higher family incomes have comparatively better schools. In higher education, Black and Latino students, and students from low-income households, are also less likely to gain admission and enroll in highly selective colleges, which are generally better funded and have stronger alumni networks that lead to better job opportunities than other, less selective, institutions. Sean Reardon and colleagues found that, despite comprising 36% of the high school class of 2004, Blacks and Latinos accounted for less than 11% of the enrollment in highly selective colleges and universities. The variation in public and private resources that are allocated to compulsory and tertiary education for different economic classes of households is a third factor contributing to educational inequality. Research shows that students in low-income school districts have lower educational attainment than their peers in more affluent neighborhoods. Similarly, in higher education, highly selective universities spend more per student and have higher graduation rates than less selective institutions. Given that minorities and lower-income students are enrolled disproportionately in the latter category of universities with lower per-student spending amounts, it is not surprising that educational inequality occurs across and within all levels of education. Measures of Educational Inequality

Measurements of the disparity between students’ educational experience are often expressed in a comparison of their achievement outcomes, although there is recent research that explores the use of the Gini index as well as educational opportunity metrics using attainment data. The more common achievement outcome measures include standardized test scores, grade point average, dropout rates, and college enrollment and completion rates. Educational inequality measures and compares the differences in education opportunity by SES. The resultant disparity is generally known as the achievement gap. In the United States, for example, although the achievement gap in reading has narrowed along racial lines, it has climbed steadily when student scores from the top and bottom 10% of household income groups are compared, all else being equal. Whereas the achievement gap in reading by income level was

398

Income Inequality and Educational Inequality

about 0.80 standard deviations for children born in the 1970s, it has increased 40% to 1.25 standard deviations for those born in 2001. This widening achievement gap points to a disparity in the type and quality of education provided—in other words, a widening of educational inequality. Consequences of Educational Inequality

While the benefits of increased education to the individual and, on average, to society are clear, the impact of growing educational inequality imposes a societal cost. Children from low-income families are less prepared for formal schooling than those from higher income groups. They attend schools with fewer resources and score lower on standardized tests. As they progress through high school, they are more likely to drop out. For those who succeed in enrolling in 4-year colleges, they are also less likely to graduate. Educational inequality therefore reinforces existing patterns of income inequality rather than serving as a pathway to opportunity. Rising educational inequality perpetuates instead of reversing current rising income inequality. These two types of inequalities are strongly associated with each other, and their attendant societal costs are similar as well.

Income or Education Policy? Governments have economic, social, and, in some instances, moral incentives for intervening in education markets to ensure that all citizens have at least an equal opportunity of access to equivalent education resources, and not just those who would have gone on to college with or without governmentfunded student financial aid. Many public education policies have been fashioned with this intent, which has proven both theoretically and practically difficult to achieve. Policies intended to create equal educational opportunities include academic standards for K-12, such as the Common Core State Standards, which aim to equalize instruction both across and within schools; charter schools, which offer students alternative educational options; and Title I funding, which provides increased resources for schools serving large proportions of low-income students. Fundamental to these policies is the assumption that equalizing educational opportunities can equalize economic outcomes. An alternative hypothesis raised by some observers advances the notion that education policy cannot overcome the great disadvantages

posed by poverty and, instead, government should focus on reducing poverty to narrow educational inequities. Supporters of this alternative view point to the strong association between parental SES and home neighborhood with student achievement and favor policies such as improving the conditions of housing, increasing the availability of health care, and offering transfer payments to low-income families. Only once income inequality is reduced, they argue, will equality of educational opportunity and outcomes be attainable. Guilbert C. Hentschke and Shirley C. Parry See also Achievement Gap; Capitalist Economy; Educational Equity; Social Capital; Socioeconomic Status and Education

Further Readings Carnevale, A. P., Rose, S. P., & Cheah, B. (2011). The college payoff: Education, occupations, lifetime earnings. Washington, DC: Georgetown University Center on Education and the Workforce. Carnoy, M. (2011). As higher education expands, is it contributing to greater inequality? National Institute Economic Review, 215, 34–47. Coleman, J. S. (1966). Equality of educational opportunity (COLEMAN) study (EEOS) (ICPSR06389-v3). Ann Arbor, MI: Inter-university Consortium for Political and Social Research. Gordon, R. J., & Becker, I. D. (2008). Controversies about the rise of American inequality: A survey (National Bureau of Economic Research Working Paper Series No. 13982). Cambridge, MA: National Bureau of Economic Research. Organisation for Economic Co-operation and Development. (2011). Growing income inequality in OECD countries: What drives it and how can policy tackle it? Paris, France: Author. Pickett, K., & Wilkinson, R. (2009). The spirit level: Why greater equality makes societies stronger. New York, NY: Bloomsbury Press. Reardon, S. P. (2011). The widening academic achievement gap between the rich and the poor: New evidence and possible explanations. In G. J. Duncan & R. J. Murname (Eds.), Whither opportunity? Rising inequality, schools and children’s life chances (pp. 91–115). New York, NY: Russell Sage Foundation. Rothstein, R., & Santow, M. (2012). A different kind of choice: Educational inequality and the continuing significance of racial segregation (Working Paper). Washington, DC: Economic Policy Institute.

Individuals with Disabilities Education Act

INDIVIDUALS WITH DISABILITIES EDUCATION ACT The Individuals with Disabilities Education Act (IDEA) is a federal law that mandates the provision of special education to students with disabilities. Originally approved in 1975, the law was reauthorized as the Individuals with Disabilities Education Improvement Act (also IDEA) in 2004. This law regulates how states and public agencies provide early interventions and special education services to children from birth until age 21. Understanding the provisions of this law is an important component of understanding education finance and policy in general in the United States, as the law covers approximately 6.5 million students. It is also important because the law provides detailed provisions regarding the federal education funding for these students and also provides significant rules and regulations for states to follow in order to obtain federal funding. Overall, this law provides a framework within which states operate and causes a struggle between the balance of the rights of students with disabilities and states’ limited resources. This entry provides background on the IDEA along with its historical development over the past few decades. It also explains in detail the most recent reauthorization of the law.

Background Until recent decades, very few children with disabilities—in the early 1970s, for example, only one in five—were educated in U.S. public schools, and those who were educated often did not receive an education appropriate for their needs. Many children with disabilities were housed in state institutions and received only basic care with little to no education. For example, in the 1960s, around 200,000 disabled students were housed in state institutions. Indeed, some states even had laws excluding students with certain disabilities from receiving a public education.

The Historical Development of IDEA The IDEA has a long history and has its roots in the civil rights movement. After segregation by race in public education was outlawed by the U.S. Supreme Court, issues surrounding children with disabilities began to rise to the forefront. Advocates for children with disabilities argued that if segregation by race

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was outlawed, then the exclusion of students with disabilities should also be outlawed. The Elementary and Secondary Education Act of 1965 (PL 89-10)

The far-reaching Elementary and Secondary Education Act was signed by President Lyndon B. Johnson on April 11, 1965. This law, which had a significant effect on education more broadly, when amended in 1966, was one of the first to address issues related to access to a public education by students with disabilities. In particular, it provided federal funding for the education of blind, deaf, and intellectually disabled (at the time called mentally retarded) students. It also created the Bureau of Education for the Handicapped and the National Advisory Council (now called the National Council on Disability). This law was significant because it acknowledged the economic reality that educating students with disabilities costs more money than educating students without disabilities, and that this money needs to be allocated to states. The Education of the Handicapped Act of 1970 (PL 91-230 Title VI)

The Education of the Handicapped Act of 1970 was the first federal law to deal exclusively with students with disabilities, defining “handicapped children” as children who were mentally retarded, hard of hearing, deaf, speech impaired, visually handicapped, seriously emotionally disturbed, crippled, or affected by other health impairments. It expanded the Elementary and Secondary Education Act of 1965 by providing more program funding. It also created new programs for training teachers of students with disabilities and technical centers to assist states and school districts in dealing with issues surrounding special education. Most important, it established that to receive federal funding, states must adopt the goal of providing students with disabilities full educational opportunities. The Education for All Handicapped Children Act of 1975 (PL 94-142)

The Education for All Handicapped Children Act of 1975, signed by President Gerald R. Ford on November 29, 1975, not only amended the Education of the Handicapped Act of 1970 but also signified a shift in the treatment of children with disabilities by greatly extending the role of the federal

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government. It changed the disability category of “crippled” to “orthopedically impaired,” and it included children with specific learning disabilities in its language. The law also provided grants to help states and school districts provide for the education of children with disabilities. The law had a number of purposes. One was to ensure that students with disabilities had access to a free and appropriate public education, meaning that 1. services were to be provided at public expense and under public supervision and direction; 2. services were to meet the standards of the state educational agency; 3. appropriate preschool, elementary, or secondary school education was to be included; and 4. the requirements of individualized education programs (IEPs)—the goals set for a child with a disability during the school year, as well as any special support needed to help achieve those goals—were to be met.

The law did not define what constituted an “appropriate” education, however, which spurred much subsequent litigation. Another purpose of the Education for All Handicapped Children Act of 1975 was to lay out the details of IEPs, which were required for every student with a disability. IEPs were to be designed to give each student with a disability a specific and individually meaningful educational program developed by a team of parents, educators, and other qualified school staff. A third purpose of the law was to ensure that the rights of children with disabilities and their parents were protected. The law established administrative procedures for parents who wished to dispute decisions about their children’s special education services and IEPs. The law also required students with disabilities to be serviced in the least restrictive appropriate environment, so that they could be educated with their nondisabled peers to the greatest extent possible. The Handicapped Children’s Protection Act of 1986 (PL 99-372)

The Handicapped Children’s Protection Act of 1986 clarified that parents must be allowed to help develop an IEP for their children and were entitled

to a hearing by the state education agency if they disagreed with aspects of the IEP. If they were dissatisfied with the decision of the hearing, they could file a suit in a state or federal district court. The law granted the courts the authority to award attorneys’ fees to parents who won such lawsuits, reversing a previous Supreme Court decision that banned the award of attorneys’ fees. The Infants and Toddlers with Disabilities Act of 1986 (PL 99-457)

The Infants and Toddlers with Disabilities Act of 1986 extended the coverage of the Education for All Handicapped Children Act of 1975 to include all children between the ages of 3 and 5 with disabilities and established a new federal program to cover babies with disabilities from birth to age 2. The Individuals with Disabilities Education Act of 1990 (PL 101-476)

The Individuals with Disabilities Education Act of 1990 emphasized the person instead of the disability and changed the term handicap to disability. The act also added autism and traumatic brain injury to the list of disabilities and required that IEPs include transition services for students, starting at age 16. The Individuals with Disabilities Education Act Amendments of 1997 (PL 105-17)

The Individuals with Disabilities Education Act Amendments of 1997 evaluated, strengthened, and expanded IDEA. In particular, it focused on the quality and level of educational opportunity, rather than just access to education, for children with disabilities. It required that IEPs include measureable goals and details on how students would achieve those goals. It also strengthened the role of parents in the process, encouraged the resolution of differences between schools and parents using nonadversarial mediation, and addressed issues concerning the discipline of children with disabilities, which had been a source of considerable litigation over the years. A significant change was to relax the requirements of previous laws with respect to specifying particular disabilities, making it possible for school districts to indicate that a child under age 5 was experiencing developmental delays, rather than identifying the child as having a specific disability, and to provide services accordingly. The amendments also highlighted the need to recognize racial, ethnic,

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and linguistic diversity and to prevent inappropriate identification based on those characteristics. The Individuals with Disabilities Education Improvement Act of 2004 (PL 108-446)

The Individuals with Disabilities Education Improvement Act of 2004, signed on December 3, 2004, by President George W. Bush, reauthorized and amended the IDEA. Most notably, it aligned IDEA with the requirements of the No Child Left Behind Act. In particular, it required states to establish targets for the participation of children with disabilities in state assessments and to create and report proficiency rates for these children. The amendment also continued the commitment of the federal government to support highly qualified personnel with special education and early intervention experience, including early intervention staff, classroom teachers, therapists, counselors, psychologists, and program administrators. The law is divided into a number of provisions or parts. The contents of these parts are described below. Part A: General Provisions

Part A presents the law’s justification, as well as information about the state of education of students with disabilities at the time of the passage of the Education for All Handicapped Children Act of 1975. It also defines the meanings of the terms used in the rest of the law and outlines the purposes of the law. Part B: Assistance for Education for All Children With Disabilities

Part B outlines the mechanisms and the rules of the federal funding program. Funds are delivered to the state educational agency, which then distributes them to local educational agencies. To receive funding, states must provide the federal government information about their selection procedures, programs, and safeguards in place to ensure that programs are delivered properly. The funds are meant to cover approximately 40% of those needed for special education under IDEA, although the program has never been fully funded. Part B also defines educational standards for students with disabilities between the ages of 3 and 21 and mandates that states must develop programs for professional development and knowledge dissemination.

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Special education scholars have divided Part B into six principal aspects: 1. Free appropriate public education, whereby all special education services are provided free of charge and must be supervised and directed under the public system and meet current state standards 2. A least restrictive environment—that is, states are charged with educating students with disabilities alongside their peers without disabilities to the maximum extent appropriate 3. Zero reject, meaning that all students with disabilities are eligible for services and that states are responsible for locating, identifying, and evaluating all children in their state with disabilities 4. Protection in evaluations, meaning that evaluation methods must not be discriminatory with respect to race, ethnicity, or linguistic diversity and that a variety of assessments, rather than just one, must be used to evaluate students with disabilities, with the determination to be made by a group of highly qualified professionals and the child’s parents, followed by the development of an IEP for the child 5. Procedural safeguards to ensure that children with disabilities receive a free and appropriate education 6. Parent participation, whereby parents’ rights are to be respected, and they are to be considered equal partners in the IEP process Part C: Infants and Toddlers With Disabilities

Part C covers children from birth to age 3 who have a disability or are developmentally delayed. It mandates the development of interagency programs to deliver early intervention programs for children with disabilities and their families and relies on various state agencies instead of the school districts and local education agencies used in Part B. Part C also defines Individualized Family Service Plans, which are similar to IEPs but also include the needs of the family. These plans are mandatory for every infant or toddler protected under Part C and are developed by a multidisciplinary and interagency team, which includes parents and may also include other relatives and advocates for the child, such as family friends and the child’s pediatrician. Under Part C, infants and toddlers are guaranteed the right, free of charge, to a service coordinator who

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assists the family in obtaining services; helps coordinate services, evaluations, and assessments; monitors the delivery of services; and helps determine whether appropriate services are being provided. The service coordinator also facilitates the transition plan from Part C to Part B. Part D: National Activities to Improve Education of Children With Disabilities

Part D establishes funding for support programs aimed at improving the education of children with disabilities, and it is intended to help state educational agencies reform and improve their systems for personnel preparation and professional development in early intervention, education, and transition services to improve the outcomes of children with disabilities. Part E: The National Center for Special Education Research

Part E establishes the National Center for Special Education Research, the mission of which is to sponsor research to expand knowledge and understanding of the needs of children with disabilities in order to improve their developmental, educational, and transitional results. It also sponsors research related to services provided under IDEA and evaluates the implementation and effectiveness of IDEA. Within these parts of the law, a number of mandates and rules are included. First, the law allows school districts to use up to 15% of their IDEA Part B funds to develop and implement early-intervention services for students who had not been identified with a disability but who were struggling in school. Local education agencies that disproportionately identified children from certain racial or ethnic backgrounds as having disabilities would have to reserve the full 15% of Part B funds for early intervention services. Examples of early intervention services are speech and language services, medical services, nutrition services, occupational and physical therapy, and psychological services. The law mandates that the services and aides required by students’ IEPs be based on peerreviewed research to the maximum extent possible. It also changed the requirements for special education eligibility. In particular, parents, state and local educational agencies, and other state agencies, henceforth, could request an initial evaluation of a child with parental consent, and the eligibility determination would have to be made within 60 days of

the request. The student’s IEP team would have to examine the programming the student had received previously to make sure that the student’s particular deficiencies were not related to previous poor programming and/or services. Also the determination of specific learning disabilities could no longer use a discrepancy formula—a measure of the difference between the student’s intellectual ability and his or her academic achievement—since it takes many years for a discrepancy to be revealed, during which time interventions could not be provided. The law also mandates that school districts employ a response to intervention framework, whereby students at risk for poor learning outcomes are identified by universally screening all students in the district and by monitoring the progress of these students while providing evidence-based interventions. The law also made many key changes regarding discipline procedures. For example, it gave school personnel the authority to consider unique circumstances on a case-by-case basis and allowed students to be removed for “serious bodily injury.” If a child with disabilities was suspended for 10 or more days in a school year, the local education agency would have to hold a manifestation determination hearing, which determines whether the child’s conduct was the result of his or her disability, within 10 school days of any decision to change the child’s placement as a result of the violation of a code of student conduct. In addition, the law introduced a “stay-put” provision under which a child could not be moved automatically to a new placement if the infraction was deemed to cause danger to other students. In that event, the local education agency, the child’s parents, and the IEP team were to review the child’s file and determine if the conduct was caused by the child’s disability or was the result of the agency’s failure to implement the child’s IEP. Finally, the law requires that each state must submit an annual plan to the Office of Special Education and Rehabilitative Services, part of the U.S. Department of Education, which is responsible for enforcing, monitoring, and implementing certain aspects of IDEA, and for conducting audits of states to also ensure their compliance with the law.

Conclusion Since IDEA became law, more children with disabilities are attending neighborhood schools, graduating from high school, enrolling in postsecondary

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programs, and are being employed as adults. In addition, early intervention services are provided to hundreds of thousands of infants and toddlers each year, and children with disabilities have experienced incredibly large academic gains over the past few decades. However, the IDEA funds only cover a portion (about 10%) of the special education expenditures of each state. The rest of the expenditures are covered by state and local funds. The differences in state finance systems has caused special education enrollment rates to vary significantly between states despite being governed by the same federal law. For instance, in 2003, enrollment in special education ranged from about 10% in California to about 20% in Rhode Island. These enrollment differences point to significant differences in providing services to children with disabilities across geographic areas. Along with state finance systems and geography, it has also been shown that socioeconomic status and culture play a role in disability identification. Therefore, the differences in identification and service provision across states and demographic groups need to be explored further and the legislation changed to address these discrepancies. There also continues to be much dispute and litigation regarding issues surrounding aspects of the IDEA. Much of this litigation has surrounded issues of parents wanting increased services for their children, while school districts have been trying to balance the needs of the students with the economic reality of their budgets. This struggle will be ongoing as long as the special education budgets of states and school districts do not meet the wants and needs of the students. Much has changed in the past few decades due to IDEA and its predecessor legislation. In the early 1970s, very few children with disabilities were educated in public schools; by 2009, 13.5% of all students in public schools had a disability covered under IDEA. Given its success in increasing access to public education by students with disabilities, the IDEA has been copied by numerous other countries around the world. Future reauthorizations and amendments of the law will continue to be drafted in an attempt to improve both access to and the quality of education for students with disabilities. Elizabeth Dhuey See also Access to Education; Adequacy; Elementary and Secondary Education Act; Special Education Finance

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Further Readings Alexander, K., & Alexander, M. D. (2009). Rights of students with disabilities. In K. Alexander & M. D. Alexander (Eds.), American public school law (pp. 561–637). Belmont, CA: Wadsworth Cengage Learning. Bateman, B. D. (2011). Individual education programs for children with disabilities. In J. M. Kauffman & D. P. Hallahan (Eds.), Handbook of special education (pp. 91–106). New York, NY: Routledge. Bush, G. W. (2004). Remarks on signing the Individuals with Disabilities Education Improvement Act of 2004. Weekly Compilation of Presidential Documents, 40(49), 2897–2898. Martin, E. W., Martin, R., & Terman, D. L. (1996). The legislative and litigation history of special education. Future of Children, 6(1), 25–39. Rozalski, M., Miller, J., & Stewart, A. (2011). Least restrictive environment. In J. M. Kauffman & D. P. Hallahan (Eds.), Handbook of special education (pp. 107–118). New York, NY: Routledge. U.S. Department of Education, Office of Special Education and Rehabilitative Services. (2010). Thirty-five years of progress in educating children with disabilities through IDEA. Washington, DC: Author. Yell, M. L., & Crockett, J. B. (2011). Free appropriate public education. In J. M. Kauffman & D. P. Hallahan (Eds.), Handbook of special education (pp. 77–90). New York, NY: Routledge. Yell, M. L., Katsiyannis, A., & Bradley, M. R. (2011). The Individuals with Disabilities Education Act: The evolution of special education law. In J. M. Kauffman & D. P. Hallahan (Eds.), Handbook of special education (pp. 61–76). New York, NY: Routledge.

Website U.S. Department of Education website on IDEA: http://idea .ed.gov/

INFRASTRUCTURE FINANCING STUDENT ACHIEVEMENT

AND

Facilities, or school buildings and grounds, are the largest component of what is, generally, referred to as school infrastructure. This entry describes how school districts finance school infrastructure before reviewing how such spending relates to educational outcomes. Most state school finance systems have historically addressed wealth-based inequities in current spending while largely overlooking similar disparities in spending on infrastructure and facilities. A primary source of this discrepancy is the

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unsurprising focus of policymakers and school officials on spending within the classroom, primarily on teacher compensation, rather than for the physical classrooms themselves. Another reason for focusing on current spending activities, as opposed to capital, is the nature of public budgeting, which centers on matching sufficient revenues to desired expenditures on an annual basis. While it is fairly easy to track infrastructure spending, there is still a limited understanding of how quality school facilities and infrastructure are distributed within states and districts due to the lack of regular uniform facility assessments. Existing research has also struggled to provide consistent evidence of any significant link between school facilities and student outcomes. This is likely due, in part, to persistent methodological and measurement challenges. State courts, though, have clearly acknowledged the relevance of school infrastructure to the education process in a series of school finance cases. The following sections consider the challenges to achieving equitable infrastructure financing under current systems, the logic of considering school infrastructure from an adequacy perspective, and the methods of measuring facility and infrastructure quality before discussing the avenues by which student outcomes may be directly, or indirectly, affected by the resulting infrastructure.

Adequacy, on the other hand, considers whether a district’s existing level of facility quality, or funding available for infrastructure, meets some absolute threshold or standard. State funding of facilities and infrastructure, when provided, typically comes in the form of intergovernmental aid often attempting to alleviate the disparities that arise from this system of local capital finance. Given the variation in school infrastructure and the capacity to finance new investment, state efforts to address disparities benefit from, first, measuring existing infrastructure needs across school districts. Needs assessments allow states to prioritize funding and direct limited resources to critical projects. Such prioritization is made difficult in the absence of a set of standards establishing a baseline for facility and infrastructure quality. State aid frequently consists of matching grants to recognize the differences in local resources and to equalize, to varying degrees, the ability of districts to invest in facilities and infrastructure. Even such wealth-adjusted state aid programs can fall short of goals when school districts struggle to receive voter approval for raising the required local match through borrowing. Possibly for this reason, there is some evidence that lump-sum grants more effectively reduce inequities in school district capital spending. Facility and Infrastructure Equity

Financing Infrastructure and Facilities Paying for school infrastructure largely remains a local function, despite increasing contributions from state governments. School districts typically issue long-term debt backed by their taxing authority to finance infrastructure investments. With such debt issuance commonly requiring voter approval, the ability to finance capital spending differs across communities based on the existing property tax base and resident characteristics. Even alternative funding mechanisms, such as local option sales taxes, similarly disadvantage school districts with more limited wealth and economic activity. Concerns over existing capital finance systems are based on both the equity and adequacy of school infrastructure and its funding. Equity concerns center on the relative quality of and resources available for school facilities and infrastructure. For example, an equitable distribution of school facilities would mean that there is little variation in quality across school districts within a state or that resources available for facilities investment are roughly comparable.

School finance reforms, often prompted by litigation, have successfully reduced the within-state inequity of current education spending over the past few decades. Within-state school facility equity, though, has received significantly less attention despite the dominant role of local funds in supporting capital outlays. Since the mid-1990s, capital financing systems have drawn the attention of the courts with school facility finance litigation in a number of states, including Alaska, Arizona, Arkansas, Colorado, Idaho, Louisiana, New Jersey, New Mexico, and Wyoming. Evidence suggests that schools with more limited resources do indeed spend less on school facilities and that facilities are neglected in times of fiscal stress. Not only do fiscal constraints result in lower capital outlays in poorer districts, but they also reduce maintenance spending on the existing physical plant. In general, capital spending has been found to be lower for school districts with high poverty, high percentages of limited English proficient students, and in urban and rural school districts compared

Infrastructure Financing and Student Achievement

with suburban districts. Conversely, higher capital spending accompanies larger property tax bases. Such disparities remain even following large investments in school infrastructure during the decade beginning in the mid-1990s. While most generalizations about the distribution of quality school infrastructure are based on capital spending rather than on direct measures of infrastructure quality, national survey-based assessments support the notion that infrastructure quality is inversely correlated with the share of poor students. The primarily local system of capital financing and dependence on debt secured by a school district’s tax base means that small, and often rural, districts are especially disadvantaged with regard to infrastructure investment. For these groups, the inability to raise sufficient local funds, mainly for facilities, reflects both a smaller tax base and an inability to benefit from scale economies due to lower overall student enrollments and population density. While rural school districts face significant financing challenges, higher land and construction services costs may serve as barriers to infrastructure investment in some urban and suburban school districts. Achieving equity in school facilities and infrastructure financing remains a challenge and is an unsettled issue, especially given that nearly a dozen states remain uninvolved in financing such activity. Facility and Infrastructure Adequacy and Measurement

The adequacy of school facilities underlies recent school finance litigation. This is the case in Arizona, for example, where school districts are free to raise funds locally to provide facilities that exceed the fully state-funded standards. The definition of an adequate school facility is difficult to agree on, though, given that links between facilities and student outcomes have been difficult to demonstrate. Regardless, some states have instituted school facilities standards, and guides to facility appraisal exist, which set forth recommended guidelines for everything from classroom space to recreational field size. This focus on adequacy, rather than solely equity, acknowledges that the theoretical benefits of facilities in education are expected to dissipate after infrastructure reaches a certain minimum level of quality. Therefore, equity may not be the overriding objective when considering school facilities. Unlike the difficulty in equalizing school infrastructure, which would require leveling up investments, an approximate level

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of adequacy in school facilities appears feasible since policymakers, given sufficient resources, have ample control over the physical environment via capital spending and maintenance standards. Assessing the adequacy, or equity for that matter, of school facilities depends on being able to measure their quality. Unfortunately, this is more challenging than it sounds since quality is subjective, infrastructure quality changes over time in response to use and maintenance, and the most critical elements of quality remain unclear. School infrastructure disparities are also commonly considered at the district level despite the diversity that exists within a district’s schools. There are two main approaches, dictated by the limited available information, to measure the quality of school facilities and infrastructure. The first takes historical financial inputs as a proxy for the quality or quantity of a school district’s infrastructure. Cost-based measures may tell us little about the current quality of facilities since we do not know the effectiveness of the original spending or differences in local capital costs. The second method, which is closer to the perspective of school administrators, is the measurement of the condition or characteristics of existing facilities based on surveys of school personnel or by trained professionals based on standardized criteria. To date, more than 10 states maintain school facility inventories or have surveyed their school infrastructure. Appropriate methods of measuring school infrastructure, and comparing it on the basis of quality, remain unsettled.

Effects of School Facilities on Educational Outcomes Concerns over equitable financing and adequate school facilities implicitly assume that school infrastructure matters for students and the education process. As noted, the empirical evidence is mixed, but there are a number of reasonable paths by which school infrastructure quality can affect student learning. Economists often consider education in terms of a production function, where educational outcomes are a product of the labor and capital used in the education process. The education process is dominated by labor including teachers, administrators, and counselors, with capital, such as facilities, transportation, and technology, serving a secondary role. Facilities and school infrastructure are expected to influence the learning process directly by shaping the educational environment experienced by

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students and indirectly by affecting teachers and parents. The potential sources of direct facility influence on student outcomes are many. At a high level, the available facilities in a school district and the ability to finance new facilities dictate both school size and, to a lesser degree, class sizes. A school district that would like to maintain relatively small schools may not have that option depending on the existing facilities. If a school lacks additional classrooms, then it may be forced to increase average class sizes, resulting in crowding. Classrooms and other instructional spaces may also be functionally inadequate for modern instructional techniques depending on configuration and available technologies. For example, science curricula may be dictated by the availability of lab facilities, performing arts offerings may depend on the existence of an auditorium, and student research opportunities may be curtailed by limited Internet access. Other prominent sources of potential influence within school buildings are environmental quality (including indoor air quality and temperature control), lighting and sound, and overall aesthetics. These structural characteristics of a school may affect intervening variables such as “time on task” if poor conditions lead to reduced teaching time, increase student absenteeism, or disrupt the learning process. For example, indoor air quality issues may lead to increased health-related absences, and classrooms exposed to external noise may interfere with students’ ability to concentrate. Outright neglect, inadequate maintenance and upkeep, or subpar facilities and infrastructure may signal to students that their education is not being taken seriously. Students may respond to such negative physical cues with increased apathy. The other main avenue by which student outcomes may be influenced by school infrastructure is more indirect. There is evidence that facilities affect teacher quality, turnover, and self-reported effectiveness. Teachers, especially those who are most mobile, prefer to work in schools with high-quality facilities and infrastructure. Adequate classrooms, the availability of offices for teacher preparation, and up-to-date technologies are all expected to improve teacher effectiveness. Surveys of parent priorities for schools indicate that facilities are an important consideration. Just as facilities have signaling effects for students, parents assess schools based on their physical attributes, and this has implications for where their children end up enrolling. Connected to the sorting of households into schools with higher

quality facilities, studies find that homeowners value school infrastructure based on the capitalization of such investment in home prices. Given the described relationships of school facility quality to the major education production function inputs, it is apparent why the courts have been open to arguments over disparities in school infrastructure. A consensus is growing that investment in school facilities and infrastructure is valued by parents and has positive implications for teacher performance and stability. According to the broader literature, though, there remains little consistent evidence that the characteristics of facilities directly affect student outcomes once a minimum level of adequacy is reached. This is not to say that researchers have not identified compelling relationships between school facilities and academic outcomes in isolated settings. The inconsistent evidence, despite clear expectations that school infrastructure and facilities matter for student outcomes, is often attributed to the tremendous variation in research settings and differences in measurement of infrastructure or facility quality. There are also inherent challenges to studying the impacts of broadly defined capital investment in the absence of baseline information about previous and current physical conditions and students. Existing capital financing mechanisms should continue to receive attention from policymakers and the courts as technology demands grow and school districts naturally compare their facilities and infrastructure with neighboring districts. States will be depended on for any desired improvement, whether prompted by the courts, advocacy groups, or legislators, in the equalization of capital financing and the adequacy of school facilities and infrastructure. Todd L. Ely See also Adequacy; Bonds in School Financing; Capital Financing for Education; Education Production Functions and Productivity; Educational Equity; General Obligation Bonds

Further Readings Arsen, D., & Davis, T. (2006). Taj Mahals or decaying shacks: Patterns in local school capital stock and unmet capital need. Peabody Journal of Education, 81, 1–22. Buckley, J., Schneider, M., & Shang, Y. (2005). Fix it and they might stay: School facility quality and teacher retention in Washington, D.C. Teachers College Record, 107, 1107–1123.

Instrumental Variables Duncombe, W., & Wang, W. (2009). School facilities funding and capital-outlay distribution in the states. Journal of Education Finance, 34, 324–350. Filardo, M. W., Vincent, J. M., Sung, P., & Stein, T. (2006). Growth and disparity: A decade of U.S. public school construction 1995–2004. Washington, DC: 21st Century School Fund, Building Educational Success Together. Neilson, C., & Zimmerman, S. (2011). The effect of school construction on test scores, school enrollment, and home prices (IZA Discussion Paper No. 6106). Bonn, Germany: Institute for the Study of Labor (IZA). Odden, A., & Picus, L. O. (2008). School finance: A policy perspective (4th ed.). Boston, MA: McGraw-Hill. Picus, L. O., Marion, S. F., Calvo, N., & Glenn, W. J. (2005). Understanding the relationship between student achievement and the quality of educational facilities: Evidence from Wyoming. Peabody Journal of Education, 80, 71–95. Schneider, M. (2002). Do school facilities affect academic outcomes? Washington, DC: National Clearinghouse for Educational Facilities. Sciarra, D. G., Bell, K. L., & Kenyon, S. (2006). Safe and adequate: Using litigation to address inadequate K-12 school facilities. Philadelphia, PA: Education Law Center.

INSTRUMENTAL VARIABLES The method of instrumental variables (IV) allows estimation of the causal effect of a treatment when the treatment is not randomly assigned. IV requires that the researcher identify a variable that affects the treatment status but has no other impact on the outcome. This variable is referred to as an instrument or an instrumental variable. The instrument induces variation in the treatment status that isn’t attributable to the unobserved characteristics of the subject, allowing the researcher to isolate the causal effect of the treatment. This entry provides an example of how IV works, followed by a more detailed discussion of IV methodology and its assumptions. The entry then discusses IV in the context where the impact of a treatment differs across individuals and illustrates how IV can be used to correct for data mismeasurement in statistical analysis. It concludes with a discussion of education research that has employed IV methods.

Example of Use of IV Suppose a researcher wishes to understand the causal effect of attending private school on mathematics

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achievement. Comparing the math test scores of a sample of children who attended private school with those of children who attended public school may paint a misleading picture of the impact of private school. Specifically, the difference in achievement will reflect the impact of private school attendance as well as unobserved differences in family background between private school and public school children. For example, children who attend private school may have more educated parents and consequently have the resources and support to perform well academically, even if they attended public school instead of private school. To implement IV in this context, the researcher needs an instrument that is uncorrelated to family background but affects the probability a student attends private school. Suppose the researcher offers a random set of public school students a voucher to attend private school. Furthermore, suppose that no children attended private school in the sample unless offered the voucher. Whether a student was offered a voucher is the instrument in this example. Because of random assignment, the difference in math achievement between those offered a voucher and those who were not reflects the causal impact of being offered a voucher. However, this still doesn’t answer the question of what is the impact of private school attendance on math achievement if some families fail to use the voucher and remain in public school. If the only way being offered a voucher affects math achievement is through private school attendance, then the effect of being offered a voucher simply equals the effect of attending private school multiplied by the fraction of students who used the voucher. For example, if the fraction of those offered a voucher who accepted was 0.3 and the average impact on a math test of attending private school was to answer two additional questions correctly, then the impact of being offered a voucher would be 0.6 extra questions answered correctly, or 0.3 multiplied by 2. The data allow the researcher to measure both the fraction of students who attend private school after being offered a voucher, 0.3, as well as the impact of being offered a voucher, 0.6 extra questions answered correctly. IV implicitly uses these numbers to estimate the impact of attending private school, which equals the impact of being offered the voucher divided by the fraction of voucher recipients who attended private school, or 0.6/0.3 ⫽ 2 extra questions answered correctly.

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Instrumental Variables

Methodology

Understanding and Testing the Assumptions

IV estimation is often implemented using a method called two-stage least squares. In this method, the researcher first predicts a treatment status using an instrument. This is called the first stage.

The first assumption of IV is that the instrument determines the treatment status at least in part. In the context of the voucher example, receiving a voucher must cause at least some families to enroll their children in public school. This assumption is readily testable by examining whether the estimate γ1 is significantly different from 0. If this assumption does not hold, the estimate of the treatment effect β1 will be very imprecisely estimated and potentially misleading. The second assumption is that the instrument is uncorrelated to the unobserved factors that affect the outcome. If this assumption doesn’t hold, IV estimates of the treatment effect will be invalid. To see why this is the case, consider the initial example. If the voucher was given to the poorest families instead of randomly, the estimated impact of receiving a voucher would reflect both the impact of private school attendance and the characteristics of the family. With just one instrument per treatment, this assumption is not testable. However, tests do exist for when the researcher has multiple instruments for a single treatment. Typically, researchers make arguments based on their knowledge of the institutional setting about why the instrument is unlikely to be correlated with an individual’s unobserved characteristics. In the context of the voucher example, if one knew that the vouchers were randomly assigned, it would seem reasonable that being offered a voucher would be uncorrelated to everything else that might affect math performance except whether the student went to private school.

treatmenti ⫽ γ0 ⫹ γ1zi ⫹ νi,

(1) where treatmenti is the treatment status of individual i, zi is the value of the instrument, and νi represents unobserved factors that also determine treatment status. γ0 and γ1 are parameters that show how the instrument relates to the treatment. In the context of our initial example, treatmenti indicates whether a child attends private school, zi shows whether the family is offered a voucher, and γ1 indicates the impact that receiving a voucher has on the probability the student attends private school. Usually, this first-stage equation is estimated using ordinary least squares and can include additional controls. This equation allows researchers to predict treatment status on the basis of the value of the instrument. This predicted treatment status equals the ordinary least squares estimate of γ0 plus the estimate of γ1 multiplied by the value of the instrument. Once the first stage is estimated, the researcher estimates the relationship between the outcome and the predicted treatment status. This is called the second-stage equation and can be written as follows: outcomei ⫽ β0 ⫹ β1 predicted treatmenti ⫹ εi.

(2) Note that outcomei represents the outcome of individual i, predicted treatmenti is the predicted treatment status calculated from the first stage, and εi represents random factors besides the predicted treatment status that might affect the outcome. Again, additional controls can be included in the specification. β1 represents the impact of the treatment on the outcome. β0 and β1 can be estimated using ordinary least squares, though usually both equations (1 and 2) are estimated by statistical packages simultaneously. Again referring to our initial example, the outcome would be performance on the math test. Predicted treatment would equal the probability a child attends private school based on whether he or she was offered a voucher or not. β1 would equal the impact of attending private school on math performance.

When Impact of Treatment Varies Across Individuals If the impact of treatment varies across individuals, IV can only show the average effect of the treatment on the individuals whose treatment status was affected by the instrument. In the context of the voucher example, IV can only identify the impact of going to private school on those children who attended private school having been offered the voucher. It cannot identify the impact of treatment on children whose parents refused the voucher. Even this interpretation requires that an instrument only affect treatment in one direction. In other words, while it is fine for an instrument to have no effect on some individuals, if an instrument increases the probability that some individuals will take the

Intergovernmental Fiscal Relationships

treatment and decreases the probability that others will, the resulting estimate cannot be interpreted.

IV as a Fix for Measurement Error In addition to solving the problem of nonrandom assignment of a treatment, IV can also be used to overcome the problem of classical measurement error in regression contexts. This problem arises when a regressor (right-hand side variable) of interest is measured with error. Suppose instead of using math performance as an outcome, it was used as a control variable. In this case, a math test may be a noisy indicator of the variable of interest, which is a student’s latent math ability. This measurement error causes the estimated coefficient on the regressor to be too close to zero. The noisier the measure, the more severe this bias will be. This problem can be solved if one has a second noisy measure of the regressor. In this case, one can use one of the noisy measures as an instrument for the other. For example, a student’s performance on one math test may be used as an instrument for the same student’s score on a different math test taken on a different day. The resulting IV estimate of the coefficient will be unbiased by the measurement error.

Examples of IV Analyses IV has been used frequently by education researchers in a variety of settings. Lawrence Katz, Jeffrey Kling, and Jeffrey Liebman use random assignment of housing vouchers as an instrument to see how moving to a low-poverty neighborhood affects a variety of outcomes, including the type of school children attend. Brian Jacob uses the situation of whether a household was in a public housing project scheduled for demolition as an instrument for receiving a housing voucher. He does so to examine the impact of leaving a high-poverty housing project on academic achievement. Kevin Milligan, Enrico Moretti, and Philip Oreopoulos use changes in compulsory schooling laws as an instrument for educational attainment to measure the effect of education on the likelihood an individual votes. Caroline Hoxby uses variation across years in cohort sizes within a school as an instrument to discover the impact of class size on student achievement. Jacob and Lars Lefgren use performance on standardized tests relative to a district cutoff to construct instruments that determine the probability a student attends summer school and is retained in his or her grade. They examine the impact of these treatments on subsequent

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academic achievement. Orley Ashenfelter and Alan Krueger use IV to solve the problem of measurement error in self-reported educational attainment. They use a sibling report of education as an instrument for an individual’s own educational attainment when examining the impact of years of education on earnings. Lars Lefgren See also Measurement Error; Omitted Variable Bias; Ordinary Least Squares; Quasi-Experimental Methods; Randomized Control Trials; Regression-Discontinuity Design

Further Readings Ashenfelter, O., & Krueger, A. (1994). Estimates of the economic return to schooling from a new sample of twins. American Economic Review, 84, 1157–1173. Greene, W. H. (2011). Econometric analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall. Hoxby, C. M. (2000). The effects of class size on student achievement: New evidence from population variation. Quarterly Journal of Economics, 115, 1239–1285. Imbens, G. W., & Angrist, J. D. (1994). Identification and estimation of local average treatment effects. Econometrica, 62, 467–475. Jacob, B. A. (2004). Public housing, housing vouchers, and student achievement: Evidence from public housing demolitions in Chicago. American Economic Review, 94, 233–258. Jacob, B. A., & Lefgren, L. (2004). Remedial education and student achievement: A regression discontinuity analysis. Review of Economics and Statistics, 86, 226–244. Katz, L. F., Kling, J. R., & Liebman, J. B. (2001). Moving to opportunity in Boston: Early results of a randomized mobility experiment. Quarterly Journal of Economics, 116, 607–654. Milligan, K., Moretti, E., & Oreopolous, P. (2005). Does education improve citizenship? Evidence from the U.S. and the U.K. Journal of Money, Credit and Banking, 88, 1667–1695.

INTERGOVERNMENTAL FISCAL RELATIONSHIPS Intergovernmental fiscal relationships are designed to allow the different levels of government in a federal system to coordinate their provision of public goods and services. We often think of decisions about taxation and public spending as stemming from a

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Intergovernmental Fiscal Relationships

single “government.” Much more prevalent, however, is a multilevel system with fiscal responsibilities vested in both central and lower level governments. In the United States, for example, the power to tax and spend is held by more than 89,000 governmental jurisdictions: 1 federal, 50 state, 3,033 county, 19,492 municipal, 16,519 township, 13,051 school district, and 37,381 special district. Consequently, public finance in the United States operates through a complex federal system of interactions among multiple levels of government. The field of study that examines the functions of different levels of government and their interactions when providing public goods and services is fiscal federalism. This field has received much attention over the past few decades due, in part, to an extension of the theory of social goods to the problem of state and local governments. The operation of a federal system of government finance entails the use of intergovernmental grants, sometimes called grants-in-aid. These are transfers of funds from one government to another, generally from a higher level government to a set of lower level governments. These grants are of many types and serve different purposes. This entry examines the economic functions that are assigned to the different levels of government and the advantages of such a decentralized system of governance. The theory of intergovernmental grants and their role in coordinating government activities is discussed, along with the econometric evidence on their effects. The entry concludes with some general implications for intergovernmental grant policy.

It is clearly not sensible for these stabilization functions to be assigned to state or local governments. First, state and local governments operate as open economies within the national marketplace. Consequently, any stimulative effects of state or local fiscal measures such as increased public expenditures will not be fully captured by the originating jurisdiction but will leak into the broader economy through trade and commerce. Any such leakages are substantially smaller when fiscal measures are undertaken at the national level. Second, stabilizing fiscal policy requires periodic budgetary deficits or surpluses with corresponding borrowing and debt repayment. These activities are problematic for local governments, which have less ready access to capital markets. Even more compelling reasons can be given for assigning monetary policy to the central government. Decentralized monetary policy would be seriously compromised in its effectiveness by the openness of the subnational economy, and the power to print money would invite irresponsible decisions by state or local governments.

Economic Responsibilities in a Federal System

Externalities: A public good with benefits that accrue only to members of a single community is called a local public good. In reality, however, local communities often impose externalities, both positive and negative, on each other, but would ignore or discount these external effects when allocating resources. The result would be economically inefficient from society’s point of view. A spatial externality, or “spillover,” occurs when the spatial distribution of the costs or benefits of government services is not confined to the boundaries of the providing government. Nonresidents either pay part of the cost (e.g., a nonresident landlord pays local property tax) or enjoy part of the benefit (e.g., a graduate of the local public schools lives and works in another community) of the public program. Such spatial externalities can result in socially inefficient government taxation and spending levels. With a cost spillover,

In the United States, many important public activities are assigned to state and local governments, including education and public safety, while the federal government is responsible for other functions, such as national defense and Social Security. At the same time, all three levels of government spend money on public welfare. And revenue raised by one level of government is often spent on another. In light of this complexity, the question arises What is the optimal assignment of economic responsibilities across the levels of government in a federal system? Macroeconomic Functions

Most economists agree that taxing and spending decisions designed to impact employment and price levels should be made by the central government.

Microeconomic Functions

These functions of government seek to improve the efficiency and equity of supplying public goods. The objective is to allocate resources to public goods so as to maximize social welfare. A system composed exclusively of decentralized governments could lead to an inefficient supply of public goods for several reasons.

Intergovernmental Fiscal Relationships

local residents underestimate the true social cost of the public good and demand too much of it, while a spillover of benefits leads residents to underestimate social benefits and demand too little. A classic economic solution to this common problem is to internalize the externality—that is, to compel the decision maker to consider the true social costs and benefits of the service. In the case of spatial externalities, the socially optimal allocation of resources would be achieved by making the government jurisdiction big enough to include all consumers who pay costs or enjoy benefits. Scale economies in the provision of public goods: For some public services, the unit cost (e.g., cost per person) falls as the number of users increases. Different services, of course, are subject to different scale economies (e.g., police, sanitation, education, and/or national defense), thereby providing a rationale for overlapping jurisdictions, with each jurisdiction responsible for those services appropriate for the jurisdiction’s size. Inefficient tax systems: Taxes levied by local jurisdictions are unlikely to be socially efficient. That is, localities are likely to choose taxes that can be exported to nonresidents. Such tax shifting could lead communities to purchase too many local public goods. Specifically, if a locality can shift some of the tax burden to other communities, the locality’s perceived marginal cost will be lower than the marginal social cost and more than the efficient amount of public goods will be purchased. Equity issues: Maximizing social welfare may require income transfers to the poor. Most economists would agree that taxpayer mobility rules out reliance on local governments to pursue goals regarding income distribution. In the United States, spending on income maintenance is largely a federal function, carried out through programs such as Social Security, the Supplemental Nutrition Assistance Program, Supplemental Security Income, and the earned income tax credit.

Advantages of a Decentralized System Despite these issues, it is clear that a decentralized system of government is not without advantages. Two such advantages are the ability of decentralized government to tailor public goods and services to local tastes and the fostering of competition

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among governments, thereby increasing efficiency. These advantages are nicely summed up in the principle of subsidiarity, which holds that public decisions should be assigned to the lowest level of government (i.e., closest to the people) competent to make them. This principle, first enunciated in connection with the financing of local public education, stems from the idea that a local government’s greater proximity to the people makes it more responsive to citizens’ preferences than central government. And the more preferences vary within an area, the greater the benefits of decentralization. If citizens can choose among communities, government managers have a greater incentive not only to supply the mix of public goods most closely matching the citizens’ preferences but also to produce them more efficiently. Furthermore, Raymond Fisman and Roberta Gatti found that the more decentralized a country’s fiscal system, the less corrupt its government is likely to be, all else being equal.

Coordination: Revenue Collection Two general types of intergovernmental relationships are designed to assist the lower levels with revenue collection. One mechanism provides revenue relief to decentralized governments in the form of deductions and credits granted for taxes paid at a lower level in the computation of tax liability at the higher level. Deductibility means that the tax base for one governmental level is reduced by the amount of tax paid to another level. For example, in the United States, the federal personal income tax currently permits deduction of state and local income and property taxes. With a tax credit, the tax levied by one governmental level serves as full or partial payment of tax liability owed to another level. Current U.S. law allows a variety of federal personal income tax credits, the largest being the earned income tax credit, for qualified low-income individuals. Deductions and credits help state and local governments finance their operations but do not assist them with tax administration and compliance. Mechanisms for these purposes include the coordinated tax base and the supplemental, or “piggyback,” tax rate. With the former, one government sets its tax on some stage of the tax structure of another government. For example, several states base their individual income tax on the federal government’s adjusted gross income. Under the latter, a lower level of government applies its own tax rate to

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Intergovernmental Fiscal Relationships

a tax base used by a higher level. The higher level of government assumes responsibility for tax administration and enforcement. Most local sales taxes are piggybacked on the state sales tax.

Coordination: Expenditures— Role of Intergovernmental Grants Intergovernmental grants, sometimes called grantsin-aid, are transfers of funds from one government to another, generally from a higher level government in the federal system to a set of lower level governments. Grants are of different types and are intended to improve the operation of a federal system of government finance. In the United States, grants from the federal government increased both in real terms and as a proportion of total federal outlays between 1960 and 2007. The economists Harvey Rosen and Ted Gayer have found that grants as a percentage of state and local expenditures have also increased. Grants are a particularly important revenue source for local governments, with federal and state grants making up about 35% of total local general revenues, according to the U.S. Bureau of the Census. One explanation for the growth in intergovernmental grants is that over the past 50 years the demand for services historically supplied by state and local governments (e.g., education, public safety, and/or transportation) has grown more rapidly than state and local revenues, primarily property and sales taxes. Federal revenues, on the other hand, have grown automatically, largely due to the progressivity of the federal personal income tax. Purposes of Grants

Intergovernmental grants serve four distinct functions in a federal fiscal system: (1) to correct for externalities that arise from the structure of subnational governments, (2) to redistribute resources among regions or localities, (3) to substitute one tax structure for another (e.g., to take advantage of scale economies in tax collections), and (4) as a macroeconomic stabilizing mechanism. To serve these various purposes, governments have designed grants of various characteristics. A grant may be intended for a specific service or for any purpose. A grant may be allocated by formula or require an application in connection with a particular project. The potential grant size may or may not be limited. Finally, another important distinction is whether the grant

funds must be matched in some proportion by the recipient government. Categorical Grants

A grant earmarked for a specific program is the most prevalent type offered by the federal and state governments. The dominant state categorical program provides grants for local education. There are several types of categorical grants. A grant is lump sum, or nonmatching, if the amount does not change as a recipient government changes its taxes or expenditures. Matching grants, in contrast, require recipient government taxes or spending, with the size of the grant dependent on the recipient government’s response. Typically, a specific matching grant offers to match each dollar of recipient tax or expenditure on the target service (e.g., education) with R grant dollars, to be spent on that service. R is the grant’s matching rate. Generally, the share financed by the grant (M) is M ⫽ R/(1 ⫹ R), and the local marginal tax price (P) of an additional dollar of service is P ⫽ 1 ⫺ M ⫽ 1 ⫺ [R/(1 ⫹ R)] ⫽ 1/(1 ⫹ R). Both matching and nonmatching categorical grants may be allocated either by formula or on a project basis and may be either open-ended (i.e., no limit on the grant amount) or closed-ended (i.e., grant amount is limited). Project categorical grants are more prevalent than formula grants, while openended categorical grants are relatively rare. General (or Unconditional) Grants

Such grants, without use restrictions, are rare among federal government grants but more common among state grants. These grants, usually intended to provide general financial assistance, are almost always formula based. If the formula includes factors outside the recipient government’s control, such as population or per capita income, the grant is a pure lump sum. In contrast, if the allocation formula includes factors that are subject to government control, such as tax effort, then the amount of the grant can be influenced by the recipient government. This approach, used for federal and some state revenue sharing, constitutes a type of matching grant, although the total amount of grant aid is limited and the matching rate varies. The best known general-purpose grant was the U.S. General Revenue Sharing Program. Initiated in 1972, the program provided grants to state and local governments. The funds were first divided among the states by a formula that included population, per

Intergovernmental Fiscal Relationships

capita income, and tax effort, with one third of a state’s allocation retained by state government and two thirds distributed by formula to local governments. The state share was terminated in 1983, and the local government program expired in 1987. Block Grants

These are specific grants earmarked for very broadly defined categories. As such, they fall in an area between narrowly defined categorical grants and grants with no use restrictions. In most cases, block grants are general grants because the use categories are broad enough to allow most recipient governments leeway to reallocate other funds.

Economic Effects of Grants: Theory Intergovernmental grants may affect recipient government fiscal decisions in one of the two ways: (1) via an income effect by increasing resources available to provide government services or (2) through a price effect by increasing resources and reducing the recipient’s marginal cost of additional services. This analysis relies on the theories of political economy and individual demands for government services. The field of political economy applies economic principles to the analysis of political decision making and employs models that assume that individuals view government as a mechanism for maximizing their self-interest. Individual demands for government services must be coordinated by a political choice system. If political decisions are made by voting, then the effect of the grant on a government’s decisions is determined by the grant’s effect on the decisive voter (see the entry “Median Voter Model,” this encyclopedia). Accordingly, the standard economic analysis of the expected effects of intergovernmental grants begins with the effects on individual demands. An increase in available resources, which arises from a lump-sum (i.e., nonmatching) grant, increases demand through an income effect. A matching grant, on the other hand, increases demand through a price effect. Given the characteristics of the grant program and the local political choice (i.e., voting) system, the economic effects of the grant can be predicted. Microeconomic theory predicts that a decrease in price will have a greater effect on consumption than an increase in income, even if that increase is large enough to give the consumer the same choices as the price decrease. When the price of a good or

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service is reduced, consumers are influenced by both price (or “substitution”) and income effects. For normal goods, both effects are an incentive for consumers to purchase more of the reduced-price good. When consumers receive an increase in income, purchasing power increases, but there is no change in relative prices. Consequently, if the income effect of a price reduction is of the same magnitude as the income effect of an income increase, the price decrease should affect consumption more. That is, the income effects are the same, but the price cut exerts an additional substitution effect. This theoretical result implies that an open-ended matching grant will increase government spending on the target service by a greater amount than an equal size lump-sum grant—that is, a lump-sum grant large enough to allow the recipient government to choose the same expenditure as selected with the matching grant.

Econometric Evidence on Grant Effects The effect of a matching grant on a recipient local government’s spending on the target service depends on the price elasticity of demand for the good. If the demand for the public good is price inelastic, a matching grant will increase government spending by less than the grant amount, thereby allowing local funds to be spent in other ways, including paying for local tax relief. The demand for most state and local government services is price inelastic. For example, literature surveyed by Ronald Fisher and Leslie Papke estimates the price elasticity of demand for education between 0.15 and 0.50. Econometric evidence supports some theoretical predictions about the general direction and relative magnitude of grant effects. First, open-ended, categorical matching grants increase spending on the target service by a greater amount than specific lump-sum grants of the same amount. Second, estimated spending effects of lump-sum grants vary widely, with spending increases ranging from ¢20 to $1 per dollar of grant. A third finding, however, does not comport with theory. While theory would predict that lump-sum grants and income increases in the same amount exert identical influences on government spending, much evidence reveals that lump-sum aid is more stimulative of government spending than an equal increase in residents’ income. This unintuitive finding has been dubbed the “flypaper effect,” indicating that money tends to “stick in the sector where it hits.”

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Internal Rate of Return

Implications for Intergovernmental Grant Policy Despite some mixed evidence on grant effects, a few major conclusions have emerged from the research. First, an open-ended categorical matching grant is the best mechanism for increasing a grantee government’s spending on the target service. A grant’s matching rate should be equal to the nonresident’s share of benefits from the target service. In that way, the lower local price of the service will induce an increase in spending to the efficient (socially optimal) level. Second, unrestricted lump-sum grants are preferred to matching grants as a means of redistributing resources across local jurisdictions. Third, categorical lump-sum and closed-ended matching grants should generally be avoided. Categorical restrictions on a lump-sum grant will have no effect on the grantee government’s spending on the target service unless the recipient would prefer to spend less than the grant amount. Otherwise, local funds can be shifted to other uses, including tax relief. And close-ended matching grants become lump sum once the grant limit is reached. Michael F. Addonizio See also Block Grants; Categorical Grants; Centralization Versus Decentralization; Median Voter Model; Public Choice Economics; Spillover Effects

Further Readings Addonizio, M. F. (1991). Intergovernmental grants and the demand for local educational expenditures. Public Finance Quarterly, 19, 209–232. Coons, J. E., Clune, W. H., III, & Sugarman, S. D. (1970). Private wealth and public education. Cambridge, MA: Belknap Press of Harvard University Press. Fisher, R. C. (2006). State and local public finance (3rd ed.). Mason, OH: Cengage South-Western. Fisher, R. C., & Papke, L. E. (2000). Local government responses to education grants. National Tax Journal, 53, 153–168. Fisman, R., & Gatti, R. (2002). Decentralization and corruption: Evidence across countries. Journal of Public Economics, 83, 325–346. Mikesell, J. L. (2010). Fiscal administration: Analysis and applications for the public sector (8th ed.). Belmont, CA: Wadsworth. Musgrave, R., & Musgrave, P. (1976). Public finance in theory and practice. New York, NY: McGraw-Hill. Rosen, H. S., & Gayer, T. (2010). Public finance (9th ed.). New York, NY: McGraw-Hill.

INTERNAL RATE

OF

RETURN

The internal rate of return (IRR) is essentially the rate of return a project generates over its life span. More technically, the IRR is the discount rate at which all negative cash flows (cash outflows) and positive cash flows (cash inflows) of a project sum to zero after being discounted back to the present time (discounting is necessary to adjust for the fact that a dollar today does not have the same value as a dollar at some point in the future). The calculation of a project’s IRR can be a helpful tool in the evaluation of whether or not an entity (corporation, individual, etc.) will receive a rate of return large enough to compensate for the time value of money as well as the risk associated with the project. The general rule when utilizing a project’s IRR is that the project should be accepted as long as the IRR exceeds the minimum required rate of return for the project. This entry will demonstrate how an IRR is calculated, how an IRR is used for project evaluation, and the similarities between IRR and the net present value (NPV) and explain some caveats to the usage of IRR.

Calculation of IRR To calculate the IRR for a project, it is necessary to obtain estimates of the cash inflows and outflows throughout the life of the project, as well as the timing at which each inflow or outflow will occur. The timing is necessary due to the fact that a dollar received 5 years in the future is worth less today than a dollar received 1 year in the future (referred to as the time value of money). Once the cash flows have been identified, the IRR is calculated as the discount rate for which the sum of all cash flows discounted back to the present time is zero. This process is commonly referred to as the discounted cash flow procedure. The equation for the discounted cash flow procedure is defined as follows: CF1 CF2 CFn , DCF = CF + + +$+ n 0 (1 + r )1 (1 + r )2 (1 + r )

(1) where DCF represents the discounted cash flow of the project, CF represents the cash flow expected to occur in the period defined by the subscript, and r represents the discount rate for the project. In the calculation of the IRR, the estimated cash flows are

Internal Rate of Return

plugged into the equation, and the rate is calculated such that the discounted cash flow equals zero. The rate that results in a discounted cash flow of zero for the project is therefore the IRR of the project. In other words, the IRR is the return the project will generate over the life of the project assuming the cash flows entered into the equation are correct. The actual calculation of this result is quite complex mathematically if the cash flows occur over a number of years. Therefore, financial calculators or computer spreadsheets are often utilized to calculate the value of r, which sets the discounted cash flow equal to zero.

Using the IRR to Decide on a Project The main purpose and most common use of calculating a project’s IRR is to aid in the decision-making process for accepting or rejecting projects. In general, if the IRR on the project exceeds the required rate of return, then the project should be accepted. If it does not exceed the required rate of return, then the project should be rejected. The required rate of return refers to the return that, for the entity, is the minimum that must be received for it to invest in the given project. It should take into account factors such as the riskiness of the project, the timing of cash inflows and outflows, and the returns on similar alternative investments. The intuition for this decision rule is simple: If an entity requires a minimum return of X% and the project will not provide at least this return, then it does not adequately compensate the entity for its investment and the risk that the entity is taking on. Consider a simple example. An entity is considering a project that will cost it $1,000 upfront and will return $400 each year for the next 3 years. The sum of the outflows is $1,000, and the sum of the inflows is $1,200 ($400 × 3). However, the inflows arrive at different points in time and, therefore, must be discounted to their present value. The IRR of this project is the rate at which the inflows and outflows offset each other when discounted back to the present time. In this case, the IRR is approximately 9.7%. This is demonstrated by the following: $0 = −1,000 +

400 1

(1.097 )

+

400 (1.097 )

2

+

400 (1.097 )

3

.

(2) If the entity required anything less than 9.7%, then this project should be accepted. However, if it required anything greater than 9.7%, this

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project would not meet its expectations and should be rejected.

Similarities Between IRR and NPV A similar rubric for the decision to accept or reject projects is the NPV decision rule. The calculation of NPV is also based on the principle of discounted cash flows. While IRR calculates the rate of return at which the discounted cash flow is equal to zero, NPV inputs the required rate of return into Equation 1 and calculates the discounted cash flow given that required rate of return. The value of all of the cash flows discounted back at this required rate of return is called the NPV of the project. Utilizing the NPV, there is a rule of thumb that is similar to that used in the case of the IRR. If the NPV is greater than zero, then the project should be accepted, and if not, it should be rejected. In general, the NPV rule and the IRR rule should result in the same decision. If the IRR is greater than the required rate of return, it also implies that the NPV using the same required rate of return would be greater than zero. However, as will be shown in the next section, there are caveats to this generality.

Caveats to the IRR Rule The general rule that projects with IRRs greater than the required rate of return should be accepted does have some caveats. This rule is based on the assumption that firms have a cash outflow at the beginning of the project with cash inflows in subsequent years. When this assumption does not hold true, the decision rule can be changed. There is even the possibility of multiple IRR solutions, which can result in a more difficult interpretation. Let’s first consider a situation where an entity is borrowing capital to be paid back over a number of years. In this case, the IRR actually represents the interest rate the entity is paying on the borrowed capital. Therefore, the decision rule reverses as the entity should only accept the loan if the IRR is lower than the maximum rate it is willing to pay. The decision becomes even more complex in the case where cash inflows and cash outflows are mixed throughout the life of the project. In this situation, it is possible that two distinct rates may result in the discounted cash flow being equal to zero. Consider the following example where an entity invests $1,000 (outflow of $1,000) today and will have cash flows of $1,000, $2,000, and negative $2,100 over the following 3 years. To find the IRR

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of this scenario, one must solve the r, which satisfies the following equilibrium: $0 = −1,000 +

1,000 1

(1+ r )

+

2,000 (1+ r )

2

+

−2,100 (1+ r )3

.

(3) Both 14.528% and 28.342% solve the above equation. If the entity’s required rate of return were 18%, it would appear that it should both accept (28.342% > 18%) and reject (14.528% < 18%) the project. In this case, the IRR rule is inconclusive. However, using the NPV method, it becomes evident that the project has a positive NPV utilizing the entity’s 18% required rate of return. This implies that the entity should accept the project. Scenarios like this one demonstrate why NPV should likely be the preferred methodology for project evaluation as it is more easily interpreted in some situations. However, in the majority of instances, the IRR rules and NPV rules will result in the same solution. There are few clearly illustrative examples of IRR in education finance as educational investments tend not to manifest monetarily. Consider a school constructing a new science lab that requires the purchase of computers. Though an educational investment, it is unlikely that the monetary inflows would exceed the upfront outflows. It is difficult to exactly quantify in monetary terms what are the benefits of the lab. One might consider wages, but attributing the portion of wage to the investment in the lab is nearly impossible. Without expressing the benefits of education in monetary terms, the educational investment will likely have a negative NPV and IRR, because only the upfront cost and ability to sell off the used computers are considered. Brian R. Walkup and Matthew D. Hendricks See also Discount Rate; Present Value of Earnings

Further Readings Brealey, R. A., Myers, S. C., & Marcus, A. J. (2012). Fundamentals of corporate finance (7th ed., pp. 226–251). New York, NY: McGraw-Hill/Irwin. Brigham, E. F., & Houston, J. F. (2013). Fundamentals of financial management (13th ed., pp. 367–392). Mason, OH: South-Western Cengage Learning. Ross, S. A., Westerfield, R. W., & Jordan, B. J. (2010). Essentials of corporate finance (7th ed., pp. 230–253). New York, NY: McGraw-Hill/Irwin.

INTERNATIONAL ASSESSMENTS International assessments of student performance provide student-level test score data across multiple countries, either across or within diverse world regions. Due to their significant value for informing policy and research and given the increasing ease of collecting and managing large-scale data, such assessments have grown in prominence over the past few years. Data are gathered using the same or similar questionnaires and tests to enable crossnational comparisons. They allow the identification and comparison of high- and low-performing education systems. Unlike data from national assessments, these data provide cross-national insights and the opportunity for education systems to learn from one another. International assessments are administered by several agencies and vary in terms of grades covered. Mathematics, followed by language and science, seem to be the most widely assessed subjects. This entry reviews some of the key international assessment efforts and highlights their uses and limitations. The International Association for the Evaluation of Educational Achievement administers two widely used assessments: (1) the Trends in International Mathematics and Science Study (TIMSS) and (2) the Progress in International Reading Literacy Study (PIRLS). Each has been conducted multiple times since 1995. More than 50 education systems participated in the most recent administrations of TIMSS and PIRLS. TIMSS assesses mathematics and science performance in Grades 4 and 8. PIRLS assesses reading literacy in Grade 4. The International Association for the Evaluation of Educational Achievement also administered the International Civic and Citizenship Education Study in 2009 and the Computer and Information Literacy Study in 2013. Both of these studies gathered data from eighthgrade students in multiple countries. The Programme for International Student Assessment (PISA), a widely used international assessment administered by the Organisation for Economic Co-operation and Development, assesses 15-year-old students in reading, math, and science. The most recent administration of PISA included more than 60 countries. There are also several regional data collection efforts worth noting. In Latin America and the Caribbean, the Laboratorio Latinoamericano de Evaluación de la Calidad de la Educación (Latin American Laboratory for Assessment of the Quality

International Assessments

of Education) has conducted two regional assessments. The most recent was the 2006/2007 Segundo Estudio Regional Comparativo y Explicativo (SERCE, Second Regional Comparative and Explanatory Study), which assessed reading, writing, and mathematics among third- and sixth-grade students in 16 countries, as well as sixth-grade science in 9 countries. Since 1995, the Southern African Consortium for Monitoring Educational Quality (SACMEQ) has overseen data collection efforts in sixth-grade language and mathematics performance from 15 education systems. In 1991, CONFEMEN (Conférence des ministres de l’Éducation des États et gouvernements de la Francophonie, or Conference of Education Ministers of Countries Using French in Common) initiated the Programme d’Analyse des Systémes Educatifs de la CONFEMEN (PASEC, Program for the Analysis of Education Systems of CONFEMEN). While PASEC started with 6 countries, it now includes 22, all but 4 in Africa. PASEC gathers data on French and mathematics performance from students in Grades 2 and 5. While the majority of datasets are available for download, some require permission from the data collection agency. These agencies often provide user guides and explanations about sampling processes and instrument construction. Although data from the same source are generally comparable across years, strict comparisons over time may be difficult because test administration and measurement details can vary. International assessment data have been invaluable for educational policy. They provide educational systems with in-depth assessment and greater knowledge of strengths and weaknesses relative to neighboring countries. Yet participation may be financially and politically costly. The world’s two most populous countries, China and India, do not participate in international assessment efforts on a national basis. Similarly, several large, low-income economies such as Bangladesh and Afghanistan are also absent from systematic assessment efforts. Poor test results can lead to extensive media coverage, national debates, and introspection. A few countries that repeatedly perform well have also emerged as global education leaders, attracting many imitators. In fact, the stakes of participating in international assessments are so high that the national decision to participate has emerged as an important area of research. International assessment data are invaluable for researchers because in addition to student test

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scores, they provide rich information on students’ demographics, home background, aspirations and attitudes, classrooms, and teachers. Data may also include principal-reported information on principals’ background, school infrastructure, school composition, and community participation. Several datasets provide indices of key measures such as attitudes and infrastructure. Careful data collection and documentation allow researchers to merge data from students, classrooms, teachers, and schools to create hierarchical or clustered analytical frameworks. Most international assessment data also include sample weights that allow researchers to make country-level generalizations about students, teachers, or schools at a particular grade level. Large sample sizes ensure that complex statistical operations seldom run into sample size problems. Data collection processes can also create unique opportunities for analysis. In 2011, TIMSS and PIRLS were conducted in many of the same schools, offering a comprehensive assessment of mathematics, science, and reading in the participating countries. Perhaps most important, large cross-national comparisons offer researchers variation in policy-relevant variables that is absent in single-country studies. International assessments are not without limitations. The most crucial challenge is to ensure true cross-national comparability. This is particularly challenging when measuring students’ socioeconomic status. While socioeconomic status serves as a key control variable for most policy-relevant analysis aimed at isolating the importance of teacher or school inputs, not all datasets provide satisfactory socioeconomic status variables or indices. Crossnational comparability is also challenging because students are exposed to different types and intensity of curricula in different countries. Yet several assessment efforts address this limitation with detailed planning and careful test design. Because most international assessment data do not provide preand posttest scores (PASEC is the only exception), it is difficult to make causal inferences. While a few innovative scholars have applied quasi-experimental techniques to these data, the scope for techniques such as propensity score matching or regression discontinuity is not immediately apparent. The data are also limited by the grades they cover; in most cases, researchers are limited to one or two grades. One of the most serious challenges to the generalizability of TIMSS, PIRLS, and PISA is the limited representation of low-income countries. Many countries are deterred by heavy financial and technical burdens of

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participation. Recent donor-funded efforts such as the Early Grade Reading Assessment and volunteerdriven efforts like Uwezo in East Africa may become important sources of international assessment data from developing countries, along with SERCE, SACMEQ, and PASEC. Amita Chudgar and Thomas F. Luschei See also International Datasets in Education; International Organizations; National Datasets in Education

Further Readings Kamens, D. H., & McNeely, C. (2010). Globalization and the growth of international educational testing and national assessment. Comparative Education Review, 54(1), 5–25. Postlethwaite, T. N. (2004). What do international assessments tell us about the quality of our school systems? (Background paper prepared for the Education for All Global Monitoring Report 2005: The quality imperative). Paris, France: United Nations Educational, Scientific and Cultural Organization. Retrieved from http://unesdoc.unesco.org/images/0014/001466/146692e.pdf

INTERNATIONAL DATASETS IN EDUCATION The past several decades have witnessed a growing demand for educational reform within a wide range of countries. To this end, researchers and policymakers have recognized the crucial role that comparative analyses of different educational systems can play in reforming a nation’s school system. The inability to do so helps explain the recent emergence of international datasets in education. Now, cross-national estimates of student competencies are available through a series of tests given regularly by two different international organizations. The International Association for the Evaluation of Educational Achievement (IEA) administers two of them. One is the Progress in International Reading Literacy Study (PIRLS), which is a study of the reading literacy of fourth-grade students. The other is the Trends in International Mathematics and Science Study (TIMSS), which estimates the mathematics and science achievement of participating nations’ fourthand eighth-grade students. A second organization, the Organisation for Economic Co-operation and Development (OECD), administers the Programme

for International Student Assessment (PISA), which measures 15-year-old students’ reading, mathematics, and science literacy. For more than two decades, these various international assessments have provided the public, policymakers, and researchers with comparative indicators of education outcomes across a wide range of countries, and data, to assess educational policies both within and across countries. The existence of extensive individual-level data on student performance has led to an extensive body of research seeking to explain cross-country differences in educational outcomes. Most commonly, the various datasets have been used to evaluate the impact of countries’ educational policies, the institutional features of their educational system, the educational resources available to students, and features of students’ home environment on students’ educational outcomes. More recently, the results of these assessments have also been used to examine the impact of educational outcomes in countries on cross-national differences in income, income growth, and income inequality. This entry first introduces the PIRLS and TIMSS and is followed by a brief discussion of the PISA. It then presents a comparison of the three sets of tests and discusses how the various test results have been used. A final section discusses an international dataset on adult literacy.

PIRLS and TIMSS PIRLS and TIMSS are both administered by the IEA, which is an independent association of various national and governmental research agencies. The IEA conducts cross-national comparative studies of educational achievement and education policy. The two main tests that IEA administers—PIRLS and TIMSS—differ in their frequency, subject matter, target population, and scope. PIRLS assesses the reading literacy of fourth graders and is administered every 5 years. The first round of PIRLS dates to 2001, and included 36 nations and regions. A second assessment occurred in 2006 and involved 45 countries and regions. The last PIRLS, administered in 2011, tested approximately 325,000 students in 57 countries and regions. Each complete PIRLS test entails a total of 10 passages, 5 of which are of a literary nature and 5 of which are primarily informational in content. Every student taking the PIRLS—the test is identical in each country—is given 80 minutes to complete two of the tests’ 10 passages. Each passage is accompanied by a series of questions about the passage;

International Datasets in Education

half are multiple-choice questions, and the other half requires students to construct their own responses. Based on how these two series of questions are answered, each student is then assigned a complete scale score that has been estimated for him or her as described in greater detail below. The scale score for each student is calculated on the PIRLS scale, which ranges from 0 to 1,000. PIRLS scores are normed at the center point of the scale (500) as a point of reference that remains constant from assessment to assessment. TIMSS measures mathematics and science achievement among a nation’s fourth and eighth graders. Since the first cycle of tests in 1995, TIMSS tests have been administered on a regular 4-year cycle, with the fifth and most recent cycle occurring in 2011. In the 2011 round, more than 60 countries and regions participated, and about 500,000 students took that year’s assessments. TIMSS Advanced, which measures students’ competencies in advanced mathematics and physics in their final year of secondary school, was conducted in 1995 and 2008, and will be administered for a third time in 2015 when the next regular TIMSS assessments is administered. As with PIRLS, nations participate in TIMSS by selecting random samples of their nations’ schools; classrooms within those schools are then randomly selected to take both the science and math TIMSS tests. Students taking a TIMSS test take a portion of an entire test, and answer both multiple-choice questions as well as others that require constructed responses. Each cycle of the TIMSS entails both science and math tests administered to both fourth and eighth graders. Like the PIRLS, TIMSS results are also standardized to have a mean of 500 and a standard deviation of 100. The fourth- and eighth-grade TIMSS tests are sufficiently different in content and in difficulty so that direct comparisons of scores at the two grade levels are not very meaningful. For both PIRLS and TIMSS, students answer detailed questions about their home environment, teachers and principals provide information about the school and classroom learning environment, and researchers collect information on national-level policy and curriculum.

Programme for International Student Assessment PISA is a series of tests administered by the OECD, an intergovernmental association of industrialized

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democracies. PISA was first administered in 2000 and testing cycles have continued every 3 years, most recently in 2012. Thirty-two countries participated in the first PISA tests in 2000, and 67 are scheduled to participate in the next round of tests (PISA, 2015). All OECD member countries take part in PISA, although today more non-OECD countries than members participate in PISA assessments. PISA can be distinguished from the TIMSS and PIRLS in two important ways. First, its tests are given to students based on their age rather than grade level. PISA targets students approaching the end of their compulsory education, and as such, it is administered to students between the ages of 15 years 3 months and 16 years 2 months. Second, the purpose of PISA is to assess students’ ability to apply concepts and knowledge to real-world problems. TIMSS and PIRLS, on the other hand, focus more on the skills and academic content taught in school. PISA assessments are given in mathematics, science, and reading. Each round of tests concentrates on one subject matter in depth, estimating students’ competency in a range of the subject matter’s subdomains. Less comprehensive assessments are administered to cover the other two subject matters and are designed to simply estimate overall student performance in the subject area. Every 3 years, PISA rotates the subject that its assessment investigates in depth. PISA tests are timed, with each student taking 2 hours of a 6½-hour test. Students answer both multiple-choice questions, as well as questions requiring a constructed response. As with the TIMSS and PIRLS, PISA results are normed around a score of 500 with a standard deviation of 100 for all OECD member countries. As with the TIMSS and PIRLS, each student taking the PISA also follows this up by answering questions about his or her background, with others filling in information about the classroom, the school, and national policy.

Comparing the Tests All three sets of international tests rely on a similar methodology. Participating countries must test a minimum number of students in randomly selected schools, with weights chosen so that national estimates can be generated from the sample. Because all the tests are very lengthy, participating students are given an exam that contains only a portion of the entire test. Based on the results of their test segment, five probable value test scores are randomly generated for each student based on a statistically

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generated probability distribution. Analyses based on individual-level test scores typically use the information available from all five plausible values, whereas descriptions of test scores across aggregated groups of students—for instance, average scores for a nation—are based on averaging students’ five plausible values. As mentioned, TIMSS and PIRLS are administered to students based on their grade level, whereas the PISA is based on a student’s age. For the former, this means that in schools selected for participation in either TIMSS or PIRLS, entire classrooms within the school participate. By contrast, PISA assesses the mathematics and science literacy of 15- or 16-yearold students, and as such, students across a range of grades are assessed in the selected schools. TIMSS and PIRLS assessments are based on the curriculum that students in Grades 4 and 8 are expected to have been taught. The purpose of PISA assessments, on the other hand, is to measure the extent to which students have gained the lifelong learning objectives of applying their learning and knowledge to real-world contexts, drawing not only from school curricula but also from learning that may have occurred beyond the boundaries of the classroom. While PISA does gather background information on school practices and resources, it does not examine curricular and pedagogical practices. All three assessments include participating nations from around the world, but the sets of students who participate in each are not identical. Because of its home in the OECD, PISA focuses on the 30 OECD-member countries, reporting results separately for OECD and non-OECD countries. About two thirds of those countries participating in PISA are European nations, and very few are from the Middle East. By contrast, only about one third of the countries participating in TIMSS are from Europe, whereas Middle Eastern countries are well represented. There is a large overlap between those countries participating in the TIMSS and those participating in the PIRLS; in 2011, all but 7 of the 57 countries and regions participating in the PIRLS also participated in the TIMSS. In general, all three sets of tests are well regarded by experts in educational assessment, and a strong correlation at the national level exists among them. One important point of dispute exists in the differences between PISA and the TIMSS and PIRLS, especially with regard to mathematics. The PISA math test is based on a constructivist, problem-solving

approach to math that is controversial amid mathematicians, among others. Critics of this approach to teaching math tend to favor the more traditional approach to mathematics instruction featured in the TIMSS. A few countries, such as Finland and New Zealand, that favor the constructivist approach to math instruction perform much better on the PISA than they do on the TIMSS; some claim that the PISA results overstate the level of math capability in these countries. These critics also urge caution in relying on PISA results to reach conclusions about the comparative strength of various educational systems.

Use of Results The primary goal of the various international assessments is to provide information that can help improve educational practices and policies. Typically, participating countries use test scores to examine patterns of educational outcomes and to monitor trends within their own countries. Since results are widely distributed by journalists and numerous subnational, national, and international organizations, released test results always gain significant attention among the public at large as well as among state and national politicians and policymakers. Some countries participate at the subnational as well as the national level. For instance, Florida, Connecticut, and Massachusetts participated as individual states in the 2012 PISA. Subnational participation requires that students taking the test be representative of students within that state or region. In national assessments, by contrast, participating students within a state are representative of the nation, not the state; thus, national assessments do not provide separate information on state performance. National participation in international tests can also allow subnational regions indirectly to benchmark themselves against student performance in other countries. The U.S. Department of Education, for example, has embarked on a project linking scores on TIMSS with its national counterpart called the National Assessment of Educational Progress. This has permitted states to translate their students’ performance on the mathematics National Assessment of Educational Progress to its equivalent value on the TIMSS mathematics assessments. Both individual and aggregated test scores are used extensively by researchers to assess the role of students’ socioeconomic background characteristics, school-level resources, educational policies, and

International Organizations

educational practices such as the early tracking of students or the use of exit exams on students’ educational outcomes. Particular attention has focused on distinguishing the role of home environment from school resources and on identifying the school-level and national-level factors most strongly correlated with student performance. The roles of teacher preparation, classroom size, school resources, school climate, and class content on student outcomes are among the more important features of students’ educational experience that researchers have investigated through PISA, PIRLS, and TIMSS datasets.

Adult Literacy While the TIMSS, PIRLS, and PISA now provide fairly consistent information on the academic competencies of students within and across countries, little is known about comparative literacy levels across adults in different countries. The best data come from periodic cross-national assessments of adult literacy that have been conducted since 1994. That year marked the beginning of the International Adult Literacy Survey, which was administered in 20 countries. Countries were also surveyed again in 1996 and 1998. In 2003 and 2008, the Adult Literacy and Lifeskills Survey replaced the International Adult Literacy Survey. Both tests measured the literacy and numeracy skills of a representative sample of participating countries’ 16- to 65-year-olds. However, no adult literacy test has been administered since 2008, and the number of participating countries has waned over the years. Katherine Baird See also International Assessments; International Organizations; National Datasets in Education; Organisation for Economic Co-operation and Development

Further Readings International Association for the Evaluation of Educational Achievement. (n.d.). TIMSS and PIRLS. Retrieved from http://timssandpirls.bc.edu/ Loveless, T. (2013, March). How well are American students learning? Part 1: The 2013 Brown Center report on American education. Houston, TX: Brown Center on Education Policy. Retrieved from http://www. brookings.edu/~/media/Research/Files/ Reports/2013/03/18%20brown%20center%20 loveless/2013%20brown%20center%20report%20 web.pdf

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National Center for Education Statistics. (n.d.). Trends in International Mathematics and Science Study (TIMSS). Retrieved from http://nces.ed.gov/Timss/ Organisation for Economic Co-operation and Development. (n.d.). OECD Programme for International Student Assessment (PISA). Retrieved from http://www.oecd.org/pisa/

INTERNATIONAL ORGANIZATIONS This entry addresses four international organizations: (1) the World Bank, (2) the International Monetary Fund (IMF), (3) the World Trade Organization (WTO), and (4) the Organisation for Economic Co-operation and Development (OECD). Each organization emerged after World War II with the general goal of collaborating with governments to promote economic development and growth in member countries. For scholars in education economics and finance, each of the organizations is of interest because of its complex and potentially major effects on the education sector. This entry explores each organization’s mission and effects on education.

Overview The effectiveness of international organizations is fiercely contested in the political economy research literature. Scholars within international organizations typically argue that loans, technical assistance, and aid have supported economic development and growth in client countries. In the case of the education sector, these scholars claim that the involvement of international organizations in education has improved the quality and quantity of education, citing, for instance, the rapid rise in worldwide educational enrollment and attainment. Studies and reports produced by the international organizations typically assess the impact of an educational intervention and make the case for continued and often expanded involvement; intervention shortcomings and improvements may also be discussed. At the other end of the discourse, some scholars use neoliberal theories to critique the involvement of international organizations in the education sector. Since the 1960s, the term neoliberal has been used pejoratively by these scholars to describe an economy in which market interests take precedence over societal interests, and in which the market economy causes conflict rather than harmony. Such scholars

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have argued that international organizations serve neoliberal aims by virtue of being unaccountable and serving the interest of rich countries, the wealthy, and corporations. Researchers in education economics and finance study the direct and indirect effects of international organizations on education sector participants, including students, parents, teachers, and administrators. A direct effect refers to an organization’s explicit involvement in proposing, designing, and supporting a policy in the education sector, such as the World Bank loaning money to build schools or the OECD collecting detailed educational data for policy analysis on school accountability. An indirect effect refers to educational policy as a by-product, intended or unintended, of a separate policy. In some instances, an organization may indirectly affect education through policies in other sectors such as infrastructure, agriculture, and health; for example, an IMF- or WTO-supported trade reform creates employment opportunities for adults, which improves their income and the educational outcomes of their children. For a number of reasons, it is challenging to test the direct and indirect effects of an organization as well as the claims that advocates and critics make about these effects. It is difficult to disentangle the effects of one organization from other actors. For example, the building of roads, and the subsequent effect on family incomes and improved educational outcomes, may be attributed to the World Bank, IMF, WTO, local firms, politicians, and voters. Moreover, it is not possible to credibly estimate counterfactual outcomes of no involvement by an international organization. For example, would South Korea’s economic and educational achievements have been possible without the involvement of international organizations? Alternatively, would Latin American economies have fared better without international organizations? Since scientific inquiries on such questions are difficult, if not impossible, debate continues on the effects of international organizations. The remainder of this entry presents the stated missions of international organizations and their aspirational and actual effects on education.

World Bank With a mission to alleviate global poverty, the World Bank was founded in 1944 at a conference held at Bretton Woods, New Hampshire. It is based in Washington, D.C., with other offices in low- and

middle-income countries. As of 2013, it had 184 member countries and employed around 12,000 people. The World Bank provides loans to governments at preferential rates for long-term investments in productive activities such as education, agriculture, health, environmental protection, infrastructure, and governance. It also awards grants to the poorest countries, provides technical assistance, supports data collection, and conducts relevant research. The World Bank’s direct effect on education has had three phases. In the first phase (early to mid1980s), the World Bank emphasized the expansion of public higher education; this policy was inspired by the contributions of highly educated workers to the post–World War II economic success of Germany and Japan. Eventually, the World Bank reduced its support for higher education partly because of concerns about high per-student costs and brain drain. In the second phase (late 1980s to mid-2000s), the World Bank recommended school access for all children at the primary and secondary education levels. The World Bank initially hired architects to design and build schools in client countries, but later, it began focusing on making loans to improve primary and secondary education (e.g., textbooks and teacher training). It also advocated for the expansion of private schools, citing the limitation of tax bases, increased pressure on public school systems, and cases of private school advantages. In the current phase, the World Bank acknowledges the importance of all levels of education (from early childhood to higher education) and accountability in the education sector. The World Bank’s support in noneducation sectors has affected parental income, which indirectly affects the educational outcomes of children. Agricultural development and environmental protection affect student nutrition and health, which in turn affect student learning and cognitive development. Infrastructure such as roads and public transportation reduce transportation costs and safety concerns, and electricity makes it easier to study for longer hours. The World Bank has also indirectly affected education through its data and publications. Of all international organizations, the World Bank has received the most attention from education analysts. Critics have noted that World Bank loan officers are rewarded for lending money but are not held accountable for the results. Economic studies on the ineffectiveness of foreign aid on economic growth have been detrimental to the World Bank’s

International Organizations

aid efforts. Facing such criticisms and the overall reduction in loan funds, the World Bank had plans for a major restructuring in 2014.

International Monetary Fund The IMF was the second agency founded at the Bretton Woods conference of 1944. Also based in Washington, D.C., the IMF is supported by its 188 member states and has a staff size of approximately 2,700 in 154 countries. It was designed to help countries avoid national-level economic problems by giving short-term loans called structural adjustment programs. To qualify for loans, countries must adopt austere fiscal, monetary, and trade policies. Critics have argued that the short-term nature of the loans—typically between 2.5 and 4 years—is insufficient for countries recovering from economic and political crises. Neoliberal scholars criticize the IMF, citing the deficiency of structural adjustment programs in sub-Saharan Africa (1980–1999) and the exacerbation of the financial crises in Latin America (the 1970s to the 1980s) and East Asia (1997–1999). The literature has mostly focused on the effects of certain IMF-initiated structural adjustment programs that led to cuts in government spending on education, thereby reducing public funds available for teacher salaries and maintaining school quality. Some studies have documented declining educational outcomes (e.g., lower educational attainment) in Latin America and sub-Saharan Africa coinciding with the IMF programs. From a methodological perspective, however, researchers have been unable to establish whether this relationship is causal or correlational. Moreover, it is unclear whether educational outcomes would have fared better or worse in the absence of IMF intervention.

World Trade Organization The Bretton Woods conference paved the way for the General Agreement on Tariffs and Trade or GATT (1948–2000) and later the General Agreement on Trade in Services or GATS (1995–2000). In 2001, the WTO replaced the GATT and GATS, retaining the mission of boosting economic development and growth of all participating countries by reducing trading barriers on goods and services worldwide. Based in Geneva, the WTO is a relatively small organization with only 640 staff. The pillar trade agreements of WTO set the rules on trade in goods and services and the rules for the protection of intellectual property rights that affect international trade

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in goods and services. While governments and firms are responsible for trade decisions, the WTO facilitates negotiations, conflict resolution, and the creation of formal trade agreements. Before the creation of the WTO, the GATT years were associated with rapid increases in exports in developing countries; the most successful cases of export-led economic growth were those of newly industrialized East Asian countries. However, industrialized countries experienced even larger gains from GATT. Critics asserted that the GATT typically sided with the interests of powerful nations and corporations; for example, industrialized countries were able to protect their agricultural sector from imports, while developing countries could not. The formation of the WTO is partly a gesture of increased commitment to developing countries. The GATT’s, GATS’s, and WTO’s effects on education are not well researched. Presumably, intellectual property rights directly affect the cost of textbooks and educational software. The indirect effects of this WTO regulation are potentially large. The WTO’s negotiated terms on intellectual property rights determine whether life-saving drugs, such as those for HIV/AIDS, are sold at affordable prices. Furthermore, agreements on trade rules for goods and services affect family incomes and expenditures on education.

Organisation for Economic Co-operation and Development Established in 1948, the Paris-based OECD comprises 34 democratic and developed countries. The OECD provides a forum for member governments to cooperate in addressing economic challenges. It is explicit in its focus on spurring economic growth by restoring confidence in markets, healthy public finance, democracy, and labor market skills. Unlike the World Bank and IMF, the OECD cannot enforce compliance with its decisions. The OECD’s actual role has focused on conducting and disseminating transnational research and policy ideas. Its most notable contribution in education is its coordination of the Programme for International Student Assessment (PISA), which contains detailed data on 15-year-old students and school characteristics. Since its first round in 2000, PISA has been widely used in education policy making because it contains unusually good proxies for mathematics, science, and reading skills that are valued by employers and thus provide economic

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Investing in Innovation Fund (i3)

benefits (e.g., knowledge, real-life problem solving, and lifelong learning). Critics argue that PISA neglects social issues in education such as culture, civics, and indigenous languages. Furthermore, the PISA results are criticized for causing lower scoring countries to adopt unproven educational reforms. Overall, supporters and critics agree that PISA has contributed to dialogue across member countries on improving the effectiveness of the education sector. For example, Finland’s success on PISA has spurred dialogue within and across governments.

1960–1990. International Journal of Educational Development, 23, 315–337. Mundy, K., & Ghali, M. (2009). International and transnational policy actors in education: A review of the research. In G. Sykes, B. Schneider, & D. Plank (Eds.), Handbook on education policy research (pp. 717–734). New York, NY: Routledge. Reimers, F. (1994). Education and structural adjustment in Latin America and sub-Saharan Africa. International Journal of Educational Development, 14, 119–129. Stiglitz, J. (2003). Globalization and its discontents. New York, NY: W. W. Norton.

Conclusion In summary, there are five points about the roles of World Bank, IMF, WTO, and OECD in education. First, of the four organizations discussed in this entry, only World Bank and OECD have directly engaged in the education sector. Second, through involvement in noneducation sectors, all four organizations may indirectly affect the education sector. Third, the organizations are polarizing and engender strong dichotomous responses from analysts and observers. Fourth, there is a lack of data appropriate for studying the organizations’ internal dynamics and little independent research of organizational and project effectiveness. Finally, it is difficult to disentangle the effect of one organization from others, including governments, nongovernmental organizations, and private corporations. For these reasons, disagreements about the effects of international organizations on education are likely to continue. M. Najeeb Shafiq See also Cost-Effectiveness Analysis; Economic Development and Education; Globalization; International Datasets in Education; Organisation for Economic Co-operation and Development

Further Readings Bhagwati, J. (2007). In defense of globalization (2nd ed.). New York, NY: Oxford University Press. Easterly, W. (2014). The tyranny of experts: Economists, dictators, and the forgotten rights of the poor. New York, NY: Basic Books. Ginsburg, M., Cooper, S., Raghu, R., & Zegarra, H. (1990). National and world system explanations of educational reform. Comparative Education Review, 34, 474–499. Heyneman, S. (2003). The history of problems in the making of education policy at the World Bank,

INVESTING FUND (I3)

IN INNOVATION

The Investing in Innovation Fund, also known as i3, is an education research grant program that was established through the U.S. Department of Education (ED). The program provides competitive grants to eligible partnerships between nonprofit organizations and schools or school districts. The i3 grants were established using funds from the American Recovery and Reinvestment Act of 2009 to test new ideas, validate what works, and scale up the most effective approaches in education policy and practice in the United States. The i3 program represents a significant shift in the role of the federal government in education from mandating compliance to encouraging innovation. This entry outlines the rationale, purpose, and key elements of the i3 fund.

Rationale The i3 fund is based on a combination of four primary factors: first, an increased recognition that new solutions are required for the improvement of education; second, research and development are key drivers of innovation; third, most schools and districts do not have the capacity to design, conduct, and evaluate new programs and practices; and, fourth, federal sponsorship and resources may facilitate the sharing of best practices nationwide.

Purpose The ED states that the purpose of the i3 fund is to (a) identify and validate solutions to educational challenges, (b) support the expansion of effective solutions across the United States, and (c) serve

Investing in Innovation Fund (i3)

more students in those programs. The i3 projects are designed to improve the evidence available to practitioners and policymakers about which practices work, for which types of students, and in what contexts they are effective. Applicants are required to meet high methodological standards with intensive evaluation and technical assistance with the research design that determines their inclusion in the What Works Clearinghouse.

Eligibility Requirements The purpose of i3 is to be accomplished through partnerships that include schools and school districts, and which can also include nonprofit organizations such as institutions of higher education. Applicants must be either local educational agencies or nonprofit organizations that are partnered with a local educational agency or with a consortium of schools. Applicants must have demonstrated practices that have a positive impact on improving student achievement, closing achievement gaps, decreasing dropout rates, increasing high school graduation rates, or increasing college enrollment and completion rates. Grantees must also conduct an independent evaluation of their project to estimate the impact of their proposed practice.

Priorities In 2013, grantees were required to address one of eight priorities identified by the ED. These priorities were as follows: (1) improving the effectiveness of teachers or principals; (2) improving low-performing schools; (3) improving science, technology, engineering, and mathematics education; (4) improving academic outcomes for students with disabilities; (5) improving academic outcomes for English learners; (6) improving parent and family engagement; (7) using technology effectively; and (8) serving rural communities.

Tiers of Funding i3 grants are made at three levels depending on the evidence base to support the research. The lowevidence grants are called development projects and must show evidence of promise or strong theory. A second type, validation grants, must show moderate evidence and be able to scale to the regional level. The third level, scale-up projects, must show strong evidence of effectiveness and be able to scale to the

425

national level. The requests for proposals, funding amounts, and level of evidence required vary for each of these three award types. In fiscal year 2013, the maximum amount for development grants was $3 million, validation grants could be no more than $15 million, and scale-up grants were capped at $25 million. A percentage of the funds for each project must come from nonprofit organizations such as foundations that support educational innovations. In the first year, this percentage was set at 20% for each of the three grant types, although this has been changed so that the percentage of required match varies across the three award categories; the new percentages are lower for scale-up (5%) than for validation (10%) or for development (20%).

Costs The i3 requests for proposal request applicants to include estimates of cost of interventions that are the focus of the research. The cost requirement in the requests for proposal is to identify overall costs associated with the scale-up of the intervention to various levels. The cost requested of applicants is the accounting—not economic—cost of the educational intervention. Grantees may include additional more rigorous economic analysis such as cost-effectiveness analysis, cost-benefit analysis, or econometric methods in their applications, and some grants that include such analysis have been funded. Many of the projects, especially validation and scale-up projects, are randomized control trials that have the potential to provide rigorous information about both the educational outcomes associated with different education policies and also the economic efficiency of different education policy options.

Awards In the first 4 years of the i3 fund, the ED has awarded 117 grants—77 development grants, 35 validation grants, and 5 scale-up grants. In fiscal year 2010, the first year of the competition, the ED awarded 49 grants totaling $646 million, but after that, the department lowered the amount of grant funding to between $135 million and $148 million for 20 to 25 grants in each of the next 3 years. The ED reports that all of the scale-up and validation grants funded in 2010 and 2011 meet What Works Clearinghouse standards either with or

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without reservations and that 35 of the 47 development grants meet those standards. There is not yet any evidence of the impact of the i3 program since the first cohort of projects from the 2010 grants are scheduled to be completed in the fall and winter of 2015, and the evaluation results are to be completed during the winter and spring of 2015–2016. Linda Goetze See also Educational Innovation; Evolution in Authority Over U.S. Schools

Further Readings Smith, K., & Petersen, J. (2011). Supporting and scaling change: Lessons from the first round of the Investing in Innovation program. Wellesley, MA: Bellwether Education Partners. Strange, M. (2011). Taking advantage: The rural competitive preference in the Investing in Innovation program. Washington, DC: Rural School and Community Trust. U.S. Department of Education. (n.d.). Investing in Innovation Fund (i3). Retrieved from http://www 2.ed.gov/programs/innovation/index.html

J concluding with an overview of the influence of labor markets on the demand and supply of job training programs.

JOB TRAINING Job training and vocational education provide occupation-specific skills that can overlap, but are also distinct from, formal systems of compulsory and tertiary education. However, while both types of education are similar, job training differs from vocational education in that the skills students learn in job training programs lead to a specific job, while the skills associated with vocational education can prepare students for a range of jobs within a given industry. Job training systems, both in the United States and globally, exist to contribute to the overall efficiency of labor markets with the end result of encouraging economic growth and development. In the United States, these systems have historically focused on manufacturing and trade skills, but as jobs in labor-intensive heavy industries such as manufacturing and chemical refinement have decreased by more than 33% in the past 15 years, much of the current focus in job training programs is on skills related to labor market segments that have proven to be more robust, as discussed later in this entry. This entry includes a brief synopsis of the history of job training programs in the United States, followed by a discussion of current funding and approaches to program governance. The next section describes the characteristics of students being served by job training programs,

Historical Overview Job training and vocational education have existed in the United States since the early 19th century, but the current model of vocational training programs traces its genesis to the Smith-Hughes Act of 1917. This act of Congress was the first to set aside federal funding specifically to provide job training to students, albeit primarily to those interested in gaining agricultural skills. Under the funding formula provided by SmithHughes, students who took vocational classes were discouraged from spending more than half of their school day studying academics, laying the foundation for a formal separation between students on the vocational and academic tracks. The model laid out in the original Smith-Hughes Act continued to be the basis for job training in America’s secondary education system well into the 1950s, providing a flexible framework that allowed the federal government to adapt the vocational system to the various employment challenges that arose over the years. During the Great Depression, the emphasis was on a wide variety of employable skills, shifting to war-related production training during World War II, and then moving to training aimed at the light industry and technological innovations that characterized the 1950s.

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Job Training

Starting in the early 1960s with the Manpower Development and Training Act and the Vocational Education Act, however, the federal government shifted control and funding authority to the states, a change that greatly diversified vocational offerings and provided an even more flexible approach to meeting the needs of local labor markets. This period saw an expansion of work-based training programs that allowed students to receive course credit while simultaneously being employed and the beginnings of job training related to the more technologically complex jobs that began to characterize the labor market in the second half of the 20th century. The demise of Smith-Hughes also meant that vocational and academic education could be reintegrated, a shift that contributed to the precipitous drop in vocational enrollments at the high school level, as discussed below. In recent years, the federal government has taken a renewed interest in overseeing vocational education and job training. The current version of the Vocational Education Act of 1963, renamed the Carl D. Perkins Career and Technical Education Act in its most recent reauthorization in 2006, provides funding for programs that lead to postsecondary certifications and ultimately to employment in high-needs fields such as nursing, computer science, and culinary arts.

Funding and Program Governance Because of vocational education’s close relationship to employment and the labor force, job training programs are often supported by government funding and administered through public organizations. In the United States, agencies such as the Office of Workforce Investment and the Employment and Training Administration fund job training programs through grants to secondary education providers and to community colleges, which have historically played a central role in this market segment. As with other segments of the postsecondary education market, private, for-profit schools have increased their presence in vocational education and job training in recent years. In the areas where community college programs and programs at the forprofit schools overlap, competition between the two types of schools is fierce, with the for-profit schools making significant inroads into the market share of enrollments. At the same time, for-profit schools that offer 4-year degrees often recruit public community college graduates; in this way, the public not-forprofit and the private for-profit models for vocational education governance can complement each other.

Students in the Job Training System Job training programs tend to serve students from lower income backgrounds and those with parents who do not have a college education. At the secondary school level, this has fostered a divide along socioeconomic and racial and ethnic lines, with lowincome and minority students being overrepresented nationally in vocational education. This trend is reflected in the overall decline in vocational education at the secondary level over the past 30 years, as schools have shifted away from vocational course offerings, electing instead to place greater relative emphasis on nonvocational college attendance after high school. Between 1982 and 1998, participation in vocational education at the secondary level declined by 8.7%, while participation in college preparatory coursework increased by 30.2%, according to the National Center for Education Statistics. More recently, that trend has continued, with the average number of high school credits that students earn in vocational education declining from 18.0 credits in 1990 to 13.1 credits in 2009. This trend mirrors a widely held presumption that increasing returns, both individual and social, result from increased years of education. Analyses of future job growth suggest that students will need some type of training beyond high school, although this increased training need not be limited to academic skills. At the postsecondary level, however, enrollment in vocational education and job training programs has been steadily increasing, suggesting perhaps that many students interested in job-related skills have chosen to delay their career preparation until after high school. The number of certificates awarded in career-related fields by vocational education programs in the United States increased by approximately 67% between 2000 and 2010 (the most recent year for which statistics are available). Within that 10-year time span, the largest year-over-year increases were between 2006 and 2010, a trend that could suggest a correlation between participation in vocational education programs and the condition of the national economy. Involvement in job training programs represents a significant investment for students. Because most students enrolled in these programs are learning in settings outside the compulsory school system, most of them pay some amount of tuition to attend. For students taking courses in publicly funded community colleges or government-sponsored job training programs, the cost is relatively low. For students enrolled in private trade schools or for-profit credentialing programs, the cost is typically higher.

Job Training

During the 2011–2012 school year, community college students paid, on average, $2,918 for a year of study, while students pursuing similar courses of study at private schools paid $14,125. These costs are offset by the availability of federal student financial aid and private loans, although the default rates for borrowers who attend community colleges and trade schools is well above the average student loan default rate. In 2010, student-borrowers who attended programs that could be completed in 2 years or less experienced a 12.5% rate of default, while the rate of default across the general population of student-borrowers was 9.2%.

Job Training in the Marketplace The primary function of job training programs is to provide workers with skills that will be useful in the labor market. As such, these programs have historically been responsive to the needs of employers. For example, the need for more nurses, medical lab technicians, and home health aides created by growing demand from aging baby boomers has been met by vocational programs, which more than doubled the number of graduates in the health sciences between 2000 and 2010. Students in this area now make up 42.2% of vocational education’s annual graduating class, the largest segment of the market. In second and third place in terms of graduates are consumer services and manufacturing; taken together, these three employment fields made up nearly 80% of the students enrolled in vocational programs between 2000 and 2010. It is no coincidence that health sciences, consumer services, and manufacturing (which includes construction trades) represented the bulk of high-demand labor market segments during that same period.

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In sum, job training programs serve to educate consumers in search of practical, employable job skills and to connect students with jobs on graduation. Although the employment rate for vocational graduates has fallen by almost 10% in recent years, people with credentials from vocational programs are still employed at a rate of 79.7%. As employment in the 21st century becomes more and more dependent on having concrete sets of specialized skills, job training programs will continue to be an integral part of the education system. Guilbert C. Hentschke and Andrew L. LaFave See also Continuing Education; Licensure and Certification; Vocational Education

Further Readings Gordon, H. (1999). The history and growth of vocational education in America. Needham Heights, MA: Allyn & Bacon. Hayward, G., & Benson, C. (1993). Vocational-technical education: Major reforms and debates, 1917-present. Washington, DC: Office of Vocational & Adult Education. Lazerson, M., & Grubb, W. (1974). American education and vocationalism. New York, NY: Teachers College Press. Levesque, K., Laird, J., Hensley, E., Choy, S. P., Cataldi, E. F., & Hudson, L. (2008). Career and technical education in the United States: 1990–2005. Washington, DC: National Center for Education Statistics. National Center for Education Statistics. (2011). Postsecondary and labor force transitions among public high school career and technical education participants. Washington, DC: Government Printing Office.

E N C Y C L O P E DI A O F

Education Economics & Finance

Editorial Board Editors Dominic J. Brewer New York University Lawrence O. Picus University of Southern California

Managing Editor Rochelle Hardison University of Southern California

Editorial Board Bruce D. Baker Rutgers University Eric Bettinger Stanford University Eric R. Eide Brigham Young University Margaret E. Goertz University of Pennsylvania Douglas N. Harris Tulane University Guilbert C. Hentshcke University of Southern California Jennifer Imazeki San Diego State University Kieran M. Killeen University of Vermont Tatiana Melguizo University of Southern California Anthony Rolle University of South Florida

E N C Y C L O P E DI A O F

Education Economics & Finance Editors

Dominic J. Brewer New York University

Lawrence O. Picus

VOLUME

University of Southern California

2

Copyright © 2014 by SAGE Publications, Inc.

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14 15 16 17 18 10 9 8 7 6 5 4 3 2 1

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Contents Volume 2 List of Entries vii Reader’s Guide xi L M N O P Q

431 451 463 495 511 591

R S T U V W

601 629 723 817 825 835

Appendix A: Resource Guide 839 Appendix B: Chronology 845 Appendix C: Glossary 849 Index 855

List of Entries Ability-to-Pay and Benefit Principles Access to Education Accountability, Standards-Based Accountability, Types of Accreditation Achievement Gap Adequacy Adequacy: Cost Function Approach Adequacy: Evidence-Based Approach Adequacy: Professional Judgment Approach Adequacy: Successful School District Approach Adequate Yearly Progress Administrative Spending Adult Education Age-Earnings Profile Agency Theory Allocative Efficiency American Association of School Administrators Association for Education Finance and Policy Auxiliary Services

Charter Schools College Choice College Completion College Costs. See Tuition and Fees, Higher Education College Dropout College Enrollment College Rankings College Savings Plan Mechanisms College Selectivity Common Core State Standards Community Colleges Finance Comparative Wage Index Compensating Differentials. See Hedonic Wage Models Compound Annual Growth Rate Comprehensive School Reform Compulsory Schooling Laws Continuing Education Contracting for Services Cost Accounting Cost of Education Cost-Benefit Analysis Cost-Effectiveness Analysis Credential Effect Cultural Capital Cumulative Annual Growth Rate. See Compound Annual Growth Rate

Baumol’s Cost Disease Behavioral Economics Benefits of Higher Education Benefits of Primary and Secondary Education Bilingual Education Block Grants Bonds in School Financing Brown v. Board of Education Budgeting Approaches

Data Envelopment Analysis Demand for Education Department of Defense Schools Deregulation Desegregation Difference-in-Differences Digital Divide Discount Rate Distance Learning District Power Equalizing District Size

Capacity Building of Organizations Capital Budget Capital Financing for Education Capitalist Economy Categorical Grants Central Office, Role and Costs of Centralization Versus Decentralization Charter Management Organizations vii

viii

List of Entries

Dropout Rates Dual Enrollment Dual Labor Markets Due Process Early Childhood Education Econometric Methods for Research in Education Economic Cost Economic Development and Education Economic Efficiency Economics of Education Economies of Scale Economies of Scope. See Economies of Scale Education and Civic Engagement Education and Crime Education Finance Education Management Organizations Education Production Functions and Productivity Education Spending Education Technology Educational Equity Educational Innovation Educational Vouchers Effect Size Elasticity Elementary and Secondary Education Act Enrollment Counts Enrollment Management in Higher Education Equalization Models Evolution in Authority Over U.S. Schools Expenditures and Revenues, Current Trends of Extended Day External Social Benefits and Costs Factor Prices Faculty Contracts. See Faculty in American Higher Education Faculty in American Higher Education Faculty Tenure. See Faculty in American Higher Education Family Educational Rights and Privacy Act Federal Perkins Loan Program Federal Work-Study Program Financial Literacy and Cognitive Skills Fiscal Disparity Fiscal Environment Fiscal Neutrality Fixed-Effects Models Foregone Earnings For-Profit Higher Education Fund Accounting

Gainful Employment General Educational Development (GED®) General Obligation Bonds GI Bill Globalization Governmental Accounting Standards Board Guaranteed Tax Base Hedonic Wage Models Higher Education Finance Homeschooling Horizontal Equity Human Capital Income Inequality and Educational Inequality Individuals with Disabilities Education Act Infrastructure Financing and Student Achievement Instrumental Variables Intergovernmental Fiscal Relationships Internal Rate of Return International Assessments International Datasets in Education International Organizations Investing in Innovation Fund (i3) Job Training Labor Market Rate of Return to Education in Developing Countries Licensure and Certification Local Control Lotteries for School Funding Lotteries in School Admissions Market Signaling Markets, Theory of Measurement Error Median Voter Model Merit Pay. See Pay for Performance Moral Hazard Nation at Risk, A National Assessment of Educational Progress National Board Certification for Teachers National Center for Education Statistics National Datasets in Education National Science Foundation Neighborhood Effects: Values of Housing and Schools New Institutional Economics

List of Entries

No Child Left Behind Act Nonwage Benefits

Reliability Risk Factors, Students

Omitted Variable Bias Online Learning Opportunity Costs Opportunity to Learn Ordinary Least Squares Organisation for Economic Co-operation and Development Outsourcing. See Contracting for Services

Salary Schedule San Antonio Independent School District v. Rodriguez SAT School Boards School Boards, School Districts, and Collective Bargaining School District Budgets School District Cash Flow School District Wealth School Finance Equity Statistics School Finance Litigation School Quality and Earnings School Report Cards School Size School-Based Management Schools, Private Schools, Religious Segmented Labor Market. See Dual Labor Markets Selection Bias Serrano v. Priest Service Consolidation Sheepskin Effect. See Credential Effect Social Capital Socioeconomic Status and Education Special Education Finance Spillover Effects Stafford Loans State Education Agencies State Education Codes Student Financial Aid Student Incentives Student Loans Student Mobility Supplemental Educational Services

Parcel Tax Parental Involvement Partial and General Equilibrium Pay for Performance Peer Effects Pell Grants Percentage Power Equalizing. See Guaranteed Tax Base Performance Evaluation Systems Permanent Income Philanthropic Foundations in Education Policy Analysis in Education Portfolio Districts Preschool. See Early Childhood Education Present Value of Earnings Price Discrimination Principal-Agent Problem Private Contributions to Schools Private Fundraising in Postsecondary Education Private School Associations Privatization and Marketization Professional Development Program Budgeting Progressive Tax and Regressive Tax Propensity Score Matching Property Taxes Public Choice Economics Public Good Public-Private Partnerships in Education Pupil Weights Quantile Regression Quasi-Experimental Methods Race Earnings Differentials Race to the Top Randomized Control Trials Reduction in Force Regression-Discontinuity Design

Tax Burden Tax Elasticity Tax Incidence Tax Limits Tax Yield Teacher Autonomy Teacher Certification. See Licensure and Certification Teacher Compensation Teacher Effectiveness Teacher Evaluation

ix

x

List of Entries

Teacher Experience Teacher Intelligence Teacher Pensions Teacher Performance Assessment Teacher Supply Teacher Training and Preparation Teacher Value-Added Measures Teachers’ Unions and Collective Bargaining Technical Efficiency Theory of the Firm Tiebout Sorting Title I Tracking in Education Tragedy of the Commons Transaction Cost Economics Tuition and Fees, Higher Education

Tuition and Fees, K-12 Private Schools Tuition Tax Credits Two or Three Tier Funding Programs. See Equalization Models Unfunded Mandates University Endowments U.S. Department of Education Validity Value-Added Model. See Teacher Value-Added Measures Vertical Equity Vocational Education Weighted Student Funding

Reader’s Guide Accountability and Education Policy

Special Education Finance State Education Agencies State Education Codes Teacher Autonomy Teacher Effectiveness Teacher Evaluation Teacher Performance Assessment Teachers’ Unions and Collective Bargaining Tracking in Education U.S. Department of Education Weighted Student Funding

Access to Education Accountability, Standards-Based Accountability, Types of Accreditation Achievement Gap Adequate Yearly Progress American Association of School Administrators Association for Education Finance and Policy Brown v. Board of Education Capacity Building of Organizations Common Core State Standards Comprehensive School Reform Compulsory Schooling Laws Desegregation Educational Equity Elementary and Secondary Education Act Gainful Employment Individuals with Disabilities Education Act International Assessments International Organizations Investing in Innovation Fund (i3) Local Control Median Voter Model Nation at Risk, A National Assessment of Educational Progress National Datasets in Education National Science Foundation No Child Left Behind Act Organisation for Economic Co-operation and Development Performance Evaluation Systems Philanthropic Foundations in Education Policy Analysis in Education Portfolio Districts Race to the Top SAT School Boards School Report Cards

Budgeting and Accounting in Education Finance Adequacy: Successful School District Approach Administrative Spending American Association of School Administrators Auxiliary Services Block Grants Bonds in School Financing Budgeting Approaches Capital Budget Capital Financing for Education Categorical Grants Central Office, Role and Costs of Cost Accounting Cost of Education Cost-Benefit Analysis Cost-Effectiveness Analysis Enrollment Counts Fund Accounting Governmental Accounting Standards Board Higher Education Finance Intergovernmental Fiscal Relationships Philanthropic Foundations in Education Program Budgeting School District Budgets xi

xii

Reader’s Guide

School District Cash Flow Service Consolidation Weighted Student Funding

Education Markets, Choice, and Incentives Agency Theory Capitalist Economy Centralization Versus Decentralization Charter Management Organizations Charter Schools Compound Annual Growth Rate Comprehensive School Reform Deregulation Dual Labor Markets Economic Efficiency Education Management Organizations Education Production Functions and Productivity Educational Equity Educational Innovation Educational Vouchers Evolution in Authority Over U.S. Schools Factor Prices Globalization Homeschooling Local Control Lotteries in School Admissions Market Signaling Markets, Theory of Median Voter Model Moral Hazard Neighborhood Effects: Values of Housing and Schools New Institutional Economics Opportunity to Learn Parental Involvement Partial and General Equilibrium Pay for Performance Philanthropic Foundations in Education Portfolio Districts Principal-Agent Problem Private Contributions to Schools Private Fundraising in Postsecondary Education Private School Associations Privatization and Marketization Public Choice Economics Public Good Public-Private Partnerships in Education Risk Factors, Students Salary Schedule School-Based Management Schools, Private

Schools, Religious Spillover Effects State Education Codes Student Incentives Student Mobility Theory of the Firm

Equity and Adequacy in School Finance Ability-to-Pay and Benefit Principles Access to Education Achievement Gap Adequacy Adequacy: Cost Function Approach Adequacy: Evidence-Based Approach Adequacy: Professional Judgment Approach Adequacy: Successful School District Approach Allocative Efficiency Association for Education Finance and Policy Bilingual Education Brown v. Board of Education Comparative Wage Index Desegregation District Power Equalizing District Size Due Process Education Finance Educational Equity Equalization Models Expenditures and Revenues, Current Trends of Guaranteed Tax Base Horizontal Equity Infrastructure Financing and Student Achievement Lotteries for School Funding Progressive Tax and Regressive Tax Property Taxes San Antonio Independent School District v. Rodriguez School District Wealth School Finance Equity Statistics School Finance Litigation Serrano v. Priest Special Education Finance Title I Unfunded Mandates Vertical Equity Weighted Student Funding

Financing of Higher Education Baumol’s Cost Disease Benefits of Higher Education

Reader’s Guide

College Choice College Completion College Dropout College Enrollment College Rankings College Savings Plan Mechanisms College Selectivity Community Colleges Finance Dual Enrollment Enrollment Management in Higher Education Faculty in American Higher Education Federal Perkins Loan Program Federal Work-Study Program For-Profit Higher Education Gainful Employment GI Bill Higher Education Finance Pell Grants Private Fundraising in Postsecondary Education Stafford Loans Student Financial Aid Student Loans Tuition and Fees, Higher Education Tuition Tax Credits University Endowments

Key Concepts in the Economics of Education Age-Earnings Profile Agency Theory Baumol’s Cost Disease Behavioral Economics Capitalist Economy Centralization Versus Decentralization Cultural Capital Demand for Education Deregulation Discount Rate Economic Development and Education Economic Efficiency Economics of Education Economies of Scale Education Production Functions and Productivity Education Spending Educational Equity Educational Vouchers Elasticity External Social Benefits and Costs Factor Prices Foregone Earnings Human Capital Internal Rate of Return

Market Signaling Markets, Theory of Moral Hazard New Institutional Economics Opportunity Costs Partial and General Equilibrium Permanent Income Policy Analysis in Education Price Discrimination Principal-Agent Problem Progressive Tax and Regressive Tax Public Choice Economics Public Good Public-Private Partnerships in Education Social Capital Socioeconomic Status and Education Spillover Effects Tax Burden Tax Elasticity Tax Incidence Tax Limits Tax Yield Technical Efficiency Theory of the Firm Tiebout Sorting Tracking in Education Tragedy of the Commons Transaction Cost Economics Vertical Equity

Private and Social Returns to Human Capital Investments Access to Education Achievement Gap Adult Education Age-Earnings Profile Benefits of Higher Education Benefits of Primary and Secondary Education College Completion College Dropout College Enrollment College Rankings College Savings Plan Mechanisms College Selectivity Comparative Wage Index Continuing Education Credential Effect Demand for Education Discount Rate Dropout Rates Early Childhood Education

xiii

xiv

Reader’s Guide

Economic Development and Education Education and Civic Engagement Education and Crime Education Spending Federal Work-Study Program Financial Literacy and Cognitive Skills Foregone Earnings Gainful Employment General Educational Development (GED®) GI Bill Human Capital Income Inequality and Educational Inequality Internal Rate of Return Job Training Labor Market Rate of Return to Education in Developing Countries Market Signaling Nonwage Benefits Present Value of Earnings Race Earnings Differentials Risk Factors, Students School Quality and Earnings Service Consolidation Spillover Effects Student Mobility Vocational Education

Production and Costs of Schooling Ability-to-Pay and Benefit Principles Adequacy Adequacy: Cost Function Approach Adequacy: Evidence-Based Approach Adequacy: Professional Judgment Approach Adequacy: Successful School District Approach Administrative Spending Adult Education Allocative Efficiency Baumol’s Cost Disease Capacity Building of Organizations Capitalist Economy Central Office, Role and Costs of Compound Annual Growth Rate Contracting for Services Cost Accounting Cost of Education Cost-Benefit Analysis Cost-Effectiveness Analysis Data Envelopment Analysis Department of Defense Schools Digital Divide Distance Learning

District Size Dual Enrollment Economic Cost Economies of Scale Education Production Functions and Productivity Education Technology Educational Innovation Elasticity Enrollment Counts Evolution in Authority Over U.S. Schools Extended Day External Social Benefits and Costs Hedonic Wage Models Homeschooling Infrastructure Financing and Student Achievement Intergovernmental Fiscal Relationships Online Learning Peer Effects Price Discrimination Professional Development School Boards School District Budgets School Size Social Capital Socioeconomic Status and Education Supplemental Educational Services Teacher Compensation Teacher Experience Technical Efficiency

Revenue and Aid for Schools Bilingual Education Block Grants Bonds in School Financing Capital Financing for Education Categorical Grants Early Childhood Education Education Finance Enrollment Counts Equalization Models Fiscal Environment Fiscal Neutrality General Obligation Bonds Guaranteed Tax Base Individuals with Disabilities Education Act Infrastructure Financing and Student Achievement Lotteries for School Funding Parcel Tax Private Contributions to Schools

Reader’s Guide

Progressive Tax and Regressive Tax Property Taxes Pupil Weights School District Cash Flow School District Wealth Special Education Finance State Education Agencies Tax Burden Tax Elasticity Tax Incidence Tax Limits Tax Yield Title I Tuition and Fees, K-12 Private Schools Tuition Tax Credits Unfunded Mandates

Statistical Methods in the Economics of Education Data Envelopment Analysis Difference-in-Differences Econometric Methods for Research in Education Economic Cost Effect Size Family Educational Rights and Privacy Act Fiscal Disparity Fixed-Effects Models Instrumental Variables International Datasets in Education Measurement Error Median Voter Model National Center for Education Statistics National Datasets in Education Omitted Variable Bias Ordinary Least Squares Organisation for Economic Co-operation and Development Peer Effects Present Value of Earnings

xv

Propensity Score Matching Pupil Weights Quantile Regression Quasi-Experimental Methods Randomized Control Trials Regression-Discontinuity Design Reliability Selection Bias Tiebout Sorting Validity

Teachers and Teacher Labor Markets Comparative Wage Index Dual Labor Markets Faculty in American Higher Education Hedonic Wage Models Licensure and Certification National Board Certification for Teachers Nonwage Benefits Pay for Performance Performance Evaluation Systems Private Fundraising in Postsecondary Education Professional Development Reduction in Force Salary Schedule School Boards, School Districts, and Collective Bargaining Teacher Autonomy Teacher Compensation Teacher Effectiveness Teacher Evaluation Teacher Experience Teacher Intelligence Teacher Pensions Teacher Performance Assessment Teacher Supply Teacher Training and Preparation Teacher Value-Added Measures Teachers’ Unions and Collective Bargaining

L associated with a certain level of education. The costs of education include direct costs, such as tuition, fees, uniforms, school transportation, and private tutoring; the indirect costs include the earnings foregone while studying and not working. Data on benefits and costs are obtained from nationally representative household surveys, labor force surveys, or both. Household surveys are multipurpose surveys that include data on income, expenditure, and other items (e.g., gender, ethnicity, educational attainment, and/or time use); direct costs can be inferred from educational expenditure, and indirect costs can be inferred from earnings of child and adult workers. Usually, labor force surveys offer better data on earnings and foregone adult labor earnings but do not include data on educational expenditure or child labor earnings. There are two methodological options for estimating or computing the LMRRE with these data: (1) the full method and (2) the Mincerian method. The first method, referred to as the full method, is identical to formulas used for computing the rate of return to physical capital, such as a bridge, farm, or house. The full method formula is

LABOR MARKET RATE OF RETURN TO EDUCATION IN DEVELOPING COUNTRIES The pioneering education economist Theodore Schultz began his 1979 Nobel lecture with “Most of the people in the world are poor, so if we knew the economics of being poor we would know much of the economics that really matters.” Schultz inspired a large body of research that considers the educational decisions (i.e., enrollment and attainment) of individuals and their families. Education economists assess these decisions using the private labor market rate of return to education (henceforth, LMRRE). Essentially, the LMRRE is a measure (expressed as a percentage) of whether monetary educational costs are worth incurring for future monetary labor market benefits. LMRRE estimates help education economists and policymakers understand patterns and determine interventions; for example, low LMRRE estimates for secondary education may explain low enrollment in secondary schools and lead to the abolishment of tuition and fees in secondary schools. This entry examines the estimation methods, evidence, limitations, and possibilities of LMRRE studies of developing countries. In general, LMRRE estimates from developing countries are larger but less accurate than estimates from industrialized countries.

n

B −C

t t = 0, ∑ t −1 t =1 (1 + r)

where B is the benefit, C is the cost, t is the year in a series ranging from 1 to n (where n is the last year of employment), and r is the internal rate of return or LMRRE. The computations are then completed using a spreadsheet such as Microsoft Excel. For each time period, five columns may be used: (1) age,

Methods For individuals and their families, the benefits of education include after-tax labor market earnings 431

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Labor Market Rate of Return to Education in Developing Countries

(2) direct costs, (3) indirect costs or foregone earnings, (4) benefits or earnings, and (5) net benefits, which is equal to benefits minus direct costs and indirect costs. The mean benefits and indirect costs are computed using samples of wage-earning workers for a particular age, and the mean direct costs are computed using samples of students. Net benefits are initially negative and eventually become positive. The spreadsheet solves for r by using the net benefits values from 1 to t. Typically, separate analyses are conducted for males and females. The majority of LMRRE estimates from developing countries have been produced not by the full method but by an econometric technique, the Mincerian method, developed by the late labor economist Jacob Mincer. Economists have used this method extensively to estimate LMRRE in industrialized countries. Part of the Mincerian method’s appeal is its modest data requirements: Direct costs are not included, and smaller sample sizes can suffice. An LMRRE estimation using the Mincerian method involves the following five columns of data: (1) after-tax earnings, (2) the natural log of earnings (because actual earnings have large ranges), (3) educational attainment (either years of schooling or dummy variables for levels of education), (4) years of work experience, and (5) years of work experience squared (to reflect the curvilinear nature between earnings and experience). The unit of observation is a full-time wageworker, and the data are fitted using ordinary least squares regression and statistical packages such as Stata and SPSS. Separate analyses are typically conducted for males and females.

Interpretation and Patterns There are at two least scenarios in which an individual or family will not invest in a particular level of education. The first is when poverty and high costs make schooling infeasible and therefore not an option. But even if a family is able to afford education, they may choose to not invest if the LMRRE is small or negative. Indeed, a positive LMRRE is a necessary but insufficient condition for investment in education because the family may also hope for a LMRRE to exceed the returns from noneducation investments (e.g., land or business), bank interest rates on educational loans (in case the family needs to borrow for financing education), and family discount rates (indicating the preference for current consumption over future consumption).

From a policy perspective, a higher LMRRE should encourage educational attainment. Low LMRRE is a policy cue to improve educational quality and create jobs for skilled workers. Harry Patrinos and George Psacharopoulos have created a database of worldwide LMRRE estimates from 1960 to 2005. They conclude that, on average, the LMRRE are positive for all levels of education. This satisfies the minimum condition for investment in education. However, families may choose not to invest in education if feasible noneducation investments offer higher returns. The Patrinos and Psacharapoulos database reveals several LMRRE patterns. First, the LMRREs are higher for primary and tertiary education and lower for secondary education. According to studies from 52 developing countries, the LMRRE is 23.0% for primary education (vs. below primary education), 17.9% for secondary education (vs. primary education), and 21.1% for tertiary education (vs. secondary education) in developing countries. Second, across developing regions, LMRREs are highest in sub-Saharan Africa (ranges between 24.6% and 37.6%), followed by the Latin America and the Caribbean region (17%–26.6%), and the returns in Asia and the Middle East and North Africa are comparable (13.6%–20%). Third, the returns by level have been changing over time. Patrinos and Psacharapoulos illustrate in Figure 1 that the LMRRE for primary education has drastically fallen over time, from nearly 30% in

30

25

20

Tertiary

15 Secondary 10 Primary 5 0 1950

Figure 1

1960

1970

1980

1990

2000

2010

LMRRE Patterns in Developing Countries, 1960–2005

Source: Colclough, Kingdon, and Patrinos (2010, p. 739).

Labor Market Rate of Return to Education in Developing Countries

1960 to 8% in 2007; the returns to secondary and tertiary education have only slightly declined. The reasons for this shift are unclear; some hypotheses blame the declining quality of primary education, while others attribute the shift to decreasing wages because of an increased supply in workers with primary education.

Limitations and Possibilities The available LMRRE estimates provide a general picture of the labor market benefits of education in developing countries. Nevertheless, such estimates suffer from the same methodological issues as studies from industrialized countries, such as a lack of data on student ability and school quality. In addition to these issues, LMRRE estimates from developing countries suffer from further methodological issues. Direct Cost Neglect

In industrialized countries, direct costs for public education are small because of free tuition, fees, and transportation; parents make modest contributions toward clothing, supplies, and food, while private tutoring is relatively uncommon. In developing countries, direct costs are common and large relative to household incomes, especially for the poorest households. Since studies using the Mincerian method cannot incorporate direct costs, the resulting LMRRE estimates are exaggerated. For example, in a study of LMRRE in rural Bangladesh, the return to secondary education fell from 24.8% (Mincerian method) to 9.8% (full method) after the inclusion of direct costs. (Childhood) Indirect Cost Neglect

Conventional LMRRE analyses consider the indirect cost of education in the form of foregone adult earnings that are incurred at postsecondary levels of education. In studies of industrialized countries, the assumption of zero indirect cost during childhood is sensible because child labor bans are enforced. In developing countries, however, child labor persists; according to the International Labor Organization, there are 200 million child laborers. Thus, foregone child labor earnings are indirect costs that arise because of the time spent at school and studying rather than working. One reason for the neglect of this indirect cost is that child labor data have only recently become available. Again, in the study of rural Bangladesh, the LMRRE estimate for primary

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education fell from 31.0% (Mincerian method) to 14.3% (full method) after the inclusion of indirect costs incurred during childhood. Measurement of Informal Employment Earnings

Developing countries are characterized by dual economies: the formal sector and the informal sector. Between 20% and 80% of workers are employed in the informal sector, which is beyond government regulation, taxation, and observation. There are many obstacles to obtaining earnings data for informal sector workers. For example, fearing legal action, informal sector workers are likely to underreport earnings to survey staff. Another issue is determining the earnings of self-employed informal sector workers; in particular, family farm and family business income are not attributable to a single worker and instead accrue to the entire household. Thus, researchers have no choice but to either include faulty data or omit the informal sector workers. Anecdotal evidence suggests that the LMRRE for informal sector employment is lower than that of formal employment. Thus, for individuals and families engaged in informal employment, educational investment is less attractive than what LMRRE estimates suggest. Female Self-Selection

Female self-selection is a concept that addresses the difference between the women who self-select or choose to participate in the formal labor force versus the women who choose not to participate. Because of female self-selection, LMRRE estimates obtained from samples of full-time female wageworkers do not reflect the prospective LMRRE of other females. In LMRRE studies on industrializing countries, James Heckman’s Nobel Prize–winning two-step correction technique is usually combined with the Mincerian method to correct for selfselection. Though used for female LMRRE estimates in developing countries, the correction technique is unsuitable where the majority of women are outside formal sector wage employment. Thus, the LMRRE estimates for women are especially unreflective of the realities girls and women experience. For this reason, some studies only report male LMRRE estimates. Sensitivity to Life Expectancy

LMRRE estimates using both the full method and the Mincerian method typically assume that the

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child goes on to complete the entire work life, which usually ends at the common retirement age of 60. This assumption, however, is inappropriate for several sub-Saharan African regions, where the average life expectancy is often below 40 years because of the HIV/AIDS epidemic, malaria, and conflict. Since short life expectancy reduces the years of benefits, the standard assumptions and estimation methods exaggerate the LMRRE. For most individuals and families in sub-Saharan Africa, the prospective LMRRE is far lower than that suggested by available estimates. LMRRE estimates’ sensitivity to life expectancy can easily be explored with the full method by reducing t.

Conclusion This entry examined the attractiveness of education for individuals and families in developing countries, who make up most of the world population. Studies suggest that LMRRE are in the 17.9% to 23.0% range and that the benefits of primary education have declined considerably since 1960. Though useful, existing studies typically exaggerate the attractiveness of education as an investment for individuals and families in developing countries. Nevertheless, investment in education continues not only because of its monetary LMRRE but also because of its numerous nonmonetary benefits, such as happiness and health, that are not accounted for in LMRRE estimates. M. Najeeb Shafiq and Yuan Zhang See also Age-Earnings Profile; Benefits of Primary and Secondary Education; Demand for Education; Dual Labor Markets; Economic Development and Education; Globalization; International Datasets in Education; Opportunity Costs

Further Readings Bennell, P. (1996). Rates of return to education: Does the conventional pattern prevail in Sub-Saharan Africa? World Development, 24, 183–199. Colclough, C., Kingdon, G., & Patrinos, H. A. (2010). The changing pattern of wage returns to education and its implications. Development Policy Review, 28, 733–747. Edmonds, E. (2008). Child labor. In T. P. Schultz & J. Strauss (Eds.), Handbook of development economics (Vol. 4, pp. 3607–3709). Amsterdam, Netherlands: Elsevier. Patrinos, H. A., & Psacharopoulos, G. (2010). Returns to education in developing countries. In D. Brewer &

P. J. McEwan (Eds.), Economics of education (pp. 44–51). Amsterdam, Netherlands: Elsevier. Psacharapoulos, G. (1996). A reply to Bennell. World Development, 24, 201. Schultz, T. W. (1980). Nobel lecture: The economics of being poor. Journal of Political Economy, 88, 639–651. Shafiq, M. N. (2007). Household rates of return to education in rural Bangladesh: Accounting for direct costs, child labor, and option value. Education Economics, 15, 343–358.

LICENSURE

AND

CERTIFICATION

Teacher licensure in education is a credentialing arrangement that requires prospective teachers to earn a license from a state authority in order to work as a professional educator in public schools in that state. The goal of licensure is to ensure that all teachers meet a minimal standard of quality. The licensure and certification of teachers plays a critical role in current education policy debates. There is an emerging consensus that teachers have a greater impact on students’ academic achievement than any other school-related factor, such as facilities or school leadership. Teacher licensing is a regulatory requirement that influences the size and composition of the teacher labor pool by lowering or raising the cost of entry for individuals considering the teaching profession. Teacher licensing and certification procedures, therefore, have important implications for state and local policymakers attempting to shape the pool of teacher candidates to maximize student achievement in K-12 schools. This entry begins by describing traditional routes to teacher preparation and will discuss some possible shortcomings of the traditional route. It concludes with a description of alternative teacher certification strategies that currently exist in the United States.

Brief Overview Individuals must possess the necessary license for the subject and grade level they wish to teach, or they can be prevented from obtaining employment as a public school teacher. Teaching licenses expire after a set amount of time and must be renewed for a teacher to remain qualified to work as a professional educator in the public school system. Every state has different requirements regarding provisional license applications and licensure renewal

Licensure and Certification

procedures and timelines. The traditional approach to earning a license may require individuals to complete a specified set of course work, pass a standardized exam, or both. In egregious cases of teacher misconduct, states may withdraw teacher licenses so that those individuals would no longer be legally permitted to teach. In 1834, Pennsylvania became the first state to administer a competency exam for prospective teachers wishing to obtain a state certificate. By the 1870s, almost all states were administering similar exams assessing, at a minimum, reading, writing, and arithmetic. By the beginning of the 20th century, state policies governing licensure and certification requirements started to become more homogeneous and typically involved completion of an approved teacher training program rather than passing an exam or applying for a local certificate. Today, all local educational agencies are required to ensure that every teacher hired to teach a core academic subject is “highly qualified,” as laid out by the federal No Child Left Behind Act of 2001. The criteria for being regarded as “highly qualified” include being certified or licensed by the state, holding a bachelor’s degree from a 4-year institution, and demonstrating competency in teaching skills and the subject areas being taught. Because nearly all certification programs also require applicants to have bachelor’s degrees, there is effectively very little difference between current certification requirements and the “highly qualified” teacher requirement of the No Child Left Behind Act. Requirements for teacher licensure vary considerably by state, but most states have reciprocity agreements in place to ensure teaching licenses are accepted across states. Because all public school teachers must be certified in the state in which they teach, these reciprocity agreements provide a process for out-of-state applicants to obtain a state license without having to retake the course work or exams that were necessary for their initial license. In many cases, the new state will issue a provisional teaching license while the individual completes any remaining requirements for certification.

Traditional Teacher Preparation The most common route for earning a professional educator’s license in the United States is the traditional route, which involves completing a stateapproved teacher education program from an

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accredited college or university. Teacher candidates apply directly to a college or university where they study educational theories of child development, are trained in practical pedagogical skills, and complete clinical experience made up of observations and hands-on practice in actual classrooms. An elementary training curriculum, for example, might include math and reading content in addition to courses on classroom management, lesson planning, how to assess student learning, and strategies for working with specific subgroups such as English Language Learners or students with special educational needs. A secondary training curriculum, on the other hand, might focus a larger portion of the teacher’s training on developing specific content knowledge. Some states, such as California and Texas, don’t allow undergraduates to major in education, and candidates must complete a postbaccalaureate program of teacher preparation instead. Recent surveys of teachers suggest that more than 80% of teachers are trained and certified through traditional collegebased undergraduate programs or traditional college-based graduate programs. At the heart of this form of teacher training is the idea of transmitting the craft through practice, careful observation, and induction into the profession. To be issued a teacher license by this route, individuals must meet a set of requirements, which differ by state. Typically, candidates submit college transcripts showing they have completed the necessary education course work, test scores from subject area tests such as the Praxis examinations, and a processing fee. Many states also require fingerprinting prior to granting licensure. The Praxis exam series is the most commonly administered teacher competency test, used by more than 40 states. Individual states set their own requirements for passing scores and may require teachers to take the Praxis I Pre-Professional Skills Test, which measures basic reading, writing, or math skills; and/ or the Praxis II Subject Assessments, which measure specific skills and knowledge in a content area. The Praxis exams consist of a combination of multiplechoice questions and essays. The theoretical justification for teacher examinations and the teacher certification processes is to guarantee at least a minimum level of teacher quality. Nevertheless, some observers maintain that traditional teacher preparation strategies actually do not ensure a basic level of teacher quality, much less attract the highest quality teachers into classrooms across the country.

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Possible Shortcomings of the Traditional Model Traditional certification has been criticized by some policymakers and observers, particularly those in the so-called education reform community, as an ineffective way to ensure teacher quality for two reasons. First, these critics contend, certification requirements do not successfully screen out lowquality applicants and ensure some base level of teacher quality. Second, mandatory teacher licensure and certification policies can exacerbate labor pool shortages in hard-to-staff subjects and geographical areas by acting as a barrier to entry. If the critics are correct, then certification policies may well have the effect of weakening the overall quality of teachers by limiting the supply of teacher candidates without guaranteeing any minimum level of quality. The certification requirement has a particularly strong impact on the supply of teachers in the areas of upper-level math and science. First, traditional programs housed in colleges of education generally graduate fewer future teachers prepared to teach in these subject areas. Potentially effective teacher candidates with bachelor’s degrees in these subjects may be dissuaded from entering teaching because of the need for an additional credential beyond the undergraduate degree in the content area. Moreover, midcareer engineers (or others with solid math or science training) wishing to teach mathematics or science would be unable to enter the classroom as a teacher without acquiring the necessary certification credential. Because talented teachers with training in math and science are in short supply, they are highly sought after and find work in relatively affluent areas; thus, the scarcity of qualified math and science teachers is particularly problematic in economically disadvantaged urban and rural locations. To expand the pool of potential teacher candidates overall, and to address shortages in critical geographic areas or subjects in particular, policymakers in many states have devised alternative routes to licensure and certification.

Alternative Routes to Licensure and Certification Alternative certification programs are designed to attract potentially effective teachers who have not obtained a bachelor’s or master’s degree in education but who may have an interest in and an aptitude for teaching. Proponents of alternative routes propose that deep subject knowledge is paramount

for good teaching, especially at the secondary school level, and seek to bring individuals with a major in a specific content area into teaching. Moreover, proponents put forth alternative routes to teaching as strategies to address teacher shortages in key areas. California, New Jersey, and Texas began developing alternative teacher certification programs in the 1980s. By 2007, 47 states had developed alternative certification routes into teaching. Candidates in alternative certification programs typically take education courses at night and during summers and may be assigned a mentor teacher during the school year. Alternative certification programs tend to be run by local school districts, nonprofit organizations, or education schools. These nonselective programs have the effect of opening up the profession to a wider pool of potential teachers but provide little assurance of teacher quality. Nevertheless, the vast majority of alternatively certified teachers have entered the classroom through these state-run programs with nonrestrictive entry requirements. In recent years, programs such as Teach for America and The New Teacher Project’s Teaching Fellows program have emerged to train accomplished college graduates and midcareer professionals to teach in high-poverty schools. Candidates in these alternative programs are generally selected based on success in undergraduate school and their content knowledge and, typically, have earned a degree in the subject they plan to teach. Such programs typically recruit graduates of top colleges and provide the teacher candidates with a short period of training during the summer months, approximately 5 to 6 weeks, before they enter the classroom as teachers. Candidates are normally required to make a 2- to 3-year commitment and earn the same starting salary as other beginning teachers in that school district. Critics contend that the short training period provided by these selective alternative certification programs is inadequate to transmit the necessary pedagogical knowledge and classroom experience necessary for effective teaching. Furthermore, many alternatively certified teachers trained through these programs do not remain in teaching once their initial commitment expires, prompting criticism of the programs for exacerbating high teacher turnover rates in low-income schools. Currently, despite the heated debate in the education policy world on the effects of programs such as Teach for America, fewer than 1% of teachers across the country have been

Licensure and Certification

certified through these selective alternative certification programs.

The Evidence on Certification Route Research on the link between teacher’s initial certification status and teacher effectiveness has not demonstrated strong evidence of a meaningful relationship. While there have been several nonrigorous studies claiming an advantage for one type of certification or another, the best evidence can be found in the results of experimental random assignment studies. From these rigorous studies, we can conclude that neither traditionally certified teachers nor alternatively certified teachers are superior across the board in terms of fostering greater student achievement. In fact, because the characteristics of programs vary greatly, whether traditional or alternative, the variation in teacher quality within certification groups is much greater than differences in teacher quality between groups. This implies that the greatest disparities between very high and very low quality teachers occur within certification group. Both truly excellent and truly ineffective teachers can be found among those who went through traditional certification and those who took alternative certification routes; there is no clustering of effective teachers within one certification group. Nevertheless, recent studies suggest that highly selective alternative certification programs may be particularly effective; two random assignment studies of the Teach for America program have found that its teachers produce significantly greater gains in math for secondary students. Finally, it is important to note that much of the policy debate on the teacher certification issue, as well as the research, asks whether the traditional strategy or alternative strategy is superior. This may be the wrong question to ask. It is possible that different strategies work better for different types of teachers. The traditional training model may well be effective and appropriate for the elementary and middle levels, where pedagogical training may matter more than content and where the supply of potential teachers is plentiful. On the other hand, alternative pathways may be an effective option for dealing with teacher shortages in upper-level science and math classes.

Conclusion Both traditional and alternative routes to teacher certification may be necessary in order to fill a variety of teaching vacancies with effective candidates.

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In some cases, either traditionally licensed or alternatively licensed teachers may seek additional credentialing as a signal of their professional accomplishments. The National Board for Professional Teaching Standards offers a supplementary credential intended to signal advanced teaching ability. This challenging, performance-based, and peer-reviewed process is designed to signal advanced knowledge and skills and expert teaching practices. In 2013, the National Board reported that more than 100,000 teachers nationwide had obtained National Board certification. Current trends suggest that the future of teacher licensure will incorporate all three types of certifications—(1) traditional, (2) alternative, and (3) the optional National Board credential. The available evidence suggests that teacher quality does not vary systematically as a result of certification route and that both effective and ineffective teachers can be found within both the traditional and alternative certification groups. Gary Ritter and Anna J. Egalite See also National Board Certification for Teachers; No Child Left Behind Act; Teacher Effectiveness; Teacher Supply; Teacher Training and Preparation

Further Readings Boyd, D., Lankford, H., Loeb, S., Rockoff, J., & Wyckoff, J. (2008). The narrowing gap in New York City teacher qualifications and its implications for student achievement in high-poverty schools. Journal of Policy Analysis and Management, 27(4), 793–818. Clark, M. A., Chiang, H. S., Silva, T., McConnell, S., Sonnenfeld, K., Erbe, A., & Puma, M. (2013). The effectiveness of secondary math teachers from Teach for America and the Teaching Fellows programs (No. 2013–4015). Washington, DC: U.S. Department of Education, National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences. Retrieved from http://ies.ed.gov/ncee/pubs/20134015/ pdf/20134015.pdf Constantine, J., Player, D., Silva, T., Hallgren, K., Grider, M., Deke, J., & Warner, E. (2009). An evaluation of teachers trained through different routes to certification: Final report (No. 2009–4043). Washington, DC: U.S. Department of Education, National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences. Retrieved from http://www .mathematica-mpr.com/publications/pdfs/education/ teacherstrained09.pdf

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Kane, T. J., Rockoff, J. E., & Staiger, D. O. (2008). What does certification tell us about teacher effectiveness? Evidence from New York City. Economics of Education Review, 27(6), 615–631. Podgursky, M., Monroe, R., & Watson, D. (2004). The academic quality of public school teachers: An analysis of entry and exit behavior. Economics of Education Review, 23(5), 507–518. Sass, T. R. (2011). Certification requirements and teacher quality: A comparison of alternative routes to teaching (Working paper). Retrieved from http://www2.gsu .edu/~tsass/pdfs/Alternative%20Certification%20 and%20Teacher%20Quality%2011.pdf Shuls, J. V., & Ritter, G. W. (2013). Teacher preparation: Not an either-or. Phi Delta Kappan, 94(7), 28–32.

LOCAL CONTROL In the education sphere, the notion of “local control” refers to decision-making authority at the school or school district level, rather than at the county, state, or federal level. Local control includes power over a broad range of education functions, not just the ability to raise revenue through taxation or bonds. This authority is vested within the broader educational governance structure of a state and can include a variety of functions, including revenue generation, resource allocation, facilities planning/management, structure/organization, staffing, training/professional development, curriculum and instructional materials, testing/assessment, and others. Across the United States, states have different educational governance structures, with a variety of decision-making powers vested to the different players in the system. Some states have strong local control—giving local schools or school districts the bulk of the decision-making power—while other states concentrate more on the decision-making authority at the state level. This entry will include some background on the historical development of local control, before describing the opposing views of the phenomenon in current times. It will then outline the different stakeholders involved in decision making that reduces or increases local control and the variations in the extent of local control in different states. It concludes with two examples of modern innovations in local control: (1) site-based budgeting and (2) the creation of charter schools.

Historical Development of Local Control The education system in the United States, though varied across states, has evolved over time from one rooted in local control (with oversight activities in the form of development and technical support from the state) to one in which much of the decisionmaking and financial authority is given to the state. Over time, the trend of control has shifted from local school districts to something that is more dispersed and in which the state and federal government—in addition to a wide variety of additional actors—are more dominant. The early manifestations of the U.S. education system began as local endeavors to educate the youth of particular communities. This system developed out of necessity, as there was no existing national or state educational governance structure but a demand for public education, and a normative tradition of local decision making over myriad policy areas. The primary goal of each individual system was to efficiently develop the proficiency—including both basic skills as well as character—required by and adhered to by the local community. This backdrop of local control contrasted with Europe and elsewhere, where strong centralized states have been the norm, the presumption being in favor of greater number of decisions being made centrally with minimal local control. Local communities began by developing their own education systems, which over time combined and transformed into the complicated governance system that now exists. Over approximately the past 50 years, the role of the state in the U.S. system has shifted from oversight activities in the form of development and technical support to an increasingly assertive role through a variety of functions (in particular standards, testing, and accountability). This mirrors broader trends in governance across policy issues in light of globalization, interdependence, interconnectivity, and higher mobility. Additionally, the view developed that the educational system can and should simultaneously produce higher levels of both efficiency and equity. To fulfill this equity goal, the system of governance trended toward higher levels of state and national control. The concentration of control has shifted from local districts to something more vertical and multifaceted, with an increase in both the number and power of state-level stakeholders. This concentration of control varies greatly by function. For example, in California, revenue generation is overwhelmingly dominated by the state, but training

Local Control

and professional development incorporates district, county, state, and federal institutions. The federal government has also become an increasingly important actor in educational governance in recent years. First through the No Child Left Behind legislation and more recently with the Race to the Top grant program, the federal government has used financial incentives to shape state and district actions. For instance, to be eligible for Race to the Top grants, states had to adopt standards developed in concert with other states, leading many states to adopt the Common Core State Standards. At the same time, it has been argued that with the increase in the federal role in education governance, there has been a resurgence of local control as local actors devise strategies to implement federal initiatives while asserting control over local schooling.

Opposing Views of Local Control The primary assertion made by proponents of local control is that those closest to the ground—in other words, to the students—are best equipped to understand the specific educational needs, challenges, and opportunities of their local classroom, school, or district. Proximity to the classroom, the argument goes, offers insights into how best to educate specific groups of students. These insights result in different approaches to meeting educational needs, reflecting local values and unique circumstances. Opponents of local control argue that it produces inequity, with the most highly qualified teachers opting to work in the most affluent neighborhoods where local resources are greater than in higher poverty areas. Opponents of local control further assert that the centralization and standardization of education governance lead to reductions in pervasive resource inequities and can ultimately reduce the achievement gap among subgroups of students. Top-down structures have been used to implement a wide range of policies aimed at creating systemic changes, including those targeting equality of opportunity and addressing racial disparities. Many education reforms of the Modern Era, such as the emphasis on standards-based education, standardized testing, and accountability for continuous student improvement under the No Child Left Behind Act, as well as more recent standardization efforts seen with the adoption of the Common Core State Standards, shift control away from local decision makers and concentrate them instead at the state and federal levels.

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Stakeholders Involved in Local Control The prevalence and power of different stakeholders at different levels of a state’s education governance system serves to either facilitate or restrict the extent of local control in a given state. School districts, often viewed as the primary local service delivery units in education, comprise many actors contributing to the decision-making processes at this level, including members of local boards of education, superintendents, district personnel, and their staff. At the school level, principals, assistant principals, senior leadership teams or head teachers, and teachers may all play a role in deciding matters of local control. In some cases, and on some key issues, unions and other interest groups also play major roles at the local level. Above the local level, various educational governance structures exist, including county, regional, state, and federal agencies with various levels of decision-making authority. County offices, though they do not play a part in every state, typically serve as an intermediate governance step. In some states, the county-level offices play major roles and have significant decision-making authorities. At the state level, there are several bodies that may have initially been created for oversight but have increasingly assumed larger levels of control over time. These vary state by state but can include the governor, a secretary of education and/or a chief state school officer, the legislature, a state board of education, a department of education, a teacher credentialing commission, and various other agencies, unions, and commissions. At the federal level, legislative, judicial, and executive branch stakeholders, including the U.S. Department of Education, exert both oversight and varying degrees of decision-making control.

National Variations in the Extent of Local Control There are wide variations in the extent of control maintained by local communities across different functions and different states. Nationally, the functions most likely to be distributed to state-level actors include revenue generation, facilities planning and management, structure and organization of schools and districts, curriculum, and testing/assessment. Inversely, the functions most likely to be controlled at the local level, or at least shared to some degree, include resource allocation, staffing, and

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training/professional development. Still, the question of local control is not a binary, yes/no, proposition but rather something much more nuanced and complex. Control in many circumstances is distributed and diluted among a number of levels in the educational governance system, and every state in the country has developed its own decision-making system. Within the broad functional categories of education policy outlined above, there are considerable differences state to state. For example, on the question of revenue generation, states vary regarding whether the state is only permitted education-related taxing agency. States such as Alaska, Connecticut, and Maryland do not allow local taxing, but California, Arkansas, and Arizona are among the majority of states that allow local taxing. Similarly, states vary widely on the question of curriculum determination. In some states (including Georgia, New York, and North Carolina), the state board solely determines the curriculum, while in another set of states (including Utah, Idaho, and Illinois), it is a consultative and collaborative process between states and local districts, and in states like Rhode Island, the state only develops the standards, with local and regional boards left to develop curriculum to meet those standards. Similar variation is seen in functions that typically are more of the local purview to begin with, such as charter schools. In many states (including Pennsylvania, Oregon, Ohio, and Colorado), local districts serve as the charter authorizers, granting local districts a high degree of control over selecting and overseeing, as well as renewing or closing, the charter schools in their district, but in another set of states (including New York, North Carolina, Rhode Island, and South Dakota), the State Board of Education or similar body authorizes charter school creation. Last, the level of democratic accountability over education officials that is vested in local stakeholders varies widely. In this context, democratic accountability refers to how much direct power a given population has over the selection of its education policymakers. Examples of variance in democratic accountability include but are not limited to (a) whether officials are elected or appointed; (b) whether elections occur on a general election cycle, where turnout rates are higher and officials with more public salience are on the ballot, or off-election cycles; and (c) whether education officials appear on the ballot as partisan or

nonpartisan candidates. States vary widely across these and other criteria. For example, in states such as Texas, Colorado, and Alabama, the State Board of Education is directly elected by the population on a general election cycle ballot. In other states (e.g., Florida, Idaho, and Illinois), the State Board of Education is appointed (typically by the governor or the legislature). Importantly, the same variation across a number of criteria of democratic accountability applies to the chief state school officers, county supervisors, local superintendents, and local school boards.

Innovations in Local Control Charter Schools

The enactment of state laws over the past 20 years in 42 states and the District of Columbia allowing for the creation of charter schools is one of the most significant innovations aimed at establishing local control of public schools. As public schools operated under a performance contract with an authorizing body such as a local school district or university for an initial contract period of between 5 and 15 years, per different state laws, charter schools are an example of vesting control at the school level over a number of functions that are otherwise controlled at the district, county, or state level. The core underlying concept of the charter school movement is to hold schools accountable for performance—via the threat of charter nonrenewal—in exchange for autonomy over many of the functions generally controlled by the district or state and, at the same time, to provide parents increased public schooling options for their children. Charter schools are publicly funded, and although the specifics vary by state law, they tend to have decision-making power over their budget and key functions such as staffing, curriculum, and instructional models; their school mission and culture; and the structure and length of the school day and school year. While autonomous over these areas, state and federal laws prohibit charter schools from charging tuition and require charter schools to be nonsectarian, serve students with disabilities, and offer open enrollment through a lottery if more students wish to enroll than there are spaces available. According to the National Alliance for Public Charter Schools, as of January 2013, there were 6,004 operational charter schools in the United States serving more than 2.3 million students. The growth of the charter system has been significant: From 2008 to 2012,

Lotteries for School Funding

student enrollment has increased 80%, and the number of schools grew by 40%. Site-Based Budgeting

School districts across the country have also experimented in recent years with site-based budgeting, which gives school principals budgetary control of their schools while maintaining many of the other district and state requirements waived for charter schools. The processes vary from state to state, with some school districts requiring their schools to apply for this increased autonomy, while others award it to their high-performing schools. In some cases, districts have required that all their schools take on budgetary control. Proponents of site-based budgeting—similar to those in favor of local control generally—argue that greater budgetary responsibility leads schools to make more efficient decisions around how resources are allocated. For example, under a site-based budgeting system in Los Angeles, individual schools would have to pay for substitute teachers out of their own budgets. Schools in the Los Angeles Unified School District began realizing the direct costs and effects of this, which led them to take active steps in reducing teacher absences. As a result, those schools are free to allocate resources elsewhere. Opponents of site-based budgeting make similar assertions as those in favor of greater centralization and standardization in education governance. Joanna Smith, Nicholas Perry, and Hovanes Gasparian See also Central Office, Role and Costs of; Centralization Versus Decentralization; Charter Schools; School-Based Management; State Education Agencies

Further Readings Brewer, D. J., & Smith, J. (2008). A framework for understanding educational governance: The case of California. Education Finance and Policy, 3(1), 20–40. Marsh, J. A., & Wohlstetter, P. (2013). Recent trends in intergovernmental relations: The resurgence of local actors in education policy. Educational Researcher, 42(5), 276–283. Perry, M. (2013). Site-based budgeting: A new age of district finance. Leadership, 42(5), 8–11. Wohlstetter, P., Smith, J., & Farrell, C. C. (2013). Choices & challenges: Charter school performance in perspective. Cambridge, MA: Harvard Education Press.

LOTTERIES

FOR

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SCHOOL FUNDING

Currently, 44 states operate state lotteries, which generate small but not insignificant revenue for these states. Of these 44 states, 23 states earmark the lottery proceeds for the support of education. This entry discusses how much revenue the lotteries raise and the educational activities that states fund with lottery revenues. It goes on to consider why states choose to adopt lotteries, whether lotteries actually increase state spending on education, and what equity issues arise with lotteries for education.

Background Lotteries have a long history in the United States. In the colonial period, lotteries were a common means of financing public projects, including roads, schools, churches, and public facilities, and lotteries were used during the Revolutionary War and Civil War to supply and support troops. Lotteries continued to operate during the 19th century but were organized and marketed by private sector firms. Due to an increase in fraud and abuse associated with the lotteries operated by these firms, the states and the federal government moved to make lotteries illegal. From the end of the 19th century to 1964 there were no legal, government-operated lotteries in the United States, although there were many failed attempts between 1930 and 1963 to institute government lotteries. But finally, in 1963, a proposal to adopt a state lottery was passed, and in 1964, New Hampshire became the first state in 70 years to operate a state lottery. Lottery adoptions spread across the United States as other states followed this initial success (see Table 1). Thirteen states had lotteries by 1975, 22 by 1986, 32 by 1992, 38 in 2003, and in 2011, 44 states, including the District of Columbia, had a state lottery. Of the 44 states with a lottery, 23 have been identified as earmarking all or some of the lottery proceeds for the support of education. In 2011, the 44 states had lottery ticket sales of $54.7 billion and generated $18.3 billion in net proceeds, or $62.04 per capita. On average, about two thirds of gross revenue is used to pay prizes and to fund lottery administration, with one third remaining as net proceeds. In 2011, the 23 education lotteries had sales of $38.7 billion and net proceeds of $13.1 billion. These 23 states account for 72.7% of the population of the states with lotteries and 71.6%

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Table 1

State Lotteries With Proceeds Earmarked for Education Year Started

State

Sales (in $1,000)a

Proceeds (in $1,000)a

Proceeds Per Capitab ($)

Proceeds as a % of State Allocation to K-12c

Arkansas

1981

437,801

92,483

31.48

3.4

California

1985

3,438,578

1,340,264

35.56

3.9

Florida

1988

3,781,550

1,186,992

62.28

14.4

Georgia

1993

3,109,295

848,072

86.40

12.5

Idaho

1989

135,379

36,480

23.02

2.8

Illinois

1974

2,264,685

663,446

51.55

8.3

Michigan

1972

2,139,205

700,227

70.90

6.7

Missouri

1986

981,542

300,297

49.96

10.1

Nebraska

1993

123,711

30,294

16.44

2.5

New Hampshire

1964

216,229

62,415

47.35

6.9

New Jersey

1970

2,489,474

931,136

105.56

9.9

New Mexico

1996

129,756

41,421

19.89

1.7

New York

1967

6,986,288

2,695,497

138.48

11.5

North Carolina

2006

1,358,387

437,070

45.26

5.7

Ohio

1974

2,439,667

747,381

64.74

7.5

Oklahoma

2005

211,373

92,381

24.37

3.4

Oregon

1985

838,595

554,766

143.28

18.8

South Carolina

2002

973,013

268,076

57.29

7.8

Tennessee

2004

1,108,861

445,701

69.60

11.6

Texas

1992

3,599,037

1,006,330

39.20

5.1

Vermont

1978

89,935

31,379

50.09

2.3

Virginia

1988

1,398,843

445,683

55.05

8.1

Washington

1982

478,516

138,978

20.35

2.0

a. Sales per $1,000 and proceeds per $1,000 from “Income and Apportionment of State-Administered Lottery Funds: 2011,” U.S. Census Bureau (http://www2.census.gov/govs/state/11lottery.pdf). b. Proceeds per capita calculated using U.S. Census Bureau data. c. Proceeds as a percentage of state allocation to K-12 education calculated using U.S. Department of Education data.

of net lottery proceeds. The amount of education lottery net proceeds per capita in 2011 was $61.04, with a range from $16.44 in Nebraska to $143.28 in Oregon. Table 1 provides information on each of the 23 education lotteries.

Use of Lottery Proceeds What gets funded from the proceeds of education lotteries differs widely across the 23 states (see Table 2). Some states allocate the revenue directly to local

school systems. For example, California allocates an equal amount per pupil to all school districts, while New York allocates the proceeds using the same formula as used for allocating state aid. In these cases, the local school district determines how the funds will be used. Some states, for example, Michigan, Ohio, Texas, and Virginia, deposit the proceeds into a state education fund, which the legislature then appropriates, but with no special restriction on what is funded so long as it is for education. In other states, for example, Oregon and Oklahoma,

Lotteries for School Funding

Table 2

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State Use of Education Lottery Proceeds

State

Use of the Lottery Proceeds

Arkansas

The lottery is used to fund scholarships and grants to Arkansas citizens who attend in-state nonprofit colleges and universities, including both public and private and 2- and 4-year colleges and universities.

California

The state is required to provide at least 34% of lottery revenues to public education, from kindergarten through higher education, plus several specialized schools. All segments of public schools receive equal funding per pupil. Since 1985, 77% of the cumulative distribution of lottery revenue to education went to K-12 schools.

Florida

The lottery funds the Bright Futures Scholarship Program, which assists students in pursuing postsecondary educational and career goals. It also funds new construction, renovation, remodeling, and major repair and maintenance of K-12 educational facilities and assists school districts in meeting class-size reduction requirements.

Georgia

The lottery is used to fund HOPE (Helping Outstanding Pupils Educationally) tuition grants, scholarships, or loans to undergraduate college students and teachers, and the prekindergarten program for 4-year-olds. The lottery can be used for technology improvements, but no funds have been allocated to that purpose since 2003.

Idaho

The lottery is used to fund construction and renovation of public school buildings.

Illinois

Most of the lottery is allocated to K-12 education and capital projects. In addition, proceeds from cause-specific games are used to fund a wide array of benevolent organizations.

Michigan

The lottery is allocated to the School Aid Fund, which the legislature then appropriates.

Missouri

The lottery is used to support a variety of programs, including the A+ Scholarship Program, online-only schools, special education, construction of college and university buildings, library acquisitions, and educational scholarships. The legislature appropriates the proceeds. In fiscal year 2013, about 63% was allocated to K-12 and 37% to higher education.

Nebraska

The lottery proceeds are distributed by formula, with education receiving 44.5% of the proceeds. The legislature determines the specific use of these funds.

New Hampshire

All of the revenue is earmarked for education, with the legislature appropriating the proceeds.

New Jersey

The lottery is for a variety of education purposes, including county college capital improvement initiatives; school nutrition efforts; the Marie Katzenbach School for the Deaf; the operation of centers for the developmentally disabled, state psychiatric hospitals and homes for veterans; higher education tuition and grants; and aid to colleges and universities. In 2011, 63% went to higher education.

New Mexico

The lottery is used to fund the Legislative Lottery Scholarships, which pays 100% of tuition for eight consecutive semesters of eligibility for students attending New Mexico public colleges, universities, and technical colleges.

New York

The lottery is used to support education. Lottery proceeds are distributed to local school districts by the same statutory formula used to distribute other state aid to education.

North Carolina

The lottery supports education, including reduced class size in the early grades, academic prekindergarten programs, school construction, and scholarships for needy college and university students.

Ohio

The lottery is used solely for the support of elementary, secondary, vocational, and special education programs as determined in appropriations made by the legislature. (Continued)

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Table 2

(Continued)

State

Use of the Lottery Proceeds

Oklahoma

The lottery proceeds have been divided between higher education and K-12 education, with each receiving 45%. At the higher education level, uses for the lottery funds include tuition grants, loans and scholarships, renovations and expansions, capital outlay projects, technological upgrades, and endowed chairs; at the elementary and secondary level, uses of the funds include compensation for public school teachers, early childhood development programs, construction, and technological upgrades. The other 10% has gone to the Teachers’ Retirement Fund and a fund to assist districts with school consolidation.

Oregon

The lottery is used for a variety of purposes as determined by voters and the legislature. For 2011–2013, 59% of the lottery was distributed among four areas within education: (1) the Education Stability Fund (a rainy day fund to avoid cuts in school funding), (2) general-purpose state funding for schools, (3) funding for colleges and universities, and (4) bonds for school construction. Lottery revenue is also used for economic development and parks and natural resources.

South Carolina

The lottery is used for educational purposes and programs. The legislature appropriates the lottery proceeds. Over the past 10 years, 75% has gone to higher education, mainly to fund scholarships and grants, while 22% has been used for K-12 education, mainly for enhancement programs for grades K-5.

Tennessee

The lottery is used mainly to fund an educational scholarship program.

Texas

The lottery proceeds are transferred to the Foundation School Fund, the primary source of state funding for school districts. The legislature determines the appropriation of these funds.

Vermont

The lottery proceeds go to the state’s Education Fund, the main source of state funding for school districts. The legislature determines how these funds will be used.

Virginia

The lottery is used to support public K-12 education. The legislature determines which specific programs are funded.

Washington

The lottery is used to fund college scholarships, higher education financial aid programs, and early childhood education programs for 3- and 4-year-olds from low-income families.

Note: Information from states’ lottery websites.

the use of the proceeds are restricted to certain educational purposes, but the legislature can decide the allocation within, and sometimes between, these purposes. In other states, for example, Georgia and Tennessee, state law or the state constitution specifies the particular programs that the legislature can fund with lottery proceeds. For example, in Georgia, the lottery proceeds can be used only for three specific purposes, but the legislature can decide the allocation between these purposes. Several states allocate a significant percentage of the proceeds to higher education, particularly for scholarships and grants as in the case of Arkansas, Georgia, New Mexico, and Tennessee. Several states allocate a substantial amount of the proceeds for construction and renovation of school facilities. A few states earmark only a share of the lottery proceeds to

education, for example, California, Nebraska, Oregon, and Washington.

Lottery Adoptions and Promotion The use of lottery proceeds for education appears to play an important role in the decision to adopt a lottery and in its promotion. Studies of lottery adoption have found that the probability of adopting a lottery is larger the worse the fiscal conditions of the state, the larger the number of neighboring states with a lottery, the higher the state’s tax burden, the greater the potential for lottery sales to nonresidents, the larger the state’s average income, the greater the other forms of gambling, and the smaller the number of religious conservatives. It is believed that to overcome the opposition to lotteries, for example,

Lotteries for School Funding

from those concerned with the fact that lotteries are more likely to be played by low-income households or those who oppose the lottery on moral grounds, state legislatures specified that the lottery proceeds would be used for politically popular purposes and, in particular, for education. Studies support this premise. One study found that earmarking the lottery for education significantly reduced the oppositional effect that religious conservatives had on the probability of lottery adoptions. Other studies provide indirect evidence, for example, that the probability of adopting a lottery was higher in states in which education spending per capita was low, and that the percentage of voters approving a state lottery in a county was greater the lower the quality of education in that county. These results suggest that public support for the lottery was motivated by a desire to increase education spending. Lottery websites prominently feature that lottery funds are used to support education. Since the explicit goal of lotteries is to maximize net proceeds, using the support for education to promote lottery playing suggests that lottery administrators believe that ticket sales will be higher if citizens think that the proceeds will be used for education. The importance of education funding for lotteries may help explain why seven states (Illinois, Missouri, Oregon, Texas, Vermont, Virginia, and Washington) changed the use of their lottery proceeds to include earmarking for education, either in whole or in part.

Earmarking and Fungibility In most states, the legislation establishing lotteries includes language stating that lottery proceeds shall be used to supplement, not supplant, existing resources for educational programs and purposes. But one of the significant questions surrounding education lotteries is whether the education lotteries actually do increase spending on education. Lotteries may not translate into a dollar-for-dollar increase in education expenditures, either because lotteries decrease other revenue or there is a reduction in appropriations for education from other revenue sources. For example, research has found that expenditures on lotteries reduce purchases of goods and services that are taxed under the sales tax, which reduces sales tax revenue. A recent study found that charitable donations to education-related organizations fell with the adoption of a new education lottery, implying that education funding by these organizations likely decreases when a state adopts a

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lottery. In addition, lottery revenue fluctuates from year to year, although the variation is negatively correlated with fluctuations in other revenues. In the absence of earmarking, one should expect that the lottery proceeds would be used for the same purposes as any other significant increase in revenue, namely, an across-the-board increase for most budget categories. But would earmarking lottery proceeds result in larger increases in education expenditures, say by an amount about equal to lottery proceeds? Economic theory suggests that we should not expect that outcome. Most earmark revenue is fungible—that is, interchangeable with other revenues, to a greater or lesser extent. With earmarked revenue, the government has two pots of money to use to fund education, the lottery proceeds and general fund revenue. The lottery revenue has to be spent on education, but the government could reduce the appropriation from the state’s general fund and use that revenue to fund other programs. Thus, the government’s use of the lottery proceeds could be the same whether the lottery proceeds are earmarked or not. The extent to which earmarked revenue is fungible will depend on the specific situation. Consider the case of New York and California, which allocate lottery revenue directly to the local school system. It would be very easy for either state to reduce the funds that they would have otherwise provided to the local school system. And dedicating the lottery proceeds to the state education fund would not prevent the legislature from reducing education funds from other sources. The extent to which states do substitute lottery proceeds for other revenue sources depends on the behavior of the elected officials and the ability of citizens to monitor the budget process. When an education lottery is first introduced, the legislators may feel a moral obligation to increase spending on education by the amount of the lottery proceeds. Likewise, citizens may be able to initially monitor how lottery proceeds affect spending on education. However, over time, the legislature might approve smaller increases in revenue from the general fund than it would have in the absence of the lottery. And citizens would be hard pressed to demonstrate that the legislature did so. Thus, one might expect that spending on education would increase initially by the amount of lottery proceeds, but over time, we might observe that the lottery proceeds are substitutes for other revenue. Thus, in a few years, total education funding would be about the same as it

446

Lotteries for School Funding

would have been without earmarking the lottery proceeds. Concern for the possibility that lottery proceeds would simply replace existing funding led some states to specify that the revenue had to be used to fund new programs, making it difficult for elected officials to reduce spending on education programs currently being funded. Georgia is an example. The governor made it very clear that he did not want the lottery money to be used as a substitute for other funds, so he specified that the lottery revenue could be used only to fund three new programs, namely, (1) postsecondary student merit aid, (2) a prekindergarten program, and (3) technology improvements. Studies of the effect of the lottery on education spending in Georgia suggest that the state did not substitute the lottery proceeds for other revenue. What do we actually observe regarding the effect of education lotteries on expenditures for education? Many studies have been conducted that find that expenditures on education did not increase commensurate with the increase in education lottery revenue. These studies use different measures of education spending and employ different empirical techniques. Studies have concerned total spending on education, education spending per student, education spending as a share of total state expenditures, and the growth in education spending. Many of these studies are comparisons of a time series of education spending pre- and postlottery, either just of the states with education lotteries or states with and without an education lottery. Other studies use empirical models that capture a few of the nonlottery factors that might explain the level of education spending. Some studies focus on just one state, while others consider several states. The consensus from these studies is that lottery proceeds are fungible and, thus, that education spending did not increase as a result of earmarking the lottery proceeds. However, these studies suffer from a number of methodological limitations, and thus, one should be careful in drawing firm conclusions. There are a few studies that find that education lotteries do increase expenditures on education. A recent study found that 50 to 70 cents of each dollar of lottery proceeds find their way to local school systems and that local school systems use about 80% of the additional state funds to increase education spending. In contrast, in states in which the lottery proceeds go to the general fund, education spending increases by about 30 cents for every dollar of lottery profit, which is a little less than state

spending on education as a percentage of total state spending. The study also finds that in states that earmark lottery proceeds for purposes other than education, lottery proceeds have no effect on state spending on education.

Equity of Lotteries Most individuals consider the lottery as a voluntary contribution to the state treasury. But playing the lottery is no different than deciding to drink a beer, since you don’t have to play the lottery and you don’t have to drink a beer. The proceeds from the lottery are thus just as much a tax as the tax one pays on beer. Given that about one third of the price of a $1 lottery ticket goes to the state to pay for education or other earmarked purposes, in effect the tax rate on the lottery is 50%—that is, 33 cents divided by the 66 cents for the expected payout and administrative cost. So the lottery imposes a very high tax rate. An issue is how regressive is the lottery tax. Most of the studies that measure the incidence of the lottery find that it is regressive. One approach is to estimate the income elasticity of the lottery (i.e., the percentage change in lottery tickets purchased divided by the percentage change in income). If the income elasticity is less than one, then the share of income spent on lotteries decreases as income increases. The estimates of the income elasticity vary across studies, but most studies estimate an elasticity of less than one. An interesting study by Ross Rubenstein and Benjamin Scafidi (2002) compared how household expenditures on the Georgia lottery and the benefits of the programs funded by the lottery varied by income. They found, for example, that households with an income between $15,000 and $25,000 had net annual spending on the lottery of $323 compared with $144 for households with incomes between $50,000 and $75,000. They also found that the average benefits from the programs funded by the lottery (the HOPE scholarship and the prekindergarten program) were $138 and $257 for the two income groups. So not only is the lottery regressive but also the benefits of the programs funded by the lottery accrue more heavily to higher income households. Related to the issue of equity is whether lowincome players are also the heavy players—that is, whether low-income players are more likely to be addicted lottery players. There is also some evidence

Lotteries in School Admissions

that lotteries are addictive and that very heavy lottery players are older and have higher incomes. David L. Sjoquist See also Education Finance; Education Spending; Elasticity; Progressive Tax and Regressive Tax

Further Readings Clotfelter, C. T., & Cook, P. J. (1989). Selling hope: State lotteries in America. Cambridge, MA: Harvard University Press. Combs, K. L., Kim, J., & Spry, J. A. (2008). The relative regressivity of seven lottery games. Applied Economics, 40(1–3), 35–39. Coughlin, C. C., Garrett, T. A., & Hernández-Murillo, R. (2006, May/June). The geography, economics, and politics of lottery adoption. Federal Reserve Bank of St. Louis Review, 88(3), 165–180. Evans, W. N., & Zhang, P. (2007). The impact of earmarked lottery revenue on K-12 educational expenditures. Education Finance and Policy, 2(1), 40–73. Grote, K. R., & Matheson, V. A. (2011). The economics of lotteries: A survey of the literature (Faculty Research Series, Paper No. 11–09). Worcester, MA: Department of Economics, College of the Holy Cross. Retrieved from http://college.holycross.edu/RePEc/hcx/GroteMatheson_LiteratureReview.pdf Kearney, M. S. (2005). The economic winners and losers of legalized gambling. National Tax Journal, 58(2), 281–302. Pantuosco, L., Seyfried, W., & Stonebraker, R. (2007). The impact of lotteries on state education expenditures: Does earmarking matter? Review of Regional Studies, 37(2), 169–185. Pierce, P. A., & Miller, D. E. (1999). Variations in the diffusion of state lottery adoptions: How revenue dedication changes morality politics. Policy Studies Journal, 27(4), 696–706. Rubenstein, R., & Scafidi, B. (2002). Who pays and who benefits: Examining the distribution consequences of the Georgia lottery for education. National Tax Journal, 55(2), 223–238.

LOTTERIES

IN

SCHOOL ADMISSIONS

Lotteries are one mechanism elementary and secondary schools in the United States use to determine which students to admit. Lotteries involve the random selection of students to be admitted from among a larger group of eligible applicants. The use

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of admissions lotteries in the United States has gone hand in hand with policies promoting public school choice. Lotteries allow choice policies to be implemented in a manner viewed as fair and equitable. A side benefit of lotteries is that random selection of students for admission can be a natural experiment used by researchers to measure schools’ impacts on student outcomes. This entry describes how lotteries have developed in conjunction with various forms of school choice in recent decades. It then describes how lotteries work, presents critiques of school lottery systems, and explains how lotteries have been used in research on school effectiveness. The entry concludes with thoughts on the future of lotteries in school admissions.

Lotteries and School Choice Admissions lotteries require schools to be oversubscribed—to have more applicants than available seats. Lotteries are not typically used in public school systems with neighborhood enrollment zones determining students’ school assignments. Such systems are designed so that each school serves all students within its enrollment zone. When policies allow some choice for parents as to which public school their children may attend, however, the possibility of oversubscription arises because some schools may get more applicants than they can admit. Lotteries are not the only mechanism oversubscribed schools use to determine which students to admit. Some schools give enrollment preference based on students’ prior academic achievement, economic need, race or ethnicity, or having a special talent. Another common alternative is a first-come, first-served enrollment mechanism, where schools admit the earliest applicants. Among alternatives, lotteries are popular because of their perceived fairness. In theory, lotteries give all interested students an equal likelihood of admission. So when states or school districts implement choice policies, admissions lotteries often arise as a result. Common school choice policies include districtwide choice (open enrollment), magnet schools, and charter schools. Private schools represent another form of choice, at least for families with sufficient economic resources. School choice has become more common over the past two decades in the United States, leading to greater potential use of lotteries. According to the National Center for Education Statistics, the proportion of students attending a public school of choice increased from 11% to 16%

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Lotteries in School Admissions

from 1993 to 2007. In higher education, choice is universal, although admissions lotteries are rare. School choice can also be found in other countries; for example, New Zealand uses systemwide school choice, with students choosing from schools throughout the country. Under the districtwide choice, any student within district boundaries may apply to any district school. Several large districts use this policy, including Chicago, Illinois; Minneapolis, Minnesota; and Charlotte-Mecklenberg, North Carolina. Since students can apply to any school, some may be oversubscribed, and districts typically hold admissions lotteries in some or all of these schools, using either separate lotteries at each oversubscribed school or a single, unified districtwide application and lottery process. Districts may introduce choice in a more limited form by opening magnet schools or schools with a specialized curriculum or focus to which any district student can apply. Some oversubscribed magnet schools admit students using nonrandom criteria, but others use admissions lotteries. In the 1990s, states began passing legislation authorizing the establishment of charter schools, another form of public school choice. Charter schools are public schools operating partially or fully outside of traditional public school districts, under contracts (charters) issued by state-approved agencies. Most charter schools are open-enrollment schools, and many are oversubscribed and hold admissions lotteries, as required by many states or if they receive funding from the federal Public Charter Schools Program. Admissions lotteries are less common in private schools, which typically admit students nonrandomly. However, an increasing number of districts and states have begun using systems whereby students receive vouchers they can use at private schools. In some cases, such as in Milwaukee, Wisconsin, private schools accepting vouchers that are oversubscribed must hold admissions lotteries.

How Do Lotteries Work? To implement an admissions lottery, schools identify students interested in and eligible for admission, determine the number of seats to be filled via lottery, and use a random mechanism to determine the students to be admitted. The simplest case involves a single-school lottery, such as at an oversubscribed charter or magnet school. After announcing a lottery date and application period, the school determines

the number of seats to be filled by lottery using the difference between the school’s capacity and the number of students to be admitted outside the lottery (i.e., those exempt from the lottery). Exempt students include returning students from the previous year, implying that admissions lotteries typically fill seats at the school’s entry grade. Other exempt applicants may include siblings of currently enrolled students, children of faculty and other staff, or other preferred groups. If the number of nonexempt applicants exceeds the number of seats to be filled, the school is oversubscribed and must hold a lottery. The lottery may be mechanical, relying on some physical process for random selection, such as drawing applicants’ names blindly from a hat. Many schools favor this approach for public lotteries because of its transparency and familiarity to most parents. Alternatively, the lottery may be computerized, with a computer program randomly selecting applicants. Either way, schools randomly select both students to be admitted immediately and a randomly ordered waiting list of those not admitted. This allows the admissions process to remain random after the lottery, as some lottery winners decline the admissions offer and must be replaced. For admissions goals other than random selection, schools sometimes employ stratified lotteries, which treat different groups of applicants differently. Under vertical stratification, schools give absolute preference to applicants from a particular group (e.g., economically disadvantaged families). Students within and outside the preferred group are randomized separately, with admissions offers and wait-list spots going first to the preferred group and then—after the preferred applicant list is exhausted—to others. Alternatively, schools wishing to ensure a predetermined balance of admitted student characteristics (perhaps to promote school desegregation) may use horizontal stratification. Here, schools determine the number of applicants to be admitted from each group and then hold separate lotteries for each. Districtwide lotteries are similar to those for single schools, but there are some differences. Some districts with open-enrollment systems ask students to submit applications ranking their desired schools in order of preference and guarantee admission to students’ neighborhood school. Students whose top choice is their neighborhood school would thus be admitted to the school. To fill the remaining seats, districts could use an algorithm ensuring that as many students as possible are admitted to

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Most view admissions lotteries as an equitable way of admitting students to popular schools, since each lottery participant has an equal likelihood of admission. Thus, lotteries can help ensure that popular schools do not admit only the most advantaged or high-achieving students. However, there has also been criticism of lotteries used by choice schools, some of which is tied to overall concerns critics have about school choice programs. The education historian Diane Ravitch has been a prominent critic of charter schools, including their admissions lotteries. In a review of the 2010 documentary Waiting for Superman, she lamented the need for popular charter schools to hold lotteries that publicly deny admission to interested students and families, contrasting this practice with traditional public school systems that do not require lotteries for students to gain admission. Other criticism of school lotteries involves their timing and transparency. Some lotteries occur many months before the school year and may have application requirements not understood by all families. This may shut out students from families unaware of lottery requirements or unable to complete these requirements in time. Finally, lotteries could have an adverse effect on lottery losers, often from disadvantaged families already feeling ill-served by public schools. These families may be further demoralized by an unsuccessful lottery outcome, as was shown dramatically in Waiting for Superman, which highlighted public lotteries held by popular charter schools. Recent research evidence also suggests that lottery outcomes themselves (rather than lottery school attendance) may affect students’ academic performance. In particular, winning a lottery may boost students’ level of motivation and effort in school.

effects on student outcomes. The fact that lotteries are random implies that among students participating in a lottery, there should be no systematic differences between those admitted and those denied admission (other than admission to the school). Thus, later differences in these students’ outcomes can be attributed to the effect of the school rather than other factors. By contrast, a design that compares outcomes among students choosing to attend a school versus those not making that choice is subject to selection bias, whereby nonrandom self-selection into the school rather than the school itself explains later differences in student outcomes. This approach has been used to study the effects of charter schools and magnet schools. Lotteries held in districts with open-enrollment policies have been used to study the effects of students’ admission to their top-choice school, and whether preferences for schools revealed on districtwide applications correspond with estimated school effects. The value of lottery-based research has been widely noted. A 2006 panel convened by the Center on Reinventing Public Education, a research organization affiliated with the University of Washington, to suggest national guidelines for studying charter schools noted that among designs, lottery-based methods have the greatest internal validity. By comparing outcomes of students randomly selected to be admitted with those randomly denied access, differences in subsequent outcomes can be attributed to the lottery school rather than to self-selection of students into the school. A limitation of lottery-based research involves its limited external validity. It yields estimated effects of only oversubscribed schools holding lotteries, which tend to be the most popular schools, and perhaps the best schools. Another limitation is that lottery-based research can only measure effects of schools relative to a counterfactual situation of the schools that lottery losers attend, which can be difficult to measure and inconsistent across schools. For example, School A’s lottery losers may attend lower quality district schools than School B’s lottery losers. Thus, impacts may be more positive at School A than School B even if its teachers, curriculum, and other aspects of quality are similar.

Research Based on School Lotteries

The Future of School Lotteries

Researchers can use lottery outcomes at a particular school as a source of exogenous variation in school attendance, allowing them to estimate the school’s

With the increasing presence of school choice in public school systems, the number of oversubscribed schools and admissions lotteries is also likely to

their top-choice school. Alternatively, the algorithm might ensure that as many students as possible are admitted to one of their top two or three schools, even if not their top choice. Because districtwide lottery mechanisms may be complex, districts typically use a computerized approach.

Criticism of Admissions Lotteries

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increase. Lotteries are generally viewed as an equitable approach for admitting students to popular schools and can help ensure that choice programs do not give advantaged students greater access to the best schools. These benefits of school lotteries are more likely if schools and districts make their entire admissions process—including the lottery— transparent and easy to understand. This includes thoroughly documenting and publicizing application procedures, rules guiding the lottery, and lottery outcomes—who was admitted and who was waitlisted. Such lottery documentation may reassure parents about the fairness of the school admissions process. A secondary consequence of thorough lottery documentation is that it may serve as a resource for researchers studying choice schools. Philip Gleason See also Charter Schools; Educational Vouchers; Selection Bias

Further Readings Cullen, J. B., Jacob, B. A., & Levitt, S. (2006). The effect of school choice on participants: Evidence from randomized lotteries. Econometrica, 74(5), 1191–1230. Grady, S., & Bieleck, S. (2010). Trends in the use of school choice: 1993 to 2007 (NCES 2010–004). Washington, DC: National Center for Education Statistics. McEwan, P. J., & Olsen, R. B. (2010). Admissions lotteries in charter schools. In J. Betts & P. T. Hill (Eds.), Taking measure of charter schools: Better assessments, better policymaking, better schools (pp. 83–112). Lanham, MD: Rowman & Littlefield. Ravitch, D. (2010, November). The myth of charter schools. The New York Times Review of Books, 57. Retrieved from http://www.nybooks.com/articles/ archives/2010/nov/11/myth-charter-schools/ Tuttle, C. C., Gleason, P., & Clark, M. (2012). Using lotteries to evaluate schools of choice: Evidence from a national study of charter schools. Economics of Education Review, 31, 237–253.

M Information Asymmetry

MARKET SIGNALING

Information asymmetry occurs when information is not distributed perfectly across actors, causing a basic market failure. Signaling is an approach individuals and organizations utilize to attempt to overcome information asymmetries. There are two types of information actors provide to each other, fixed information (indices) and alterable information (signals). Indices consist of details about the actor that are unalterable by the actor, such as age, race, and ability. The market signals consist of the alterable information actors provide, such as education and experience. Information asymmetries occur when one party has better information on a particular index or signal. An example of this occurs in labor negotiations where the employee knows his or her own ability but a potential employer does not. In general, signals (e.g., a particular education level) are used when indices (e.g., ability) are unobservable to at least one agent.

Formalized by the economist A. Michael Spence, market signaling theory explains how actors make joint decisions in the presence of information asymmetries. Signaling theory originally developed to better understand how employers choose employees when the abilities of employees are unknown to the employer and hiring is a costly investment for an organization. Employers value potential employees based on the perceived, yet unknown, value of the potential employee to the organization. Potential employees who could withstand the rigors of education are assumed to be more productive than those with less education; thus, potential employees may use their education as a signal to an employer of their higher potential ability. Alternatively, organizations may screen potential employees by requiring a minimum level of education, essentially restricting the hiring pool to only those able to meet the high costs and demands of an education. The emphasis on solving problems of information asymmetry, or how individuals and organizations evaluate the unknown traits of others, makes the theory of market signaling an important topic in explaining individual educational choices. This entry describes the problem of information asymmetries, the potential benefits and costs associated to actors providing informational signals, the signaling equilibrium, and the implications of market signaling theory for human capital theory.

Signaling Benefits The benefits of signaling for both individuals and organizations are generally realized through efficiencies in the matching process. For a high-productivity individual, the benefits of signaling his or her productivity occur through receiving a higher wage from an employer willing to pay more for increased productivity. For the organization, signals allow the employer to be more confident that a candidate for a high-wage job is highly productive. Combined, the signal allows a more efficient matching process

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when individual productivity is unknown prior to hiring an applicant.

Signaling Costs The costs of signaling to an individual are many and include fees, effort, and the opportunity costs of obtaining the signal. An individual will invest in a signal as long as the signal produces an expected net benefit to the individual. The amount of education an individual will acquire depends on its discounted utility, or how much of a benefit the individual expects to receive over a lifetime compared with the costs of education in the present. In the educational context, the key focus of market signaling concerns an agent wanting to maximize labor market returns through the choice of the level and type of education to receive. While the knowledge gained through schooling serves as an investment in human capital by the student, the type of education a student receives (e.g., vocational education, liberal arts education) will signal to the employer the potential abilities that agent could bring to the job. Similarly, the level of education a student reaches (e.g., graduating from high school, taking some college courses, graduating from college) signals to employers his or her potential productivity. Additionally, completing a degree could signal a potential employee’s degree of perseverance. Beyond the labor market, individuals wishing to attend certain colleges will attempt to signal their potential value as students of that college. Belonging to a high school honor society, participating in athletics and/or clubs, and engaging in community service convey additional information about students to college admissions offices beyond GPAs (grade point averages) and standardized test scores. Thus, the standard postsecondary education enrollment processes are filled with signaling and screening activities on both the individual and organizational sides.

Signaling Equilibrium Spence contends that organizations hire under uncertainty. The signals the potential employee sends to the employer will give the employer a better understanding of whether to invest in an individual and, if so, how much to invest. By gathering more data on the potential employee, the employer reduces the amount of unknown information about the potential employee and will better determine whether or not to offer the position and at what wage. As employers learn whether or not they correctly interpreted signals in the initial hiring, they adjust their probabilistic beliefs concerning

the signals and invest differently in the next round of hiring. As the quality of the signal increases, the quality of the match increases leading to efficiency gains. Unlike human capital theory, which posits that more years of education increases worker productivity due to knowledge gained, market signaling theory does not rely on an assumption that education increases the productivity of a worker. Alternatively, signaling theory provides a rationale to obtain higher levels of schooling as a signal of ability and not as a means to increase skill. At this extreme, within the signaling model the value of schooling is created through the obtainment of a credential and not the obtainment of increased skill. The key assumption needed for market signaling to produce efficiency gains is that the signal is related to the unobservable trait valued by the potential employer. Thus, signaling improves efficiency simply by providing a means to produce better employee-employer matches.

Implications Market signaling provides a theory through which people can understand why individuals choose certain levels of education, educational institutions, and other educational paths. While human capital theory focuses on the investment of skills and knowledge of an individual, market signaling focuses on how employers interpret a potential employee’s choices and potential to benefit the organization. Market signaling theory’s emphasis on how utilitymaximizing individuals choose education and how profit-maximizing organizations desire individuals with specific educational backgrounds makes market signaling theory a key theory concerning why individuals choose an educational path. Bradley Curs and Jason Evans See also College Choice; Cost-Benefit Analysis; Human Capital; Opportunity Costs

Further Readings Connelly, B. L., Certo, S. T., Ireland, R. D., & Reutzel, C. R. (2011). Signaling theory: A review and assessment. Journal of Management, 37(1), 39–67. doi:10.1177/0149206310388419 Spence, M. (1973). Job market signaling. Quarterly Journal of Economics, 87(3), 355–374. doi:10.2307/1882010 Spence, M. (2002). Signaling in retrospect and the informational structure of markets. American Economic Review, 92(3), 434–459. doi:10.1257/00028280260136200

Markets, Theory of Weiss, A. (1995). Human capital vs. signaling explanations of wages. Journal of Economic Perspectives, 9(4), 133–154. doi:10.1257/jep.9.4.133

MARKETS, THEORY

examples of imperfect competition. It concludes with applications of the theory of markets in finance and education.

Demand, Supply, and Market Equilibrium

OF

In most societies, resources are allocated by the government, the market, or a mixture of both. A market can be defined as a place where an exchange of a particular good or service between buyers and sellers occurs. Market forces refer to the behavior of buyers and sellers in a market. Consensus on price is the key determinant of transactions. In theory, markets are not centrally organized and no single agent can influence market price. Thus, prices are determined by the market, but prices also have the largest influence on the behavior of individual producers and consumers in a market. Economists have traditionally focused on the way different markets function. Markets are relevant in education even in instances where there is significant government operation in the provision of education (e.g., in determining the prices of textbooks or food). Moreover, the theory of markets has been increasingly applied to the production of schooling itself in recent decades. This entry provides an overview of the theory of markets. First, it describes the supply-demand interaction that determines equilibrium market price and quantity. Next, it outlines the key underlying assumptions, including perfect competition, before discussing

The economist Alfred Marshall developed the supply-demand model in the latter half of the 19th century. The supply-demand model is routinely used in economics and is often the starting point for studying the price of products. In a single market, prices and quantity are determined by the interplay of supply and demand. The relationship between buyers and sellers naturally evolves over time, resulting in price changes from the short run to the long run. Figure 1 graphically illustrates demand, supply, and the market equilibrium. Demand

The demand of a good is determined by several factors, including its price as well as the income, age, and preferences of the consumer. The “law” of demand states that the lower the price of a good, the greater the quantity demanded, ceteris paribus—that is, assuming that other factors influencing quantity demanded such as income are held constant and do not change simultaneously with prices. In other words, prices and quantity demanded are inversely related; thus, consumers buy more when goods are cheaper. The law of demand is graphically represented by the downward-sloping demand curve in Figure 1.

Supply Curve Price

Surplus

Market Equilibrium Equilibrium Price

Shortage

Equilibrium Quantity

Figure 1

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Demand Curve

Quantity

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The demand curve depicts the preferences and behavior of buyers. It is important to differentiate between a shift in demand and movement along the demand curve. A movement along the demand curve happens when quantity demanded changes in response to a change in price, ceteris paribus. A shift in demand occurs when other factors such as changes in income or taste affect the quantity demanded at each price. Supply

The supply of a good depends on a variety of factors such as its price, the technological knowledge of the industry, as well as the costs of inputs. The “law” of supply states that firms will produce larger quantities at higher prices, ceteris paribus. The upward-sloping supply curve in Figure 1 reflects the assumption that higher prices are needed to motivate larger output. The supply curve illustrates the behavior of sellers. There are many parallels between the concepts and terminology of demand and supply. In essence, supply and demand are opposite sides of the same coin. Equilibrium Price and Quantity

The market can be viewed as a process where the producers and consumers of goods and services collectively establish the price. The intersection of the aggregated market supply and demand curves determines the equilibrium market price and quantity. This is the particular price and quantity where what producers are willing to sell (quantity supplied) equals what consumers are willing to buy (quantity demanded). The independent actions of buyers and sellers move the market toward equilibrium. However, as Figure 1 illustrates, when quantity demanded exceeds quantity supplied, a shortage occurs. When quantity supplied exceeds quantity demanded, there is a surplus. And when there is a surplus, there is an incentive for the price to drop as goods otherwise go unsold. Conversely, when there is a shortage, there is an incentive for prices to rise. This model developed by Antoine Augustin Cournot and Alfred Marshall examines one market at a time and is considered a partial equilibrium model. For more complex scenarios, economists employ general equilibrium models that analyze several markets together.

Key Underlying Assumptions Assumptions About Buyers and Sellers

Economists typically make three main assumptions about how market participants behave: self-interest,

rationality, and scarcity. First, buyers and sellers engage in self-interest behavior and are assumed to be pursuing personal goals. Stated differently, producers and consumers are doing what is best for them individually. However, this assumption of selfinterest guiding the motives and actions of market participants is not mutually exclusive with altruistic aims. Second, buyers and sellers engage in rational behavior. Rationality implies that decisions are carefully thought out through a deliberative process that considers the benefits of an action as well as the costs or consequences. Third, market participants face scarcity. There are limited resources to satisfy the unlimited wants and needs of consumers. Hence, choices are necessary. The market functions to adjust price to reflect changes in supply and demand in the most efficient fashion. Prices are the main signals to producers and consumers. Producers are able to respond to the changing demands of the consumer through market prices. In theory, there are no barriers to entry and exit, and producers are guided by profit maximization. Each producer determines the quantity to produce that maximizes his or her profits. Producers continue to earn expected profits as high prices and profit potential attract new producers to a market. Eventually, the increase in the number of producers drives down the price of their product. Lower prices increase consumer demand, but prices can also signal product quality and the value of brands. Sellers seek to maximize profits, whereas buyers maximize satisfaction (or “utility”) based on their preferences and budget constraints. The dynamics of markets also encourage “survival of the fittest” and are presumed to increase efficiency and innovation as producers of goods and services compete to improve offerings to customers. Competition is the driver of markets. There are many buyers and sellers and thus multiple alternatives. Competitive markets lead to the optimal allocation of resources due to price signals. In a well-functioning market, resources are used in the most efficient manner. This presumably provides opportunities for economic development and generating wealth as evolving consumer wants and needs would be met by efficient and responsive suppliers. Perfect Competition

Perfect competition is a key concept in the theory of markets and how prices are determined. There are five main assumptions of perfect competition.

Markets, Theory of

First, there are many firms producing the same good. In other words, there is no product differentiation, and numerous firms engage in the industry. A monopoly violates this assumption. Second, each firm is profit maximizing. In other words, firms are directing resources where price equals marginal cost—the cost of producing an additional unit of a product is the same as the price determined by the market. Third, each firm is a price taker. The actions of a firm do not influence the market price. Fourth, information is perfect, and prices are known by all market participants—both producers and consumers. Fifth, transactions are costless. Buyers and sellers can make exchanges without incurring costs.

Imperfect Competition and Market Failures In reality, the assumptions of perfect competition are often not satisfied. There are few examples of a perfectly competitive market in practice. Most markets have some extent of imperfect competition when one or more of the assumptions of perfect competition are violated. For example, there may be barriers to entering an industry so that some firms are no longer price takers and can influence prices in some direct or indirect way. In the event that markets do not efficiently allocate goods and services, a market failure occurs. There are numerous reasons why markets may fail, including market power of a supplier (e.g., monopolies and oligopolies), incomplete information about products and their quality, and externalities. There are two common cases of imperfect competition: monopoly and oligopoly. A monopoly occurs when a single producer dictates market prices as buyers have no substitute suppliers. An oligopoly is when a few large producers dominate the market. In both instances, competition is reduced and producers can influence market prices. These cases of imperfect competition also represent an inefficient use of resources due to the added costs incurred by other actors in the market and the abnormal or extra profits reaped by the monopolists and oligopolists.

Applications The theory of markets predicts and explains how a change in market conditions influences equilibrium price and quantity. The theory of markets is widely applied across multiple industries. For example, the efficient market hypothesis in finance states that the stock market is rational and embodies all available information. The growth of behavioral

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economics, which posits that consumers may not be rational and may be affected by mass psychology, has challenged the notion that the markets are efficient. Stock prices may reflect consumers’ irrationality, and critics of market efficiency point to the recent burst in the dot-com and housing bubbles as evidence. Empirical evidence on the functioning market suggests that a market is perfectly competitive and efficient only in theory. However, the theory of markets is pervasive and useful. Applications such as the efficient market hypothesis provide an insightful way to use the analysis of past behavior to predict future trends and thus shape strategies. Markets in K-12 Education

Historically, K-12 schooling has been allocated at the government level. Government involvement in education has been primarily motivated because of externalities. In this case, markets would underproduce education as individuals may not take into account social benefits in decisions about obtaining education. In other words, the social benefits of education serve as the main reason why governments usually make schooling mandatory at the primary and secondary levels. However, concerns regarding efficiency and the effectiveness of the centrality of governments in the provision of education have resulted in the emergence of market-based mechanisms in the allocation of educational resources. In recent decades, “market-based” reforms such as charter schools and portfolio districts have grown in popularity in education. The key levers of change for these educational policies rely on the theory of markets. Given that schooling is widely considered a public good with positive externalities, markets tend to undersupply education, prompting governments to make schooling mandatory. The application of the theory of markets to education reforms has sought to disentangle the regulation, financing, and provision of education in attempts to reduce the role of governments in education and bring about greater efficiency. In the mid-20th century, the economist and Nobel Prize recipient Milton Friedman introduced the concept of educational vouchers. Vouchers split the financing of education from the provision of education. Friedman argued that allowing parents to choose which schools their children attend would better satisfy schooling preferences, catalyze competition among schools for students, and lead to an overall more efficient use of resources. He proposed

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educational vouchers that allow parents to use government funds to pay for their children to attend the school of their choice. Vouchers have been tried in more than a dozen states, with Indiana creating the first statewide voucher program in 2011. Other examples of introducing some forms of market competition into public education include charter schools (publicly financed, privately operated schools of choice) and portfolio districts (districts with several school types, providers, and authorizers). Under the portfolio management model, districts are akin to financial managers and schools are analogous to assets in a portfolio—successful models are expanded, while failing schools are closed. Dominic J. Brewer and Richard O. Welsh See also Charter Schools; Educational Vouchers

Further Readings Browning, E., & Zapan, M. (2009). Microeconomics: Theory and applications (10th ed.). Hoboken, NJ: Wiley. Bulkley, K. A. (2010). Introduction: Portfolio managment models in urban school reform. In K. A. Bulkley, J. R. Henig, & H. M. Levin (Eds.), Between public and private: Politics, governance, and the new portfolio models for urban school reform (pp. 3–26). Cambridge, MA: Harvard Education Press. Chubb, J. E., & Moe, T. M. (1990). Politics, markets, and America’s schools. Washington, DC: Brookings Institution Press. Henig, J. (2010). Portfolio management models and the political economy of contracting regimes. In K. Bulkley, J. Henig, & H. Levin (Eds.), Between public and private: Politics, governance, and the new portfolio model for urban school reform (pp. 27–52). Cambridge, MA: Harvard Education Press. Marshall, A. (1920). Principles of economics (8th ed.). London, UK: Macmillan. Nicholson, W., & Snyder, C. M. (2011). Microeconomic theory: Basic principles and extension (11th ed.). Mason, OH: South-Western Cengage Learning. Smith, A. (1937). The wealth of nations. New York, NY: Modern Library. (Original work published 1776)

MEASUREMENT ERROR Measurement is the process of ascribing a number to some abstract concept, such as cognitive ability or school quality. This number, called the measured value, is an approximation of the abstract concept’s

true value. Measurement error is the amount that a measured value deviates from the true value. Depending on its source, measurement error can be random or systematic. Each type of error affects either the measurement’s validity (i.e., the accuracy or how closely a measured value coincides with the true value) or reliability (i.e., the precision or how consistently a measurement produces the same results). It is important to know the causes of each type of error and how to minimize their effects in order to avoid utilizing misleading measurements. This entry explains random and systematic measurement error and discusses ways to prevent or reduce them.

Random Measurement Error Measurement error is random if it occurs by chance. This type of error happens because measurement is naturally variable. Simply by chance, researchers obtain slightly different results each time they take a measurement. For example, suppose a researcher is measuring some cognitive ability by timing how long it takes a student to complete a task. The true time cannot be measured since the researcher can neither stop the timer at the exact moment the student completes the task nor start it precisely when the task begins. The researcher will, merely by random chance, start and stop the timer slightly too early or too late. The difference between the true time that the student spends on a task and the time that the timer reveals is random measurement error. If many measurements are taken with random error, some measurements will be higher than the true value, while others will be lower. But because measured values that are too high counterbalance the measured values that are too low, random error will theoretically equal zero. This fact and the law of large numbers (a theorem from probability theory that states that as a procedure is repeated, the average value of its outcomes will approach the value that the outcomes are theoretically expected to be) led to the following result: Random error will essentially equal zero when many measured values are averaged. Hence, random error will not cause the average measured value of some abstract concept to deviate from its true value if enough measurements are obtained. However, individual measured values themselves will vary around the true value to a greater degree as random error is increasingly present (i.e., more random error increases the standard deviation of the measured values). Random error does not compromise the validity of a measurement

Median Voter Model

but weakens its reliability. It does not bias comparisons in a particular direction but increases the uncertainty surrounding them. Given this nature of random error and the law of large numbers, taking many repeated measurements will sufficiently neutralize its effects. Having more data not only statistically eliminates the deviation between the average measured value and the true value but also decreases the standard deviation of the measured values as more of them closely approximate the true value.

Systematic Measurement Error Systematic measurement error occurs when deviations from the true value are the result of some persistent, ordered pattern. While random error leads to measurements that are higher or lower than the true value, deviations from systematic error occur in one direction. For example, suppose a large school district is measuring the quality of its high schools by asking its high school administrators to report their respective schools’ graduation rates. Administrators, especially those of schools where the true graduation rate is low, have an incentive to overstate their graduation rate. The administrators also might refrain from responding, resulting in a problem called survey nonresponse. Ultimately, the measured graduation rate will deviate from the true graduation rate because of a persistent and ordered pattern, namely, schools with lower graduation rates tend to overstate or to not report their graduation rates. Unlike random error, systematic error will not equal zero on average. All measured values bearing systematic error deviate from the true value in the same direction, so averages of those measured values will also deviate consistently and predictably from the true value in the same way. Systematic error weakens the validity but not the reliability of the measurement. The best way to address systematic measurement error is to identify its sources and to correct or compensate for the resulting deviations. Triangulating, or using multiple methods to measure the same abstract concept, may help the researcher detect systematic error. If different methods of measurement yield different results, then systematic error may be present. It is then the researcher’s responsibility to ascertain that systematic error is the cause of these differences, to identify its source, and to develop a reasonable correction or explanation for it.

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Additional Ways to Address Measurement Error Additional steps may be taken to address measurement error. Researchers constantly attempt to find ways to more accurately and consistently measure the true value of some abstract concept. Concepts being measured should also be clearly defined. Moreover, researchers typically craft and pilot their measuring instruments to ensure that they function as desired. Various statistical procedures, such as factor analysis, which tests whether a small number of common factors are present among a larger number of indicators, or item response theory, which weighs items in a scale more heavily if they are more influential, may be used both to improve and to test the validity and reliability of a measuring instrument. Research methods should be chosen carefully to avoid introducing error. For instance, a poor sampling procedure may leave the study sample unrepresentative of the wider population. This problem is called selection bias. Measurements of some characteristic of the population will contain systematic measurement error if such a sample is used. Finally, following research protocols and properly using measuring instruments as well as carefully recording results further reduces the likelihood and magnitude of measurement error. Patrick J. Wolf and Albert Cheng See also Econometric Methods for Research in Education; Reliability; Selection Bias; Validity

Further Readings Rossi, P. H., Lipsey, M. W., & Freeman, H. E. (2004). Measuring and monitoring program outcomes. In Evaluation: A systematic approach (7th ed., pp. 203–232). Thousand Oaks, CA: Sage. Viswanathan, M. (2005). Measurement error and research design. Thousand Oaks, CA: Sage. Willink, R. (2012). Measurement uncertainty and probability. West Nyack, NY: Cambridge University Press. Wooldridge, J. M. (2012). Introductory econometrics: A modern approach (5th ed.). Mason, OH: SouthWestern, Cengage Learning.

MEDIAN VOTER MODEL The median voter model has proven to be a useful working hypothesis of how public school budgets

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Median Voter Model

are determined. Specifically, this model, derived from formal political theory, predicts that under certain circumstances two candidates competing for elective office will converge to identical policy positions. These are the positions preferred by the median voter—that is, the voter whose preferences are exactly in the middle of the distribution of all preferences. In the realm of government spending, the model predicts that the winning expenditure level would be the median of the various expenditure levels most preferred by individual voters. In empirical work, economists have adopted the median voter framework and identified the median voter as the voter with median income and facing the median tax price for the government expenditure. In this way, the median voter model allows for the analysis of social problems in terms of the preferences of a single individual, the decisive median voter. This entry examines the process of majority voting and specifies the conditions under which the median voter theorem applies. The entry also discusses the theoretical basis of the model and summarizes empirical findings. Finally, the entry comments briefly on some aspects of the median voter outcome.

The Median Voter and Government Spending A fundamental characteristic of government services is that one quantity is provided to all consumers. Accordingly, decisions about the quantities of government services to be supplied or spending levels will be decided by voting. Whether by a vote of the people (direct democracy) or by the people’s elected representatives (representative democracy), the victorious choice in a majority vote is the one that receives at least 50% plus one of the votes. An important but often misunderstood point about majority voting is that the winning alternative is selected not because it is the preferred choice of a majority of voters, but because it is the only choice that could receive majority support. A concern about majority voting is that it may fail to produce a clear-cut winner. This problem may arise if each voter does not have single-peaked preferences; that is, a most preferred alternative and declining utility as the choices move away from that alternative. For example, each voter has a most preferred level of government spending. As expenditures increase or decrease from this most preferred level, the voter’s level of preference decreases. When this is not true for all voters, majority voting may fail to produce a consistent choice. Consider

a choice among three possible levels of government spending, denoted by E1, E2, and E3, from low to high. Assume the preferences of three voters, Rush, Mitt, and Hillary, toward the alternative spending levels are as follows:

Voter

First Choice

Second Choice

Third Choice

Rush

E1

E2

E3

Mitt

E2

E3

E1

Hillary

E3

E1

E2

Hillary most prefers a high level of government spending, but prefers the low level to the median amount. That is, her preferences are not single peaked. As spending is decreased from her most preferred high level, Hillary becomes less happy until spending becomes very low and Hillary’s happiness (utility) increases. When preferences exhibit this characteristic, majority voting may fail to yield a consistent choice. (Preferences would exhibit this characteristic if one believes that a particular government program, e.g., early childhood education, must be generously funded to be effective. If the program is not funded at a high level, the voter would expect the program to be ineffective and would view a low spending level to be less wasteful than the median level.) In this example, if the alternatives are considered two at a time, E1 would be chosen over E2 (Rush and Hillary outvote Mitt) and E3 would win over E1 (Mitt and Hillary defeat Rush). It would appear, then, that E3 is the most preferred. But when paired against E2, E3 loses as Rush and Mitt opt for the median spending level and outvote Hillary. Thus, the voting is inconsistent: E3 beats E1, E1 beats E2, but E2 beats E3. Although each individual voter’s preferences are consistent, the community’s preferences are not. The winner depends on the order in which the votes are taken. This phenomenon is referred to as the voting paradox. In such a case, the median voter rule does not apply.

Economic Theory and Evidence Economic theory and econometric evidence reveal that people’s demand curves for government spending are downward sloping, implying that preferences are single peaked. Consequently, the majority voting model of government choice is well suited for

Moral Hazard

single-purpose governments such as school districts or transportation authorities. Of course, there are other instances when majority voting may be inconsistent; for example, with vote trading or other strategic behavior. Such tactics are more likely to characterize voting in legislative bodies as opposed to general voter elections. However, in light of the many fiscal decisions made by legislative bodies, the question arises whether governmental fiscal decisions can be represented as if they were made by the participatory majority-voting process. This question is addressed by the median voter theorem, which is stated by the economist Ronald Fisher (1996) as follows: If voters’ preferences are single-peaked, if the choice to be made by voting is represented along a continuum, and if voters act on their true preferences, then the choice selected by majority vote is the median of the desired outcomes. (p. 75)

Applying this theorem to the choice of government spending suggests that if all individuals’ demand curves for government services are downward sloping, then the spending level selected by majority voting will be the median of these individuals’ desired spending levels.

Aspects of the Median Voter Outcome A fundamental quality of the median voter’s choice of government expenditure is voter dissatisfaction with the outcome. In general, the outcome selected by majority vote will be the preferred outcome of a minority of voters. Indeed, it is possible that the outcome is the preferred choice of only the median voter. The median voter model, requiring as it does that voters choose among alternative desired outcomes, calls for compromise, which requires some voter dissatisfaction. Furthermore, the level of public expenditure selected by majority voting will, in general, not be economically efficient. The efficient level of government spending requires that the sum of individuals’ marginal benefits equals the marginal production cost of the public goods. The spending level selected by majority voting, however, requires only that the median voter’s marginal benefit equal his or her marginal tax cost. This inefficiency will generally arise because majority voting fails to account for the intensity of voter preferences. Michael F. Addonizio See also Local Control; Public Choice Economics; Public Good; Tiebout Sorting

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Further Readings Arrow, K. (1951). Social choice and individual values. New York, NY: Wiley. Atkinson, A. B., & Stiglitz, J. E. (1980). Theories of the state and public economics. In Lectures on public economics. New York, NY: McGraw-Hill. Fisher, R. C. (1996). Public choice without mobility: Voting. In State and local public finance (2nd ed.). Chicago, IL: Irwin. Gramlich, E. M., & Rubinfeld, D. L. (1980). Micro estimates of public spending demand functions and tests of the Tiebout and median-voter hypotheses. Journal of Political Economy, 90, 536–560. Inman, R. (1978, Winter). Testing political economy’s ‘as if’ proposition: Is the median voter really decisive? Public Choice, 33, 45–65. Romer, T., & Rosenthal, H. (1979). The elusive median voter. Journal of Public Economics, 12, 143–170. Rosen, H. S., & Gayer, T. (2010). Political economy. In Public finance (9th ed.). New York, NY: McGraw-Hill.

MERIT PAY See Pay for Performance

MORAL HAZARD Moral hazard is a type of principal-agent problem. Principal-agent problems arise when two entities, a principal and an agent, enter into a contract, but the principal in the contract does not have full information about the agent or about the agent’s actions. This entry distinguishes between moral hazard— principal-agent problems involving hidden actions— and principal-agent problems involving hidden information. The entry discusses moral hazard in employment contracts and identifies a potential solution to the problem of moral hazard in employment contracts. Examples related to the economics of education are provided.

Moral Hazard and Principal-Agent Problems Insurance contracts are often used as examples of principal-agent problems. When it is legal to do so, a company selling health insurance policies will usually charge a higher price to someone in poor health than it would charge to someone in good health. In this example, if the person buying the insurance policy—the agent—is in poor health, he

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might want to hide information about his health from the insurance company—the principal—in order to obtain insurance at a lower price. This is an example of a principal-agent problem with hidden information. Similarly, an agent might choose to buy an insurance policy from a principal that protects the agent’s home from fire or theft. Here, the insurance company might charge a higher price to someone who routinely acts carelessly by leaving the stove on after leaving the house, thereby increasing the probability that the house will burn down, or leaving the front door unlocked or open after leaving the house, increasing the probability that items will be stolen from the house. In this example, the agent might want to hide from the principal the fact that he regularly leaves the stove on and his doors unlocked. Otherwise, if the principal learned of these actions, the principal might choose to charge the agent a higher price for the home insurance policy. This second type of principal-agent problem, involving asymmetric information about the agent’s actions, is known as moral hazard. Moral hazard can be contrasted with the first type of principalagent problem, which involves hidden information, not hidden actions. Moral hazard can be seen in many areas of education: • Students collaborating on a group project may not exert the same amount of effort as would be the case if each student worked individually. • Income-contingent student loan repayment plans allow individuals who took out loans to finance their college education to make loan payments proportionate to their annual incomes. While these plans may increase the share of college graduates entering low-paying but socially valuable professions, such as teaching or social work, they also decrease individuals’ incentives to attain high-wage employment after college. • Educating a child is a team effort involving students, teachers, parents, school administrators, and policymakers. The potential for moral hazard arises for all members of this team. For example, a child may choose not to study if failure on an exam can reasonably be attributed to poor teaching rather than student shirking. A primary goal of many school accountability policies is to reduce the potential for moral hazard among teachers and educators.

Moral Hazard and Employment Contracts Moral hazard can also result from employment contracts. In the case of employment contracts, the employer is the principal, and the employee is the agent. An employee’s productivity may be difficult to observe, and even when an employee’s productivity can be observed, the costs associated with monitoring that productivity may be high. If it is difficult and/or costly to monitor productivity, the employee may choose to exert less effort—to shirk—and otherwise be less productive than would be the case if the employer were able to observe the employee’s productivity perfectly. This potential for moral hazard can create an obstacle preventing employers and employees from entering what would otherwise be mutually beneficial contracts. The potential for moral hazard exists in employment contracts between school districts and their employees. How much a student knows and can do at the end of a school term depends on a number of factors: how much the student knew and could do at the beginning of the school term, the student’s effort, the student’s potential to learn, the student’s teachers and other school personnel, the student’s home environment, and so on. Measuring how much a student has learned over the course of a school term is challenging in itself; determining how much of that learning can be attributed to each of the factors listed above—student effort, teachers, home environment, and so on—presents an even greater challenge. As a result, teachers, principals, and other school employees may be less productive than would be the case if it were easy to observe each employee’s productivity.

Multiperiod Employment Contracts as a Solution to the Moral Hazard Problem Contracts can be negotiated to obviate employment situations with the potential for moral hazard. Edward Lazear argues that to discourage employee shirking, employers and employees may engage in explicit or implicit multiperiod contracts that pay employees less than the full value of their production in the early periods of the contract and more than the full value of their production in the later periods of the contract. Under such a contract, the employer need not constantly monitor the employee’s productivity. Instead, the employer can conduct unannounced occasional observations of the employee’s productivity. Employees who appear to be fully productive during these observations will retain their positions through to the later periods of the contract,

Moral Hazard

when the compensation is greater. Employees who appear to be shirking will be dismissed. However, Lazear and others note that such a multiperiod contract may not solve the moral hazard problem. A multiperiod, delayed-compensation contract could incentivize employers to dismiss employees before the later, higher compensation periods of the contract, regardless of employees’ observed productivity. To avoid this second moral hazard problem, further rules regarding termination can be added to contracts to prevent employees who otherwise fulfill the terms of the contract from being terminated in the later periods of the contract. Lazear also notes that employers’ concerns about their reputation could also mitigate the incentive to dismiss employees in the later periods of their contracts. Salaries of school teachers and administrators are often determined by a fixed salary schedule under which employees’ salaries increase with their years of experience. Research has shown that, on average, teacher productivity increases little after the first 4 or 5 years of experience, leading some to question the rationality of the fixed salary schedule. It is possible that the fixed salary schedule, together with tenure for teachers who are observed to be

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productive and engage in no malfeasance, exists to solve the potential problem of moral hazard in employment contracts of teachers and school administrators. Eric Larsen See also Economic Efficiency; Pay for Performance; Performance Evaluation Systems; Principal-Agent Problem; Teacher Evaluation

Further Readings Carnoy, M., & Loeb, S. (2002). Does external accountability affect student outcomes? A cross-state analysis. Educational Evaluation and Policy Analysis, 24(4), 305–331. Goldhaber, D., DeArmond, M., Player, D., & Choi, H. (2008). Why do so few public school districts use merit pay? Journal of Education Finance, 33(3), 262–289. Hanushek, E., & Raymond, M. E. (2002). Sorting out accountability systems. In W. M. Evers & H. J. Walberg (Eds.), School accountability (pp. 75–104). Stanford, CA: Hoover Institution Press. Lazear, E., & Moore, R. (1984). Incentives, productivity, and labor contracts. Quarterly Journal of Economics, 99(2), 275–296.

N NATION

AT

among youths and young adults as evidence of U.S. educational decline—a decline that commission members asserted would impose significant economic and social costs on the United States. The commission pointed to inadequate curriculum, particularly in the high schools, as the root cause of declining achievement:

RISK, A

Arguably the most impactful education report in U.S. history, A Nation at Risk was authored by the National Commission on Excellence in Education, an 18-member group appointed by Terrel H. Bell, President Ronald Reagan’s Secretary of Education. Completed in 18 months and released in April 1983, in the depths of a severe economic recession, the 36-page report created an immediate sensation with its blunt opening statement:

Secondary school curricula have been homogenized, diluted, and diffused to the point that they no longer have a central purpose. In effect, we have a cafeteriastyle curriculum in which the appetizers and desserts can easily be mistaken for the main courses. . . . This curricular smorgasbord, combined with extensive student choice, explains a great deal about where we find ourselves today. (p. 18)

Our Nation is at risk. Our once unchallenged preeminence in commerce, industry, science, and technological innovation is being overtaken by competitors throughout the world. . . . The educational foundations of our society are presently being eroded by a rising tide of mediocrity that threatens our very future as a Nation and a people. . . . If an unfriendly foreign power had attempted to impose on America the mediocre educational performance that exists today, we might well have viewed it as an act of war. As it stands, we have allowed this to happen to ourselves. . . . We have, in effect, been committing an act of unthinking, unilateral educational disarmament. (p. 5)

The migration of students from college preparatory and vocational programs to “general track” courses came in for particular criticism, along with the rise of “minimum competency” exams (then required for high school graduation in 37 states) that “fall short of what is needed, as the minimum tends to become the ‘maximum,’ thus lowering educational standards for all” (p. 20). The commissioners also decried falling academic expectations, with students doing little homework, school districts lowering high school graduation requirements, colleges responding with lower admission standards, and U.S. students falling behind their international peers in the completion of coursework in math and science. Publication of A Nation at Risk triggered what many have called the first wave of comprehensive

The report cited declining SAT scores from 1963 to 1980, falling scores on standardized achievement tests, poor performance on international assessments, the increase in remedial math courses on public college campuses across the United States, and the incidence of functional illiteracy

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state and federal educational reforms, focusing on such initiatives as boosting high school graduation requirements, raising standards for entry into the teaching profession, instituting merit pay and market-sensitive pay for teachers, making local and state standardized tests more stringent, and increasing the amount of instructional time in school as well as the amount of homework given. Among the report’s many recommendations, two resonated particularly strongly with the press and the public: (1) strengthened high school graduation requirements and (2) higher standards for new entrants into the teaching profession. As to the former, the commissioners asserted that all high school students should study “The Five New Basics”: (1) 4 years of English, (2) 3 years of mathematics, (3) 3 years of science, (4) 3 years of social studies, and (5) ½ year of computer science. College-bound students were advised to add at least 2 years of a foreign language. Furthermore, the commission proposed that foreign language study begin in elementary school and that schools supplement the new basics with courses in the arts and vocational education. As for teacher preparation, the commission maintained that those entering the profession should be expected to meet high educational standards relating to both teaching aptitude and competence in an academic discipline. Teacher compensation should be increased and should be “professionally competitive, market-sensitive, and performance based.” Decisions regarding salary, retention, tenure, and promotion “should be tied to an effective evaluation system that includes peer review so that superior teachers can be rewarded, average ones encouraged, and poor ones either improved or terminated” (p. 30). The commission also called on colleges and universities to raise their admissions requirements and urged scholars and publishers to improve the quality of textbooks and other instructional materials. States were encouraged to critically evaluate textbooks and ensure that publishers present evidence of the effectiveness of their instructional materials.

The States Respond: The Academic Standards Movement States’ responses to the report’s call for the “New Basics” in U.S. high schools were dramatic. Virtually all states increased their graduation requirements in academic subjects, including no fewer than 36 states in 1983 and 1984 alone. Several states that

previously allowed local districts to set their own graduation requirements (e.g., California, Florida, Mississippi, and Wisconsin) adopted statewide standards. At least eight states—North Carolina, Louisiana, South Carolina, Mississippi, Illinois, Texas, Indiana, and Oklahoma—established public boarding schools. Others created residential summer schools for high-achieving students. Furthermore, 30 states raised the number of academic courses required for admission to public colleges and universities. Enrollment in academic courses surged in U.S. high schools. Graduates increased their coursework in English, math, history, science, and foreign languages. Participation in Advanced Placement (AP) courses rose as well. The College Board reported in 1989 that the 456,000 AP tests taken by high school students that year was more than double the number administered in 1983 and that the number of high schools participating in the AP program had increased by 52% during the same period. Nevertheless, despite this rise in AP enrollments, only 2% of U.S. public high school students participated in the program in 1989. A Nation at Risk also inspired changes in teacher compensation. Teachers were rarely paid or promoted on the basis of performance when A Nation at Risk was released in 1983, and there were few career advancement opportunities that did not require teachers to abandon the classroom for administration. By the early 1990s, however, dozens of states and scores of school systems had established career ladders, mentorships, and other leadership opportunities that offered teachers increased status, higher salaries, and new professional challenges without requiring that they abandon the classroom.

A Nation at Risk as Education Policy While generally held in high regard in the education community as sound education policy analysis, A Nation at Risk is not without its critics. Some educators have cited the report’s lack of attention to the social and economic underpinnings of children’s academic performance, including health, housing, family income, and neighborhood quality of life. This omission was glaring in view of the mountain of research evidence attesting to the importance of socioeconomic variables on student achievement compiled since publication of the widely heralded Equality of Educational Opportunity report by James Coleman and colleagues in 1966.

Nation at Risk, A

Furthermore, the commission’s pointed criticisms of U.S. high schools while sparing elementary and middle/junior high schools of any blame has been justifiably challenged. Children enter ninth grade with widely divergent levels of academic skills and preparation, and the report’s lack of attention to this issue was a glaring error. Finally, the report has been criticized for its fixation on the needs of business and industry, excessively emphasizing math, science, and technology-related curricula at the expense of social, moral, and aesthetic concerns, so vital to the functioning of a democracy. Some observers argued that this first wave of school reform following publication of A Nation at Risk failed to produce any substantive improvements in student achievement, because the reforms were both superficial and “top down”—that is, imposed on educators by politicians and government bureaucrats and overly focused on school inputs (e.g., longer school day or year, increased graduation requirements, etc.) and basic skills. The reforms, in their view, failed to change the content of instruction or directly involve teachers in the reform process. Other critics found the reformers’ attempts to teach a rigorous academic curriculum to a much broader student body undermined by superficial coverage of subject matter, poorly prepared teachers, and ineffective pedagogy. This criticism, of course, is properly directed at implementation of the commission’s reforms, not the merit of the reforms themselves. Indeed, many observers trace the origins of the contemporary academic standards movement to A Nation at Risk. The report’s strong emphasis on curriculum, in their view, was a precursor to the concept of systemic reform that inspired the adoption of national education goals by the state governors at their historic 1989 education summit in Charlottesville, Virginia.

The Economic Analysis The grand scope of education reform triggered by A Nation at Risk undoubtedly was attributable to the report’s unmistakable linkage of school reform with the nation’s economic well-being. At the time of the report’s release, the U.S. economy was caught in the grip of a severe recession that began in 1979. The U.S. unemployment rate had reached 9.6% in 1983, with 10.7 million Americans looking for work, and another 1.6 million abandoning their job search altogether. Productivity growth, the source of improved living standards, was alarmingly low,

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having dropped precipitously from 2.6% annually during 1962–1973 to an anemic 0.9% annually during 1973–1986. As a consequence, real wage growth virtually disappeared, falling from 2.6% annually between 1962 and 1973, to a miniscule 0.3% during the latter period. A Nation at Risk clearly pointed to deterioration in the public schools as a significant, if not primary, cause of this decline in U.S. productivity growth. This perceived linkage between U.S. economic prosperity and the quality of the nation’s public schools is most clearly stated in the report’s section titled “The Risk”: The world is indeed one global village. We live among determined, well-educated, and strongly motivated competitors. We compete with them for international standing and markets, not only with products but also with the ideas of our laboratories and neighborhood workshops. America’s position in the world may once have been reasonably secure with only a few exceptionally well-trained men and women. It is no longer. . . . Knowledge, learning, information, and skilled intelligence are the new raw materials of international commerce and are today spreading throughout the world as vigorously as miracle drugs, synthetic fertilizers, and blue jeans did earlier. If only to keep and improve on the slim competitive edge we still retain in world markets, we must dedicate ourselves to the reform of our educational system. (pp. 6–7)

This alleged linkage between the extremely low productivity of the U.S. economy in the early 1980s and the quality of the nation’s public schools, while clearly the source of much of the report’s public support, was almost certainly mistaken. The steep productivity decline that began around 1973 afflicted the entire industrialized world, including France, Germany, and Japan. Moreover, the decline was much too precipitous to be blamed on relatively gradual changes such as a decline in the quality of the workforce. There were more logical explanations of the slowdown. Worldwide economic growth came to an abrupt halt in late 1973 by the Arab oil embargo and the subsequent quadrupling of oil prices by the Organization of Petroleum Exporting Countries. Prices for other raw materials also rose precipitously in 1973 and 1974 due, in part, to crop failures around the world. These events ignited a round of inflation in most countries, and a deep

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worldwide recession soon followed. The United States and Japan, but not Europe, had largely recovered from the severe stagflation of the mid1970s when Organization of Petroleum Exporting Countries again cut oil supplies in 1979 and ignited another round of international stagflation, from which the United States was slow to recover. Of course, the productivity of the labor force, defined as the output produced by each hour of work, depends on the skill levels of workers. But many other factors contribute heavily to worker productivity, including the quantity and quality of the capital equipment available to the labor force, the pace and character of technological change, and the way that labor is organized in the production process. The weakness of the commission’s theory linking the U.S. productivity problem to a decline in the quality of public schools became readily apparent a decade later. By 1994, the United States had become the most competitive economy in the world. In aggregate terms, the economy was booming: unemployment had fallen below 5.5%, inflation was a mere 2.7%, and labor productivity, the ultimate measure of the economy’s efficiency, was growing faster than in the two previous decades. Educational outcomes in U.S. public schools, on the other hand, had changed little between 1980 and 1994, and yet the United States was on the threshold of its longest peacetime period of economic growth in its history.

School Quality and Economic Growth The foregoing analysis notwithstanding, it seems obvious that as societies become more educated and their labor forces become more skilled, their economies should grow faster. More recent research supports this view. Prior to 1980, economic research on the impact of education estimated the financial rate of return an individual could realize by spending additional time in school. Education was considered an “input,” the number of years spent in school, rather than an “output,” or how much workers actually knew. These studies, at least in the United States, found that the rate of return on time spent in school, specifically whether people went to college, was quite low in the 1960s and 1970s. But starting in the 1980s, the “earnings gap” between high school graduates and college graduates began to grow, so the returns to an investment in college grew dramatically. Conventional theory attributed this outcome to technological growth that drove the demand for skilled workers faster than the rate

at which they could be supplied. Economists also noted that education yielded benefits to society over and above those accruing to the educated individuals. More educated individuals are not only better citizens, enhancing democratic society, but also produce more innovation, thereby accelerating economic growth. Economists have been challenged, however, when looking for a statistical correlation between the amount of schooling and economic growth (controlling for other factors affecting growth) in various countries. Some researchers have found a positive association, particularly when measuring education as an output (e.g., test scores). Taken as a whole, however, the literature on the contribution of education to economic growth remains somewhat ambiguous. For individuals, more education promotes higher labor productivity, and for entire nations, greater levels of education are associated with higher rates of economic growth. It is clear, however, that economic growth depends on much more than investment in education. The right type of government and the protection of contract and property rights necessary to promote entrepreneurship are prerequisites for the robust economic growth that spawns great improvements in living standards. (Socialist countries have often experienced little or no growth and chronically poor living standards despite their investments in education.) Perhaps the best interpretation of this literature is that education is a necessary but not sufficient condition for economic growth and its importance increases as the economy becomes more technologically sophisticated. The commission appeared to grasp the latter point to some degree, but its economic analysis glossed over the former. Furthermore, the lag between the effects of school quality on productivity and economic growth appears to be much longer than implied in A Nation at Risk. Most workers in the economy who exert the greatest impact on current levels of productivity and growth were educated decades earlier. Nevertheless, despite its flawed analysis of the impact of school quality on economic growth, publication of A Nation at Risk remains a landmark event in the history of U.S. public education. The report generated enormous interest in public education and ushered in an unbroken period of intense debate, scrutiny, and education reform in the United States that continues to the present day. Michael F. Addonizio

National Assessment of Educational Progress See also Accountability, Standards-Based; Globalization; Human Capital; Pay for Performance; School Quality and Earnings

Further Readings Baumol, W. J., Litan, R. E., & Schramm, C. J. (2007). Good capitalism, bad capitalism, and the economics of growth and prosperity. New Haven, CT: Yale University Press. Blinder, A. (1987). Hard heads soft hearts. Reading, MA: Addison-Wesley. Goldin, C., & Katz, L. F. (2008). The race between education and technology. Cambridge, MA: Harvard University Press. Hanushek, E. A. (2006). Alternative school policies and the benefits of general cognitive skills. Economics of Education Review, 25, 447–462. Murnane, R. J., & Levy, F. (1996). Teaching the new basic skills. New York, NY: Free Press. National Commission on Excellence in Education. (1983). A nation at risk. Retrieved from http://eric.ed .gov/?id=ED226006 Ravitch, D. (2010). The death and life of the great American school system. New York, NY: Basic Books. Toch, T. (1991). In the name of excellence. New York, NY: Oxford University Press.

NATIONAL ASSESSMENT OF EDUCATIONAL PROGRESS The National Assessment of Educational Progress (NAEP) is the name of a group of subject matter tests regularly administered by the U.S. Department of Education’s National Center for Education Statistics to a nationally representative set of students. The primary purpose of NAEP is to provide a widely disseminated indicator of the academic competencies of the nation’s elementary and secondary students. NAEP tests date to 1969 and are most frequently given in mathematics, reading, science, and writing. NAEP assessments serve three purposes: (1) to permit comparisons of student competencies over time, (2) to provide point-in-time snapshots that permit comparisons across geographic units as well as among subgroups of the student population, and (3) to permit correlating academic performance with features of students’ education through the collection of information on students’ educational experiences and the educational practices in the schools they attend.

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This entry begins with a brief history of NAEP, moves to discuss the various different types of NAEP assessments, and then presents some technical detail on NAEP tests. It ends with a brief comparison between NAEP and various state and international assessments.

History NAEP has been controversial since its origins. Those who initially promoted the idea of national tests ran up against strong opposition, with critics vocalizing concern over a federal encroachment into education, a misuse of test results, and an overreliance on test scores in policy making. This controversy led to NAEP’s establishment in 1964 through a private initiative with voluntary participation that was funded by the Carnegie Corporation. Only later did NAEP become a congressionally mandated program administered and overseen by the federal government. The first national assessments took place in 1969 and have continued every couple of years since. The subject areas NAEP assesses have both expanded and periodically changed over the years. In addition to mathematics, reading, science, and writing, NAEP assessments today include the arts, civics, economics, geography, and U.S. history. In 2014, assessments will extend to technology and engineering literacy, and others in foreign language and world history are under development. Beginning in 1990, U.S. states were provided the option of participating in trial assessments that would permit the release of state-level estimates of student performance. This is now a permanent feature of NAEP, and almost all states voluntarily participate in state-level assessments. Initially, state assessments were distinct from national assessments, but today, state assessments are combined so as to be representative of the nation as well. A second expansion of NAEP’s scope occurred in 2002, when select urban school districts were allowed to participate in district-level assessments; about a dozen urban districts currently participate in this ongoing trial that provides district-level estimates of student performance.

Types of NAEP Assessments Consistent with its different goals, NAEP consists of several distinct types of assessments. One objective of NAEP is to capture students’ proficiency at key stages of their education in various subject matters. To this end, tests are periodically administered in

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various subjects to a sample of the nation’s 4th, 8th, and 12th graders. These tests, termed main assessments, can vary over time as the tests are designed to reflect current expectations of what content matter and skills students should know. Most main assessments are given to a nationally representative group of students, typically numbering around 10,000 to 20,000, with state-level estimates of student performance not available. Since 1990, however, the National Center for Education Statistics has provided states with the option of participating in NAEP’s fourth- and eighth-grade mathematics, reading, science, and writing tests so that they can gain estimates of the performance of public school students within their state. State and national assessments in mathematics and reading for fourth and eighth graders are done at least every 2 years. In assessments involving state data, about 150,000 students nationwide are tested. Estimates from all other of NAEP’s main tests, and all tests given to 12th graders, are not available at the state level. Main assessments at the national level provide estimates of all of the nation’s students, with results available separately for students attending both public and private institutions. A second set of NAEP tests is referred to as longterm assessments; these have been in place since 1986 and are now administered every 4 years. Long-term assessments, which most commonly cover mathematics, reading, science, and writing, are designed to permit assessing national trends over time. Rather than being administered to representative samples of grade-level students, the long-term assessments are administered to a representative group of the nation’s 9-, 13-, and 17-year-old students and thus test students in different grades. Long-term assessments are designed to be consistent over time so that results in different years are directly comparable. Due to some minor differences between NAEP’s main and long-term assessments, the scores from each cannot be directly compared.

Technical Detail of NAEP NAEP estimates of student proficiency come in the form of both scale test scores and achievement levels. Scale scores provide a numeric indicator of students’ content knowledge and skills in a particular subject. Achievement levels, on the other hand, group student scores into the categories of below basic, basic, proficient, and advanced. Most NAEP assessments contain both multiple-choice answers as well as constructed responses where students supply their own answer.

NAEP tests are long and, within a subject area, cover a large number of thematic areas so that the tests are capable of assessing a range of student competencies in a single subject matter. In both of these senses, NAEP distinguishes itself from typical assessments administered by U.S. states, as these tend to be both shorter and focused on assessing a limited range of competencies. Because of the length of NAEP exams, students who take them take only a portion of one test, with the test score for that portion used to generate an estimated “plausible value” test score for the entire test. Sampling weights for all students within a particular school are used to generate national-level (and where applicable state- or district-level) estimates. At the national level, NAEP provides estimates for average student competency in the subject matter, as well as the distribution of achievement across students; competency levels are also estimated for subpopulations of the population, broken down by gender, ethnicity and race, socioeconomic status, disability status, and linguistic background and are made available for both private and public school students. In main NAEP tests that include state-level estimates, state results are also available for these subpopulations, provided that the numbers tested are sufficient to allow for making state-level estimates. The same holds true for the trial urban district assessments. By law, NAEP can provide neither individual nor school-level results.

Comparison of NAEP With State and International Assessments NAEP assessments are distinct from tests given at the state level. Unlike NAEP, the results of statelevel assessments are available at the individual and school level. State tests, which are mandated under the federal No Child Left Behind Act, are tied to state standards and state curriculum and, thus, vary from state to state in terms of both content and rigor. Like NAEP, state tests categorize student performance into various levels of proficiency. The U.S. Department of Education periodically aligns state tests with NAEP to compare state proficiency standard with those used by NAEP. These comparisons reveal that states define student proficiency using a wide range of standards, and with few exceptions, state proficiency standards are significantly below those embodied in NAEP. The National Center for Education Statistics is also engaged in an ongoing effort to convert NAEP scores into Trends in International Mathematics and

National Board Certification for Teachers

Science Study scores in eighth-grade mathematics and science so that states can compare their students’ proficiency with those of students in other countries. This effort, as well as others, indicates that NAEP proficiency levels are similar to the international standards reflected in international assessments given to students around the world. One common criticism of NAEP, in fact, is that its standards are unrealistically high and, therefore, mislead the public about the level of student competency in the United States. According to the NAEP, for instance, a majority of the nation’s students are not proficient in mathematics or reading. Another common criticism of NAEP is the potential for subjectivity in student scores, since about 40% of NAEP questions require constructed responses. Many also level the same criticism at NAEP that they do of all standardized tests: A student’s competency in a particular subject matter cannot be reduced to one number arrived at through taking a test. One limitation of NAEP is that the cross-sectional property of the test, as well as the limited information available about the test takers and their schools, limits its usefulness for assessing policy. Yet the widespread availability of NAEP data makes interpretative misuse of it a common practice. Katherine Baird See also Centralization Versus Decentralization; International Datasets in Education; Local Control; Nation at Risk, A; National Center for Education Statistics; National Datasets in Education; No Child Left Behind Act

Further Readings Phillips, G. W. (2010). International benchmarking: State education performance standards. Washington, DC: American Institutes for Research Retrieved from http:// www.air.org/files/AIR_Int_Benchmarking_State_Ed__ Perf_Standards.pdf Sawchuk, S. (2013, July 24). When bad things happen to good NAEP data. Education Week. Retrieved from http://www.edweek.org/ew/articles/2013/07/24/37naep .h32.html?tkn=XWSFjintSc1tarwBowLttmHfKt77SXIk UIav&cmp=ENL-EU-NEWS1 Stedman, L. (2009, March). The NAEP long-term trend assessment: A review of its transformation, use, and findings. Paper commissioned for the 20th anniversary of the National Assessment Governing Board, Washington, DC. Retrieved from http://www.nagb.org/ content/nagb/assets/documents/who-we-are/20anniversary/stedman-long-term-formatted.pdf

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Website National Center for Education Statistics website for the National Assessment of Educational Progress: www .nationsreportcard.gov

NATIONAL BOARD CERTIFICATION FOR TEACHERS The National Board for Professional Teaching Standards (NBPTS), commonly referred to as the National Board, was founded in 1987 for the purpose of establishing standards for what accomplished teachers should know and be able to do. The creation of the NBPTS was prompted by calls for education reform in the 1980s in such reports as A Nation at Risk (1983) and A Nation Prepared: Teachers for the 21st Century (1986). The NBPTS began with a 63-member board of directors, two thirds of whom were teachers, who were charged with establishing a voluntary process by which teachers could become certified as accomplished through demonstrating that they met an ambitious set of national standards. The rationale adopted by the NBPTS is similar to that for other professions, such as medicine and law, whereby accomplished individuals in the profession have a substantive role in determining standards for entry and advancement in the profession. After establishing the standards, the NBPTS collaborated with measurement experts to design the performance-based assessments that teachers would complete to earn certification. The first National Board certified teachers (NBCTs) earned their certificate in 1994. By the end of 2012, more than 100,000 teachers had achieved National Board certification, representing approximately 3% of teachers nationwide. The Carnegie Corporation of New York provided an initial $1 million to support the work of the NBPTS, and in 1991, the federal government began annual appropriations for the NBPTS. This entry includes information about the certification process, availability of incentives for NBCTs, and evidence regarding effectiveness and equity. The entry concludes with a discussion of future directions for the NBPTS.

Certification The NBPTS awards certificates in 25 areas representing 16 different subjects and spanning PreK-12. The assessment consists of a multimedia portfolio

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that demonstrates knowledge of content and pedagogy and evidence of student learning. The portfolio includes videotaped samples of teaching practice, evidence in the form of student work samples, and reflective commentaries that demonstrate how the evidence provided addresses each standard being examined. Candidates also complete timed response exams through an online assessment center. The process for earning a National Board certificate takes a minimum of 1 year. Candidates must hold a valid teaching credential and have at least 3 years of teaching experience. The cost for the assessment process is $2,500. Over the years of the program, a number of incentives and supports for teachers wishing to obtain National Board certification have been offered. Approximately 60% of states and some local school districts have provided incentives and supports that have been funded through a variety of public and private sources. Examples include full or partial subsidies for NBCTs for the cost of certification, professional development resources and networks during the certification process, and increased compensation for NBCTs, with some additional stipends if NBCTs teach in high-poverty or low-performing schools. The rates of participation in National Board certification vary greatly across states and are correlated with the availability and type of financial incentives being offered. States with the greatest number of NBCTs include North Carolina, Florida, South Carolina, Washington, California, and Illinois.

Evidence on Effectiveness and Equity Research on the National Board certification program has examined a variety of topics. In several survey-based studies, NBCTs report that participation in the certification process provided them with useful professional development that improved their teaching practice, irrespective of whether they earned certification. Some studies have found that NBCTs are more likely to engage in teacher leadership and mentoring roles, and others have noted that National Board certification serves as a signal of teacher quality. The majority of research studies conducted have examined the impact that NBCTs have on student performance, using a variety of methodological approaches, time periods, and sample sizes. Additionally, a large portion of these impact studies use data from the states of North Carolina and Florida, where both a comparatively large portion

of NBCTs reside and where databases are available to conduct these types of analyses. In 2008, the National Research Council conducted its own analysis that included a review of 25 rigorous studies. The council’s study concluded that students taught by NBCTs had higher achievement test gains than those taught by comparable groups of non-NBCTs, although the differences varied by state. The council noted that some of the studies that were reviewed found mostly positive impacts, while others found mixed results or no impact. Some studies have also found that the positive impact on student learning is greater for students of color and students from low-income families. As is generally the case for research examining the impact of individual teachers on student learning, there is considerable debate about the appropriate measures and analytic methods employed, and the need for an ongoing research program to examine a variety of impacts has been noted by the NBPTS. In addition to deliberations about the impact that the NBPTS has on student learning, some questions have been raised about the equity of distribution of NBCTs across schools and about cost. Especially in the earlier years of the National Board program, comparatively lower proportions of NBCTs were located in high-poverty or low-performing schools. This prompted some states to offer additional bonuses for NBCTs who teach in these challenging schools, and progress has been made, with the NBPTS reporting that nearly half of NBCTs now teach in high-poverty schools. However, since the economic recession in recent years, state-provided support for NBCTs has been declining.

Future Directions The NBPTS is in the process of streamlining its certification process and expanding its scope. One of the first changes made was the addition of a process called Take One that allows teachers to sample a portion of the National Board certification process before deciding if they wish to pursue a certificate. Take One scores can be applied to a candidate’s subsequent certification process. In 2010, the NBPTS began field testing a National Board certification for school principals and will launch this certification once field testing and validity studies are complete. The NBPTS is also in the midst of revamping the National Board certificates for teachers. Changes being made include a reduction in certification fees, more flexibility in completing assessments, and the

National Center for Education Statistics

inclusion of student surveys and multiple measures of student academic progress in the portfolio. These changes will begin to take effect in 2014–2015. Finally, discussions are under way about the development of a national exam for entry into the teaching profession, with the NBPTS potentially overseeing the development of the standards and assessment for this exam. Margaret Plecki See also Licensure and Certification; Professional Development; Teacher Effectiveness; Teacher Training and Preparation

Further Readings Cannata, M., McCrory, R., Sykes, G., Anagnostopoulos, D., & Frank, K. A. (2010). Exploring the influence of National Board Certified Teachers in their schools and beyond. Educational Administration Quarterly, 46(4), 463–490. Harris, D., & Sass, T. (2009). The effects of NBPTScertified teachers on student achievement. Journal of Policy Analysis and Management, 28(1), 55–80. National Research Council. (2008). Assessing accomplished teaching: Advanced-level certification programs. Washington, DC: National Academies Press.

NATIONAL CENTER FOR EDUCATION STATISTICS The National Center for Education Statistics (NCES) is one of the principal agencies of the Federal Statistical System of the United States, a decentralized network of federal agencies that produce statistical data. Part of the Institute of Education Sciences at the U.S. Department of Education, NCES is the primary federal office responsible for gathering, analyzing, reporting, and disseminating data on foreign and domestic education systems. NCES activities include the following: collection of administrative data from schools, districts, and states; administration of representative surveys; and oversight of national assessments that permit analyses across subgroups of the U.S. student population. Data products at NCES include both universe data collections, which include data on all subjects in a population, and nationally and internationally representative sample survey data. NCES makes much of its data available on its website as public-use data files;

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datasets that contain confidential, individually identifiable information may be made available under a license as restricted-use files. The depth and breadth of quantitative education research data that NCES collects and manages is unparalleled in the United States. NCES collects a wealth of quantitative education research data useful for informing federal, state, and local policymakers, school officials, researchers, journalists, and the public about education in the United States. This entry discusses the purpose and organization of the NCES and some of the reports and datasets the NCES produces.

Overview of the Purpose and Organization of NCES In 1867, Congress created the Department of Education in large part, as the legislation establishing it said, “for the purpose of collecting such statistics and facts as shall show the condition and progress of education.” Today, NCES is the primary office within U.S. Department of Education that fulfills this purpose. The Office of the Commissioner directs NCES policy and operations. The Office of the Deputy Commissioner supports NCES data suppliers and users, ensures statistical rigor through the Statistical Standards Program, and maintains the licensing system for restricted-use data. Four NCES divisions are responsible for different content areas. (1) The Early Childhood, International and Crosscutting Studies Division manages the areas of early childhood and international education, as well as U.S. school crime and safety; (2) the Elementary/Secondary and Libraries Studies Division oversees surveys dealing with elementary and secondary education, including longitudinal studies, at the national, state, and local levels; (3) the Postsecondary, Adult, and Career Education Division conducts surveys and studies addressing postsecondary, adult, and career and technical education; and (4) the Assessment Division administers the National Assessment of Educational Progress (NAEP), a collection of periodic, multisubject, nationally representative assessments of public school students that began in 1969 and continues today.

Prominent NCES Reports and Data Collections NCES produces annual, cross-sectional, longitudinal, and occasional reports, as well as data collections that cover a wide array of education topics. Its

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three annual reports are among its most notable publications. The Condition of Education is a collection of statistical indicators that reflect important recent developments in education using the most current statistical data. The Digest of Education Statistics is an annual report that provides up-to-date data on national, long-term trends in education. In contrast, the Projections of Education Statistics provides key education statistics for recent years along with projections for a decade into the future. The majority of other NCES publications are based on numerous NCES data collections, an illustrative selection of which follows. The Common Core of Data collection includes universe data on states, local education agencies, and public and private schools; state and school district finance data and dropout rates; and teacher, school, and district data on teacher compensation. The datasets found in Common Core of Data serve as sampling frames for many education surveys in and outside of NCES. The Early Childhood Longitudinal Studies program consists of three longitudinal studies that include data on (1) a 1998–1999 kindergarten cohort followed through the eighth grade, (2) a 2001 birth cohort followed through kindergarten, and (3) a 2011 kindergarten cohort to be followed through fifth grade. Early Childhood Longitudinal Studies data collections include multiple interviews across multiple years and include a variety of assessments. The Schools and Staffing Survey includes data on American public and private elementary and secondary schools, principals and teachers based on survey of teachers, principals, schools, and school district officials. Schools and Staffing Survey includes data on school operations, climate, staff, and a variety of school and educator workplace conditions. Schools and Staffing Survey also includes follow-up principal and teacher surveys to gauge educator labor markets decisions. NCES has a series of longitudinal surveys that includes the National Longitudinal Study of the High School Class of 1972, High School and Beyond (1980), the National Educational Longitudinal Study (1988), The Education Longitudinal Study of 2002, and, most recently, the High School Longitudinal Study of 2009. Each survey measures similar constructs over time and follows students through high school and into early adulthood. The longitudinal surveys include multiple and repeated assessments, as well as high school and postsecondary transcript data.

NCES conducts postsecondary education surveys such as the Integrated Postsecondary Education Data System, which includes universe data on postsecondary institutions and serves as the frame for most postsecondary surveys. The National Postsecondary Student Aid Study combines administrative and survey data on postsecondary students and focuses on how those students finance their education. NCES participates in several periodic international data collections. The Program for International Student Assessment includes data on 15-year-olds’ literacy in reading, mathematics, and science. The Trends in International Mathematics and Science Study focuses on the mathematics and science achievement of fourth and eighth graders. The Progress in International Reading Literacy Study focuses on fourth graders’ reading abilities. NAEP is a serial nationally representative multisubject assessment program governed by the National Assessment Governing Board and administered by NCES to assess 4th-, 8th-, and 12th-grade students, most frequently in mathematics, reading, science, and writing. NAEP began in 1969 and longterm trends in achievement can be investigated with NAEP data, although content areas have been added over time. Nat Malkus See also International Datasets in Education; National Assessment of Educational Progress; National Datasets in Education; U.S. Department of Education

Further Readings U.S. Department of Education, National Center for Education Statistics. (2005). Programs and plans of the National Center for Education Statistics. Washington, DC: Author.

Website National Center for Education Statistics: www.nces.ed.gov

NATIONAL DATASETS IN EDUCATION The past 50 years have seen a proliferation in the collection of large national education datasets as government agencies such as the National Center for Education Statistics (NCES) and the Bureau of Labor Statistics have fielded surveys designed to facilitate analyses on questions of interest to the

National Datasets in Education

agencies. These data collection efforts have generated a wealth of secondary data ripe for analysis by researchers in economics of education and related fields. This entry provides an introduction to national datasets available for the analysis of educational issues at different units of aggregation (institutions vs. individuals) and covering different points in individuals’ educational careers. Most of these datasets are designed to be nationally representative, although a few that are somewhat more narrowly targeted are included as well.

Institution-Level Datasets Many datasets are collected at the level of the institution, providing only aggregate information at the level of the school, district, state, or nation rather than allowing individual-level analysis. Two datasets, the Common Core of Data (CCD) and the Private School Universe Survey (PSS) provide information on the full set of K-12 schools operating in the United States in a given year. These surveys collect basic information on factors such as enrollment figures, school demographics, student-to-teacher ratios, and school location; the CCD also includes information on state and district revenues and expenditures. In a similar vein, the Integrated Postsecondary Education Data System (IPEDS) collects institutionlevel data on colleges and universities. Unlike the CCD and PSS, the IPEDS is not strictly a census; reporting is mandatory only for institutions receiving federal student aid, so institutions that do not accept Pell grants or federal student loans do not need to submit information. The IPEDS provides data on factors such as student enrollments, student progression, and school finances. Researchers can obtain measures of the selectivity of postsecondary institutions separately through another dataset, the NCES-Barron’s Admissions Competitiveness Index Data Files, which has been compiled five times between 1972 and 2008. At a broader level of analysis, researchers can find information on student achievement trends over time through the National Assessment of Educational Progress (NAEP), which comprises several different tests. The national NAEP has been administered to 4th, 8th, and 12th graders since 1969. Test coverage varies from year to year, both in terms of the subjects tested and of content within subject areas. By contrast, the NAEP long-term trend test, administered to 9-, 13-, and 17-year-olds, has had broadly

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similar coverage in terms of content within each subject since its inception. Therefore, while researchers can use the national NAEP to compare how well each cohort of students performs on tests aligned to current thinking about what should be taught in each grade level, the long-term trend NAEP allows researchers to compare how different cohorts perform on the same set of skills. While the national and long-term trend NAEP tests offer pictures of achievement aggregated to the national level, state NAEP tests (first administered in 1990) and the trial urban district assessment administrations of the NAEP allow aggregation of results to the state and urban district levels. As of 2013, NAEP tests represented the only way to compare student performance across states and districts nationally. In the future, implementation of tests tied to the Common Core State Standards should provide an additional source of comparison of student performance across schools, districts, and states. These datasets offer many advantages. In particular, the CCD, PSS, and IPEDS offer data on a broad range of institutions, providing researchers, administrators, and policymakers basic information on nearly every education institution in the nation, from kindergarten through universities offering doctoral degrees. However, the aggregate nature of the data does not support more fine-grained analysis of how educational settings affect students’ academic achievement and attainment; for such analyses, researchers must turn to datasets that collect information at the individual level.

Individual-Level Datasets Since many individual-level datasets follow children longitudinally, datasets are categorized here by the age range at which they initially collect data. Early Childhood

A handful of surveys have specifically focused on data collection in children’s preschool years. The Early Childhood Longitudinal Study-Birth Cohort sampled roughly 14,000 children among the 2001 birth cohort of children and followed them through kindergarten entry. The survey used child assessments; interviews with parents, child care providers, and teachers; and interviewer observations to gauge sample children’s development, family environments, child care and preschool experiences, and kindergarten entry experiences. While the survey unfortunately does not follow children past kindergarten,

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the extraordinarily detailed information available over four waves of data collection in children’s first 5 years represents the richest available source of nationally representative data on early childhood development and children’s school readiness. The Pre-Elementary Education Longitudinal Study provides longitudinal data on roughly 3,000 children with disabilities who were ages 3 to 5 in the study’s first wave in 2003. The children were followed through 2008, and child assessments were collected in addition to interviews with families, teachers, and principals. While smaller than the Early Childhood Longitudinal Study-Birth Cohort, Pre-Elementary Education Longitudinal Study provides an invaluable resource for researchers tracing the development of children with special educational needs. K-12

The original Early Childhood Longitudinal Study-Kindergarten (ECLS-K) followed a cohort of children who started kindergarten in 1999 through their eighth-grade year. In addition to assessing children, the ECLS-K collected extensive information on their home and school environments through parent and teacher interviews. A second version of the ECLS-K was launched in 2010–2011 for a new cohort of students; survey administrators plan to follow this wave of children through fifth grade. Taken together, the two surveys provide researchers with the ability to compare the elementary school experiences of children who started kindergarten before and after the introduction of important educational policies such as No Child Left Behind Act. Several other studies follow children starting in their adolescent years. The Bureau of Labor Statistics has fielded two longitudinal studies of youths starting in their adolescence and following them indefinitely: the National Longitudinal Survey of Youth-1979 (NLSY-79) and the National Longitudinal Survey of Youth-1997 (NLSY-97). The NLSY-79 sampled roughly 12,500 youth who were ages 14 to 22 when they were interviewed in the inaugural 1979 survey year; they have been followed annually or biennially since. The nearly 9,000 NLSY-97 respondents entered the sample at ages 12 to 17 in the 1997 data collection wave and have been followed 15 times since. Both studies include child assessments as well as information about home environments, substance abuse, educational expectations, educational attainment, employment outcomes, and family formation.

The length of these longitudinal studies render them a unique source of data on the evolution of subjects’ educational, familial, and labor experiences through their adult lives. Several other studies have specifically examined the period encompassing students’ transitions from adolescence into their early adult years, providing rich data on factors that predict college-going, college success, and early employment outcomes. For instance, the National Longitudinal Study of Adolescent Health (Add Health) has followed roughly 20,000 students from their adolescent years (7th through 12th grades) in 1994–1995 into their early adulthood through four waves of data collection; the most recent wave was fielded in 2008. Add Health offers somewhat less depth in terms of educational measures than many other longitudinal datasets. However, it offers several unique features, notably including the collection of detailed data on students’ social networks and peer groups. The NCES has also maintained a series of datasets that follow children from their high school years into young adulthood. This series includes the National Longitudinal Study of the High School Class of 1972 (NLS-72), the High School and Beyond (HS&B) study, the National Education Longitudinal Study of 1988 (NELS: 88), the Education Longitudinal Study of 2002 (ELS: 2002), and the High School Longitudinal Study of 2009 (HSLS: 09). Domains studied vary by survey but generally include student assessments as well as data on educational attainment, work histories, and family formation. The consistency with which new studies have been fielded over time allows researchers to explore trends in the experiences of different cohorts as they navigate their transitions to adulthood. The years and age-ranges covered vary in each study. The NLS-72, the inaugural NCES survey of students’ high school-to-adulthood transitions, followed roughly 16,500 students from the 1972 cohort of high school seniors through young adulthood up to 1986. The NLS-72 is particularly useful in studying the educational opportunities of disadvantaged youth, as it includes an oversample of students attending schools in low-income areas and with significant minority enrollments. The HS&B study followed roughly 58,000 students from the sophomore and senior classes in 1980 biennially until 1986; the sophomore class was then surveyed again in 1992. In addition to allowing prospective analysis on students’ 10th- through 12th-grade years, HS&B was the first NCES longitudinal study

National Datasets in Education

to collect high school transcript data for students; all of the subsequently launched studies in this series followed suit. The NELS: 88 surveyed roughly 25,000 students who were in eighth grade in 1988 and followed them through four waves of data collection until 2000. The NELS offers an advantage over previous surveys in allowing study of the transition from middle school to high school. The ELS: 2002 updates the high school transition series by following 15,000 members of the 2002 high school sophomore class through four waves of data collection until 2012. While both the ELS and NELS were initiated when students were in lower grades, both surveys “freshened” their samples in students’ senior years to ensure that they were representative of high school seniors. They can therefore be used with the NLS-72 and HS&B senior cohorts to compare the experiences of seniors over time. Finally, the HSLS: 09 surveyed more than 23,000 9th-graders in fall 2009 and has had one follow-up so far, in students’ 11th-grade year (spring 2012). Other follow-up waves are planned in 2016 and 2021. The HSLS: 09 features a particular focus on students’ decisions to go into science, technology, engineering, and math (STEM) fields. The HSLS: 09 therefore conducts teacher interviews only with math and science teachers; student assessments are limited to assessing mathematical thinking; and school counselor interviews focus particularly on how students are placed in math and science courses. While the HSLS will therefore prove an especially rich resource for researchers interested in STEM education, it will also provide slightly less detail than some previous high school surveys on students’ educational influences and development in other subject areas. Two studies funded by the National Center for Special Education Research allow study specifically of students with disabilities starting in their K-12 years. The Special Education Elementary Longitudinal Study followed 13,000 students in special education starting in 2000, when they were ages 6 to 12, through 2006. The study therefore examines students as they transition from elementary to middle and from middle to high school. The National Longitudinal Transition Study-2 tracks the transition from adolescence to adulthood of students using special education services. Roughly 9,500 children entered the study in 2000 when they were 13 to 16 years old and were followed until 2010. Data collection for the Special Education Elementary Longitudinal Study and National

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Longitudinal Transition Study-2 surveys included direct assessments, as well as interviews with students, parents, teachers, and special education teachers. Taken together with the Pre-Elementary Education Longitudinal Study, this set of surveys provides a unique resource for researchers interested in the experiences of student with disabilities at different points in their educational careers. Finally, a handful of studies provide data specifically on student and educator perspectives of school safety in K-12 students’ schools. The School Crime Supplement to the National Crime Victimization Survey, fielded by NCES and the Bureau of Justice Statistics over eight waves from 1989 to 2009, provides data on students’ exposure to criminal and bullying behaviors at school. The survey targets students ages 12 to 18. A complementary survey, the School Survey on Crime and Safety provides principal reports from 3,500 K-12 schools on school crime and discipline from five waves of data collection between 1999–2000 and 2009–2010. College

Several datasets provide individual student-level data on students starting in their college years. The National Student Clearinghouse’s StudentTracker system provides student-level records of enrollment and degree receipt for nearly all individuals observed entering postsecondary institutions. StudentTracker records have been maintained since 1993. However, while it provides unparalleled breadth of coverage, it provides less depth of detail on students’ experiences than most other individual-level surveys. Another major survey, the National Postsecondary Student Aid Study, has been conducted using periodic cross-sectional samples since the 1986–1987 school year. It provides a nationally representative data source on student enrollment, financial aid, and educational expenses for both graduate and undergraduate populations but provides little data on other aspects of students’ postsecondary experiences and cannot follow students over time. Two other major longitudinal studies have been spun off from the National Postsecondary Student Aid Study samples collected in different years. The Beginning Postsecondary Students study has followed three cohorts of students from their first year in postsecondary education (in 1990, 1996, and 2004) through their college careers and their initial transitions to employment. The most recent cohort included nearly 16,700 students. The Baccalaureate

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and Beyond (B&B) Longitudinal Study has also followed three cohorts of graduating seniors (the classes of 1993, 2000, and 2009) through their early work years. While both allow researchers to study college students’ transition into their careers, each dataset brings unique advantages. For instance, since the Beginning Postsecondary Students samples college freshmen, researchers can prospectively look at how students’ college experiences are associated with their choices of majors and ultimate career paths; the B&B collects only retrospective reports on most aspects of students’ college experiences. However, the B&B study provides more detail on certain postbaccalaureate experiences, such as experiences in graduate schools. It also provides special advantages to researchers interested in teachers, as it oversamples graduates who plan to enter teaching professions. Life Course

Several other datasets aim to report on the educational experiences of children at different points in the life course. One type of study in this vein collects cross-sectional data on subjects at different points in the life course. For instance, the National Household Educational Survey is a repeated crosssectional survey conducted every 3 or 4 years. The National Household Educational Survey has a core coverage of questions asked in each administration, with a varying set of supplemental topics in each administration. This survey provides important snapshot information on national trends in educational attainment, but because it is not a panel, researchers cannot follow the educational trajectories of the same individuals over time. There are also several studies that follow families longitudinally. These studies provide data beginning from birth for children born into survey families, and these children are followed indefinitely. For instance, the NLSY-79 Children and Young Adults study follows the children of female respondents in the NLSY-79. Likewise, the Panel Study of Income Dynamics, which has followed families longitudinally since 1968, features three waves of its Child Development Supplement (1997, 2002, and 2007). Both of these family longitudinal datasets include detailed data on child development, including cognitive, health, and social outcomes, as well as data on family processes and parenting practices. These studies are unique in the richness of data they provide on children’s experiences throughout their lives,

and the historical detail that they provide on children’s families. However, they provide less in-depth data on any given period of children’s lives than do the longitudinal datasets covered above. Educators

Finally, several datasets can be used to examine the experiences of educators. The Schools and Staffing Survey (SASS) collects data on the experiences of K-12 public and private school teachers and principals. Survey topics include respondents’ educational and professional backgrounds, evaluations of working conditions, and evaluations of professional development opportunities, among others. The survey has been fielded seven times between the 1987–1988 school year and the 2011–2012 school year. However, because the SASS is cross-sectional, it cannot be used to follow the same teachers over time. A spin-off study, the Beginning Teacher Longitudinal Study addresses this shortcoming and follows the roughly 2,000 first-year teachers interviewed as part of the 2007–2008 wave of the SASS over 5 years. At the collegiate level, the National Survey of Postsecondary Faculty fielded four waves of surveys between 1987–1988 and 2003–2004, with sample sizes ranging up to 35,000 faculty. Sample domains in the survey include academic and employment backgrounds, faculty rank, working conditions, and compensation. Like the SASS, the National Survey of Postsecondary Faculty is cross-sectional and cannot trace changes in individuals’ experiences over time.

Special Considerations There are several considerations researchers should keep in mind with these datasets. Some datasets have special properties that make them particularly advantageous for certain types of analyses. For instance, the Panel Study of Income Dynamics, NLSY-79, NLSY-97, NLSCYA, AddHealth, and Early Childhood Longitudinal Study-Birth Cohort surveys include sibling groups, which allows for family fixed-effects research designs. Other datasets include oversamples of potentially interesting populations (e.g., Add Health oversamples Black students from well-educated families; the B&B 2009 cohort oversampled STEM graduates, etc.). Researchers may find such designs useful for the study of special populations. Finally, while many of these datasets have some publicly available portions, some require

National Science Foundation

special licensing permission to gain access to more sensitive data. Cassandra M. D. Hart See also Econometric Methods for Research in Education; International Datasets in Education; National Assessment of Educational Progress; National Center for Education Statistics

Further Readings Bureau of Labor Statistics. (2013). National Longitudinal Surveys (NLSY-79, NLSY-97, NLSCYA datasets). Retrieved from https://www.nlsinfo.org/ Carolina Population Center. (2013). Add health. Retrieved from http://www.cpc.unc.edu/projects/addhealth Institute for Social Research. (2013). PSID studies. Retrieved from http://psidonline.isr.umich.edu/Studies .aspx National Center for Education Statistics. (2013). Surveys and programs (CCD, PSS, IPEDS, NCES-Barron’s, NAEP institutional datasets; ECLS-B, ECLS-K, ECLS-K2010, NLS-72, HS&B, NELS: 88, ELS: 2002, HSLS: 09, NCVS-SCS, SSOCS, NPSAS, BPS, B&B, NHES individual student-level datasets; SASS, BTLS, NSOPF individual educator-level datasets). Retrieved from http://nces.ed.gov/surveys/ National Center for Special Education Research. (2013). Research programs (PEELS, NLST2 datasets). Retrieved from http://ies.ed.gov/ncser/projects/ National Student Clearinghouse. (2013). StudentTracker. Retrieved from http://www.studentclearinghouse.org/ colleges/studenttracker/

NATIONAL SCIENCE FOUNDATION The National Science Foundation (NSF) is an independent federal agency that is responsible for support of scientific and engineering research conducted primarily in American universities. Research supported by the foundation has provided knowledge of the global dynamics and concentrations of gases, such as carbon dioxide, ozone, and nitrous oxide in the atmosphere, made possible the ability to predict giant solar storms, afforded early warnings of severe weather, revealed the existence of plate tectonics and uncovered its role in earthquakes and tsunamis, investigated techniques for the reverse engineering of the brain, and developed materials capable of repelling water, capturing solar rays, and preventing the occurrence of icing, among many other notable

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studies and investigations. The NSF also has supported elementary and secondary school teacher education and graduate fellowships. From an initial appropriation of $225,000, the NSF budget grew from $3.5 million in 1950 to $1 billion in 1980 and to more than $7 billion today. As a result, the NSF plays a significant role in the economy of higher education institutions and the nation. This entry is a brief description of the history, organization, and activities of the foundation.

History The NSF was founded by an Act of Congress in 1950 and signed into law by President Harry S Truman. The seminal idea for the foundation had come from a 1945 report requested by President Franklin D. Roosevelt from Vannevar Bush, director of the Office of Scientific Research and Development, an emergency wartime government agency that had supported or overseen many of the scientific and technological efforts that had enabled the Allied forces to achieve victory in World War II. Bush called for the creation of a national research foundation whose members would be appointed by the president “on the basis of their interest in and capacity to promote the purposes of the Foundation.” Bush also recommended that the members choose the director of the foundation to protect the choice from political influences. After a prolonged period of contentious debate and compromise, the bill presented to President Truman by the Congress called for the establishment of an independent federal agency, the NSF, with a mission, according to its website, “to promote the progress of science; to advance the national health, prosperity and welfare; to secure the national defense” and with a policy-making board of 24 persons, the National Science Board, and a director, each to be appointed by the president, with the advice and consent of the Senate, for terms of 6 years.

Research Support: Basic and Applied From the beginning, the NSF has served as the federal agency solely responsible for the support of basic research in all the various disciplines of the mathematical, natural, physical, and engineering sciences. While the medical sciences were included initially, they were later placed under the purview of the National Institutes of Health and were no longer the primary responsibility of the NSF. Although it is difficult, if not impossible, to differentiate between

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the so-called pure or basic and applied research during the early days of the NSF, there was strong resistance on the part of the scientific community to the inclusion of support for applied research in the mission of the foundation. In the mid-1960s, largely due to pressure from Congress, the NSF charter was revised to include applied research. NSF operates under the premise that its charter for supporting scientific research means that it supports good, high-quality research regardless of whether it is considered basic or applied. The proposal for such research must survive stringent peer review and be considered sufficiently meritorious for financial support. It must reflect the potential for adding measurably and significantly to the knowledge base. Today, NSF identifies and supports cuttingedge research through the work of program administrators within the divisions of the seven principal research directorates: 1. Biological Sciences 2. Computer and Information Sciences and Engineering 3. Education and Human Resources 4. Engineering 5. Geosciences 6. Mathematical and Physical Sciences 7. Social, Behavioral and Economic Sciences

Examples of NSF’s ongoing contributions to American science are its administration of the U.S. Antarctic Program, which provides facilities and support for research activities in Antarctica, its provision of radio and optical telescopes for astronomy research at several sites across the nation, and its funding for the construction and operation of vessels and instrumentation used for oceanographic research.

Behavioral, Economic, and Social Sciences While the physical and natural sciences have generally experienced strong support, the behavioral, economic, and social sciences have had an uncertain tenure at NSF. Originally, they were not explicitly included among the scientific areas for which the foundation was responsible, although some small social science grants were occasionally supported by the natural sciences divisions. An organizational unit specifically focused on the social sciences was created at the end of the first decade of NSF’s existence.

Even so, they have been very much at the mercy of the attitudes and ideological temperaments of the changing administrations and Congresses and the financial support that they have received throughout the life of the foundation has ebbed and flowed as a result.

Engineering In 1981, as a result of a long campaign by the engineering community and often with concomitant resistance from those sectors most protective of the foundation’s commitment to the support of fundamental science, a directorate of engineering was established at NSF. Until then, the administration and support for engineering projects had been the province of the directorate responsible for managing the mathematical and physical sciences programs of the foundation. The foundation’s support for engineering research and education aims to build and strengthen a national capacity for innovation that can lead over time to the creation of a new shared wealth and a better quality of life. NSF created the directorate for engineering in the belief that support for engineers is crucial for strengthening their capacity to innovate and translate scientific understanding into new industrial and technological capabilities with immense economic and social benefits to the public. The foundation established an ongoing program for the development of a series of engineering research centers focused on converting scientific breakthroughs and emerging technologies into innovations that can strengthen our national ability to compete in the global economy of technological concepts and artifacts. Engineering research centers are multidisciplinary and consist of organizational structures that bring together universities, industrial organizations, and other entities with a diverse collective staff of faculty, students, and researchers focused on technological innovation and entrepreneurship. A central component of the engineering research centers is to encourage and prepare students at the precollege level for possible engineering careers by exposing them and, where appropriate, their teachers to engineering concepts, principles, and activities.

Science Education and Manpower Development NSF has played a major role in supporting science education efforts throughout its existence. Among its undertakings, the foundation has supported summer institutes for teachers to address the need to

Neighborhood Effects: Values of Housing and Schools

provide opportunities for elementary and secondary teachers to enhance their knowledge and skills in mathematics and science, has made grants for equipment and instrumentation in school and university laboratories, and has played a major role in providing financial support for graduate students participating in research activities. Over the past two decades, NSF has instituted a number of programs designed to increase the participation of women and underrepresented minorities in science and engineering education and research. These programs are considered essential in the nation’s efforts to increase its capacity to compete in science and technology globally. John Brooks Slaughter See also Categorical Grants; Economic Development and Education; Education Spending; Faculty in American Higher Education; Investing in Innovation Fund (i3); Teacher Effectiveness

Further Readings Bush, V. (1945). Science—the endless frontier: A report to the president for a program for postwar scientific research. Washington, DC: U.S. Office of Scientific Research and Development (Reprinted 1960, National Science Foundation). Retrieved from http://www.nsf. gov/od/lpa/nsf50/vbush1945.htm Lomask, M. (1976). A minor miracle: An informal history of the National Science Foundation. Washington, DC: National Science Foundation.

Website National Science Foundation: nsf.gov/about

NEIGHBORHOOD EFFECTS: VALUES OF HOUSING AND SCHOOLS The majority of children in the United States attend a neighborhood school in close proximity to their home. Consequently, the demographic and economic composition of neighborhoods and the social processes therein can be consequential for schools. A large body of research has attempted to understand the processes that give rise to neighborhood sorting and the consequences of such sorting for child and adult educational and economic outcomes. Research on neighborhood effects attempts to estimate the

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causal effect of living in a particular neighborhood on individual’s educational and economic outcomes such as test scores, high school graduation, college attendance, employment status, or wages. This entry provides an overview of research on neighborhood effects. First, it describes the mechanisms that lead to neighborhood sorting, with a focus on stratification by income levels and race. Next, it discusses some of the methodological challenges present in identifying the effects of neighborhoods and provides a conceptual overview of mechanisms through which variation in neighborhood composition might influence children. Finally, the entry discusses some of the key pieces of empirical research that inform the question of whether neighborhoods matter for children’s schooling outcomes, paying particular attention to the most rigorous studies that have the strongest causal warrant.

Mechanisms Leading to Neighborhood Sorting How do neighborhoods come to exist in the form that we observe them? Typical economic models specify that people sort into neighborhoods based on preferences for locally produced public goods (goods enjoyed by all without exclusion and without rivalry), peer composition, and property taxes. Preferences for these items are bounded by price constraints and income levels. Charles Tiebout describes municipalities as offering varying services at different tax rates. Given that preferences for these services vary across individuals, as does their ability to pay, it is theorized that individuals will move from one community to another until they find the one that maximizes their utility. Economists have tried to model this selection process by looking at housing prices. If housing prices are taken as a proxy for welfare (how much one values residing in a neighborhood), then housing prices can be used to model consumer preferences. For example, if housing prices are higher in neighborhoods where the only difference is better performing schools, we would say that housing prices have capitalized on school quality; that is, consumer preferences for higher quality schools are captured in the price of neighborhood houses. Many studies examine what kind of neighborhood characteristics are capitalized in home prices. These studies consistently find that house prices are sensitive to school outputs, as measured by student achievement scores, and are generally less sensitive to school inputs, as measured by per-pupil expenditures.

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Tax capitalization, or the response in housing prices to property taxes, is also heavily studied. Wallace E. Oates’s groundbreaking study in 1969 was the first to empirically document the negative relationship between property taxes and property values. This finding has been echoed in more recent work. For example, evaluations of Proposition 2½ in Massachusetts, which placed caps on property taxes, find that tax-constrained communities experienced increases in property values, leading to increased local revenues, despite the tax constraint. Two important lessons can be learned from this. First, neighborhoods with the same tax rate can raise additional revenues for local goods, such as schools, if property values are higher. Second, neighborhoods that wish to improve the quality of their local goods by imposing higher property taxes can actually decrease available revenues if the increase in property taxes results in a decline in property values. This can lead to unintended consequences, whereby low-income communities tax themselves at great rates to provide comparable education relative to higher income neighborhoods. But these high taxes then can lead to further reductions in housing prices, resulting in an overall reduction in available tax revenues. A result of the residential mobility, preferences, and price constraints described above can be neighborhood stratification. Since housing prices limit access to communities, it follows that price largely dictates certain community characteristics. Individuals are willing to pay more for neighborhood characteristics that they deem valuable. Their ability to pay is restricted by their own characteristics, such as income or family size, meaning that more affluent families can afford to pay for higher quality neighborhoods. In addition to sorting due to income and preferences for local goods, sorting also occurs along racial lines. Neighborhood racial segregation can be caused by two mechanisms. First, because income is correlated with race (African Americans and Hispanics have, on average, lower incomes than Whites and Asians), any racial segregation we observe may be partially effected by income differences, for the reasons described above. The second source of racial segregation can come from preferences for neighborhood ethnic composition. In his classic 1969 article, Thomas Schelling found that high levels of segregation could arise even if individuals had only modest preferences for same-race peer composition. Extensions of this work have found

that preferences against ethnic isolation (rather than preferences for same-race peers) also lead to high levels of segregation.

Neighborhood Effects on Educational Outcomes Methodological Challenges

Given sizeable variation in average educational outcomes between neighborhoods, many researchers have been interested in whether this variation is attributable to the selection process, whereby observed differences in achievement, for example, are due to differences in the types of individuals residing in different neighborhoods, or to the features of neighborhoods themselves. Researchers have struggled to disentangle these two mechanisms. Charles Manski was one of the first to formalize these difficulties, demonstrating that the identification of direct neighborhood effects will be, in most cases, extremely difficult. Following his seminal work, neighborhood mechanisms have been disaggregated into three components. Neighborhood influences have been described as endogenous interactions—in which an individual’s actions may be influenced by the actions of others around them—and contextual interactions—in which an individual’s actions vary based on neighbors’ social characteristics such as age, race/ethnicity, or income level. Influences due to the selection process have been described as correlated effects. Correlated effects occur when an individual’s actions mirror his or her social surroundings, not because the surroundings have affected the individual’s actions but because the individual and his or her social group tend to behave similarly. Endogenous and contextual effects reflect distinct ways that individuals may be influenced by their social environments, while correlated effects are not caused by social interactions. The methodological challenge is to disentangle endogenous and contextual effects from correlated effects. Another important challenge in neighborhood research is that there is ambiguity about what constitutes a neighborhood. Most studies of neighborhood effects define neighborhoods using census tracts, zip codes, or other administrative units. These definitions are often used simply because this is how data are most readily available, rather than for strong conceptual or theoretical reasons. Some researchers have argued that such geographies are less salient to families and children than geographies defined in other ways. Some alternative approaches to defining

Neighborhood Effects: Values of Housing and Schools

neighborhoods include measuring residents’ own definitions of their neighborhood boundaries, using information about average travel time to capture the average distance that can be traveled to carry out regular activities, or mapping the configuration of streets that may support or inhibit social interaction more than spatial proximity alone. The first problem with such alternative definitions is that these data are not widely available and, second, they can vary among individuals living on the same block or for a single household or individual over time. The theoretical literature suggests that neighborhoods can be viewed as spaces that define residents’ exposure to different types of people, behaviors, and social and physical environments and places to which individuals can potentially have a sense of attachment and belonging. Since neighborhood effects research has generally relied on predefined administrative boundaries, its scope may be limited for uncovering and understanding neighborhood effects. Mechanisms

Though teasing out the causal effects of neighborhoods on individual outcomes is challenging empirically, there are a variety of conceptual reasons to believe that neighborhoods have an independent impact on children, net of individual and family characteristics. Research on neighborhoods suggests that neighborhood characteristics such as the quality of schools, poverty, or crime influence families and children in a number of ways. These mechanisms can be summarized into two broad categories: (1) the quality of institutions and (2) social organization and interaction with peers and adults. Quality of Institutions

Perhaps the most obvious reason that student outcomes vary across neighborhoods is due to differences in the quality of public schools. Historically, schools were primarily funded through local property taxes; however, there has been a general shift from local property taxes to state aid. Currently in the United States, about 44% of revenues come from local sources, 40% come from the state, and the remaining 16% come from federal contributions. Most spending inequality is now attributable to between-state differences. For example, in 2010, based on data from the National Center for Education Statistics and U.S. Census, the districts with the highest student poverty rates (top 25%) nationally received about $2,000 fewer revenues per

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pupil than the districts with the lowest poverty rates (bottom 25%). Within states, however, the poorest districts received about $550 more revenues per pupil than the richest districts. In other words, poor states spend less than rich states, but on average, within states, poor districts spend more than rich districts due to greater need. Whether spending actually matters for student achievement is a separate issue, and this line of research, referred to as education production function literature, has provided mixed results. Some recent studies have tried to estimate the causal effect of changes in per-pupil expenditures resulting from court-ordered finance reform on student achievement, and these studies do find a positive relationship between spending and achievement, though it is fairly modest. Nevertheless, it may be the case that a dollar of spending “purchases” more achievement in wealthier districts if those districts use resources more efficiently. One way this might happen is if schools in wealthier districts have access to a higher quality pool of teachers. Research has consistently shown that quality teachers are schools’ most valuable resource. Many studies have further documented the difficulties that urban schools with high concentrations of disadvantaged students have in recruiting and retaining high-quality teachers. This is driven partially by differences in teacher pay across districts, teacher preferences for working in schools that are closer to their home, and teacher preferences for working with easier-to-serve student bodies. The distribution of teachers across schools serving different neighborhoods is important since there is substantial variation across teachers in the ability to raise student test scores. Variation in access to quality teachers is likely to explain at least some of the difference in effectiveness across schools. In addition to variation across neighborhoods in access to well-funded schools and high-quality teachers, neighborhoods also differ in the quality of their other institutions, such as social and health services and the quality of local policing. More affluent neighborhoods may provide parents with access to more child care providers, as well as more public libraries, recreational programs and activities, parks, religious institutions, and social service providers. The availability and quality of institutions may be influenced by public policy, but they are also likely to be determined by neighborhood socioeconomic characteristics. For example, there may be more after-school programs and public libraries in higher

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income neighborhoods, because residents demand such services and have the means to pay for it. Lower income neighborhoods may be worse off than others because they have weaker institutions and also because needy families in such neighborhoods may overtax the existing institutions. Social Interactions With Peers and Adults

Neighborhood composition may also have direct effects on academic outcomes through peer effects, sometimes referred to as “contagion” or “epidemic” theories. On the negative side, the prevalence of more students with behavioral problems or tendencies toward violence may negatively affect the outcomes of other children at their school by making them feel less safe or decreasing the classroom time spent on academic tasks. On the positive side, having a higher concentration of high-achieving students means that teachers can spend more time helping students with different needs, and students who might not otherwise be inclined to work hard mimic the behaviors of their hard-working peers. Indeed, an important 2001 study by Bruce Sacerdote, about the beneficial effects of having a high-achieving college roommate, found that the random assignment of a roommate with a higher than average SAT score positively improved the roommate’s college grade point average. There is also speculation that culture and norms regarding academic achievement may vary across neighborhoods and affect academic achievement. The seminal work by John Ogbu, for example, argues that socially isolated neighborhoods facing severe economic and racial segregation may be cut off from middle-class individuals and institutions. This isolation leads to the development of cultural norms that devalue schooling as a means to upward mobility since there are few role models in these areas that achieved success via education.

Evidence on Neighborhood Effects on Schooling Outcomes As mentioned above, it is very difficult to disentangle the selection into neighborhoods (correlational effects) from direct neighborhood effects (endogenous and contextual effects). Many observational studies using regression analysis have tried to estimate the association between a given outcome of interest (e.g., high school graduation, achievement scores, and mental health) and some socioeconomic or demographic characteristic of a

neighborhood (e.g., poverty rates and ethnic composition), whereby a neighborhood is defined by census tract (small subdivisions of counties that average around 4,000 people), zip code, or metropolitan statistical area (core urban areas with a population of 50,000 or larger). These studies are often plagued by selection bias. The Project on Human Development in Chicago Neighborhoods is one of the most comprehensive observational studies of neighborhood effects on children’s outcomes conducted to date. It includes careful measurement of detailed features of neighborhood characteristics and social processes and multiple surveys of parents and children. Studies using these data show that living in a disadvantaged neighborhood (measured by a composite of poverty rates, unemployment, female-headed households, percentage Black and percentage under the age of 18) reduces students’ test scores an amount equivalent to missing 1 to 2 years of schooling. In many studies, there is worry about bias since it is difficult to fully control for all correlated effects. In light of this problem, some more rigorous studies have looked for quasi-random variation in neighborhood quality. For example, some researchers have used metropolitan area economic measures (e.g., unemployment, median income, and college completion) to isolate exogenous variation in schoollevel disadvantage. Other studies have compared the educational outcomes of siblings raised in different neighborhoods. Perhaps unsurprisingly, these studies have produced mixed results. The inconsistency in the results could be driven by variation in the way researchers define neighborhoods, differences in the types of neighborhood measures used, or to variation in methodological sophistication. An ideal way to study the causal effects of neighborhoods is a random shock to neighborhood quality. One way to do this would be to move individuals from a disadvantaged neighborhood to a more affluent neighborhood. This occurred in the 1970s, when a series of class action lawsuits against local and federal public housing authorities in Chicago resulted in the Gautreaux Assisted Housing Program. Vouchers were offered to African American families to move to less segregated suburban neighborhoods. Children of voucher recipients had significant improvements in their later educational attainment. Compared with the surveyed students who remained in the city of Chicago, suburban movers were four times less likely to have dropped out of school, more likely to be in a college track in high school, twice as likely to attend any college, and close to seven

Neighborhood Effects: Values of Housing and Schools

times as likely to attend a 4-year college. This study contained many of the same selection problems as previous studies, as voucher recipients were not randomly selected, but the findings and methods helped pave the way for a more ambitious program. Following the findings from the Gautreaux program, the U.S. Department of Housing and Urban Development extended its effort to conduct an experiment in altering individuals’ neighborhood makeup in a way that would be more conducive to making causal inferences. This program, the Moving to Opportunity demonstration, has been under way in five cities—Baltimore, Boston, Chicago, Los Angeles, and New York—since 1994. Through a lottery system Department of Housing and Urban Development provided rental assistance to help very low-income families move from poor urban areas to relatively better-off neighborhoods. This random change in neighborhood quality has allowed researchers to investigate whether neighborhood composition has a direct effect on a variety of child and adult outcomes. Studies from this experiment have provided mixed results. While children in families that received vouchers saw declines in rates of behavioral problems, asthma attacks, and injuries requiring medical attention, no significant effects on student achievement outcomes have been found. One reason for this may be because many of these neighborhood transfers occurred within school district boundaries, so that, while neighborhood quality increased, school quality did not. Moreover, there is substantial variation in average treatment effects across sites. In a summative review of the Moving to Opportunity studies, the authors Julia Burdick-Will, Jens Ludwig, Stephen W. Raudenbush, Robert J. Sampson, Lisa Sanbonmatsu, and Patrick Sharkey conclude that moving from a very disadvantaged to a less disadvantaged neighborhood improves children’s test scores. Thus, in Baltimore and Chicago, where poverty rates are extremely high, neighborhood transfers did result in improvements to educational outcomes. This is true even if the mobile children spent many years growing up in very disadvantaged neighborhoods and whether or not the moves are associated with modest changes in either neighborhood racial segregation or school quality.

Conclusion In sum, research on neighborhoods has sought to understand the processes that lead to neighborhood sorting as well as the effects of neighborhoods

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on individual outcomes. Studying neighborhood effects is complicated both by the ambiguity inherent in defining “neighborhoods” and by the fact that individuals generally choose where to live. Since people choose where to live, disentangling the causal effects of neighborhood environments from other unobservable characteristics that may be associated with educational outcomes is challenging. The most rigorous empirical evidence shows that neighborhoods do matter—living in a highly disadvantaged neighborhood depresses students’ educational achievement and attainment. Unfortunately, these most credibly causal studies do not tell us very much about the mechanisms that lead to this relationship. Demetra Kalogrides and Kenneth Shores See also Capital Financing for Education; Education Spending; Selection Bias; Social Capital; Socioeconomic Status and Education; Tiebout Sorting

Further Readings Boyd, D., Lankford, H., Loeb, S., Ronfeldt, M., & Wyckoff, J. (2010). The effect of school neighborhoods on teachers’ career decisions. In G. J. Duncan & R. J. Murnane (Eds.), Whither opportunity? Rising inequality, schools, and children’s life chances (pp. 377–396). New York, NY: Russell Sage Foundation. Burdick-Will, J., Ludwig, J., Raudenbush, S. W., Sampson, R. J., Sanbonmatsu, L., & Sharkey, P. (2010). Converging evidence for neighborhood effects on children’s test scores: An Experimental, quasiexperimental, and observational comparison. In G. J. Duncan & R. J. Murnane (Eds.), Whither opportunity? Rising inequality, schools, and children’s life chances (pp. 255–276). New York, NY: Russell Sage Foundation. Durlauf, S. (2004). Neighborhood effects. Handbook of Regional and Urban Economics, 4, 2173–2242. Gennetian, L. A., Sciandra, M., Sanbonmatsu, L., Ludwig, J., Katz, L. F., Duncan, G. J., . . . Kessler, R. C. (2012). The long-term effects of moving to opportunity on youth outcomes. Cityscape, 14(2), 137–168. Hanushek, E. (1997). Assessing the effects of school resources on student performance: An update. Educational Evaluation and Policy Analysis, 19(2), 141–164. Manski, C. F. (1993). Identification of endogenous effects: The reflection problem. Review of Economic Studies, 60, 531–542. Oates, W. E. (1969). The effects of property taxes and local public spending on property values: An empirical study

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of tax capitalization and the Tiebout hypothesis. Journal of Political Economy, 77(6), 957–971. Orr, L., Feins, J. D., Jacob, R., Beecroft, E., Sanbonmatsu, L., Katz, L. F., . . . Kling, J. R. (2003). Moving to opportunity interim impacts evaluation. Retrieved from http://www.huduser.org/publications/fairhsg/mtofinal .html Sacerdote, B. (2001). Peer effects with random assignment: Results for Dartmouth roommates. Quarterly Journal of Economics, 116(2), 681–704. Sampson, R. J., Morenoff, J. D., & Gannon-Rowley, T. (2002). Assessing “Neighborhood Effects”: Social processes and new directions in research. Annual Review of Sociology, 28, 443–478. Schelling, T. (1969). Models of segregation. American Economic Review, 59(2), 488–493. Tiebout, C. (1956). A pure theory of local expenditures. Journal of Political Economy, 64(5), 416–424.

NEW INSTITUTIONAL ECONOMICS New institutional economics (NIE) is the study of the institutions of social, political, and economic interactions. Because these interactions occur in uncertain environments and the choices of individuals can be unpredictable, institutions—the formal and informal rules established by society—are developed to provide structure to, and reduce the uncertainty of, human interaction. Formal institutions include laws and regulations, while informal institutions include norms of behavior, cultural customs, and codes of conduct. Regardless of the type of institution, to be credible, they must be associated with enforcement mechanisms and consequences if not followed. A common way of thinking about institutions is to consider them the rules of the game and individuals and organizations as the game’s players. Like any game, the creation and enforcement of rules shapes the way the game is played and its possible outcomes. NIE seeks to understand this process in social, political, and economic settings but recognizes that individuals and organizations can also affect the rules of the game. Accordingly, NIE moves beyond the static models of neoclassical economics to a dynamic theory that includes institutional and organizational change. Overall, NIE seeks to explain the existence of institutions, how they shape human interaction, how they develop and evolve, and, in certain instances, how they can be reformed. This entry provides a brief overview of new institutional economics and its relevance to education.

It begins by presenting the role of institutions as a response to the uncertainty of interaction between individuals and continues by discussing the different types of institutions that can develop. The entry then addresses the interaction between institutions and organizations and how each can effect changes in the other. The entry concludes with a discussion of NIE in the context of education. While an obvious application is to examine the influence of institutions on the landscape and organizations of education, it is also important to examine the influence education can have on shaping institutions in general.

Uncertainty and Institutions In social, political, and economic settings, the sources of uncertainty are manifold. However, in modeling human interaction in markets, neoclassical economic theory makes assumptions that preclude the presence of uncertainty. While this provides a benchmark for market outcomes, the utopian predictions of these models are often at odds with empirical evidence. The disparity between theory and evidence has prompted the development of improved theories that address the inability of these models to truly capture the importance of uncertainty and its effect on human interaction. Through the inclusion of a theory of institutions, NIE provides a refinement of neoclassical economic theory. While neoclassical economic theory and NIE both build on the assumptions of scarcity and competition, NIE rejects the assumptions that individuals have perfect information and unbounded rationality, where unbounded rationality is the unlimited ability to process information from which predictable decisions are made. Instead, NIE suggests that information is often limited and asymmetric, and that participants are bounded in their ability to make rational choices, which in turn create uncertainty in interaction. Efforts made by individuals to reduce uncertainty increase the cost of transacting and can distort outcomes. As transaction costs increase so too does the potential for distorted outcomes, making institutions more important. To reduce both the level of uncertainty and the cost of acquiring information, institutions are created. The incorporation of institutions into theory allows NIE to go beyond strictly looking at how markets determine prices and outputs. NIE scholars suggest that the focus of neoclassical economics on abstract market settings has only provided a partial account of what matters for market outcomes.

New Institutional Economics

Furthermore, although this focus has led to comprehensive theories on how markets can fail, it has fallen short in describing how other social arrangements and institutions aimed at correcting those failures can fail as well. NIE recognizes that all social arrangements and institutions are subject to failure in meeting the benchmarked outcomes of neoclassical models. This recognition has led NIE scholars away from comparing outcomes to neoclassical predictions in favor of comparing the outcomes of social arrangements and institutions to each other. This approach can be seen in the evolving debate over the public provision of education. The longstanding reasoning for the government provision of education has been based on the notion that private markets will fail to meet the socially optimum level of education as predicted by neoclassical models. However, NIE asks whether a similar failure occurs in the public provision of education. From an NIE perspective, it becomes a question of understanding and comparing the institutions in place in education and the incentives they create. Although this matter is intensely debated, the empirical research is far from reaching a consensus.

Institutional Environment and Institutional Arrangements A key distinction in NIE is between the institutional environment and institutional arrangements. Although both can be formal or informal and share in providing structure to interactions by defining and limiting the choices of individuals, the development and scope of their structures are different. The institutional environment provides the overall framework in which humans interact, while institutional arrangements are focused on structuring specific interactions that occur within the institutional environment. The two types of institutions are discussed in more detail below in the context of education. Institutional Environment

The institutional environment is composed primarily of laws, social norms, and customs. These institutions define and limit choices for individuals. They can be thought of as institutional constraints in that they constrain the behavior of individuals by defining what they are prohibited from doing. However, these institutions also define the conditions under which individuals are responsible for certain activities. From the perspective of law and education, one can think of the Tenth Amendment

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to the U.S. Constitution—powers not granted to the federal government by the Constitution, nor prohibited to the states, are reserved to the states or the people—as giving states purview over the provision of education. Additionally, the Fourteenth Amendment—specifically the equal protection clause—prohibits states from discriminating against their citizens in the provision of education. Each state then contributes to the institutional environment through its respective constitutions and laws, thereby adding further constraints and responsibilities to the institutional environment. Most institutional constraints in the institutional environment are not developed purposefully like constitutions and laws. Rather, informal institutions such as social norms, customs, and culture are developed over time as consequence of repeated human interaction. Although not deliberately constructed, NIE scholars suggest that these constraints are perhaps more significant in shaping the institutional environment than are formal institutions. Furthermore, informal institutions can also influence the formal constraints of the institutional environment. For example, the social value that citizens place on education could be considered a cultural norm and can have a significant influence on the creation of the formal institutions of education. Institutional Arrangements

Institutional arrangements are the rules and agreements that individuals create to oversee specific exchanges. Because of uncertainty, all market exchanges are associated with some degree of transactional costs. To reduce these costs, institutional arrangements—including contracts and organizational structures—are created. However, these arrangements are themselves associated with costs. NIE seeks to understand the nature of transaction costs, both external and internal to the institutional arrangements, and how they are related to various outcomes. Transaction cost economics (TCE) has made a significant contribution to understanding institutional arrangements. TCE theorizes that institutional arrangements are the governing structures that protect transacting parties from the costs inherent to their particular exchange. From this perspective, TCE assumes that institutional arrangements will be chosen such that transaction costs are minimized. While NIE builds on the theories of TCE, it maintains that institutional arrangements are not

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exclusively guided by cost minimization strategies but are subject to additional preferences such as social and cultural norms. NIE has provided insights into various conditions under which institutional arrangements are made. Asset specificity is one of many topics that have particular interest in understanding institutional arrangements in the context of education. Asset specificity relates to the ease with which physical and human capital can be redeployed to other productive purposes. In cases of high asset specificity, meaning that capital is not easily transferred to other productive uses, institutional arrangements are particularly important for protecting trading partners from changing circumstances where one could take advantage of the other. Without this protection there would be a tendency to underinvest in capital. Several institutional arrangements can be employed under conditions of high asset specificity, including long-term contracts. Tenure and salary schedules can be seen as an institutional arrangement related in part to the somewhat asset-specific nature of teaching due to training and certification requirements.

Institutional and Organizational Change One of the essential contributions of NIE is the movement of economics from a static theory to a dynamic theory that can examine change. Much of the research in this area has been on the role of institutions and economic development, although it speaks to the greater issue of how institutions and organizations interact to shape changes in one another. At the core is the assumption that institutions and organizations have an interdependent relationship. The institutional framework creates incentives that result in the development and evolution of organizations. However, individuals within organizations also have an incentive to alter the institutional framework if they perceive that they could do better under a different institutional framework. These alterations depend on how individuals perceive their environment and how they process that information. It is important to note that efficiency is not the only factor that influences changes in the institutional framework. NIE scholars suggest that this insight is crucial to understanding why we observe different performance of economies over time. Although institutional and organizational change has primarily been examined in the context of economic development and performance, the

application of NIE to understanding change in educational institutions and organizations could be a promising avenue for future research in understanding differences in development and performance over time in education.

The Relationship Between Institutions and Education Institutions play a significant role in the market for education. As illustrated above, institutions shape the educational environment by defining responsibilities and constraints. In meeting these responsibilities, educational organizations create various institutional arrangements that provide further structure to the provision of education. However, as the preceding section suggests, the relationship between institutions and organizations is not unidirectional. Educational organizations can also influence educational institutions, creating a process of learning and feedback through which the institutional framework can change. Another factor to consider is the influence education can have on institutions in general. By fostering human and social capital, education is a key resource to the institutional framework. Education can enable individuals to interpret their environment more effectively and can improve how they learn from interactions. This recognition reduces the level of uncertainty associated with interactions and, combined with improved learning, can influence the institutions that develop. The incorporation of NIE into the study of education is still developing. What NIE provides is an extension of neoclassical theories that suggest that the private provision of education is subject to failure. Instead, because of uncertainty and imperfect institutions, NIE suggests that the public provision of education is also subject to failure. This is not to suggest that either method is preferred. Rather, NIE suggests that a productive avenue for research is to examine under what circumstances certain institutional frameworks work best. Therefore, the provision of education may look very different from state to state or even district to district, depending on the formal and informal institutions that develop. Nathan Barrett See also Behavioral Economics; Human Capital; Markets, Theory of; Public Choice Economics; Social Capital; Transaction Cost Economics

No Child Left Behind Act

Further Readings Coase, R. H. (1937). The nature of the firm. Economica, 4(16), 386–405. Coase, R. H. (1960). The problem of social cost. Journal of Law and Economics, 3(1), 1–44. Ménard, C., & Shirley, M. M. (Eds.). (2005). Handbook of new institutional economics. Dordrecht, Netherlands: Springer. North, D. C. (1990). Institutions, institutional change and economic performance. Cambridge, UK: Cambridge University Press. Rowan, B., & Meyer, H. D. (Eds.). (2006). The new institutionalism in education. Albany: State University of New York Press. Williamson, O. E. (1985). The economic institutions of capitalism: Firms, markets, relational contracting. New York, NY: Free Press.

NO CHILD LEFT BEHIND ACT The No Child Left Behind Act (NCLB) is the name given to the 2001 reauthorization of the Elementary and Secondary Education Act (ESEA) passed by the U.S. Congress. NCLB represents the federal government’s deepest foray into public education in the United States as it mandated standardized testing of students, consequences for underperforming schools, and minimum standards for teachers. While overwhelmingly passed in both houses of Congress and lauded by many, the law was also highly criticized for some of its provisions, including setting unreasonable performance targets and an overreliance on standardized tests. Research has shown improvements in student achievement since NCLB, but its provisions have also produced many unintended consequences, including a narrowing of curricular offerings and cheating scandals. This entry provides an overview of NCLB, criticisms of the law, the law’s effects, and its future prospects.

History The U.S. Constitution makes no mention of education, leaving the authority and policy making in education historically to local and state governments. The passage of the ESEA by Congress in 1965 as part of President Lyndon B. Johnson’s War on Poverty thus represented a significant change in the federalist structure of education in the United States. The original intention of the act was to improve educational equity by family income by providing

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federal funds to school districts serving students from low-income families. The ESEA has been reauthorized seven times since 1965, taking on new authority with each version. The roots of NCLB began with the landmark 1983 report, A Nation at Risk, which asserted that America’s schools were failing and cited weak curriculum as one of the major reasons. This report had a large impact on state education policy, as a wave of states adopted standards and accountability systems in the 1990s. In fact, by 2001, all but one state had set some type of academic standards system. The reauthorization prior to NCLB, called the Improving America’s Schools Act, occurred in 1994 during the Clinton administration and set preliminary standards and accountability requirements that would be further developed in NCLB. Whereas previous versions of the ESEA used different standards for Title I and non-Title I students, Improving America’s Schools Act required that states adopt the same standards for all students. Prior to NCLB, ESEA primarily used funding to attempt to narrow inequality in student achievement by family income. NCLB is significant because it represents the federal government’s attempt at shaping the goals and outcomes of public education. This shift is based on the recognition that funding alone had not been effective in addressing educational equity, as well as an embrace of standards and accountability reform enacted in many states in the 1990s. Another factor in the creation of NCLB was a widespread belief that the decentralized and fragmented nature of the public education system in the United States was problematic because of the mixed messages and priorities communicated to schools from different levels of government. The provisions of NCLB apply to all U.S. elementary and secondary public schools, but the federal government mandated provisions under NCLB by tying them to federal Title I funding of public schools. Established under the 1965 version of the ESEA, Title I provides federal funding to schools with a large population of low-income students. Under NCLB, schools with 35% or more students from low-income families, as defined by the U.S. Census, are eligible for Title I funds. Although states are not technically required to adopt the provisions of the law, the funding provides states with a strong incentive to do so as approximately half of all public schools in the United States receive Title I funding, which totaled more than $13 billion in 2013.

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Theory of Action NCLB’s theory of action is based on the premise that high standards and measurable goals, coupled with incentives, can improve educational outcomes with the school as the level of intervention. The rationale is rooted in the principal-agent problem, which concerns the alignment of the actions of a contracted agent with the goals of a principal contractor. The problem arises when the agent acts in self-interest instead of as the principal intends, or acts based on asymmetric information not available to the principal. In public education, schools are the agents who are contracted by the state, through the school district, to accomplish the task of educating children, while the state education agency is the principal. The principal-agent problem in education arises when schools’ instruction is not aligned with the goals of the state, either because of different notions of the overall purpose of education or because schools’ perception of what is best for students differs from that of the state. To promote alignment between schools and states, NCLB uses several mechanisms. First, state academic standards are utilized to communicate what schools are expected to teach students. Second, standardized tests provide objective outcomes against which goals can be set to measure the performance of schools. Third, the threat of sanctions and possibilities for rewards based on outcomes is intended to incentivize educators to align their practices with educational standards. Fourth, school report cards provide information on school performance for parents with the goal of making schools more accountable to parents. These mechanisms are based on several assumptions. First, it is assumed that the measures used to evaluate schools are valid, reliable, and transparent and that they capture what is expected of schools. A second assumption is that educators have the knowledge and capacity to use available data to inform practices, as well as the flexibility to accomplish their goals. Third, it is assumed that the prescribed rewards and sanctions are effective in motivating schools to change their behavior. These assumptions are necessary, but not sufficient, for the efficacy of accountability systems to have their intended effect.

Provisions Testing. Under NCLB, states had to create standardized tests that were aligned with state learning standards. The law requires states to test students in math and reading every year in 3rd through

8th grades and once in 10th through 12th grades, as well as once in science in each of elementary, middle, and high school. At least 95% of students in each school have to participate in testing, as well as the same percentage of students in each subgroup in a school, including by race/ethnicity, socioeconomic status, special education, and English Language Learner. While testing is mandated by the federal government, states have the discretion to create their own tests and to determine the score required for proficiency levels. A major provision of NCLB is the requirement that 100% of students meet these proficiency levels by the end of the 2013–2014 school year. To gradually meet this goal, states set progressive annual targets called annual measurable objectives for schools each year. Adequate Yearly Progress. Schools have to demonstrate adequate yearly progress (AYP) based on a formula for their overall student population, as well as for all demographic subgroups. High school graduation rates are included as well as test scores as an additional measure of performance for high schools, and states are required to choose another measure for elementary and middle schools. If any of these measures are not achieved, a school is determined to not have met AYP. NCLB also includes several little-known alternative methods of meeting AYP that were determined state by state. Consequences. NCLB’s accountability system included consequences for schools that did not meet AYP for two consecutive years. After 2 years of failing to meet AYP, schools are provided technical assistance, and students have a choice to attend other public schools. A third consecutive year of not meeting AYP forces schools to offer supplemental educational services, including private tutoring, to low-performing students. Schools that do not meet AYP for a fourth year are labeled as requiring “corrective action,” which may include a complete replacement of staff, the introduction of new curriculum, and extended instructional time. After 5 years of insufficient progress, schools are required to draft a plan to restructure that will be implemented if the school does not improve in the next year. Restructuring includes closing the school, converting it into a charter school, or turning it over to the state or a private company to operate. Report Cards. In an effort to make school performance data more available to the public,

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NCLB requires states to provide annual report cards with student achievement both overall and by subgroup, as well as by school district. Districts are also required to provide school-by-school achievement data. Teachers. NCLB also seeks to increase teacher quality in schools serving students from low-income families. Starting in 2006, every public school teacher in core content areas was required to be “highly qualified,” which is defined as meeting three criteria: (1) is fully certified or licensed by the state, (2) holds at least a bachelor’s degree from a 4-year institution, and (3) is proficient in the subject matter that the teacher teaches. States have discretion to set the details of these criteria, particularly around certification and subject matter proficiency. All new teachers hired starting in the 2002–2003 school year have to fit the definition of “highly qualified.” The law also sets standards in education and evaluation for paraprofessionals hired with Title I funding. In addition, schools have to notify parents of students in classes taught by teachers who do not meet the state’s standards of a highly qualified teacher. In recognition of the difficulties of staffing in some areas, the U.S. Department of Education (ED) offered states flexibility around these requirements, particularly in rural areas and in science classes.

Reaction Support. The passage of NCLB was initially applauded by many observers for several reasons. First, the act was praised for bringing attention to traditionally low-achieving demographic groups of students, including low-income students, students from ethnic or racial minorities, English Language Learners, and special education students. Because NCLB mandated that these students be tested and their results reported, NCLB proponents argued, schools and districts could no longer hide any poor treatment of these difficult-to-educate students, and the extent of achievement gaps would become more evident. Second, standardized tests created a measure of school effectiveness in achievement that could be compared across schools, within states. Making these measures of school effectiveness public provided parents with objective information for deciding between schools for their children. Third, sanctions for schools that did not meet AYP focused support on the neediest schools. NCLB was also lauded by school choice advocates for including

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choice provisions for students attending schools in need of improvement. Criticisms. Despite its overwhelming bipartisan passage by Congress, NCLB also had its critics. One of the major criticisms of NCLB was its 2014 goal of proficiency. The notion that every student would test on grade level in reading and math was an unreasonable target. Also, under NCLB, 100% proficiency technically only meant 95% since it was this percentage of students’ assessment scores that states were required to report, and schools with small numbers of students in subgroups were not required to test these students nor report their results. NCLB was also criticized for its strong reliance on test scores as measures of school performance. Opponents point to the multiple purposes of education, including the development of students’ civic duties, morals, and workforce skills, of which cognitive ability is only one facet. Furthermore, students are only tested regularly in two subjects—math and reading—and tests can only capture a narrow band of the knowledge and skills that schools should be imparting to students. Another criticism of NCLB was that, because states could set their own proficiency levels, the term proficiency itself was far from an absolute determination. Additionally, without any guidelines around proficiency levels, NCLB created the potential that states could essentially appear to have high percentages of proficient students by setting a low bar for their level of proficiency. For example, Colorado set proficiency levels at the 50th to the 90th percentiles, while Texas set its reading proficiency standard at the 25th percentile and its mathematics standard at the 44th percentile. Clearly, these states would have substantially different proficiency rates. A fourth criticism of NCLB was a lack of state and district capacity. The law mandated increased responsibility for states and districts in creating and administering testing, as well as providing support to schools in need of improvement, but often without providing sufficient funding. In fact, by 2005, school districts in several states had filed suit against the ED for inadequate funding of the law’s requirements.

Effects Positive. Several studies have been conducted on the effects of NCLB on student achievement, although identifying the specific effect of the law is challenging in a dynamic policy environment. Many

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studies on the effect of NCLB on student achievement suffer from lack of a credible comparison group, no alternative assessment, and partial effects. One of the most rigorous studies was conducted by Thomas Dee and Brian Jacob with data used from the National Assessment of Educational Progress, which is a low-stakes test with no consequences for schools or students. The authors found that NCLB resulted in an increase in average fourth-grade math achievement and a more modest increase in average eighth-grade math achievement but had no effect on reading scores. These increases in achievement were found for both high- and low-achieving students, as well as for traditionally low-achieving subgroups of students. Apart from effects on achievement, NCLB has also resulted in the increased alignment of curriculum and instruction with standards and assessments. Schools and districts are increasingly analyzing test score data to improve instruction, the placement of students, and the allocation of resources within schools. Additionally, most schools met NCLB’s requirements for staffing highly qualified teachers in core courses, although research has shown that this designation is not strongly associated with teacher effectiveness. Negative. Despite research indicating positive effects of NCLB on student achievement, the act has also been found to have some negative unintended consequences. First, given the possibility of sanctions for poor test results, schools have focused resources on the tested subjects, particularly math and reading. This focus has resulted in the narrowing of curriculum, minimizing, if not altogether eliminating, classes of nontested content, such as art and music, in favor of math and reading instruction. Furthermore, within math and reading classes, some teachers have focused on the narrow range of concepts and skills that appear on the state test in what is known as “teaching to the test.” Second, given the primacy of proficiency status in the determination of AYP, some schools have targeted their efforts on students just below the proficiency cutoff. A shift in resources to these “bubble kids” often comes at the expense of other students, particularly high and low achievers. Third, there have also been a number of cases in which schools have resorted to manipulation in an attempt to meet AYP. One example of this manipulation is schools reclassifying students who are less likely to score at the proficiency level into nontested subgroups. Another example is found in reports of cheating at the

classroom, school, and even district level, with revelations of staff members in several major urban school districts changing students’ test answers.

Post-NCLB NCLB was slated to be reauthorized in 2007 but, as of the end of 2013, Congress had not yet acted. In 2011, the ED, citing some of the criticisms of NCLB mentioned above, as well as state innovations in standards, testing, and accountability systems, granted each state a chance to apply for a waiver of NCLB requirements. As of October 2013, 42 states, the District of Columbia, and a group of California school districts have received waivers in exchange for the creation of new accountability systems. One of the major changes from NCLB for states receiving waivers is a shift from an absolute measure of school performance—AYP—to a relative measure. Instead of setting a standard for schools to meet to determine whether they are in need of improvement, many states that received waivers chose to identify schools in need of improvement by ranking all Title I schools by multiple measures or a composite of measures and selecting the bottom 15% of schools for the designation. Not surprisingly, this move to relative measures of identifying priority and focus schools has resulted in substantially fewer schools being labeled as in need of improvement than under NCLB. Many states used the opportunity provided in the flexibility waivers to dispose of some of the most highly criticized aspects of NCLB, but many of those criticized elements remain. While the extension of flexibility waivers to the states represents some degree of power ceded to the states, the federal government retains authority in the realm of school accountability with the waiver approval process, potential ESEA reauthorization, and expansion of the accountability domain. In addition to school accountability measures, the ED also required states to construct teacher accountability systems in their ESEA flexibility requests. Dominic J. Brewer and Matthew Duque See also Accountability, Standards-Based; Adequate Yearly Progress; Elementary and Secondary Education Act

Further Readings Dee, T., & Jacob, B. (2009). The impact of No Child Left Behind on student achievement. Journal of Policy Analysis and Management, 30(3), 418–446.

Nonwage Benefits Dee, T., Jacob, B., & Schwartz, N. (2013). The effects of NCLB on school resources and practices. Educational Evaluation and Policy Analysis, 35(2), 252–279. Forte, E. (2010). Examining the assumptions underlying the NCLB federal accountability policy on school improvement. Educational Psychologist, 45(2), 76–88. Hess, F. M., & Petrilli, M. J. (2006). No Child Left Behind primer. New York, NY: Peter Lang. Hyslop, A. (2013). It’s all relative: How ESEA waivers did—and did not—transform school accountability. Washington, DC: New America Foundation. Retrieved from http://newamerica.net/sites/newamerica.net/files/ policydocs/ItsAllRelative-12-17-2013.pdf Krieg, J. M. (2008). Are students left behind? The distributional effects of the No Child Left Behind Act. Education Finance and Policy, 3(2), 250–281. O’Day, J. A., & Smith, M. S. (1993). Systemic reform and educational opportunity. In S. H. Furhman (Ed.), Designing coherent education policy: Improving the system (pp. 250–312). New York, NY: Jossey-Bass. Peterson, P., & West, M. (2003). No Child Left Behind? The politics and practice of school accountability. Washington, DC: Brookings Institution Press. Polikoff, M., McEachin, A., Wrabel, S., & Duque, M. (2014). Waive of the future? School accountability in the waiver era. Educational Researcher, 43(1), 45–54. Sunderman, G., Kim, J., & Orfield, G. (2005). NCLB meets school realities: Lessons from the field. Thousand Oaks, CA: Corwin Press.

NONWAGE BENEFITS Nonwage benefits (also called fringe benefits or simply benefits) represent compensation in addition to salary or wages, such as health insurance, paid leave, and retirement benefits. In the public education sector, nonwage benefits generally represent a considerable share of the total compensation of public education employees (e.g., teachers). More than one fourth of expenditures tied to instruction go toward nonwage benefits. Moreover, benefits such as summer leave and traditional retirement packages play a prominent role in shaping the debate around policies in education finance. This entry begins with a discussion of major categories of benefits. It then outlines prominent trends and tensions with respect to these categories in the sphere of education.

Categorization of Nonwage Benefits There are two primary approaches to categorizing nonwage benefits: first by their treatment under

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federal tax law and second by their function. The federal tax law approach divides benefits into two groups, mandatory and voluntary. Mandatory benefits include Social Security retirement (Old-Age and Survivors Insurance), Social Security Disability Insurance, Medicare Part A (Social Security Hospital Insurance), workers’ compensation, unemployment insurance, Medicaid, Supplemental Security Income, and public assistance. These benefits (subject to need-based eligibility) represent federal and state government programs funded by payroll taxes. Voluntary benefits may be fully taxable, tax exempt, tax deferred, or other tax-preferred benefits. Fully taxable benefits include vacation, paid lunch, severance pay, and cash bonuses. Tax-exempt benefits include employee and dependent health insurance, retiree health insurance, dental and vision insurance, Medicare Part B (Social Security Supplementary Medical Insurance), qualified transportation allowances, educational assistance, and child care. Taxdeferred benefits include defined-benefit pension plans, defined-contribution retirement plans, and other vehicles sheltering cash from taxation until retirement, as with the 401(k) and 403(b) plans, or to finance the education of children, as with 457 plans. Other tax-preferred benefits include life insurance, long-term disability insurance, and a variety of leaves of absence, notably sick leave. The function approach clusters benefits by their intended purpose, and it often reflects the way that employers and employees think about and discuss nonwage benefits. These groupings tend to include both mandatory and voluntary benefits. For example, a seminar on retirement benefits offered by a public school district to its employees may cover both mandatory and voluntary benefits such as a defined-benefit pension plan, Social Security retirement, and a 403(b) plan. Moreover, employers may also have staff devoted to managing benefits related to health care and/or leave benefits, both of which represent prominent functional groupings. However they are categorized, discussions about nonwage benefits are complicated by several factors. The first complication is that the insurance industry is regulated at the state level. Second, in the public sector, federally driven mandatory benefits such as Social Security retirement are interrelated with state pension plans. In 13 states, including large ones such as California, Illinois, and Texas, some or all state and local government employees do not participate in Social Security by virtue of their involvement with state pension plans. As

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a consequence, the proportion of compensation devoted to different categories of nonwage benefits varies significantly among states. Similarly, local preferences expressed in school board policies or collective bargaining agreements create variation within states.

Disproportionate Importance in Education A small number of key trends and tensions dominate the debate about nonwage benefits in public education. Nonwage benefits often offset perceived deficiencies in salaries and wages earned by teachers and other groups of employees in public schools, and efforts by school districts to alter the compensation mix are often confronted by the deeply rooted sociocultural expectations (i.e., summer vacations) of teachers. Debates about compensation in the public education sector are often muddled by the complexity of nonwage benefit packages and the disproportionate importance of benefits as a share of total compensation. What follows is a brief survey of key trends and tensions around salient nonwage benefits. Paid Leave

For teachers, paid leave pertains to a contractual work year, often 10 months (summer vacation tends not to represent paid leave, though widespread use of provisions that spread salary payments over the entire year make the distinction somewhat cloudy to the general public and confound efforts to compare teachers’ salaries to those earned by 12-month employees). Paid leave comprises a variety of distinct types, including vacation, sick, personal, maternity and family, bereavement, leave for jury or military duty, and sabbatical leave. The majority of paid leave is accounted for by sick and personal leave. Typically, school employees are allotted some number of days per year to be used for personal reasons or illness. Some states fix the number of days allocated; others provide a floor or minimum number to be allocated. Sick day allowances range from more than 10 days to less than 20 days, and generally two or three personal days are allowed. Employees are also sometimes able to accumulate some or all unused sick days for future conversion to cash payments or retirement income. Personal days tend not to roll over or accumulate. The topic of paid leave has received increasing scrutiny in recent years in light of evidence that suggests a

positive relationship between student achievement and teacher attendance. Retirement Benefits

Most employers in the United States provide some type of retirement plan option for employees. The two most common types of retirement plans are (1) defined-benefit plans and (2) defined-contribution plans. Today, most private sector employers offer defined-contribution plans, in which employers, employees, or both make a defined contribution to an individual investment account set up through the employer. The benefit available to an employee on retirement is tied directly to contributions and investment earnings on the account. Conversely, most public sector employees, including public school teachers, participate in definedbenefit pension plans. These plans offer retirees a guaranteed stream of income for life, funded from a common investment pool managed by a board of trustees. Typically, employees and employers pay into a plan some percentage of annual salary. The value of this benefit is dictated by the employee’s final average salary (often the highest average taken over 3 consecutive years) and years of service. Many plans adjust payments for inflation, though there is considerable variation in how this is done. The economic crisis of 2008 intensified the issues surrounding defined-benefit pension plans, as many pension investment pools suffered from balances insufficient to cover their liabilities. Concerns about underfunding have driven a wave of reforms. However, there are also grounds for concern about the relationship between traditionally defined benefits pension plans and a shift in work and career expectations of those entering the workforce in the 21st century. In particular, the extended vesting period of defined-benefit pension plans may discourage interstate mobility and may undercut retirement security for some workers. Research offers some guidance about how changes in retirement benefits would affect the quality of the teaching workforce, but the fiscal exigencies of current payments into pension plans tend to dominate debate. Insurance

Teachers and other school employees typically receive several nonwage benefits under the category of insurance. These benefits include modest life

Nonwage Benefits

insurance policies, income insurance against shortor long-term disability, and employer subsidies for premiums on health, dental, and vision insurance. The Affordable Care Act has elevated the profile of health insurance benefits, and the subject is both dynamic and complicated. In education, the tension around retirees’ access to employer-sponsored health insurance creates additional tension and uncertainty. In particular, promised access of this kind does not enjoy the same kinds of guarantees that pertain to retirement income from a definedbenefit pension.

Future of Nonwage Benefits Given the complexity of nonwage benefits, their outsize importance as a facet of total compensation, and their potential use as policy levers around strategic goals such as improving student achievement, there is liable to be continued and abundant debate about what mix of benefits is best. The past 40 years have seen a surge in research in education policy, with the importance of quality teachers and school leaders emerging as the dominant theme. Because teachers and leaders represent the most important school-based factor influencing student achievement, expectations that changes in nonwage benefits could produce desired outcomes are unlikely to subside. Raegen Miller and Rachel Perera See also Age-Earnings Profile; Present Value of Earnings

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Further Readings Costrell, R., & Podgursky, M. (2009). Teacher retirement benefits. Education Next, 9(2), 58–63. Retrieved from https://mospace.umsystem.edu/xmlui/bitstream/ handle/10355/8373/TeacherRetirementBenefits.pdf? sequence=1 Freeman, R. (1981). The effect of unionism on fringe benefits. Industrial and Labor Relations Review, 34(4), 489–509. Retrieved from http://www.jstor.org/ stable/2522473 Hansen, J. (2010). An introduction to teacher retirement benefits. Education Finance and Policy, 5(4), 402–437. doi:10.1162/EDFP_a_00012 National Center on Teacher Quality. (2013). NCTQ teacher contract database. Retrieved from http://www .nctq.org/districtPolicy/contractDatabaseLanding.do Podgursky, M. (2003). Fringe benefits: AFT and NEA teacher salary surveys. Education Next, 3(3), 71–78. Wepman, N., Roza, M., & Sepe, C. (2010). The promise of cafeteria-style benefits for districts and teachers. Seattle, WA: Center on Reinventing Public Education. Retrieved from http://www.crpe.org/sites/default/files/rr_crpe_ Benefits_Dec10_0.pdf Woodbury, S., & Hamermesh, D. (1992). Taxes, fringe benefits and faculty. Review of Economics and Statistics, 74(2), 287–296. Retrieved from http://www.jstor.org/ discover/10.2307/2109660

Website U.S. Department of Education, National Center for Education Statistics, Education Finance Statistics Center: http://nces.ed.gov/edfin/

O affect an outcome variable. The linear regression model assumes that the independent variables have a linear effect on the expected value of the outcome (dependent) variable. In other words, the outcome variable equals a linear combination of the independent variables plus an error term. The coefficient (β) associated with an independent (X) variable measures the effect of a 1-unit increase in that variable on the expected value of the dependent (Y) variable. These population coefficients are not known, and one method of estimating them is ordinary least squares (OLS) estimation. The OLS estimates of the coefficients will be unbiased if the error term is uncorrelated with the included independent variables. However, independent variables that are not controlled for explicitly in the regression model are implicitly relegated to this error term. So if an independent variable is excluded from the regression model that both influences the outcome variable and is correlated with one or more independent variables that are included, the error term will be correlated with the included independent variables and the OLS coefficient estimates will suffer from OVB. OVB is the reason why in nonexperimental methods it is difficult to make causal claims about the effect of an independent variable on an outcome variable. One must argue that all omitted variables are uncorrelated with the included variables for this to be the case. This must hold not only for all omitted variables that are measured in the data but also for any omitted variables not included in the data. While in principle this assumption can be tested for measured

OMITTED VARIABLE BIAS Omitted variable bias (OVB) occurs when an important independent variable is excluded from an estimation model, such as a linear regression, and its exclusion causes the estimated effects of the included independent variables to be biased. Bias will occur when the excluded variable is correlated with one or more of the included variables. An example of this occurs when investigating the returns to education. This typically involves regressing the log of wages on the number of years of completed schooling as well as on other demographic characteristics such as an individual’s race and gender. One important variable determining wages, however, is a person’s ability. In many such regressions, a measure of ability is not included in the regression (or the measure included only imperfectly controls for ability). Since ability is also likely to be correlated with the amount of schooling an individual receives, the estimated return to years of completed schooling will likely suffer from OVB. This entry first discusses OVB as it applies to the linear regression model. It then discusses conditions under which such bias occurs, situations in which OVB is likely to be absent, and OVB in other nonlinear models. Finally, the details of a linear regression model are given to show how OVB can arise in this context.

OVB in Linear Regression Models In statistics, multivariate models attempt to control for all the relevant independent variables that 495

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variables, it cannot be tested for unmeasured variables. For example, a regression of class size on student test scores as part of a study of teacher quality may include several independent variables, such as students’ tests score in the previous year, teacher experience, and teacher certifications. However, it may be difficult to precisely measure teacher quality. If high-quality teachers also tend to be given classes with more students, then by not including an adequate measure of teacher quality in the regression model, the OLS regression estimate of class size on student achievement may suffer from OVB. The benefit of a properly conducted randomized controlled trial is that it eliminates OVB as a problem. This occurs not because all relevant variables are included in the regression model but because if the randomization is done correctly, then all the omitted variables, both measured and unmeasured, are uncorrelated with the treatment variable. So although there may be omitted variables, they do not result in biased estimates in this context. One classic randomized controlled trial is the Tennessee Project STAR (for Student Teacher Achievement Ratio) experiment that was studied by Alan Krueger and Diane Whitmore among others. In this experiment, students and teachers were randomly assigned to small classes (13–17 students), regular classes (22–25 students), or regular classes with a teacher’s aide for kindergarten through third grade. Outcomes examined were achievement test scores in elementary school, as well as longer run outcomes such as college test-taking behavior. Other quasi-experimental methods can be used to eliminate OVB. One popular method is a regression discontinuity design. This method is used when a researcher is interested in the effect of some particular treatment on an outcome variable, and whether an individual receives the treatment or not is based on the value of a variable (sometimes called the “running” variable), where an individual receives the treatment (or at least is eligible to receive the treatment) if his or her value on this running variable is below (or in some cases above) a cutoff value. Researchers would use regression discontinuity design when, for example, individuals cannot precisely manipulate the running variable around the cutoff. So estimates of the effect of the treatment on a dependent variable for those “near” the cutoff point won’t suffer from OVB. For example, in many community colleges, whether or not an individual is required to take remediation courses is based on whether or not the individual’s score on a test is below or above a certain

threshold. Researchers have used this to examine the effect of remediation on various college outcomes such as persistence and college graduation. OVB can occur in multivariate contexts other than the linear regression model. For example, it can occur when modeling dichotomous dependent variables using logit or probit models or when analyzing duration data using hazard models. In these nonlinear models, biased or inconsistent estimates may result when important variables are excluded even if they are uncorrelated with the included predictor variables. So in some nonlinear models, biased estimates of a treatment effect may occur even when the data are from a randomized experiment. For example, suppose that colleges randomly assign roommates in dormitories and you are interested in studying peer effects on college performance by seeing whether the students assigned higher ability roommates persist longer at the university. In this case, a study that uses a duration model to estimate the effect of a highability student on persistence may suffer from OVB even though any unmeasured variable is uncorrelated with the ability of a student’s roommate.

Technical Details How OVB can arise in a linear regression context can be seen clearly in a linear regression model with two independent variables that will be denoted by X1 and X2. In this circumstance, it is possible to derive the magnitude of the OVB if one of the variables, for example, X2, is excluded from the estimation. So assume that the true regression model for the dependent variable Y is Y = β0 + β1X1 + β2X2 + ε,

(1)

where the error term ε satisfies all the assumptions of the standard linear regression model: has mean zero, is uncorrelated with both X1 and X2, has errors that are uncorrelated with each other, and the errors have a variance that is the same for all observations. As mentioned above, if this model is estimated by OLS regression, the estimates of β1 and β2, which will be denoted by βˆ 1 and βˆ 2, are unbiased—that is, E(β1) = βˆ 1 and E(β2) = βˆ 2. Suppose, however, that a regression model is estimated by OLS regression that omits the independent variable X2. Will the estimate of β1 still be unbiased? In general, the answer is no. In this case, the estimate of β1 can be shown to satisfy the equation.

Omitted Variable Bias

∑ i=1(Yi − Y )( X1i − X1 ) , βˆ 1 = n 2 ∑ i=1( X1i − X1 ) n

(2) – – where X1 and Y denote the mean of X1 and the mean of Y, respectively; the subscript i denotes a particular observation; and there are n observations in the sample. The OVB of the estimate βˆ 1 is formally defined as the difference between its expected value E(βˆ 1) and the population value β1: OVB = E(βˆ 1) − β1. To derive the size of the bias, first substitute Equation 1 into Equation 2. This yields

∑ i=1 (Yi − Y )( X1i − X1 ) βˆ 1 = n ∑ i=1 ( X1i − X1 )2 n (β ( X − X1 ) + β 2 ( X 2i − X 2 ) + ε i − ε )( X1i − X1 ) ∑ i =1 1 1i , = n ∑ i=1 ( X1i − X1 )2 n

(3)

where we have also used the fact that − − − Y = β0 + β1X1 + β2X2 + − ε,

where ε– is the mean of ε: ε=

1

Brian P. McCall

∑ εi . n

Performing some additional algebra lets you rewrite Equation 2 as

∑ i=1( X2i − X2 )( X1i − X1 ) + ∑ i=1( εi − ε ) ( X1i − X1 ) . n n 2 2 ∑ i=1( X1i − X1 ) ∑ i=1( X1i − X1 ) n

See also Econometric Methods for Research in Education; Ordinary Least Squares; QuasiExperimental Methods; Randomized Control Trials; Regression-Discontinuity Design

n

(4)

Now, taking the expected value of both sides of Equation 4, treating the Xs as fixed, and noting that by assumption the error term is uncorrelated with X1, we have

( )

The OVB will be 0 if either β2 = 0 or αˆ 1 = 0. Assuming that β2 ≠ 0 and αˆ 1 ≠ 0, then the sign of the bias depends on the signs of β2 and αˆ 1. If both β2 and αˆ 1 are positive or are both negative, then the OVB for the OLS estimate βˆ 1 overestimates the effect of X1 on Y. If β2 is positive and αˆ 1 is negative, or vice versa, then the OVB for the OLS estimate is βˆ 1 negative, and on average βˆ 1 underestimates the effect of X1 on Y. Note that the OVB does not vanish as the sample size increases but instead converges to β2α1, where α1 is the population regression parameter for the regression of X2 on X1. In other words, the estimate βˆ 1 is also inconsistent. In the case of more than two predictor variables, the sign of the OVB on the estimated coefficients of the included independent variables that arises from omitting one or more variables is more difficult to determine. However, no OVB will occur if the omitted variable(s) are uncorrelated with all the included predictor variables. Since the omitted independent variables are contained in the error term, the assumptions of the standard regression model still hold under these circumstances.

n

i =1

βˆ = β1 + β2

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∑ i=1( X2 − X2 )( X1i − X1 ) ⎤⎥ + E ⎡⎢ ∑ i=1 εi ( X1i − X1 ) ⎤⎥ n n 2 2 ⎥ ⎢ ⎥ ⎢ ∑ i=1( X1i − X1 ) ⎢⎣ ⎥⎦ ⎢⎣ ∑ i =1 ( X1i − X1 ) ⎥⎦ ⎡

n

⎡

n

n

E βˆ 1 = β1 + β2 ⎢

∑ i=1( X2 − X2 )( X1i − X1 ) ⎤⎥ . n 2 ⎢ ∑ i=1( X1i − X1 ) ⎥⎥⎦ ⎢⎣

= β1 + β2 ⎢

(5) The term that multiplies β2 in Equation 5 equals the OLS estimate of the slope parameter from a regression of the variable X2 on X1 and will be denoted by αˆ 1. So OVB = E(βˆ 1) − β1 = β2 αˆ 1.

Further Readings Cameron, A. C., & Trevedi, P. K. (2005). Microeconometrics: Methods and applications. New York, NY: Cambridge University Press. Gail, M. H., Weiand, S., & Piantadosi, S. (1984). Biased estimates of treatment effect in randomized experiments with nonlinear regressions and omitted covariates. Biometrika, 71, 431–444. Krueger, A. M. (1999). Experimental estimates of education production functions. Quarterly Journal of Economics, 114, 497–532. Krueger, A. M., & Whitmore, D. M. (2001). The effect of attending a small class in the early grades on college-test taking and middle school test results: Evidence from Project STAR. Economic Journal, 111, 1–28. Martorell, P., & McFarlin, I. (2011). Help or hindrance: The effects of college remediation on academic and labor market outcomes. Review of Economics and Statistics, 93, 436–454.

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Wooldridge, J. M. (2009). Introductory econometrics: A modern approach (4th ed.). Mason, OH: South-Western Cengage Learning.

ONLINE LEARNING There has been a dramatic increase in online course offerings and enrollments in the past decade. According to a 2013 report by I. Elaine Allen and Jeff Seaman, nearly one third of all U.S. postsecondary students taking courses in 2011 were enrolled in at least one online course. Similarly, the number of K-12 students taking online courses through schools has grown from 45,000 in 2000 to 4 million in 2011. Scholars also predict that nearly 50% of all high school courses will be online by 2019. The promise of providing greater access to a wider range of populations appeals to administrators, with the majority of chief academic officers reporting that online learning is a critical aspect of their long-term strategies. Though there has been exponential growth, it is still unclear whether online courses are more effective in terms of learning outcomes than their faceto-face counterparts. This entry discusses types of online learning, provides a brief overview of the central debates in the research on online learning, and discusses the primary challenges confronting online learning. The entry draws on the following definition of online learning by Watson, Murin, Vashaw, Gemin, and Rapp (2013): Teacher-led education that takes place over the Internet, with the teacher and student separated geographically, using a web-based educational delivery system that includes software to provide a structured learning environment. It may be synchronous (communication in which participants interact in real time, such as online video) or asynchronous (communication separated by time, such as email or online discussion forums). It may be accessed from multiple settings (in school and/or out of school buildings). (p. 8)

Well-designed courses can provide multiple pathways for learning and interaction. Students have opportunities to access course materials, communicate with instructors, give and receive feedback, and track performance potentially in one online site.

Types and Contexts of Online Learning Virtual Schools

Virtual schools arose in the 1990s to meet the varying needs of K-12 students. They provide opportunities for students to take one or all of their courses online and have enrolled nearly 1 million K-12 students. The courses are often offered through local education agencies and allow students seeking remedial or accelerated learning opportunities, from rural to urban areas, to learn outside of a classroom. Courses may also be synchronous or asynchronous. In a synchronous virtual school, teachers and learners are required to be online at the same time. In an asynchronous virtual school, students can access courses at anytime. Florida Virtual Schools is the first statewide Internet-based public high school in the United States. It offers 120 courses and full-time virtual instruction. Additionally, schools around the world can contract with Florida Virtual Schools to provide courses to students. They also help schools reduce class size by offering opportunities for learning outside of physical classrooms and serves atrisk students by allowing opportunities for credit recovery. Learning Management Systems

Learning management systems are a collection of software tools that allow for the online delivery of course materials, management, and interaction. They are customizable and allow instructors to add course content and sections that are unique to a course. In addition to course materials, students can post completed assignments and track their grades. Both students and teachers can also post to the course wall, post to the discussion forum, and chat. Blackboard and Moodle are two commonly used learning management systems. Blended or Hybrid Learning

Many schools and universities incorporate online learning into their curricula in various fashions. K-12 institutions offer online courses, and postsecondary programs offer entire degree programs online. Blended learning, also called hybrid learning, refers to having multiple delivery systems for more than one course. Delivery systems include digital and face-to-face options and can include a combination of computers, the Internet, prerecorded lectures, and videos. Blended-learning classrooms are defined as

Online Learning

having anywhere between 30% and 79% of content facilitated through an electronic medium. New Models of Online Education

Massive open online courses (MOOCs), developed at the beginning of the 21st century, are notable for their large scale. MOOCs provide content to thousands of students in one course and give badges or certificates for courses completed. Notable MOOC providers include Coursera, Udacity, and edX. Universities have also begun to produce MOOCs, and some schools are incorporating MOOCs into their degree programs. MOOCs have the potential to reduce overcrowding in university courses and accelerate time to degree for students. Some universities have spoken out against MOOCs because of their potential to supplant the role of the professor, particularly in entry-level courses. MOOCs have also been criticized because of the difficulty in accurately assessing progress and the high dropout rate. In 2006, the educator Salman Khan founded the Khan Academy, a nonprofit website that provides “microlectures” and video tutorials on a range of subjects. There are more than 2,500 videos on Khan Academy. Students can take courses on Khan Academy and receive badges attesting to successful completion of the course. The organization has received support from the Bill & Melinda Gates Foundation and from Google. In 2007, Apple launched iTunes University, the largest repository of online educational resources. This format allows accredited K-12 schools and universities to manage and distribute educational content in audio, video, and PDF formats. Content includes course lectures, lab demonstrations, language lessons, and other relevant material. Schools can use the platform to distribute learning to their own students or to make it open to the public. As of 2013, there have been more than 1 billion downloads on iTunes University. Academic Outcomes and Online Learning

There has been much debate about the effectiveness of online learning versus face-to-face learning. Various studies comparing both forms of delivery have been conducted, though questions remain about the level of rigor in their methodology. The U.S. Department of Education’s 2010 meta-analysis of 235 of these studies from 1985 to 2002 showed an effect size of zero, indicating online learning both

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underperforms and outperforms courses delivered in traditional formats. The meta-analysis and review of studies of online learning versus face-to-face learning suggested a slight advantage for courses delivered online. It is important to note, however, that the bulk of the studies reviewed did not include K-12 learners. Scholars note that this advantage may be attributed to factors such as the amount of time on task rather than the delivery mode per se. In addition, effect sizes were larger when curriculum materials differed in the online versus face-to-face forms of delivery. Research shows that there are differences in students’ attitudes about online learning versus face-to-face learning, with many favoring the latter to asynchronous online courses. In addition, students are more likely to complete traditional courses than they are to complete asynchronous courses. Other differences have been noted in the performance of students in K-12 virtual schools managed by for-profit organizations. Students in these schools are less likely to meet adequate yearly progress goals under the No Child Left Behind Act than are students in traditional charter schools. Reasons for these differences may include reduced social interaction, fewer collaborative learning opportunities, and less individual attention from instructors. Another important finding in the 2010 metaanalysis was that hybrid learning yielded the best achievement outcomes of any of the programs examined. This was particularly the case in hybrid programs having supports that catered to specific learner needs. Again, increased time on task and modified content were credited with producing the benefits of hybrid learning, not necessarily the particular medium of instructional delivery. More methodologically rigorous research is needed on achievement in this area and online learning more broadly.

Challenges Facing Online Education As the field of online learning grows, challenges persist. Internet access has grown in the United States and abroad but is not universal. Though reduced dramatically in the past decade, the digital divide remains, and many Americans lack access to broadband and, in some cases, home computers. This problem is magnified in parts of the developing world where access to information and communications technologies may be difficult for people living in remote and rural areas.

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Additionally, as online schools become more widespread, accreditation becomes another challenge. Schools seeking to develop online programs must navigate interstate commerce and education laws. Training for educators teaching online programs is another issue, as are credentialing and certifications that are required by some but not all online programs. Copyright issues also remain a challenge in online programs. Institutions struggle to find ways to disseminate information in a way that promotes education but protects copyright holders. Some members of the online learning community have issued a call for a common educational marketplace to promote greater integration between online learning programs across state lines. The Academic Common Market Electronic Campus is a function of the Southern Regional Education Board that allows students to pay in-state tuition for online learning programs not offered in a student’s home state. Participation includes the 16 southern states that are members of the Southern Regional Education Board. Some policymakers are calling for relaxed regulation of online learning programs. The Lumina Foundation has worked to get regional higher education accreditation for institutions offering online learning, and for regional accreditors to allow institutions to participate in a state authorization reciprocity agreement. This would allow for the creation of a national set of reciprocity agreements for online learning programs, so that online learning operators would not have to forge separate agreements with the accrediting institutions of each state in which they wish to operate. This agreement would attempt to bypass this process. As of 2010, more than 10% of American college students attended a for-profit college. For-profit colleges cater to students’ desires for flexible class schedules, accelerated time to degree, and training attached to the demands of the labor market. Additionally, for-profit schools may be an alternative to overcrowded community colleges and public universities. Many for-profit colleges offer online or hybrid courses. For-profit colleges face low rates of completion and high rates of loan default. Some colleges have been accused of predatory lending practices. In 2011, the U.S. Department of Education and the Obama administration launched an investigation into for-profit colleges. The federal government has since increased regulations of for profits. Policy changes require schools to disclose

their graduation rates to applicants and to get permission from government before chartering new degree programs.

The Classroom of the Future The rapidly changing technical landscape will bring with it continued changes in online learning. The use of tablets, for example, is transforming what constitutes a learning space and the nature of learning more broadly. Science, technology, engineering, and math classrooms are being redefined by students’ ability to perform higher order tasks in their “handheld labs.” The promise of these devices has prompted a number of school districts to implement one to one (1:1) tablet programs for their students. These programs allow for each student to have a computing device. Early studies show enhanced engagement and learning outcomes associated with tablet use, though more research is needed. What is clear is that there will be an integral role of mobile devices in the next generation of online learning and in the classroom of the future. Brendesha Tynes and Sharla Berry See also Access to Education; Digital Divide; Distance Learning; Education Technology

Further Readings Allen, I. E., & Seaman, J. (2013). Changing course: Ten years of tracking online education in the United States. Oakland, CA: Babson Survey Research Group and Quahog Research Group. Barbour, M. (2011). Today’s student and virtual schooling: The reality, the challenges, the promise. Journal of Open, Flexible and Distance Learning, 13(1), 5–25. Beldarrain, Y. (2006). Distance education trends: Integrating new technologies to foster student interaction and collaboration. Distance Education, 27(2), 139–153. Bell, B. S., & Federman, J. E. (2013). E-learning in postsecondary education. Future of Children, 23(1), 165–185. Bernard, R. M., Abrami, P. C., Lou, Y., Borokhovski, E., Wade, A., Wozney, L., . . . Huang, B. (2004). How does distance education compare with classroom instruction? A meta-analysis of the empirical literature. Review of Educational Research, 74(3), 379–439. Borgman, C. L. (Ed.). (2008). Fostering learning in the networked world: The cyberlearning opportunity and challenge (Report of the NSF Task Force on Cyberlearning). Washington, DC: National Science

Opportunity Costs Foundation. Retrieved from http://www.nsf.gov/ pubs/2008/nsf08204/nsf08204.pdf Cavanaugh, C. (2001). The effectiveness of interactive distance education technologies in K-12 learning: A meta-analysis. International Journal of Educational Telecommunications, 7(1), 73–78. Keegan, D. (1996). Foundations of distance education. London, UK: Routledge. Morey, A. I. (2004). Globalization and the emergence of for-profit higher education. Higher Education, 48(1), 131–150. Shachar, M., & Neumann, Y. (2010). Twenty years of research on the academic performance differences between traditional and distance learning: Summative meta-analysis and trend examination. MERLOT Journal of Online Learning and Teaching, 6(2), 318–334. Tierney, W. G., & Hentschke, G. C. (2007). New players, different game: Understanding the rise of for-profit colleges and universities. Baltimore, MD: Johns Hopkins University Press. U.S. Department of Education, Office of Planning, Evaluation, and Policy Development. (2010). Evaluation of evidence-based practices in online learning: A metaanalysis and review of online learning studies. Washington, DC: Author. Watson, J., Murin, A., Vashaw, L., Gemin, B., & Rapp, C. (2013). Keeping pace with K-12 online and blended learning: An annual review of policy and practice. Durango, CO: Evergreen Group. Retrieved from http:// kpk12.com/cms/wp-content/uploads/EEG_KP2013-lr.pdf Zhao, Y., Lei, J., Yan, B., Lai, C., & Tan, H. S. (2005). What makes the difference? A practical analysis of research on the effectiveness of distance education. Teachers College Record, 107(8), 1836–1884.

OPPORTUNITY COSTS Given that a decision maker has to choose how to use a limited resource, the opportunity cost is the value of the best use of the resource other than the use actually chosen. No matter what choices are made in education, or in any other endeavor, there is always an opportunity cost. This entry describes opportunity costs, provides numerous instances within education finance and policy where opportunity costs are relevant, describes how the concept of opportunity cost can be used to help determine whether decision makers are making the best use of available resources, and discusses how measures of opportunity cost can differ depending on what the observer considers valuable.

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For example, a school district may unexpectedly have additional funds to spend. The school board can consider using the funds to hire additional teachers, to renovate and expand school buildings, to pay for long-desired but previously unaffordable school activities, to pay for more and better training of existing staff, or many other possibilities. However the school board decides to spend the funds, there will be valuable opportunities foregone. The value of the best opportunity not chosen is the opportunity cost. For the sake of simplicity, say the school board must spend all of the additional funds on one of the potential categories mentioned above. Say hiring additional teachers is the choice made. Also assume that it has been determined that renovating and expanding school buildings is the most valuable choice that is not taken. Then, the value of renovating and expanding the school buildings is the opportunity cost. If the value of a use of a resource is greater than its opportunity cost, then the resource is being put to its best possible use. In our example, if the value of hiring the additional teachers is greater than the value of the school building renovation and expansion (the opportunity cost), then the unexpected additional funds (the resource) are being put to their best possible use. The concept of opportunity cost applies whenever budget decisions are made. To see this, think about when state governments determine their budgets. Typically, the total dollar amount that can be assigned to expenditures in a state budget is based on the forecast of total state revenues. Some of the revenues may be statutorily dedicated to specific purposes, but much of the revenue raised typically goes into what is often called the general fund. The governor and legislature have discretion over how the dollars in the general fund are spent. There are many different programs and purposes for which the general fund can be potentially used. Policy decisions could be made to allot funds to the major expenditure categories (e.g., education, health, natural resources, public safety, transportation, etc.) at historically traditional proportions; or perhaps a new governor may choose to budget disproportionately more funds for education at the expense of the other categories. Some programs may be eliminated, while others are expanded, and so on. The opportunity cost would be calculated by comparing the best alternative budget with the budget chosen. Specifically, the opportunity cost would be the value of the elements

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Opportunity Costs

that are present in the best alternative budget but are not present with the budget chosen. If the value of the elements that are in the chosen budget but not in the alternative budget exceeds the opportunity cost, then the governor and legislature have put the available state funds to their best possible use. Although the previous examples have dealt with using funds on a specified choice, the concept of opportunity cost applies to the use of any type of resource. Time, classroom space, and slots for university admissions are some other types of resources, in addition to funds, for which there are opportunity costs once choices about their use are made. Say a state legislature passes a law requiring that high school students must pass a financial literacy class in order to graduate. There is value for students to learn the basics of dealing with money while still young, increasing the likelihood that they make sound financial decisions going forward. The law in effect requires that high school staff and students use the resources of time, classroom space, and funds in a specific way. The opportunity cost of passing the law is the cumulative (for high schools across the state) value of the best alternative use of the classroom time, teacher preparation time, student studying time, classroom space, funds spent on instructional materials, and so on that must be utilized for financial literacy classes due to the law. Whether or not the value of a choice made exceeds its opportunity cost often depends on how one measures value. Depending on the observer’s views, the value of a resource choice could be measured by (a) change in percentage of students who pass state or national standardized tests, (b) change in a different measure of student learning, (c) change in estimated lifetime earnings of students, (d) change in the appreciation and understanding of culture, (e) change in crime rate, (f) change in the employability of students once they graduate, (g) numerous potential other categories, or (h) any combination of these categories. Thus, someone whose only measure of value is change in the percentage of students who pass state or national standardized tests, which typically don’t include the arts, would likely have a smaller measurement for the opportunity cost of switching resources from the arts to more time, space, and funds used for English, mathematics, and science, than would an individual who includes change in appreciation and understanding of culture in his or her measurement of value. The concept of opportunity cost is useful when considering many other decisions in education.

When considering whether or not to attend a 4-year college, there is the foregone salary and experience from instead working full-time or perhaps another use of time. When choosing a major, there is the opportunity cost associated with the value of choosing a different major. When a professor chooses to spend extra time preparing a lecture, there’s the opportunity cost associated with instead having spent that time with family or on research. When a teacher decides to devote extra time to a particular topic, there is the opportunity cost of having spent that time differently. When a school board considers giving raises to teachers, there is the opportunity cost of using those funds for other purposes. When a college decides to dedicate limited space on campus to one specific purpose, there is the opportunity cost of using that space for another purpose. When parents and their high school–age child decide to spend time and money in SAT course preparation, there is the opportunity cost associated with using that time and those dollars differently. When a school board decides whether a second assistant principal should be hired and assigned to each school, there is the opportunity cost of using the necessary funds for a different purpose. When a student decides to go to a review session in preparation for a chemistry exam, there is the opportunity cost of using that time in another way. Considering opportunity costs can help individuals make better informed decisions throughout the field of education. Lawrence S. Getzler See also Budgeting Approaches; Cost of Education; CostBenefit Analysis; Cost-Effectiveness Analysis; Economic Cost; Economics of Education; Foregone Earnings; Policy Analysis in Education; School District Budgets

Further Readings Frank, R. (2005, September 1). The opportunity cost of economics education. New York Times, p. 1. Retrieved from http://www.nytimes.com/2005/09/01/ business/01scene.html?_r=1& Murnane, R. J., Singer, J. D., & Willett, J. B. (1989). The influences of salaries and “opportunity costs” on teachers’ career choices: Evidence from North Carolina. Harvard Educational Review, 59(3), 325–346. Palmer, S., & Raftery, J. (1999). Opportunity cost. British Medical Journal, 318(7197), 1551–1552. Pinera, S., & Selowsky, M. (1978). The opportunity cost of labor and the returns to education under unemployment

Opportunity to Learn and labor market segmentation. Quarterly Journal of Economics, 92(3), 469–488. Rahman, H., Seldon, J., & Seldon, Z. (2012). The opportunity cost of education: Where do the lost years go? Journal for Economic Educators, 12(1), 43–52.

OPPORTUNITY

TO

LEARN

Standardized testing has become the norm in K-12 education in the United States and beyond. When these assessments are used to advance or retain students, and to gauge the effectiveness of schools or educators, the interpretation of results raises a fundamental question: Do they measure ability of students or the opportunities and resources provided to students to learn the material? This question is the basis for opportunity to learn (OTL). The definition of OTL broadly refers to the extent to which students are exposed to concepts on which they are tested and provided with the necessary conditions to learn expected content. OTL focuses on resource inputs likely to affect student outcomes. This entry provides a history of the concept, along with a description of how it has been used in recent research and practice. As the name suggests, the intention of specifying OTL is to draw attention to whether or not all children have comparable prospects to learn curriculum and achieve high standards. Students in low-income and high-minority schools have traditionally been presented with inequitable access to quality teaching, differentiated curriculum, and equipment. OTL draws attention to persistent inequities between schools, districts, and states with different levels of resources (e.g., spending per pupil, experienced or qualified teachers, etc.) with the intention of ensuring equal opportunities for students, regardless of where they live. Many argue that without assurances of OTL, content and performance standards and linked accountability systems may exacerbate existing achievement gaps. Over time, the definition of OTL has evolved based on the needs of its varying audiences—from researchers to politicians. While OTL originated in the research community as a variable to enable valid comparative analyses, the concept was later adopted by policymakers to advance efforts that promote equity.

History of OTL OTL surfaced as a research tool in the 1960s. At that time, researchers were concerned with whether

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students had been exposed to specific content and processes that would allow them to answer standardized test questions. Researchers used OTL to document relationships between particular concepts, such as exposure to content, and student outcomes on assessments. In this way, OTL allowed researchers to compare content as intended to be taught at the systems level with content as actually taught in the classroom. In this role, OTL was found to be highly predictive of student outcomes; students who were given access to content and high-quality instructional practice achieved higher scores on standardized assessments. Early studies utilized the concept to ensure valid cross-national comparisons of studies of mathematics achievement and to explain differences in test results across national boundaries as well as within single systems. Over time, researchers have expanded the definition of OTL beyond exposure to content, including a range of other variables likely to influence student performance. Some examples include teacher quality, facility conditions, learning environments, interactive engagement opportunities among students, and access to technology. Because of this wide variation, the definition of OTL in studies depends largely on the researchers’ individual inquiries. In the 1990s, the concept of OTL gained traction in the policy arena, with particular regard to standards-based reform and accountability. In 1992, the National Governors Association created a task force to investigate defining and enforcing OTL standards at the federal level. At the time, the debate about the role of OTL in policy was intensifying. Some lawmakers argued that if students, schools, and school systems were to be held accountable for achieving content and performance standards based on assessment results, assurances were needed that all students had equal access to learning the standards and material to be tested—regardless of whether they resided in a state with high per-pupil funding or low per-pupil funding. The adoption of specific OTL standards, they argued, would provide this guarantee. This debate was followed in 1994 by the signing of the Goals 2000 Act, a law that codified a set of education goals concerning, among other things, student academic achievement and school completion and resources for states and communities to develop content and performance standards and aligned assessments to ensure that all students reach these goals. In its original form, the legislation included a provision for states to receive grant

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funding to create and implement their own set of OTL standards, with specific attention paid to classroom procedures. The development of state OTL standards was to be voluntary and address a range of factors, including curricula, instructional materials, technology, professional development, teacher qualifications, and safety. This grant provision was later repealed in appropriations legislation; in its place, Goals 2000 was updated to include the provision of federal technical assistance to states to create plans for increasing equity in school finance. (Funding for Goals 2000 never materialized, so these provisions were not enacted.) The potential of standardizing OTL in Goals 2000 sparked considerable debate. Detractors challenged the narrow scope of enforceable standards, as they could only control school factors, not necessarily home and environmental issues influencing student learning. Critics also questioned the burden such standards could place on schools already in need. Some went so far as to argue that OTL standards imposed an unfunded mandate on states to provide these resources. These debates further illustrated the difficulty of framing standardized conceptions of equity. OTL was featured less prominently in the 2001 passage of the No Child Left Behind Act. While it did not explicitly include language on OTL, the law called for all schools to cover common standards and employ highly qualified teachers and also presented families from low-performing schools with school choice options and supplemental educational services (i.e., tutoring). Although the act was intended to improve exposure to standards-based curriculum for all students, critics of the law noted that when testing is used to determine content coverage, it can actually limit students’ OTL content beyond the tested boundaries. Critics of the act have also argued that the law did not provide equal opportunities to learn for all students, but rather, it exacerbated status quo inequities; for example, by incentivizing the exclusion of lower performing children from state testing.

OTL Today While discussions surrounding the importance of schools, school systems, and states guaranteeing all students opportunities to learn persist today— particularly within debates over school funding and resource adequacy (a concept closely related to OTL)—the adoption of OTL standards has not been

central in recent policy dialogue. Practical and political considerations help explain this shift. First, most agree that it is difficult to achieve a consensus on the definition of OTL and what it entails. OTL is also difficult to measure. For example, there may not be one set of conditions that applies to all students, as the factors contributing to successful learning may vary from class to class and from school to school. Validly measuring such a highly variable and complex set of processes relating to curriculum and instruction would be challenging. The associated costs of measurement of standards could be prohibitive, and enforcement of OTL standards could prove to be expensive, perhaps requiring redistribution of resources or resulting in costly legal disputes. Second, the incorporation of OTL and OTL standards into policy would likely face significant political opposition. Much like the debates occurring in 1994 over Goals 2000, many interests view efforts to enforce and regulate OTL as an attempt to centralize control over education and challenge traditional local control over school governance. As such, as Lorraine McDonnell has argued, the enduring value of OTL and OTL standards in education policy may be as an instrument of hortatory policy: It defines a vision of equitable education and can serve to persuade others to buy into that vision. Although the term has not been prominent in recent policy discussions, scholars have continued to apply the ideas of OTL to research on standards-based reform and accountability policy. For example, Andrew Porter and colleagues’ studies measuring the rigor of state standards and their alignment with instruction have highlighted significant cross-state variation and disparities in OTL. Some scholars and advocates have used this research to argue for the adoption of national standards and aligned assessments in the hopes of equalizing access to high-quality curriculum and instruction. Julie A. Marsh and Alice Huguet See also Accountability, Standards-Based; Adequacy; No Child Left Behind Act; Unfunded Mandates

Further Readings Elmore, R., & Fuhrman, S. (1995). Opportunity-to-learn standards and the state role in education. Teachers College Record, 96(3), 432–457. Guiton, G., & Oakes, J. (1995). Opportunity to learn and conceptions of educational equality. Educational Evaluation and Policy Analysis, 17(3), 323–336.

Ordinary Least Squares McDonnell, L. M. (1995). Opportunity to learn as a research concept and a policy instrument. Educational Evaluation and Policy Analysis, 17(3), 305–322. Moss, P. (2008). Assessment, equity, and opportunity to learn. Cambridge, UK: Cambridge University Press. Porter, A. C. (1995). The uses and misuses of opportunityto-learn standards. Educational Researcher, 24(1), 21–27. Porter, A. C., McMaken, J., Hwang, J., & Yang, R. (2011). Common core standards: The new U.S. intended curriculum. Educational Researcher, 40(3), 103–116. Porter, A. C., & Polikoff, M. S. (2009). National curriculum. In T. L. Good (Ed.), 21st century education: A reference handbook (pp. 434–442). Thousand Oaks, CA: Sage. Stedman, J. B., & Riddle, W. C. (1998). Goals 2000: Educate America Act implementation status and issues. Washington, DC: Library of Congress, Congressional Research Service. Wang, J. (1998). Opportunity to learn: The impacts and policy implications. Educational Evaluation and Policy Analysis, 20(3), 137–156.

ORDINARY LEAST SQUARES Ordinary least squares (OLS) is perhaps the most commonly used estimation technique in econometrics, and it forms the backbone of most empirical work in the economics of education. It is used to estimate a relationship, causal or otherwise, between a dependent (or outcome) variable and one or more independent (or explanatory) variables. There are many reasons why the OLS estimator is preferred: It is easy to calculate, easy to interpret, and has nice properties given some fairly weak assumptions. This entry provides a brief overview of each of these aspects of the OLS estimator.

The Mechanics of OLS The purpose of most empirical work is to estimate a causal relationship between an explanatory and a dependent variable—that is, the effect of the explanatory variable on the dependent variable while holding all else constant. For example, a researcher may be interested in estimating how class size (the explanatory variable) causally affects student test scores (the dependent variable). Researchers are often interested in estimating causal effects because they are most relevant for policy. In the example above, to accurately project the effect of a policy that reduces class sizes, one must estimate the effect of class size

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reductions while holding all other factors that might impact test scores constant. OLS can provide estimates of this causal effect under some conditions. If those conditions are not met, OLS instead provides estimates of a partial correlation between class size and student test scores. Partial correlations may be useful in some contexts, such as simply describing a relationship, but they are rarely useful in a policy context. The focus of this section is to describe the conditions under which OLS estimates lend themselves to a causal interpretation. Suppose a researcher has collected data on a dependent variable (y) and n explanatory variables (x1, x2, . . ., xn). It is a common practice to model the causal relationship between the dependent variable and explanatory variables in a linear function of unknown parameters (β0, β1, . . ., βn): y = β0 + β1x1 + β2x2 + . . . + βnxn + ε,

(1)

where β0, β1, . . . , βn are unknown parameters to be estimated (by OLS or other means) and ε is an error term, which captures variation in y that is unrelated to variation in the explanatory variables included in the model. The unknown parameters are assumed to measure (or approximate) the causal relationship between each explanatory variable and the dependent variable. For example, β1 measures the effect of x1 on y, while holding all of the other observed explanatory variables and unobserved factors constant. The key question the researcher faces is how to estimate these unknown parameters. The method of OLS offers one option. The intuition behind the method of OLS is that it chooses estimates of the unknown parameters so that the linear model presented in Equation 1 fits the data as closely as possible. To do so, the OLS estimator does what its name suggests—it produces estimates of the parameters that minimize the sum of the squared differences between the dependent variable and the linear function itself. Deriving the formula for the OLS estimates is beyond the scope of this entry. In practice, researchers rely on standard statistical programs (Excel, Stata, SPSS, SASS, etc.) to produce OLS estimates.

Interpreting OLS Estimates The OLS estimates of the parameters in Equation 1, often called coefficient estimates, have a simple interpretation. Each coefficient estimate of beta (denoted as βˆ 0, βˆ 1, . . . βˆ n) gives an approximation of the

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change in the expected value of the dependent variable for a 1-unit increase in the explanatory variable attached to the coefficient, while holding all of the other explanatory variables constant. For example, βˆ 1 gives an estimate of the change in the expected value of y for a 1-unit increase in x1, while holding all of the other explanatory variables (x2, x3, . . . , xn) constant. Notice that the interpretation does not include holding the error term constant, so the OLS estimates do not automatically lend themselves to a causal interpretation unless we make an assumption about the relationship between the explanatory variables and unobserved factors included in the error term. The nature of this assumption is discussed in the next section. A closer examination of the mechanics of OLS estimation shows more clearly what OLS estimates represent. It is a well-known result in econometrics that the OLS estimate of β1 from Equation 1 is the same as the OLS estimate obtained from the regression of y on the residuals from the regression of x1 on all of the other explanatory variables in the model. Here, the residuals are simply the difference between each observed x1 and the estimated regression function obtained from regressing x1 on all of the other explanatory variables. This result demonstrates that the OLS estimates represent the relationship between the dependent variable and variation in each explanatory variable that is unexplained by variation in the other explanatory variables. In other words, OLS coefficient estimates measure how y varies with roughly independent variation in each explanatory variable.

Properties of OLS Estimators The key to most education research is establishing a credible case for causality. This section discusses the conditions that are necessary for the OLS estimator to give reliable estimates of the causal effects defined in Equation 1. In practice, most of the debate over the validity of empirical results in education research is focused on whether the estimators used in the research meet the conditions outlined here. An estimator is generally viewed favorably if it satisfies two conditions. First, and most important, it should produce estimates that are, on average, equal to the causal effect, a property known as unbiasedness. Second, if the estimator is unbiased, it ought to have a small variance so that any error in estimating the causal effect is likely to be small. The concept of unbiasedness refers to whether the distribution of an estimator is centered on the

parameter of interest, which, in this case, is assumed to be an unknown causal effect. An estimator is unbiased if, for any sample size, the estimator has an average value that is equal to the causal effect of interest. That is, if the OLS estimator from a given model is unbiased, then if one were to continually resample and reestimate the empirical model, the average of the OLS estimates will be equal to the causal effect of interest. Unbiasedness says that the average of all of the estimates obtained under resampling will be equal to the effect of interest. There will be variation across estimates, hence the need for standard errors, which characterize this variation. In order for OLS estimators to be unbiased, they must satisfy what are known as exogeneity conditions. The OLS estimator is unbiased if, conditional on each explanatory variable in the model, the error term has an expected value that is equal to zero. In the context of the model in Equation 1, this condition is E[ε | x1, x2, . . . , xn] = 0. More intuitively, for this exogeneity condition to hold, each explanatory variable must be partially uncorrelated with unobserved factors that influence the dependent variable. Although the conditions necessary for unbiased OLS estimators may seem simple, researchers often face considerable challenges in establishing a credible case for exogeneity. Endogeneity, the violation of the exogeneity condition, can arise from several sources. The most common sources of endogeneity arise from omitted variables, measurement error, reverse causality (or simultaneity), and nonrandom sampling from the population. Whatever the cause, when unobserved factors that influence the dependent variable are also correlated with an explanatory variable, the result is that the OLS estimator is generally biased and is therefore not guaranteed to produce estimates that are, on average, equal to the causal effect. When estimators are biased, researchers are often able to predict the direction of bias. When an OLS estimator is likely to produce estimates that are larger than the causal effect, we say that the estimator is biased upward or suffers from upward bias. Downward bias occurs when an estimator is likely to produce estimates that are smaller than the causal effect. While it is important that an estimator produce estimates that are, on average, equal to the causal effect of interest, a second concern is the variance or precision of the estimator. The variance of an estimator characterizes how far the estimates are likely to fall from the mean. A larger variance implies a larger

Ordinary Least Squares

dispersion in the estimates produced by the estimator. The ideal estimator is one that is unbiased and has a small variance, so that it produces estimates that are likely close to the causal effect of interest. So far, we have seen that the OLS estimator is easy to calculate and interpret. In addition, it is unbiased under well-known conditions. All of these characteristics help explain the popularity of the OLS estimator. There is, however, another major advantage of OLS over alternative estimators. Under additional assumptions regarding the variance and covariance of the error term (the homoscedasticity assumption and uncorrelated errors), the OLS estimator has the smallest variance among all potential unbiased estimators of the parameters in Equation 1. This result is known as the Gauss Markov theorem, and it implies that under the assumptions mentioned here, OLS is the best estimator to use to estimate the causal parameters in Equation 1 because it is unbiased and produces the most precise estimates of all potential alternative estimators.

An Example: The Causal Effect of Class Size on Student Achievement An example of estimating the causal effect of class size on student achievement is useful in illustrating the major concepts discussed in this entry. Suppose a researcher has collected data on class size (size), student test scores (score), and teacher experience (exper) and specifies the following empirical model: Score = β0 + β1size + β2exper + ε.

(2)

Here, β1 is assumed to measure (or approximate) the causal effect of class size on student test scores, and β2 is assumed to measure the causal effect of teacher experience on test scores. The error term (ε) captures all other factors that are related to test scores, including, but not limited to, parental inputs, school quality, and peer influences. The OLS estimate of the causal effect of class size from Equation 2 (βˆ 1) has the following interpretation: βˆ 1 gives an estimate of the change in average test scores given a one-child increase in the class size, while holding teacher experience fixed. Similarly, βˆ 2 measures the change in average scores given a oneyear increase in teacher experience, while holding class size fixed. Again, note that the OLS estimates are not necessarily estimates of causal effects, since changes in class size and teacher experience may

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coincide with changes in the factors included in the error term. For example, class size may be correlated with parental inputs if the most supportive parents tend to enroll their children in small classes. In this case, the OLS estimator will likely produce an estimate of the class size effect that is larger than the causal effect (upward bias). Recall that the OLS estimator is unbiased only if the error term is uncorrelated with each explanatory variable included in the empirical model. In this case, the OLS estimator of the causal effect of class size and teacher experience is unbiased if these explanatory variables are uncorrelated with parental inputs, school quality, peer influences, and other factors included in the error term. In addition, correlation between the error term and class size may arise if class size is measured with error in the data, if student test scores have a causal impact on class size (reverse causality), or if the sample used to estimate Equation 2 is not a random sample from the population. In all of these cases, the OLS estimator will be biased. Generations of researchers have relied on OLS estimates to form our understanding of important relationships that exist in the data. It is often tempting to project policy effects using these relationships, but often these projections are made on the basis of correlations rather than on reliable estimates of causal effects. As an education researcher or consumer of education research, it is important to understand that the policy relevance of most empirical results depends on the likelihood that an exogeneity assumption holds. If it does, then the study provides useful evidence of a causal effect. If a study’s estimator suffers from endogeneity, then one ought to interpret the results with caution and avoid inferring policy effects, since the results may simply reflect correlation rather than causation. Matthew D. Hendricks and Brian R. Walkup See also Instrumental Variables; Measurement Error; Omitted Variable Bias; Quasi-Experimental Methods; Regression-Discontinuity Design; Selection Bias

Further Readings Greene, W. H. (2008). Econometric analysis. Upper Saddle River, NJ: Prentice Hall. Wooldridge, J. M. (2002). Econometric analysis of cross section and panel data. Cambridge: MIT Press. Wooldridge, J. M. (2012). Introductory econometrics: A modern approach. Stamford, CT: Cengage Learning.

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ORGANISATION FOR ECONOMIC CO-OPERATION AND DEVELOPMENT The Organisation for Economic Co-operation and Development (OECD) is an international organization that provides a forum to compare policy experiences, analyze common problems, identify good practices, and coordinate domestic and international policies to achieve sustainable economic growth around the world. The OECD plays a major role in the field of economics of education by providing domestic and international educational indicators as well as educational data derived from international studies. This entry explains the origins and structure of the OECD and briefly describes the main initiatives conducted by this organization within the domain of education.

Origins and Structure The OECD began its existence on September 30, 1961, when it replaced the Organization for European Economic Cooperation, originally organized in 1948 to administer the Marshall Plan (also known as the European Recovery Program), a U.S. initiative for the reconstruction of Europe after World War II. Originally, the OECD was a 20-nation association, but this number has increased over the years with the addition of new countries. As of 2013, there were 34 member countries, although the OECD also maintains relationships with 70 other associated countries. The headquarters of the OECD is in Paris. Unlike other intergovernmental organizations such as the World Bank or the International Monetary Fund, the OECD has no financial resources for loans or subsidies. Its sole function is the cooperation among nations, essentially on domestic policies with the aim of learning from each other’s experience. The organization is divided into various directorates and departments on specific or sectorial issues. Each unit collects and analyzes a unique body of data that allow comparisons of statistics across countries about the issues that affect their economies. This work is usually published as a way to promote discussions by experts and policymakers from member countries, who participate in specialized committees and groups for more than 200 areas. Among those topics, education represents one of the main focuses of research. Responsibility for the collection and analysis of educational data falls

to the Directorate for Education and Skills, which devotes a major effort to developing and examining an enormous volume of quantitative and internationally comparable indicators. These data are published annually in a report titled Education at a Glance. This publication provides educational policymakers and practitioners an excellent instrument not only to examine their own national education systems but also to compare them with other countries’ performance.

Surveys In addition to this annual report, the OECD conducts different international surveys to examine the level of achievement of populations and the factors that influence them. The most well-known study is the Programme for International Student Assessment (PISA), which aims to evaluate education systems worldwide by testing the skills and knowledge of 15-year-olds, an age at which students in most countries are nearing the end of their compulsory time in school. This survey was officially launched in 1997 in response to member countries’ demands for regular and reliable data on the knowledge and skills of their students. Since 2000, when the first survey took place, it has been conducted every 3 years, with a focus given to one subject in each year of assessment. The survey is planned to be conducted at least until 2015; thus, there will be available data from six different years (2000, 2003, 2006, 2009, 2012, and 2015). The number of countries or regions participating in PISA has increased from 32 in 2000 to 67 expected to participate in PISA in 2015. The survey tests reading, mathematical, and scientific literacy in terms of general competencies— that is, how well students can apply the knowledge and skills they have learned at school to real-life challenges. Apart from the results obtained in the tests, the students and their school principals also fill out two different questionnaires to provide additional information that can help analysts interpret the results and explore connections between how students perform in PISA and factors such as migration, gender, and students’ socioeconomic background, as well as students’ attitudes about school and their approaches to learning. The resulting dataset has been used to perform multiple empirical studies about different topics of research in the field of economics of education.

Organisation for Economic Co-operation and Development

The OECD is also interested in assessing the foundation skills of member countries’ adult populations to monitor how well prepared they are for the challenges of the modern knowledge-based society. For that purpose, this organization recently developed an international adult literacy survey called the Programme for the International Assessment of Adult Competencies, which assesses literacy, numeracy, and problem-solving skills of adults between 16 and 65 years of age in 28 countries. The survey has been designed to determine what activities adults perform in their daily lives (e.g., reading, finding information, and using computers and technology) and to learn about their education and work experience. This information will help countries better understand how education and training systems can foster key cognitive and workplace skills needed for economies to prosper. This initiative is a continuation of two previous large-scale assessments of young people and adults implemented in the OECD region: the International Adult Literacy Survey and the Adult Literacy and Life Skills Survey. The OECD also carries out an international survey focused on the working conditions of teachers and the learning environment in schools, known as the Teaching and Learning International Survey. Its aim is to help countries review and develop policies that foster the conditions for effective schooling. For this purpose, the program examines lower secondary education teachers and the principals of their schools with the aim of providing policy-relevant data about the role and functioning of school leadership; teachers’ training and professional development; and teachers’ beliefs, attitudes, and pedagogical practices in the classroom. The first round of the survey

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was conducted in 2008 with the participation of 24 countries; the second cycle took place in 2013, and the number of participants increased to 33. José Manuel Cordero Ferrera See also International Assessments; International Datasets in Education; International Organizations; Performance Evaluation Systems

Further Readings Goldstein, H. (2004). International comparisons of student attainment: Some issues arising from the PISA study. Assessment in Education: Principles, Policy & Practice, 11(3), 319–330. Organisation for Economic Co-operation and Development. (2012). Education at a glance 2012: OECD indicators. Paris, France: Author. Retrieved from http://www.oecd.org/edu/EAG%202012_e-book_ EN_200912.pdf Organisation for Economic Co-operation and Development. (2013). PISA 2012 assessment and analytical framework: Mathematics, reading, science, problem solving and financial literacy. Paris, France: Author. Retrieved from http://dx.doi .org/10.1787/9789264190511-en Woodward, R. (2009). The organisation for economic co-operation and development (OECD). Abingdon, UK: Routledge.

OUTSOURCING See Contracting for Services

P For most of their history, school districts in California financed their operations through local ad valorem property taxes. However, in 1978, Californians approved Proposition 13, which created a statewide uniform property tax of 1% of assessed value and limited increases in assessed value to 2% per year. Furthermore, Proposition 13 prohibits local governments, including school districts, from increasing the property tax rate. In other states, local communities have the power to increase property tax rates to fund local services, with some restrictions. Thus, Proposition 13 severed the link between the demand for local public services and the financing of such services. The effect of Proposition 13 was a dramatic reduction (57%) in property tax revenue. The state replaced much of this lost revenue in school districts, permanently shifting the responsibility for funding schools to the state. However, some communities searched for ways in which to increase local education spending. Despite the fact that the overall goal of Proposition 13 was to limit the ability of local governments to raise taxes, its interpretation by the courts and subsequent legislation created a new form of taxation available to local governments: the parcel tax. Proposition 13 required that all “special taxes” win approval from two thirds of voters. In the 1982 court case City and County of San Francisco v. Farrell, the California Supreme Court defined special taxes as those earmarked for a special purpose. School districts, using the revenue for education, could levy a special tax. The school district parcel tax was thus created and the first measure passed in 1983.

PARCEL TAX Schools in the United States receive funds from a variety of sources, including the federal, state, and local levels of government. The primary source of local funding is the ad valorem property tax, which is based on the assessed value of real property, with the tax rate typically set by local voters. In 2010– 2011, ad valorem property taxes benefitting public schools in the United States totaled $176 billion, or about 30% of total revenue. In contrast, some jurisdictions also impose non–ad valorem taxes. These are fees charged per housing unit or per square foot rather than on the value of the property. For example, some municipalities impose non–ad valorem taxes for services such as solid waste disposal, street lighting, or fire protection. These fees usually appear on the property tax bill but are listed as a fee for service rather than a tax charged against the value of property to support general governments. One unique type of non–ad valorem property tax is the parcel tax. The parcel tax was created in California in response to the property tax limitations imposed by voters in the 1970s. Because the parcel tax is only known to occur in California and differs from the other types of non–ad valorem taxes described above, this entry will explain the creation of the parcel tax in California, document its use by school districts, and compare the characteristics of districts that have successfully passed a parcel tax with those that have never offered a parcel tax. 511

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Despite this new form of taxation to support local services, parcel taxes are relatively rare in California. Since 1983, only about 20% of California’s school districts have offered a parcel tax. Of these offered, about 60% have passed with the necessary twothirds approval from voters. In 2010–2011, parcel taxes raised nearly $317 million statewide, representing less than 1% of total K-12 funding. About 10% of school districts enrolling about 10% of students receive parcel tax revenue averaging $584 per pupil. If parcel taxes were instead allocated equally across the state on a per-pupil basis, districts would receive slightly more than $50 per pupil. Although parcel taxes are only a small share of total K-12 revenue statewide, they provide about 7% of revenue to districts that have successfully passed a parcel tax. In some districts, parcel taxes are more significant, providing up to one third of a district’s budget ($3,700 per pupil). One might expect support for parcel taxes to be higher in districts with wealthier and more educated residents, who typically demand higher levels of public spending and have more disposable income to spend on education. Indeed, median household income averages more than $85,000 in districts with parcel taxes, compared with $60,000 in districts that have never offered a parcel tax. Furthermore, nearly half of all residents in districts with parcel taxes have a college education compared with a quarter of residents in districts that have never proposed a parcel tax measure. In addition to having wealthier residents, districts with parcel taxes have fewer low-income students, English Language Learners, and students of color. Districts with parcel taxes are more likely to be elementary districts that serve only students in Grades K-8. They are also more likely to be small and have fewer school-age children per household, reflecting a lower tax price since any parcel tax revenue raised is spread over fewer children. Finally, districts that offer and pass parcel taxes are primarily located in the nine-county San Francisco Bay Area, where three quarters of all parcel tax measures have been proposed. This region of the state generally has more highly educated and wealthy residents. In public opinion polls, this region also tends to have more favorable attitudes toward taxes and government services. Parcel taxes are unique to California and reflect the complicated state-local fiscal relationship. They are the only way for school districts to increase

tax revenues under the constraints of Proposition 13. Yet parcel taxes have been offered by very few districts and are concentrated among small, wealthy, and homogeneous communities primarily located in the San Francisco Bay Area. Critiques of the parcel tax focus on its regressivity and the high vote threshold necessary to pass. In response, some districts have experimented with alternative pricing schemes based on size or the use of the property, though some of these measures have been overturned by the courts. California has contemplated lowering the threshold necessary to pass a parcel tax from two thirds to 55%, which may induce more districts to attempt a parcel tax or increase the passage rate of those offered. Margaret Weston See also Progressive Tax and Regressive Tax; Property Taxes; School District Wealth; Tax Limits

Further Readings Brunner, E. J. (2003). The parcel tax. In J. Sonstelie & P. Richardson (Eds.), School finance and California’s master plan for education (pp. 187–212). San Francisco: Public Policy Institute of California. Chavez, L., & Freedburg, L. (2013). Raising revenues locally: Parcel taxes in California school districts. Oakland, CA: EdSource. Lang, B., & Sonstelie, J. (2014). The parcel tax as a source of local revenue for California public schools. Unpublished manuscript. Retrieved from http://www .utexas.edu/cola/_files/ms37643/ LangSonstelieParcelTax_3_5_14.pdf McGhee, E., & Weston, M. (2013). Parcel taxes for education in California. San Francisco: Public Policy Institute of California.

PARENTAL INVOLVEMENT While formal schooling undoubtedly plays a key role in shaping students’ lives, by the time they reach the age of 18, children have typically only spent 13% of their waking life at school. It is perhaps not surprising, therefore, that family characteristics are more predictive of students’ success in school than school characteristics. A wide body of literature, particularly in sociology and psychology, has examined the roles that parents play in shaping their children’s education.

Parental Involvement

The limited research in economics on parental involvement suggests that economists think of parental involvement as a part of the education production function, where direct inputs are provided by parents in an attempt to increase the achievement levels of their children. The Nobel Prize–winning economist Gary Becker has argued that children from successful families are more likely to be successful themselves due to the fact that their parents invest more time in them. Generally, the research across fields converge on four key mechanisms in the family that are thought to affect children’s educational outcomes: (1) parental expectations, (2) parental involvement, (3) social connections with and knowledge of the school system, and (4) parental investment in resources related to educational outcomes. Some parents have both the knowledge and resources to effectively implement all four of these mechanisms. The question then remains whether it is possible to design programs that might help bolster parental involvement among those who are less successful in these domains. This entry discusses the documented relationship between parental involvement and children’s outcomes, differences in rates of parental involvement along the lines of social class, and finally how researchers have attempted to determine the causal impact of parental involvement practices on children’s outcomes.

Relationship Between Parental Involvement and Child Outcomes Parent Interactions With Schools

Good parent-teacher relationships are, as to be expected, positive for students’ school performance. Parental involvement in schooling helps parents gain a clearer understanding of what is expected from their children at school and can help them understand how to enhance their children’s education in the home. Of the studies that have examined the benefits of a strong parent-teacher relationship, they have generally found that children perform better academically when their parent is more actively involved in activities such as parent-teacher conferences, school events, and open school nights. A similar relationship has been documented between parental involvement and social-emotional outcomes, such that involvement is positively associated with social skills and negatively associated with problem behaviors among preschoolers.

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Children enter preschool and kindergarten with very different levels of academic skill—well before they have had any access to formal schooling. For example, 18% of children who entered kindergarten in the United States in 1998 did not know that print reads left to right, that they need to continue to the next line when one line of text ends, or that the story ends on the last page of a book. This kind of knowledge of print is a key precursor for becoming a literate adult. Other children, however, begin school already having the ability to read words in context. These large differences in beginning skills are likely due to differences in exposure to books and reading in the home through interactions with their parents. Researchers have identified several areas in which parents’ skills and behaviors in the home can enhance their child’s education: parenting styles, reading to children, providing educational objects in the home, and providing assistance with homework. Studies suggest that children whose parents read to them during preschool begin kindergarten with stronger academic skills. The vocabulary scores of 5- and 6-year-olds are considerably higher when their mothers read to them daily, and children benefit more from having a parent rather than an outside adult (e.g., tutor) read to them. Specifically, studies have found that the quality and quantity of speech directed at children is a strong predictor of children’s later IQ (intelligence quotient). Providing more educational objects in the home is also related to higher school performance among children. Objects such as books, a computer, newspapers, and magazines provide children with the opportunity to develop better school-related skills. Helping children with homework is another way in which parents can signal the value that they place on education. There is some evidence that children do better in school when their parents help them with schoolwork, especially if the parents are college educated. However, the relationship between homework help and student achievement is hard to isolate since students who are struggling may be most likely to obtain help. Parental Time Invested

Some studies have investigated whether characteristics of families that should be associated with parental time spent with children, such as family, birth order, growing up in a single-parent household,

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or mothers’ employment, are related to children’s outcomes. There is evidence that firstborn children have better educational outcomes than their later born counterparts, and part of this difference appears to be explained by the fact that firstborns generally spend more time interacting with parents in their early years than do secondborn children. There is also some evidence that single mothers spend less total time with their children than do married mothers and that children in single-parent families spend less time doing art, playing sports, doing homework, and reading than their counterparts in two-parent families; however, most of this is accounted for by differences in socioeconomic background. Finally, it is important to note that although the spotlight is typically turned on mothers when considering parental time, the amount of time fathers spend with their children is also positively related to child outcomes. For instance, the amount of time fathers spend with their children eating meals, engaging in conversations, reading or helping with homework, and doing leisure activities is positively associated with children’s academic grades. Less is known, however, about how much the amount of time parents are involved matters relative to the quality of time spent for determining children’s outcomes.

Rates of Parental Involvement by Socioeconomic Status There is variation in degree and type of parental involvement by socioeconomic status (SES). Some have argued that much of the relationship between SES and school performance is accounted for by parenting practices. In a classic book titled Class and Conformity (1969), Melvin Kohn argues that parents’ style of interacting with their children is influenced by the organization of work in parents’ occupations. Parents who work in jobs with little autonomy are rewarded by following rules and not by questioning authority. Therefore, working-class parents tend to place more importance on obedience than do middle-class parents. On the other hand, middle-class parents tend to be in occupations that allow for more critical thinking and decision making, which lead them to encourage similar behaviors in their children. According to Kohn, middle-class parents have a less punitive style of discipline and put more emphasis on developing children’s ability to self-regulate.

SES is also related to the way that parents interact with teachers and school officials. The sociologist Annette Lareau observed very different patterns of parental involvement in working- and middleclass communities. When teachers in workingclass communities made efforts to involve parents, these parents were much less likely to participate in school activities than their middle-class counterparts. Working-class parents were less likely to attend parent-teacher conferences, to attend school open houses, or to volunteer in their child’s classroom. This was explained in part by the fact that they had more difficulty obtaining transportation, securing child care, and rearranging work schedules compared with middle-class parents who tend to have more flexible jobs, but they were also more likely to view education as being under the purview of schools. They trusted teachers and school leaders as experts and were less likely to view education as being their own responsibility. Furthermore, they were less comfortable interacting with teachers because they felt unqualified to discuss academic matters since they had relatively low levels of education themselves. SES is also related to parent-child interaction. Studies of parent-child conversation quality find that mothers who are more highly educated talk to their children more and also use more varied vocabulary than mothers with less education. Whereas workingclass parents use more directive speech, highly educated parents tend to ask children more open-ended questions that draw more elaborated responses and engage them in critical thinking. Research also suggested that more educated parents spend more time with their children. For example, one study found that mothers with a college degree or higher spend about 4.5 hours more per week engaging with their children than mothers with a high school degree or less. This relationship exists despite the fact that more educated parents also spend more time working outside of the home.

Parental Involvement Interventions While the research described above provides suggestive evidence regarding the link between parental involvement and children’s academic outcomes, it is hard to adjust for all factors that might jointly influence parental involvement and children’s achievement. Because researchers cannot randomly assign degree of involvement to parents, it is difficult to make any kind of causal claims about the effects of

Parental Involvement

specific kinds of parental involvement on children’s development. The best attempt to disentangle the effect of parent background characteristics from parental involvement practices themselves comes from research studies that randomly assign parents to interventions designed to enhance one or more of the mechanisms believed to improve children’s academic outcomes. In a chapter dedicated to discussing the challenges of determining a causal link between parental involvement and children’s academic outcomes, Frank Furstenberg (2011) describes several interventions designed to improve parenting skills and/or increase parental involvement in children’s lives. Such interventions fall into three main categories: (1) home visiting programs, (2) programs designed to directly improve parenting in the early years to promote school readiness, and (3) programs aimed at directly increasing family resources. Home visiting programs are designed to be implemented in early childhood, and the most popular model is referred to as “nurse-family partnership.” These programs are often designed as intensive programs starting as early as birth and include frequent home visits on the part of nurses to instruct parents on best practices for early infant care and development, nutrition and health, and parenting skills. Generally, these programs have been shown to reduce cases of child maltreatment and, in some cases, behavioral problems, but they have not been as effective at directly increasing school readiness. Early parental interventions are typically designed to affect parenting practices through home-school interactions with early childhood programs. Head Start, for example, has been historically known for its emphasis on parental involvement. Many randomized experiments have focused on parental involvement in early childhood. These generally have had null findings. However, positive results have been demonstrated as a result of some programs that were the most intensive, longest lasting, and implemented the earliest. Some have argued that part of the reason interventions to increase parental involvement generally show little or no effect for children is that it is difficult to change parents’ actual beliefs and parenting practices and that parents who become more involved in the first place do so because of an underlying characteristic that we cannot observe. If this is true, then null findings from such interventions do not necessarily provide evidence to indicate that parental involvement practices are not effective, but rather, it is simply difficult

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to change parenting practices once they are already established. Finally, the third approach relies on the idea that since parental involvement is correlated with SES, if parents had sufficient financial resources, they could allocate more to their children’s educational success, both in terms of their own time invested and in terms of direct financial investments. Families with limited resources experience more difficulty preparing their children for school in part because they are more likely to be stressed over money, preventing them from optimizing their time spent with their children; are more mobile and thus less likely to provide a stable and secure home environment; and have a harder time securing high-quality child care. In his chapter, Furstenberg describes two types of resource-focused interventions: (1) those designed to improve child outcomes by increasing parental education and employment and (2) those designed to increase income directly through income transfers. Greg Duncan and Pamela Morris are among those who have evaluated several of such experiments and have found modest impacts on academic achievement, but these results are typically limited to young children. However, because very few studies have investigated the effect of such resource-focused interventions on specific family processes, to date, there is limited knowledge about the specific mechanisms that might make such programs work. With such knowledge, it may be more feasible to design interventions that generate larger effects and that are longer lasting.

Conclusions Although it is difficult to know which parental behaviors have the largest impact on their children’s outcomes, parents who are highly involved in their children’s lives also tend to be those who engage in high-quality parenting behaviors and foster a highquality home environment that stimulates learning. Much of the reason why parents differ in their practice of such behaviors can be explained by a knowledge and resource gap between parents of high and low SES, such that high-SES parents have substantially more human, cultural, and financial capital to invest in their children’s development. Although there is no definitive evidence, this may suggest that policies designed to increase these forms of capital among low-SES parents may be a more effective approach to improving parenting behaviors than interventions designed to directly improve parenting

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practices alone. More research is certainly necessary to disentangle the direct causal link between efforts to improve parenting and child outcomes. Demetra Kalogrides and Rachel A. Valentino See also Selection Bias; Social Capital; Socioeconomic Status and Education

Further Readings Avvisati, F., Besbas, B., & Guyon, N. (2010, April). Parental involvement in school: A literature review. Paris, France: Paris School of Economics. Brooks-Gunn, J. (2003). Do you believe in magic? What we can expect from early childhood intervention programs. Social Policy Report, 17, 3–15. Cooksey, E. C., & Fondell, M. M. (1996). Spending time with his kids: Effects of family structure on fathers’ and children’s lives. Journal of Marriage and the Family, 58, 693–707. Duncan, G. J., Morris, P. A., & Rodrigues, C. (2011). Does money really matter? Estimating impacts of family income on young children’s achievement with data from random-assignment experiments. Developmental Psychology, 47, 1263–1279. Furstenberg, F. F. (2011). The challenges of finding causal links between family educational practices and schooling outcomes. In G. J. Duncan & R. J. Murnane (Eds.), Whither opportunity? Rising inequality, schools, and children’s life chances. New York, NY: Russell Sage Foundation. Kohn, M. (1969). Class and conformity: A study in values. Chicago, IL: University of Chicago Press. Lareau, A. (1987). Social class differences in family-school relationships: The importance of cultural capital. Sociology of Education, 60(2), 73–85. Lareau, A. (2000). Home advantage: Social class and parental intervention in elementary education. Lanham, MD: Rowman & Littlefield. Powell, D. R., Son, S., File, N., & San Juan, R. R. (2010). Parent-school relationships children’s academic and social outcomes in public school pre-kindergarten. Journal of School Psychology, 48, 269–292.

PARTIAL AND GENERAL EQUILIBRIUM Equilibrium is an important concept in economics. In common language, equilibrium refers to the state of a system where all the components are balanced and do not change. In economics, there is a specific

meaning, where equilibrium is defined as a state in which the selected interrelated variables are adjusted to each other and therefore remain static when external factors are assumed fixed. Equilibrium is an important concept in education economics, as failure to achieve equilibrium may lead to either surplus or shortage of educational goods or services. In addition, policymakers can also influence the demand and supply of a particular kind of educational goods or services by adjusting their market prices. This entry provides an overview of partial and general equilibrium and explains how this concept can be used in the analysis of education economics. The entry is divided into three main sections. The first section introduces key properties of equilibrium, the next compares partial and general equilibrium, and the final section explains how to calculate equilibrium price.

Key Properties of Equilibrium There are three important properties in the aforementioned definition of equilibrium that warrant special attention. First, the key word “selected” indicates that any economic equilibrium is achieved in the context of a set of variables “selected” by the analyst. In a standard economic analysis under perfect competition, this usually includes three indispensable variables: (1) the amount of goods or services demanded by all buyers (Qd), (2) the amount of goods or services supplied by all producers (Qs), and (3) the price of the goods or services (P). The goal of an equilibrium analysis is to determine the price at the point where the amount of goods or services sought by buyers is equal to the amount of goods or services supplied by the producers. At the equilibrium point, both buyers and producers maximize their utility, and therefore, there is no incentive either to produce or to consume more goods. As a result, agents on neither side would have an incentive to change anything at the market price, or the equilibrium price. Second, the variables are not separate from each other but, instead, are interrelated. It is often assumed that the amount of goods or services demanded is a decreasing function of price. Taking college education as an example, the increasing tuition and fees may prevent some students from attending college; on the contrary, however, the amount of goods supplied is an increasing function of price, that is, colleges will have incentive to recruit more students as the college tuition increases. Demand, supply, and price

Partial and General Equilibrium

influence each other and need to be in a static state simultaneously to achieve equilibrium. When the price is above the equilibrium, there will be an incentive for the producers to supply more goods, resulting in an excess of supply. The fact that quantity supplied exceeds the actual demand will pressure the price downward, which in turn will lead to an increase of demand. Such dynamic adjustment will continue until the equilibrium condition is achieved. Likewise, when the price is below the equilibrium point, there is an incentive for more demand, which will pressure the price upward, leading to an increase of supply. Finally, any equilibrium is achieved based on the assumption that external factors, or exogenous variables, are constant. This assumption that all else is held constant is also known as ceteris paribus. For example, the demand, supply, and price of private school may change as the quality of private school changes. The current tuition and fees charged by private schools represent the equilibrium price where the demand for private schools equals the supply, holding all the external factors constant, such as people’s income, the quality of private schools, and the quality of public schools. However, when any of these external factors change, the demand for and supply of private schools will change accordingly. For example, when the quality of private school increases, more parents and students may choose private schools; such increased demand will push the tuition of private schools upward, which, in turn, will provide incentive for private schools to recruit more students. These competing forces will dynamically adjust to each other until they reach a point where the demand for private school equals the supply, or the new equilibrium price.

Partial Versus General Equilibrium The major difference between partial and general equilibrium is whether the analysis considers the market for a single good in isolation (partial equilibrium) or multiple goods in several markets (general equilibrium). Partial equilibrium, as indicated by its name, is a condition that takes into consideration only one commodity or service in an isolated market to attain equilibrium, disregarding its impact on commodities in other markets or the potential impacts of other commodities on this particular commodity under examination. In other words, this method considers the changes in only one sector of the economy and holds all others constant—that is, ceteris paribus (other things remaining the same).

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To fully illustrate the differences between partial and general equilibrium, consider the market for math teachers. Based on a framework of partial equilibrium, the examination of K-12 math teacher salary would only consider the supply and demand for math teachers. However, in the real world, the supply and demand for math teachers may interact with several other markets to determine its equilibrium state. For example, an increase in the demand for mathematics majors in the industrial sector may lead to higher salary. Such high salary will attract more math majors from teaching positions to the industrial sector. As a result, the demand for math majors in the industrial sector may influence the supply for math teachers at school, which in turn will eventually influence the salary provided to K-12 math teachers. General equilibrium analysis recognizes such connections between markets and assumes that the equilibrium price of one commodity requires an analysis that accounts for all different commodities that are available, and it studies how these variables can adjust to achieve equilibrium in all markets at the same time.

Solving for the Equilibrium Price In education economics, policymakers and analysts often need to find out the equilibrium price of educational goods or services that could lead to balanced demand and supply. For example, schools may want to determine a reasonable salary scheme in order to attract sufficient math teachers. To solve for the competitive equilibrium salary using the partial equilibrium model, one needs to know both the supply and demand function and then solve for their equations being equal. As mentioned previously, the supply curve is an increasing function of the salary offered to math teachers, while the demand curve is a decreasing function of the salary. In a most simplified condition, both curves are linear; however, they can be replaced by nonlinear models as well, such as quadratic function, or even higher degree polynomial equations. In either case, one has three equations (demand equation, supply equation, and the equation Qd = Qs) in three variables (Qd, Qs, and P) and, therefore, can find out the equilibrium salary by equating the demand function and the supply function. The partial market equilibrium only considers the market for math teachers. However, as explained earlier, the market for math teachers may also interact with other markets, such as the industrial sector

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for individuals with a math degree. Therefore, the general equilibrium model dictates that the equilibrium salary for math teachers should consider the demand and supply for math majors in all other sectors. Consequently, the calculation of general equilibrium can be extremely complicated relative to estimating the partial equilibrium. With the advances of computing power, it is now possible to solve for general equilibrium for the whole economy. Di Xu See also Economic Cost; Internal Rate of Return; Markets, Theory of; Salary Schedule

Further Readings Black, F. (1995). Exploring general equilibrium. Cambridge: MIT Press. Chiang, A. C., & Wainwright, K. (2005). Fundamental methods of mathematical economics (4th ed.). New York, NY: McGraw-Hill/Irwin. Dixon, P. D., & Jorgenson, D. (2013). Editors, handbook of computable general equilibrium modeling (Vol. 1A). Amsterdam, Netherlands: Elsevier. Petri, F. (2004). General equilibrium, capital, and macroeconomics: A key to recent controversies in equilibrium theory. Northampton, MA: Edward Elgar. Turnovsky, S. J. (2000). Methods of macroeconomic dynamics (2nd ed.). Cambridge: MIT Press. Varian, H. R. (1992). Microeconomic analysis (3rd ed.). New York, NY: Norton.

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PERFORMANCE

Pay for performance for educators seeks to tie compensation more directly to outcomes, most frequently defined as some measure of student achievement, instructional quality, or the acquisition of specific knowledge and skills of value to the organization. Pay for performance may occur at the level of the individual educator or across teams or whole schools. The basis of pay for performance lies in the motivational theory, which suggests that higher levels of employee motivation and performance may be obtained when compensation is more closely aligned with organizational goals and individual or group outputs. This entry describes pay for performance as applied to educators, as well as giving an overview of teacher compensation practices more broadly. The entry then examines the theoretical basis of

performance pay, the development of performance pay in education over time, its role within the larger context of teacher compensation, the various forms performance pay has taken in practice, and the extant research on its effects on student achievement. Finally, several examples of performance pay in the United States are presented. Pay for performance broadly refers to a variety of approaches for incentivizing or rewarding specific outcomes or activities on the part of educators. Several different terms have been used to refer to pay for performance, including merit pay, performance pay, or incentive pay. These terms are often used interchangeably, and that is the approach taken here. Most often, performance pay plans pertain to classroom teachers but increasingly include other school-based professional staff such as principals, librarians, and instructional specialists serving students with special needs, who are English Language Learners, or who have other specific learning needs. Performance incentives may include financial rewards for attaining specific performance goals, such as targets for students’ scores on standardized tests or the learning gains students achieve in a given year. Other measures of performance may include educators’ ratings on evaluations of their practice, which most often consist of classroom or schoolbased observations conducted by their supervisors. Educators may also receive financial incentives for engaging in activities such as coursework or other forms of training to acquire specific knowledge or skills, such as new instructional approaches. Furthermore, market-based incentives may be used to help recruit and retain high-demand positions where there are shortages of qualified teachers or to attract educators to positions in challenging schools. Many performance pay plans combine several of these components. While interest in performance pay has waxed and waned in education over time, the concept has received renewed attention from federal and state policymakers in recent years due to continuing concern over student achievement levels and international economic competitiveness and research highlighting the crucial role of effective teaching in improving student outcomes. Reforming educator compensation is viewed as part of a broader, comprehensive strategy for improving teacher quality that includes initial preparation, induction, evaluation, and support. Because educator compensation is also the single largest expenditure of districts and schools, using this investment to leverage higher

Pay for Performance

performance is perhaps the most significant opportunity for improving educational productivity and efficiency in a time of resource constraints. For much of the past century, the most prevalent method by which public school teachers were paid was through what is known as the single salary schedule. Under the single salary schedule, teachers’ pay is standardized, with base pay increasing as teachers gain years of experience and college credits and degrees. Although interest in pay for performance is again on the rise, research on its effects is inconclusive. The results of studies of its impact on student achievement are mixed, with results varying by the quality of the design and implementation of the plan, grade levels taught, and tested subject measured. There seems to be more consensus in the research suggesting that pay for performance may improve teacher retention.

An Overview of Teacher Compensation An effective compensation system for districts and schools should possess several key characteristics. These include providing salaries that are competitive with other organizations within a shared labor market to ensure that an organization can successfully attract and retain talented educators, providing a suitable, predictable, and stable income commensurate with the norms of the profession and sustaining long-term affordability for the organization. To accomplish these goals, compensation for educators consists of several distinct components. The foundation of most compensation plans is base pay. This is the biweekly or monthly pay that workers receive in return for their labor. Base pay should be sensitive to the labor market conditions applicable to the organization to ensure that the level of pay is both adequate for attracting quality employees to the organization and for retaining them over time. Base pay progression determines how base pay increases over time. In the case of teachers, the single salary schedule with its matrix of years of experience and educational attainment has determined base pay progression for the vast majority of teachers in the United States since the 1920s. The single salary schedule typically forms a matrix of “steps” and “lanes” with a salary amount entered into each cell. Steps form the rows of the matrix and represent each additional year of experience earned by the teacher. Lanes represent the columns of the matrix and represent additional college credits and degrees earned. A new teacher with a bachelor’s

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degree and no experience would earn the salary amount posted in the upper left-hand cell of the matrix, and he/she would then earn salary increases with each additional year of experience gained and/ or college credits or degrees earned. The schedule’s highest salary would be found in the lower righthand cell of the matrix. Advantages of the single salary schedule include objectivity, predictability, and efficiency. The basis for pay raises is clear and objective, and teachers can easily determine the amount of their salary at various points in their career. From a management perspective, the administrative costs required to manage the salary schedule are minimal due to the objective, quantitative nature of the salary criteria—years of experience and college attainment. However, research shows that neither experience nor educational attainment is a reliable indicator of teacher quality or performance. Studies indicate that while the first 3 to 5 years of experience does have a beneficial effect on teacher performance, additional years of experience do not provide additional benefit. Additional college credits and degrees have been found to have little effect on teacher performance. This suggests that a significant investment in educators’ salaries is being directed ineffectively at factors that have little influence on teacher performance. In addition to base pay, teachers may also receive variable pay for assuming extra duties such as coaching, serving as an academic department chair, or supervising student activities. These payments are not part of base pay but are paid in addition to base pay only as long as the individual continues to serve in these roles. In its many forms, pay for performance in education extends, and in some cases remakes, this compensation structure.

Theoretical Framework A number of theories from the psychological and management literature have been developed to explain the motivation of employees and the effects of both financial and nonfinancial incentives. However, Victor Vroom’s expectancy theory of motivation is perhaps the leading theory used for guiding the design of incentive programs for employees—including educators. Under expectancy theory, an individual’s motivation is a function of how strongly she values a particular outcome and the extent to which she believes a given level of performance will lead to that outcome. According to expectancy theory, financial incentives work best to motivate higher levels of performance when the

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following conditions are met. First, the goals of the incentive system must be of value, or valence, to the individual. Incentives will have less of an effect when attached to outcomes employees do not value or support. Second, individuals must have expectancy, or the belief that the goals supported by the incentive system are achievable and within their control. That is, they possess the knowledge, skills, tools, and support to achieve the desired outcomes. Finally, individuals must perceive a direct connection, or instrumentality, between the effort they put forth and the earning of incentives. Keeping these conditions in mind when designing and evaluating, performance pay plans may serve to avoid the mistakes that have derailed so many attempts at performance pay over the past several decades.

A Brief History of Performance Pay in Education Although performance pay is still uncommon in districts and schools across the country, the concept is not new. Early forms of performance pay date back several centuries in England and to at least the mid19th century in the United States. In response to inequitable compensation practices in schools in the United States in the early 20th century, particularly by race or ethnicity and gender, reformers sought to standardize pay by adopting the single salary schedule in the 1920s. The single salary schedule was eventually adopted by the vast majority of public school districts and continues to be the predominant method for paying teachers in the United States. Renewed interest in pay for performance emerged in the 1980s following the release of A Nation at Risk in 1983, an assessment of the quality and competitiveness of the nation’s educational system by President Reagan’s National Commission on Excellence in Education. The report was highly critical of the country’s educational system and warned that if academic rigor and student performance were not improved, the country would rapidly lose competitiveness in the global economy. The influential report was the catalyst of a series of reform efforts, including merit pay for educators. However, a significant number of merit pay plans initiated over the next decade failed, suffering from design flaws and unreliable measures of educator performance. Richard J. Murnane and David K. Cohen, among others, argued that the work of teachers was unsuitable for merit pay due to the complex nature of teaching, a lack of consensus about the

goals of education (e.g., raising standardized test scores vs. developing higher order thinking skills), and difficulties with measuring the outputs of teaching. These criticisms coupled with intense opposition on the part of teachers led to declining interest in performance pay, particularly in the public sector. However, the growing body of research pointing to the importance of teacher quality to improving student achievement has led to a renewed interest in alternative forms of educator compensation as a strategy for improving teacher quality. For example, studies by William Sanders using Tennessee teacher and student data have found that students who are assigned to ineffective teachers over several years have much lower learning gains than students assigned to more effective teachers. Several states, such as Florida, Minnesota, and Texas, have implemented statewide policies supporting performance pay plans. At the federal level, the Teacher Incentive Fund program has invested nearly $1.6 billion in grant funding since 2006 to support the development of performance pay plans at the school, district, and state levels. Still, performance pay is rare in the public schools. A recent study estimates that only about 3.5% of the nation’s public school districts report using some form of pay for performance, although evidence suggests that the rate may be much higher among charter and private schools. In the most recent comprehensive assessment of the extent to which performance pay has been implemented in the United States, Michael Podgursky’s analysis of a 1999– 2000 national dataset on school staffing found that 36% of charter school administrators and 22% of private school administrators reported using some form of performance pay, compared with only 6% of administrators from traditional public schools.

Forms of Performance Pay Performance pay takes many forms, ranging from alternative types of variable pay that incentivize educator performance or behaviors and are appended to the traditional single salary schedule structure to completely restructured plans that incorporate elements of performance or knowledge and skills development within base pay itself. While the former type of performance pay is most commonly used, an increasing number of districts and schools are turning to entirely new salary structures that supersede in part or entirely the elements of

Pay for Performance

experience and educational attainment found in the single salary schedule. The predominant form of performance pay consists of variable pay for incentivizing performance or behaviors while leaving the components of base pay and base pay progression inherent in the single salary schedule unchanged. Based on the goals and emphasis of the organization, variable pay may consist of bonuses to reward performance or to incentivize certain behaviors such as engaging in professional development, or use market incentives to attract educators to hard-to-staff positions or schools. Performance-based variable pay offers annual bonuses to educators who meet or exceed certain criteria based on factors such as student performance on standardized tests, on standardsbased evaluations of educator practice, or a combination of both. Some plans, such as Denver Public Schools’ ProComp compensation plan, provide financial incentives for teachers to engage in professional development aligned with district or school priorities or to earn the challenging National Board Certification by the National Board of Professional Teaching Standards. A growing number of districts have employed market incentives that pay bonuses to teachers from competitive fields such as mathematics, science, or special education. Districts may also offer bonuses as an incentive for teachers and principals to accept positions in hard-to-staff, highneeds schools. More radical pay for performance plans jettison the single salary schedule in favor of a new salary structure based on performance or knowledge and skills levels. Variations of these plans may include some vestige of the single salary schedule, such as a limited number of experience steps within each performance or knowledge and skills level (in the former case earned only if performance expectations at that level are maintained), or may incorporate a base salary increase for earning a master’s or doctoral degree. Variable pay incentives may also be incorporated into these plans. Examples of school districts implementing alternative pay plans such as these include those in Denver, Colorado; Houston, Texas; Minneapolis, Minnesota; and Washington, D.C. An example of a performance-based salary schedule would include a novice performance level for new teachers with no experience, a proficient performance level for teachers who meet the minimum requirements of performance for continued employment, a highly proficient performance level for teachers who meet or exceed a higher standard

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of performance, and finally an expert level for those teachers who meet or exceed a high level of performance and take on leadership roles such as mentor or instructional coach. Under this model, teachers receive a significant base pay increase as they move up to the next highest performance level. This simple four-level model could be expanded to include four to five annual steps within levels to provide some amount of base pay progression for those teachers who fail to advance to the next highest performance level. A permanent base pay increase could also be offered within each performance level for teachers earning a master’s degree or a doctorate. Performance pay may be paid on the basis of individual or team performance. Under individual performance pay plans, individual educators receive incentive pay based on measures of their own performance or behavior. However, concern has been raised over the effect individual performance pay for educators may have on the collaborative work of teaching. Will individual incentives increase competition among educators and reduce cooperation and collaboration—an important element of effective schools? As an alternative, team-based or whole school–based performance awards have been devised to reward collective performance without inhibiting collaboration.

Research on the Effectiveness of Performance Pay The results of studies undertaken to date on the effects of performance pay are best described as mixed. Significant differences in the design of the performance pay plans studied, in the fidelity of the plans’ implementation across schools, and in the studies’ research approach complicate interpreting and summarizing results across studies. The findings of research on the effects of performance pay plans, both individual and schoolwide, on student test scores range from no effect to mixed (depending on grade level and subject tested) to uniformly positive effects. However, several recent studies using a randomized, experimental design method failed to find any significant effects of pay for performance on student achievement. Several studies of pay-forperformance plans in Texas have found positive effects on teacher retention even in cases where there was no effect on student achievement. The research indicates that the design and implementation of pay-for-performance plans play a significant role in their effectiveness. Important

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considerations include the size of the incentives, with larger incentives more likely to be effective; acceptance and buy-in by teachers; and a strong sense on the part of teachers that they have the capability, resources, and support to achieve the desired outcomes.

Examples of Performance Pay Plans The most widely acknowledged performance pay plan is the ProComp program in the Denver school district. First implemented in 2005, ProComp replaced the single salary schedule with a comprehensive pay plan that incorporates elements of both performance and knowledge and skills-based pay. Teachers may earn pay increases, some in the form of permanent raises, others as annual bonuses, for meeting or exceeding student achievement growth targets, for performing well on evaluations of instructional practice, for engaging in professional development, and for earning advanced degrees, licenses, and certificates. The plan also includes market incentives for teachers filling hard-to-staff subject areas, such as mathematics, special education, and English language instruction, and for working in high-needs schools. Performance-based variable pay exists for both individuals and whole schools. In 2010, the Washington, D.C., public schools initiated its performance pay plan, IMPACTplus. Under IMPACTplus, the district retained its traditional single salary schedule but added annual performance bonuses of up to $25,000 for teachers earning highly effective evaluation ratings. Teachers may also earn permanent pay increases by moving up the salary schedule more quickly if they earn highly effective ratings for two or more consecutive years. Under the new pay plan, a highly effective first-year teacher could earn nearly $80,000, with the potential to earn as much as $131,540 after just 9 years of teaching. An example of a state-supported performance pay program is Minnesota’s Q Comp. Initiated in 2005, Q Comp provides up to $260 per student for participating school districts ($240 for charter schools) to design and implement alternative teacher compensation plans. State regulations require participating districts and charter schools to develop alternative compensation systems that incorporate pay for performance based on student achievement and objective evaluations of teacher practice. Participating districts and public charters must also implement teacher induction and mentoring programs, a standards-based teacher evaluation system,

and a research-based professional development system. Mark L. Fermanich See also Licensure and Certification; Teacher Evaluation; Teacher Training and Preparation; Teachers’ Unions and Collective Bargaining

Further Readings Lawler, E. (1990). Strategic pay: Aligning organizational strategies and pay systems. San Francisco, CA: Jossey-Bass. Murnane, R. J., & Cohen, D. K. (1986). Merit pay and the evaluation problem: Why most merit pay plans fail and a few survive. Harvard Educational Review, 56(1), 1–18. Odden, A., & Kelley, C. (2002). Paying teachers for what they know and do: New and smarter compensation strategies to improve schools (2nd ed.). Thousand Oaks, CA: Corwin Press. Odden, A., & Wallace, M. (2008). How to create world class teacher compensation. St. Paul, MN: Freeload Press. Podgursky, M. J., & Springer, M. G. (2007). Credentials versus performance: Review of the teacher performance pay research. Peabody Journal of Education, 82(4), 551–573. Podgursky, M. J., & Springer, M. G. (2007). Teacher performance pay: A review. Journal of Policy Analysis and Management, 26(4), 909–949. Springer, M. G., Ballou, D., Hamilton, L., Le, V., Lockwood, J. R., McCaffrey, D. F., . . . Stecher, B. M. (2010). Teacher pay for performance: Experimental evidence from the Project on Incentives in Teaching. Nashville, TN: Vanderbilt University, Peabody College, National Center on Performance Incentives. Vroom, V. H. (1964). Work and motivation. New York, NY: Wiley.

PEER EFFECTS While researchers have been exploring the impact peers in educational settings can have on student outcomes ever since James Coleman conducted the seminal work of the relationship between various educational inputs and outcomes in 1966, many still debate whether these effects, known as “peer effects,” exist and whether they can be measured. When researchers analyze peer effects, it is often thought of as a “spillover effect” in which the behavior and academic abilities of students within the education process can affect the educational

Peer Effects

outcomes of their peers. Whether these peer effects actually manifest is a critical policy question because they could affect the success or failures of a number of policies, including school choice, economic and racial/ethnic desegregation, and tracking and detracking programs. For example, if peers can affect the outcome of their fellow students, charter schools would have a strong incentive to recruit the best students away from traditional public schools (often referred to as “cream skimming”) to enhance the production of education in the charter schools. This loss of high-achieving students within the traditional public schools would diminish the positive peer interaction in these schools and, ultimately, the quality of education. Furthermore, the economist Sandra Black provides evidence that families may pay a premium for housing in neighborhoods with high-quality schools. If peers can have a large effect on the quality of schools, then the quality of peers can affect the residential choices of families, which in turn could affect the racial/ethnic integration within neighborhoods. Theoretically, these peer effects can manifest both through direct and indirect interactions of students. A direct effect refers to peer-to-peer interactions (e.g., learning from a peer’s question, helping each other with homework, and discussion of aspirations and ambitions) as well as being included or isolated from a group. An indirect effect refers to a teacher’s response to the individual and his or her peers in the classroom. Responses to behavioral disruptions in the classroom, the pace and rigor with which the teacher presents material based on the ability of students, or the possibility that teachers could be more or less motivated by the ability of the students could exemplify this indirect peer effect. This entry discusses how peer effects are identified; research on peer effects at the preschool, K-12, and higher education levels; and challenges in estimating peer effects.

Identifying Peer Effects To examine whether peer effects exist, one could imagine researchers naively examining the relationship between the ability and/or demographic mix of peers within a school, grade, or classroom and individual student achievement gains. In fact, much of the early research did just that. However, beginning in the 1990s, Charles F. Manski and Robert A. Moffitt argued that estimating the peer effect is complex because of the nonrandom sorting (or what economists call endogenous sorting) of students

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across schools, grades, and classes; the simultaneous educational and influential effects peers can have as well as correlated unobservables (e.g., unmeasured school resources); and the related errors-invariable problem. While the terms are technical, the actual problems are intuitive. For instance, endogenous sorting results because families may choose to live in certain neighborhoods, to attend certain schools, and possibly to select certain teachers that make them systematically different from those who do not make this decision. Furthermore, principals may sort students into particular classes, and highquality teachers may be assigned to high-ability students. Because these activities are not observed in data, researchers cannot control for these activities and may falsely attribute improved student achievement to the peers. The simultaneity problem (also known as a reflection problem) makes it difficult to estimate the effect of peers on student outcomes because a student’s achievement can be affected by the achievement of his or her classmates, and vice versa. Finally, a common example of the errors-invariables problem is the mismeasurement of real income using free and reduced-price lunch eligibility in schools. Some students do not apply for subsidized school meals even though they qualify, so a student who lives in a neighborhood with peers who receive free and reduced-price lunch might also be poor but is not captured by this proxy. This correlation will lead to an upward bias of the effect of free and reduced-price lunch on achievement and bias the estimate of the peer effect. All of these challenges have raised questions of whether we can accurately estimate peer effects. Nevertheless, many have tried, as discussed below.

Peer Effects in Early, Primary, and Secondary Education Beginning with the 1966 Coleman Report, much of the early work examining peer effects was based on race and ethnicity, rather than on the ability of peers. The Coleman Report indicated that academic achievement for Black students was higher when they were in schools with a higher proportion of White students. However, the Coleman analysis may not have accounted for certain unobserved effects. For instance, schools with a greater percentage of White students may have unmeasured resources that are not accounted for in the model but could influence why Black students at these predominately White schools performed better than their peers at predominately Black schools. Furthermore, parents

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of Black students who choose to send their child to a predominately White school may be systematically different from those who send their children to a predominately Black school, constituting a selection effect. Failure to account for selection and unobservable characteristics may lead to a biased estimate of peer effects. Randomly assigning students to schools and classrooms could deal with these challenges, but it is often impractical. Therefore, researchers often proxy the ability of a student’s peers with mean peer achievement from the previous year’s class—that is, lagged achievement. Using lagged achievement often fails to fully capture the current peer behavior due to measurement error. One type of this error could stem from the possibility that the performance on a single test may not be a perfect measure of a student’s ability, which may lead to an underestimation of peer influence. Eric Hanushek and colleagues argue that lagged achievement may also be problematic if the reference group and the individual share experiences that are directly related to the outcome but are not accounted for in a model. A shared experience could be an unobserved shock, or unexpected event, in the previous grade, such as a highly effective teacher. Overall, researchers began concluding that using lagged test scores was not a sufficient solution for dealing with the challenges laid out by Manski and Moffitt. However, more recent research has taken steps to gain more accurate estimates of the peer effect. With the recent increase in standardized testing, accessibility of student-level data for more years in more grade levels has allowed researchers to produce estimates of peer effects that account for the identification problems using instrumental variables analysis, policies where assignment is random, or taking advantage of natural variation in peers as students progress through school. In addition, whereas much of the early research on peer effects focused on race as the peer variable, the field has shifted toward a greater emphasis on peer ability. Nonetheless, some researchers continue to explore issues of race in the context of peer effects. Caroline M. Hoxby and Gretchen Weingarth have found that contextual effects of race or socioeconomic status appear to be minimal once peer ability is accounted for. Yet Joshua D. Angrist and Kevin Lang argue that some patterns of within-group effects of minorities in mixed classes are evident (e.g., higher performing peers of the same race as the student of interest increase the student’s achievement).

The results from these more recent approaches often show nuanced results. For instance, Mary Burke and Tim R. Sass illustrate that classroom effects tend to be larger than grade and school effects, once other factors are controlled for, as the classroom facilitates more peer interactions than the grade or the school. Burke and Sass also show that, once teacher effects are accounted for, the size of the peer effect estimate is smaller. Preschool and elementary school students, who, according to Gary T. Henry and Dana K. Rickman, may experience more direct effects from peers due to self-contained classes where groups of students stay together throughout the school day, appear to benefit from having highability peers in their classrooms and not be particularly hurt by having low-ability peers. At the middle and high school levels, Hoxby and Weingarth demonstrate that a student may be better off grouped with students near his or her own ability than with students at varied levels of ability.

Peer Effects in Higher Education For students in postsecondary education, particularly for students in residential settings, research has measured the effect of random roommate assignment on academic achievement and social outcomes. Bruce Sacerdote finds that peer effects appear to be more important in terms of social interactions than academic performance. For instance, at highly selective colleges and universities, peer effects determine whether the student joins a fraternity or sorority and, conditional on joining, which fraternity or sorority the student selects. While academic effort and choice of major do not appear to be influenced by roommates or at the dorm level, high-achieving roommates improve grades of lower achieving roommates, while low-performing roommates neither help nor hurt low- or high-achieving roommates.

Conclusion While it has long been assumed that peer effects do exist, it has often been difficult for researchers to estimate these effects free from the challenges raised by Manski and Moffitt of simultaneity, correlated unobservables, and endogeneity. However, as empirical research on peer effects has developed, researchers have more fully addressed these challenges with stronger research strategies and have found that peer effects are important effects in the education production function. As vouchers and school assignment

Pell Grants

policies become more prominent, where peer effects represent a possible mechanism for improving student achievement, it is essential to continue to refine these models in order to produce less-biased estimates of peer effects and determine whether students of different backgrounds and ability levels experience peer effects of varying magnitudes. Ron Zimmer and Jonathon Attridge See also Charter Schools; Education Production Functions and Productivity; Fixed-Effects Models; Instrumental Variables; Measurement Error; Neighborhood Effects: Values of Housing and Schools; Selection Bias; Tiebout Sorting; Tracking in Education

Further Readings Angrist, J., & Lang, K. (2004). Does school integration generate peer effects? Evidence from Boston’s Metco Program. American Economic Review, 94(5), 1613–1634. Burke, M. A., & Sass, T. (2013). Peer effects and student achievement. Journal of Labor Economics, 31(1), 31. Hanushek, E., Kain, J. F., Markman, J. M., & Rivkin, S. G. (2003). Does peer ability affect student achievement? Journal of Applied Economics, 18(5), 527–544. Henry, G., & Rickman, D. K. (2007). Do peers influence children’s skills development in preschool? Economics of Education Review, 26(1), 100–112. Hoxby, C. M., & Weingarth, G. (2006). Taking race out of the equation: school reassignment and the structure of peer effects (Unpublished manuscript). Harvard University, Cambridge, MA. Manski, C. F. (1993). Identification of endogenous social effects: The reflection problem. Review of Economic Studies, 60, 531–542. Moffitt, R. A. (2001). Policy interventions, low-level equilibria, and social interactions. In S. N. Durlauf & H. P. Young (Eds.), Social dynamics (pp. 45–82). Washington, DC: Brookings Institution Press. Sacerdote, B. (2001). Peer effects with random assignment: Results for Dartmouth roommates. Quarterly Journal of Economics, 116(2), 681–704.

PELL GRANTS The Federal Pell Grant Program is authorized under Title IV of the Higher Education Act of 1965 (20 USC § 1070). Pell grants, the foundation of federal grant aid for higher education, are need-based scholarships for undergraduate students from low-income

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families. The scope and scale of the Federal Pell Grant Program has grown tremendously over time. For the 1973–1974 school year, 176,000 students received $47.6 million in Pell aid. In 2011–2012, 9.4 million students received Pell grants, and expenditures totaled $33.6 billion, or 19% of total undergraduate student aid. The effect of Pell grants on student demand for college is uncertain; eligibility for and receipt of Pell aid appears to have little impact on college enrollment, perhaps due to complex application processes alongside institutional responses to outside aid. This entry covers eligibility determination before turning to a brief sketch of research on Pell grants.

Eligibility Students seeking Pell grants apply via the Free Application for Federal Student Aid (FAFSA), typically filed several months before college enrollment. The FAFSA collects information pertaining to the student, the student’s parents, and if applicable, the student’s guardians. This includes demographic information, family size, number of family dependents in college, federal income tax information, and other financial data, such as bank balances, asset values, and untaxed income. Students must indicate at least one college on the FAFSA to receive federal aid eligibility details. Once a student submits his or her FAFSA, the Central Processing System (the U.S. Department of Education automated system that processes applications for federal student aid) uses application fields to calculate the expected family contribution (EFC), the amount that the student and his or her family are expected to provide for expenses incurred during college. Allowances for taxes and basic living expenses are subtracted from a family’s income to yield the family’s available income. Then, the EFC is derived from the family’s available income along with a percentage of net assets, divided by the number of college students in the family (excluding parents). Pell eligibility is determined at well-defined EFC thresholds. In the 2011–2012 academic year, for example, the threshold was $5,273; full-time applicants with EFC values at or below $5,273 were eligible for at least $555 in Pell grant aid for the academic year. Students with zero EFC were eligible for the maximum award of $5,550. Awards are prorated for part-time students. The collective value of need-based and merit-based aid is bounded

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by recipients’ total cost of attendance at an eligible college, including tuition and fees, room and board, books and supplies, and dependent care. Pell awards are among the first aid distributions counted toward students’ total aid, and maximum Pell awards alone are typically less than the cost of attendance. Thus, Pell awards are rarely scaled back for exceeding the cost of attendance.

Research The Pell program is the largest vehicle for federal financial aid and the largest distributor of needbased grants nationwide. As with all need-based aid vehicles, Pell grants are intended to relieve financial constraints that prevent otherwise-capable students from enrolling in college, persisting through college, and ultimately completing college. Pell grants have a large footprint on the landscape of financial aid for postsecondary students, but the question of whether Pell grant eligibility and receipt advances students’ educational attainment is unresolved. Analyzing the impact of Pell grant aid on college enrollment is difficult for several reasons, chiefly because Pell grants are not distributed at random. Eligibility for need-based aid coincides with other factors that affect college enrollment. Some researchers have addressed this methodological problem by examining the decisions of students who are right at the eligibility threshold. Research in this vein finds that Pell grant aid has little to no effect on college enrollment. An exception is for older, nontraditional students, who are somewhat more likely to attend college in light of Pell aid. Less research has been devoted to how Pell grant aid affects postenrollment student outcomes: persistence through college, college grade point average, college major, or degree completion. Limited work in this area suggests that Pell grants may increase the probability that students continue to enroll in college after their first year. One explanation for why Pell grants have a small or negligible effect on student demand for college stems from the hurdles presented by the FAFSA application process. The FAFSA is a time-consuming and complex instrument for determining student need. An experimental assessment of a program that assisted families in completing the FAFSA found significant impacts of application assistance on college enrollment and Pell receipt. Another mechanism that may reduce Pell’s impact on college enrollment is institutional capture of federal financial aid, that is, the idea that colleges and universities react to

outside funding from Pell grants by increasing tuition or reducing institutional aid. In much the same way that tax credits for higher education crowd out (i.e., reduce) the aid that institutions offer on their own, colleges and universities may let federal Pell aid supplant institutional grants and scholarships that students would have received in the absence of Pell. Recent work on this topic finds evidence that colleges not only value Pell-eligible students but that they also capture a significant portion of Pell aid in the form of lower institutional aid. Celeste K. Carruthers and Jilleah G. Welch See also College Enrollment; Student Financial Aid; Tuition and Fees, Higher Education; Tuition Tax Credits; U.S. Department of Education

Further Readings Bettinger, E. (2004). How financial aid affects persistence. In C. M. Hoxby (Ed.), College choices: The economics of where to go, when to go, and how to pay for it (pp. 207–238). Chicago, IL: University of Chicago Press. College Board. (2012). Trends in student aid: 2012. Washington, DC: Author. Retrieved from http://trends .collegeboard.org/sites/default/files/student-aid-2012-fullreport-130201.pdf Dynarski, S., Scott-Clayton, J., & Wiederspan, M. (2013). Simplifying tax incentives and aid for college: Progress and prospects (Working Paper No. 18707). Cambridge, MA: National Bureau of Economic Research. Hansen, W. L. (1983). Impact of student financial aid on access. In J. Froomkin (Ed.), The crisis in higher education (pp. 84–96). New York, NY: Academy of Political Science. Kane, T. J. (1995). Rising public college tuition and college entry: How well do public subsidies promote access to college? (Working Paper No. 5164). Cambridge, MA: National Bureau of Economic Research. Rubin, R. B. (2011). The Pell and the poor: A regressiondiscontinuity analysis of on-time college enrollment. Research in Higher Education, 52(7), 675–692. Seftor, N. S., & Turner, S. E. (2002). Back to school: Federal Student aid policy and adult college enrollment. Journal of Human Resources, 37(2), 336–352. Turner, L. J. (2013, March). The road to Pell is paved with good intentions: The economic incidence of federal student grant aid (Working Paper). College Park: University of Maryland. Retrieved from http://econweb .umd.edu/~turner/Turner_FedAidIncidence.pdf Turner, N. (2012). Who benefits from student aid? The economic incidence of tax-based federal student aid. Economics of Education Review, 31(4), 463–481.

Performance Evaluation Systems U.S. Department of Education, Office of Postsecondary Education. (n.d.). 2011–2012 Federal Pell Grant Program End-of-Year Report. Retrieved from http:// www2.ed.gov/finaid/prof/resources/data/pell-2011-12/ pell-eoy-2011-12.pdf

PERCENTAGE POWER EQUALIZING See Guaranteed Tax Base

PERFORMANCE EVALUATION SYSTEMS Performance evaluation or appraisal systems comprise the guidelines, procedures, and methods used by organizations to systematically monitor and judge the work of organizational units and individuals against desired criteria or standards of quality and productivity. Performance evaluation systems (PES) face unique theoretical, technical, and practical challenges in evaluating the work of schools and teachers. This entry provides an overview of PES in economics and public policy and then examines their application in the context of education policy and practice in relation to four key dimensions: (1) the purpose of the evaluation, (2) the frameworks defining the aspects of performance to be evaluated, (3) the design and methods of evaluation, and (4) the uses of the information collected for assessing and improving performance. The final section considers standards of quality for PES in education. PES have their roots in economic theories of productivity, where performance is improved by setting standards, accurately and fairly measuring the outcomes of interest, and creating meaningful rewards and sanctions for the actors responsible for these outcomes. These theories have a long history in scientific human resource management, but they have also been applied extensively to personnel evaluation in government and public organizations in all sectors. PES ultimately seek to answer essential questions about the degree to which individuals in all types of organizations have achieved their objectives and the necessary courses of action for improvement. In education, the logic of performance evaluation is central to local, state, and national standardsbased accountability policies in many countries.

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In the United States, it is a key component of K-12 federal education policy (e.g., the No Child Left Behind Act of 2001 and the Race to the Top initiative of 2009) and the driving force behind efforts to develop or revamp state and district school and teacher evaluation systems. These policies reflect persistent concerns about student performance in national and international assessments, and extensive evidence that credentials and seniority have little relation to student outcomes. These concerns have led to calls to reform traditional bureaucratic evaluation to monitor the performance of smaller units of the education system, moving from traditional comparisons of state and district statistics to indices that reflect productivity at the school level and, more recently, at the teacher level.

Purpose of Evaluation (Why Evaluate) Systematic performance evaluation can serve a variety of purposes in education systems. Summative evaluations gather evidence as the basis for some kind of final or discrete decision (e.g., a district computing an index of student performance as the basis for assigning bonuses to teachers), while formative evaluations focus on gathering useful information to guide improvement efforts and processes (e.g., a principal observing a lesson and offering feedback to the teacher on how to improve a certain aspect of his or her instruction). A key goal common to most PES in education entails improving student learning outcomes by driving concurrent improvements in school and classroom practice. A parallel goal is to develop and promote systemwide adoption of frameworks of professional practice to provide criteria for successful work by teachers and principals. Operationally, PES can also seek a variety of specific policy goals, including identifying underperforming schools and teachers for assistance, intervention, and (when necessary) sanction; certifying principals and teachers for promotion and career advancement; identifying high-performing schools and teachers for receiving incentives and rewards, or as the basis for performance pay policies; and guiding district professional development policy and practice, among others. Thus, in practice, most PES in education seek to support a combination of summative and formative purposes. While this is efficient from a policy perspective, it can pose technical complications and demands for resources in system design and implementation, as different goals may call for different data, procedures, and instruments.

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Standards and Frameworks (What to Evaluate) In economics and business administration, the key outcomes of interest in performance evaluation (e.g., income, employment, sales) are often available and relatively easy to measure. However, in education, there is less consensus around the appropriate metrics to assess quality and productivity. In recent decades, standards-based accountability systems have popularized benchmarks for assessing student achievement and have increased standards or frameworks of professional practice to guide teacher and principal development. Student achievement benchmarks comprise basic standardized indicators of academic achievement (e.g., standardized test scores, graduation rates), course grades and other teacher-generated marks, and a variety of cognitive and noncognitive indicators that may be harder to measure and occur over large spans of time (e.g., earnings, career success, citizenship, life satisfaction). Teaching is a complex and multidimensional profession, and consequently, teaching standards not only include basic markers of qualification (i.e., credentials, seniority) and psychological traits like knowledge of content and pedagogy, but they also extend to applied knowledge and skill in classroom practice; transactions and communications with administrators, peers, students, and parents; and contributions to the broader community, among others—standards for principals additionally include dimensions such as academic and instructional leadership, feedback and support to teachers, and organizational management.

of teacher practice related to student achievement and also provide evidence of differentiated instruction within classrooms. Parent surveys can be valuable for assessing teacher efforts to engage families and the effects on student well-being not captured by the standard academic metrics. Peer evaluation mechanisms are commonly used for formative evaluation purposes to promote collection of locally relevant data as the basis for discussion and reflection among groups of colleagues. Inspector visits by expert administrators are used to assign qualitative ratings of performance and to provide feedback and guidance to school personnel. Finally, portfolios can be used to compile contextualized artifacts of practice (e.g., lesson plans, assessments) as evidentiary basis for evaluating teacher and principal performance. Each of these methods has unique strengths for evaluating different aspects of performance, but each also has significant limitations. Value-added models face questions about attribution of student learning gains to individual teachers, the stability of estimates over time, and diagnostic value for improving teaching practice. Classroom observation is costly and may not provide reliable measures on a large scale, owing to natural inconsistencies in human judgment and complex rubrics and instruments. Student surveys face issues of accuracy and consistency (with younger children), bias and validity (with older students), and statistical complications with constructing aggregate indicators from student data. Portfolio assessments place a substantial burden on teachers and are also difficult and costly to score reliably.

Methods and Measures (How to Evaluate) PES rely on a variety of methods and measures to collect data related to the standards and frameworks that inform the evaluation. Student achievement has progressively become the dominant measure of interest in standards-based evaluation; it is commonly measured through value-added models that capture learning growth over time to avoid unfairly comparing schools and teachers serving very different types of students. Supervisor ratings reflect qualitative judgments of performance based on evidence collected across the year by principals and others in supervisory roles, including standardized protocols for systematic classroom observation. Customer satisfaction or feedback surveys are also gaining acceptance as a cost-effective method for collecting information about teachers from students and parents; student surveys can produce reliable indicators

Inferences and Consequences (How to Use the Information) The value of combining objective outcome data with subjective (but rigorous and systematic) judgments of performance is well known in program evaluation and psychology and is increasingly acknowledged in the economics literature. In education, there is wide consensus that valid and useful evaluation requires multiple measures to offer a comprehensive picture of performance, clear distinction among individuals, and relevant and effective feedback. Multiple measures can also facilitate stakeholder buy-in and reduce incentives for fraud. Importantly, there are different ways of combining a set of related measures for performance evaluation. In some cases, PES keep the measures separate but use them in combination to inform formative

Performance Evaluation Systems

and summative evaluation: Conjunctive models specify joint decision rules or criteria of performance for the measures (e.g., tenure for beginning teachers requires satisfactory or higher scores in the observation and student survey, and above the first quartile of the value-added distribution). Disjunctive or complementary models similarly require meeting q out of p criteria (e.g., meeting two of the three criteria in the previous example). These models are useful for formulating and conveying policy goals, and for performance diagnosis, but may lead to inefficiencies in analyses and make results difficult to convey to individuals and the public. Alternatively, PES may adopt compensatory models to create composite indices (i.e., weighted averages) to summarize information across measures—allowing high performance on one indicator to compensate for lower performance on another. Summary indices are useful for conveying policy priorities and for monitoring results, but they require weighty conceptual and statistical assumptions for weighting and merging information from a set of measures. Empirical weights can be derived through a variety of methods (e.g., outcome prediction, factor analysis, and reliability weighting), but in practice, most PES adopt policy weighting schemes that reflect the priorities and values placed on different aspects of performance. In selecting an appropriate combination model, PES must also carefully examine how different sources of measurement error influence the reliability of resulting indicators and particularly the accuracy and policy dependability of inferences about specific individuals. The technical literature shows that factors such as the sample size of student surveys or the number of observers or observations can substantially influence reliability. It also shows that the choice of model matters for composite reliability and that a single unreliable component can significantly lower the reliability of the aggregate.

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and the resources available to help them improve their performance. This is critical because the formative uses of the information collected largely determine the effects on improving school processes and student outcomes. The quality and effectiveness of a PES can be evaluated in relation to technical properties of instruments, methods, and procedures or their policy goals, operation, and consequences. In the United States, the Office of Personnel Management and Office of Management and Budget certify that PES in federal agencies meet guidelines of technical and operational quality. In education, the American Recovery and Reinvestment Act of 2009, which established the Race to the Top grant competition, set parameters for evaluating state and district PES against standards of technical and policy quality of its components. These include factors such as comprehensiveness, rigor of standards, technical quality of assessments, data systems to support instruction, focus on improving teaching and leadership, and turning around low-achieving schools, among others. Similarly, state laws increasingly regulate the quality of local PES. In Florida, for example, Section 1012.34 of state law defines not only the purpose of district teacher and administrator PES (increasing student learning by improving the quality of instruction, administration, and supervision) but also their theoretical basis, focus, frequency, characteristics, and components. As with Office of Management and Budget certification standards, RTT and state guidelines establish standards of quality for performance evaluation to ensure that these systems are able to reliably distinguish among schools, administrators, and teachers. José Felipe Martinez See also Accountability, Standards-Based; Education Production Functions and Productivity; Measurement Error; Professional Development; Teacher Evaluation

Using and Evaluating PES In addition to the theoretical and technical issues involved, education systems face their most critical challenge in effectively using the information collected to inform and support professional development and improvement efforts for all actors involved. Notably, large-scale studies such as the Schools and Staffing Survey or the Teaching and Learning International Survey show widespread teacher dissatisfaction with the quantity and quality of the feedback and guidance they receive from PES

Further Readings Davis, S., Kearney, K., Sanders, N., Thomas, C., & Leon, R. (2011). The policies and practices of principal evaluation: A review of the literature. San Francisco, CA: WestEd. Hamilton, L. (2005). Lessons from performance measurement in education. In R. Klitgaard & P. C. Light (Eds.), High performance government: Structure, leadership, incentives (pp. 381–405). Santa Monica, CA: RAND Corporation.

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Permanent Income

Mansky, C. (2004). Measuring expectations. Econometrica, 72, 1329–1376. Mehrens, W. (1989). Combining evaluation data from multiple sources. In J. Millman & L. Darling-Hammond (Eds.), The new handbook of teacher evaluation (pp. 322–336). Newbury Park, CA: Sage. Nardo, M., Saisana, M., Saltelli, A., Tarantola, S., Hoffman, A., & Giovannini, E. (2005). Handbook on constructing composite indicators: Methodology and user guide (OECD Statistics Working Paper No. JT00188147). Paris, France: Organisation for Economic Co-operation and Development. National Research Council. (2008). Assessing acomplished teaching: Advanced-level certification programs (M. Hakel, J. K. Anderson, & S. Elliot, Eds.). Washington, DC: National Academies Press. Organisation for Economic Co-operation and Development. (2013). Teachers for the 21st Century: Using evaluation to improve teaching. Paris, France: Author. Stecher, B., & Kirby, S. N. (2004). Organizational improvement and accountability: Lessons for education from other sectors. Santa Monica, CA: RAND Corporation.

PERMANENT INCOME Many economic problems involve the allocation of resources across time; for example, how much money to consume now and how much to save for future consumption, or how much money to invest in facilities to be used in the future. Optimal decisions about how much to spend or invest at a moment in time depend not only on the resources available at that moment but also on the resources that will be available in the future. For example, a school district with a given level of current property tax revenues is more likely to make capital investments that will be paid off over time if it expects rising revenues in the future rather than falling revenues. Permanent income is a measure of the amount of income that a household, or other economic decision maker, expects to have available on average at future time periods. The permanent income hypothesis says that, if households prefer to consume about the same amount of money every year, they will consume an amount equal to their permanent income, saving money in years when their current income is greater than their permanent income and borrowing money (or drawing down savings) when their current income is less than their permanent income. Permanent income is thus a better measure of the

socioeconomic status of households than is current income, and it is also a better predictor of expenditures on education. This entry describes the origins of the theory of permanent income in macroeconomics and discusses ways in which the theory has been applied to the economics of education. The economist Milton Friedman developed the theory of permanent income in 1957 to explain why households vary in their response to changes in income. Consider a household with an income of $5,000 per year every year. Then the household’s permanent income is also $5,000 per year, and we would expect the household to consume $5,000 each year. Now suppose the household’s income increases to $6,000 in the current year. How will its consumption change? It depends on whether the household expects the increase to continue in future years or not. If the increase will continue in all future years, then it is called a permanent increase in income; if it will only occur in this year, then it is called a transitory increase in income. If the increase is a permanent increase, so that the household’s income will now be $6,000 per year every year, then its permanent income also rises to $6,000 per year. In this case, we expect the household to increase its consumption to $6,000 as well; its marginal propensity to consume out of this increase in permanent income is 1 (i.e., its consumption increases by $1 for every $1 increase in permanent income). But if it does not expect its income to rise in future years, then it cannot do that. It could increase its consumption to $6,000 this year and keep consumption at $5,000 in all future years. But that means the household’s consumption level will vary from year to year. If the household instead prefers to smooth consumption over time, then it would do better to save most of this one-time increase in income to use for future consumption and to increase consumption this year by only a small amount. Its marginal propensity to consume out of transitory income increases will be much less than 1. Suppose, for simplicity, that the household is infinitely lived, meaning that it plans to continue spending forever (i.e., it plans for its descendents as well as its current members) and hence has no final year in its plan. Then it should invest the entire transitory gain and increase its consumption every year by the amount of the interest earned on the investment. That is, if the interest rate is 5%, the household can increase consumption to $5,050 every year by drawing $50 in interest income from its investment. In that case, the household’s marginal propensity to consume out of a transitory increase in income will

Philanthropic Foundations in Education

be 0.05. In general, it will be equal to the interest rate or a little more if the household has a finite life and thus does have a final year in its plan. We then say that the household’s permanent income has risen to $5,050 as well. Friedman used the theory to predict that households will consume more of an increase in income if they expect it to be permanent and consume less of it if they expect it to be transitory. Permanent income has been applied to the economics of education to explain spending decisions by governments and to measure the influence of resources on educational outcomes. Since permanent income is not directly observable, it must be calculated; this can be done in different ways, depending on the data available to calculate it. Stephen Schmidt and Therese McCarty use an explicit forecasting model to calculate permanent income for state governments using a 21-year panel dataset. They find that states increase education spending when permanent income rises, but do not increase spending when transitory income rises, consistent with the theory. Because the permanent income of states changes very slowly over time, state-level fixed effects, if used, will capture much of the effect of permanent income in regressions of education spending that only include current income. Estimated income elasticities from regressions that use both current income and fixed effects, but no measure of permanent income, should therefore be interpreted only as elasticities with respect to transitory income. Jesse Rothstein and Nathan Wozny use average income over a 15-year period as a measure of permanent income for families. They find that permanent income explains about twice as much of the Black-White test score gap as current income explains. Arnaud Chevalier, Colm Harmon, Vincent O’Sullivan, and Ian Walker forecast earnings for a panel of households, and they use the predicted earnings as a measure of permanent income and the residuals as measures of transitory income. They find that, when estimating with instrumental variables, permanent income of parents significantly affects children’s education levels, while parental education levels, which may proxy for permanent income when the latter is not included, are no longer significant when it is included. Stephen J. Schmidt See also Achievement Gap; Demand for Education; Income Inequality and Educational Inequality; Instrumental Variables; Present Value of Earnings; School District Wealth

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Further Readings Chevalier, A., Harmon, C., O’Sullivan, V., & Walker, I. (2005). The impact of parental income and education on the schooling of their children (IZA Discussion Paper No. 1496). Retrieved from http://cep.lse.ac.uk/ seminarpapers/24-06-05-WAL.pdf Friedman, M. (1957). A theory of the consumption function. Princeton, NJ: Princeton University Press. Rothstein, J., & Wozny, N. (2013). Permanent income and the Black-White test score gap. Journal of Human Resources, 48(3), 510–544. Schmidt, S., & McCarty, T. (2008). Estimating permanent and transitory income elasticities of education spending from panel data. Journal of Public Economics, 92, 2132–2145.

PHILANTHROPIC FOUNDATIONS IN EDUCATION Philanthropic foundations are tax-exempt, nonprofit organizations recognized by Section 501(c)(3) of the U.S. Internal Revenue Code as existing to distribute funding for charitable purposes. These organizations are restricted from participating in political and legislative activities, and they are required to distribute a certain amount of money annually from their asset base according to an Internal Revenue Service formula. Philanthropic foundations have a long, yet evolving history of making contributions to education in the United States, and they have made investments across the education spectrum, from early childhood to K-12 to postsecondary. Foundations range from small, local donors focused on a single issue to large, national or even international grantmakers that have an interest in myriad aspects of education. The proper role of philanthropic foundations in education is often debated, for while they are capable of providing the needed resources for disadvantaged groups, these taxexempt organizations allocate their resources outside the standard democratic processes and without public accountability. The topic of philanthropic foundations is relevant for understanding education finance for three reasons. First, although the contributions that foundations make directly to schools and districts are relatively small when placed in the context of overall public education spending, these dollars can be very influential by allowing schools and districts to experiment with new approaches. Second,

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philanthropic investments have allowed for the expansion of educational providers that operate outside the traditional systems, such as charter schools and colleges that provide credit for demonstrated competency rather than course completion. Third, philanthropic foundations are increasingly attempting to shape the public policies that determine how public expenditures for education at all levels are distributed. This entry includes an overview of the historical development of philanthropic foundations in education, followed by a discussion of the size and scope of foundation giving to education in the United States. It concludes with an outline of the debate over the proper role of philanthropic foundations in general, and in education specifically.

History and Evolution of Philanthropic Foundations in Education American education, at all levels, has long benefited from contributions from philanthropy. Established in the late 19th and early 20th centuries, important early philanthropic foundations—the Rosenwald Fund (established by the former CEO of Sears, Roebuck and Company), the Anna T. Jeanes Fund, and the Slater Fund—helped to expand training of African American teachers and to build more than 5,000 schools for African American children across the South. The most prominent philanthropic foundations of the 20th century include the Rockefeller, Ford, and Carnegie foundations. The Rockefeller and Ford foundations had a similar approach to giving that was primarily dedicated to support for postsecondary educational institutions and research, but they both have also contributed to improving educational opportunities in the South, especially for African Americans. The Ford Foundation has also invested to promote education policy reforms, to improve secondary schools, and to support doctoral fellowships for African American students and faculty. Andrew Carnegie’s foundation, the Carnegie Corporation of New York, made notable investments in public libraries and, through the establishment of the Carnegie Foundation for the Advancement of Teaching, to improve public education. The Foundation for the Advancement of Teaching has not operated as a donor but as an independent policy and research center that has helped shape colleges and universities and also high school academic programs. Since the modern education reform movement began in the early 1980s, the role of the

philanthropic foundation in the American education system has evolved in many ways, particularly with the entrance of a number of new philanthropists who are focused on this issue. In the late 20th century, philanthropies such as the Ford Foundation and the Annenberg Foundation began making high-profile grants directly to urban school systems to spur the creation of new programs. During the 1990s, the Annenberg Foundation launched the Annenberg Challenge, which at the time was the largest ever investment in education by a foundation. It involved $500 million in foundation grants plus $600 million in matching funds to 18 projects that supported approximately 2,400 schools. The funds went to a variety of efforts, including a focus on providing additional professional development for teachers. While some reports on this initiative have been positive, critics have observed that this large infusion of philanthropic funding that predominantly sought to leverage existing capacities in traditional school systems did not lead to sustainable long-term changes. In the early days of the 21st century, several new foundations with substantial resources and an interest in K-12 education have emerged, including the Bill & Melinda Gates Foundation, the Broad Foundation, the Michael & Susan Dell Foundation, and the Walton Family Foundation. These foundations are trying new approaches that have tended to focus their grantmaking outside the traditional public education system, funding competitors to the district system such as charter and private schools. Some of these newer philanthropic foundations are also seeking to change public policy on issues as varied as teacher evaluations, school funding formulas, accountability systems, and curriculum standards. In addition, these newer philanthropies have tended to take strategic approaches to their grantmaking, treating grants like venture capital investments that are based on strategic plans, cost-benefit analyses, and performance benchmarks for measuring returns. The tradition of large foundations investing in postsecondary education has continued, but the focus of these investments has similarly begun to evolve. Where philanthropies once invested in capital projects at colleges and universities, in teacher training programs, or in university-sponsored research, large foundations have begun turning their attention to shaping how colleges and universities deliver education and how federal financial aid policies are structured. Newer foundations—such as the Bill & Melinda Gates Foundation, the Lumina Foundation, and the Kresge Foundation—seek to

Philanthropic Foundations in Education

alter the traditional postsecondary experience so that more students can gain access and complete their degrees in a timely manner. For example, in 2009, the Bill & Melinda Gates Foundation helped create Complete College America, a nonprofit organization that advocates for a number of reforms to state higher education policies, including reducing the amount of time required to earn a degree, reforming remediation programs, increasing data transparency, and tying state funding allocated to colleges and universities to outcomes such as course completion and graduation rates rather than the number of students enrolled.

Philanthropy’s Size and Scope According to the Foundation Center, the number of grantmaking foundations and grants has expanded significantly from 21,887 foundations distributing $1.9 billion in 1975 (the first year for which such information is available) to 81,777 foundations distributing $49 billion across all causes in 2011. Similarly, the role of philanthropy in the K-12 education system has continued to grow over time as well. In 1998, the earliest year for which consistent data are available, the 50 largest foundations providing grants for elementary and secondary education programs provided roughly $384 million. By 2011, that figure had more than doubled to just over $1 billion in inflation-adjusted dollars. The types of projects in K-12 education funded by philanthropic foundations have also changed significantly during that time. In 1998, 8 of the 10 largest grant recipients in K-12 education were school districts or district-affiliated nonprofit organizations. By 2011, only 1 district (Washington, D.C.) was in the top 10, with the other 9 spots dominated by groups that work outside the traditional education system, such as Teach For America, the Charter School Growth Fund, KIPP (Knowledge Is Power Program) charter schools, Children’s Scholarship Fund (a private school voucher provider), and The New Teacher Project. Notably, the amount of philanthropic support relative to all public expenditures for K-12 education is small, about 0.3% of the roughly $585 billion allocated by federal, state, and local governments each year. But the level of influence leveraged by these dollars can be significant for at least three reasons. First, the majority of public funding currently going to school systems is nondiscretionary, meaning that policymakers have already prescribed

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how those dollars should be spent and for what purpose. By contrast, foundation grants offer district leaders discretion in starting new programs or introducing new policies. Second, even though foundations are prohibited by their tax status from lobbying, they can influence public policy by educating policymakers and opinion leaders, by filing lawsuits to seek judicial intervention, and by working with other advocacy organizations to build support for certain policy positions. In this way, foundations can influence the rules that govern all schools, as well as how significant streams of government funds are allocated. Finally, foundations have more recently shown the ability to create outsized influence by making grants outside the traditional district system. By providing resources for district competitors and engaging families in school choice programs, foundations have supported mechanisms that place pressure on districts to reform or face losing additional students. The size and scope of philanthropic foundations in postsecondary education have also changed. Data from the Foundation Center show that grants made to support higher education totaled roughly $1.1 billion in 1998. By 2011, that figure had increased to nearly $1.7 billion. However, there have been two notable changes: (1) the largest grantmakers in higher education have changed and (2) the total dollar amount donated by individual foundations has gotten smaller, even as overall giving has expanded in this category. In 1998, the largest funders were the Lilly Endowment, the Robert Wood Johnson Foundation, and The W. K. Kellogg Foundation. Together, they donated roughly $443 million. By 2011, the largest donor to higher education was the Bill & Melinda Gates Foundation, which made 79 grants totaling $75 million. The two next largest donors were the Duke Endowment and the Andrew W. Mellon Foundation. Combined, the top three foundations provided just $170 million. Despite these changes, the top grant recipients continue to be well-known universities, such as Columbia, Yale, and Stanford.

Debate Over the Role of Philanthropic Foundations The proper role of philanthropic foundations in a democratic society generally, and in education specifically, has long been debated. The critics of philanthropic foundations have several arguments, most of which concern the lack of public

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accountability and transparency for these organizations. Specifically, the positions foundations take and the resources they distribute, potentially having significant impacts on the well-being of large numbers of citizens, are decided by private boards, directors, and staff, with no public oversight. In addition, it is often difficult for the public to track how foundations are distributing funds without accessing federal reporting documents. Accountability and transparency can also be hindered, critics note, because foundations neither have sufficient incentives to evaluate the impacts of their giving nor are they required to publish results even when they do conduct an evaluation. Critics add that while the money donated to foundations provides an immediate tax benefit to the donors, the public often does not benefit immediately because the majority of donations go into foundation investment funds. Regarding the role of foundations in education, some critics have argued that a lack of diversity in the strategies of the largest donors for reforming public education can prevent new ideas and approaches from being pursued by schools and policymakers. Proponents of philanthropic foundations view these organizations as beneficial because they provide incentives for the wealthy to spend their money on public goods, such as education, rather than on private consumption. They also argue that philanthropic foundations are accountable because the reputation of donors is tied to the success of their philanthropic investments. Moreover, regarding criticisms that foundations are uninterested in understanding the impact of their giving, foundation proponents point to the recent efforts of several high-profile foundations to systematically evaluate the performance of grantmaking strategies and even to begin publishing reports about successes and failures, as the William and Flora Hewlett Foundation has prominently done. According to some proponents, an added benefit of the independence of foundations is that it allows them to take risks and fund innovative new ideas that might benefit public education and to make controversial decisions because they do not have to seek democratic approval or adhere to public opinion. Finally, proponents point out that private foundations can provide resources for causes that help politically disenfranchised groups, whose preferences are not reflected in government priorities, which are guided by the politically powerful. Marc J. Holley and Matthew J. Carr

See also Block Grants; Education Spending; Private Fundraising in Postsecondary Education; University Endowments

Further Readings Donohue, J. T., Heckman, J. J., & Todd, P. E. (2002). The schooling of southern Blacks: The roles of legal activism and private philanthropy, 1910–1960. Quarterly Journal of Economics, 117(1), 225–268. Hess, F. M. (Ed.). (2005). With the best of intentions: How philanthropy is reshaping K-12 education. Cambridge, MA: Harvard Education Press. Katz, S. (2012, March 25). Beware big donors. Chronicle of Higher Education [Online]. Retrieved from http:// chronicle.com/article/article-content/131275/ Parry, M., Field, K., & Supiano, B. (2013, July 14). The Gates effect. Chronicle of Higher Education, 59(42), A18–A23. Reckow, S. (2013). Follow the money: How foundation dollars change public school politics. New York, NY: Oxford University Press. Reich, R. (2013, March 1). What are foundations for? Boston Review [Online]. Retrieved from http://www .bostonreview.net/forum/foundations-philanthropydemocracy

POLICY ANALYSIS

IN

EDUCATION

The public importance assigned to education has increased steadily over the past half-century, as state and federal authorities have come to rely ever more heavily on schools to address problems ranging from social inequality to national economic competitiveness in a globalized economy. Federal and state spending on education has grown dramatically since 1960, and the efforts of state and national governments to direct the work of teachers and schools through policy have grown commensurately. In California and other states, the education code has ballooned in recent decades, while federal laws governing the allocation of education revenues have become more and more prescriptive as to how federal money can be used and to what ends. Public officials count on schools to accomplish a wide variety of social and economic goals, which include everything from increasing college completion rates among otherwise disadvantaged students to reducing the incidence of bullying on playgrounds. Each of these many goals brings with it a policy, or a set of policies, to guide educators toward its

Policy Analysis in Education

accomplishment. One natural result is the everexpanding profusion of statutes, rules, and regulations under which schools must operate. Another is the corresponding torrent of policy analysis, as scholars and others seek to evaluate the impact of educational policies, to identify gaps and weaknesses in current policy frameworks, and to devise new policies that will be more effective in accomplishing public goals. This entry provides an overview of policy analysis in education, reviews some of the major contributions that policy analysis has made to educational policy, identifies the circumstances in which policy analysis has its greatest impact, and proposes some strategies to increase the impact of policy analysis in policy debate.

The Nature of Policy Analysis Policy analysis seeks to determine which among a set of alternative policy arrangements is most likely to achieve the goals of the policy. Policy goals include not only the specific outcome to be achieved (e.g., closing achievement gaps) but also the conditions under which the goals might be accomplished (e.g., how much it will cost to accomplish the goal and who will bear the price). The tools of economic analysis are generally well suited to these kinds of questions, and economists consequently dominate the field of policy analysis, in education as in other issue areas. (The broader field of policy research has a more catholic set of goals and methods.) Policy analysis relies heavily on other disciplines as well, including sociology, political science, and law, and professional associations in the field, including, for example, the Association for Public Policy Analysis and Management, draw their membership from across the social sciences. The tools and tricks of the trade are taught in a steadily growing number of graduate programs in education policy, supported by a wide variety of textbooks and handbooks. The volume of research focused on educational policy is expanding rapidly, with findings from current policy analyses in education published in specialized journals including Educational Evaluation and Policy Analysis and Education Finance and Policy, among many others. Most policy analysis is conducted under contract to particular clients, which commonly include the federal, state, and local agencies with direct responsibilities in the education system. Legislation often includes a requirement that specific policy initiatives be evaluated after some period of time, to determine

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whether the program should be expanded, replicated, or terminated. For example, some recipients of funds from the federally sponsored Teacher Incentive Fund were given larger grants that would allow them to fund a rigorous evaluation of the program’s impact. In the best of cases, these evaluations may produce interesting research findings, but too often the relationship with the client shapes the research questions or controls the findings in ways that limit their research value.

Why Policy Analysis Has a Limited Impact on Policy The recent efflorescence of policy analysis has greatly increased our understanding of some important policy issues and initiatives, including those aiming to expand parental choice in the education system and increase the efficiency and flexibility of the teacher labor market. A vast body of research on charter schools, for example, has greatly deepened our understanding of how parents make choices about the education of their children, of the sources of innovation in schools, and of the impacts of expanded public school choice on student performance. Recent work on teacher labor markets has illuminated many of the reasons why policies aimed at improving the quality of teaching have limited effects, including the preferences of teachers for “easier” and more familiar assignments, the weakness of most teacher preparation programs, and the inherent difficulty of evaluating the quality of teaching. In fact, the goals of policy analysis include not only the accumulation of new knowledge but also guidance to policymakers about policy choice, and support for policy learning and continuous improvement in the education system. Even though academic understanding of several key policy issues has grown, however, the impact of policy analysis on policy learning and policy choice remains limited, for two main reasons. First, education policies are only rarely designed to support careful evaluation or analysis. Most policy innovations are adopted and implemented simultaneously in all schools. Pilot projects and policy experiments are rare. This makes it difficult if not impossible to measure policy impacts, because there are no baseline data and no control groups. In California, for example, the state legislature established a program in 1996 that provided funding to all of the state’s school districts to support reduced

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class sizes in the early elementary grades. The state has subsequently spent billions of dollars to keep class sizes small, but it is impossible to learn whether this vast expenditure had any impact on student learning, because the policy was implemented simultaneously in all school districts throughout the state. This is beginning to change for the better. Some recent federal initiatives including the Teacher Incentive Fund and the Investing in Innovation program have been designed to support careful analysis of policy impact. In addition, new statistical techniques including regression-discontinuity designs offer stronger leverage on policy impacts even under nonexperimental conditions. Even when policy analysis produces solid evidence on the effects of different policy choices, however, research still generally weighs lightly at best in the scales of policy judgment. For example, two decades of research has shown that class size reduction at politically feasible levels is a costly and generally ineffective way to increase student learning, but the impact of research on policy has been barely perceptible when measured against the strong preferences of unions, teachers, parents, and students. On issues where the evidence is less strong, the impact of research on policy is even weaker. The obvious question is why this should be the case. There are many answers to this question, which include obstacles on both sides of the relationship between policymakers and researchers. As a rule, policymakers do not pay much attention to research, in education or in other fields. The incentives that elected officials face encourage them to make decisions that are informed and guided by other, more powerful influences than research, including ideology, interest, authority, and common sense. They will happily cite findings from policy analysis to bolster their arguments in support of or opposition to specific initiatives, but only when these findings comport well with their existing policy preferences. They rarely (if ever) shift their policy preferences in response to research findings. By the same token, most policy analysts pay surprisingly little attention to policy. The incentives that researchers face encourage them to adhere closely to the norms and conventions of academic research, even when these work to limit rather than enhance policy impact. Their work is targeted at an academic and not a policy audience, and the rhetorical conventions of academic writing virtually ensure that it is inaccessible (and largely incomprehensible) to nonacademic readers. Moreover, most scholars are

reluctant to draw strong policy conclusions from their research, which is rarely designed to answer the questions that policymakers need to answer. The gap between policy analysis and policy is widened further by the ready availability of “research” produced by partisan or advocacy organizations. Much of this work is explicitly or implicitly motivated by the desire to advance particular policy positions and only incidentally, if at all, by scholarly curiosity. The profusion of tendentious “research” reports debases the currency of research in general, as reports and studies are increasingly deployed as ammunition in the service of policy argument and not as guides to deeper understanding of policy issues or sources of insight into effective public policies.

When Policy Analysis Does Have an Impact Under some circumstances, however, policy analysis can have a much larger impact on policy decisions in the education system. In developing countries, for example, the ostensibly research-based policy recommendations of the World Bank and other international agencies often trump the preferences of local governments because they are directly tied to financial resources. Cash-strapped governments in Africa and Latin America adopt the policy preferences of their external partners in exchange for direct or indirect assistance. In the United States., similarly, many state governments and local school districts have signed on to the policy agenda of the U.S. Department of Education in return for funds made available under programs including Race to the Top and the Teacher Incentive Fund. Private foundations have played a similar role in the United States and abroad. The Bill & Melinda Gates Foundation’s enthusiasm for small schools and rigorous teacher evaluation has driven change in school districts and schools across the country. In California, the James Irvine Foundation has spent more than $100 million to support the development and diffusion of the Linked Learning approach to high school reform. When funds are scarce, marginal investments that support specific policy changes—whether or not these are guided by policy analysis—can have an outsized impact.

Bridging the Gap Between Policy Analysis and Policy As noted above, policymakers have no special interest in research. They will make use of findings from

Portfolio Districts

policy analysis when these can help advance goals that are otherwise important to them, but they will make policy decisions with or without research support for their choices. Increasing the impact of policy analysis on policy decisions therefore depends on efforts by researchers to bridge the divide between policy analysis and policy. There are a variety of ways in which this might happen, but one of the most promising is the establishment of specialized intermediary institutions that focus directly on this challenge. Examples include the National Education Policy Center, Policy Analysis for California Education, and the Education Policy Center at Michigan State University. As a general proposition, the mission of these organizations is not to produce additional research but rather to “translate” research findings into language and formats that are accessible to policymakers. David N. Plank See also College Completion; Elementary and Secondary Education Act; Evolution in Authority Over U.S. Schools; Philanthropic Foundations in Education; U.S. Department of Education

Further Readings Plank, D. N. (2012). Minding the gap: Making connections between research and policy-making. In C. Conrad & R. C. Serlin (Eds.), SAGE handbook for research in education: Engaging ideas and enriching inquiry (2nd ed., pp. 43–58). Thousand Oaks, CA: Sage. Reckhow, S. (2012). Follow the money: How foundation dollars change public school politics. New York, NY: Oxford University Press. Sykes, G., Schneider, B., & Plank, D. N. (Eds.). (2009). AERA handbook of education policy research. New York, NY: Routledge. Weimer, D., & Vining, A. R. (2010). Policy analysis: Concepts and practice (5th ed.). New York, NY: Pearson Education.

PORTFOLIO DISTRICTS The portfolio strategy is a new approach to providing public education, with the goal of creating highquality schools and improving student outcomes at scale. Paul Hill and his colleagues at the Center on Reinventing Public Education first coined the term portfolio management in the late 1990s. Districts that use a portfolio strategy are referred to as portfolio

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districts. This entry describes the seven components that define the portfolio strategy and the history of how the portfolio concept emerged. It also summarizes the arguments against portfolio districts and the research to date on outcomes. The portfolio strategy gives schools control of budgeting and hiring and holds them accountable to common performance standards. Several large school districts, most notably New York City and New Orleans, began to implement the portfolio strategy in the early 2000s. As of early 2014, nearly 40 cities are actively pursuing a portfolio strategy, some more deeply than others. Some cities, including New Orleans, have moved to a system made up almost entirely of charter schools. Others, like New York City and Denver, have a mix of charter schools and district-run schools. As discussed in a 2009 report by Hill et al., Portfolio School Districts for Big Cities, Typically, mayors or state officials introduce the idea when they are forced to take responsibility for the schools in a time of crisis. . . . The portfolio idea breaks up myriad political deals among school board members, between the board and unions, and between the central office bureaucracy and schools and neighborhood groups, that held the old system in place. (p. 7)

However, many other districts turn to the idea because they are stuck—they may be seeing some gains in some schools, but they are troubled by stagnant growth and persistently low-performing schools, while their top-performing schools experience long wait lists. Portfolio districts sponsor both traditional schools run by district employees and schools run by independent organizations under different rules. School districts become performance managers, giving school leaders and teachers freedom to operate but holding them accountable for school performance. Portfolio districts work to expand the number of high-performing schools and reduce the number of low-performing ones.

The Seven Components of the Portfolio Strategy Although portfolio strategies differ depending on local circumstances, the complete strategy includes implementation of seven components. The full details of the seven components can be found on the research section of the Center on Reinventing

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Public Education website, under portfolio strategy. The components are 1. good options and choices for all families, allowing families to choose their neighborhood school or another that meets their children’s needs; 2. school autonomy, with principals and teachers deciding how and what to teach; 3. pupil-based funding, with funding tied to the student rather than staff positions; 4. a talent-seeking strategy that develops staff members and recruits employees from a variety of sources, including top school districts, charter schools, and training programs; 5. multiple sources of support for schools, including the school district and private organizations; 6. performance-based accountability for schools, in which schools must meet a common, clearly defined set of performance measures; and 7. extensive public engagement, in which districts detail outcomes for students, explain opportunities for families and staff members, and clearly communicate on their progress.

District leaders implementing a portfolio strategy work to create a process of continuous improvement through which schools can adapt to emerging needs and evidence about effective instruction and services.

How the Portfolio Strategy Emerged The concept of a school district as manager of a diverse portfolio of schools was developed recently. As detailed by Hill et al. (2009) in Portfolio School Districts for Big Cities, the portfolio strategy emerged in the early 21st century as a successor to four reform efforts that had neither fully succeeded nor fully failed: common standards and assessments for all schools, district decentralization to increase schools’ freedom of action, result-based accountability for schools, and increased diversity of public schools via investment in new instructional designs and support of new school providers via chartering. The portfolio district idea incorporates elements of all those ideas, so they work together not separately. A district managing a portfolio of schools serves students by providing schools in many ways— supporting existing schools that serve students well, developing new sources of help for educators in

struggling schools, creating new options for students in schools where student gains have been low for many years, encouraging new schools that incorporate promising ideas about how to meet the needs of a particular group of students, and seeking promising ideas about instruction and school culture from groups both inside the school system and in the broader community (and nationally). Such a district might both operate some schools in the traditional way and sponsor some schools in new ways, via chartering, contracting with independent groups, and increasing freedom of action for educators employed in district schools. (pp. 6–7)

The portfolio strategy is an approach to continuous improvement in which schools are held equally accountable for student achievement, less productive schools and arrangements are ended, and more productive schools and arrangements are sustained or expanded. Portfolios in different locations vary based on local needs, capacity, and experience.

Critiques of the Portfolio Strategy The portfolio strategy has inspired both academic critiques and political opposition. Jeffrey R. Henig has said that while a portfolio strategy may make school districts more flexible and less bureaucratic, it also could result in superficial change without resolving the main problems of delivering education. Also, Henig said, a portfolio strategy carries the risk of empowering private providers while eroding mechanisms for democratic oversight and control. Public opposition to portfolio implementation often comes from local teachers’ unions or community groups that oppose partnerships with charter schools, school closures, and school-based accountability measures.

Research on Outcomes in Portfolio Districts Research on portfolio districts is still emerging and, to this point, has focused more on implementation than on outcomes. Research from New York City has traced improvements in student outcomes back to the district’s portfolio reforms. There is not yet enough broader, systematic research on how portfolio reform strategies compare with more centralized district reform strategies. Research by Hill and colleagues suggests that due to their complex nature, portfolio strategies take time to take root and demonstrate results. Hill and colleagues also point to the need to track intermediate outcomes in portfolio districts, such as the effectiveness of the labor pool and

Present Value of Earnings

the likelihood that students are better off as a result of school closures than staying in a low-performing school. Robin Lake and Christine Campbell See also Accountability, Standards-Based; Central Office, Role and Costs of; Centralization Versus Decentralization; Charter Schools; Educational Innovation; Public-Private Partnerships in Education; School-Based Management

Further Readings Bulkley, K., Henig, J., & Levin, H. (2010). Public and private: Politics, governance, and the new portfolio models for urban school reform. Boston, MA: Harvard Education Press. Center on Reinventing Public Education. (2012). Logic model: How education challenges add up to 7 portfolio components. Seattle, WA: Author. Center on Reinventing Public Education. (n.d.). The portfolio strategy is built on seven key components. Seattle, WA: Author. Retrieved from http://www.crpe.org/research/ portfolio-strategy/seven-components Hill, P., Campbell, C., & Gross, B. (2012). Strife and progress: Portfolio strategies for managing urban schools. Washington, DC: Brookings Institution Press. Hill, P., Campbell, C., Menefee-Libey, D., Dusseault, B., DeArmond, M., & Gross, B. (2009). Portfolio school districts for big cities: An interim report. Seattle, WA: Author. Retrieved from http://files.eric.ed.gov/fulltext/ ED506952.pdf Hill, P., Pierce, L., & Guthrie, J. (1997). Reinventing public education: How contracting can transform America’s schools. Chicago, IL: University of Chicago Press. Lake, R., & Hill, P. (2009). Performance management in portfolio school districts. Seattle, WA: Author. Voices in Education. (2010, November 29). Q&A with Jeffrey R. Henig [Blog post]. Retrieved from http://hepg .org/blog/48

PRESCHOOL

earnings. Income is most commonly measured in annual terms; just about everyone knows their approximate yearly earnings from work. Moreover, just about everyone has at least a vague idea of the average yearly earnings of an occupation besides their own. Thus, the commonly used yardstick for measuring income is in terms of dollars earned annually; however, for some purposes, this annual yardstick is not sufficient. Some examples are weighing the net financial effect of going to college, estimating the economic damages in a wrongful-death lawsuit, and quantifying the value of human capital. The appropriate yardstick for purposes such as these is the present discounted value of expected future earnings over the remaining work career (the present value of earnings for short). Although the PVE is clearly more difficult to measure than annual earnings, conceptually, it is not difficult to construct a reasonable estimate. The basic formula for the PVE is as follows: T

PVE =

PRESENT VALUE

OF

EARNINGS

Present value of earnings (PVE) is defined as the present discounted value of expected future

E

t ∑ t, t =0 (1 + r)

(1)

where t denotes each future year from the current period (t = 0) to the last period of work, Et is the expected earnings in each future year, and r is the annual discount rate (which is assumed to be constant over time). In some applications, such as estimating economic damages in legal cases, expected future earnings are weighted by the expected probability of survival to each future year. As shown in Equation 1, estimation of the PVE requires forecasting future earnings, specifying the expected length of the work career, and specifying the discount rate. The standard procedure for forecasting future earnings requires constructing an age profile of earnings (i.e., average earnings at each age, denoted Ea) and specifying the expected rate of growth in wages, g (which is also assumed to be constant over time). Thus, the formula for the PVE becomes T

See Early Childhood Education

539

PVE =

∑ t =0

Ea (1 + g)t (1 + r)t

.

(2)

The following sections describe the factors that influence the calculation of PVE, such as age profile of earnings, growth in earnings, and discounting. The entry concludes with an example of a common application of PVE.

540

Present Value of Earnings

Age Profile of Earnings Average earnings rise rapidly until about age 40, are roughly constant until the early to mid-50s, and decline rapidly after the mid-50s. This is illustrated in Figure 1. This figure is derived using data from the 2011 American Community Survey under the assumptions that potential work careers begin at age 16 and end at age 75 (2,296,232 observations). Age 16 is a natural beginning point for legal reasons, and age 75 is somewhat arbitrarily chosen to make the average potential work career 60 years (retirement at age 65 and a 50-year potential work career is frequently used instead). By age 76, only 13% of the sample report having earnings (compared with 38% having some earnings at age 66). In some applications, it can be appropriate to calculate the average age-earnings profile of those working. In general, however, it is important to account for periods of unemployment and nonparticipation in the labor market (i.e., time spent in education, child rearing, and retirement).

Growth in Earnings Earnings generally rise over time (more than what is due only to inflation), presumably because technological change makes workers more productive (earnings can also rise over time if hours of work

continually increase). Despite the recent severe recession, average real earnings (i.e., earnings after removing the effect of inflation) in the United States in 2011 were 26.8% higher than in 1975 (derived using data from the 1976–2012 March Social and Economic Supplement of Current Population Surveys). The average annual growth rate in real earnings over this 36-year period was 0.66%. The rates were very different for women and men though—1.90% and 0.08%, respectively. If real earnings are expected to continue to grow in the future, then the expected average earnings of, say, 46-year-old women in 2041 will be greater than for 46-year-old women in 2011 (specifically, if the expected growth rate of real earnings is 0.66% per year, expected earnings will be 21.8% greater in 2041 than in 2011). Applying expected growth in real earnings makes a significant difference in the estimation of lifetime earnings. Figure 2 uses the data shown in Figure 1 and applies a growth rate of 0.66% per year. Thus, this chart represents the expected future earnings of an average 16-year-old American in 2011. The undiscounted sum of the average lifetime earnings shown in Figure 1 is $1,208,560 for women and $2,067,929 for men. These sums shown in Figure 2 are $1,464,654 and $2,535,680, respectively. Interestingly, if real earnings continue to grow at 1.9% per year for

$60,000 Male Female $50,000

$40,000

$30,000

$20,000

$10,000

$0 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74

Age

Figure 1

Average Earnings in 2011

Source: From data in the 2011 American Community Survey. Retrieved from http://www.census.gov/acs/www/

Present Value of Earnings

women and 0.08% per year for men, the expected lifetime earnings of an average 16-year-old woman in 2011 ($2,132,674) would slightly exceed the corresponding figure for a man ($2,119,055). The estimates above are sensitive to the assumed growth rate of earnings. Moreover, there is considerable uncertainty about this value because the rates of earnings growth have varied considerably over time. Real earnings growth is sometimes stagnant for prolonged periods, particularly in the past decade (indeed, average real earnings were lower in 2011 than in 2001). Thus, although the PVE is a relatively straightforward concept, calculating it can be problematic.

Discounting Future earnings are discounted because money received now can be put into financial assets that earn interest, thus yielding more money in the future than the money initially received. This is often referred to as the time value of money. This needs to be accounted for dollar values to be comparable over time, and therefore, future dollars are discounted into the PVE. If one projects the expected real growth rate earnings into the calculation as above, the appropriate

discount rate is a real interest rate, which is the stated interest rate less the rate of inflation. It is also appropriate to use a risk-free real interest rate (rates of return are uniformly higher on riskier assets to compensate for the risk). Although someone might choose to invest in a risky asset (because of the higher expected rate of return), it is inappropriate to impose this choice because people generally perceive risk as a real cost. The rate of return on short-term U.S. Treasury bills is generally accepted as the closest to a risk-free interest rate. As with the expected growth rate of earnings, there is also uncertainty about the value of the risk-free real interest rate (also due to its volatility), and a wide range of values have been used in the economics literature. The most common value used in recent work is probably 3%, which is somewhat higher than historical evidence on the real rate of return on short-term Treasury bills (which, depending on the time period, has generally been about 1.5% or so). A slightly high rate is commonly chosen to make the present value estimates err on the conservatively low side. Table 1 takes the estimates of future earnings shown in Figure 2 and shows the PVE under some different discount rates. The estimates are clearly very sensitive to the assumed discount rate.

$80,000 Male Female $70,000

$60,000

$50,000

$40,000

$30,000

$20,000

$10,000

$0 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74

Age

Figure 2

541

Expected Real Future Earnings for an Average 16-Year-Old in 2011

Source: From data in the 2011 American Community Survey. Retrieved from http://www.census.gov/acs/www/

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Price Discrimination

Table 1

Expected Present Value of Lifetime Earnings (in US$) for an Average 16-Year-Old in 2011 r = 0%

r = 1%

r = 3%

r = 6%

r = 10%

Female

1,464,654

1,098,066

651,226

335,924

167,127

Male

2,535,680

1,867,651

1,070,169

525,961

247,303

Source: From data in the 2011 American Community Survey. Retrieved from http://www.census.gov/acs/www/

Table 2

Expected Present Value of Earnings (in US$) for an Average 16-Year-Old in 2011 Female

Male

Less than high school

216,561

464,847

GED®

346,199

544,572

High school diploma

428,713

753,070

Some college

556,120

943,782

Associate’s degree

696,394

1,062,024

Bachelor’s degree

913,908

1,623,704

Master’s degree

1,119,558

1,918,033

Professional degree

1,782,628

3,083,529

Doctorate degree

1,574,759

2,202,209

Source: From data in the 2011 American Community Survey. Retrieved from http://www.census.gov/acs/www/

Education; Compound Annual Growth Rate; Discount Rate; Foregone Earnings; Human Capital

Further Readings Barrow, L., & Rouse, C. (2005). Does college still pay? Economists’ Voice, 2(4), 1–8. Borjas, G. (2013). Labor economics (6th ed.). New York, NY: McGraw-Hill/Irwin. Gilbert, S. (2011). The value of future earnings in perfect foresight equilibrium. Journal of Forensic Economics, 22(1), 21–41. Heckman, J. J., Moon, S. H., Pinto, R., Savelyev, P. A., & Yavitz, A. (2010). The rate of return to the HighScope Perry Preschool Program. Journal of Public Economics, 94(1), 114–128. Hunt, G., & Trostel, P. (2005). Annual estimates of human capital by state: 1976–2000. Review of Regional Studies, 35(1), 8–37. Trostel, P. (2010). The fiscal impacts of college attainment. Research in Higher Education, 51(3), 220–247.

A Typical Application Table 2 presents a common application of the PVE concept. The table shows the average PVE across different levels of education attainment. These estimates are derived using the data shown in Figure 2, with a real discount rate of 3%, and under the assumption of “traditional” career paths (i.e., the work career is assumed to begin at age 18 for those not completing high school, 19 for GED® and high school graduates, 20 for those with some college but no degree, 21 for associate’s graduates, 23 for bachelor’s graduates, 25 for master’s graduates, and 27 for professional and doctorate graduates). These estimates quantify the significantly higher earnings associated with education attainment after taking into account both the foregone earnings while in school and the time value of money. Philip Trostel See also Age-Earnings Profile; Benefits of Higher Education; Benefits of Primary and Secondary

PRICE DISCRIMINATION Price discrimination is a corporate strategy whereby a seller offers the same product to customers at different prices. This practice allows sellers to appeal to a wide range of customers and capitalize on opportunities to maximize profits. The word discrimination often has a negative connotation. However, in terms of finance, the term discrimination merely denotes how sellers can sway market price to meet the demand of buyers. In the U.S. education context, price discrimination generally is discussed and debated as it applies to the postsecondary level. In higher education, price discrimination denotes a scenario in which academies charge unlike tuition prices to students for the same quality of education. This practice can be done at both university and departmental levels. For price discrimination to occur, the seller must have the ability to adjust price.

Price Discrimination

Price discrimination is also used by a seller offering a product that has a strong consumer demand with few alternatives. This is done because customers are willing to pay more for a given product and its perceived benefits. This entry provides examples of price discrimination in the private sector and in higher education.

Price Discrimination Examples In the private sector, price discrimination is routinely practiced—for example, by the airline industry. Factors such as one-way versus round-trip tickets, duration of stay, promotions, and when the flight was purchased all affect the overall price of tickets. The airline industry itself is able to control the market price for the tickets based on demand and the number of passengers on the flight. Such price discrimination is used to offer incentives to customers to book flights on planes that are not full or during off-peak hours (red-eye flights), as well as to maximize profits on flights that are in higher demand. The number of flights into a given airport also affects ticket prices. For example, a passenger whose destination is a rural regional airport has fewer flight options than the passenger who is flying to a major city. The airline industry is able to charge a higher price per ticket simply because the buyer has fewer options. Another example of price discrimination in the private sector is the cost of a movie ticket. Afternoon shows are cheaper because ticket demand is lower, and perhaps because customers have less money as they are not at work during the day. The theater owner adjusts the market price of the tickets because he or she is willing to make less money per ticket instead of no money at all. By contrast, evening and weekend shows have a greater ticket demand. The theater owner can then raise the market price to accommodate the increased demand, time preference, and perhaps higher disposable income of customers. The elasticity of demand allows the theater owner an opportunity to maximize profit and to appeal to a wider range of customers as well.

Price Discrimination in Higher Education Universities engage in a similar type of price discrimination, charging different rates based on state residence, perceived quality of education, and type of degree program. Larger state schools have a greater number of applicants applying for admission annually than smaller schools. Large state schools also have the resources and ability to attract students.

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Because demand for admission is high, students are more willing to pay more for tuition because of the superior perceived quality of education they will receive. When smaller and less competitive schools are competing for the same high-quality students as larger institutions, they may offer lower tuition rates (through scholarships) as a way to attract highly qualified prospective students. Moreover, within different universities, different degree programs will adjust the market price of tuition to meet the demand, or lack of demand, in order to maximize profit returns. Outstanding state universities also have the ability to attract quality students from other states. Consequently, they are able to charge out-of-state residents a higher tuition rate because the student is able to justify paying a greater amount of money for a quality education. Private universities also have the ability to exercise price discrimination in terms of tuition cost. For example, religiously affiliated institutions may offer lower tuition rates to students who are of the same faith as the religious sponsor of the university. Students of other faiths who choose to attend such a university show a willingness to pay a higher rate of tuition because of the perceived benefit of its high quality of education. Luke J. Stedrak See also Access to Education; Benefits of Higher Education; Capitalist Economy; Higher Education Finance; Tuition and Fees, Higher Education

Further Readings Doti, J. L. (2004). Is higher education becoming a commodity? Journal of Higher Education Policy and Management, 26(3), 363–369. Lawson, R., & Zerkle, A. (2006). Price discrimination in college tuition: An empirical case study. Journal of Economics and Finance Education, 5(1), 1–7. Lovenheim, M. F. (2011). The effect of liquid housing wealth on college enrollment. Journal of Labor Economics, 29(4), 741–771. Morrison, R. (1992). Price fixing among elite colleges and universities. University of Chicago Law Review, 59(2), 807–835. Mumper, M. (2001). The paradox of college prices: Five stories with no clear lesson. In D. E. Heller (Ed.), The states and public higher education (pp. 39–63). Baltimore, MD: Johns Hopkins University Press. Rolfe, H. (2003). University strategy in an age of uncertainty: The effect of higher education funding on

544

Principal-Agent Problem

old and new universities. Higher Education Quarterly, 57(1), 24–47. Rothschild, M., & White, L. (1995). The analytics of the pricing in higher education and other services which the consumers are inputs. Journal of Political Economy, 103(3), 573–586. Vedder, R. (2004). Going broke by degree: Why college costs too much. Washington, DC: AEI Press.

PRINCIPAL-AGENT PROBLEM The principal-agent problem refers to a dynamic between principals (those who need work accomplished) and agents (those contracted to carry out the work). Traditionally, principal-agent theory has been used in the field of economics to describe the relationships between boards of directors and executives at firms. The applicability of this theory has recently been expanded to other fields, including education policy, particularly as a framework underpinning performance pay for district superintendents, school leaders, and teachers. This entry discusses the assumptions of the economic theory behind the principal-agent problem and provides examples of its use in current education policy debates.

Key Assumptions The conventional principal-agent model has four primary assumptions, which are discussed in turn below. The degree to which these assumptions hold in reality varies according to the context in which the model is being applied. To begin, the first assumption is that both principals and agents are rational actors seeking to maximize their own utility. The goal of principals is to have their work accomplished to standard and efficiently with minimal costs. At the same time, agents have the incentive to complete the work sufficiently well to maintain the contract but without any additional effort or resource expenditure. The second assumption involves the potential misalignment of goals between principals and agents. This might be expected since both actors are assumed to be self-interested. Problems can arise for principals who incur agency costs when there is misalignment between the goals of the two parties. These costs include those associated with employing monitoring mechanisms.

Third, it is assumed that there is information asymmetry between the principal and the agent when the agent has specialized skill or knowledge that the principal lacks. This asymmetry can contribute to the barriers that principals have in monitoring the agent’s efforts and true costs, and gives the agent a relational advantage. Moreover, when combined with the second assumption about the likelihood of goal misalignment, this information asymmetry can give rise to moral hazard. Moral hazard occurs when agents, whose effort level is hidden from principals, do not act in good faith, thereby increasing the risk to the principals of nonperformance. The final assumption of the basic principal-agent model is that principals can set contractual provisions unilaterally. Under these occupational circumstances, the principal will have tremendous power, which can to some extent be employed to balance out the agency costs described above.

The Education Policy Context In the past two decades, scholars have begun to propose revisions to the conventional principal-agent model. One key revision examines the notion that there is a single principal and single agent involved in the transaction. In the education policy context, this revision is useful when considering that there are often multiple principals and that sometimes principals also play the role of agent. For example, school leaders act as agents when they are implementing the directives of their principals—school boards— but they act as principals in monitoring the behavior of teachers, the frontline agent in the education context. The fact that there are multiple principals and agents playing multiple roles can exacerbate the goal misalignment problems in these relationships. These problems can be made even worse because, in the public education policy context, there exist a diversity of mandates from multiple policy principals with varying levels of authority. Traditionally, principals have sought to mitigate the potential for moral hazard through setting of incentives whereby agents share in the risk of nonperformance. These incentives can come in the form of bonuses. In factory settings, for example, principals often pay agents for piecework. In the education policy context, school boards (principals) have historically attempted to address monitoring and information asymmetries, and the associated moral hazard, by regulating inputs. For example, public school teachers were required

Private Contributions to Schools

to complete a state-authorized licensure program. More recently, however, policymakers have begun to use outcome-based incentives, such as pay for performance. Over the past 50 years, teachers have been paid according to the single salary schedule. School leaders have largely had their compensation based on these schedules as well. Under these compensation schemes, teachers and school leaders, acting as agents for school boards, have been rewarded equally regardless of their effectiveness. The risk that students will not be served to the level desired by the school boards has largely been borne by these principals who pay educator salaries, and not their agents, the teachers or school leaders. The idea behind these educator compensation reforms is that the policy principal can signal to the agent about the priorities that matter most by providing explicit rewards for certain behaviors. For example, if school boards have the goal of seeing student achievement improve, they can create incentives for school leaders and teachers to maximize their discretionary effort and to devote their resources to the goal of the policy principals (the school board). Not only is a mechanism built in to monitor agent behavior, but the agent also then begins to share in the risk. As a result, the potential for moral hazard is reduced. A central feature of using pay for performance as a policy solution to resolve the principal-agent problem in education has been the use of student academic test scores. By tying higher levels of compensation to increases in test scores, education policy principals (i.e., school boards) are attempting to insert a transparent apparatus for monitoring agent behavior. The success of adopting these types of compensation structures to solve the principal-agent problem is complicated because in the education policy context, the fourth assumption of the model often does not hold. Particularly in some collective-bargaining states, in which state laws either require or allow unions to negotiate the terms of teacher compensation, principals do not have the power to set the contractual provisions unilaterally. The negotiations between teacher unions and district officials over the use of pay for performance for teacher compensation have often been contentious, particularly regarding the role of standardized academic test scores as the mechanism for measuring the value of a teacher. Marc J. Holley

545

See also Agency Theory; Moral Hazard; Pay for Performance; Theory of the Firm

Further Readings Goldhaber, D., DeArmond, M., Player, D., & Choi, H. (2008). Why do so few public school districts use merit pay? Journal of Education Finance, 33(3), 262–289. Miller, G. J. (2005). The political evolution of principalagent models. Annual Review of Political Science, 8, 203–225. Waterman, R. W., & Meier, K. J. (1998). Principal-agent models: An expansion? Journal of Public Administration Research and Theory, 8(2), 173–202.

PRIVATE CONTRIBUTIONS TO SCHOOLS Public schools in the United States have long relied on private contributions to augment school resources. These private contributions include both in-kind contributions, such as donations of goods and services, and monetary contributions, such as direct financial donations. Public education is by definition maintained at public expense, through federal, state, and local tax revenues, for all children without charge. At the same time, school systems in recent decades have been pursuing private contributions with increased sophistication and purpose. As documented by Ron Zimmer and colleagues, the impetus for greater levels of private support comes largely from the convergence of changes in states’ school finance systems and state budget environments that have threatened K-12 spending. Since the 1970s, a majority of states have faced legal challenges to their school finance systems on the grounds that their systems have failed to provide adequate and/or equitable funding. In response, school funding in many states has moved away from its traditional dependence on local property taxes and toward greater dependence on state support. At the same time, states have faced taxpayer revolts of varying magnitude in the form of tax rollback initiatives, tax limitation measures, and spending caps. And as support for education has shifted to the states, difficult economic times of varying intensity in different states have meant lower tax revenues and tight budgets for public education. This move to state financing of education comes at a time when governance reforms, such as site-based management

546

Private Contributions to Schools

and charter school reforms, are pushing for more educational decision making at the local level. Private funding provides a way to increase flexibility in how educational funds are spent and allows families to match their willingness and ability to pay for education with their preferred level of education for their children. This entry explores what we know about private contributions to public schools, issues raised by private contributions, and recent initiatives to change the dimensions of private contributions to schools as well as extend their reach.

Documenting Private Contributions to Schools Private givers to schools are as diverse as are their forms of giving and the organizations through which their giving is funneled. Parents have always been a key source of giving to public schools. Parental contributions include monetary donations through fundraisers and direct donations, as well as contributions of time for organizing events and fundraisers or supporting teachers in classrooms. In a pilot study of private resources in public schools, Ron Zimmer, Cathy Krop, and Dominic Brewer found that while parental involvement was the most common form of “giving” to public schools, other contributors play a significant role in providing both monetary and in-kind support. Local businesses provide a variety of support to public schools, offering donations of everything from meeting space to school supplies, student mentors, and gift certificates to schools in their own cities and in close proximity to their business locations. Corporations are also key givers to public education, with the resources both to help finance school programs and to act as advocates for specific policies at the national and local levels. Large philanthropic foundations provide significant financial contributions to public education through partnerships, grants, and award programs. Other organizations including community-based organizations and colleges and universities offer a host of academic, social, and vocational services to schools. Generally, private contributions are channeled through a school- or district-based organization. Parent-Teacher Associations (PTAs), part of a national association, are the most common schoolbased organization, with about 23,000 local chapters throughout the United States; they typically focus on national or state educational issues and are service organizations. Other groups include Parent-Teacher

Organizations (PTOs) and Booster Clubs, which are generally not affiliated with a national organization and are generally fundraising organizations serving an individual school. A fourth type of organization that has recently come into prominence is the local education foundation (LEF). LEFs are tax-exempt, nonprofit organizations generally operating at the district level but independent from the school districts they serve and tending to focus on a smaller number of donors giving larger amounts of money. Their structure and position outside the public school system allow them to write grants, secure donations of services or funds, and establish districtwide programs. Although the largest growth of these foundations occurred after 1989, the boom started earlier in some states that had newly approved, restrictive property tax measures. In California, for example, the number of LEFs doubled in the state between 1978, following the passage of Proposition 13, and 1980—from 22 to 46. The California Consortium of Education Foundations estimates that there are at least 650 educational foundations currently operating in the state. Data on the types and quantity of private contributions to schools are not systematically collected. And in-kind support is inherently harder to quantify than monetary support. Much of what we know about private contributions to education comes from anecdotal reports, often in the news media. Schools and school districts generally do not report private contributions in their official budget documents, and even when they do, these are not reported as a distinct source of revenue. However, recent research literature has focused on monetary contributions raised by local education foundations and other school-based organizations. Nonprofit organizations with tax-exempt status are required to report their revenues and expenses to the state and federal governments. Using these data as a base, for example, Eric Brunner and Jennifer Imazeki reported that contributions from LEFs, PTAs/PTOs, Booster Clubs, and Urban Foundations to California’s public schools grew sharply between 1992 and 2001, from $123 million to $238 million in constant 2001 dollars. More recently, the Public Policy Institute of California, based on an analysis of tax filings, reported that California K-12 foundations, PTAs, and Booster Clubs raised about $1.3 billion in 2007, up from $70 million in 1989. While LEFs and other school fundraising organizations are clearly an important component of private support, they do not alone

Private Contributions to Schools

present, and current data do not allow, a comprehensive and systematic picture of private contributions to public education.

Issues Raised by Private Contributions to Schools There are both opportunities and concerns raised by private contributions to public schools. Private contributions help schools and districts to purchase and maintain programs that would not otherwise have been possible. They also give communities flexibility in educational spending decisions and are one of the few instruments available to parents trying to obtain a higher quality of education for their children. Private contributions, whether in-kind or monetary, also foster relationships between schools and their communities. At the same time, they raise concerns about both the growing dependence on private donors for day-to-day operations of schools and inequities in private contributions that may exacerbate the inequities that already exist across schools. Brunner and Imazeki examined the relationship between family income and school-level monetary contributions per pupil, summarizing the distribution of contributions per pupil among elementary and middle schools by quintiles of family income. They found that all schools clearly do not benefit from these private contributions. For example, only 0.4% of schools in the lowest income quintile were able to raise more than $100 per pupil, while 23.3% of schools with an average family income of $86,321 or more raised more than $100 per pupil. At the same time, the researchers concluded that, overall, contributions have not led to large inequities in the distribution of resources among high- and low-income schools. Schools raising particularly high levels of contributions, more than $500 per pupil, are rare. While it is true that a small number of schools raise extraordinarily large amounts, the vast majority of students attend schools where private monetary contributions are small and have almost no effect on inputs. Looking at another aspect of private contributions to schools, Kathleen Herrold and Kevin O’Donnell documented parent participation in school-related activities. The Parent and Family Involvement in Education Survey of the National Household Education Surveys Program provides a look into parent in-kind contributions to schools. Using these data, Herrold and O’Donnell found that 26% of parents in households with incomes below

547

the poverty threshold, as determined by the federal government, volunteered in their child’s school or served on a school committee compared with 51% of parents in households with incomes above the poverty threshold. Similarly, 45% of parents below the poverty threshold participated in school fundraising compared with 70% of parents above the poverty threshold. This research focuses on the distribution of private contributions to schools, not on their effect on school outcomes. Private contributions to schools may affect student performance not only because of the resources they buy directly but also because they represent the involvement of the local community. The question of how private contributions affect student performance remains for future work.

Looking Ahead There are current initiatives to change the dimensions of private contributions to schools as well as extend their reach. Across California, for example, districts are considering whether to centralize their fundraising and distribute it more evenly among their schools. This creates a way for school boards and foundations to look across school needs. The equity-promoting standards released by the National Commission on Civic Investment in Public Education, for example, suggest, at a minimum, that private giving be aggregated across schools and shared equally across an entire school district. It remains to be determined if centralizing and distributing funds across a larger unit than the school would take away parents’ and community members’ incentives to give, as some suggest. On an international level, there are calls to boost private contributions to education, in part to help countries reach by 2015 the six Education for All goals established in 2000 through a global movement led by UNESCO (United Nations Educational, Scientific and Cultural Organization). In a recently released policy paper, UNESCO argued that private sector education funding has been stagnant, lags far behind private sector contributions in other global development sectors, such as health, and represents only 5% of all aid to education. Recommendations for improving and increasing private sector contributions for education include calls for private organizations to provide sufficient funding over several years to ensure sustainability of initiatives, better alignment of private support with government priorities and countries’ needs, and better evaluations

548

Private Fundraising in Postsecondary Education

of the impact of private sector contributions to education. Cathy Krop See also Education Finance; Expenditures and Revenues, Current Trends of; Fiscal Disparity; Parental Involvement; Philanthropic Foundations in Education; School District Budgets; School Finance Equity Statistics

Further Readings Addonizio, M. F. (1999). New revenues for public schools: Alternatives to broad-based taxes. In W. J. Fowler (Ed.), Selected papers in school finance, 1997–99 (pp. 89–110). Washington, DC: U.S. Department of Education, National Center for Education Statistics, Office of Educational Research and Improvement. Brunner, E., & Imazeki, J. (2003, September). Private contributions and public school resources. San Diego, CA: San Diego State University, Department of Economics, Center for Public Economics. Herrold, K., & O’Donnell, K. (2008). Parent and family involvement in education, 2006–07 school year, from the National Household Education Surveys Program of 2007 (NCES 2008–050). Washington, DC: U.S. Department of Education, National Center for Education Statistics, Institute of Education Sciences. Merz, C., & Frankel, S. S. (1995). Private funds for public schools: A study of school foundations. Tacoma, WA: University of Puget Sound. United Nations Educational, Scientific and Cultural Organization. (2013, January). Private sector should boost finance for education (Education for All Global Monitoring Report, Policy Paper No. 05). Paris, France: Author. Zimmer, R., Krop, C., & Brewer, D. J. (2003, Spring). Private resources in public schools: Evidence from a pilot study. Journal of Education Finance, 28(4), 484–522. Zimmer, R., Krop, C., Kaganoff, T., Ross, K. E., & Brewer, D. J. (2001). Private giving to public schools and districts in Los Angeles County: A pilot study. Santa Monica, CA: RAND Corporation.

PRIVATE FUNDRAISING IN POSTSECONDARY EDUCATION Private fundraising is the process of soliciting voluntary contributions from individuals, foundations, businesses, or other nongovernmental sources to supplement the revenue of the recipient public or

private institution, for example, community college, college, or university. Fundraising from private rather than government sources for education is part of the broader concept of philanthropy. Other terms used in postsecondary education to describe the philanthropic process include development and institutional advancement. These terms refer to much more comprehensive approaches to supporting and growing the institution through activities beyond fundraising. This entry covers private fundraising in postsecondary education, focusing on its history, purpose, and magnitude; federal tax benefits; and the nature of the work involved.

Magnitude of Private Fundraising in Postsecondary Education Voluntary contributions from private, nongovernmental sources to public and private nonprofit postsecondary institutions in the United States amounted to $31 billion in fiscal year 2011, according to the National Center for Education Statistics (NCES). This amount has increased over the years with fluctuations due to changing economic conditions. When measured in dollars adjusted for inflation, total voluntary support increased from $2.3 billion in 1949 to $31.2 billion in 2010, an increase of 1,253% according to NCES data. This level of growth can be attributed to population growth, increased numbers of college graduates, an expanding national economy, and favorable tax laws. Private, nonprofit postsecondary institutions rely more heavily on private fundraising than do public institutions and generally raise more than twice the funds in the aggregate than do their public counterparts. The magnitude of private fundraising for both types of postsecondary institutions is impressive and is an important revenue source regardless of institutional control. The magnitude of private fundraising in postsecondary education also is shown in the market value of the endowments of all institutions, which was reported by the NCES as $415.7 billion in 2011. According to the National Association of College and Business Officials, endowments are the permanent funds of a nonprofit institution, consisting of cash, securities, or property, with the investment income used to help finance the ongoing operations of the institution. Donors make gifts of cash and pledges, in-kind tangible items of value, stocks and bonds, real estate, and personal property, such as works of art

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and books. Through estate planning, donors can include gifts to the institution through wills, trusts, annuities, life insurance, savings, or real estate. Some gifts are restricted for a specific use, while others are unrestricted and can be used for any legal purpose determined by the institution’s governing board or administrative officials. Gifts can be designated for current use or for the institution’s permanent endowment, whereby a portion of the annual investment earnings can be used. Based on NCES data, nearly 60% of private gifts are used for current operations, including student scholarships, faculty support, athletics, program enhancements, and in-kind gifts, while the remaining 40% are used for construction of new facilities or major renovations. Private gifts come from several sources, including alumni, nonalumni individuals, corporations, foundations, and religious organizations. Successful private fundraising for educational institutions is not limited to the United States, as evidenced by a study in the United Kingdom reporting that in 2011–2012, the higher education sector secured total new funds of £774 million. According to the study, the University of Cambridge and the University of Oxford completed billion-pound fundraising campaigns. These data illustrate the growing importance of private fundraising internationally. From a historical perspective, some wealthy donors in England provided the earliest contributions of money and goods for the emerging colleges in the American colonies.

Federal Tax Benefits The federal government, through several provisions in the U.S. tax code, has provided economic incentives for voluntary gifts to higher education. Section 170 allows an income tax deduction for any charitable contribution to or for the use of a qualifying educational institution, subject to certain limitations. Private colleges and universities and the foundations of public institutions qualify for tax-exempt status under the provisions of Section 501(c)(3) of the tax code. An educational organization described in the tax code generally is exempt from federal income tax on contributions received, income from activities that are substantially related to the purpose of the organization’s tax exemption, and investment income. The tax code has complex rules governing the tax-exempt status of charitable organizations and for the charitable contributions deduction.

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History of Private Fundraising for Postsecondary Education Private fundraising for postsecondary education in the United States has played an integral role in the general development and expansion of higher education since the creation of the first college in the American colonies. John Harvard’s deathbed bequest in 1638 of half of his estate and the books of his library to the college established in Cambridge by the General Court of the Massachusetts Bay Colony 2 years earlier is generally considered to be the first private gift to an American college. History is replete with examples of colleges being named after generous donors. Merle Curti and Roderick Nash, in their seminal history of philanthropy in American higher education, noted that these and other gifts were not only critical to the survival of the colonial colleges but they also helped establish techniques of fundraising and a habit of philanthropy that have continued to the present time. Private giving to postsecondary institutions has evolved over time and has gone through various phases generally associated with social and economic changes in the United States that gave rise to more substantial gifts from individuals of wealth or from emerging private foundations endowed by wealthy industrialists in the late 19th and early 20th centuries. Many of the early gifts to colleges were made for narrow purposes such as constructing buildings or providing for current expenses. Later gifts, particularly in the period after 1865, tended to be larger in scale and were used in a more transformative fashion, for example, for expanding educational opportunities for women and African Americans and enhancing the academic and operational standards of the recipient institutions. In the early and mid-20th century, new trends emerged with the organization of alumni, increasing appeals to individuals having no previous connection to an institution, appeals to donors on behalf of the growing athletic programs, and the beginning of gifts from businesses and corporations. The institutionalization of organized fundraising in colleges has been associated with the growing number of alumni who began to raise money on behalf of their alma mater. To take advantage of emerging alumni organizations, colleges hired professional fundraisers and created offices on campuses devoted to these efforts. Intercollegiate athletic programs began to engage in their own fundraising efforts, with the effect of linking individuals to the

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institutions. Business corporations and their related philanthropic foundations emerged as another important source of private revenue for postsecondary institutions. Changes in federal and state tax laws fostered corporate giving to postsecondary education by allowing corporations to deduct a certain percentage of their net taxable income for charitable contributions to higher education. Even with this tax advantage, as some have suggested, many stockholders and executives were ambivalent, since the prevailing attitude was that any corporate gift should have a direct benefit for the company and should not be for general educational purposes. Motivation notwithstanding, corporate giving to postsecondary institutions has become an important source of private revenue. By the end of the 20th century, private fundraising was a critical part of the funding model for private and public institutions. Several trends were apparent, including the growth and sophistication of the fundraising apparatus of institutions; the growing ambition of fundraising goals, including billion-dollar campaigns; and the large-scale gifts of wealthy entrepreneurs—often called transformational donors—who approached giving with a more active engagement in the process and a focus on the results of their gifts. When considered in its historical context, philanthropy has influenced American postsecondary education in many ways. While historically some donors intervened in academic affairs by influencing personnel decisions or using money as leverage for the promotion or restriction of certain ideas, such cases are probably the exception and not the rule. Private gifts to postsecondary education have been and will continue to be an important revenue source for postsecondary institutions.

The Work of Private Fundraising In postsecondary fundraising, one must take into account the sequential nature of the process, with initial attention to identifying likely donors and understanding their motivations to give. Postsecondary donors include corporations, foundations, and individuals. Corporate donors support higher education for a variety of reasons that stem from an enlightened self-interest in which the corporations receive something in return, such as a positive corporate image and publicity, a tax advantage, or workforce development training programs that

benefit the corporation’s industry. Corporations also may support educational institutions in close proximity to their local operations or those in which their employees or the employees’ dependents enroll. Foundations are motivated to make donations by several factors: to provide community support, address the foundation’s sociopolitical concerns, continue a historical role, and/or launch a start-up project. Private foundations that are recognized by the tax code as 501(c)(3) tax-exempt organizations are required to pay out 5% of their assets each year to maintain their tax-exempt status, which is a significant tax benefit motivation. A 2012 Bank of America study of high-net-worth philanthropists noted that, except for the very highest levels of income and wealth, American families— at all levels of income and wealth—contribute roughly equal percentages of their income to charity; of note, 80% of the respondents gave to education. Research findings suggest that there is a significant opportunity for higher education to increase total philanthropic gifts by targeting high-net-worth individuals. To develop this opportunity, institutions must enhance the strength of the individual prospect’s social relationship to individuals within the organization as well as the prospective donor’s integration into the institution. Strategic management of these relationships directly benefits fundraising success: A highly engaged donor is more likely to become the highest dollar amount donor. This opportunity must be managed closely. Due to multiple opportunities to support charitable organizations, individual donors—especially those of significant wealth and income—expect more from their philanthropic participation than traditional donors and may wish to influence organizational operations. Postsecondary institutions worldwide are seeking additional sources of funding, and alumni offer a significant funding opportunity. Alumni often have a natural affinity for the schools they attended and will provide support through service and financial contributions when given the opportunity. Private, 4-year higher education institutions have a long history of successfully capitalizing on alumni support and legacy or intergenerational alumni relationships, while public institutions tended not to treat alumni with any special deference until state funding was significantly reduced in recent decades. Four-year public institutions created alumni associations that were separate functions from the academic organization

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to build networks and relationships, but community colleges have lagged far behind in both creation of alumni associations as well as investment in alumni resource development. Community colleges’ alumni programs have struggled for a number of reasons, including very small staff, poor contact record keeping, lack of support from the institutional leadership, and lack of alumni engagement. Alumni giving has increased at 2- and 4-year institutions, but the giving level is institution dependent. Scholars note that alumni donors from both 2- and 4-year institutions are older and wealthier and report having a positive student experience. Employees and former employees have a natural and close connection to the institution and, as a group, are likely to support its mission and understand its most critical needs. Current employees are typically encouraged to make multiyear pledges and donate through payroll deduction. The amount of employee giving is heavily institution dependent, and additional research in this area could be done to capture more information about this unrecognized source of funding. Effective fundraisers identify the confluence of mutual needs and interests of the donors, the institution, and the community. There are many reasons motivating donors to give, including efforts to end a disease through research, to gain recognition, compassion, and tax benefits. Wealthy individuals are often motivated to make substantial gifts because of the sense of personal fulfillment and satisfaction they experience from the act of donating itself or because they can see the impact and outcomes of their philanthropic activity. These donors often provide annual gifts on an ongoing basis. In their study of the motivations of community college donors, Linnie S. Carter and Molly H. Duggan indicated that the primary motivations were “doing good” in their community, returning the goodwill they had received, and continuing a family tradition of giving.

The Fundraising Process The fundraising process is implemented in sequential stages that begin with research about the institution itself, its environment, and its donors and potential donors. Next, objectives are developed based on the institution’s mission and goals as well as the research findings. Programming and donor cultivation activities engage donors through communication and

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information sharing as well as positive media relations, which help build relationships with the institution and its leaders. When a gift is solicited and made, fundraisers engage in stewardship to nurture the donor relationship by monitoring the use of the gift and communicating the long-term impact of the gift to the donor. According to Kay Sprinkel Grace and Alan Wendroff, the fundraising cycle is best expressed as an ongoing “transformational infinity loop” in which the donor identification, cultivation, gift, and stewardship process is unending. Who Does the Work?

The institution’s president is the leader of the fundraising team and is responsible for articulating the vision of the institution and the case for support, setting institutional priorities, creating a climate of institutional support, serving as an example by making his or her own significant gifts, empowering constituents, cultivating and soliciting gifts, and supporting the plans and implementation strategies of the development team. Foundation trustees have multiple responsibilities, including taking an active role in identifying, cultivating, soliciting, and stewarding donors. Under the president’s leadership, fundraising is implemented by the fundraising staff, which is uniquely positioned: half in the academic world and half in the community. Whether the fundraising staff is centralized or decentralized depends on the institution, although most community college fundraising teams are small and centralized. A decentralized model is common in 4-year colleges and universities because of the manner in which fundraising began on these campuses: Deans and faculty members began raising funds for specific projects in their units. In the decentralized model, deans have an elevated fundraising responsibility. Typically found at a large university, a centralized resource development model is led by a vice president, and each college may have at least one development officer housed within the unit who is responsible for the unit’s fundraising. In this model, unit fundraisers are typically supported by a central development operation that may include research, planned giving, corporate and foundation giving, and annual giving. Regardless of organization, the chief development officer must work closely with the president and deans. In a 2012 survey of CEOs and chief fundraisers, the Council for the Advancement and Support

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of Education identified three keys to success in this relationship: (1) open communication, (2) collaboration, and (3) respect. Scholars have identified three key roles for professional fundraising staff: (1) educator, (2) impresario, and (3) fundraising consultant. The duties and tasks of the lead fundraising officer have been noted by researchers and include the following: identifying unmet institutional needs and opportunities, locating private sources of donations, making or facilitating gift requests, motivating key institutional personnel, and managing investments. Significant differences exist in fundraising at 2- and 4-year colleges and universities, the most notable being the number of fundraising staff. Fouryear institutions hire numerous development staff to serve in alumni affairs, planned giving, corporate and foundation relations, and giving of major gifts, while 2-year institutions commonly have one or two full-time development staff responsible for all fundraising activities. The number of development staff is directly related to the funds raised: Larger staffs resulted in increased total dollars raised. How Are the Funds Used?

Colleges and universities ask donors to fund student scholarships, new or renovated campus buildings, faculty research and teaching, and a wide variety of programs and projects that are not funded through state funding allocations or tuition. Trends

The potential for growth in alumni support is an elemental strength of the public university as well as the 2-year community colleges because of the number of graduates from these public institutions. Engaged alumni could significantly increase their giving patterns if institutions implemented best practices for alumni fundraising, such as obtaining the college leadership’s full support to incorporate alumni relations into the college’s business plans and ensuring accurate contact information for critical alumni communication. Scholars argue that there is a “new generation” of megagift donors who are interested in making a breakthrough that could improve society. These donors want to actively engage in the gift’s implementation and make sure that institutions adhere to their intentions. Megacampaigns that raise hundreds of millions or billions of dollars abound. Harvard University announced a $6.5-billion campaign late in 2013. According to Thomas Bakewell, these

exceptionally large campaigns require many elements to be successful—from an exemplary vision to institutional buy-in—but experienced presidential and fundraising leadership is critical. William E. Sparkman and Paula Lee Hobson See also Community Colleges Finance; National Center for Education Statistics; Philanthropic Foundations in Education; Private Contributions to Schools; University Endowments

Further Readings Bakewell, T. (2006). Mega campaigns for colleges and universities: Achieving success. International Journal of Educational Advancement, 6(3), 253–257. Bank of America. (2012). The 2012 Bank of America study of high net worth individuals: Issues driving charitable activities amount wealth households. Charlotte, NC: Author. Brumbach, M. A. (2006). The chief development officer: A job analysis (New Century Series, Resource Paper No. 13). Washington, DC: Council for Resource Development. Carter, L. S., & Duggan, M. H. (2010). Philanthropic motivations of community college donors. Community College Journal of Research and Practice, 35(1–2), 61–73. Coutinho, S., & Toomse-Smith, M. (2013). Giving to excellence: Generating philanthropic support for UK higher education 2011–12. London, UK: NatCen Social Research. Retrieved from http://www.philanthropyimpact.org/sites/all/files/downloads/ross-case_ survey_11-12_report_final.pdf Curti, M., & Nash, R. (1965). Philanthropy in the shaping of American higher education. New Brunswick, NJ: Rutgers University Press. Grace, K. S., & Wendroff, A. L. (2001). High impact philanthropy: How donors, boards and nonprofit organizations can transform communities. New York, NY: Wiley. National Association of College and University Business Officers. (2013). 2012 NACUBO: Commonfund study of endowments. Washington, DC: Author. Snyder, T. D., & Dillow, S. A. (2013). Digest of education statistics 2012 (NCES 2014-015). Washington, DC: U.S. Department of Education, National Center for Education Statistics, Institute of Education Sciences. Retrieved from http://nces.ed.gov/pubs2014/2014015.pdf U.S. Congress, Joint Committee on Taxation Report. (2012). Background and present law relating to tax benefits for education (JCX-62–12). Retrieved from https://www.jct.gov/publications .html?func=startdown&id=4474

Private School Associations

PRIVATE SCHOOL ASSOCIATIONS In the United States and internationally, numerous organizations of private schools and colleges exist. These associations, which represent member schools, can serve a wide variety of functions for their constituents. Nearly all private school associations sponsor professional conferences and other services, including publications and online resources, to share best educational practices. Many also act as clearing houses for data and information about their sector of the private school universe, sharing data not only with the members but also with the researchers and the general public. In addition, some associations engage in advocacy and lobbying activities as well as provide accreditation and teacher certification services for member schools. Some also provide administrators of member schools with legal education and even offer legal representation to their member schools. It should be noted that such accreditation and certification activities are often directed solely at internal constituents such as parents, schools, and affiliated denominational colleges and are often not equivalent to nor accepted as alternative to regional accreditation and state teacher certification. Religious school associations are also active in providing approved curricula and curricular materials to private schools. The sections that follow provide background on the major associations.

Notable Associations Numerous private school associations exist in the United States and North America, as well as internationally in countries with extensive and well-developed private education sectors. Although it is not within the scope of this article to address the full galaxy of private school associations, it is possible to identify certain leading groups that represent the range of such organizations. National Association of Independent Schools (NAIS)

NAIS was established in 1962 through the merger of two predecessor organizations and today serves 1,700 independent day and boarding schools worldwide. The focus of NAIS is to represent and serve American and other independent schools, meaning nonprofit, private college preparatory schools that are generally nondenominational or have a limited

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and nonexclusive religious character. NAIS emphasizes professional development and support for independent schools and their professionals, data collection, and information dissemination. NAIS is particularly active in training current and aspiring independent school heads. NAIS does not provide accreditation and certification services and requires its member schools in the United States to possess appropriate regional certification, although NAIS does also adhere to a statement of Principles of Good Practice. NAIS headquarters are located in Washington, D.C. Catholic School Associations

Catholic schools, except for those run as independent schools, are often under the primary control and leadership of regional dioceses and/or specific Catholic religious orders. For example, the Catholic Schools Accreditation Association located at Fordham University provides accreditation services for all Catholic schools in New York in association with the New York Archdiocese. Nonetheless, several key national Catholic school organizations do exist. The National Association of Private Catholic and Independent Schools (NAPCIS) represents smaller private Catholic schools that are organized outside of the Catholic Church hierarchy. NAPCIS provides accreditation and certification services primarily to non-Diocesan schools that teach within the Catholic tradition. NAPCIS is especially focused on assisting with the start-up and creation of new independent Catholic schools in the United States as well as providing curriculum assistance to member schools. NAPCIS’s offices are located in Sacramento, California. Although not an actual association of schools, perhaps the most influential organization in the Catholic school universe is the National Catholic Education Association (NCEA). NCEA acts as a professional organization for more than 200,000 Catholic school teachers, administrators, and staff members, including some in Catholic higher education, as well as their schools (see the Association of Catholic Colleges and Universities), and emphasizes the professional development of its members. However, it also serves Catholic schools in general through national marketing efforts. NCEA does not engage in lobbying activities and does not provide accreditation or certification programs, although it does administer assessments of Catholic educational effectiveness, such as ACRE (Assessment of

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Catachesis/Religious Education). NCEA headquarters are located in Arlington, Virginia. Evangelical Protestant Associations

As is characteristic of evangelical Christianity in general, evangelical Protestant schools are represented by a plethora of organizations, especially within the nondenominational realm. Unlike other private school associations, many of these organizations reject regional accreditation, where legally permissible, and state teacher certification, emphasizing their religious doctrines and views. Often, these associations openly reject secularist curricula and see themselves in direct opposition to the curriculum of public and nonreligious private schools. Such Christian school associations are often more active than others in lobbying and legislative efforts, especially in the support of private school choice programs and in the elimination of state oversight of private schools, and in preparing and distributing curricular texts and other materials. A significant example of such associations is the Association of Christian Schools International (ACSI), located in Colorado Springs, Colorado. Founded in 1978, ACSI represents 3,000 Christian schools in the United States and 20,000 worldwide, as well as 120 affiliated colleges (which it does not accredit). It does provide school accreditation as well as teacher and administrator certification services. ACSI is especially active in providing legal and legislative services and publishing curricular materials. In 2008, ACSI unsuccessfully sued the University of California on behalf of a member school and several students whose high school biology credits were rejected based on their creationist course orientation. Other Christian school associations include the Association of Classical and Christian Schools (235 schools), the American Association of Christian Schools, and the National Christian Schools Association (120 Schools). While all represent private Christian schools within the evangelical tradition, it should be noted that considerable differences exist between these organizations, including ACSI, and similar smaller groups not only in terms of theology, doctrine, and educational perspectives but also in terms of their services and activities. For example, the Association of Classical and Christian Schools, whose offices are located in Moscow, Idaho, emphasizes classical education as well as Christian values, with an emphasis on Latin instruction, and opposes regional accreditation. In contrast, although the National Christian Schools Association, whose

headquarters are in Oklahoma City, Oklahoma, also provides its own accreditation process; it emphasizes outside accreditation, including state and regional agencies, for its member schools. The American Association of Christian Schools, while headquartered in East Ridge, Tennessee, is particularly active in legislation and maintains an office in Washington, D.C., as well. RAVSAK

RAVSAK (transliterated from the Hebrew as Reshet Batei Sefer K’hilat’im, meaning Network of Jewish Community Day Schools) is one of the larger Jewish school associations in America and is located in New York City, New York. RAVSAK and similar organizations tend to have a narrower focus than other private school associations, concentrating almost entirely on providing curricular services for member schools that emphasize Jewish traditions. It also has programs for training school administrators but does not appear to engage in accreditation or certification activities. Numerous smaller and even informal Jewish education associations also exist.

Private College and University Associations Overall, associations representing private colleges and universities tend to have more limited and focused purposes than those representing private K-12 schools. Since postsecondary institutions do not require certified faculty and staff, and place a higher emphasis on formal regional or national accreditation, these associations do not generally provide such services. Instead, they are much more focused on advocacy, including education and legal issues, and on research and information. Two of the best established private college and university associations are the National Association of Independent Colleges and Universities and the Association of Catholic Colleges and Universities, both headquartered in Washington, D.C. The former represents a broad spectrum of more than 1,000 private, nonprofit institutions, including liberal arts colleges, research universities, religious institutions, specialty postsecondary schools, and 2-year colleges. Its primary emphasis is on public policy and legislation affecting private higher education, particularly at the national level. It also supports a number of national initiatives such as student voter registration, financial aid development, and a system of institutional rankings. In addition, it is a significant source of data and research on private colleges and universities.

Privatization and Marketization

The Association of Catholic Colleges and Universities represents only Catholic institutions and primarily focuses on building cooperation and sharing information between these institutions nationally and internationally, as well as serving as a point of communication with the Catholic Church hierarchy and other denominational groups including NCEA. Luke M. Cornelius See also Accreditation; Licensure and Certification; Schools, Private

Further Readings Legal Citation Schools Association of Christian International v. Stearns, 679 F. Supp. 2d 1083 (C.D. Cal. 2008)

Websites American Association of Christian Schools: http://www .aacs.org/ Association of Catholic Colleges and Universities: http:// www.accunet.org/ Association of Christian Schools International: http://www .acsi.org/ Association of Classical and Christian Schools: http://www .accsedu.org/ The Catholic School Accreditation Association: http:// catholicschoolaccreditation.org/ National Association of Independent Colleges and Universities: http://www.naicu.edu/ National Association of Independent Schools: http://www .nais.org/ The National Association of Private Catholic and Independent Schools: http://napcis.org/ National Catholic Education Association: http://www.ncea .org/ National Christian School Association: http://www.national christian.org/ RAVSAK: http://www.ravsak.org/

PRIVATIZATION AND MARKETIZATION Privatization is an overarching term that describes several subtrends in the education sector. Borrowing from the work of the educational economists Clive Belfield and Henry Levin, privatization may be defined in two primary but not mutually exclusive ways. The first way of thinking about privatization is

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that it is the transfer of assets, activities, and responsibilities from public or government-run institutions to private individuals or organizations. This definition is probably the most commonly understood, and examples include nationalized industries that are handed off to private shareholders, the administration of prisons subcontracted out to private providers, and sports stadiums being taken over by private interests. A second way of thinking about privatization, more common in policy circles and among those interested in economics, relies on the idea of liberalization. That is, privately held but tightly regulated organizations or firms are deregulated to allow for greater freedom to act in markets. Perhaps the most notable example from the latter half of the 20th century was the deregulation of airlines, a policy change that allowed for more intense competition between vendors, ultimately driving down costs for travelers. Liberalization’s companion, marketization, fits within this definition as new markets are created as alternatives to publicly run monopolies. Although marketization certainly exists in the United States, it has been more common in Europe, where centrally planned economies were more usual. This entry includes a division of the broad category of privatization into smaller subsets. The entry continues with a discussion of the current trends in privatization and concludes with thoughts on the future of educational privatization and its place in the evolving education marketplace.

A Typology of Privatization in Education Although traditional privatization and liberalization are key concepts to broadly describe the changes taking place in the education market, they are insufficiently specific. Keeping these two definitions in mind, privatization may be broken down into three main categories: (1) private provision, (2) private funding, and (3) private decision rights. In theory, these “pure” types of educational privatization exist separately and are uniquely contained in institutions. In practice, however, these three types of privatization are not mutually exclusive, often overlapping onto each other, sometimes coexisting in the same school. Private Provision

Education services can be provided by both public and private actors. Private actors in this case include (but are certainly not limited to) religious schools, private universities, and test preparation

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academies. In many places, students and their families choose to forgo the publicly funded options that the state has provided in favor of private education, and they make this choice for any of a wide variety of reasons. In the case of schools run by religious organizations, families might prefer the spiritually inflected curricula that these schools are able to employ over the secular approaches used in public schools. Private universities are also able to offer services to students that are generally unavailable at public institutions, although the disparity between the most prestigious public and private schools in the tertiary systems is much smaller than at the compulsory level. Finally, smaller operations like test preparation academies offer a service that public schools do not have the time or inclination to provide, and the families of students who take college admission tests and other high-stakes examinations often feel the need to get a “leg up” on the competition by turning to private provision. The prevalence of all these examples varies widely by location. In the Philippines, for instance, close to 75% of college students are enrolled in private universities; in much of Europe, the percentage is close to 0. Private Funding

The funding for education services can come from private individuals or organizations rather than from public subsidies or other governmental sources. Most private schools charge their students some level of tuition, whereas most public schools are free. The notable exception to this rule involves the tuition-charging public universities in the United States, where students are charged some percentage of the true cost of their education; the remaining cost is subsidized by the government. Sometimes the tuition at private schools is shared in some way between the families of the students and the government, as in the voucher programs that have blossomed across much of Latin America. In the case of private funding, privatization occurs when some fraction of the total cost of education provision is paid by individuals rather than by the government. Private Decision Rights

Even though some level of education is required in most parts of the world, students and their families are usually free to choose the manner of provision. If a school or other organization is not properly meeting the needs of its students, the students are free to

leave the school to find a provider better suited to their needs or desires. Similarly, students and families who might like to exercise their freedom to exit, but who cannot for various reasons, often demand (and are given) the ability to communicate their dissatisfaction to the school. The “voice” of education consumers, coupled with the freedom to exit, serves as a counterbalance to the authority of the provider; here, privatization occurs when public institutions give up some of their power to make decisions to the private consumers of the product. Privatizing educational decision rights can mean giving parents an increased voice or the ability to exit, even if all the schools are publicly run. A common example of this type of privatization is intradistrict open enrollment. Large urban school districts in cities like Las Vegas and Chicago provide opportunities for families to choose which school in the district their children will attend. By providing families with the option to exit schools that are not meeting their children’s needs, districts have privatized the school attendance decision right. Families have always been able to exit the public system entirely by enrolling their children in private schools, but by allowing families to choose educational alternatives within the public system, open-enrollment programs provide incentives for students to remain in public schools while exercising private decision rights.

Trends in Privatization Although the focus here is the privatization of education, it is important to note that education is only one segment of an increasingly privatized world. In the 1990s, privatization across all industries reached a high-water mark, with close to US$500 billion of state assets passing into private hands worldwide. One key source of this privatization was the selling off of nationalized industries in the countries that made up the former Soviet Union, but privatization in the West kept pace. In the first decade of the 21st century, approximately US$487 billion worth of public assets were privatized; the privatization of education falls squarely within this global trend of liberalization. In South America, both Colombia and Chile have experimented extensively with a variety of education reforms, with mixed results. In 1981, Chile implemented a voucher program that covered nearly 90% of that country’s students. School

Privatization and Marketization

vouchers, which function as types of both provision privatization and, through the choice that the programs incentivize, decision rights privatization, are one of the most widespread policy solutions to issues of school choice. Researchers investigating the Chilean voucher program have found that there appears to be no difference in terms of educational outcomes between students who attend private schools through the voucher program and those who attend public schools, but regardless of the effectiveness of the policy, it remains one of the largest decision-rights privatization experiments in the world. With similar results, between 1991 and 1997, Colombia implemented a voucher program that effectively subsidized half of the cost of private schooling for low-income students. Like the Chilean program, the Colombian program represents a form of both funding privatization and decision-rights privatization. In primary and secondary education in the United States, vouchers remain a politically contentious reform, but one which has nevertheless spread in some form to at least six states. The voucher program in Louisiana, for example, allows students with household incomes within 250% of the federal poverty guideline to receive annual funding equal to the per-pupil state allocation. As with the South American examples, voucher programs in the United States have had mixed results, with the majority of research demonstrating that student-level outcomes remain unchanged by the presence of vouchers in districts. Privatization at the compulsory level in the United States, however, has tended to exist more as a “hollowing out” of public institutions. In an example of provision privatization, many school districts in the United States have contracted out formerly publicly provided services, including student transportation, food service, and custodial services. Although the actual delivery of instruction in the public school system remains largely public in most school districts, schools are also turning increasingly to curricula developed by private, for-profit firms. This is an instructive example of the ways in which private and public provision can exist simultaneously within the same institution. Tertiary education systems are experiencing a similar hollowing out in the United States, but for colleges and universities, privatization takes a slightly different form. Private, for-profit colleges make up the fastest growing segment of the higher education market, with the total number of students

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enrolled growing from 766,000 in 2001 to 2.4 million in 2010. Along those same lines, enrollment in for-profit colleges increased by 225% between 1998 and 2008, while total enrollment in all types of degree-granting institutions grew by just 31%. While very few public colleges and universities are being converted holistically into private schools, the higher education sector as a whole is undergoing a sea change, as more and more private, for-profit schools enter the market. Individual schools may not be undergoing privatization, but the sector as a whole certainly is.

Implications of Privatization for the Future Education has been undergoing privatization for quite some time; in theory, education privatization is as old as the idea of public education. Globally, most nations operate some form of public education, and in most places, families are free to choose whether or not to avail themselves of that public education. In this sense, the privatization of decision rights on the demand side of the provision equation is almost universal. More explicit experiments with school choice—the voucher programs discussed above, or the more contemporary portfolio model adopted by school districts in Chicago, Illinois, and Washington, D.C.—are currently under way, creating an unprecedented degree of freedom to choose from among education options. Public schools turning to private funding sources, a practice with well-documented drawbacks, has nevertheless allowed students all over the world to experience learning in new ways. From high-profile programs like those privately funded by SpaceX and 3M that underwrite instruction in the STEM (science, technology, engineering, and mathematics) fields to smaller nonprofit grants administered by the National Gardening Association and Pioneer Drama (both of which privately fund K-12 theater programs in the United States), schools are taking advantage of private funding to fill the gaps left by overextended government purses. Even the privatization of education provision, the rarest type of privatization in its pure form, has made an impact on the education landscape. Universities have come to rely on online content management systems from the private sector to support the growing need for Internet-based distance education. The privatization of curricula—a trend that has almost always existed in the form of textbook publishing

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and distribution—has also increased as a result of the diversification of content delivery. And, in parts of the world where what counts as a public school is tightly regulated and where the barriers to entry into the public sector are high, the opening of private schools has rapidly picked up speed. In India, private schools now educate an estimated 31% of school-age children; in the urban centers, that figure increases to almost half of the overall school-age population. Similar figures exist all over the developing world, where aid from nongovernmental organizations has incentivized the founding of new private schools. But what does all this mean? Whether the divestment of public education by governments is purposeful or not remains unclear. In the case of privatizing provision and funding, the motivations are obvious: To provide a higher standard of education for students than the one made available by the public, schools need to seek out private sources of funding that better suit their needs. In the case of privatizing decision rights, governments are often eager to encourage competition both among public schools and between public and private institutions, in hopes that increased competition will improve the effectiveness and efficiency of education provision across the board. The increased privatization of public education has created an unprecedented opportunity for interests from both the public and private spheres to become actively involved in shaping the production and consumption of education services. Impacts over the long run may be intended, such as increased options, or unintended, such as greater inequality of education services. Industries that will require workers to have certain levels or types of education are able to directly influence the way those future workers are taught. Through active partnerships, public institutions are able to harness the power of private firms to enrich the curriculum with ideas and experiences from the private sector. Although privatization has accompanied public education for many decades, reports of the private “takeover” of public education have been greatly exaggerated. Instead, the opening of education markets through the privatization of funding, provision, and decision rights has created a much higher level of flexibility for agents on both the demand and supply side of education. There is no reason to suspect that this trend will come to an end anytime soon. Guilbert C. Hentschke and Andrew L. LaFave

See also Education Finance; Educational Vouchers; Public Choice Economics; Public-Private Partnerships in Education

Further Readings Angrist, J., Bettinger, E., & Kremer, M. (2004). Long-term consequences of secondary school vouchers: Evidence from administrative records in Colombia (Working Paper No. w10713). Washington, DC: National Bureau of Economic Research. Belfield, C. R., & Levin, H. M. (2002). Education privatization: Causes, consequences, and planning implications. Paris, France: International Institute for Educational Planning. Belfield, C. R., & Levin, H. M. (2005). Privatizing educational choice: Consequences for parents, schools, and public policy. Herndon, VA: Paradigm. Bracey, G. W. (2002). The war against America’s public schools: Privatizing schools, commercializing education. Boston, MA: Allyn & Bacon. Burch, P. (2009). Hidden markets: The new education privatization. New York, NY: Routledge. Davies, B., & Hentschke, G. C. (2006). Public-private partnerships in education: Insights from the field. School Leadership & Management, 26(3), 205–226. Desai, S., Dubey, A., Vanneman, R., & Banerji, R. (2008). Private schooling in India: A new educational landscape (India Human Development Survey, Working Paper No. 11). Retrieved from http://www .ihds.umd.edu/IHDS_papers/PrivateSchooling.pdf Health, Education, Labor, and Pensions Committee. (2012). For-profit higher education: The failure to safeguard the federal investment and ensure student success. Washington, DC: U.S. Senate. Hentschke, G. C., & Wohlstetter, P. (2007). K-12 education in a broader privatization context. Educational Policy, 21(1), 297–307. Levin, H. M. (2009). An economic perspective on school choice. In M. Berends, M. G. Springer, D. Ballou, & H. J. Walberg (Eds.), Handbook of research on school choice (pp. 19–34). New York, NY: Routledge. McEwan, P. J., & Carnoy, M. (2000). The effectiveness of private schools in Chile’s voucher system. Educational Evaluation and Policy Analysis, 22(3), 213–239. Organisation for Economic Co-operation and Development. (2009). Privatization in the 21st century: Recent experiences from OECD countries. Paris, France: Author. Stiglitz, J. E. (1999). Knowledge as a global public good. Global Public Goods, 1(9), 308–326. Tsang, M. (2002). Comparing the costs of public and private schools in developing countries. In H. M. Levin &

Professional Development P. J. McEwan (Eds.), Cost-effectiveness and educational policy (pp. 111–138). Larchmont, NY: Eye on Education.

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3. University extension or adult education programs 4. College courses in teacher’s subject field

PROFESSIONAL DEVELOPMENT Considerations of human capital—the collective knowledge, skills, and competencies of individuals that contribute to their productivity—are vital in discussions regarding investments in public education. Because student outcomes depend on highquality educators, policymakers at all levels of the education system have focused on quality teachers as a critical resource in attempts to reach the ambitious goals embedded in current high-stakes accountability reforms. While much attention has been devoted to the preparation of high-quality entry-level teachers, leaders throughout the education system have also recognized the importance of ongoing professional development as a way to enhance the capacity of the existing teacher workforce. Though professional development varies across school systems in terms of dominant approaches, level of investment, and effectiveness, researchers and policymakers alike have come to recognize that investing in human capital through professional development is an essential component of school improvement efforts. After a brief overview of the common approaches to professional development, this entry describes the levels of investment in professional development activities and presents what is known about the effectiveness of different forms of professional development. Professional development activities take a variety of forms. Traditionally, professional development has encompassed a range of formal, structured activities in which practicing teachers engage— usually outside of the classroom—to further develop their teaching skills, learn new skills or content, and familiarize themselves with new education policies that affect their teaching and classroom practices (e.g., changes to the curriculum, new standards, and assessment programs). For instance, a 1998 National Center for Education Statistics analysis of data from the Schools and Staffing Survey identified five broad categories of teacher professional development: 1. District-sponsored workshops or in-service programs 2. School-sponsored workshops or in-service programs

5. Activities sponsored by professional associations

More contemporary conceptualizations of what counts as professional development, however, include shifts in the use of time (e.g., to provide common planning time for teachers of the same subject) and in the creation of collaborative structures for teachers to learn from one another (e.g., through teacher networks or mentor programs). More recent National Center for Education Statistics analyses of Schools and Staffing Survey data reflect these trends; for example, a 2006 report includes several additional categories of professional development that illustrate these evolving conceptualizations. In addition to many of the forms of professional development identified in the 1998 report, the 2006 report included regularly scheduled collaboration with other teachers on issues of instruction; individual or collaborative research on a topic of interest; mentoring and/or peer observation and coaching; observational visits to other schools; participation in a network of teachers; and induction programs. Clearly, teacher professional development includes a wide range of activities aimed at improving the knowledge, skills, and abilities of teachers, and school systems are currently making substantial investments in various forms of teacher professional development.

Investments in Professional Development Investments in educator professional development require resources such as time and money, and these costs are shouldered by a variety of individuals and organizations. The justification for these expenses is the belief that professional development will promote better teaching and, ultimately, improved student outcomes, so these resources are generally thought to be investments in greater productivity. While some research documents the costs of teacher professional development in specific states and districts or to particular levels of the educational system (e.g., the district budget), the actual level of resources committed to teacher professional development is largely unknown in a comprehensive sense, in part because professional development accounting is often buried within program-level expenditures. Furthermore, educational interventions like professional development generally require a variety

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of hidden and widely dispersed resources, some of which may not translate into additional expenditures, and consequently, many may not appear in any accounting record. For example, professional development requires substantial time of personnel, sometimes compensated and sometimes not. To the degree that individuals are willing to donate their time, the overall explicit price tag of the initiative will decrease. However, this investment of time is a real cost and must be recognized as such. Likewise, to the degree that the cost burden is distributed in such a way that external sources of support cover substantial portions of the cost, the burden on the school or school system will decrease. Nonetheless, shifting the distribution of the cost burden does not affect the overall cost of the initiative. Despite the limited systematic data on the full costs of educator professional development, existing research provides some information about professional development expenditures. These studies show considerable variability in spending across schools, districts, individuals, and initiatives. While some research reports higher figures, most estimates of per-teacher annual spending on professional development range from $2,000 to $3,500, values that are approximately 3% to 6% of national average annual wages for elementary, middle, and secondary teachers in 2012. Expenditures on professional development are estimated to account for between 2% and 4% of districts’ operating expenditures, though some studies suggest that these expenditures could consume more than 8% of operating expenses. However, caution should be exercised in drawing broad conclusions about the level of investment in professional development, given the limited number of studies and the range of methods used to estimate costs. Other studies also have examined what professional development dollars purchase or how the money is spent. For instance, a study by the Education Commission of the States reports that the majority of district professional development dollars is spent on in-service training days (40%), while the largest share of state spending on teacher professional development goes to university subsidies for graduate programs in education (36%). Other studies examining expenditures in terms of conventional budgeting categories report that the vast majority of professional development expenditures are associated with spending on personnel time, including teacher participants, trainers, and coaches. When future salary obligations (e.g.,

increments on a salary schedule associated with earning an advanced degree) are included as a cost, they represent the single largest taxpayer investment in staff development, though controversy surrounds the appropriateness of including this expenditure in the calculation of the total cost of professional development.

Identifying Promising Forms of Professional Development Professional development activities take a variety of forms, which have evolved over the past decade. As noted above, traditional approaches to teacher professional development have included workshops sponsored by schools and districts, college courses in a teacher’s subject field, and seminars and conferences sponsored by professional associations. These approaches to professional development generally have required afterschool time, in-service days, or release time during the school day for teachers to participate, and participation in some form of professional development is generally part of a teacher’s contractual agreement. More contemporary conceptualizations of what counts as professional development include shifts in schools’ organizational structure and use of time to create collaborative arrangements for teachers to learn from one another in the context of the school day (e.g., through teacher networks, mentor programs, and common planning time for teachers of the same subject or grade). In general, the evolution from traditional to more contemporary forms of professional development has involved moving from an understanding of professional development as a district-driven process of providing training and information through a menu of alternative activities to an approach that emerges from local needs and interests, is relevant to specific school communities, and is open to a wide variety of methods. While limited research has been conducted on the level of investment being made in teacher professional development, even less is known about the effectiveness of teacher professional development strategies. Researchers have generated lists of the characteristics of “high-quality” professional development. For instance, Willis Hawley and Linda Valli describe the eight research-based principles that the National Partnership for Excellence and Accountability in Teaching suggest will promote high-quality professional development. According to this report, professional development should

Professional Development

1. be based on analyses of the differences between actual student performance and student learning goals, 2. involve teachers in the identification of what they need to learn and in the development of the learning experiences in which they will be involved, 3. be primarily school based and built into the day-to-day work of teaching, 4. be organized around collaborative problem solving, 5. be ongoing and involve follow-up and support for further learning—including support from sources external to the school that can provide necessary resources and new perspectives, 6. incorporate the evaluation of multiple sources of information on student outcomes and instruction, 7. provide opportunities to gain an understanding of the theory underlying the knowledge and skills being learned, and 8. be connected to a comprehensive change process focused on improving student learning.

Studies of the effectiveness of these new conceptualizations of professional development have recently begun to emerge; this new body of research generally suggests that professional development interventions are more effective at improving student outcomes when they adhere to these standards of high-quality professional development. A growing body of research is also emerging around induction programs, a particular form of professional development targeted at new teachers. Induction programs range in their design, often including orientation sessions, mentoring opportunities, classroom observation, and other forms of support. A recent study by Steven Glazerman and colleagues adds to the existing literature by using a high-quality rigorous evaluation design to examine the effects of comprehensive induction services. The treatment group—that is, the teachers who participated in the induction activities that the researchers were interested in studying—included teachers who received comprehensive induction support, and the control group included teachers who received less comprehensive (and more common) services. The analysis found no impact of participation in comprehensive induction on teacher attitudes, teacher practices, student test scores, or teacher retention

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after 1 year. However, when the authors examined the specific types and intensity of support received by teachers across the treatment and control groups, they found that some components of induction programs may affect outcomes. Specifically, students of teachers who received coaching and feedback on their teaching scored higher than students of teachers who did not receive these services. In addition, teacher retention was higher among teachers who had an assigned mentor, received guidance in content areas (math and literacy), or engaged in content-specific and pedagogical professional development. Since these findings were not generated through experimental methods, the authors caution readers against making causal inferences from them. Still, the results of this study are noteworthy given their ability to provide insights for future research and their addition to the knowledge base on a prevalent approach to educator human capital development. Until recently, the only form of professional development that has a substantial body of research analyzing its impact on student achievement is the earning of graduate degrees. For example, research by Dan D. Goldhaber and Dominic J. Brewer has demonstrated a small but consistently positive effect of teachers’ advanced degrees on high school student achievement. However, these findings are limited to high school mathematics and science. Evidence on the impact of advanced degrees at the elementary level and in other subject areas remains mixed and inconclusive. In summary, the goals of education reform are unlikely to be realized without enhancing the capacity of existing school personnel through high-quality professional development. School systems—from the state to the school levels—are making considerable investments of time and money in the ongoing professional development of educators. While research provides broad principles of high-quality professional development, studies regarding the effectiveness of specific forms of professional development are recent and relatively few in number. More work is clearly needed to guide policymakers and school leaders in this area. Jennifer King Rice and Kathleen Mulvaney Hoyer See also Expenditures and Revenues, Current Trends of; Teacher Effectiveness; Teacher Training and Preparation

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Program Budgeting

Further Readings Choy, S. P., & Chen, X. (1998). Toward better teaching: Professional development in 1993–94 (NCES 98–230). Washington, DC: U.S. Department of Education, National Center for Education Statistics. Choy, S. P., Chen, X., & Bugarin, R. (2006). Teacher professional development in 1999–2000: What teachers, principals, and district staff report (NCES No. 2006–305). Washington, DC: U.S. Department of Education, National Center for Education Statistics. Darling-Hammond, L., & Sykes, G. (Eds.). (1999). Teaching as the learning profession: Handbook of policy and practice. San Francisco, CA: Jossey-Bass. Education Commission of the States. (1997). Investment in teacher professional development: A look at 16 districts. Denver, CO: Author. Ferguson, R. F., & Ladd, H. F. (1996). How and why money matters: An analysis of Alabama schools. In H. F. Ladd (Ed.), Holding schools accountable: Performance-based reform in education (pp. 265–298). Washington, DC: Brookings Institution Press. Glazerman, S., Dolfin, S., Bleeker, M., Johnson, A., Isenberg, E., Lugo-Gil, J., . . . Britton, E. (2008). Impacts of comprehensive teacher induction: Results from the first year of a randomized controlled study (NCEE No. 2009–4034). Washington, DC: U.S. Department of Education, National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences. Goldhaber, D. D., & Brewer, D. J. (2000). Does teacher certification matter? High school teacher certification status and student achievement. Educational Evaluation and Policy Analysis, 22(2), 129–146. Hawley, W. D., & Valli, L. (1999). The essentials of effective professional development: A new consensus. In L. Darling-Hammond & G. Sykes (Eds.), Teaching as the learning profession: Handbook of policy and practice (pp. 127–150). San Francisco, CA: Jossey-Bass. Killeen, K. M., Monk, D. H., & Plecki, M. L. (2002). School district spending on professional development: Insights available from national data. Journal of Education Finance, 28(1), 25–50. Rice, J. K. (2009). Investing in human capital through teacher professional development. In D. Goldhaber & J. Hannaway (Eds.), Creating a new teaching profession (pp. 227–250). Washington, DC: Urban Institute Press.

PROGRAM BUDGETING Program budgeting is one of many approaches used to develop and monitor public agency budgets. Developed mostly for federal programs (many

in the military), program budgets outline the goals, mission, and anticipated outcome of public agency activities and then develop a cost budget (including assignment of personnel) to complete the task. Program budgeting assumes a multiyear process, and if fully implemented, it includes careful review of alternative approaches to service provision along with detailed evaluations and review within the budget process. This entry provides the background on program budgeting along with a description of why it has not been implemented widely in school systems in the United States.

Background Program budgeting is an approach to budgeting developed from a special study of resource allocation in the U.S. Department of Defense conducted by the RAND Corporation in the 1950s. Many federal programs of the mid-1960s required school districts to implement program budgeting to receive federal funds. Dade County, Florida, was the first district to implement program budgeting concepts. This provided a stimulus for national dissemination, and a number of other large school districts developed their own version of program budgeting by the early 1970s. Unfortunately, program budgeting did not live up to the early hype. Its use was eventually discontinued at the federal level, and program budgeting systems in many states were abandoned in the 1970s. Today, many public agencies rely on versions of program budgeting that differ from early implementations but are designed to provide more data on the allocation and use of resources to meet the agency’s mission and goals.

Components of Program Budgeting There are seven major steps in the development of a program budget for a school district. These are as follows: 1. Identification and definition of the school district’s mission, goals, and objectives, along with a clear statement of desired outcomes 2. Specification of alternative approaches or programs for achieving the outcomes 3. Translation of each alternative program into fiscal and nonphysical requirements, including planned expenditures and proposed revenue sources for each program—typically done for a multiyear period and not for a single budget cycle

Program Budgeting

4. Assessing the cost-effectiveness of each alternative 5. Choosing the alternative that offers the best course of action for the school district 6. Review and evaluation of each program focused on the extent to which the desired outcomes were realized 7. Using the evaluations to cycle back into the budget cycle to develop better and more cost-efficient program alternatives for meeting desired outcomes

Program budgeting is a “top-down” decisionmaking approach. Moreover, the specification of alternative program options and costing them out in detail are very labor intensive, often requiring additional budget staff. As a result, program budgeting has only found limited acceptance in school districts.

Strengths and Weaknesses of Program Budgeting Program budgeting offers a number of improvements over traditional line-item budgets. Its primary strength is that it requires the school district to improve its planning capabilities. Districts that have given up on pure program budgeting have sometimes continued the long-term planning processes. Such planning efforts can be helpful as districts establish long-term student performance goals. A second strength of program budgeting is its focus on accountability, performance, and achievement of objectives. Program budgeting requires the establishment of clear goals and objectives and forces planners to determine what resources are necessary to achieve those goals. When properly implemented, spending decisions are made on the basis of what is needed to achieve the district’s goals and not on some other basis such as “That’s the way we have always done it in the past.” It also helps decision makers consider the full range of available options and requires the evaluation of several options before selecting the one that appears to be the best for meeting the district’s needs. One of the weaknesses or problems with program budgeting is its cost. Allan Schick estimated that to fully implement a program budgeting system takes as long as 5 years. During that time, regular budget staff are often required to do their previous jobs as well as complete all of the required documentation for a program budget. For example, reaching an agreement on the goals of a program can be a difficult task, further complicated by the need to garner

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public input. Once an agreement on the goals has been reached, a number of alternative strategies might be developed. If seven such options exist, the staff have to analyze the costs and potential outcome of all seven, which potentially is seven times as much work compared with if only one option is considered. Schick found that as budget deadlines approached, staff often set these analyses aside in favor of getting the job done using the familiar tools of the past. As a result, program budgeting made little headway in gaining acceptance in school district finance offices. Another feature of program budgeting that has limited its effectiveness in education is the bias toward centralized planning. This is contradictory to many current efforts to shift the responsibility for decision making and budget authority to school sites.

Conclusion The cost of implementing program budgets, combined with the extensive investment in developing program budgeting systems, has kept most school districts from moving extensively away from lineitem budgets, which are easier to manage and administer, particularly in an environment where there are potentially year-to-year fluctuations in revenues and expenditure needs. Another approach used by school systems has been to simplify the program budgeting process, providing descriptions of each program along with outcome goals. The “program” budget then identifies the expenditure needs of each program and totals them to get school and district budgets. Often missing from this approach are the careful review of alternatives and the evolution of each program with an eye toward improvement in the future. As the data capacity of school systems grows, and as demand for stronger outcome accountability becomes more important to measuring school district performance, finding ways to link student performance to budgeting systems through program budgets may be used by more school systems. Lawrence O. Picus See also Budgeting Approaches; Education Spending; School District Budgets

Further Readings Hartman, W. T. (2003). School district budgeting. Lanham, MD: Scarecrow/Education in partnership with Association of School Business Officials International.

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Lee, R. D., Johnson, R. W., & Joyce, P. G. (2012). Public budgeting systems. Burlington, MA: Jones & Bartlett Learning. Schick, A. (1971). Budget innovation in the states. Washington, DC: Brookings Institution Press. Wildavsky, A. (1988). The new politics of the budgetary process. Glenview, IL: Scott Foresman.

PROGRESSIVE TAX REGRESSIVE TAX

AND

The terms progressive, regressive, and proportional are used to describe the effect of a tax on private income distribution. Most economists define these concepts in terms of average tax rate, or ratio of tax paid to income. If the average tax rate increases with income, the tax system is progressive; if it falls, the tax is regressive. A tax is proportional if the average tax rate is constant across all income levels. This entry discusses the principles of tax equity and the measurement of the economic burden of a tax. It concludes with a discussion of the roles of economics and ethics in the consideration of fairness in taxation. Since at least the time of Adam Smith, economists and social philosophers have considered the requirements of a “good” tax system. One such requirement calls for an equitable distribution of the tax burden. That is, each taxpayer should pay his or her “fair share.” However, while everyone agrees that the tax system should be fair, there is no such agreement as to the definition of “fair share.” Ultimately, the matter of fairness in taxation is an ethical and not an economic question, but a question on which economics can shed considerable light. Economic insights into the nature and extent of income inequality and the ways in which taxpayers respond to alternative tax structures may inform our pursuit of fair taxation. Among the conceptual approaches taken toward tax equity, two theoretical principles have been emphasized. The first is the so-called benefit principle, which holds that an equitable tax system is one in which each taxpayer contributes in proportion to the benefits he or she receives from government services. The benefit principle, therefore, addresses public spending as well as taxation. The second is the “ability-to-pay” principle, which dates back to the 16th century and has enjoyed the support of prominent thinkers such as Jean-Jacques Rousseau, Jean-Baptiste Say, and John Stuart Mill. Under this

concept, the matter of raising tax revenue equitably is divorced from the issue of public spending. For any given revenue target, each taxpayer is called on to contribute according to her ability to pay. Given that actual tax policy is largely determined without regard to particular spending categories, the ability-to-pay principle is usually emphasized in tax policy discussions, particularly by advocates of redistribution. Each approach is beset with operational difficulties. The benefit principle requires knowledge of expenditure benefits for individual taxpayers, while the ability-to-pay approach calls for some way to gauge this ability. These challenges are not trivial. And the benefit approach suffers from the further limitation of being incapable of serving the redistributive aims of taxation. This is a serious shortcoming given the dual functions of allocation and redistribution generally assigned to a tax system. The ability-to-pay approach is better suited to serve redistributional goals and, more generally, address the problem of overall tax structure design. The ability-to-pay principle requires that equal amounts of tax be paid by individuals with equal ability to pay and appropriately unequal amounts of tax be paid by individuals of differing abilities to pay. The former requirement is referred to as horizontal equity and the latter as vertical equity. Both requirements are part of the same broader principle of “equal treatment,” but horizontal equity is often considered more fundamental, applying to tax policy the widely accepted doctrine of equality under the law.

Economic Burden of a Tax To determine whether a tax system distributes its burden fairly across individuals, we must understand how taxes change the distribution of private real income. This change is the economic incidence of the tax. Analysis of tax incidence rests on the economist’s view that, contrary to much conventional wisdom, taxes are paid by people—workers, stockholders, consumers, landlords, and so on— and not by business or corporations. The question then becomes, How should individuals be classified for purposes of incidence analysis? A traditional approach is to classify individuals by their role in the production process—that is, by the inputs, or factors of production, they supply. This approach focuses on how the tax system changes the distribution of income among workers, owners of capital,

Progressive Tax and Regressive Tax

and landlords, and it is referred to as the functional distribution of income. This approach, however, while well suited for an economy of an earlier time when property owners did not work and workers did not own property, does not work well for the contemporary U.S. economy, where many workers own common stocks (often in retirement accounts) and saving accounts and where many people with substantial capital holdings work full time. A more modern approach is to examine how taxes affect the distribution of total income across income classes: the size distribution of income. With information on the proportion of people’s income derived from labor, capital, and land, we can convert changes in the functional distribution of income arising from the imposition of a tax into the size distribution. These changes in private real income—the economic incidence of the tax—must be distinguished from the statutory incidence, which indicates who is legally responsible for a tax (e.g., a firm, landlord, etc.). Because prices may change in response to the tax, individuals and firms will change their behavior in an attempt to shift the tax burden to others. Consequently, the statutory incidence often fails to reveal who bears the ultimate burden, or economic incidence, of the tax. The true economic incidence of a tax arises from both an individual’s source and use of income. For example, a tax on cigarettes may trigger an increase in cigarette prices, redistributing income away from smokers on the use side. But this price increase may reduce the amount of cigarettes demanded, requiring lower output. This would lower the income of factors (labor and capital) employed in cigarette production, the source side. Thus, the overall incidence of the tax depends on how both uses and sources of income are affected. In practice, however, economists generally ignore the effects on the sources side when analyzing a tax on a commodity (e.g., a cigarette tax) and ignore the uses side when considering a tax on an input (e.g., an income tax). Incidence analysis usually considers the distribution of the revenue burden of a tax. But incidence is a relative concept, so the burden of a tax must be compared with something. One possibility is to measure the combined effects of levying taxes and government spending financed by those taxes. Under this approach, known as balanced-budget incidence, the distributional effect of a tax depends on how the government spends the money. A limitation of this approach is that tax revenues are generally not earmarked for particular expenditures. Consequently,

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an alternative approach, differential tax incidence, ignores how government spends the revenue and focuses instead on how incidence differs when one tax is replaced by another tax that raises the same amount of revenue. The hypothetical tax used for comparison is often assumed to be a lump-sum tax, the only tax that cannot be avoided or shifted by changing behavior. Incidence analysis then requires a determination of which prices change and by how much as a result of the tax. Again, while the prices of both consumer goods and services and factors of production can change, thereby affecting individuals through both the use and the source of income, incidence analysis often focuses on the source side with a factor tax and on the use side with a commodity tax.

Measuring Tax Progressivity Once the burden of a tax is determined, that burden is usually characterized by its effect on income distribution. A general structure for an income tax involves a definition of taxable income (the tax base) and a schedule of tax rates, or percentages of the tax base owed as tax. For purposes of tax analysis, public finance economists generally adhere to the HaigSimons definition of income: Income is the money value of the net increase in an individual’s power to consume during a specified time period, usually a year but sometimes a longer period. Policymakers have used the Haig-Simons criterion to define income broadly, including wages and salaries, business profits, rents, royalties, dividends, interest, and other items. A flat rate, or proportional tax, applies a single rate to the entire base. A progressive structure applies higher rates as the tax base increases, while a regressive structure applies lower rates as the tax base increases. In a progressive tax structure, the marginal tax rate is the rate applied to the last dollar of taxable income. The measurement of progressivity can be controversial because several reasonable approaches can be taken. One approach holds that the greater the increase in average tax rate as income rises, the more progressive the tax system. An alternative view gauges progressivity according to the elasticity of tax revenue with respect to income (i.e., the percentage change in tax revenue divided by the percentage change in income)—the more elastic the revenues, the more progressive the tax system. These two approaches, while similar in concept, are not equivalent. Consider, for example, a proportional increase (say, 10%) in everyone’s tax liability.

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By the first definition, this change would make the tax system more progressive. By the second measure, however, the progressivity of the system would remain unchanged.

What Is Fair? The Role of Economics Fairness in taxation, like fairness in any realm, is an ethical issue involving value judgments. Economists have no advantage over anyone else in making such judgments, but careful economic analysis can clarify issues and identify trade-offs involved in these matters. Ultimately, we rely on the political process to make choices about replacing one tax with another, choices that usually create winners and losers. The concept of progressive taxation has traditionally enjoyed strong support in the United States. And both principles of fair taxation discussed above—the benefit principle and the ability-to-pay principle—may be interpreted as supporting progressive taxation. The benefit principle has been interpreted as suggesting that because individuals and households with higher income and wealth benefit more from the protections that government provides, including defense, police, a criminal justice system, and so forth, they should bear a higher tax burden. How much greater, of course, is an ethical, and not a technical, question. The ability-to-pay principle also calls for progressive taxation, not because such a structure is related to the benefits an individual receives from government but because it reflects an individual’s ability to bear a tax burden. Where a tax payment may lower a wealthy person’s consumption of a luxury good (e.g., travel or entertainment), such a payment may deprive a poor person of food or health care. However, while this reasoning is persuasive and even compelling, it is impossible to prove. We simply cannot make the interpersonal comparisons required to fully validate this argument. To paraphrase the economists Joel Slemrod and Jon Bakija, we can no more quantitatively compare across individuals the sacrifice caused by having less money than we can compare the pain caused to two people by a pinprick. For these reasons, economists no longer try to derive an “ideal” degree of progressivity from basic principles of fair taxation. Instead, they focus on the economic consequences, or costs, of different degrees of tax progressivity. These costs, termed the excess burden of taxation, are the burden imposed by the tax over and above the revenue generated and arise from the disincentive effects of a tax. A progressive tax, in effect, penalizes the efforts of people to improve their economic circumstance—working

hard, getting an education, starting a business, and so on. Estimating the excess burden that arises when people cut back on these efforts in an attempt to avoid a progressive income tax gives us insight into the trade-off posed by progressive taxation—how to balance the potential social benefits of a more equal distribution of after-tax income against the excess burden of a highly progressive tax structure. Ultimately, the resolution of that trade-off is an ethical decision that depends on both the value society places on a more equal distribution of income and an understanding of the behavioral response to progressive taxation. Michael F. Addonizio See also Ability-to-Pay and Benefit Principles; Elasticity; Horizontal Equity; Tax Burden; Tax Elasticity; Tax Incidence; Vertical Equity

Further Readings Ballard, C. L. (1988). The marginal efficiency cost of redistribution. American Economic Review, 78, 1019–1033. Browning, E. K. (2002). The case against income redistribution. Public Finance Review, 30, 509–530. Eissa, N., & Hoynes, H. (2006). Behavioral responses to taxes: Lessons from the EITC and labor supply. In J. Poterba (Ed.), Tax policy and the economy (Vol. 20, pp. 73–110). Cambridge: MIT Press. Musgrave, R. A. (1994). Progressive taxation, equity, and tax design. In J. B. Slemrod (Ed.), Tax progressivity and income inequality (chap. 10, pp. 341–356). Cambridge, UK: Cambridge University Press. Rosen, H. S., & Gayer, T. (2010). Public finance (9th ed.). New York, NY: McGraw-Hill. Slemrod, J., & Bakija, J. (2004). Taxing ourselves (3rd ed.). Cambridge: MIT Press. Triest, R. K. (1994). The efficiency cost of increased progressivity. In J. B. Slemrod (Eds.), Tax progressivity and income inequality (chap. 5, pp. 137–169). Cambridge, UK: Cambridge University Press.

PROPENSITY SCORE MATCHING Propensity score matching (PSM) is a statistical technique used in the economics of education to estimate the effects of an intervention (e.g., tutoring, dropout prevention, class-size reduction, job training) using observational data where assignment to the intervention occurs naturally and is nonrandom.

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PSM forms an observationally similar group of individuals using a propensity score (PS). The PS is the probability that an individual receives the intervention given a set of observed variables measured before the intervention. The PS is used to make the treated group and the comparison group similar on observed variables. This entry discusses why PSM is used and how it is implemented, describes the challenges, and concludes with a list of software to implement PSM.

Why Use PSM? PSM is a possible alternative to an experimental study when data already exist on the treated group, and the formation of the comparison group is feasible and possible with existing data sources. Estimates based on observational nonexperimental data are vulnerable to selection bias, which occurs when observable and unobservable differences before the intervention, rather than factors related to the intervention, account for observed outcomes. For example, participants may self-select into an intervention, perhaps because they are interested or motivated, or participants are purposefully assigned to an intervention because they meet eligibility criteria. The program participants (“treated”) may differ in observable and unobservable characteristics before their assignment to the intervention from individuals who could have participated but did not (“comparison”). These differences may be related to the outcomes and could bias the estimated results if changes in the outcomes are attributed as intervention impacts when in fact they are due to preexisting differences between the two groups. An experimental study may not be possible if, for example, random assignment would be unethical; this may be the case if participants would be victimized (e.g., school bullying) or the cost is prohibitive due to expected low participation rates and high rates of dropout. In observational studies, participating in an intervention will likely depend on other factors that influenced participation. This fact, which will result in nonequivalence between the treated and comparison groups, or selection bias, can lead to inaccurate estimates of the intervention’s impacts on outcomes. PSM has been shown to reduce selection bias, and it separates the causal effects of the intervention from the effects associated with preexisting differences more effectively than regression analysis without matching. The benefits of PSM are that participation in the intervention occurs naturally, and so the impacts can

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be examined in larger samples than would be possible under a randomized study. If selection bias is reduced, more plausible assertions of causality can be made when interpreting an intervention’s impacts on outcomes. Studies using PSM could also permit greater in-depth analysis to inform policy and practice and improve understanding of social and educational interventions.

Conditions for Unbiased Estimates PSM is most likely to yield unbiased estimates when there are no unobserved or unconfounded sources of bias. That is, unbiased estimates are achieved when important factors (or types of selection bias) are accounted for in forming the comparison group. PSM works best when a rich and diverse set of potentially confounding variables (e.g., variables that are related to assignment to the intervention and those likely to be significantly related to the outcome of interest) are used to form the comparison group. The PSM method will likely be effective when the condition for the intervention is a uniform instance, there is only one version of the intervention, and it is consistent across all persons, locations, and time points. If the outcomes for one person in the sample are related to the exposure of another person in the sample, known as interference in the sample, then attempts to minimize bias may be less successful. The assertions of high consistency and lack of interference in the sample are known as the SUTVA (stable unit treatment value assumption). PSM typically requires a comparison group of large size relative to the treated group. This is to ensure that there will be a sufficiently large pool of comparison cases so that a high proportion of the treated cases will successfully match. Furthermore, to ensure a large number of successful matches, the distribution of PSs in the comparison group must overlap well with the distribution of PCs in the treated group. There are additional data requirements. All confounding factors and outcome variables must be measured in the same fashion across both groups. This includes how the data are collected, the content of the measures, and the timing and methods of data collection. Estimates of an intervention’s impacts will be invalid if the measures and procedures are not uniform across both groups. This may especially be a concern when administrative datasets collected for monitoring and accountability purposes are used for research. It may be necessary to adjust survey weights if the treated and comparison cases are

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obtained from multiple data sources. Other complex, multilevel, or hierarchical survey data structures, for example, when students are grouped in classrooms, or classrooms are grouped in schools, may have to be accounted for to ensure unbiased estimates. PSM has been used in many studies related to education: Peter Mueser and colleagues applied PSM to estimate the effects of job training on earnings; Roberto Agodini and Mark Dynarski used it to examine dropout prevention programs; and Elizabeth Wilde and Robinson Hollister used it to examine class-size reduction programs.

Implementation The PS can be estimated using standard models such as logistic regression. An important step is to include the confounding variables that affect participation in the intervention. Eligibility and admission criteria, and/or factors affecting the decision or selfselection into the intervention, for example, should be included in the estimation of the PS. Variables used in estimating the PS should not be affected by assignment to the intervention. Once the PS is estimated, the next step is to choose an algorithm to form an observationally similar comparison group. “Nearest neighbor” matching is the most straightforward algorithm and entails finding a comparison case with the closest PS value for each treated case. A “one-to-one match” is the most common, where a single treated case is matched with a single comparison case. An alternative is to “match with replacement,” where each comparison case may be a match to multiple treated cases. Another algorithm is “caliper matching,” which selects all cases within a specified range of the PS. In this instance, there is no limit to the number of comparison cases that can be matched to a single treated case as long as the PS value is within the specified range. “Stratification” or “subclassification,” which forms groups of treated and comparison cases with similar PS, is yet another frequently used matching technique. The number of times each comparison case is used as a match should be monitored, and this information is included when the impacts of the intervention are estimated. There is a bias-efficiency trade-off in choosing a matching algorithm that allows for multiple comparison cases. If there are multiple cases with similar PS values to each treated case, the sampling variance can be reduced due to the larger sample size. On the other hand, if there are

multiple comparison cases that are poor matches, it will increase bias because of additional matches that are less similar in characteristics to the treated group. Once the comparison group is formed, the next step assesses whether it is similar to the treated group in all observed preexisting characteristics, excluding the outcome affected by the intervention. At a minimum, for all observed variables that characterize the estimated PS, there should be no statistically significant differences in variable means between the two groups using a t-test, for example. Another common approach is to graphically review the distributions of the PS values for both groups to assess how well they overlap. It is important to assess the quality of cases matched and whether the groups formed are robust or sensitive to the chosen matching algorithm. If there are large differences, one option is to discard the observations with extreme or nonoverlapping PS values. Conducting the analysis with observations where there is a strong overlap in the PS values should lead to a more robust inference. Any statistical differences between the groups on observed variables should have been eliminated after matching. If differences remain, the PS model is reestimated, or a different matching algorithm is considered to help improve the match. Once both groups are shown to be observationally similar, the impacts of the intervention are estimated by comparing the treated with the comparison group. The estimates can be done by averaging the differences in outcomes across each pair of treated and comparison cases—this is known as the average treatment effect. Alternatively, the intervention’s impacts can be estimated using linear regression, logistic regression, or hierarchical modeling (what model is used will depend on the type of data), controlling for possible confounding variables to adjust for remaining residual imbalance.

Challenges One challenge in estimating the impacts of educational intervention is that the organization of education system has a multilevel structure, which needs to be accounted for when applying PSM. SUTVA may be violated if the intervention yields other possible outcomes affected by the multilevel structure in which it occurs instead of one outcome for each student’s exposure to the intervention. Another challenge is that some of the variables related to participation may not have been measured

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or are measured imperfectly, resulting in hidden bias. If important variables are unobserved or not measured, it cannot be assumed that they are randomly distributed across the two groups. Furthermore, even if treated and comparison groups are similar on all observed variables, there is no such certainty that the same holds for unobserved variables. There is no direct or simple way to assess the sensitivity to an unobserved variable or to know the magnitude of this problem. One approach previously used is to construct several sets of PS values using different methods and then examine the robustness of the findings across the different methods. Missing values in observed variables are another challenge. There have been empirical tests of various methods for handling missing values in the context of estimating a PS model. These methods include using cases with complete data and discarding cases with missing values, and imputing missing values either through a regression, mean imputation, or multiple imputation method. In the multiple imputation methods instead of imputing a single value for each missing value, a set of plausible values are imputed resulting in multiple datasets, then each dataset is analyzed using standard procedures and combining the results from these multiple analyses.

Available Software There are currently four main software packages that can be used to implement PSM. They include R, Stata, SAS, and SPSS. Even though SAS has not developed a procedure for PSM, there are several publications available that provide macros to implement PSM. The R and Stata packages include programs to perform sensitivity analysis to an unobserved confounder. Annie Georges and Peter Lovegrove See also Econometric Methods for Research in Education; Quasi-Experimental Methods; Randomized Control Trials; Selection Bias

Further Readings Agodini, R., & Dynarski, M. (2004). Are experiments the only option? A look at dropout prevention programs. Review of Economics and Statistics, 86, 180–194. Caliendo, M., & Kopeinig, S. (2005). Some practical guidance for the implementation of propensity score matching (Discussion Paper No. 1588). Berlin, Germany: Institute for the Study of Labor (IZA). Retrieved from http://ftp.iza.org/dp1588.pdf

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Hong, G., & Raudensbush, S. W. (2006). Evaluating kindergarten retention policy: A case study of causal inference for multilevel observational data. American Statistical Association, 101, 901–910. Luellen, J. S., Shadish, W. R., & Clark, M. H. (2005). Propensity scores: An introduction and experimental test. Evaluation Review, 29, 530–558. Mueser, P. R., Troske, K. R., & Gorislavky, A. (2007). Using state administrative data to measure program performance. Review of Economics and Statistics, 89, 761–783. Stuart, E. A., & Rubin, D. B. (2007). Best practices in quasi-experimental designs: Matching methods for causal inference. In J. Osborne (Ed.), Best practices in quantitative methods (pp. 155–176). Thousand Oaks, CA: Sage. Wilde, E. T., & Hollister, R. (2007). How close is close enough? Evaluating propensity score using data from a class size reduction program. Journal of Policy Analysis and Management, 26, 455–477.

PROPERTY TAXES In fiscal year 2010, U.S. public schools secured, on average, 13% of revenues from federal sources, 43% from state sources, and 44% from local sources. The federal government raises most of its revenue through personal and corporate income taxes (exclusive of payroll taxes). State governments rely mostly on income and sales taxes, though states vary in their relative utilization of these taxes. In addition, more than 40 states have lotteries, and more than half of these earmark a portion of the proceeds for education. Alternatively, both dependent and independent school districts raise most of their local revenues through property taxes. Property taxes are taxes levied by governments on property (e.g., land and buildings). Dependent school districts are components of a parent government, such as a city, a township, or a county. Independent school districts have their own taxing authority. This entry highlights the history, administration, and efficacy of property taxes to fund U.S. public schools.

History To understand the prominence of property taxes in education finance warrants brief consideration of the historical antecedents of property taxes and public school systems. Likely modeled after feudal lords’ collection of rents, colonial legislators levied property taxes to finance public services such as defense.

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Later, the newly formed states levied property taxes to make improvements (e.g., clear land and build transportation systems). The federal government even used the property tax intermittently (i.e., 1798 and 1814–1816) to settle war debts. Throughout the 19th century, state and local governments (i.e., governments below state level) established the property tax as their primary revenue source. Though a state-by-state comparison would reveal some differences, the property tax began with a land tax, which was later extended to a tax on improvements (e.g., buildings) and in some jurisdictions personal property (e.g., equipment). The tax also evolved from a uniform tax (e.g., a common tax per acre) to an ad valorem tax (i.e., a tax based on market value). It was during the 19th century that many states also developed public school systems, a period referred to as the common school movement. Though each system evolved independently, in general, states first passed legislation that would permit communities to establish schools and levy local taxes to support their operations. Next, states began to encourage communities to establish schools by providing land and some fiscal support. Last, states passed compulsory education laws and undertook a more active role in school finance and governance. In 1902, property taxes accounted for 53% of state revenues and 89% of local revenues. As states established new revenue sources in the 1920s and 1930s, state-level property taxes were supplanted in total, or in large part, by income and sales taxes. Only a few state governments now rely on property taxes. The property tax continues to play a central role in school finance, though its share of total revenues declined throughout the 20th century as intergovernmental grants (e.g., state aid to local school districts) increased. Since 1990, the property tax comprised, on average, 35% of total school revenues and 80% of local revenues.

Administration It is important for anyone interested in school finance to understand property tax administration. School districts do not administer the property tax. State or county legislatures enact laws that govern property tax policy, and agencies specify administrative standards (e.g., property valuation practices) and professional requirements (e.g., assessor certification), monitor performance through audits, and provide technical assistance to local government officials, called assessors. Some assessors are locally

elected officials, while others are appointed for fixed or indefinite terms. Some argue that appointed assessors are preferable to elected officials, as the latter might be subject to political influence. When stated simply, the property tax can be represented by the following formula: Base (expressed in dollars) × Rate (expressed as a percentage) = Levy (expressed in dollars)

Complexity emerges when one begins to explicate these terms. What is generally true about property tax administration nationally varies considerably among states and may vary among jurisdictions within states. Discovery

Viewed broadly, assessors perform a series of steps. The first, discovery, requires that assessors take stock of all property in their jurisdiction, which can require mapping real property using geographic information systems, as well as physically inspecting improvements (e.g., buildings). Classification

Assessors must then classify each property. The property tax is a tax on wealth that takes three general forms: (1) real property, (2) tangible personal property, and (3) intangible personal property. Real property refers to land and improvements (e.g., buildings) and can be further classified as residential (e.g., single-family homes), commercial (e.g., retail store), industrial (e.g., factory), agricultural (e.g., farm), or special purpose (e.g., church). Tangible personal property refers to nonreal property that has physical substance, such as jewelry, automobiles, machinery, equipment, and inventories. Intangible personal property refers to items that lack physical substance, such as stocks, bonds, and cash. While local governments in all states include real property in the tax base, and most include some forms of income-producing tangible personal property (e.g., machinery and equipment), few include intangible personal property. The rationale for excluding non-income-producing personal property and intangible personal property is that it is easy to conceal and often difficult to value, requiring a high level of voluntary compliance. Valuation

Once classified, the assessor must assign a value to the property, a task referred to as assessment.

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The principle of highest and best use guides the assessment process. Put simply, the principle dictates that assessors value property as if the owner used it in a way that generated the greatest return. In some instances, highest and best use valuation can lead to unwanted results. Farmland valued at its highest and best use, say, a residential housing tract, might force the farmer to sell the land. In an effort to counter these effects, most jurisdictions make exceptions to highest and best use valuations. Preferential use provisions enable assessors to value property based on current use, without tax consequence if the property’s use changes. Deferred tax provisions value property based on current use but require owners to pay back taxes if use later changes. Provisions for contracts and agreements allow owners to contract for a period of time during which they agree to use the property for a given purpose in exchange for relief from highest and best use valuation. Many analysts question whether preferential use provisions and other exceptions to highest and best use achieve the goal of preserving farmland and other green space. Depending on the type of property under consideration, assessors employ one of three strategies to determine highest and best use: market data approach, income approach, and replacement cost (less depreciation) approach. The market data approach values property in reference to other, recently sold, comparable properties. Assessors use this approach to value residential properties, using characteristics such as acreage, square footage, and the number of bedrooms. Data-based technologies have greatly enhanced assessors’ ability to employ the market data approach. Assessors commonly use the income approach to value income-producing property, such as commercial and rental property, for which recently sold, comparable properties may not exist. The income approach requires that the assessor determine the present value of the entity’s future earnings, a challenge given that they must estimate future revenues and expenses, as well as select an appropriate discount rate. Assessors may employ the income approach when valuing mineral properties, recognizing that land values decrease as mineral reserves deplete. The replacement cost approach values property at the cost the owner would incur to replace the property, less depreciation. Replacement cost is the expenditure needed to replace the property. Depreciation is an accounting concept that recognizes a decline in

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the cost of property due to use. An assessor might employ the replacement cost approach when an entity’s income-generating property is located in multiple jurisdictions, as would be the case for factories owned by a national corporation. There are also cases when income-producing property extends across multiple taxing jurisdictions, such as railroads and utility lines. Such property is often valued by centralized assessing offices using one of the aforementioned approaches. Once the total value of the property is determined, the state will allocate to localities a proportional share (unit value) based on a sensible metric (e.g., miles of rail). Assessment Role

Once assessors determine a property’s market value (also called full value), they assign an assessed value and log the relevant data in an assessment role (e.g., owner’s name, address, and property characteristics). The assessment role is a formal, certified record of property within a jurisdiction. Most jurisdictions assess properties at a uniform fraction of market value (e.g., 70%). The practice, called fractional assessment, can lessen transparency. Consider a home where the assessed value is much lower than market value. The taxpayer might assume that they are being undertaxed, making it unlikely they would appeal their assessment. Most states monitor local valuation practices by conducting ratio studies. A ratio study compares the assessed market values of a set of properties to their recent sales prices. Frequent and predictable property reappraisal cycles (e.g., every year) also contribute to transparency. Full Exemptions

For practical purposes, assessors do not value all property. Statutes or state constitutions may exclude special purpose property from the base. For example, all states exclude federal, state, and local government property (e.g., offices, prisons, and parks). The rationale behind prohibiting one governmental unit (e.g., school district) from taxing another (e.g., public library) is that doing so simply results in a shift of public dollars. Similarly, nearly all states exclude religious property, charitable property, and educational property but differ in whether the exemptions are mandated (i.e., required) or authorized locally (i.e., discretionary). The rationale behind the exclusion of nonprofit properties is that these organizations provide benefits that governments would otherwise need to provide.

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The exclusion of nonprofit property from the tax base concerns some analysts. First, the exemption benefits only nonprofits with real property, not all nonprofits. Nonprofits that lease space may bear a portion of the property tax by paying higher rents. Second, exclusion of governmental, charitable, and religious property can erode the tax base in districts with high concentrations of exempt property, placing greater burdens on others to pay for public services. Third, the local community may not benefit from the excluded property. Consider charitable properties whose activities provide regionwide, statewide, or national benefits. Last, exclusions benefit most those organizations with higher property values, not necessarily those that provide the greatest local benefit. To address these concerns, many local governments, including school districts, collect payments in lieu of taxes. These payments are voluntary payments made by property tax-exempt organizations that compensate the local government in full, or in part, for services (e.g., schooling for children who live in graduate student housing and attend a local school district). Because these payments are negotiated between the local government and an individual organization, they are often ad hoc and contentious. Economic Development Incentives

To encourage economic development, state and local governments may use abatements and tax increment financing. In general, abatements reduce property taxes in full, or in part, for certain classes of property (e.g., commercial) or a single parcel. Abatements vary with regard to whether the jurisdictions’ tax revenue loss is borne locally or is reimbursed by the state. Abatements also vary by term (e.g., 10 years) and eligibility requirements (e.g., minimum investment) and whether it is granted automatically or is at the discretion of local officials. Many states require firms to pay back the abatement if they do not meet its conditions (e.g., investment threshold). Few states prohibit the abatement of school property taxes, though jurisdictions sometimes allow school officials to participate in granting decisions. Most states also permit local governments to use tax increment financing (TIF) to encourage development. A TIF district splits property tax revenues into two streams: (1) property taxes levied on predevelopment, base year assessed value, and (2) property taxes levied on postdevelopment, incremental increases in assessed value. The local government

(e.g., school district) receives the base year share, and the TIF district receives the incremental share. The TIF district may use the tax revenue to improve public services (e.g., roads), pay bonded debt, or subsidize additional private development (e.g., building improvements). Like abatement programs, TIF programs vary among and within states (e.g., term and eligibility requirements). Similarly, few states restrict TIF districts from diverting the incremental taxes from school districts, and only some allow school officials to participate in the decision to establish a TIF district. Some states reimburse local governments directly for losses in property tax revenues, while others do so indirectly by making adjustments to local wealth variables in state aid distribution formulae. Other states do not compensate local governments, including school districts, for revenues lost. The rationale behind abatement and TIF programs is straightforward. Property tax incentives will lower the cost of doing business in a jurisdiction and may, therefore, attract new firms, retain existing firms, or encourage property improvements. In the near term, the developed or redeveloped property may result in job creation and increased property values (and taxes) as new workers compete for homes and community appeal is enhanced. In the long term, the firm will provide additional tax property revenues when the abatement or TIF lapses. The emergence of abatement programs can be traced to just after World War II, when southern states sought to attract industry from the Northeast and the Midwest. Over time, local governments began to view abatements as a means to compete with neighboring jurisdictions rather than as an interstate or interregional economic development strategy. This phenomenon has prompted concern among some analysts and policymakers. Findings suggest that commercial firms generally make location decisions based largely on factors other than relative property taxes (e.g., proximity to consumers). Thus, jurisdictions may grant abatements to firms that would have located in the area regardless. Others question the net benefits that accrue to communities that grant abatements (i.e., benefits less foregone property tax revenues). Still others note that the public generally has little understanding of abatements’ budgetary effects. Some states prepare tax expenditure budgets that make explicit the property revenues foregone due to preferential tax treatment, including economic development incentives,

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full exemptions, assessment limitations, and residential property tax relief programs. Homestead Exemptions

Many states exempt, in part, the market value of residential property. Often called homestead exemptions, these initiatives provide tax relief to broad classes of homeowners. Fixed-dollar exemptions decrease assessed property values by a stated amount (e.g., $30,000), which lowers effective tax rates (i.e., property taxes paid divided by property value) as property values decline. Other jurisdictions offer partial exemptions based on a stated percentage of residential property value (e.g., 20%). These programs produce equal effective tax rates but provide greater tax savings for those with higher property values. Partial exemptions may be increased for homeowners who are also elderly, disabled, veterans, or have limited income. Some states reimburse local governments, including school districts, for revenues lost from homestead exemptions. Circuit Breakers

Most states provide targeted residential property tax relief to those who demonstrate or are assumed to have a limited ability to pay (e.g., low income, elderly, and disabled). In this form of tax relief, termed circuit breakers, homeowners receive a credit on their state income taxes for some portion of property taxes paid. Single threshold circuit breakers limit property tax payments to a stated percentage of income (e.g., 4%). Multiple threshold circuit breakers provide incremental tax relief (e.g., property tax limited to 2% of first $20,000 of income, 4% of the next $40,000 of income, and 6% of income more than $60,001). Sliding-scale circuit breakers also specify income brackets and provide greater relief to those with lower income levels (e.g., 50% relief for incomes less than $25,000, 25% relief for incomes between $25,001 and $50,000, and no relief for incomes more than $50,000). Because landlords may shift property taxes to tenants in the form of higher rents, most states have circuit breaker-like provisions for renters whose income falls below specified thresholds. Analysts note that circuit breakers that use age rather than income to determine eligibility may provide relief to high-income homeowners. Tax Rate and Levy

Once an assessor specifies a jurisdiction’s total assessed value (i.e., tax base), local officials can set

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a tax rate that yields the desired revenue level, called the tax levy. Most states require that local jurisdictions tax all property classes at the same rate. Thus, the basic property tax formula can be restated as follows: Rate (expressed as percentage) = Levy (expressed in dollars)/ Base (expressed in dollars)

Suppose that a school district’s tax base is $800 million and it needs to raise $20 million to meet its budget needs. The district would impose a tax rate of 0.025 ($20 million/$800 million). Districts commonly express tax rates as percentages (e.g., 2.5%) or millage rates (e.g., $25 per $1,000 of assessed value). A mill is an old English coin equal to 1/10th of one cent. In an effort to make property tax administration transparent, many states have enacted the so-called truth-in-taxation (full disclosure) laws. These provisions require taxing jurisdictions to disclose publicly (e.g., newspapers and public hearings) differences between proposed tax rates or levies and prior year figures. Limits on Assessments, Tax Rates, and Levies

Most states limit local governments’ ability to increase property taxes through one or more of the following mechanisms collectively referred to as tax and expenditure limits (TELs): assessment increase limits, tax rate limits, and levy limits. Assessment limits bound the amount assessed values can increase in a given year using either a stated percentage (e.g., 3%), the rate of inflation, or both. Assessment limits typically apply only to residential property. Rate limit provisions limit the property tax rate (e.g., 10 mills). Levy limits restrict the property tax revenue a jurisdiction can raise in a given year, typically in reference to prior year amounts (e.g., 2% or the rate of inflation, whichever is less). Assessment and levy limitations are generally adjusted upward to account for new construction and property improvements. Many states also enable taxpayers to override limits on assessments, tax rates, and levies in their jurisdiction (e.g., school district) by a simple majority vote or some higher percentage. Analysts regard the use of assessment limits alone as nonbinding (i.e., ineffective), because jurisdictions can simply raise tax rates to secure more revenues. Alternatively, tax rate and levy limits alone can reduce property taxes. Though TELs can provide property tax relief, their use has several implications. States may compensate local governments for lost revenues

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by increasing aid, thereby diminishing local fiscal control. Alternatively, states may fail to make local governments fiscally whole, a likely result in periods of fiscal stress. Several analysts report that TELs decrease school spending and, as a result, can lower student performance. TELs also often allow reassessment to market value when property is sold. The result, referred to as the welcome stranger tax, can result in two identical properties having substantially different tax burdens. This phenomenon violates the principle of horizontal equity (equally situated taxpayers have equal tax burdens). In addition, some argue that reassessment at sale decreases homeowner mobility and can therefore alter real estate market values. Most significant, as with partial exemptions, for a given budget, the preferences given to one set of taxpayers (e.g., homeowners) will need to be compensated by other taxpayers (e.g., commercial). Tax Collection and Appeal

Once a year, school boards issue a warrant, a legal directive that authorizes tax collectors to collect property taxes on their behalf. The tax collector then notifies owners’ of taxes due and other information as required by law (e.g., full value). Once notified, the collection process affords taxpayers one or more avenues to appeal the factors used to determine their liability, including classification, valuation, and exemption; this process is referred to as tax certiorari proceedings. Districts often establish tax certiorari reserves to settle disputes with taxpayers.

Efficacy California’s Proposition 13 was a watershed moment in U.S. property tax history. Passed in 1978, the referendum limited local governments’ (including school districts) ability to increase property assessments and tax levies, curtailing greatly their revenueraising capacity over the long term. Soon after, other states adopted property tax limitations, a period commonly referred to as the “property tax revolt.” Reasons given for the revolt include taxpayers wanting to decrease the size of government, perceived inefficiencies in the provision of government services, and supposed inequities in the tax system. A series of polls taken by the Advisory Commission on Intergovernmental Relations throughout the 1980s and the early 1990s confirmed the public’s low regard for local property taxes, deeming them worse than state income and sales taxes and, in some years,

worse than the federal income tax. New York’s passage of a property tax levy limit in 2011 suggests that the property tax revolt is ongoing. Public Finance Perspective

How property taxes should and do affect governments and taxpayers is a well-developed subfield in public finance (sometimes referred to as public sector economics). With regard to governments, most view the property tax as a stable tax. A stable tax yields predictable revenues from year to year, enabling governments to budget accurately, even in a fluctuating economy. The property taxes’ stability follows, in part, from administrative practices such as infrequent revaluation and, in part, because owners view property as a long-term investment. Others note that local property taxes are a key component of fiscal federalism—the system that defines U.S. public school finance. Fiscal federalism takes advantage of both centralized and local forms of government. Centralized governments, such as states, can redistribute resources from localities with more fiscal capacity to those with less, which may then foster equity (or adequacy) in service provision. Local governments, such as school districts, can provide a tax-service mix that aligns with community preferences, and concomitantly, communities can hold agencies accountable for the efficient provision of services. As Charles Tiebout famously theorized, individuals who are dissatisfied with the tax burden–service mix in their community will move to one that better satisfies their wants. The property tax also has perceived shortcomings, some of which have elicited policy responses (e.g., circuit breakers). Foremost among the taxes’ perceived shortcomings is that the tax is inequitable. Definitive answers to questions about property taxpayer equity are illusive. Difficulties arise, in part, due to varying conceptions of equity and, in part, due to measurement issues. All agree that property taxes should comply with the principles of horizontal and vertical equity. Horizontal equity requires that taxpayers who are equally situated have equal tax burdens. Analysts define property tax burden as annual property tax divided by annual income, though some propose using a longer time frame such as total property taxes paid in a residence divided by income earned during the period. Vertical equity holds that unequal taxpayers have unequal burdens. Sorting taxpayers into unequal groups implicates two additional principles. The benefit principle holds

Property Taxes

that tax burdens should be allocated in reference to the benefits derived from public services. Stated simply, the greater the benefit, the greater the burden. The ability-to-pay principle allocates burdens in reference to one’s ability to pay taxes, regardless of benefits. Whether the property tax rests on the benefit principle or the ability-to-pay principle is debatable. Some hold that those with higher relative property values may benefit from property tax– funded services, such as fire and police. Others hold that the property tax rests primarily on the abilityto-pay principle due to the difficulty of assigning service benefits to individual taxpayers, including the benefits of public schooling. To assess taxpayer equity, the ability-to-pay principle requires further specification—how to measure one’s relative burden. Progressive taxes increase tax burdens as income increases. Regressive taxes decrease tax burdens as income increases. Proportional taxes assign equal burdens across income levels. Theorists and policymakers regard regressive taxes as inequitable. Contemporary debates, however, make clear that policymakers’ preference for progressive versus proportional taxes varies. Accordingly, judgments about property taxpayer equity generally follow from assessments about whether and the degree to which the tax is regressive. These assessments pose numerous measurement challenges due to difficulties in determining tax incidence. The statutory incidence of the property tax refers to the individual or entity that pays the tax (i.e., writes the check). Statutory incidence, however, may not reflect who actually bears the burden of the property tax, a concept referred to as the economic incidence. Consider that a business may be able to pass along property taxes to consumers through higher prices, to employees through lower wages, and to owners through lower dividends; a landlord might be able to pass along property taxes to tenants through higher rents; and a homeowner can pass a portion of his or her property tax to others through the federal deduction for local taxes. Measurement issues confound analysts’ ability to determine the economic incidence of the property tax and, therefore, whether it is regressive in the aggregate or for specific classes of taxpayers. Nevertheless, efforts to account for these shifts offer the following general observation: The property tax is likely regressive for the lowest income taxpayers, proportional for middle-income taxpayers, and proportional or progressive for the very highest income taxpayers.

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The same analysts, however, note that state and local preferential property tax policies can also shift burdens among taxpayers. For example, an abatement granted to a business might make the property tax more regressive (or less progressive), while a homestead exemption or circuit breaker that favors lower income taxpayers could have the opposite effect. Difficulty in determining the economic incidence of the property tax also confounds analysts’ ability to determine the tax’s efficiency—the extent to which the tax distorts behavior. Whether the property tax is distortionary has resulted in two opposing viewpoints: (1) the benefit view and (2) the capital (or new) view. The benefit view, an extension of the Tiebout effect, holds that the property tax is a benefit tax (or user charge) for public services (including schools) and results in residents making sound tax service decisions. Furthermore, the benefit of local public services and their associated costs (i.e., taxes) are capitalized in property values. For example, good schools, all else equal, will increase housing values. Alternatively, the new view holds that the property tax is a tax on capital and produces multiple distortions, including discouraging improvements and new development and influencing the location decisions of firms and residents. Empirical efforts do not reveal conclusively which view reflects best how property taxes affect behavior. School Finance Perspective

The public finance literature on property taxes is extensive and multidimensional. Alternatively, the school finance literature focuses largely on one element of the property tax—the association between local property tax bases and district revenues. Among the earliest known treatments of the issue was Ellwood P. Cubberley’s 1905 doctoral dissertation in which he reported that districts with higher property wealth per pupil can generate greater revenues for a given tax rate and, therefore, provide more educational services than less resourced districts—a result he deemed inequitable. As the 20th century advanced, school finance analysts documented this phenomenon in increasingly nuanced ways, benefitting from the availability of more comprehensive district-level fiscal and demographic data.

Conclusion The property tax is an important component of local government finance, particularly for school districts. As with other broad-based taxes (e.g.,

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income), policymakers continue to modify the property tax in ways that address its alleged shortcomings. With regard to individual taxpayers, states have enacted programs to address inequities (e.g., circuit breakers) and dissatisfaction (e.g., homestead exemptions). States have also provided incentives to property owners to encourage green space preservation (e.g., preferential use provisions) and business development (e.g., abatements). As noted, difficulty in determining the economic incidence of the tax obscures our ability to draw definitive conclusions about the tax’s efficacy. With regard to school finance, differences in district property wealth will lead to differences in total revenues in the absence of state and federal funding. One needs to recognize, however, that evidence suggests that the use of other local taxes, such as income or sales, would produce similar results. Thus, policymakers need to recognize that it is not the property tax per se that yields differential revenues but the degree to which states rely on a local revenue source to fund schools and the measures they take to compensate for differences in local fiscal capacity. Brian O. Brent See also Ability-to-Pay and Benefit Principles; Horizontal Equity; Local Control; Progressive Tax and Regressive Tax; School Finance Litigation; Tax Burden; Tax Elasticity; Tax Incidence; Tax Limits; Tax Yield; Tiebout Sorting; Vertical Equity

Further Readings Augustine, N. Y., Bell, M. E., Brunori, D., & Youngman, J. M. (Eds.). (2009). Erosion of the property tax base: Trends, causes, and consequences. Cambridge, MA: Lincoln Institute of Land Policy. Bowman, J. H., Kenyon, D. A., Langley, A., & Paquin, B. P. (2009). Property tax circuit breakers: Fair and costeffective relief for taxpayers. Washington, DC: Lincoln Institute of Land Policy. Brunori, D. (2003). Local tax policy: A federalist perspective. Washington, DC: Urban Institute Press. Cubberley, E. P. (1905). School funds and their apportionment (Unpublished doctoral dissertation). Teachers College, Columbia University, New York. Dalehite, E. G., Mikesell, J. L., & Zorn, C. K. (2005). Variations in property tax abatement programs among states. Economic Development Quarterly, 19(2), 157–173. Dornfest, A., Van Sant, S., Anderson, R., & Brown, R. (2010). State and provincial property tax policies and administrative practices (PTAPP): Compilation and

report. Journal of Property Tax Assessment & Administration, 7(4), 5–112. Fisher, G. W. (1996). The worst tax: A history of the property tax in America. Lawrence: University Press of Kansas. Fisher, R. C. (2009). Property taxes for local finance: Research results and policy perspectives (Reconsidering property taxes: Perhaps not so bad after all) (Working Paper No. WP09RF1). Washington, DC: Lincoln Institute of Land Policy. International Association of Assessing Officers. (2010). Standard on property tax policy. Kansas City, MO: Author. Kenyon, D. A. (2007). The property tax: School funding dilemma. Cambridge, MA: Lincoln Institute of Land Policy. Kenyon, D. A., & Langley, A. M. (2010). Payments in lieu of taxes: Balancing municipal and nonprofit interests. Cambridge, MA: Lincoln Institute of Land Policy. Lincoln Institute of Land Policy and George Washington Institute of Public Policy. (2013). Significant features of the property tax [Data file]. Retrieved from http://www .lincolninst.edu/subcenters/significant-features-propertytax/Report_Value_Standard_and_Assessment_Ratios.aspx Lowry, D., & Sigelman, L. (1981). Understanding the tax revolt: Eight explanations. American Political Science Review, 75(4), 963–974. National Education Association. (1998). Understanding the property tax. Washington, DC: Author. Oates, W. E. (2001). Property taxation and local government finance. Cambridge, MA: Lincoln Institute of Land Policy. U.S. Census Bureau. (2012). Public education finances: 2010. Washington, DC: Author.

PUBLIC CHOICE ECONOMICS Public choice economics is the application of economic principles to the study of collective decision making in politics and government. It developed, in part, as a challenge to classic economic theories that assume government impartially serves the collective interest through policies guided by economic models. Instead, public choice economics recognizes that the government decisions are influenced by individuals—including voters, politicians, and bureaucrats—pursuing their own self-interest and the rules by which those interests are aggregated into a collective choice. This recognition has helped inform the understanding of government decision making and how those decisions can diverge from the predictions of classic economic theories.

Public Choice Economics

This entry provides an overview of public choice economics and its application to education. It begins by clarifying the role of government from an economic viewpoint. This perspective suggests that, in addition to the role of protecting its constituents, the primary function of government is to correct market failures. Market failures occur when individuals, pursuing their own self-interest in private markets, fail to fully account for the socially desired allocation or distribution of resources. With the role of government defined in this manner, questions remain as to how the socially desired allocation and distribution of resources are determined and how those preferences are aggregated into a collective decision or choice. The entry continues by addressing these questions through the application of public choice economics. The principles of public choice economics are presented and followed by a discussion of its key insights, which highlight how government, like private markets, can fail to achieve social preferences for the allocation and distribution of resources. The entry concludes by applying these insights to the government financing and provision of education.

The Role of Government and Collective Choice Market Failure

Economic principles assume that individuals respond to incentives and, although people can incorporate the concern for others into their preferences, that their preferences are primarily motivated by self-interest. Under certain market conditions, such as externalities and public goods, this behavior contributes to the inefficient allocation or inequitable distribution of goods commonly referred to as market failure. Although not the specific focus of this entry, market failure is the key factor justifying collective action through government. Market failures occur when economic markets fail to achieve the efficient allocation or distribution of goods. There are many reasons why markets fail, and examples of such markets are abundant; this entry, however, limits its focus to externalities, public goods, and inequity. Externalities

Typically, the interactions between individuals in private markets affect only those individuals involved in the transaction. Consumers and producers agree to a market price based on their own

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preferences. However, sometimes the consumption and production of goods and services in private markets affect individuals not directly involved in the transaction. These effects can be either positive or negative and are commonly referred to as externalities. Because of self-interest, consumers and producers do not consider these effects when interacting in private markets. Accordingly, the market price does not account for the external benefits or costs imposed on individuals outside the direct transaction, and the socially desired amount of the good is under- or overprovided, respectively. Although subject to debate, education is often associated with positive externalities. Students receive the direct benefits of education, but society can also benefit. Potential benefits include increased civic engagement and higher economic productivity. Accordingly, society would prefer students to consume more education. However, an increase in consumption would mean that students (or their parents) would also incur more educational expenses. Because students (or their parents) are only concerned with the direct benefits, they would be unwilling to incur these costs and will consume less than the socially desired amount of education. Public Goods

Public goods are goods that are nonexcludable and nonrivalrous in consumption. Nonexcludable means that individuals cannot effectively (without significant cost) be excluded from consuming a good. Nonrivalrous means that an individual’s consumption of a good does not diminish another person’s ability to consume that same good. Because of these characteristics, there is little motivation for a producer to provide the good privately since individuals cannot be effectively charged for their consumption. In the case of public goods, the market will fail to provide the socially efficient amount. Similar to externalities, the classification of education as a public good is a matter of some debate. Education can largely be considered a private good in that its consumption is excludable and rival to some extent. For example, the presence of private schools demonstrates that education can be excludable, and students can, up to a point, be added to classrooms without diminishing other students’ educational experience. However, the potential positive externalities discussed above are considered by many to be public goods.

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Education and Equality of Opportunity

Beyond concerns of allocative efficiency, the collective interest is also concerned with the equity of market outcomes. These concerns are motivated by societal judgments of what is considered fair. Equity and equality are distinct concepts, as a fair outcome does not necessarily mean an equal outcome. However, in education, equality of opportunity is considered equitable. If left to the market, the concern is that many would not be able to purchase levels of education that would provide them with equal opportunities. While equality of opportunity is a vague concept, it is widely cited as one of the most significant factors motivating the intervention of government. Government and Collective Action

Whatever the reason for market failure, individuals can act collectively and call on government to intervene. Traditional economic theories suggest that government, through institutions (rules and policies) and at times through the direct financing and provision of goods and services, can improve market outcomes by correcting market failures. However, these theories assume that the process of collective action does not influence the decisions ultimately made.

Insights of Public Choice Economics Public choice economics examines how governments make decisions. Unlike traditional economic theories, public choice economics suggests that the process by which decisions are made should also be considered when evaluating the role and outcomes of government intervention as compared with economic market alternatives. Government Failure

The implicit assumption of traditional economic theory is that government is an impartial entity that chooses policies according to the predictions of economic models. Public choice economics challenges this assumption. It sees government not as an impartial entity but, instead, reasons that if self-interest motivates individuals in private economic markets, then they are likely motivated by that same self-interest in collective action. Furthermore, if in the pursuit of self-interest individuals can contribute to market failure, then there may also be circumstances under which an analogous government failure can occur. The potential for government failure has changed

the way the role of government is perceived. It is no longer a foregone conclusion that government should always intervene to correct market failures. Rather, public choice economics reasons that government intervention may result in outcomes that are worse than the market outcomes they sought to correct. Furthermore, public choice economics seeks to identify sources of government failure and, where possible, ways to minimize it. Public choice economics seeks to understand the nature of these failures by examining the process of collective choice and politics through the lens of economics. Specifically, public choice economics examines how the individuals convey their preferences to the government through voting, how the individuals in government incorporate those preferences into decision making through the political process, and how those decisions are implemented by bureaucracies. Voting

Through collective action, individuals work together to achieve outcomes that they would otherwise not be able to achieve through economic markets. This is not to suggest, however, that people will necessarily agree on those outcomes or on the policies through which those outcomes are achieved. In economic markets, individuals reveal their preferences through the price mechanism. The intensity of one’s preferences is demonstrated by his or her willingness to pay, and the cost and benefit of the transaction is incurred solely by the individuals involved in the exchange. On the other hand, in collective action, individuals reveal their preferences through voting. Each person has one vote, thus limiting the ability to reveal the intensity of his or her preferences. Instead, the intensity of preferences is determined by the aggregation of votes, and decisions are made subject to satisfying the majority rule. Each person in the collective group must then share in the costs and benefits of the majority decision regardless of their vote. Given these characteristics, the majority rule and the size of the collective group have important consequences. A unanimous voting rule eliminates the possibility that individuals will need to comply with decisions for which they did not vote. While this may seem ideal, in practice, getting people to agree unanimously may be prohibitively difficult, and as the size of the collective group increases, it may become impossible. Conversely, a simple majority

Public Choice Economics

rule—meaning just over half—reduces the cost of decision making, but potentially forces more individuals to comply with outcomes for which they did not vote. Thus, the choice of the majority rule represents a balance between reducing the cost of decision making and reducing the potential for forced compliance. Public choice scholars suggest that the ideal majority rule depends on the voting matter at hand. For example, if the potential for the minority voting group to be exploited by the majority is low, then a simple majority rule can suffice. Under simple majority rule, the median voter theorem suggests that the winning outcome will be the one most preferred by the median voter. Rational Ignorance

Voting requires time and effort on the part of the individual. Whether it is voting on a specific issue or for a representative to make choices on one’s behalf, individuals can incur significant costs in acquiring information. However, the expected benefit to a given voter may not justify the cost of acquiring information. Regardless of how well-informed a voter becomes, there is still uncertainty associated with the costs and benefits of the outcome. Not only is the outcome itself associated with uncertainty, but any single voter’s vote is unlikely to matter for the result. The uncertainty coupled with the unlikely chance that an individual’s vote will affect the outcome suggest that it is rational for a voter to remain ignorant of candidates and policies and even abstain from voting. Special Interest Groups

The benefit-cost calculation of voting may limit the incentive for the individual to acquire information and vote. Yet small groups have the potential to receive concentrated benefits from voting outcomes and may be more likely to voice their preferences. This can yield outcomes that favor the minority at the expense of the majority. Furthermore, if the costs of the outcome are spread among a large enough group, there is little incentive for them to incur the costs associated with organizing in order to change the outcome. Federalism

A federalist system consists of separate but overlapping levels of government, each with different responsibilities. The basis for determining the bounds of responsibility for each level of government is the

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scope of market failure. In other words, a federalist system seeks to assign the task of addressing a given market failure to the level of government whose bounds match the spillovers of that market failure. However, there is still overlap in that higher levels of government create institutions under which lower levels of government must operate. But by reducing the size of the collective group, the costs of decision making are reduced. This also allows for individuals to “vote with their feet” by locating in communities that match their preferences. This has particular relevance for the local provision of education and the evolution of school finance and the institutions of education. The Political Process

Voting is rarely done by the entire collective group on an issue-by-issue basis. Instead, individuals vote for representatives who then vote on their behalf. While this arrangement reduces the cost of decision making, it presents costs of its own. This is because representatives, acting out of self-interest, may not always make choices that align with the electing majority. Accountability

Typically, voters have only two candidates to choose from in an election—one from each major political party. But there are a number of issues to be decided on, and it is unlikely that a candidate’s views will match the views of a given voter on many of them. To simplify this problem, candidates will often adopt a platform that reduces the dimensionality of the issues to a few key points that may even include nonpolicy factors. Although this makes it easier for voters to select a candidate, it may make it more difficult to hold the elected candidate accountable. This is reinforced by the presence of rational ignorance and special interest groups. It may be in the interest of individual voters to concern themselves only with outcomes on a few key issues. If an elected official can satisfy those preferences while also supporting the preferences of special interest groups, then accountability pressures may remain low even if the outcomes of policies are detrimental to society at large. Rent Seeking

The potential lack of accountability pressures can cause special interest groups and politicians to engage in rent-seeking activities. Rent seeking is

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the expenditure of resources in an effort to obtain returns that are higher than those earned at normal competitive levels. Special interest groups may expend a large amount of resources to protect their interests by influencing politicians. Politicians are receptive to these interests because the support that they receive helps perpetuate their incumbency. Whether or not rent-seeking behavior is successful, its presence reduces the well-being of society because resources are diverted away from productive uses in an effort to capture rents by manipulating the institutions that govern markets. Successful rent seeking distorts market incentives and competition, thereby further reducing the well-being of society. Bureaucracy

Poor implementation can lead even the most welldesigned and well-intended policy to government failure. Like voters and politicians, bureaucrats are guided by self-interest and are unlikely to be completely objective in their interpretation and application of policies. Specifically, public choice economics suggests that bureaucrats act in ways that serve their interests by increasing their budgets and authority. The ability for bureaucrats to engage in these activities is supported by the fact that legislation is often rather ambiguous and open to interpretation. This gives bureaucrats a significant amount of discretionary authority. It is also difficult for politicians to hold bureaucrats accountable because bureaucrats typically are more knowledgeable about their own respective areas. This allows bureaucrats to be vague about which parts of their agency are vital to meeting their objectives, allowing them to present inflated budget proposals that are more difficult to reject, thus leading to a burgeoning government.

The Provision of Education Although the presence and magnitude of market failure in education is subject to debate, it is seen as the primary reason for government intervention in the financing and provision of schooling. From a public choice perspective, however, government intervention in the market for education may lead to an analogous government failure. This source of failure has provided the basis for reform proposals focused on the introduction of market mechanisms such as charter schools and voucher programs. The call for these types of reform has spawned an intense debate over the status quo of government provision of education.

Although private markets are capable of providing education, approximately 90% of all primary and secondary students attend public schools, making the financing and provision of education primarily a government enterprise. Accordingly, the allocation and distribution of educational resources are subject to the decision-making process of politics and government, as opposed to an economic market of consumers and producers. The established role of government in education has been due in large part to concerns that private markets would underprovide the socially desired level of education and that distributional inequalities in access and quality would be pervasive. In other words, private markets would fail to meet the collective interest of individuals. Since the 1960s, however, economists have examined the process by which collective action influences the provision of education and have suggested that the ability of the current system to meet the collective interest is also subject to failure. Given these failures, they suggest the quality of education may be improved by replacing the status quo of public provision with market-oriented solutions of competition and choice. These competing views have spawned a debate that has reemerged and intensified over the past two decades. The renewed debate is due in large part to mounting evidence of declining relative achievement of U.S. students compared with students from other developed nations, even while U.S. spending per pupil has more than doubled. Critics of the government provision of education suggest that the way in which collective decisions are made and the failures they produce are unlikely to be resolved with increased spending and other reforms. They point to economic markets and competition as the avenue by which our education system can improve. Proponents of the status quo contend that these measures may not fully capture the collective interest of individuals’ preferences for education, suggesting that student achievement is only a part of what society values from the public provision of education. They suggest that the collective interest can only be represented by collective choice and the government financing and provision of education. In addition to providing the basis for the debate over the public provision of education, public choice economics is also useful in examining how this debate takes shape in the political arena. Perhaps more important, public choice economics helps inform us how—through the process of collective

Public Good

action—voting, politics, and bureaucracies may affect changes to the status quo of the provision of education. Nathan Barrett See also Allocative Efficiency; Charter Schools; Educational Vouchers; Markets, Theory of; Median Voter Model; New Institutional Economics; Public Good; Schools, Private

Further Readings Arrow, K. J. (2012). Social choice and individual values (Vol. 12). New Haven, CT: Yale University Press. Brunner, E., & Sonstelie, J. (2003). Homeowners, property values, and the political economy of the school voucher. Journal of Urban Economics, 54(2), 239–257. Buchanan, J. M., & Tullock, G. (1965). The calculus of consent: Logical foundations of constitutional democracy (Vol. 100). Ann Arbor: University of Michigan Press. Chubb, J. E., & Moe, T. M. (1988). Politics, markets, and the organization of schools. American Political Science Review, 82(4), 1066–1087. Cullen, J. B., Jacob, B. A., & Levitt, S. D. (2005). The impact of school choice on student outcomes: An analysis of the Chicago Public Schools. Journal of Public Economics, 89(5), 729–760. Dee, T. S. (1998). Competition and the quality of public schools. Economics of Education review, 17(4), 419–427. Friedman, M. (2009). Capitalism and freedom. Chicago, IL: University of Chicago Press. Hoxby, C. M. (Ed.). (2007). The economics of school choice. Chicago, IL: University of Chicago Press. Levin, H. M. (1991). The economics of educational choice. Economics of Education Review, 10(2), 137–158. Niskanen, W. A. (1971). Bureaucracy and representative government. New Brunswick, NJ: Transaction Books. Olson, M. (2009). The logic of collective action: public goods and the theory of groups (Vol. 124). Cambridge, MA: Harvard University Press. West, E. G. (1994). Education and the state: A study in political economy (Rev. 3rd ed.). Indianapolis, IN: Liberty Fund. (Original work published 1965)

PUBLIC GOOD In countries around the world, the state is the main supplier of educational services. State provision is often justified on the grounds that education is a public good. This entry reviews the economist’s

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definition of public goods, explains why education is not in fact a public good under this definition, and identifies other justifications for state provision of educational services. For economists, pure public goods have two main attributes: They are nonrival and nonexcludable in consumption. To say that a public good is nonrival in consumption is to say that one person’s consumption of the good does not diminish the quantity or quality of the good that is available for the consumption of others; the marginal cost of additional consumption is zero. Adding new listeners to the audience of a radio broadcast, for example, does not diminish the enjoyment of those who have already tuned in. To say that a good is nonexcludable in consumption is to say that once a public good has been produced, there is no practical way to exclude additional consumers from its enjoyment. Once a radio program has been broadcast, for example, there is no way to prevent additional listeners from enjoying the program. Most goods are both rival and excludable in consumption. Adding new listeners to a radio broadcast has no effect on the pleasure of other listeners, but adding new members to the audience in a concert hall may harm the enjoyment of those who are already there in multiple ways, perhaps including reduced air quality and increased ambient noise. It is impossible to prevent potential listeners from tuning in to a radio broadcast, but it is a simple matter to exclude those who do not pay from the audience in a concert hall or from the purchase of a recording. The canonical example of a pure public good is national defense. Once a state has created a nuclear deterrent, for example, the protection that it provides is freely and equally available to all who reside within the national territory, without regard to their numbers, location, or preferences for defense spending. Consumption of the presumed safety offered by a nuclear shield is therefore nonrival and nonexcludable. Other examples of pure public goods include street lighting and various environmental goods including clean, unpolluted air. Pure public goods must be provided by the state, because markets fail when goods are nonrival and nonexcludable in consumption. When consumption of a good is genuinely nonrival, the marginal cost of consumption (e.g., the cost of adding one additional listener to a radio broadcast) is zero, while the cost of production and the marginal utility of consumption (e.g., the pleasure one additional listener derives from the broadcast) are both greater

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Public-Private Partnerships in Education

than zero. Under these conditions, many consumers will choose to understate their true preferences, so as to pay less for the good than it is worth to them or than it costs to produce. When consumption is nonexcludable, all consumers have free and equal access to the good, whether or not they have paid anything for it. Also, many consumers will act as “free riders,” consuming as much as they like of the good without contributing to its cost. When both of these circumstances prevail, markets will produce too little or none of the goods in question, and the state must provide them directly.

Is Education a Public Good? For economists, education is clearly not a pure public good, because it is both rival and excludable in consumption. As with other private goods, the consumption of educational services is rival, because the marginal cost of consuming these services is significantly greater than zero. For example, adding one more student to a classroom may diminish the educational services provided to other students by placing new demands on the attention and energy of the teacher or on the supply of crayons. Education is also excludable. Prospective students can readily be prevented from consuming educational services by the simple expedient of charging tuition or by limiting participation in school to those who reside in a specific geographic area. In principle, therefore, education need not be provided by the state. It could instead be bought and sold in a market like other private goods—and, in fact, it often is. Parents pay very high prices to enroll their children in high-quality preschools and prestigious universities or to purchase houses in school districts with excellent public schools. Tutors and test preparation providers market their services aggressively. In some places grades, diplomas, and access to teachers’ time are widely if not openly available for sale. In the market for schooling, households receive the educational services that they pay for; those that cannot or will not pay the price are barred from consuming these services. The state nevertheless continues to play an outsized role in the education system, providing many educational services directly, subsidizing many others, and extensively regulating both public and private actors in the system. The deep involvement of the state in the education of its citizens must be justified not by the claim that education cannot be provided through the market but rather on the

basis of two other arguments. On the one hand, the dominant public role in the education system is justified on the grounds that schools are critical agencies of socialization, responsible for acquainting young people with the knowledge and values requisite to effective citizenship. On the other hand, state involvement in the education system is justified on the grounds that the market for schooling necessarily fails to produce an optimal level of educational output and likewise fails to distribute educational outcomes equitably. Among the reasons adduced for market failure in education are the presence of important positive externalities (i.e., the benefits that accrue to others because of the education that an individual receives), asymmetrical information between producers and consumers (i.e., the challenge that students face in evaluating the quality and value of specific educational opportunities), and risk aversion and high time preference among the consumers of educational services (i.e., individuals’ reluctance to invest in education even when future returns are large). State provision and/or regulation of educational services can reduce or eliminate many of these problems. David N. Plank See also Discount Rate; Economies of Scale; External Social Benefits and Costs; For-Profit Higher Education; Privatization and Marketization

Further Readings Belfield, C. R., & Levin, H. M. (Eds.). (2007). The price we pay: Economic and social consequences of inadequate education. Washington, DC: Brookings Institution Press. Buchanan, J. M. (1967). Public finance in democratic process: Fiscal institutions and individual choice. Chapel Hill: University of North Carolina Press. Gutmann, A. (1987). Democratic education. Princeton, NJ: Princeton University Press. Wolf, A. (2002). Does education matter? Myths about education and economic growth. London, UK: Penguin Books.

PUBLIC-PRIVATE PARTNERSHIPS IN EDUCATION The term public-private partnerships (PPPs) refers to any type of cooperation between public and private entities regarding the provisioning and financing of

Public-Private Partnerships in Education

certain goods, services, or both. In education, PPPs are increasingly being considered as potential solutions toward improving educational outcomes and expanding access to education. This entry applies a well-known framework in education to organize PPPs alongside their educational objectives and the policy tools used for reaching these goals. The entry concludes with a short overview of the evaluation of PPPs in education and describes promising directions for future research.

Modes of Educational Provision Accepting that education has both private and public benefits, few people would disagree that government ought to play some role in education. Because of education’s benefits to society at large, it would seem appropriate for governments to financially support compulsory education. However, this is different from stating that education should be provided by the government as well. In his 1955 landmark article “The Role of Government in Education,” the Nobel Prize laureate Milton Friedman argues that private provision of education is to be preferred. More recently, Andrei Shleifer, in his 1998 article “State Versus Private Ownership” states that compulsory education is a clear example of a service for which, in many contexts, the case for government provision has become indefensible. Instead, he argues that the innovative potential of private entities, coupled with strong incentives for cost reduction and major emphasis on consumer satisfaction, should be exploited. While some countries make a sharp distinction between the roles of the public and private sector in terms of education financiers and education providers, in most countries, private actors have historically been involved in education in several ways (e.g., faith-based schools and textbook manufacturers). The past few decades have seen a renewed interest in alternative approaches to educational governance, which can be partially understood through concurrent developments in the global political economy, as depicted in a 2012 article by Susan L. Robertson, Karen Mundy, and Antoni Verger. As a consequence, recent years have witnessed a notable expansion of the private sector’s role in the financing and provision of education services in both the developing and the developed countries. Of particular importance has been the emergence of relatively sophisticated forms of such private involvement in education through PPPs. This extension of PPPs into social

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policy areas, such as education, represents an important shift from the traditional model of government procurement for the delivery of public services. Despite the increased attention that they have been receiving from policymakers and researchers in recent years, there is still little agreement about what exactly PPPs in education entail or how to classify and evaluate them. A recent World Bank report defines a PPP in education in general terms as a system that recognizes alternative options for providing education services besides public finance and public delivery. Relative to systems in which either exclusively the public sector or private sector is involved in education, PPPs instead refer to education systems in which both sectors have entered into a partnership and in which both can have a role in the provision and/or the finance of education. The next section uses an existing conceptual framework to classify PPPs in order to better understand the different varieties of PPPs that can be imagined along this spectrum of public and private involvement.

A Framework to Understand and Classify PPPs in Education The existing body of literature describing PPPs in education is primarily composed of case studies, which mostly focus on the specificities of each PPP, rather than on their common characteristics. (Comprehensive classifications for a variety of PPPs in education, together with detailed reviews, can be found in Norman LaRocque’s 2008 article on PPP in basic education, and in the 2009 study by Harry Patrinos, Felipe Barrera Osorio, and Juliana Guáqueta.) Although this is a useful strategy to assess individual PPPs, it complicates the task of identifying similarities underlying PPPs, which would be necessary for providing some general guidance and recommendations on them. Importantly, the existing literature gives rise to the following primary question: Why are there so many different types of PPPs in education? More specifically, what are the reasons that a great number of education systems implement different versions of PPPs, thereby often moving away from the more traditional systems of public schooling? The simple answer to this question is that PPPs are designed for serving education systems operating in different contexts. Countries and their education systems differ in terms of the challenges they face and the outcomes they pursue to improve. They also differ in the policy instruments they have at their

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disposal for realizing these goals. These three features, which are interdependent, streamline a way to understand the apparent idiosyncrasy of PPPs in education. This entry applies a well-known framework developed by Henry Levin to organize the evidence of PPPs alongside their educational objectives and the policy tools used for reaching these. Levin’s framework is applicable not only to the study of PPPs but also to other forms of educational provision and finance (e.g., Levin used it to study voucher plans, school choice, and charter schools; other examples include its utilization to study privatization and social justice by Levin, Ilja Cornelisz, and Bárbara Hanisch; community college transfers by Tatiana Melguizo, Lisa Serra Hagedorn, and Scott Cypers; decentralization and privatization by Helga Cuéllar-Marchelli; and religious schools by Geoffrey Waldford). In contexts where the education system fails to deliver the desired outcomes, PPPs are conceived and implemented for a variety of reasons and can increase competition, quality, access, and diversity in education. Depending on the economic setting of a country, current challenges affect the supply of education, the demand for education, or both. Regardless of the contexts served by the different PPPs—thus, irrespective of the relative economic prosperity of countries and the failures of their education systems—the ultimate objectives of PPPs can be organized alongside a common conceptual framework. The four dimensions of educational outcomes considered in the framework are (1) productive efficiency, (2) freedom of choice, (3) equity, and (4) social cohesion as discussed in Levin’s 2002 study. A common goal of the vast majority of PPPs in education is to increase productive efficiency, albeit pursued in different ways. The emphasis of PPPs is centered on improving this educational objective, targeting the lack of quality, broadly defined, that (arguably) characterizes the initial situation of strictly public provision of education. PPPs include innovative proposals designed to improve matching of local demands and settings as to increase productive efficiency (e.g., school-based initiatives). Particularly in developing countries, PPPs also pursue productive efficiency by increasing the supply of, and access to, education (e.g., the management of government schools in Pakistan or “comanagement” governance strategies in Brazil). PPPs are often designed to foster productive efficiency by promoting competition in the supply of education

(e.g., school management initiatives such as charter schools in the United States). Greater competition in education also foments freedom of choice and enlarges the supply and demand for education. In some cases, greater choice with PPPs may reflect the result of adding fully, or partially, privatized modes of education provision to the existing system of public schools, with education services relatively comparable across school types. However, in other cases, where freedom to choose among schools is already high, a system of PPPs in education can increase the variety of education services provided so that they serve existing different cultural preferences of their societies. Among the examples of PPPs promoting educational choice are voucher-like programs, such as the Dutch and Chilean school funding systems. Other systems include the purchase of educational services from traditionally private schools, such as those that exist in Colombia or Spain, or the school infrastructure initiatives, such as can be found in Australia or Germany. Proposals for implementing a PPP in education are often based on the claim that it will increase the system’s educational equity. In all societies, inequity arises because disadvantaged children are not served, because their demand for education is below optimal from a societal point of view, or, especially in developed countries, because some form of segregation or unequal access to education exists. The private sector’s philanthropic initiatives, purchase of educational services from private schools, adopt-aschool programs, and voucher-like programs pursue to increase educational equity. Important initiatives in this respect are the ones promoted by the United Nations’ Global Education First Initiative in developing countries, as well as those by the Bill & Melinda Gates Foundation, and similar philanthropic foundations in the United States. Most of the PPPs do not explicitly address social cohesion. However, they might still affect social cohesion in important ways. If a PPP aims at increasing access to education of disadvantaged groups, and emphasizes the involvement of the community, social cohesion, at least at the level of the groups or communities served, is likely to increase. However, whether this also reflects improvements in social cohesion at a national level does not follow straightforwardly. Some examples of initiatives designed to improve social cohesion are the adopt-a-school programs such as the one promoted by the Sindh Education Foundation in Pakistan, which aims at the mobilization of the community and at increasing parental

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engagement. On the contrary, if a PPP sought to increase the variety in educational offerings, social cohesion could actually decrease by failing to provide students with common educational experiences. To reach the above objectives, Levin’s framework distinguishes three design instruments available to policymakers: finance, regulation, and support services. For example, in terms of finance, voucher plans and charter school programs utilize public funds to support the private provision of education. Similarly, a body of regulation regarding teacher certification, curriculum, student admission criteria, and so on exists to determine eligibility for these funds. Support services include transportation, food services, and the dissemination of information regarding the schools’ characteristics to promote households’ ability to make an informed choice among the options available to them. A country’s contexts—the economic and political circumstances attached to them—are once more the key to understand how PPPs are designed in terms of these policy instruments. But this argument is fully reciprocal in that the contexts and educational goals determine the specific combination of instruments used by each PPP, and in turn, a proper design of a PPP in terms of its utilization of the three policy instruments is essential to determine its success in reaching these goals in a specific context. These bidirectional arguments are discussed in the next section. In general, all PPPs involve some type of finance formulas and sets of regulation between the public and private agents involved. Fewer PPPs also explicitly provide support services to complement the provision of education.

Evaluating PPPs in Education The framework outlined above to classify PPPs, focusing on educational outcomes and policy instruments, can also assist in evaluating their success. It was already highlighted that different PPPs arise for different reasons (e.g., lack of access to schools or demand for more diversity) and are developed within their own context (e.g., developing or developed country). As such, they can pursue different objectives (e.g., improving educational opportunities, access, or innovation) and will differ in design (e.g., public delivery vs. for-profit private delivery). All of these elements also constitute the dimensions for evaluating the success of PPPs in education. Two additional considerations are added to complete the guidelines for evaluating PPPs in education.

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The first one is the existence of trade-offs between the different educational outcomes. The existence of the trade-offs implies that there can be some tensions between educational objectives, because reaching one might not be wholly compatible with fulfilling another. Thus, some degree of compensation and resignation is implied by PPPs in education, in the sense that it is not possible to jointly achieve full levels of all the outcomes in a simultaneous fashion through a given PPP. Second, the evaluation ought to be based on a set of measures that can be used for that purpose (both for the education goals and policy instruments), such as student achievement, enrollment patterns, curriculum content, and financial accounting. Trade-Offs

Trade-offs in the educational outcomes of PPPs are likely to arise for several reasons. For example, a system that emphasizes choice, situated at the private end of the PPP spectrum, may sacrifice some degree of equity and social cohesion by allowing private elite institutions to charge high levels of tuition and to select students by ability. Vice versa, a PPP whose main goals are to increase equity and social cohesion, operating at the public end of the PPP spectrum, may reach these goals only at the expense of choice by inhibiting schools from charging tuition and/or by imposing rigid curriculum requirements. Trade-offs will be either softened or reinforced, depending on how policy instruments are employed. An emphasis on improving choice will have consequences for finance (e.g., alleviate financial constraints), regulations (e.g., avoid barriers to entry), and support services (e.g., provide transportation). Similarly, emphasizing productive efficiency also has implications for finance (e.g., allow add-ons and tuition), regulations (e.g., use performance benchmarks), and support services (e.g., disseminate information on performance). If the main emphasis for a PPP is to improve educational equity, this will largely affect finance (e.g., provide compensatory funding) and regulations (e.g., set student admissions criteria). Finally, when the objective is to safeguard social cohesion, regulations (e.g., establish common-curriculum or student body composition requirements) and support services (e.g., provide transportation) will need to be in place to ensure realizing this public objective of education. Importantly, multiple stakeholders involved in PPPs (e.g., households, schools, policymakers, and

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taxpayers) can differ in terms of their preferences toward what the optimal balance of educational outcomes should be. Satisfying competing preferences can further alter the balance between educational outcomes, both pursued and realized, of PPPs. Indicators for Evaluation

In addition to an appropriate framework and methodology, a useful evaluation relies on the existence of clear indicators for all the dimensions that are to be measured. Ideally, for the sake of comparability across the array of existing PPPs, the indicators—quantitative or qualitative measures— should be objective and universal. Although the most popular evaluation indicators in education nowadays are test scores of student achievement, many of the selected dimensions composing the PPPs design and evaluation framework are arguably to be expressed in other metrics. For instance, efficiency and education quality can be measured not only by increases in student achievement but also by increases in attainment, decreases in dropout rates, or improvements in cost-effectiveness ratios. Equity can be measured not only by improvements in the test scores of disadvantaged groups as well as achievement gaps between groups but also by participation rates, graduation rates, and inequality parameters (e.g., the education Gini coefficient, which measures the dispersion of educational spending across population groups and socioeconomic gradients). Although it is conceivable that social cohesion could be described through qualitative analysis of common curriculum content, empirically, most attention is given to the distribution of students across schools and school types, evaluating segregation and stratification with measures such as indices of isolation and dissimilarity. Freedom of choice is usually evaluated by using market concentration measures (e.g., Herfindahl index, which measures the size of schools in relation to their local education market as an indicator for the intensity of competition), the percentage of private school enrollments, the proportion of students admitted to their first-choice school, the number of schools available within a meaningful radius, or the number of different school types offered.

Conclusion As private participation in compulsory education has significantly increased over the past few decades through the development of a wide variety of PPPs,

the evidence based on their effects is starting to expand as well. Despite the clearly heterogeneous nature of PPPs in education, it is possible to draw some general implications about them by linking the educational objectives PPPs pursue to the policy tools used in designing them. For this, Levin’s framework has been used for both classifying PPPs and for setting out the dimensions for a comprehensive evaluation of their educational outcomes. Building on this, future research could also move toward evaluating which policy tools are found to be effective in realizing these goals. In addition, policymakers would also like to know whether PPPs have consequences for the resources available for public education (i.e., “crowding-out” effects) and how the outcomes of PPPs rank in terms of cost-effectiveness when compared with alternative interventions in education. This entry has suggested that evaluations based on the framework adopted here will provide useful guidance on identifying the key features of what a successfully designed and implemented public-private partnership in education entails. Emma García and Ilja Cornelisz See also Charter Schools; Economic Efficiency; Education Finance; Educational Equity; Educational Innovation; Educational Vouchers; Philanthropic Foundations in Education

Further Readings Cuéllar-Marchelli, H. (2003). Decentralization and privatization of education in El Salvador: Assessing the experience. International Journal of Educational Development, 23(2), 145–166. Friedman, M. (1955). The role of government in education. New Brunswick, NJ: Rutgers University Press. LaRocque, N. (with CfBT Education Trust). (2008). Publicprivate partnerships in basic education: An international review. Reading, UK: CfBT Education Trust. Levin, H. M. (2002). A comprehensive framework for evaluating educational vouchers. Educational Evaluation and Policy Analysis, 42(3), 269–284. Levin, H. M. (2009). An economic perspective on school choice. In M. Berends, M. G. Springer, D. Ballou, & H. J. Walberg (Eds.), Handbook of research on school choice (pp. 19–34). New York, NY: Routledge. Levin, H. M. (2012). Some economic guidelines for design of a charter school district. Economics of Education Review, 31(2), 331–343. Levin, H. M., Cornelisz, I., & Hanisch-Cerda, B. (2013). Does educational privatisation promote social justice? Oxford Review of Education, 39(4), 514–532.

Pupil Weights Melguizo, T., Hagedorn, L. S., & Cypers, S. (2008). Remedial/developmental education and the cost of community college transfer: A Los Angeles County sample. Review of Higher Education, 31(4), 401–431. Patrinos, H. A., Osorio, F. B., & Guáqueta, J. (2009). The role and impact of public-private partnerships in education. Washington, DC: World Bank. Robertson, S. L., Mundy, K., & Verger, A. (Eds.). (2012). Public private partnerships in education: New actors and modes of governance in a globalizing world. Cheltenham, UK: Edward Elgar. Shleifer, A. (1998). State versus private ownership. Journal of Economic Perspectives, 12(4), 133–150. Walford, G. (2003). Separate schools for religious minorities in England and the Netherlands: Using a framework for the comparison and evaluation of policy. Research Papers in Education, 18(3), 281–299.

PUPIL WEIGHTS A weighted pupil system is a state aid system in which pupils are given different weights based on the estimated or assumed costs of their education program. Aid is allocated on the basis of the total number of weighted students. The system is also referred to as weighted student funding, studentbased allocation, student-based budgeting, and fair student funding. Weights can be used to provide additional resources to children with specific needs and/or disabilities or can be used to accommodate variations in the cost of providing education at different grade levels. This entry describes the use of pupil weighting systems in school funding formulas. It starts with a short historical background and then provides examples of pupil weighting formulas in the states.

Background One of the challenges in school finance is to find equitable ways to distribute funds and resources to students with differing educational needs. It is widely accepted today that children living in low-income households, children who are English Language Learners (ELLs), and children with special needs will require more resources to reach educational standards than other children are likely to need. The two most common approaches for meeting the needs of these students are categorical funding programs and weighted pupil mechanisms. Categorical funding focuses specific sources of revenue toward identified students with the expectation that the funds will be

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used to meet their educational needs. Pupil weights similarly focus resources on specific children but approach the process through the general funding system by counting those children more heavily in the distribution of funds to school districts. Both of these approaches were developed to enhance the vertical equity of state funding systems and both are widely used in the states—in fact, many states use a combination of weighted pupil programs and categorical grants to meet the needs of children with different or unequal needs. Roe L. Johns and others first proposed pupil weights in 1971 as a part of their extensive study of Florida school funding. Since that time, pupil weights have been used in many states, using a range of strategies to provide schools and school districts with additional funds to meet specific student needs. The form of the weights, the complexity of the weighting structure, and the size of the weights varies across states. California’s Local Control Funding Formula, enacted in 2013, is in effect a pupil weighting system as it provides additional dollars for each additional student with particular needs, and even has a further weight for identified students when the percentage of students with these needs exceed a certain threshold. This straightforward weighting scheme differs from the complex weighting schemes used in other states. Florida, for example, at one time had some 55 different pupil weights, although the system was later simplified to 7 different weights. Pupil weighting programs are operationalized by counting the number of pupils in each school district using state guidelines and then adding on weights for qualifying students. The weights for qualified students are totaled and added to the pupil count. Funds are then distributed to school districts on the basis of weighted pupils, not the headcount (or other pupil count mechanism used in a state). Thus, if there were a weight of 0.25 for compensatory education (typically measured by eligibility for free and reduced-price lunches), each student qualifying for the compensatory program would be counted as 1.25 students when funding is distributed to school districts. The consequence of this is that there are far more weighted pupils in each school, district, or state than there are when enrollment is counted by head counts. States differ in the strength of their requirements for using the resources generated by pupil weights on those specific students, although it is assumed that the weights provide the additional resources necessary to meet the needs of those students. A major

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challenge is how to determine what the weights might be and to create a system that balances the need for additional resources with the potential for districts to overidentify students with specific needs if the weight is too high or to underidentify students with needs that are underfunded by the weighting scheme.

Examples of Weighted Pupil Systems Pupil weights were first used in Florida. At one time, Florida included weights for different grade levels as well as specific weights for the many types of student disabilities that were reported to the state and federal governments. There were also weights for children from low-income homes, children who did not speak English as their first language, and career and technical education. The system in Florida was later changed to have only seven weights. Students in Grades 4–8 are weighted as 1.0, while students in K-3 are weighted 1.089 and students in Grades 9–12 receive a weight of 1.031. There are two weights for exceptional students (those in special education) of 3.523 and 4.935, depending on the student’s disability. Finally, there is a weight of 1.147 for nonEnglish speakers and a weight of 1.035 for career education in Grades 9–12. Several states (e.g., Arkansas and California) weight at-risk students—students with particular needs—but shift to a larger weight if the concentration of such students exceeds a specific threshold. In California, once fully implemented, the Local Control Funding Formula will provide each district with a differing base grant for each of four grade spans (K-3, 4–6, 7–8, and 9–12). The variation in grade-level base grants is to provide for smaller classes in Grades K-3 and to accommodate the assumed higher costs of education in the grades above 4–6. A supplemental grant of 20% of the adjusted base grant is provided for targeted disadvantaged students—ELLs, students eligible for free or reduced-price meals, foster youths, or students who are some combination of these categories (students in this last category are only counted once). A concentration grant of 50% of the adjusted base is provided for targeted students exceeding 55% of a district’s enrollment, providing further resources to districts with large concentrations of targeted students. This relatively straightforward approach will replace a complex system of some 100 often confusing and overlapping categorical programs in California once it is fully implemented.

Deborah Verstegen has catalogued state school funding systems. She identifies 23 states that use some form of pupil weighting to provide funding for compensatory education. Where she is able to determine specific weights for students in compensatory programs, they vary from a low of 0.10 in Hawaii to a high of 1.5938 for Georgia students enrolled in alternative education programs. Some states have multiple weights for compensatory students (e.g., Georgia, Minnesota, Nebraska, and New Jersey), and some states allocate a dollar amount for each eligible student (e.g., Massachusetts and Washington). Verstegen’s analysis also considered weighting programs for ELLs. She found 36 states with some form of pupil weighting program for ELLs. Programs ranged from a flat appropriation used to assist districts through the State Department of Education in North Dakota, to dollar allocations per pupil in states such as New Hampshire and New Jersey, and pupil weights varying from a low of 0.10 in Texas to a high of 2.5102 in Georgia. There are 20 states that rely on pupil weighting systems for special education. Most have a variety of weights depending on the severity of a child’s disability and range considerably from figures as low as 0.115 to more than 6 for students with severe disabilities.

Determining Appropriate Weights The previous discussion shows that if a weighted pupil system is to be effective in establishing vertical equity and meeting the needs of the children it is intended to serve, the weights need to be computed so that they provide an appropriate level of funding. This is made more complex as programs for students needing additional assistance have changed over time. For example, as the focus on special education has shifted from treating individual disabilities outside of the regular school program to making special education as inclusive as possible (i.e., providing programs in a regular classroom setting), estimating the additional cost of special education has shifted from treatment of the disability to the treatment methods. Students who are included in regular programs with an aide (part-time or full-time) may be part of one cost category, while students who are pulled out of the regular program for part of each day would be in a second cost category, and those in full-time special education classrooms would have a third cost—each of which could be converted to a weight, depending on how a weighted pupil system is calculated.

Pupil Weights

Several studies of special education over the past 35 years have consistently found that the average costs of special education are double the costs of programs for students who do not have an individualized education program for special education. Of course that average masks the differences between children with mild reading or verbal disabilities and children with multiple and serious disabilities who require nearly constant monitoring and assistance. Some states use a census approach, weighting all special education students at a specific weight (say 2.0) and then funding a percentage of total enrollments as eligible for special education. Given the consistency of the findings on the average cost of special education, this approach has many advantages in that it takes away incentives to place students in disability categories for which the weight generates more funding than the cost of the program, and vice versa. However, this approach poses a problem when it comes to children with severe disabilities who require very expensive treatments and have a very low incidence. Rather than penalize the one district where that child resides, it may be most efficient to simply fully fund services for children with severe disabilities at the state level. The challenge of a census-based weighted pupil system then is the percentage of students who can be identified as being part of the system. If the weighting program creates incentives to overidentify children for special education, states could either carefully audit special education placements and assignments or cap the percentage of students who could be identified as requiring special education. The latter approach places the burden on local districts to carefully assess special education needs and to monitor costs. Clearly, an efficient special education program could serve more than a fixed percentage of the district’s enrollment. Similar cost issues arise in determining the weights for low-income students and ELLs. Some

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of the costing-out models used in determining whether school funding is adequate could be used to impute weights for students with special needs and for ELLs and low-income students. Currently, the weights used by many of the states vary widely and likely reflect either vastly different assumptions about program treatments for these students or have been determined on the basis of available funding rather than student needs. As more states consider estimates of adequate funding levels, more data to estimate pupil weights may be available.

Conclusion Pupil weights represent a comprehensive way for states to help school districts fund the diverse needs of student populations and offer a powerful way to approach vertical equity in the distribution of funds to districts, schools, and students. The challenge with pupil weighting programs is identifying the students who require additional resources, determining the appropriate weights for each category of students, and deciding whether or not a concentration factor should be included in some instances. Lawrence O. Picus See also Adequacy; Categorical Grants; Vertical Equity; Weighted Student Funding

Further Readings Odden, A. R., & Picus, L. O. (2014). School finance: A policy perspective (5th ed.). New York, NY: McGraw-Hill. Verstegen, D. A. (2011). Public education finance systems in the United States and funding policies for populations with special educational needs. Education Policy Analysis Archives, 19(21). Retrieved from http://epaa.asu .edu/ojs/article/view/769/923

Q inequality and the distributional consequences of educational policies and interventions. This entry provides a brief introduction to these distributional research methods and highlights several recent studies that productively used these methods in settings related to educational policy and the economics of education more broadly.

QUANTILE REGRESSION Many of the analytic techniques that are widely utilized in the economics of education focus on central tendencies. Traditional experimental analyses compare the mean on an outcome of interest for subjects randomly assigned to a treatment group with the mean for subjects randomly assigned to a control group. Regression analyses model the mean value of the outcome variable for fixed values of predictors. Quantile regression and other distributional estimators expand these mean-focused techniques to make it possible to analyze an outcome variable’s entire distribution by estimating the relationship between one or more predictor variables and a specific quantile (i.e., percentile) of an outcome. These approaches have wide applicability in discussions of educational policy and economics, where stakeholders are interested in pursuing both excellence and equity goals. Policymakers expect schools to improve the availability of human capital in a society and to narrow existing social and educational inequalities. However, these two goals are not necessarily mutually reinforcing. Some policies may boost average academic achievement even as they broaden educational inequalities. Others may depress academic achievement even as they narrow inequalities. With their analytic focus on central tendency, traditional econometric techniques may provide limited information about the distribution of educational achievement. Quantile treatment effects and quantile regression provide important opportunities to boost our understanding of educational

Conceptualizing and Measuring Consequences To contextualize these distributional estimators, it is useful to first consider the estimation of the mean effects of a simple treatment in an experimental setting. Under the potential outcomes model, each individual has two potential outcomes: one that would occur if that individual were assigned to the treatment group and the other if that individual were assigned to the control group. The fundamental evaluation problem is that the same person cannot simultaneously be in the treatment group and the control group. Experimental research designs address this evaluation problem by randomly assigning subjects to treatment and control conditions. In this setting, evaluators can assume that the odds that a subject is exposed to the treatment are unrelated to any characteristic of the subject and that the mean effect of the treatment is thus the difference between the mean for subjects assigned to the treatment group and the mean for subjects assigned to the control group. Quantile treatment effect estimators expand the logic of these evaluation techniques to investigate the difference in the outcomes of interest in the treatment and control groups at any percentile (or quantile) of the distributions. Intuitively, 591

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at any given quantile from the x-value for the control group. In this hypothetical example, the treatment group score is one standard deviation above the control group’s at every point in the test score cumulative distribution, so the treatment effect is uniform and equal to 1 everywhere. Such a uniform treatment effect need not necessarily occur. Indeed, the below discussion of applications of quantile treatment effect estimation in educational settings indicates that mean effect estimation may often obscure important variation in treatment effects across the test score distribution. Figure 2 provides a second hypothetical example to illustrate one way in which this could occur. The control group in this example again has a standard normal distribution on the outcome of interest. However, the test score distribution for the treatment group is far more compressed, with a median of 0 and a standard deviation of 0.5. This hypothetical treatment has no average effect on student achievement. However, the hypothetical treatment substantially reduces inequality in student achievement, with negative treatment effects below the median and positive treatment effects above the median. A similar approach can be used to investigate the relationship between predictors and outcome distributions in observational settings, where the assumption of treatment exogeneity is not likely to hold. Nonrandom selection is a particularly vexing problem in educational settings, where educators

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these estimators can be thought of as comparing the pth percentile score of subjects in a treatment group with the pth percentile score of subjects in a control group. By compiling these quantile treatment effects estimates across the distribution of an outcome, quantile treatment effects estimators allow inferences regarding a treatment’s consequences on the distribution of the outcome. Mechanically, quantile treatment effect estimation hinges on the comparison of the cumulative distribution function (CDF) of the outcome of interest for treatment and control groups. For any random continuous variable Z, the CDF is the proportion of the population for which Z is less than or equal to each value y. Figure 1 provides CDF illustrations for two groups. The solid line in Figure 1 represents the CDF for a standard normal distribution, and the dashed line is the CDF for a normal of mean 1 but standard distribution of 1. The y-axis in this graph represents the cumulative frequencies in the population in each group, and the x-axis represents the range of values for the outcome of interest. For purposes of illustration, assume that the solid line in Figure 1 represents the test score distribution for the students randomly assigned to the control group in a hypothetical experimental evaluation project, and the dashed line represents the test score distribution for students randomly assigned to the treatment group. To estimate the quantile treatment effect, one subtracts the x-value for treatment group

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Cumulative Distribution Function for Hypothetical Treatment With Positive Effect at Bottom and Negative Effect at Top of Distribution

Source: Thurston Domina.

Quantile Regression

may handpick students for interventions, or students or parents may self-select into programs or schools. Researchers often employ regression techniques to address this problem, arguing that the mean treatment effect is equal to the difference between students selected into treatment and control groups after controlling for relevant observed characteristics. Just as quantile treatment effect estimation extends traditional means comparisons to estimate the distributional effects of treatments, quantile regression expands the regression approach to estimate the relationship between one or more predictor variables and the distribution of the outcome, conditional on all other predictors. (Roger Koenker provides a more technical description of quantile regression estimation; Lingxin Hao and Daniel Q. Naiman provide a more detailed description of the technique geared to data analysis.) Quantile regression is particularly useful in contexts in which ceilings, floors, or outliers on the outcome variable threaten to bias traditional regression estimates, since quantile regression estimates at the median and at other points near the center of the distribution are not affected by censoring or measurement error at the tails. These quantile regression models can be interpreted in much the same way as traditional linear regressions, with two important qualifications. First, in interpreting quantile regressions, the analyst should keep in mind that specific quantile regression estimates refer to a specific point on the distribution of the dependent variable. In many cases, it is most informative to compile quantile regression estimates from several points in order to understand the relationship between treatment and outcome across the distribution. Second, it is important to remember that quantile regression models provide estimates of the relationship between given independent variables and the distribution of the dependent variable, conditional on all other independent variables. Many analysts are comfortable with conditional means in traditional regression settings, where the overall mean is equal to the weighted average of any number of subgroups. However, this is not the case for quantiles (e.g., the overall 10th percentile is not necessarily equal to weighted average of the subgroup’s 10th percentiles). The conditional distribution that is the result of a quantile regression is thus different from the unconditional distribution (undoing the conditioning for all but the independent variable that is the focus of the analysis) that is typically of interest. Neglecting that difference

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can lead to misinterpretation. Joshua Angrist and Jorn-Steffen Pischke provide further discussion of this issue. Inference with quantile regression can be carried out in various ways; Maria Kocherginsky, Xuming He, and Ying Wei lay out guidelines for inference for different sample sizes.

Quantile Treatment Effects and Quantile Regression in Educational Settings Recent studies utilize quantile treatment effect estimation and quantile regression to deepen our understanding of several educational settings by examining the distribution of educational achievement. For example, Erika Jackson and Marianne Page reevaluate data from the experiment known as Project STAR, or Student/Teacher Achievement Ratio, in which nearly 12,000 early elementary students in 79 Tennessee public elementary schools were randomly assigned to a small class (in which the target enrollment was 13–17 students), a regular-sized class (in which the target enrollment was 22–25 students), or a regular-sized class with a fulltime teacher’s aide (in which the target enrollment was 22–25 students). Earlier analyses of the STAR data consistently indicate that assignment to a small class improves student achievement test scores, with effect sizes for kindergarten and first graders of approximately 0.2 standard deviations. While Jackson and Page demonstrate that this significant positive effect held across the test score distribution, their quantile treatment effect analyses demonstrate that the effects of small class placement are considerably larger at the top of the test score distribution than at the bottom. Recent distributional analyses of school accountability policies further demonstrate the potential that these techniques offer for educational researchers. There is strong evidence suggesting that the No Child Left Behind Act and other school accountability policies have small positive average effects on student achievement. But new distributional analyses indicate that these average effects tell only part of the story. Accountability policies give schools incentives to direct teacher attention and other educational resources at students whose scores are just below proficiency thresholds set by states under these policies. Research by Derek Neal and Diane Schanzenbach and by Randall Reback that estimates policy effects on the test score distribution shows that these policies have strong positive effects on students near the proficiency threshold and weaker

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Quasi-Experimental Methods

effects on higher and lower achieving students. These effects might well have been missed had these researchers instead focused on estimation of average effects or subgroup effects. Other applications of quantile regression provide more descriptive insights into the relationship between education and social inequality. Thomas Lemieux, for example, uses quantile regression to investigate changes in the returns to postsecondary education over time and the extent to which these changes explain recent increases in wage inequality in the United States. His analyses indicate that the returns to postsecondary education increased sharply between the early 1970s and the early 2000s, particularly at the top of the income distribution. Lemieux finds, for example, that relative wages for workers with postgraduate education increased by 39 percentage points at the median between the early 1970s and the early 2000s. At the 90th percentile, the relative wages for workers with postgraduate education increased by 51 percentage points during the same time period. In other descriptive work, Andrew Penner uses quantile regression models to examine gender differences across the distribution of mathematics achievement in 22 countries. Gender differences vary across countries in both magnitude and shape: In approximately half of the countries, differences do not vary across the distribution, but in several countries, differences are more pronounced at the bottom, while in others, differences are largest at the top. Penner further highlights the relationship between gender inequality in the labor market and gender differences in mathematics achievement at the top of the distribution, arguing that social context shapes the pool of potential scientists. These studies highlight the ways in which evaluations of average impact miss heterogeneous effects throughout the distribution. Thurston Domina, Marianne Bitler, Andrew Penner, and Emily Penner Authors’ Note: Research for this entry was supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Number P01HD065704. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

See also Econometric Methods for Research in Education; Educational Equity; Policy Analysis in Education

Further Readings Angrist, J. D., & Pischke, J. S. (2009). Quantile regression. In Mostly harmless econometrics (pp. 269–292). Princeton, NJ: Princeton University Press. Hao, L., & Naiman, D. Q. (2007). Quantile regression. Thousand Oaks, CA: Sage. Jackson, E., & Page, M. E. (2013). Estimating the distributional effects of education reforms: A look at Project STAR. Economics of Education Review, 32, 92–103. Kocherginsky, M., He, X., & Mu, Y. (2005). Practical confidence intervals for regression quantiles. Journal of Computational and Graphical Statistics, 14(1), 41–55. Koenker, R. (2007). Quantile regression. Cambridge, UK: Cambridge University Press. Lemieux, T. (2006). Postsecondary education and increasing wage inequality. American Economic Review, 96(2), 195–199. Neal, D., & Schanzenbach, D. W. (2010). Left behind by design: Proficiency counts and test-based accountability. Review of Economics and Statistics, 92(2), 263–283. Penner, A. M. (2008). Gender differences in extreme mathematical achievement: An international perspective on biological and social factors. American Journal of Sociology, 114, S138–S170. Reback, R. (2008). Teaching to the rating: School accountability and the distribution of student achievement. Journal of Public Economics, 92(5), 1394–1415.

QUASI-EXPERIMENTAL METHODS Over the past 30 years, mountains of data and statistics on the achievement of students and performance of schools have become available to both the public and the researchers. In addition, recent reforms at the local, state, and national levels have led to unique policies being implemented in a wide variety of contexts. Understanding the benefits of such policies is paramount in improving the efficiency of public education in the United States, in particular, as states seek new approaches to improve student achievement with budgets that are constrained and often shrinking. With these factors in mind, this entry discusses quasi-experimental econometric approaches in the economics of education. Such approaches include difference-in-difference modeling, instrumental variables (IV), regression discontinuity, and propensity score matching (PSM). Recently, several methods have been developed with all the estimation of causal parameters in absence of a

Quasi-Experimental Methods

randomized experiment. This entry summarizes various quasi-experimental approaches, gives examples of their usage, and discusses challenges that arise in their implementation.

Quasi-Experimental Approaches A robust understanding of experimental designs aids in understanding both the motivation and the necessary assumptions for quasi-experiments to provide unbiased estimates. Experiments have many advantages and considerable appeal for empirical policy analysis. However, experiments are not without their drawbacks, and both the limitations and the disadvantages of experiments highlight the benefit of the quasi-experimental approaches discussed in this entry. Treatment Effects

Researchers Jerzy Neyman and Donald Rubin independently offered the first detailed examination of causal treatment effects. While the term treatment effects originated in medicine, in this context, a treatment may refer to policy change or a measurable input in the education process. A treatment effect for a particular individual i is defined as the difference in an outcome, Yi; based on its assignment either to treatment (indicated when the binary variable Ti is equal to 1) or to control (indicated when Ti is equal to 0). This is represented in Equation 1. βi = YiTi = 1 − (Yi|Ti = 0).

(1)

This conceptual framework allows every individual to have a completely unique response to any treatment. By weighting individuals by their likelihood of being observed in the population, we can construct the average treatment effect (ATE). Due to the linearity of the expectations operator, the ATE can be rewritten as Equation 2: ATE = E(βi) = EYiTi = 1 − E(Yi|Ti = 0).

(2)

While every individual may have a unique response to the treatment, due to our inability to observe individuals in parallel universes, ultimately, individual treatment effects are impossible to calculate. However, ATEs can be calculated by comparing a treated group with a counterfactual or control group. In a true experiment, as the treatment is randomly assigned, the difference of the means of the treatment and the control groups is all that is

595

necessary to calculate an unbiased estimate of the ATE. Likewise, this is easily estimated in a regression setting by using ordinary least squares with a binary indicator for inclusion in the group receiving treatment. β1 from the linear regression in Equation 3 will provide an identical estimate to the ATE calculated by taking the difference of the means of the treatment and the control groups (u represents random error). yi = β0 + β1Treatmenti + ui.

(3)

Noteworthy examples of randomized control trials abound in the economics of education. In addition, the U.S. Department of Education’s Institute of Education Sciences has funded many studies using randomized control trials to evaluate numerous factors that influence academic achievement. Despite the large advantages in conducting randomized trials to estimate an ATE, several trade-offs also must be considered. Randomized experiments require buy-in from local policy stakeholders to ensure that the randomization is not compromised. Furthermore, ethical or political considerations often complicate or prevent certain treatments from being randomized. Also, conducting randomized trials is often expensive, requiring years of oversight, data collection, and site visits to ensure that the program is implemented. Given these obstacles, while randomization is rightfully viewed as the gold standard in program evaluation, quasi-experiments provide compelling alternatives often easily conducted with existing data sources and infrastructure. Differences-in-Differences

Several sources of naturally occurring variation can be exploited to allow quasi-experimental estimates of ATEs. One particular common source of variation is the rolling out of policies or laws in geographic or administrative units over time. If there were no confounding time trends, meaning that the only factor changing over time was treatment, then an ATE for an outcome y can be calculated by comparing the difference in the outcome measure before and after the policy was implemented. This simple difference in the outcome measure before and after a policy is implemented is often referred to as a first difference estimator, as it relies on taking the difference of two means. This could also likely be estimated by a simple ordinary least squares regression with an indicator variable for the period after the policy change.

596

Quasi-Experimental Methods

While comparing the levels of an outcome before and after a policy change seems like a straightforward exercise, this simple comparison will arrive at biased estimates if there are unobserved trends that happen to coincide with the timing of the policy change. One approach to remove the bias that would otherwise result is to utilize a difference-indifference estimator. This approach rose to prominence in influential articles in labor economics such as the work on minimum wages by David Card and Alan Krueger. The approach relies on taking the difference of two before-and-after comparisons. A first difference is calculated by subtracting the mean of the after period from the before period for the treated unit (those experiencing the policy change). Likewise, a second difference is calculated by subtracting the mean of the after period from the before period for a suitable control unit (a group not receiving the treatment). The difference-in-difference estimator is then calculated by taking the difference of these two differences. Of course, if it was the case that there were no confounding trends, then we would expect the change in outcomes for the control group to be close to or equal to 0. Provided the treatment unit and control unit experience the same trends absent the treatment, then the difference-indifference estimator will produce unbiased estimates of the ATE. Furthermore, it can be represented in a regression model with indicators for treatment unit, and the period after the law change, as shown in Equation 4. yst = β0 + β1Treatmentst + β2Afterst + β3Treatmentst ∗ Afterst + ust

(4)

Based on the regression model in Equation 4, the estimate for β3 produces a numerically identical estimate to the difference-in-difference estimate based on sample means. Difference-in-Difference With Fixed Effects

Of course, in many instances, there is more than a single unit experiencing a change or shift in policies. Within states, many districts change policies at different points in time. Likewise, within the United States, many states will adopt similar policies, with timing of policy changes varying widely across states. When this is the case, a researcher could try to estimate a different treatment effect for each district or for each state. However, there may not be enough statistical precision to identify the effect for an individual state. Thus, a generalized

difference-in-difference model can expand on the basic difference-in-difference model by allowing each unit to adopt the treatment at a different point in time. Intuitively, it does so by estimating how a geographic unit deviates when assigned to treatment both from its long-term trend and its shared contemporaneous trends with its neighbors. The meanbased difference-in-difference model in a generalized context would be unique to each setting. However, the regression model for a generalized differencein-difference model will include geographic dummy variables (to absorb time-constant effects specific to each state) and time period dummy variables (to absorb time-constant factors shared by all geographic units). That can be expressed in Equation 5. yst = β0 + Ss + Tt + β1Treatmentst + ust.

(5)

In Equation 5, Ss refers to a geographic fixed effect (e.g., state or district), Tt refers to time fixed effect (e.g., year if the policy changed in a year), Treatmentst refers to an indicator variable describing the time period in which a particular geographic unit adopts treatment, and u represents random error. The regression could also be slightly modified to include other controls in order to increase the precision of the models or control for other factors such as state-specific time trends. Generalized difference-indifference models have been utilized by a variety of researchers in the economics of education. One complication in any difference-in-difference design concerns the selection of the control group. The underlying assumption necessary for a differencein-difference quasi-experimental design to produce unbiased estimates is that the trends of the treatment and control units are the same absent the treatment. One way of testing this underlying assumption is to test if the trends between the treatment and the control units are the same in the period prior to treatment. In addition, Alberto Abadie, Alexis Diamond, and Jens Hainmueller have offered a data-driven method to construct an optimal control group known as synthetic control design. The synthetic control design approach utilizes covariates that are predictive of the outcome variable to construct an optimal weighting matrix for the universe of all possible control groups. Potential control groups that have covariates and outcomes that most closely mirror the treatment group receive the most weight. This approach also has considerable value in confirming other common approaches of selecting control groups based on factors such as geography.

Quasi-Experimental Methods

Instrumental Variables

IV is another approach to identify causal treatment effects. In essence, IV identifies the causal effect of policy by utilizing an external factor, referred to as an IV, which influences the treatment assignment of individual without otherwise affecting the outcome of an individual. In regression models with a single endogenous regressor, this can be summarized by two equations (u represents random error). yi = β0 + β1 + Treatmenti + ui Treatmenti = α0 + α1Zi + vi.

(6) (7)

In this context because treatment is endogenous, the E(Ziui) is not 0, implying that the ordinary least squares estimator will be both biased and inconsistent. The instrument variables estimator is able to overcome the correlation between the treatment status and unobservables by using an instrument. Intuitively, the instrument is utilized to separate the part of the variation in treatment status that is actually determined by an external factor (the instrument) and the part that isn’t. Using only the variation in treatment that is due to the instrument is basically using the part of the variation in treatment that is determined outside of an individual’s control. The IV must satisfy two conditions to yield consistent estimates. The first is that the instrument is uncorrelated with the unobservables in the second stage equation, or that the instrument is excludable from the main equation. This would also be satisfied by assuming that using the instrument would be as good as random selection. The second assumption is that the instrument is relevant, or that the instrument actually explains part of the variation in treatment. If all individuals have homogeneous treatment effects, the IV estimate will result in a consistent estimate of the ATE. If, however, there are individual-specific treatment effects, then a further assumption of monotonicity is needed. This implies that the effect of the instrument is either uniformly nonnegative or uniformly nonpositive. With that assumption, the IV estimator provides a local ATE, or the treatment effect for those whose treatment status is influenced by the instrument. It is most commonly estimated through two-stage least squares but could also be estimated by generalized method of moments and limited information maximum likelihood. Finding instruments that are both excludable and relevant is challenging. Despite that challenge,

597

numerous researchers have found plausibly exogenous instruments in the economics of education. Following an influential paper by Joshua Angrist and Alan Krueger, researchers studying the financial returns to education have used the quarter of the year in which subjects were born as an instrument for years of schooling. Because a student’s birth date determines eligibility to enter school and to drop out, it can influence the amount of time a student is in school. Joshua Angrist and Victor Lavy investigated Maimonides’ Rule, a cultural norm for class sizes that influences current class sizes. They find that smaller class sizes increase student achievement, with similar magnitudes to those estimated through experiments. Dave Marcotte, Steven Hemelt, and Benjamin Hansen have utilized snowfall as an instrument to create quasi-experimental variation in instructional days. While many of these instruments are quite clever in conception, the question of excludability is challenging even with an instrument that appears completely exogenous on the surface. For instance, year-to-year variation in snowfall would on many dimensions appear to be a near-ideal instrument. It predicts instructional days and is determined completely externally to most factors influencing school achievement. However, instrumenting instructional days with snowfall could lead to bias if snowfall also influences other factors, such as delayed starts. In some cases, providing upper and lower bounds of the bias is possible. In other cases, the potential instruments shift multiple endogenous regressors and prevent causal identification of treatment effects. For instance, age relative to school entry date cutoffs may increase education attainment prior to students having the chance to drop out, but it also shifts relative to age and peer dynamics. With this in mind, considerable care must be taken to ensure that IV provide valid quasi-experimental estimates of local ATEs. Regression Discontinuity

Donald Thistlethwaite and Donald Campbell first introduced the notion of a regression discontinuity (RD) in 1960. After several decades, economists began utilizing RDs, and they have grown in popularity for analyzing questions in the economics of education. Indeed, in education, numerous discontinuities arise due to the attempts of administrators to efficiently allocate scarce education inputs. Indeed the original study by Thistlethwaite and Campbell focused on evaluating college scholarship programs

598

Quasi-Experimental Methods

that utilized thresholds in measures of academic achievement to allocate scholarships. Conceptually, the RD closely mirrors the empirical structure of an IV model. An RD is generated from the threshold in a running (or forcing) variable. For values of the running variable to the right of the threshold, the probability of treatment assignment jumps. This can be represented by two equations. yi = β0 + β1Treatmenti + gri + ui Treatmenti = α0 + α1Di + fri + vi.

(8) (9)

The variable ri refers to the running or forcing variable, f() and g() are functions, while Di is an indicator that takes on a value of 1 if the running variable is to the right of the threshold in question. In this context, Di can also be thought of as an instrument. In some cases, being to the right of a threshold may perfectly predict treatment, for instance, if having an SAT above a threshold was the only requirement to receive a scholarship offer from a university. This case is referred to as a sharp RD. In other cases, referred to as a fuzzy RD, the probability of treatment may jump by a value less than 1, for instance, if an SAT score of 1,500 was one of several requirements to receive a scholarship. The main difference concerns estimation. In the case of a sharp RD, a first-stage regression is not necessary (or even possible), as the instrument perfectly predicts treatment. In the case of a fuzzy RD, the indicator for the threshold is used as an instrument for treatment. As mentioned earlier, the use of thresholds to allocate scarce resources has provided numerous contexts for RDs in the economics of education. For example, Brian Jacob and Lars Lefgren studied the effectiveness of remedial summer education, for which participation is determined by performance on a standardized exam. Jason Lindo, Nicholas Sanders, and Philip Oreopoulos utilize a threshold in grade point average that determines whether students are punished with academic probation. The critical assumption of the RD is that the assignment to treatment at the threshold is not correlated with the unobservables. Intuitively, the RD is comparing individuals who for all purposes are identical, but some randomly fell on one side of a threshold, while others fell randomly on the other side. In short, manipulation of the running variable around the threshold is the critical threat to the RD’s ability to provide unbiased estimates of ATEs. One could

imagine that students, teachers, or principals might have incentives to manipulate the running variable if they knew in advance how treatment assignment changed at the threshold. This could occur naturally, for instance, if individuals can take a test multiple times and the researcher only observes their final score, or could occur because agents strategically select one side of the threshold over the other. Fortunately, numerous methods exist to test for violations of the underlying assumptions of the need for the RD to provide consistent estimates. Researchers should investigate predetermined covariates to confirm that those characteristics are unaffected if the treatment assignment near the threshold is as good as random selection. In addition, researchers should confirm that the underlying density is smooth at the threshold. In addition, graphical representation is a key tool to help convey the influence of the threshold on determining treatment status and the change in outcomes resulting at the threshold. Propensity Score Matching

In addition to naturally occurring events that shift the likelihood of treatment, PSM offers an alternative nonexperimental approach to estimate an ATE based on observational data. PSM estimates an ATE by first calculating a propensity score, or likelihood of selection in treatment, and then calculating the differences in outcomes for individuals in the treatment and control groups with similar propensity scores. However, Elizabeth Ty Wilde and Robinson Hollister suggested various methods of using PSM to calculate ATEs failed in reproducing the experimental estimates of class-size reductions observed in Project STAR (Student/Teacher Achievement Ratio), a widely known randomized control study on the effects of smaller class sizes. The inability of PSM to replicate experimental estimates suggests caution in PSM’s use and interpretation.

Conclusion Given the huge increase in data sources and numerous policies implemented across schools, districts, and states, quasi-experimental methods offer alternatives to randomized experiments that can still recover unbiased estimates of policy-relevant parameters. Regardless of the method employed, researchers should take great care to ensure that the necessary assumptions are met. With this in mind,

Quasi-Experimental Methods

the unique set of constraints for policymakers in education will continue to yield many quasi-experiments for researchers to study in the future. Benjamin Hansen See also Cost-Benefit Analysis; Difference-in-Differences; Econometric Methods for Research in Education; Fixed-Effects Models; Instrumental Variables; Ordinary Least Squares; Propensity Score Matching; Randomized Control Trials; Regression-Discontinuity Design

Further Readings Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program. Journal of the American Statistical Association, 105, 493–505. Angrist, J., & Krueger, A. B. (1991). Does compulsory school attendance affect schooling and earnings? Quarterly Journal of Economics, 106, 979–1014. Angrist, J., & Lavy, V. (1999). Using Maimonides' Rule to estimate the effect of class size on scholastic achievement. Quarterly Journal of Economics, 114, 533–575. Card, D., & Krueger, A. B. (1994). Minimum wages and employment: A case study from the fast-food industry in New Jersey and Pennsylvania. American Economic Review, 84, 772–793. Hansen, B. (2008). School year length and student performance: Quasi-experimental evidence (Working

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paper). Santa Barbara: University of California, Department of Economics. Jacob, B., & Lefgren, L. (2004). Remedial education and student achievement: A regression-discontinuity analysis. Review of Economics and Statistics, 86, 226–244. Lindo, J., Sanders, N., & Oreopoulos, P. (2010). Ability, gender, and performance standards: Evidence from academic probation. American Economic Journal: Applied Economics, 2, 95–117. Marcotte, D., & Hemelt, S. (2008). Unscheduled closings and student performance. Education Finance and Policy, 3, 316–338. McCrary, J. (2008). Manipulation of the running variable in the regression discontinuity design: A density test. Journal of Econometrics, 142, 698–714. Neyman, J. (1990). On the application of probability theory to agricultural experiments: Essay on principles (Section 9) (D. M. Dabrowska & T. P. Speed, Trans.). Statistical Science, 5, 472–480. Retrieved from http:// projecteuclid.org/euclid.ss/1177012032 Rubin, D. (1974). Estimating causal effects in treatments in randomized and non-randomized studies. Journal of Educational Psychology, 66, 688–701. Thistlethwaite, D. L., & Campbell, D. T. (1960). Regression-discontinuity analysis: An alternative to the ex post facto experiment. Journal of Educational Psychology, 51, 309–317. Wilde, E., & Hollister, R. (2007). How close is close enough? Evaluating propensity score matching using data from a class size reduction experiment. Journal of Policy Analysis and Management, 26, 455–477.

R discusses possible reasons for the differences in academic achievement among racial and ethnic groups and reasons for changes in earning differentials over time.

RACE EARNINGS DIFFERENTIALS Economists have been interested in the source of differences in earnings between racial and ethnic groups since at least the 1930s. Their main focus has been on whether wage differences are the result of market failure (discriminatory behavior by employers, workers, and consumers) or differences among racial or ethnic groups in productive skills. Historical data suggest that wage discrimination by race and ethnicity in the United States has declined since the 1940s but still may exist for workers in jobs associated with lower levels of education. The recent analysis of skills differences has focused on assessing whether these are mainly cognitive or noncognitive and whether they are formed early in childhood or result later from differential access to schooling quality. Earnings differentials that persist over time suggest unequal access to human capital investment opportunities, unequal treatment in labor markets, or both. Starting in the 1990s, debate has taken place over whether racial and ethnic discrimination in labor markets have disappeared and whether the reason African Americans and Hispanics earn lower incomes for a given level of education is because of their lower academic achievement. The reason for differences in academic achievement and educational attainment, and their importance in explaining earnings differences, is also subject to debate. This entry discusses data on race and ethnic earnings differences in the United States and provides explanations for those differences. It then

Data on Race and Ethnic Earnings Differences Data on race, ethnicity, and earnings in the United States from 1939 to 2011 indicate that certain ethnic groups, regardless of race, earn more than average and more than the dominant non-Hispanic White majority. For example, since at least the 1960s, Japanese Americans, Chinese Americans, and Jewish Americans all earn more than non-Hispanic Whites as a whole. African Americans, Hispanics, Filipinos, and Native Americans all earn significantly less, on average, than non-Hispanic Whites. Table 1 presents earnings data for the period 1939–2012 according to ethnic group, race group, and gender group. Within the Asian American and Hispanic groups, there are distinct subgroups that earn significantly more or significantly less than the group average. On average, Hispanics of Cuban origin earn more than those of Mexican or Puerto Rican origin. This variation is due to large differences in the socioeconomic origins of different national groups that immigrated to the United States. For instance, many professionals fled Cuba for the United States in the 1950s and 1960s after the Cuban Revolution, while immigration from Mexico during the same period was largely made up of unskilled laborers. Discrimination due to skin color may also account for some of the variation in earnings among subgroups. 601

602

Race Earnings Differentials

Table 1

United States: Annual Median Earnings by Education, Race/Ethnic Group, and Gender, 1939–2011 (Full-Time Workers; Current Dollars and Percent) Year

Category

1939

1949

1959

1969

1979

1989

1999

2006

2011

White

1,356

3,001

5,438

8,633

Black

42

55

58

63

70

71

78

Latino

62

71

72

76

70

67

62

66

69

Asian American

65

76

86

101

96

98

—

124

112

White

1,353

3,026

5,241

8,082

14,830 22,288 30,496 34,387

36,173

Black

58

73

69

73

80

80

88

88

81

Latino

88

83

82

85

89

85

73

79

84

85

99

100

86

89

—

93

82

All schooling (males, 25–34 years) 17,389 28,578 35,603 40,964

45,444

77

80

High school graduates (males, 25–34 years)

Asian American College graduates* (males, 25–34 years) White

2,719

3,760

6,788 10,549

18,394 31,279 42,173 50,471

51,582 (55,805)

Black

59

72

67

68

82

70

87

80

86 (84)

Latino

99

66

78

76

88

83

90

88

83 (86)

Asian American

73

85

85

110

91

100

—

112

107 (120)

816

2,038

3,032

4,956

39 61 83

57 87 89

63 75 99

77 90 113

White

935

2,212

3,470

5,121

Black

51

75

73

84

95

90

88

88

91

Latino

94

103

95

103

93

95

91

92

90

85

69

100

94

92

119

—

—

—

1,128 67 — —

2,491 89 — —

4,378 71 — —

6,336 101 99 104

All schooling (females 25–34 years) White Black Latino Asian American

10,226 18,613 27,296 34,034 91 87 109

90 82 109

84 76 —

37,405

81 77 119

83 78 114

9,523 15,421 20,655 25,058

28,590

High school graduates (female, 25–34 years)

Asian American College graduates (female, 25–34 years) White Black Latino Asian American

12,228 23,732 34,377 40,614 95 94 90 91 94 98 94 92 110 106 — 109

42,429 (46,400) 88 (85) 94 (88) 106 (112)

Source: Carnoy, M. (2010). Race earnings differentials. In P. Peterson, E. Baker, & B. McGaw (Eds.), International encyclopedia of education (3rd ed., pp. 288–297). Oxford, UK: Elsevier (with addition of 2011 data from U.S. Department of Commerce, Bureau of the Census, Public Use Census Sample). * For college graduates, the first 2011 figure represents those with a BA, while the figure in parentheses represents those with a BA or a higher degree. Figures before 2011 represent those with BA or higher.

Race Earnings Differentials

Despite variation within groups, persistent earnings differences can be found among broad racial and ethnic groups. From 1939 to 1979, non-Anglo men and women had significant increases in earnings relative to non-Hispanic Whites (from here on, referred to as Whites). These gains leveled off after 1979, and relative earnings actually declined for Black and Hispanic women, although Black, male college graduates continued to make gains. The median income of Blacks and Hispanics has remained lower than that of Whites or Asian Americans. These data show the earnings of fulltime, full-year workers, and so they do not reflect the higher rates of unemployment and part-time work among minority workers.

Explaining Earnings Differences Using a human capital model, individual earnings can be characterized as a function of education and experience in the labor force, with differences in earnings among various groups explained largely, if not entirely, by their average education and experience. Other possible reasons for variations in earnings include which industries a group is predominantly employed in, whether the group tends to be in public or private employment, and the region in which the group tends to be located. Some groups may have higher average levels of voluntary unemployment and part-time employment, so the number of hours worked per week or per year is also an important potential factor affecting earnings differences. In the 1990s, debate over racial and ethnic earnings differences centered on the achievement score differences among groups with the same number of years of schooling and similar experience in the labor force. Various possible explanations were given for these differences, including variations in initial endowments of ability and intelligence, family and community investments in children’s academic ability, and differences in the quality of schooling available to different groups. More recently, other researchers have looked at whether noncognitive skills are even more important than cognitive skills in explaining differences in productivity and earnings among racial and ethnic groups. To measure human capital fully, academic ability and noncognitive skills need to be measured in addition to years of schooling. Cross-sectional studies for a single year show a significant correlation between schooling level attained and earnings differences among groups.

603

African Americans, Hispanics, and Native Americans receive significantly less education than Whites and Asian Americans. In Table 2, for each year and each ethnic or gender group shown, the figure represents the percentage point increase in average income the group would have had if its education were equal to that of White males in the same year. For example, taking into account age differences, Black males in 1939 would have had 27 percentage points higher income if their education were equal to White males. White females, in contrast, would have had 9 percentage points lower income if their education were equal to that of White males. This means that because full-time working White females were more educated than White males in the 1930s, the gap between White females and males with the same average amount of education would be wider than the observed average earnings gap between all full-time White female and male workers. Observable factors other than years of schooling and age, such as the workers’ industry or region, their level of English skills, and the quality of education they received, may explain part of the gap in the earnings of Black and Hispanic workers compared with Whites. Martin Carnoy examined the earnings residual or percentage points of income difference remaining when school attainment, work experience, region of work, marital status, foreign versus native birth, and industry of work for each group are equalized by simulation to that of White, full-time-employed adult males. This research shows that there was a significant drop in the earnings residual for full-time-employed Black males between 1939 and 1982 and for Hispanic and Asian American males between 1939 and 1969. In the 1980s, the residual rose for all three groups and represented about 16 percentage points of White male income for Blacks and 13 percentage points for Hispanics. Between 1989 and 2006, the residual likely continued to rise for Hispanics and may have fallen for Blacks (see Table 1). Part of the residual for Hispanics is associated with foreign birth, which may be a function of English language capability. The low residuals for native-born Hispanics of Mexican origin, compared with that of immigrant Mexicans, suggest that after accounting for English language capacity, lower incomes for Hispanics are explained by observable differences in educational attainment, experience, and place and type of work.

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Race Earnings Differentials

Table 2

Percentage Points of Income Gain That Would Result From Equalizing Minority Education to White Male Education, by Ethnicity and Gender, Full-Time Employed, 1939–1989

Year

Latino Latino Males Females

Black Males

Black White Females Females

1939

29

18

27

22

−9

1949

17

12

18

11

−8

1959

15

10

15

9

−2

1969

12

9

14

7

0

1973

16

9

12

5

0

1979

16

8

11

6

2

1982

15

10

11

6

2

1985

15

10

11

6

2

1987

15

8

10

6

1

1989

20

14

10

7

0

Source: Carnoy, M. (2010). Race earnings differentials. In P. Peterson, E. Baker, & B. McGaw (Eds.), International encyclopedia of education (3rd ed., pp. 288–297). Oxford, UK: Elsevier. Note: The education variable is measured in 1940, 1950, 1960, 1970, 1974, 1980, 1983, 1986, 1988, and 1990; incomes refer to the previous year—hence the years in the table refer to the income year. The education gap is estimated from a simulation using a regression equation with human capital variables (years of schooling, labor force experience, and, in census years, native or foreign born). The percentages in the table should be read as the number of percentage points that a given group would have gained just from getting the same distribution of education in its labor force as White males. A negative sign means that White females would receive lower incomes, all other variables being equal, were education equalized with that of White males (White females in the labor market in those years had higher education than White males).

These residuals could reflect differences in the quality of schooling and in returns to experience, such as in the choice of or access to jobs that have a training component. They also could indicate an interaction between the quality of schooling and access to training in jobs. The amount invested in students by their families and the interaction between family background and school performance can also affect the quality of education received, so that eliminating discriminatory practices in school access does not necessarily equalize the quality of

an individual’s education. Differences among groups in returns to education and in access to jobs with more training may also result from discriminatory practices in the labor market. Racial and ethnic earnings differentials vary by gender. In 2011, Black, male high school and college graduates earned about 81% and 86%, respectively, of the annual income of Whites, while Black women earned about 90% of the incomes of full-time White women at both levels of schooling. Similarly, Hispanic, male high school graduates earned a smaller fraction of White male incomes than did Hispanic women relative to White women. Gender segregation in the labor market, and the willingness of White male employers to group minority and White women in the same category of labor, partially explains why the income gap is smaller for minority and White women than for minority and White men. In the 1940s and 1950s, Black women were highly concentrated in domestic service jobs, and their earnings were much lower than those of White women. These differences began to disappear as Black women gained more education and moved into manufacturing and clerical jobs. However, with the steady increase in the labor force participation of White women since the 1980s, the ratio of Black and Hispanic women’s earnings to White female earnings has gradually declined.

Influence of Differences in Ability and Educational Quality Recent debates over whether discrimination still exists in U.S. labor markets focus in part on whether earnings differences not explained by education and experience differences are the result of continued discrimination or differences in “ability” as measured by test scores. Research by Richard Murnane, John Willett, and Frank Levy suggests a significant contribution of higher ability, as measured by scores on mathematics tests taken in high school, to individual earnings later in life, even after accounting for years of schooling. However, some economists, such as Samuel Bowles and Herbert Gintis, argue that cognitive skills explain relatively little of the variance of earnings and productivity. To estimate the role of mathematics achievement and race/ethnicity in wages, Martin Carnoy and Karen DeAngelis used data from the National Longitudinal Survey and High School and Beyond for the 1972 and 1982 high school senior cohorts. Based on their findings, race discrimination for

Race Earnings Differentials

Blacks seems to have played an important role in the labor market for men with lower levels of schooling even in the 1980s and early 1990s, but it seems to have played little role in the labor market for college-educated Black males by the late 1980s and for Black females with either a high school education only or with a college education. For the latter groups, mathematics ability was related to earnings. Based on these results, most of the differences in male, high school graduate earnings between Blacks and Whites in 1989 and 1999, as shown in Table 1, could be attributed to racial discrimination. Most of the observed differences in earnings between Black and White college graduates, on the other hand, could be attributed to mathematics achievement differences. James J. Heckman and colleagues used the National Longitudinal Survey of Youth to explore wage differences for a sample that took the Armed Forces Qualifying Test (AFQT), a measure of ability, in 1979 and were followed from 1991 to 2000. When the AFQT scores are corrected for the level of schooling attained by the test taker, one standard deviation higher AFQT score is associated with 9% to 10% higher male wages, whereas being a Black male is associated with 14% to 19% lower wages than for White males. Hispanic and White males do not have significantly different wages after accounting for AFQT scores. For women, being Black is associated with 7% to 8% lower wages than what White women earn when the AFQT scores were accounted for, while being a Hispanic woman is associated with a wage premium of 7% to 14% compared with White women after accounting for the AFQT scores.

Sources of the Test Score Gap Researchers have offered many possible reasons for the difference in test scores between Blacks and Whites and between Hispanics and Whites. Richard Rothstein argues that much of the achievement gap between Blacks and Whites exists at age 5 when children enter kindergarten and is largely the result of differences in social conditions, including health care and nutrition, so that schools alone cannot close the achievement gap. In his studies on stereotype threat, Claude Steele has shown that Blacks and Hispanics perform worse on standardized tests when their race is invoked and has concluded that this is due to anxiety that they will confirm a negative stereotype. Sean Reardon has analyzed changes in the BlackWhite test score gap between kindergarten and the

605

fifth grade and concludes that the gap in both math and reading scores widens during this period, and that the gap grows faster among higher scoring students. This suggests that one reason for the widening gap is being in schools with a concentration of low-income students, in turn the result of residential segregation. Still, the bulk of the gap between Black and White students test scores at fifth grade is explained by the Black-White test score gap at kindergarten entrance.

Changes Over Time in Earnings Differences Just as there are many possible explanations for the test score gap, researchers have offered various explanations for why the gap in earnings between Whites and minority groups has changed over time. Table 1 shows gains for minority groups from the 1940s through the 1960s. Since then, the picture has been mixed, but the relative earnings of Black, male college graduates fell during the 1980s, while in the 1990s, Black, female college graduates saw a decline in their earnings relative to White females. Some have attributed the change to supply-side forces, specifically changes in the relative investment in human capital made by different groups, while others have attributed it to demand-side forces, specifically legal and direct employment intervention by government. Richard Freeman asserted that the 1964 Civil Rights Act and subsequent federal employment legislation in 1965 were responsible for large gains in relative earnings of Blacks in the 1960s. Other researchers focus on the relative increase in education as the reason for gains among Blacks from 1939 to 1979. Table 2 shows a correlation between education gains by Blacks between 1939 and 1979 and a reduction in the Black-White earnings gap, but that reduction appears to be mostly due to other factors. During the 1940s, income shot up for all lowincome earners relative to high-income earners, which lifted Black male relative incomes more than in any decade since. Workers also shifted from agricultural jobs to manufacturing jobs during the 1940s, which brought up Black male incomes. Blacks made large gains in education in the 1960s and 1970s, but a reduction in wage discrimination accounted for the major gains in income for Blacks. With restrictions on affirmative action in the 1980s, Black male and female incomes stopped rising relative to those of Whites. In the 1990s, when the political climate for affirmative action became more favorable, Black male relative earnings rose again.

606

Race Earnings Differentials

For Hispanics and Asian American males and females, a similar analysis suggests that education had more to do with their relative income gains than it did for Black males and Black and White females. The shift from agriculture to manufacturing also brought relative income gains for both groups in the 1940s and for Hispanics in the 1950s. Reduced labor market discrimination in the 1940s and 1960s also brought up both groups’ relative incomes. Studies on the relationship between relative wages and “premarket skill differences” between minorities and Whites have prompted calls to improve the quality of education for Blacks and Hispanics. As noted earlier, however, some researchers make the case that the Black-White achievement gap in particular results primarily from socioeconomic differences. Also, at least part of the difference in wages for Blacks and Whites is not explained by differences in education and test scores. This wage difference could be due to noncognitive differences, discrimination, or a combination of these factors. Martin Carnoy Author’s Note: This entry draws on Carnoy, M. (2010). Race earnings differentials. In P. Peterson, E. Baker, & B. McGaw (Eds.), International encyclopedia of education (3rd ed., 288–297). Oxford, UK: Elsevier.

See also Achievement Gap; Educational Equity; Human Capital

Further Readings Alonso, J. D. (2006). Noncognitive skills as determinants of Black-White educational and socioeconomic gaps (Unpublished doctoral dissertation). Columbia University, New York. Bean, F., & Tienda, M. (1987). The Hispanic population of the United States. New York, NY: Russell Sage Foundation. Becker, G. S. (1957). The economics of discrimination. Chicago, IL: University of Chicago Press. Bowles, S., & Gintis, H. (1975). Schooling in capitalist America. New York, NY: Basic Books. Bowles, S., Gintis, H., & Osborne, M. (2001). The determinants of earnings, a behavioral approach. Journal of Economic Literature, 39, 1137–1176. Brown, M., Carnoy, M., Currie, E., Duster, T., Oppenheimer, D., Schultz, M., & Wellman, D. (2003). Whitewashing race: The myth of a color-blind society. Berkeley: University of California Press.

Card, D., & Krueger, A. (1992). School quality and Black/ White relative earnings: A direct assessment. Quarterly Journal of Economics, 107(1), 151–200. Card, D., & Krueger, A. (1993). Trends in relative BlackWhite earnings revisited. American Economic Review, 83(2), 85–91. Carneiro, P., Heckman, J., & Masterov, D. (2005). Labor market discrimination and racial differences in premarket factors (Discussion Paper No. 1453). Bonn, Germany: Institute for the Study of Labor (IZA). Carnoy, M. (1994). Faded dreams: The politics and economics of race in America. New York, NY: Cambridge University Press. Carnoy, M., Daley, H., & Hinojosa, R. (1990). Latinos in a changing economy (Interuniversity Program for Latino Research). New York: City University of New York. Carnoy, M., & DeAngelis, K. (1999). Does ability influence individual earnings, and if so, by how much? [Mimeo]. Stanford, CA: School of Education Stanford University. Chiswick, B. R. (1984). Differences in education attainment among racial and ethnic groups: Patterns and preliminary hypotheses [Mimeo]. Paper presented at the National Academy of Education Conference on the State of Education, Washington, DC. Donohue, J., III, & Heckman, J. (1991). Continuous versus episodic change: The impact of civil rights policy on the economic status of Blacks. Journal of Economic Literature, 29(December), 1603–1643. Farley, R. (1986). Assessing Black progress: Employment, occupations, earnings, income, poverty. Economic Outlook USA, 13(3), 14–23. Freeman, R. (1973). Decline of labor market discrimination and economic analysis. American Economic Review, 63(2), 280–286. Heckman, J., & Kautz, T. (2012). Hard evidence on soft skills. Labour Economics, 19(4), 451–464. Herrnstein, R., & Murray, C. (1994). The Bell curve: Intelligence and class structure in American life. New York, NY: Free Press. Jencks, C., & Phillips, M. (1998). The Black-White test score gap. Washington, DC: Brookings Institution Press. Juhn, C., Murphy, K., & Pierce, B. (1991). Accounting for the slowdown in Black-White convergence. In M. Kosters (Ed.), Workers and their wages: Changing patterns in the United States (pp. 107–143). Washington, DC: American Enterprise Institute Press. Murnane, R., Willet, J., & Levy, F. (1995). The growing importance of cognitive skills in wage determination. Review of Economics and Statistics, 77(2), 251–266. Myrdal, G. (1944). American dilemma: The Negro problem and modern democracy. New York, NY: Harper & Row. Neal, D., & Johnson, W. (1996). The role of premarket factors in Black-White wage differences. Journal of Political Economy, 104, 869–895.

Race to the Top Oaxaca, R. (1973). Male-female wage differentials in urban labor markets. International Economic Review, 14(3), 693–709. Reardon, S. (2007). Thirteen ways of looking at the BlackWhite test score gap [Mimeo]. Stanford, CA: Stanford University School of Education. Reich, M. (1982). Racial discrimination. Princeton, NJ: Princeton University Press. Rothstein, R. (2004). Class and schools. New York, NY: Teachers College Press. Smith, J., & Welch, F. (1989). Black economic progress after Myrdal. Journal of Economic Literature, 27(2), 519–564. Thernstrom, S., & Thernstrom, A. (1997). America in Black and White: One nation, indivisible. New York, NY: Simon & Schuster. Welch, F. (1973). Black-White differences in returns to schooling. American Economic Review, 63(5), 893–907.

RACE

TO THE

TOP

Race to the Top (RTT) was a federally funded competitive grant program intended to spark education reform and innovation at the state level. Funding for RTT was appropriated by the American Recovery and Reinvestment Act, which was signed into law on February 17, 2009. In total, the law authorized nearly $100 billion in funding for education, most reserved to fill state funding shortfalls to retain teachers and maintain existing programs. The administration carved RTT out of the $4.35 billion set aside for the “state incentive grants” program. This entry will cover the context, design, results, and criticism of RTT. President Obama and Secretary of Education Arne Duncan expressed high hopes and expectations for the program at its launch. Historically, federal funds have been distributed through formula grants to states and districts based on measures of student need. RTT was different because, as President Obama said in announcing the grant competition, for the first time “rather than divvying it up and handing it out, we are letting states and school districts compete for it.” In an op-ed in The Washington Post on July 24, 2009, Arne Duncan called RTT “the equivalent of education reform’s moon shot” because he argued that it would help all involved in K-12 education focus their energy on “reform and innovation” rather than bureaucratic compliance and would create “a new federal partnership in education reform with states,

607

districts and unions to accelerate change and boost achievement.” RTT was in many ways an attempt to circumvent the perceived failings of the last piece of major federal education legislation, the No Child Left Behind Act of 2001. The act forced states to formally adopt new practices, but political resistance and insufficient capacity to evaluate results and enforce the law’s sanctions meant that the changes were often superficial. States “dumbed down” their standards, increasing passage rates by lowering the bar that students needed to clear to be deemed “proficient.” They also failed to offer quality supplemental services for students in schools that did not meet or exceed the law’s annual achievement objectives and often restricted the options for students wishing to leave failing schools. RTT’s design—specifically its use of competitive grants—was intended to avoid these problems by relying on incentives instead of sanctions, which, even when enforced effectively, often showed lackluster results to drive state reform.

Program Design RTT’s purpose, according to the U.S. Education Department, was to encourage and reward states “that are creating the conditions for education innovation and reform”—achieving gains in student achievement, closing achievement gaps, and ensuring student college or career preparedness. States were asked to prepare ambitious plans addressing four core education reform areas: 1. Adopting standards and assessments that prepare students to succeed in college and the workplace 2. Building data systems that measure student growth and success and informing teachers and principals how to improve instruction 3. Recruiting, developing, rewarding, and retaining effective teachers and principals, especially where they are needed most 4. Turning around the lowest performing schools

RTT placed two requirements on states as a condition for applying. First, the state’s application must be approved by the U.S. Education Department prior to being awarded money, and, second, at the time of applying, there may not be any legal or regulatory barriers to state-level data linking student achievement to teachers or principals for the purpose of evaluation.

608

Race to the Top

The U.S. Education Department recruited a group of peer reviewers, many of whom were professors at graduate schools of education or leaders in education-related nonprofits or consulting firms, to rate the states’ proposals. The department provided reviewers with a 500-point scoring rubric consisting of seven criteria with scored subcategories: 1. State success factors (125 points) a. Articulating state’s education reform agenda (65 points) b. Building strong statewide capacity to implement and sustain proposed plans (30 points) c. Demonstrating significant progress in raising achievement and closing achievement gaps (30 points) 2. Standards and assessments (70 points) a. Developing and adopting common standards (40 points) b. Developing and implementing common high-quality assessments (10 points) c. Supporting the transition to enhanced standards (20 points) 3. Data systems to support instruction (47 points) a. Fully implementing statewide longitudinal data systems (24 points) b. Accessing and using state data (5 points) c. Using data to improve instruction (18 points) 4. Great teachers and leaders (138 points) a. Providing high-quality pathways for aspiring teachers and principals (21 points) b. Improving teacher and principal effectiveness based on performance (58 points) c. Ensuring equitable distribution of effective teachers and principals (25 points) d. Improving the effectiveness of teacher preparation programs (14 points) e. Providing effective support to teachers and principals (20 points) 5. Turning around the lowest achieving schools (50 points) a. Intervening in lowest performing schools (10 points) b. Turning around lowest achieving schools (40 points) 6. General (55 Points) a. Making education funding a priority (10 points) b. Ensuring successful conditions for high-performing charter schools (40 points)

c. Demonstrating other significant reform conditions (5 points) 7. Emphasis on science, technology, engineering, and math (15 points)

Timeline The competition was initially structured in two phases. Phase 1 applications were due on January 19, 2010. Applications for Phase 2, from states that did not apply to or lost Phase 1, were due on June 1, 2010. For each phase, the peer reviewers hired by the department ranked applications based on the rubric and then invited finalists, selected based on their application’s scoring, to Washington, D.C., to present their proposals and answer reviewers’ questions. After the presentations, the reviewers were given the option to readjust the finalists’ scores. States that failed to win a grant in Phase 1 could improve their prospects for Phase 2 by reviewing all the information of other competing states. Each state could see other states’ applications, scores, reviewer comments, and videos of the finalists’ presentations. After the first two rounds, Congress made an additional appropriation for a Phase 3, to which states that were finalists in Phases 1 and 2 but did not win were encouraged to apply.

State Grant Winners Forty states and the District of Columbia applied for Phase 1 of RTT. Sixteen were selected as finalists. Two states won and were awarded money: Delaware, $100 million, and Tennessee, $500 million. Thirty-five states and the District of Columbia applied for Phase 2. Nineteen applicants were granted finalist status, and 10 states won and were awarded money: Massachusetts, $250 million; New York, $700 million; Hawaii, $75 million; Florida, $700 million; Rhode Island, $75 million; District of Columbia, $75 million; Maryland, $250 million; Georgia, $400 million; North Carolina, $400 million; and Ohio, $400 million. The nine states that had earned finalist status but had not won were allowed to apply for Phase 3. Two, South Carolina and California, did not submit Phase 3 applications. Of the seven that applied, all won and were awarded part of the $200 million allocated to that round: Arizona, $17.5 million; Colorado, $12.25 million; Louisiana, $12.25 million; Kentucky, $12.25 million; Illinois, $28 million; Pennsylvania, $28 million; and New Jersey, $27 million.

Race to the Top

RTT Results RTT sparked a legislative push for education reform in several states. Tennessee Governor Phil Bredesen called the legislature into a special session in January 2010 to debate legislation designed to better position that state’s application. Bredesen told Education Week, “The whole Race to the Top just provided a focal point for a whole range of things that might have been difficult to do in other times.” Paul Pastorek, then superintendent of education for Louisiana, told Education Week’s Rick Hess Straight Up blog later that year, “Creating a competitive fund of money for people who want to do the right thing has already proven to be effective. People have changed their laws and changed their mindsets.” After failing to win in Phase I, Louisiana passed a law mandating more rigorous teacher evaluations that factored in growth on student test scores. Although it missed getting funding in Phase 2, it went on to win a Phase 3 grant. RTT created a similar stir of legislation in other states. According to a 2010 study by Learning Point Associates, 18 states made changes to their policies governing teachers in 2009 and early 2010 in advance of the Phase 1 deadline, more than three times the five states that made changes to those policies the year prior. Thirteen states altered their laws to allow for more charter schools, 6 states removed the data “firewall” linking student performance to teachers for evaluative purposes, and 11 states passed laws requiring schools and districts to use student achievement data to evaluate teachers and determine their tenure. Some states changed their policy in several other areas in the run-up to the competition deadline, for example, changing policies governing entry into the teaching profession and increasing options to intervene in schools that perform poorly for several consecutive years. An additional significant policy outcome of RTT was the mass adoption of the Common Core State Standards. The Common Core Standards were an attempt to create the framework for a standardized curricula and assessment across multiple states, which was proposed and promoted by the National Governors Association. Although RTT didn’t explicitly require that states adopt those standards, adopting “improved standards and assessments” was one of the four major areas of reform indicated by the department, and several states opted to adopt the Common Core Standards before the standards were even finalized. All but two states (Virginia

609

and Nebraska) that applied to RTT adopted the Common Core Standards.

Praise and Criticism RTT was lauded by many as a bipartisan success story. The New York Times editorial board and columnists Thomas Friedman and David Brooks lauded the program for the dynamism they saw it as unleashing. The Times editorial board declared, “Thanks to the application process, even states that did not get grants now have road maps to reform and a better sense of what it will take to build better schools.” Chester Finn Jr., president of the right-leaning Thomas B. Fordham Institute, in August 2010, echoed the sentiments of the Times. Writing in Education Next, he said, With a relatively small (by federal standards) amount of money, (Secretary Duncan) has catalyzed a large amount of worthwhile education-reform activity in a great many places. And the directions in which he has bribed the system to move are important directions to move in. This wouldn’t have happened without the program’s competition-style design, with states vying for (relatively) scarce money.

Reactions from state leaders, nonprofit education foundation leaders, and the press were overwhelmingly positive as well. Writing in Phi Delta Kappan magazine in May 2012, Frederick M. Hess, a scholar at the American Enterprise Institute, called the coverage “an amiable conspiracy of silence” against criticism of RTT. There were, however, some pointed criticisms from both sides of the political spectrum. On the left, the National Education Association, the largest teachers’ union in the United States, voted “no confidence” on RTT. Diane Ravitch, a left-leaning education historian at New York University, wrote on the Bridging Differences blog for the Education Week website in January 2012, that President Obama “doesn’t know what Race to the Top is.” Citing Obama’s statements that we must move past the era of “teaching to the test,” Ravitch wrote that RTT “emphasizes testing at every turn, and will not allow anyone to ‘stop teaching to the test,’” and that RTT was “demoralizing teachers across the nation.” On the right, there was skepticism of the longterm value of RTT and suspicion regarding the process. Writing in National Affairs in the fall of 2012,

610

Race to the Top

Hess and Andrew Kelly, also an education scholar at the American Enterprise Institute, declared that “the program was beset by problems that outweighed its benefits.” They argued that “scoring relied on how well states complied with hopelessly vague criteria” and that the competition did more to “reward grantwriting prowess . . . than meaningful, structural changes.” Several state legislators criticized the program as more of a “race to the trough” of federal money than a race to the top. The Republican minority leader in the South Carolina Senate said that “the purpose of this (RTT) is mainly, quite frankly, to draw down federal dollars,” and a state legislator in Wisconsin observed that “this is basically a race for money, not a race to the top.”

Conclusion Many observers have pointed out that it will be difficult to form clear analyses as to how effectively the winning states implemented the plans laid out in their RTT applications and as to how implementation actually affected student outcomes. Many of the state and district leaders that led the effort to win RTT dollars were replaced in the 2012 elections, and it is unclear how their successors will see the effort through. However, as the Drew University political scientist Patrick McGuinn argued, because it was a program sponsored by a popular Democratic president that challenged teachers’ unions and other vested interests in the education system, it “has significantly influenced the intensity and character of school reform discourse across the country.” Frederick M. Hess, Michael Q. McShane, and Max Eden See also Categorical Grants; Common Core State Standards; Education Spending; No Child Left Behind Act; Teacher Evaluation

Further Readings Ahn, T., & Vigdor, J. (2013, May). Were all those standardized tests for nothing? The lessons of No Child Left Behind. Washington, DC: American Enterprise Institute. Retrieved from http://www.aei.org/ files/2013/05/17/-vigdor-and-ahn-nclb-sanctionspaper_15005080098.pdf Duncan, A. (2009, July 24). Education reform’s moon shot. The Washington Post. Retrieved from http://www .washingtonpost.com/wp-dyn/content/ article/2009/07/23/AR2009072302634.html

Duncan, A. (2009, July 24). The Race to the Top begins: Remarks by the Secretary of Education. Washington, DC: Department of Education. Retrieved from http:// www2.ed.gov/news/speeches/2009/07/07242009.html Finn, C. (2010, August 25). A sober reflection of Race to the Top results. Education Next. Retrieved from http:// educationnext.org/a-sober-reflection-of-race-to-the-topresults/ Hess, F. M. (2010, August 27). Straight up conversation: Louisiana Schools chief Paul Pastorek reflects on RTT. “Rick Hess Straight Up,” Education Week [Web log post]. Retrieved from h+ttp://blogs.edweek.org/ edweek/rick_hess_straight_up/2010/08/straight_up_ conversation_louisiana_schools_chief_paul_pastorek_ reflects_on_rtt.html Hess, F. M. (2012, May). Philanthropy gets in the ring: Edu-funders get serious about education policy. Phi Delta Kappan, 93(8), 17–21 Retrieved from http:// www.aei.org/article/education/k-12/philanthropy-gets-inthe-ring/ Hess, F. M., & Kelly, A. (2012, Fall). A federal education agenda. National Affairs, (13), 43–60. Holland, S. (2010, May 31). States change applications for round two of Race to the Top. CNN. Retrieved from http://www.cnn.com/2010/US/05/31/education.race.to .top/index.html Learning Point Associates. (2010). State legislation: Emerging trends reflected in the state phase 1 Race to the Top applications. Naperville, IL: Author. Retrieved from http://www.learningpt.org/pdfs/RttT_State_ Legislation.pdf Manna, P. (2010, Fall). The three Rs of Obama’s Race to the Top program: Reform, reward and resistance. Americas Quarterly, 4(4), 108–115. McGuinn, P. (2010, December 9). Creating cover and constructing capacity. AEI Stimulus Watch. Retrieved from http://www.aei.org/papers/education/k-12/creatingcover-and-constructing-capacity/ McShane, M., & Maranto, R. (2012). President Obama and education reform: The personal and the political. New York, NY: Palgrave Macmillan. Obama, B. (2009, July 24). Remarks by the President on education. Washington, DC: White House. Retrieved from http://www.whitehouse.gov/the-press-office/ remarks-president-department-education Ravitch, D. (2012, January 31). Does President Obama know what Race to the Top is? “Bridging Differences.” Education Week [Web log post]. Retrieved from http:// blogs.edweek.org/edweek/Bridging-Differences/2012/01/ does_president_obama_know_what.html Robelen, E. W. (2009, December 23). Race to Top driving policy action across states. Education Week, 19(16). Retrieved from http://www.edweek.org/ew/ articles/2009/12/23/16states.h29.html

Randomized Control Trials Sawchuk, S. (2010, July 4). NEA delegates vote “no confidence” in Race to the Top. “Teacher Beat.” Education Week blogs. Retrieved from http://blogs .edweek.org/edweek/teacherbeat/2010/07/neas_ delegates_vote_no_confide_2.html

RANDOMIZED CONTROL TRIALS This entry examines the role of randomized control trials (RCTs) in education research. A description of RCTs and the advantages they provide in estimating program effects is followed by a review of the potential problems that can arise when conducting RCTs. The next section presents a detailed discussion of a well-known example of an RCT in education policy, Project STAR, or Student/Teacher Achievement Ratio, also known as the Tennessee STAR experiment. This is followed by a comparison of the use of randomized experiments with that of nonrandom, quasi-experimental techniques and the conditions under which the two will yield similar results. The main question most educators and policymakers ask about any program is “Does it work?” Unfortunately, evidence is often lacking and practitioners are left to rely on intuition and on subjective reports of satisfaction levels among students and teachers. A somewhat better approach involves obtaining data about whether students participating in the program seem to excel on measures related to the program goals, but even this approach can often yield an unreliable answer because of the wellknown adage that “correlation is not causation.” To take a specific example, suppose that students who choose to participate in a college access program are already planning to go to college, and their parents went to college as well. So the outcomes for the students in college access programs would likely look very good, but this would tell us more about who chooses to participate in the program than it would about the program effects for those students. In these cases, researchers say the results are not internally valid; that is, the differences in results observed for program participants are not valid estimates of the causal effect of the program. To provide clearer and more rigorous evidence about what works, it has been more common in recent years for policymakers to give attention to randomized experiments and quasi-experiments. With randomized experiments, a group of potential participants is identified, and some of them

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are randomly selected to receive access to the program. The control group of nonparticipants is then compared with the treatment group to estimate the program effects. Randomization, which amounts to flipping a coin to determine program participation, addresses the correlation-is-not-causation problem. Neither the students nor anyone else determines who participates based on student characteristics—the determining factor is the luck of the draw. Returning to the college access example, randomization would relieve any concerns that, to begin with, the participants had higher expected college outcomes than the control group. In this case, expected outcomes for the control and treatment groups would be exactly the same except for the effects of the program. This is why RCTs are generally considered the gold standard for identifying causal effects. However, this does not always mean that an RCT is always the best research option. Problems can arise in randomized trials, including broad issues such as feasibility, generalizability, and threats to internal validity caused by attrition and noncompliance. There are also conditions under which quasi-experiments might yield results similar to experiments.

Problems That Arise With RCTs The first and most obvious problem with RCTs is that they are often infeasible. This is especially true when studying the effects of systemic education policies such as test-based accountability, standards, and others that generally can only be implemented by entire districts, states, or countries. It is difficult to randomize such large units. Even with programs that can be implemented at a small scale, such experiments require a great deal of cooperation from practitioners, many of whom are likely to be uncomfortable with the idea of deliberately withholding services from certain groups of students (i.e., the control group). In general, the ethical arguments for carrying out experiments are strongest when not all students can be served due to resource constraints and when there is little evidence with which to predict effects. Even when educational leaders agree in principle to conduct an experiment, they often falter when the time comes to actually randomize participants. Problems can also arise after an experiment has begun. In particular, it can be difficult to track students over time. So even if students are assigned to treatment at random, with this attrition problem, their data might not be observed at random.

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For example, suppose a program is successful in keeping students in high school and that student outcomes are studied each year through an in-class survey. In this case, the very fact that the program worked will mean that more treatment group students will be in school to take the survey. If data are collected for 75% of the treatment group, but only 50% of the control group, then this calls into question the internal validity of the program effect estimate. Paradoxically, the more successful the program, the worse the attrition problem usually becomes. A similar problem can arise even when it is equally easy to find participants; students participating in programs are more likely to respond to a survey about the program than someone not participating. The third problem is that students who are assigned (at random) to receive a treatment do not always participate, and, conversely, those selected for the control group often find a way to participate anyway. In some ways, these noncompliers are not really a problem as they reflect what might happen in real-life policy implementation; people who start participating in programs do not always continue participating. Program benefits are likely to be smaller as a result of nonparticipation, but if this is what would happen under regular implementation, then this is what practitioners should want to know. Intuitively, it might seem reasonable to just ignore the noncompliers and compare the outcomes of those who were assigned and participated with those who were not assigned and did not participate. The problem is that even though assignment is random, participation is not. The results from such a comparison would no longer be valid causal evidence. Rather than ignore the noncompliers, researchers often use instrumental variables (see the entry “Quasi-Experimental Methods”) to estimate “treatment-on-treated” (TOT) effects, although this approach yields estimates that only pertain to part of the population of interest. In contrast, the “intent to treat” (ITT) estimates by comparing students who were assigned to the treatment with those not assigned, ignoring participation. The advantage of the ITT estimate is that it relies strictly on randomization, though the disadvantage is that it may say little about the effect of actually participating in the program (especially if the participation rate is low). With the TOT, the advantage is that more is learned about the effect of participating, but the estimates no longer apply to the entire population of

those who would be eligible under normal operating procedures. Suppose that researchers have succeeded in carrying out an experiment, demonstrating its feasibility, and have addressed the attrition and noncompliance problems to obtain internally valid estimates. Even then, problems still arise. Experiments are often carried out by researchers who pay particular attention to the way programs are implemented and who have the expertise to do it well. Moreover, those designing and running experiments generally have strong incentives to make their programs work well and may choose a sample of participants for whom the program is likely to work best. For all these reasons, the results from experiments may not generalize to other situations. Policymakers often make programs available to a much broader range of participants than are those involved in experiments. This is not to say that the results from experiments should not be believed. Again, they are internally valid for the population represented by the sample, but they may not be externally valid or generalizable.

Example: Tennessee STAR Class-Size Experiment RCTs have been used often in education in an attempt to evaluate a variety of programs and policies. Researchers have used randomized experiments to help evaluate everything from the effect of financial incentives on student achievement to the effects of dropout prevention programs, to the effects of enrolling in Head Start. To make the above discussion more concrete, consider perhaps the most well-known example of an RCT—the Tennessee class-size experiments. During the 1980s, Tennessee policymakers became curious about the effects of small class sizes, but they recognized that they could not simply compare the outcomes of students in small and large classes. If they had, the differences in outcomes could have been due, for example, to the fact that some students attend schools with more resources because their parents are wealthy and can afford to own a home in an affluent neighborhood and school district. These students’ families can and do contribute more tax revenue to the local public schools, which in turn leads to smaller classes. Since students from wealthier families also tend to have higher test scores—it becomes unclear whether any difference in outcomes between small and large classes is really caused by smaller class size.

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To obtain better evidence, Tennessee policymakers funded the now famous Tennessee STAR class-size experiment—a randomized trial involving students in Grades K-3 in a large sample of schools from throughout the state. The many analyses still ongoing suggest that Tennessee STAR did in fact increase achievement scores for students in these grades. Because the experiment randomly assigned students to class size, other factors (like parent income) are no longer associated with class size, and the estimated effects are much more plausibly causal. Even with initial randomization, there are concerns about attrition and noncompliance. It was found that students in larger classrooms were more likely to leave the study than students in small classrooms. It was also found that nearly 10% of students switched between small and regular class sizes during the course of the experiment. This nonrandom attrition reduces the internal validity of the estimates. The switching/noncompliance does not necessarily reduce internal validity, but it does restrict how the estimates are interpreted (see discussion of ITT vs. TOT in the preceding section). Even if internally valid, results in the Tennessee STAR experiment may suffer from some of the external validity concerns mentioned earlier. For example, the small group of Tennessee districts in the STAR experiment may have reduced class sizes by hiring teachers from other nearby districts. If the teachers were replaced in their former districts by less effective or less experienced teachers, this would produce a negative effect that is not captured in the experiment. However, when the program is scaled up, the decrease in teacher quality that accompanies the smaller classes would show up and the actual effect of the scaled-up policy would be smaller than in the experiment. This is one likely reason that when California implemented smaller classes statewide, it had no discernible effect on student outcomes—in fact, lower performing districts were arguably worse off because the higher performing districts hired away some of their best teachers.

Comparing Experiments and Quasi-Experiments Given the disadvantage that results from RCTs are often infeasible and may not generalize to other samples and situations, it is worth considering how the results from RCTs compare with other methods. Quasi-experiments generally involve simply

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observing a program implemented under natural conditions, as part of the regular activities in a school, college, or other organization. For this reason, they are more likely to generalize, though they may have lower internal validity. Here, we simply provide the general intuition for each approach and then report results from “within-study” comparisons, which estimate effects from RCTs and then reestimate effects using essentially the same data but relying on quasi-experimental methods instead of randomization. If we can find circumstances under which the results are similar, so that both are internally valid, then we can avoid the problems with RCTs and use quasi-experiments, which are more often feasible and do not suffer from the same generalizability problems. Propensity score matching (PSM) attempts to eliminate selection bias by choosing a control group that is very similar to the treatment group based on observable characteristics. For example, to eliminate the selection bias in the class-size example, two school districts could be found that are similar on the basis of parent income, teacher education, perpupil expenditures, and so on but that differ in class size. Now, expand that situation to one in which there are multiple school districts, matching on many different characteristics. PSM helps select the control group that looks as similar as possible to the treatment group based on observable characteristics. Within-study comparisons with PSM are relatively plentiful. In general, these comparisons show that PSM yields conclusions different from that of experimental results. One study used data from the Tennessee STAR experiment to estimate the effects of class size. Since Tennessee STAR was a withinschool experiment, they matched each treatment class with control classes from other schools. Not only were many of the propensity score results different from the experimental results, but about half the time, they were of the opposite sign. Another analysis examines how well PSM estimates compare with experimental estimates of a dropout prevention program. The within-study comparison is with the treatment students and control students in other schools that were not part of the experiment. They found no consistent evidence to show that the propensity score results replicated the experimental results. One potential reason for this mismatch in results between PSM and RCTs is that the data used did not provide a good enough match. In particular, neither of the previous two studies were able to

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use baseline pretreatment outcomes in the matching process. Three within-study comparisons that did use baseline outcome measures found similar estimates between PSM and their experimental results. This leads to an important theme of this entry—namely, that the availability of pretreatment outcomes (longitudinal data) is often as important to quasi-experimental analysis as the method used to analyze those data. Without longitudinal data, PSM does not seem to yield the same conclusions as experiments, presumably because it accounts for only observed differences that are only moderately related to outcomes. With longitudinal outcome data, the method works better because the observed differences include the outcome of interest: The best predictor of future outcomes is past outcomes. The difference-in-differences (DD) method is generally considered an improvement over PSM because it involves comparing participants with themselves before and after treatment, as well as with a comparison group. In the simplest terms, the DD involves taking the difference of two differences (hence, the name). The first difference is between the treated and nontreated individuals/groups. The second difference is across the treated and nontreated time periods. In some cases, the comparison over time is literally with the same people—they did not experience the treatment in one period and they did experience it in the period after a new policy was adopted. In other cases, the comparison over time is with repeated samples from the same population (i.e., repeated cross sections), such as samples of people from a given state over time. So long as they are random samples, the fact that each sample comprises different individuals does not by itself introduce threats to internal validity. Whether the same individuals or repeated cross sections are used, the DD is the difference of those two differences. The DD approach largely rules out most differences in observables as well as in unobservables, since the recipient and nonrecipient is the same person or an essentially identical group. The DD method is increasingly popular in education because of the expansion of longitudinal data systems that facilitate this approach. One study uses lottery-based randomization in two magnet schools to evaluate the effectiveness of DD strategies in a within-study comparison. However, this study uses a variety of control group students, all of whom attend schools either in the same district or one nearby but did not apply to the magnet schools. The use of DD estimation in this

case greatly reduces the bias of the estimates. In fact, the results using the DD approach are not statistically different from the experimental approach. Another study uses a very similar approach when estimating the effects of charter school enrollment. In their estimates, DD effects of English test scores are marginally different from the experimental results, but results on math test scores are the same. Their results suggest that their PSM estimates are slightly closer to the experimental estimates than DD. However, as mentioned earlier, their PSM estimates include pretreatment test scores. The inclusion of pretreatment outcomes results in an estimation strategy very similar to that of DD. Although these studies suggest that the DD strategy does a good job of matching experimental estimates and therefore eliminating bias, not all studies find this to be the case. The earlier study using PSM to study dropout prevention found conflicting results using either PSM or a combined PSM-DD strategy, where the control group is created using PSM and both are observed longitudinally. Specifically, neither one consistently matches the experimental estimates. Overall, the DD strategy appears to do a better job matching estimates than a cross-sectional PSM strategy, though it is comparable with longitudinal PSM. The final method we consider, regression discontinuity (RD), can only be used when treatment assignment is based on a running variable exceeding some sort of threshold. The intuition is that, within a narrow window, only treatment status will differ on either side of the threshold—observable and unobservable characteristics will be similar on average. While certain characteristics may vary with the running variable (e.g., intelligence may increase with test scores), only treatment status will have a discontinuous “jump” at the threshold. In a “sharp” RD, the general score changes as well as the discontinuous treatment can be perfectly modeled, achieving an unbiased estimate. The most common criticism of this approach is not related to bias but instead to generalizability. Because this strategy is only viable within a small window around the threshold, this is the only population to which the results can be attributed. In the remediation example, RD would only give viable information about the effect of remediation for students who were on the margin of reaching the threshold anyway. It would not necessarily give any indication about what might happen if students far below or above the threshold were given remediation.

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There are only two known within-study comparisons in education that test the validity of RD. One reason for this is that it takes a very unusual experimental design for an RD within-study comparison to be possible. Typical within-study comparisons keep the treatment group and find a control group from outside the experiment to match the method being tested. In the RD case, the control group would have to come from outside the experiment yet still be in a situation where a running variable assigns individuals to treatment. One group of researchers analyzes a situation similar to this in the context of remedial English enrollment. Assignment to remedial English is based on SAT or ACT admission scores. If the score is within a certain range, students are placed in a lottery where “winners” are exempt from remedial English, while the “losers” are enrolled in remedial English in the fall. This creates a situation where a within-study RD comparison is possible on both sides of the range. One comparison is of lottery losers to students above the range who are exempt from remedial English. The other comparison is of lottery winners to students below the range who are automatically enrolled in remedial English. The researchers estimate four different effects of remedial English by separately comparing students who used the ACT and SAT tests for admission on two different outcome tests (a writing essay test and a multiple-choice English test). The results are not statistically different from the experimental effects. Other researchers have attempted to measure the performance of RD using Mexico’s PROGRESA (Programa de Educación, Salud y Alimentación, or the Education, Health, and Nutrition Program of Mexico, now called Oportunidades, or Opportunities). Though not exclusively education focused, part of PROGRESA’s purpose is to decrease child labor and increase school enrollment in Mexico through the use of cash transfers. As the program was rolled out, villages were randomly assigned treatment. Within treatment villages, families had to meet a threshold score for eligibility. Experimental and RD estimates are compared across genders, outcomes, and rounds of implementation. While the two methods do not match perfectly, the conclusions are the same in 10 out of 12 cases. These studies suggest that the RD design does a good job eliminating bias; however, this is the least studied of the three methods. Further research should be done before drawing a final conclusion. Also, as mentioned above, RD is only applicable

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when assignment to treatment is based on a running variable, and even in those situations, results may not be generalizable to a wider selection of individuals.

Conclusion RCTs are widely seen as the gold standard of education research, and of social science research generally, because they afford the greatest confidence in the internal validity of estimates. As noted earlier, this confidence does not mean that nothing can go wrong. Such research is sometimes difficult to carry out, and moreover, the results may not generalize; even the internal validity of the experiment can be compromised by attrition. Whether noncompliance is an issue depends on what the researcher wants to know; the ITT estimate is always internally valid, but it may be of less interest if we want to know the effects of actual program participation (TOT). Quasi-experimental methods, in contrast, are more likely to generalize, and the evidence presented here from within-study comparisons suggests that there may be some conditions under which these methods have high internal validity. Matthew F. Larsen and Douglas N. Harris See also Difference-in-Differences; Propensity Score Matching; Quasi-Experimental Methods; RegressionDiscontinuity Design

Further Readings Agodini, R., & Dynarski, M. (2004). Are experiments the only option? A look at dropout prevention programs. Review of Economics and Statistics, 86(1), 180–194. Aiken, L. S., West, S. G., Schwalm, D. E., Carrol, J. L., & Hsiung, S. (1998). Comparison of a randomized and two quasi-experimental designs in a single outcome evaluation: Efficacy of a university-level remedial writing program. Evaluation Review, 22(2), 207–244. Bifulco, R. (2010). Can propensity score analysis replicate estimates based on random assignment in evaluations of school choice? A within-study comparison (Center for Policy Research Working Paper No. 124). Syracuse, NY: Center for Policy Research. Buddelmeyer, H., & Skoufias, E. (2004). An evaluation of the performance of regression discontinuity design on PROGRESA (World Bank Policy Research Working Paper No. 3386). Washington, DC: World Bank. Cook, T., Shadish, W., & Wong, V. (2008). Three conditions under which experiments and observational studies produce comparable causal estimates:

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New findings from within-study comparisons. Journal of Policy Analysis and Management, 27(4), 724–750. Fortson, K., Verbitsky-Savitz, N., Kopa, E., & Gleason, P. (2012). Using an experimental evaluation of charter schools to test whether nonexperimental comparison group methods can replicate experimental impact estimates. Washington, DC: U.S. Department of Education, National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences. Wilde, E., & Hollister, R. (2007). How close is close enough? Evaluating propensity score matching using data from a class size reduction experiment. Journal of Policy Analysis and Management, 26(3), 455–477.

REDUCTION

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FORCE

The term reduction in force (RIF), as typically used in U.S. K-12 education, refers to the process by which there is a reduction in the number of teachers employed by local school districts. Unlike a discharge for cause, which is due to a teacher’s inadequacy, lack of performance, or personal failure, a discharge due to a necessary RIF is due to external, institutional factors. Perry Zirkel and Charles Bargerstock explain that the process and procedures for RIF are reflected in state legislation surrounding tenure laws or other teacher employment acts that establish the parameters for the suspension or dismissal of professional employees by local districts. Provisions governing the layoff of teachers can also be found in teacher contracts, including those in collective bargaining agreements (CBAs) negotiated between school districts and teachers’ unions. For example, in examining CBAs in the state of Florida, Lora Cohen-Vogel and La’Tara Osborne-Lampkin found that RIF provisions were universal. That is, these provisions were found in all 66 district-union contracts negotiated in Florida’s school districts. The scope and specificity of the provisions for RIFs vary substantially across states and districts. In this entry, an overview of the common RIF provisions will be provided. These provisions include reasons and conditions for RIFs, the criteria used to select employees for dismissal, and the guidelines used to recall staff after the resolution of issues associated with an RIF.

Conditions for RIFs Some commonly cited conditions in which reductions can be made include declining enrollments; fiscal,

economic, or budgetary constraints; reorganization or consolidation of school districts; change in the number of teacher positions; and the curtailment of programs, services, or courses. District boards also have discretion in initiating RIFs. This discretion is commonly outlined in provisions in CBAs that provide district boards the ability to initiate reductions for what is deemed “for good cause” or “just cause.” Declining Student Enrollments

Over the past few decades, public school districts across the nation have experienced shifts in student enrollments. Peaks and declines in student enrollments affect student-teacher ratios and staffing requirements. In response to declining student enrollments, districts can be required to reduce personnel. As such, declining student enrollments have been cited as a primary cause for RIFs. Fiscal, Economic, or Budgetary Constraints

Another major factor influencing staff layoffs is the loss of fiscal resources. Federal and state appropriations are a primary source of funding for districts and schools. When there are substantial cuts to federal and state education appropriations, districts are at risk of losing revenue, potentially affecting district and school resources, including school personnel. Reduced federal or state funding for special programs, as well as reductions in local tax revenue, can result in district and school budgetary constraints that require teacher cutbacks. Reorganization or Consolidation of School Districts

Changes in the organization of a school district and the consolidation of two or more school districts are also cited as reasons for RIFs. As districts reorganize or consolidate, there is the potential, in both cases, for the reduction in the number of schools within the restructured district or the newly consolidated district. In these instances, personnel may be reassigned or released due to a change in staffing needs. Reduced Teacher Turnover

Another factor that can affect districts’ staffing needs and has been cited as a reason for RIFs is the increase in the supply of teacher personnel in schools and districts due to reduced teacher turnover. Economists and educational researchers examining

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teacher workforce issues have found that, among other factors, individual teacher preferences and the constraints they face affect individual teachers’ employment decisions, which can ultimately affect the supply and demand of teachers and the teacher workforce. For example, current research suggests that teachers consider a number of factors (e.g., working conditions, salaries) about where or whether to teach. Withstanding other external factors (e.g., impending layoffs due to potential budget cuts), any reduction in personnel turnover may be the result of teachers’ decisions to remain in the teaching profession rather than to move to other jobs. While this decision can be an individual preference, it can also be based on a perceived constraint (i.e., fewer job opportunities in other professions). In addition, while there has been an increase in retirement rates over the past few decades due to the aging teacher workforce, the replacement of retiring teachers with younger teachers—who can potentially decide to stay in their positions through retirement age—can potentially affect districts’ ability to address changes in school and district personnel through normal staff attrition, particularly in schools experiencing a decline in student enrollments. Curtailment of Programs, Services, or Courses

Districts may also be required to reduce personnel when it is determined that certain programs, services, or courses are no longer needed. The public school curriculum has increasingly reduced the emphasis on certain types of programs, services, and courses. For example, home economics courses are practically nonexistent in public schools today. In contrast, we have seen an increase in advanced language, math, science, and technology courses. School districts may need to reduce the number of teachers in low-need areas in order to hire teachers to meet current needs. District Discretion

Last, while there have been legal challenges surrounding the discretionary language for specific reasons for RIF, districts can also initiate RIFs “for cause.” In these cases, broadly written language in CBAs provides districts with discretion to reduce staff personnel as needed.

Criteria Used for Selecting Teachers for Reduction Once an RIF is deemed necessary by the school district or its board, subsequent decisions must be

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made regarding which position(s) will be eliminated. Employees included in the eliminated position(s) must be identified, and criteria must be established for selecting which employees will be laid off within the affected categories. Similar to general provisions surrounding the suspension and dismissal of teachers, provisions outlining the criteria by which teachers are selected for reduction are typically outlined in state statutes and/or CBAs. Among the criteria for selecting teachers for reductions is seniority. While the definition of seniority can vary across districts, provisions granting senior teachers preferences in maintaining their posts when schools reduce positions are common in district contracts. For example, in a study conducted in Washington State, Dan Goldhaber and Roddy Theobald found that most teachers receiving RIF notices had 2 to 3 years of experience and, approximately, 2 to 3 years of seniority within their district. Districts also consider other factors in RIFs. Teachers’ contract status (e.g., annual contract vs. professional contract, nontenured vs. tenured), certifications held, and qualifications are also used to determine the order of layoffs. For example, teachers who have not met tenure requirements or who hold annual contracts must typically be released prior to a tenured teacher or a teacher who has met the requirements for a continuing contract. In examining reductions in the state of Washington, Goldhaber and Theobald’s findings revealed that the estimated probability that a first-year special education teacher received a layoff notice was 6.2%, compared with 17% for a firstyear health/physical education teacher. This evidence suggests that the type of certification a teacher holds can also be taken into consideration, particularly when teachers have similar years of service. There has been increased debate surrounding the use of measures of teacher effectiveness as criteria for informing layoffs, given evidence that suggests that the decision of which teachers to lay off has implications for student achievement. With the exception of a few notable studies, there has been little research on the use of measures of teachers’ effectiveness as a criterion for layoffs and the resulting effects on overall teacher quality. However, a study by Donald Boyd, Hamilton Lankford, Susanna Loeb, and James Wyckoff found substantial differences between teachers who would be laid off under a seniority-based system and those who would be laid off if the system instead relied on teacher valueadded models, which measure teachers’ effectiveness using gains in student achievement. The researchers’

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simulations showed that the proportion of teachers who would be terminated under either system was relatively small. Given these small percentages, the researchers posit that the interpretation of the impact on student achievement can vary. For example, the researchers note that the layoff of a teacher under a particular system can have a large impact on individual students. However, given the small percentage of teachers laid off, the difference does not have a large effect on the average achievement of students in a district. While they found that the effect size between the average value-added effectiveness of the fourth- and fifth-grade teacher workforce on student achievement was less than 2% of a standard deviation of student achievement, the researchers’ simulations showed substantial differences between the teachers who would be laid off under senioritybased systems and those who would be laid off using a value-added model system. Similar to the findings in this study, Goldhaber and Theobald’s research found differences in teacher layoffs based on seniority systems versus effectiveness-based systems.

Criteria Used for Recalling Staff There are also provisions and procedures that establish the order of recall of teachers dismissed for RIF reasons. As with selecting teachers to lay off, seniority plays a role in the recall of teachers dismissed due to RIFs. Teachers are typically recalled in inverse order (i.e., seniority) of layoffs. In some cases, districts include provisions in CBAs that prohibit the hiring of new teachers before teachers with similar qualifications who were laid off are offered a position in the district.

Difference Between Layoff for Performance and Layoff Due to RIF Robert Phay, among others, notes that there is a significant difference between discharging a teacher for cause and dismissing a teacher because of an RIF. A teacher’s dismissal due to an RIF is not associated with wrongdoing or lack of performance, but instead, it is due to some institutional factor (e.g., budget cuts, district consolidation, or school reconsolidation). The difference between layoff for performance and layoff due to RIF is critical to due process procedures associated with an employee’s discharge. For example, procedural due process requirements for RIF cases are typically much less demanding than those required in dismissal cases. Similarly, in contrast to discharge for cause hearings, where the

district carries the burden of proof for cause, in RIF cases, employees generally must demonstrate that an alleged layoff is arbitrary or without justification.

Conclusion As outlined in state statutes and, typically, in provisions in CBAs negotiated between school districts and teachers’ unions, districts may initiate RIFs for a number of reasons (e.g., reduction in personnel, budgetary constraints), including “for cause.” And while a teacher’s seniority—typically defined as the length of time employed in a district—can be used as a criterion for both RIF selection and recall, other criteria such as the type of certification held and a teacher’s contract status may also be factored into the process. La’Tara Osborne-Lampkin See also Program Budgeting; School Boards, School Districts, and Collective Bargaining; School District Budgets; Teacher Supply; Teachers’ Unions and Collective Bargaining

Further Readings Boyd, D., Lankford, H., Loeb, S., & Wyckoff, J. (2011). Teacher layoffs: An empirical illustration of seniority versus measures of effectiveness. Education Finance and Policy, 6(3), 439–454. Cohen-Vogel, L., & Osborne-Lampkin, L. (2007). Allocating quality: Collective bargaining agreements and administrative discretion over teacher assignment. Educational Administration Quarterly, 43(4), 433–461. Goldhaber, D., & Theobald, R. (2013). Managing the teacher workforce in austere times: The determinants and implications of teacher layoffs. Education Finance and Policy, 8(4), 494–527. Phay, R. (1980). Reduction in force: Legal issues and recommended policy. Topeka, KS: National Organization on Legal Problems of Education. Zirkel, P., & Bagerstock, C. (1980). The law on reduction-in-force: A summary of legislation and litigation. Arlington, VA: Educational Research Services.

REGRESSION-DISCONTINUITY DESIGN A regression-discontinuity design (RDD) is an approach for estimating the impact of a program or the effect of a policy when a randomized experiment is not possible. When an RDD can be appropriately implemented, it is viewed as perhaps the most

Regression-Discontinuity Design

rigorous possible alternative to an experimental design for estimating the impact of an educational intervention. The key to the rigor of the RDD is that the selection mechanism—the process of determining who receives an educational intervention—is fully known and accounted for in the estimation process. This allows RDDs to avoid the problem to which most nonexperimental designs are highly susceptible—selection bias—whereby program impact estimates are confounded by unobserved differences between program participants and nonparticipants that are related to later outcomes. When feasible, RDDs are an attractive option as they allow researchers to generate impact estimates with high internal validity without having to address the practical and ethical challenges of experimental designs. This entry describes the historical development and current applications of RDDs, explains how researchers implement this design, notes key concerns and limitations of RDDs, and concludes with some important extensions of the basic design. An RDD can be used when the mechanism for determining who participates in the program or when and where a policy is implemented is determined by the value of one or more known, continuous numeric measures—in particular, whether the measure has a value above or below a predetermined threshold. For example, RDD would be possible for evaluating a program in which students with test scores below a threshold were admitted to the program, while students with scores above the threshold were not admitted. Once a researcher accounts for this continuous, numeric measure (prior test scores in this case), observations of program participants on one side of the threshold and those of nonparticipants on the other side will be equal in expectation on other, unobserved characteristics. The program impact is estimated by comparing the outcomes of participants and nonparticipants while controlling in a regression framework for the variable on which program participation is based.

History and Applications Donald Campbell is widely credited with first developing an RDD, in a 1960 study with Donald Thistlethwaite, examining the effects of winning a National Merit certificate on subsequent scholarship funds and educational attainment. As described by Thomas Cook, the early work on RDDs focused mainly on theoretical developments, with few practical applications over the first few decades. Initially, the design was thought to be of theoretical interest

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but to have few practical applications. Not until the 1990s did economists and education researchers begin to apply RDDs to a broad range of situations. The key to this burst of RDD applications was that researchers realized that eligibility for many programs or policies, particularly in education, is based on a continuous variable such as household income or prior test scores. For example, a meanstested policy might be applied only when household income is below a fixed threshold or—for policies directed toward groups—the proportion of households with low incomes is below a threshold. Alternatively, eligibility for programs might be limited to low-achieving students—those with test scores below a predetermined level. Recent studies have used RDDs based on eligibility rules involving income or prior achievement to estimate the effects of early childhood education programs such as Head Start, large federal initiatives such as the No Child Left Behind Act of 2001 or the Race to the Top, financial aid for higher education, and teacher training programs, among others.

Implementing RDDs In its simplest form, an RDD requires program eligibility to be based entirely on a single variable, referred to by various names, including assignment variable, forcing variable, or rating score. All cases (individuals, households, or entire schools) with values of the assignment variable above or below a predetermined threshold or cut point participate in the program (receive the treatment); those with values on the other side of the cut point do not participate. A key assumption is that values of the assignment variable and its cut point are exogenously determined, and that they are not manipulated to ensure that individuals do or do not participate in the program. In addition, the relationship between the assignment variable and outcome of interest is assumed to be continuous in the region of the cut point. Under RDD, a program’s effect on an outcome can be estimated using a regression model where the outcome is regressed on treatment status—a binary indicator of whether the individual participates in the program—and the assignment variable. This simple version of the regression model is shown below: Yi = β0 + β1Ti + β2AVi + ei,

where Yi represents the outcome, Ti is treatment status, AVi is the assignment variable, ei is a random error term, and β0, β1, and β2 are parameters to be estimated, with β1 being the program impact.

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Figure 1 shows a hypothetical RDD graphically, with the slope of the regression line (positive, β2 > 0) representing the relationship between the assignment variable and the outcome. However, there is a discontinuity in this regression line, at the cut point. In this example, the regression line “jumps” at the cut point, as individuals with values of the assignment variable just to the right of the cut point have outcomes much higher than individuals just to the left. The size of this discontinuity—determined by β1—represents the estimated treatment effect or program impact. Impact estimates based on RDDs represent the estimated effect of the program or policy at the cut point. If the treatment effect varies across individuals according to the value of the assignment variable, an RDD estimate does not capture the average program effect among all participants; rather, it captures the marginal program effect among individuals on the margin of being eligible. In other words, the external validity of RDD impact estimates is limited. Programs may well have different impacts among individuals at the margin of eligibility than among those further from the eligibility cutoff. Consider a tutoring program for students with prior test scores below a threshold. If the effectiveness of tutoring varies for students of different ability levels, this program will affect subsequent achievement differently for the lowest achieving students (far from the threshold) than for students at the margin of eligibility. The marginal impact of a program may be a useful impact parameter for policymakers interested in either expanding or contracting eligibility for the program. Such policymakers would be less

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interested in the average impact among all program participants, but instead, they would want to know the impact among those at the margin of eligibility. For policymakers considering cutting a program entirely, in contrast, this marginal impact estimate generated by RDD would be of less interest than an average impact estimate generalizing to all program participants. A critical requirement of an RDD is that the model’s regression correctly specifies the assignment variable/outcome relationship. If the true relationship is curvilinear but the model specifies a linear relationship, the RDD may produce biased impact estimates. Researchers use a variety of approaches to avoid misspecifying this relationship, typically starting with a graphical analysis involving a scatterplot of the relationship between the assignment variable and the outcome to determine whether the nature of the relationship is obvious visually. In the regression model itself, researchers commonly allow the functional form to be different on either side of the cut point and estimate both parametric and nonparametric versions. Unless the researcher believes that the relationship is linear, specifying a more flexible parametric functional form such as a quadratic or cubic relationship between the assignment variable and outcome is useful. A nonparametric approach, such as a local linear regression, will typically be even more flexible and reduce the risk of misspecification. Under this approach, the researcher uses only observations within a certain distance (or bandwidth) of the cut point in estimating the functional form. A key aspect of this nonparametric method involves the careful selection of the appropriate bandwidth size. A drawback of more flexible estimation approaches is that they typically result in less statistical power and less precise impact estimates.

60 70 80 90 100 Assignment Variable (AV)

Simple Graphical Example of RegressionDiscontinuity Design With Linear Assignment Variable/Outcome Relationship and Positive Impact

Apart from the misspecification of the assignment variable/outcome relationship, other factors threaten the validity of RDDs. RDDs assume that absent a treatment effect, this relationship is continuous near the cut point. This assumption could be violated if another program used the same eligibility criterion as the program being studied. With this other program potentially leading to a discontinuity in the assignment variable/outcome relationship at the cut point, one could not attribute the full discontinuity to the program being studied. Researchers may assess the validity of this continuity assumption in several different ways, such as by examining relationships

Regression-Discontinuity Design

between the assignment variable and a baseline version of the outcome. The presence of a discontinuity at the cut point during this period would call into question the validity of the assumption. Another threat to the validity of RDDs involves the assumption that the assignment variable and its cut point are exogenously determined, which could be violated if a program applicant or operator manipulated the value of the assignment variable to ensure or prevent the applicant’s admission. For a program providing scholarships to students with test scores above a threshold, for example, a teacher might artificially boost the score of a wellliked student. Researchers may assess the likelihood of assignment variable manipulation using circumstantial evidence. By learning how values of the assignment variable and cut point were determined, it may be possible to rule out the possibility of manipulation. For example, the teacher in the above case would have less opportunity for manipulation if the testscoring mechanism was automated or the cut point was unknown in advance. Furthermore, the presence of manipulation may lead to an unusual frequency distribution or density of the assignment variable. While one would expect this density to be smooth or continuous, manipulation may lead to a discontinuity at the cut point if individuals are systematically moved from one side to the other. Another concern about RDDs involves its statistical power. Compared with a randomized design, an RDD with the same sample size will have substantially less power. In 1972, Arthur S. Goldberger established that the power of an RDD is lower than that of an otherwise equivalent randomized experiment by a factor of at least 2.75. Thus, RDDs are frequently used in situations involving large administrative data sources.

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the assignment variable does not fully determine participation. A caveat of fuzzy RDDs is that their impact estimates apply only to “compliers”—sample members whose participation behavior is consistent with their program eligibility status based on their assignment variable value. Other extensions of basic RDD cover situations in which circumstances deviate from the case of a single assignment variable and cut point determining program eligibility. In some cases, sample members’ program eligibility is based on multiple assignment variables. This could occur, for example, if eligibility for a scholarship program was restricted to individuals with household income below some threshold and test scores above another threshold. Various methods are being developed to address this situation. Similarly, methods are being developed to address cases where eligibility is determined by one assignment variable but multiple cut points. Extensions such as these make the evaluation of programs and policies using RDDs more complex, but they also make the approach possible in additional situations. The increasing availability of large datasets makes the application of RDDs increasingly feasible. These developments are welcome, since while RDDs are generally viewed as producing valid estimates of the impact of educational programs and policies under the right circumstances, many researchers believe that these “right” circumstances are rare. An increasing understanding of RDDs and their ability to produce valid impact estimates will be a powerful tool to researchers as they study educational programs and policies. Philip Gleason See also Econometric Methods for Research in Education; Policy Analysis in Education; QuasiExperimental Methods; Randomized Control Trials; Selection Bias

Extensions of Basic RDDs In a simple RDD, all sample members with assignment variable values to one side of the cut point are assigned to the program or policy, and those with values on the other side are denied access. Assignment to a treatment is often messier, however, with some eligible for a program not participating and others who are ineligible somehow gaining admission. Thus, instead of a participation rate of 100% on one side of the cut point and 0% on the other, the difference in participation rates is smaller. Here, researchers use a “fuzzy” RDD, which generates estimates of program impacts when

Further Readings Bloom, H. S. (2012). Modern regression discontinuity analysis. Journal of Research on Educational Effectiveness, 5, 43–82. Cook, T. D. (2008). Waiting for life to arrive: A history of the regression-discontinuity design in psychology, statistics, and economics. Journal of Econometrics, 142, 636–654. Goldberger, A. S. (1972). Selection bias in evaluating treatment effects: Some formal illustrations (Discussion Paper No. 129–72). Madison: University of Wisconsin, Institute for Research on Poverty.

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Hahn, J., Todd, P., & van der Klaauw, W. (2001). Identification and estimation of treatment effects with a regression-discontinuity design. Econometrica, 69, 201–209. Imbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142, 615–635. Thistlethwaite, D. L., & Campbell, D. T. (1960). Regression-discontinuity analysis: An alternative to the ex-post facto experiment. Journal of Educational Psychology, 51, 309–317.

RELIABILITY Reliability reflects the degree to which a test or other procedure produces a consistent measurement across repeated trials. For example, in most situations, a GPS (Global Positioning System) device yields a more reliable measurement of walking distance than a pedometer. A reliable test of mathematics would give a similar score to a particular individual who took the same test on consecutive days. An unreliable test might indicate that an individual was proficient one day and substandard the next. While reliability applies to measurement procedures of all sorts, this entry focuses on the reliability of tests in an educational context. Ensuring high levels of test reliability is important for at least three reasons. First, reliable tests are required to ensure that remedial and other services are offered to the students who stand to benefit the most. Second, high test reliability allows policymakers to better evaluate the effectiveness of academic programs, schools, and teachers. Third, predictions of future outcomes are more accurate using reliable measures of student abilities. This entry discusses why test measures vary and how this reduces the reliability. It goes on to outline how researchers measure test reliability, discuss the implications of test reliability in practical educational contexts, and provide suggestions for increasing reliability, including the use of item response theory models for evaluating test performance. The entry concludes with a discussion of the reliability of common tests.

Why Test Measures Vary Two individuals may perform differently on a test because of stable differences in their ability to perform the tasks required by the test instrument.

This will be the primary source of variation in test performance for a reliable test. One can think of this as a person’s true ability. There are a number of other reasons, besides true ability, why an individual may perform well or poorly on an exam. First, an individual may have good or bad luck as she guesses answers to questions she finds difficult. Second, an individual may suffer from fatigue, illness, or some other condition that temporarily affects his ability to perform well on the test. Third, variability in test administration or scoring may lead to variation in performance that is unrelated to the ability of the person taking the test. These sources of variation are considered test error and reduce the reliability of the test measure. They lead to a situation in which individuals with similar latent ability have different measured ability.

Measuring Reliability Using Classical Test Theory Under classical test theory, the reliability coefficient can be thought of as the squared correlation between performance on a valid test and the student’s true ability. If the reliability coefficient is 1, the test is a perfectly consistent measure of true ability. If the measure is 0, the test is completely uncorrelated with true ability. If the reliability coefficient were .81, we could infer that the correlation of the test performance with true ability was .90. Unfortunately, the reliability of a test is unknown. Instead researchers must find ways to estimate this reliability. There are several methods of doing so: (a) the test-retest method, (b) the parallel forms method, (c) measuring split-half reliability, and (d) measuring interrater reliability. Test-Retest Method. This method involves a researcher administering the same test in two different periods over which ability is presumed to remain constant. For example, one might administer a math test to a set of individuals in two adjacent weeks during the summer when no math instruction is taking place. If one believed that math ability was unchanged over this period, the correlation in performance between the 2 weeks could be interpreted as an estimate of the test’s reliability coefficient. One concern with this method is that ability may not in fact remain constant if the test periods are too far apart. Also, it may be the case that individuals learn from taking the test the first time and consequently perform better the second time.

Reliability

Parallel Forms Method. With this method, two versions of the same test are administered to test takers on the same day. The correlation between the performance across the two forms is an estimate of the test’s reliability coefficient. One challenge with this approach is ensuring that the two forms of the test are functionally identical in their ability to measure achievement. A second concern is that if the two tests are taken very close together in time, temporary factors driving the error in measured performance, such as health and testing conditions, may be common for both tests. This makes it appear as though the tests were more reliable than they actually are. The parallel forms can be administered on different days to alleviate this latter concern. Split-Half Reliability. This is measured by dividing a single test into two halves and correlating performance on one half of the test items with performance on the second half of the test items. This procedure works best when the items in each half are similar in difficulty and content. Questions from all parts of the tests should be equally represented in each half. For example, odd-numbered questions could be in one half, while even-numbered questions could be in the other half. Split-half reliability measures depend on how the test is divided. Interrater Reliability. This addresses the extent to which different graders, using the same method, produce the same measurement of ability. This is important when measuring the accuracy of assessment with free response tests, such as written essays. Interrater reliability is computed by correlating the assessed score on the same exact exams, graded by different assessors. This method only measures the error associated with variation in grading standards. Noreen M. Webb, Richard J. Shavelson, and Edward H. Haertel provide a more detailed discussion of how to measure test reliability in their chapter in the Handbook of Statistics (2006).

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78% of individuals receiving the scholarship would be in the top 10% of actual ability. However, if the reliability of the test is only 0.5, fewer than half of the scholarship winners would have deserved the award on the basis of their true ability. Test reliability also affects the measured effectiveness of academic programs, schools, and teachers. Consider the case in which a teacher is being evaluated on the basis of average student test performance. When test reliability is low, even the average of a teacher’s students’ performance may be “noisy,” or have unexplained variation as a result of error. Consequently, an effective teacher would be more likely to be evaluated as ineffective, and vice versa. The ability to predict other outcomes on the basis of test performance also depends crucially on test reliability. For example, suppose one was using ordinary least squares to predict college math grade point average (GPA) with performance data from a high school math test. A reliable test would likely show a strong association between performance on the math test and college math GPA. An unreliable test would show a substantially weaker relationship between the test and the GPA. Furthermore, predictions of the student’s college math GPA would be more accurate using a reliable test relative to an unreliable test. Other consequences of an unreliable test could include that a proficient reader is assigned an afterschool tutor on the basis of an inaccurate measurement, leading to an inefficient use of school resources, or that an effective teacher is inappropriately sanctioned due to the mismeasurement of student academic performance. It should be noted that reliability differs from validity, which indicates the extent to which a measurement actually reflects the construct of interest. A person’s opinion of her own math ability may be a reliable measure of that student’s math ability in that it provides a fairly consistent measurement. However, it only poorly reflects student’s actual math ability and is consequently invalid. Ideally, measures are both valid and reliable.

Implications of Test Reliability Test reliability has important implications regarding the usefulness of the test in determining relative skills of test takers. This is crucial for the fair and efficient allocation of accolades, sanctions, or remedial interventions. For example, suppose that students who score in the top 10% of a standardized test receive a college scholarship. If the reliability of the test is 0.9,

Increasing Test Reliability There are a number of ways to increase the reliability of a test. Perhaps the easiest way to do so is to increase the length of the test. Longer tests tend to be more reliable because the idiosyncratic errors across items tend to average out. These item-level errors reflect whether a specific test question is idiosyncratically

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easy or difficult for a particular test taker. For example, a student will tend to perform better on a geography test if asked about the capital of the U.S. state in which she lives. Another source of itemlevel error is the test taker’s luck in guessing. A second way to increase the reliability of a test is to improve the clarity of test questions. Questions that are vaguely worded can make it difficult for a test taker to respond correctly even if she knows the answer to the question. Including questions that are either too easy or too difficult reduce the reliability of the test. In particular, a very easy question provides little information regarding the ability of the test taker since virtually all individuals answer correctly. A question that is excessively difficult also provides little information about the test taker’s ability and adds error to the measure of ability since even a correct answer is mostly indicative of luck as opposed to ability. An effective question discriminates between test takers of different abilities. It should be easy for a skilled test taker and difficult for one who is less skilled.

Item Response Theory Item response theory (IRT) is a branch of psychometrics focusing on the effective design and scoring of tests. IRT methods typically involve statistical models in which individuals vary in ability and test items vary in difficulty. Some models also take into account that the probability of guessing correctly depends on the number of possible answers and that performance on some questions is more closely tied to ability than performance on others. These models generally provide a more reliable assessment of ability than is possible by simply measuring the fraction of questions answered correctly. In addition to providing an estimate of the test taker’s ability, IRT methods also produce a standard error of ability that allows researchers to understand the precision of the ability estimate. Michael J. Kolen, Bradley A. Hanson, and Robert L. Brennan explain that because IRT methods provide information on both the variance of the error and the total performance, reliability coefficients are also easy to compute. These reliability coefficients do not take into account temporary factors such as health and testing conditions that might affect performance on all items. Standardized tests, such as the ACT, typically report performance measured by IRT methods.

Reliability Measures of Common Tests Most standardized tests have quite high overall reliability measures, though the reliability may be lower for subsections of the test. For example, the reliability coefficient of the ACT composite score is .97, though the reliability coefficients of the subsections range from .85 for science and .92 for English and the reliability coefficients of the SAT subsections range from .88 to .93. Lars Lefgren See also Measurement Error; SAT; Teacher Value-Added Measures

Further Readings ACT. (2007). The ACT technical manual. Retrieved from http://www.act.org/aap/pdf/ACT_Technical_Manual.pdf accessed on 6/6/2013 Baker, F. B. (2001). The basics of item response theory (2nd ed.). Washington, DC: ERIC Clearinghouse on Assessment and Evaluation. Kolen, M. J., Hanson, B. A., & Brennan, R. L. (1992). Conditional standard errors of measurement for scale scores. Journal of Educational Measurement, 4, 285–307. Webb, N. M., Shavelson, R. J., & Haertel, E. H. (2006). Reliability coefficients and generalizability theory. In C. R. Rao & S. Sinharay (Eds.), Handbook of statistics: Vol. 26. Psychometrics (pp. 1–44). Oxford, UK: North-Holland.

RISK FACTORS, STUDENTS In schools, students who are deemed to be “at risk” have been identified as having a high probability of dropping out of school or of low academic performance. The term at risk as it relates to students came into popular use after a series of reports following the 1983 report A Nation at Risk, in which students were seen as being “at risk” of low performance, failure, and dropping out. Across the United States, many factors have been nominated and tested to predict academic failure, with many states using multiple predictors of at-risk status to identify students who may need additional support. Unfortunately, the majority of factors used to predict at-risk status are based almost exclusively on indicators of poverty and social and economic privilege. These factors, while correlated with a higher

Risk Factors, Students

probability of academic failure, are not a determinate by any means and do not provide much in the way of guideposts to assist teachers and school leaders to help their students succeed and persist in schools. This issue is important from an economics and finance perspective, since, on the one hand, if a student is identified as at risk but was actually not at risk (misidentification), then resources are appropriated to a student who may not have needed them. In addition, students and families may be unduly burdened by the categorization of being at risk, which implies a deficit in ability or behavior. On the other hand, students who are at risk, but are never identified by their schools as such, are not provided the resources that they may need to succeed. Thus, recent research has focused on determining the most accurate “flags,” or groups of factors, for identifying students with a high probability of failure from all of the varied kinds of data collected in schools, as well as for identifying which groups of students are at risk of failure for which reasons, such as academic challenges and sociobehavioral issues. Specific risk factors have been identified that are helping schools promote student persistence based on the data and performance of the students in the schools. This entry begins with an overview of the issues in assessing the accuracy of flags to identify at-risk students. It then moves to a discussion of the issue of single time point factors versus longitudinal factors. It concludes with a consideration of the issue of studying the differences between at-risk students versus studying the difference between variables that predict atrisk status and which risk factors and early signs of problems are most predictive of schooling outcomes.

Hits Versus False Alarms Research on student risk factors has historically focused on the hunt for a flag or set of flags that would identify early in a student’s academic career if the student has a high probability of academic failure or dropping out of school. These have included factors such as low socioeconomic status, parents who are unemployed, a sibling who previously dropped out of high school, numerous disciplinary incidents, low attendance, low grades, and course failure, to name just a few. While the idea behind identifying risk factors may seem straightforward in theory, it can be fairly complex in practice. This is because measuring the accuracy of risk factors has two dimensions. This work has mostly focused on risk factors associated with dropping out, but since

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dropout is highly correlated with academic failure, both are considered together here. In the first dimension of accuracy, a flag is assessed for the number of students that it correctly predicts will drop out, known as sensitivity, or “hits.” This measures the proportion of the students who had the flag who dropped out, which should be high if the flag is an accurate predictor. As an example, research on data from all sixth-grade students from Philadelphia schools in the early 2000s showed that about 50% of the students who teachers reported had “unsatisfactory behavior” eventually dropped out before finishing Grade 12. This type of risk factor would be considered a mediocre predictor, since while it captures half of the students who dropped out, it also misses the other half of students who dropped out. Conversely, the other dimension of accuracy is a measure of specificity, or the “false alarms” (false positives), which must also be taken into account. In the example above dealing with Philadelphia, about 25% of the students who graduated also had unsatisfactory behavior. Thus, this flag not only misses half of the students who drop out but also misidentifies 25% of the students who graduated as dropouts. Thus, unsatisfactory behavior is not a very accurate flag for students at risk of dropping out. One wants to maximize the hits while minimizing the false alarms to find the most accurate risk factors. In searching for accurate and useful factors related to a student’s risk of failure, one wants those that (a) identify the majority of students who will drop out without misidentifying students as dropouts who do not actually drop out, (b) relate to data that are easy to collect or where data have already been collected in schools, and (c) provides information about what to do to help the student. In these ways, accuracy is a central component of examining student risk factors, since flags that are not accurate will not only fail to identify the majority of the students who are at risk but also misidentify a large percentage of students as being at risk when they never were. These are both highly problematic issues when considering allocating resources to help students succeed in schools based on early predictor flags.

Cross-Sectional and Longitudinal Risk Factors The current research on student risk factors has identified two types of factors that are both accurate and are the types of data already collected in schools: (1) cross-sectional factors and (2) longitudinal

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factors. Cross-sectional factors are those that are collected from just one time point, such as from a single year, while longitudinal factors consist of data trajectories that are examined through time. In the first, extensive evidence from the Chicago Public Schools has demonstrated that the on-track indicator developed by the Consortium on Chicago School Research is one of the most accurate cross-sectional factors predicting the likelihood that students will drop out. This flag consists of data from ninth grade, when a student is traditionally entering secondary school (high school). A student is considered “on track” to graduate if he or she has accumulated 10 semester credits by the end of Grade 9 and has no more than one course failure in a core subject (English, mathematics, science, or social studies). Seventy-two percent of students who are not “on track” by this indicator drop out (“hits”), while only about 15% of graduates are misidentified (“false alarms”). For practitioners such as teachers and principals, the on-track indicator is a useful flag for at-risk students since it is fairly accurate, relies on data already collected in schools, and helps pinpoint where problems may be occurring for students, such as in which subjects the student is failing. School resources can then be applied to help students regain the credits needed to be on track and to help students pass required core subject courses. The second type of factor—longitudinal risk factors—has been shown to be highly predictive of academic failure. However, while these factors conform to the requirements outlined above, their accuracy comes from the use of more complex statistics than cross-sectional factors such as those used in Chicago; that is, these factors examine different statistically significant types of trajectories, sorting strong students who are on track to graduate from students who are facing multiple challenges, based on significantly different trajectory groups. As examples, focusing on students who have low growth in standardized mathematics scores from a nationally representative sample of students in Grades 7 through 12 identified about 90% of the students who dropped out and only misidentified about 7% of the graduates. This is comparable with research also from a nationally representative sample that focused on students who either had low growth in noncumulative grade point averages (GPA) from Grades 9 through 10 or had a decline in noncumulative grade point average, which identified about 92% of all students who dropped out and only misidentified 18% of the students who graduated. Thus, these longitudinal factors are more

accurate than the cross-sectional factors, but they require a focus on identifying different subgroups of long-term patterns in the data. In addition, for identifying risk factors for students who do graduate but have low academic performance, these types of subgroup trajectory studies help identify consistent patterns of student growth or decline through time.

A Focus on Students Versus Factors While much of the recent research has identified risk factors for academic failure, dropping out of school, and being overly challenged by school, emerging research has begun to focus not only on these different types of risk factors but also on the different types of students. As with the different subgroups of student trajectories discussed above, a focus on different groups of students identified from the longitudinal school records data helps break down the monolithic conception of the “student at risk” and begins to see individual types of students, since “at risk” is not a single category but, rather, is made up of many subgroups of students. In the dropout literature, this has consisted of identifying multiple types of students who drop out. These consist of three to four subgroups, which include the “jaded” or “maladaptive” student who usually makes up about one third of all of the dropouts and conforms to the traditional stereotype of a dropout with not only low academic performance but also high discipline issues. Counterintuitively, the largest subgroup of dropouts is made up of the “quiet” dropouts, who have discipline reports that are similar to graduates but also have low course grade growth trajectories. These students’ grades are increasing, but not rapidly, so they traditionally go unidentified by schools. The final subgroups are usually identified as either students whose likelihood of dropping out is difficult to predict, such as students who undergo family strife, for example, parental divorce, incarceration, or a close family death; or students who unexpectedly have an unknown issue with their transcripts in the final years right before graduation and so are surprised that they do not graduate. While these are just three types out of the many subgroups proposed in the literature, these types exemplify the point that examining both the groups of risk factors and the groups of at-risk students can help tailor interventions. As examples, students in the quiet subgroup may be overly challenged academically and could benefit from academic tutoring, while students in the jaded group may need assistance with their behavior, may

Risk Factors, Students

be overly challenged with school and so they may act out or, conversely may be bored and acting out.

Teacher-Assigned Grades In the end, one of the strongest indicators students will drop out is low teacher-assigned report card grades. Some assessment researchers have maligned grades for being somewhat unrelated to academic achievement. However, recent research has shown that grades are strong indicators of a teacher’s evaluation of a student’s ability to negotiate the social processing of school. Thus, grades appear to represent both academic knowledge and a student’s ability to conform to the social expectations of school, such as turning in homework on time, behaving well, and participating in class. These factors are thus highly correlated with overall achievement and graduation as students who can fulfill both the academic and sociobehavioral expectations of classrooms are more likely to graduate and fulfill similar expectations in college or work. Alex J. Bowers See also Benefits of Primary and Secondary Education; Dropout Rates; Nation at Risk, A; Social Capital

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Further Readings Allensworth, E. M., & Easton, J. Q. (2007). What matters for staying on-track and graduating in Chicago public high schools: A close look at course grades, failures, and attendance in the freshman year. Chicago, IL: Consortium on Chicago School Research. Balfanz, R., Bridgeland, J. M., Moore, L. A., & Hornig Fox, J. (2010). Building a grad nation: Progress and challenge in ending the high school dropout epidemic. Washington, DC: Civic Enterprises Everyone Graduates Center at Johns Hopkins University America’s Promise Alliance. Bowers, A. J., & Sprott, R. (2012). Why tenth graders fail to finish high school: A dropout typology latent class analysis. Journal of Education for Students Placed at Risk, 17(3), 129–148. Bowers, A. J., Sprott, R., & Taff, S. (2013). Do we know who will drop out? A review of the predictors of dropping out of high school: Precision, sensitivity and specificity. High School Journal, 96(2), 77–100. Gleason, P., & Dynarski, M. (2002). Do we know whom to serve? Issues in using risk factors to identify dropouts. Journal of Education for Students Placed at Risk, 7(1), 25–41. Rumberger, R. W. (2011). Dropping out: Why students drop out of high school and what can be done about it. Cambridge, MA: Harvard University Press.

S high school teaching positions, the position-based pay structure was viewed as discriminatory and was gradually replaced by the “equal pay for equal work” single salary structure. Continued reliance on the salary schedule has been attributed to a number of factors, including the schedule’s objectivity, perceived fairness, predictability, and ease of administration. Teacher unions, in particular, have been staunch defenders of the salary schedule on equity grounds, arguing that differentiating teacher pay would undermine their solidarity and morale. Teachers also have historically been very supportive of the salary schedule, although their level of support has been found to vary somewhat depending on their personal characteristics (e.g., years of experience), their teaching position, and the school climate conditions.

SALARY SCHEDULE It is now well established that teachers have a greater impact on student outcomes than any other schoolbased factor. Teachers also have the greatest impact on school district budgets, with their compensation constituting, on average, about 55% of annual expenditures. As a result, teacher pay, especially with regard to how it is determined and structured, is viewed as having important implications for educational costs and productivity. The salary schedule, sometimes referred to as the single, uniform, or unified salary schedule on account of the fact that the same schedule applies to all teachers within a district, is the long-standing approach to paying teachers used by the vast majority of U.S. school districts. This entry will provide a brief history of the salary schedule and the arguments for reforming the schedule. It will conclude with an overview of the efforts to reform the salary schedule.

Structure The salary schedule links teachers’ base pay to two qualifications: (1) the years of teaching experience and (2) the level of college credits and degrees earned. The schedule itself looks like a grid with multiple “steps” (i.e., rows) and “lanes” (i.e., columns) corresponding to increasing amounts of experience and formal education, respectively. With each additional year of experience in a district, a teacher advances a step of the schedule and receives a pay increment associated with that step. Teachers who switch districts may receive full, partial, or no credit for their prior years of teaching, depending on the new district’s policy with regard to out-of-district experience. Many districts limit the total number of steps

History The single salary schedule was first introduced in the 1920s and has been in near-universal use by U.S. districts since the 1950s. It was developed in response to dissatisfaction with the prevailing practice, which set salary levels based primarily on position and grade level, such that high school teachers, who were predominately male, received higher salaries than their predominately female elementary school counterparts. Though rationalized at the time by the greater skill level and amount of training required of 629

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in the schedule, which results in teachers reaching the maximum salary for a given level of educational attainment typically after 15 to 20 years of teaching. Similarly, the completion of a higher degree (e.g., master’s degree) and a designated amount of additional education credits (e.g., master’s degree plus 30 credits), regardless of subject area and its relation to one’s teaching field, generally advances the teacher across the schedule to a new lane and provides a pay increment associated with that move. In more than half the states, the number of steps and lanes within the salary schedule as well as the size and distribution of pay increments are determined within districts by local school boards. In the other states, a minimum salary schedule is established by the state, although districts are typically allowed to supplement the minimum. As a result, teacher salary schedules are uniform within districts but often vary across districts. The design or shape of the salary schedule, particularly in terms of the distribution of salary increments across the steps and lanes, reflects how a district or state has chosen to reward teachers for additional experience and formal education. A socalled front-loaded schedule, for example, provides relatively larger pay increments to teachers in the early years of their careers, when teachers are most likely to leave the profession or change districts, and smaller increments in their later years. In contrast, a back-loaded schedule provides larger pay increments to more experienced teachers. The design has been shown to be an important feature of the salary schedule due to its impact on teacher and student outcomes.

Arguments for Reforming the Salary Schedule At the time of its development, the two criteria used in the salary schedule to reward teachers were seen as objective, measurable proxies for teacher expertise. However, research spanning more than two decades has found virtually no connection between additional coursework or advanced degrees and teacher effectiveness, except among secondary math and science teachers with advanced, subject-specific coursework. A stronger, positive association has been found between years of experience and teacher effectiveness, although the returns to experience appear to be concentrated within teachers’ first 3 to 5 years in the profession. Critics of the salary schedule point to the inefficiency and lack of incentive and fairness associated with rewarding teachers for

attributes that are only weakly connected to teacher performance as grounds for its replacement. In addition, the rigidity of the single salary structure has proven to interfere with districts’ and schools’ ability to respond to labor market forces in their efforts to recruit and retain teachers. Research shows that prospective and practicing teachers, like individuals in other occupations, consider the relative pecuniary (e.g., salary, benefits) and nonpecuniary (e.g., working conditions) characteristics of positions when deciding whether, how long, and where to teach. Given the uniform salary structure in teaching and the widespread use of differentiated pay in other occupations, individuals who have better opportunities in nonteaching occupations or more options within teaching in terms of where to work are less likely to enter or remain in the teaching profession or to teach in certain districts and schools. This has resulted in a number of unintended consequences, including shortages of qualified teachers in particular subject fields, such as math and science; a decline over time in the proportion of individuals from the top of the academic distribution who enter and stay in the teaching profession; and an inequitable sorting of teachers across and within districts such that districts and schools with relatively less favorable working conditions are much less able to recruit and retain qualified teachers.

Efforts to Reform the Salary Schedule These shortcomings associated with the salary schedule have prompted numerous calls and efforts over the years to reform or replace it. The widely cited 1983 report, A Nation at Risk, for example, called for more competitive, market- and performance-based teacher salaries. More recently, the push toward greater school accountability as well as federal initiatives such as the Race to the Top and the Teacher Incentive Fund grant programs have prompted an increasing number of states and districts to consider and experiment with reforms to their teacher salary structures. To date, no consensus has emerged with regard to how the salary schedule should be reformed or what should replace it, though a number of approaches have been and are increasingly being implemented, most often as amendments or supplements to, rather than replacements of, the traditional structure. A commonality among the various approaches is the use of differentiated pay, which involves rewarding teachers for characteristics other than years of experience and

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formal education, such as demonstrated competencies, teaching assignments, or effectiveness, as a means to alter teachers’ choices and behaviors. Perhaps the most discussed and controversial approach to reforming teacher compensation is merit pay, also referred to as performance pay, which links at least a portion of a teacher’s compensation to one or more measures of his or her effectiveness in the classroom, such as student achievement results and formal classroom evaluations. Proponents of merit pay believe that tying pay to outcomes rather than inputs will provide an incentive for teachers to work harder and improve their performance, make teaching more attractive to individuals who might not otherwise consider teaching, and encourage more effective teachers to remain in the profession and less effective teachers to leave. Opponents, though, have raised concerns about the reliability and validity of the assessments used to measure teacher effectiveness; the potential for undesirable teacher behaviors, such as teaching to the test; and the negative impact on teacher cooperation and morale that might result. Group- or school-based performance pay is another performance-based approach, which links teacher compensation awards to the effectiveness of a group of teachers or an entire school rather than to individual teachers. As with merit pay, this reform aims to provide teachers with an incentive for greater effort and performance, with one or more measures of student achievement outcomes typically used to assess group results. This approach alleviates the concern about teacher cooperation associated with merit pay; however, concerns remain regarding the adequacy of the assessments used to measure performance and unintended effects on teacher behaviors. Skill- or knowledge-based pay seeks to differentiate teacher compensation based on teachers’ skills and competencies. Rather than (or in addition to) being rewarded for experience and education, under this system, teacher pay increments are tied to their attainment and demonstrated use in the classroom of knowledge and skills associated with effective teaching, as defined by their state or local district. The awarding of salary supplements or bonuses to teachers who earn certification from the National Board for Professional Teaching Standards is a form of competency-based pay that is already in place in a number of states and districts. Targeted economic incentives constitute another approach used by an increasing number of states and districts to amend teacher compensation. This

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approach differs from performance- and skill-based programs in that it offers additional compensation only to select subgroups of teachers, such as those with specific qualifications (e.g., teachers qualified to teach in fields where there is a shortage) or those who agree to work in specific (e.g., hard to staff) districts or schools, in an effort to attract and/or retain the types of teachers needed to address local staffing challenges. The incentives take various forms depending on the intent of the program, such as one-time or periodic bonuses, ongoing enhancements to base pay, added-experience credits, student loan waiver or housing assistance. To date, research on the effectiveness of these efforts to reform teacher pay in terms of their impact on teacher behaviors and/or student outcomes is limited and inconclusive. Nonetheless, it is clear that the long-standing approach of relying solely on the uniform salary structure to reward teachers regardless of their performance, skill level, or contribution to the needs of the organization is increasingly losing support among policymakers at all levels, who see the reform of teacher compensation as a means to address concerns regarding the teaching force and improve student outcomes. Karen J. DeAngelis See also Hedonic Wage Models; National Board Certification for Teachers; Opportunity Costs; Pay for Performance; Teacher Compensation; Teacher Supply

Further Readings Goldhaber, D., DeArmond, M., & Deburgomaster, S. (2011). Teacher attitudes about compensation reform: Implications for reform implementation. Industrial & Labor Relations Review, 64(3), 441–463. Grissom, J. A., & Strunk, K. O. (2012). How should school districts shape teacher salary schedules? Linking school performance to pay structure in traditional compensation schemes. Educational Policy, 26(5), 663–695. Johnson, S. M., & Papay, J. P. (2009). Redesigning teacher pay: A system for the next generation of educators. Washington, DC: Economic Policy Institute. Kershaw, J. A., & McKean, R. N. (1962). Teacher shortages and salary schedules. New York, NY: McGraw-Hill. Kolbe, T., & Strunk, K. O. (2012). Economic incentives as a strategy for responding to teacher staffing problems: A typology of policies and practices. Educational Administration Quarterly, 48(5), 779–813.

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Odden, A. (2000). New and better forms of teacher compensation are possible. Phi Delta Kappan, 81(5), 361–366. Podgursky, M., & Springer, M. (2011). Teacher compensation systems in the United States K-12 public school system. National Tax Journal, 64(1), 165–192. Strunk, K. O., & Zeehandelaar, D. (2011). Differentiated compensation: How California school districts use economic incentives to target teachers. Journal of Education Finance, 36(3), 268–293. Vigdor, J. (2008). Scrap the sacrosanct salary schedule. Education Next, 8(4), 36–42. West, K. L., & Mykerezi, E. (2011). Teachers’ unions and compensation: The impact of collective bargaining on salary schedules and performance pay schemes. Economics of Education Review, 30, 99–108.

SAN ANTONIO INDEPENDENT SCHOOL DISTRICT V. RODRIGUEZ San Antonio Independent School District v. Rodriguez (1973) represents the only legal case involving school finance litigation to be addressed by the U.S. Supreme Court. In Rodriguez, the Supreme Court legally upheld the Texas school funding system, finding that it was not unconstitutional under the Equal Protection Clause of the U.S. Constitution’s Fourteenth Amendment. The Court ruled that education was not a fundamental right, nor was it to be given any special legal protections under the U.S. Constitution. In essence, the Supreme Court’s decision in Rodriguez overruled a previous California school finance case, Serrano v. Priest I (1971), where the state’s supreme court ruled that education was legally protected by the federal constitution. This entry will provide a historical overview of the only case in which the U.S. Supreme Court has specifically addressed legal issues surrounding school financing.

Historical Background In Texas, the state’s public schools had been (and continue to be) historically financed through property taxes imposed on local school districts. Since property values are often higher in wealthier public school districts than in others, significant financial disparities across public school districts in per-pupil spending occurred. These financial disparities triggered a Fourteenth Amendment Equal Protection Clause legal challenge to the constitutionality of Texas’s school funding system.

Prior to the Rodiguez lawsuit, Texas had not legally addressed issues surrounding school finance reform since 1948, when the Gilmer-Aikin Laws were passed by the Texas State Legislature, allowing for increased state equalization funding to supplement local property tax revenues used to fund public education. Equalization funding provides state funds to school districts in inverse relation to the districts’ property wealth per pupil. On May 16, 1968, 400 students at the Edgewood High School in San Antonio, Texas, held a walk-out demonstration and met with district, central-office administrators demanding improved funding for school supplies, facilities, and increased salaries to attract higher quality teachers. At the time of the demonstration, approximately 90% of the students in the Edgewood Independent School District were of Mexican ancestry. On July 10, 1968, nearly 2 months after the initial student demonstration at Edgewood High School, Demetrio Rodriguez and seven other Edgewood district parents filed a class action lawsuit on behalf of Texas schoolchildren throughout the state who were poor or resided in public school districts with low property tax bases. Rodriguez and the other parents who filed the lawsuit claimed that the Edgewood district had one of the highest tax rates in Texas but only generated approximately $37 per student in property tax revenue, while Alamo Heights Independent School District, Bexar County’s wealthiest public school district, raised $413 per student in property tax revenue at a lower tax rate. Additionally, the actual tax rate per $100 of property valuation needed to equalize public education funding was significantly higher in the Edgewood district, at $5.76, than the $0.68 in Alamo Heights district.

Facts of the Case In Rodriguez, Mexican American parents whose children attended public schools in the Edgewood Independent School District filed a class action lawsuit against state school officials arguing that Texas’s system of funding its public schools was unconstitutional under the Equal Protection Clause of the Fourteenth Amendment. The plaintiff parents’ primary legal argument was that education was a fundamental right, the children attending schools in poor public school districts throughout Texas represented a suspect classification, and the Supreme Court must strictly scrutinize the state’s public school funding system. In essence, the legal burden of proof would be on the state to show that it had a

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“compelling interest” compared with a lesser, “rational” interest in maintaining the state’s school funding system. A federal trial court in Texas had ruled in favor of the parents, ruling that Texas’s school funding system discriminated on the basis of wealth and was unconstitutional under the Equal Protection Clause.

The Court’s Ruling When the Rodriguez case reached the U.S. Supreme Court, a majority of the justices reversed the decision of the trial court in favor of the State of Texas. In a 5-4 decision, the Court held that Texas’s system of school funding did not violate the Equal Protection Clause. Based on the Court’s ruling, Texas was not required to financially subsidize the state’s poorer public school districts through increased state equalization funds. The Court ruled on three specific issues in the Rodriguez case. First, it found that education was not a fundamental right under the Equal Protection Clause. Second, it determined that the plaintiff parents had not proven there to be a suspect class of poor students where the state of Texas’s alleged discrimination was directed. Third, the Court ruled that the “strict-scrutiny” test of equal protection analysis did not apply. Instead, the lesser, “rational-relationship” test of equal protection analysis applied, which only required that Texas’s school funding system have some rational relationship to a legitimate state purpose. While the Court did acknowledge the funding disparities between wealthier and poorer Texas public school districts, it held that the state’s public schools did not deny any child the opportunity to obtain an education, nor was the state’s funding system discriminatory. The majority opinion, authored by Justice Lewis Powell, states, To the extent that the Texas system of school financing results in unequal expenditures between children who happen to reside in different districts, we cannot say that such disparities are the product of a system that is so irrational as to be invidiously discriminatory.

In a dissent written by Justice William Brennan, he maintained that education was a fundamental right because it was inextricably linked to the right to vote and to the free speech rights protected by the U.S. Constitution’s First Amendment. As a result, according to Brennan’s dissent, any classification affecting education should be subject to “strict judicial scrutiny.”

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In a separate dissent, Justice Thurgood Marshall called the Rodriguez decision “a retreat from our historic commitment to equality of educational opportunity.” The 1973 Supreme Court case of San Antonio Independent School District v. Rodriguez represents an established and long-standing legal precedent permitting local, property-tax-based education funding programs at the state level—a major source of local inequality in funding today’s public schools. As a result of the Rodriguez decision, there are currently no federal constitutional or federal court legal remedies addressing school finance reform. Instead, all school finance litigation is currently restricted to state-level constitutions or courts for legal remedies. Kevin P. Brady See also School Finance Litigation; Serrano v. Priest

Further Readings Sracic, P. A. (2006). San Antonio v. Rodriguez and the pursuit of equal education. Lawrence: University of Kansas Press. Texas State Historical Association. (n.d.). The handbook of Texas. Retrieved from www.tshaonline.org/handbook/ online/articles/jrrht

Legal Citations San Antonio Independent School District v. Rodriguez, 411 U.S. 1 (1973). Serrano v. Priest, 96 Cal. Rptr. 601 (Cal. 1971), 135 Cal. Rptr. 345 (Cal. 1976) cert. denied, 432 U.S. 907 (1977).

SAT The SAT is an exam taken by a majority of collegebound students in the United States and is an important criterion in college admission decisions. In the economics of education literature, individual SAT scores are a common measure of student academic quality, while SAT scores averaged across all students in a high school or college provide a parallel measure of the academic quality of an educational institution. This entry provides a brief overview of the SAT, explains its empirical and theoretical application in the academic literature, and outlines common criticisms of the use of the exam.

History and Overview of the SAT The origins of the SAT, initially an abbreviation for the Scholastic Aptitude Test and later of the

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Scholastic Assessment Test, lie in the movement for intelligence quotient testing, whose goal was to formally assess individual mental acumen. After World War I, Carl Brigham, a Princeton University psychologist, adapted the Army Alpha multiple-choice intelligence quotient test for use as a college scholarship screening mechanism as part of an effort to expand access to the more prestigious universities of the time. Proponents of the test, including Henry Chauncey, a dean at Harvard University, closely involved with the administration of standardized tests in the military during World War II, convinced member schools of the College Board—the preeminent association of institutions of higher education at the time—to adopt the SAT more broadly as the uniformly recognized admission test for all college applicants in the early 1940s. Shortly thereafter, the Educational Testing Service was born from the merging of three educational nonprofit organizations. A primary function of the Educational Testing Service at that time (and to this day) was the administration of the SAT, one of two college admission exams currently recognized by all U.S. colleges and universities. The SAT has evolved since its inception, but its goal of assessing aptitude in the form of developed reasoning skills that are predictive of college success rather than either innate ability or subject mastery remains a key focus. Furthermore, its multiple-choice format and core quantitative and verbal exam structures are remarkably similar to those administered early in the SAT’s history, although a third writing section, including an essay, was added to the core test sections as a mandatory component in 2005. To ensure consistency between high school curricula, content coverage of SAT questions, and college curricular expectations, the College Board regularly conducts surveys of high school and college educators. Research based on a fall 2009 survey found the content of the verbal portion of the SAT to be largely consistent with U.S. high school curricula and college expectations; consistency for the mathematics exam was somewhat weaker. The study’s researchers claim that the latter is likely a product of the idiosyncratic course-based nature of mathematics courses at all academic levels. According to the College Board website, the SAT exam is administered approximately seven times annually at more than 7,000 Educational Testing Service–sanctioned locations across the nation and internationally at a student cost of $51 in 2013–2014. The registration fee includes the transmission of test results to up to

four institutions of the student’s choice. Additional reports could be requested for a fee of $11.25 per report in 2013. The SAT is graded on a normed scale ranging from 200 to 800 with a mean of 500 and a standard deviation of 100 points for each of the three sections of the SAT Reasoning Test—(1) critical reading (formally known as the “verbal reasoning section”), (2) math, and (3) writing—yielding a maximum score of 2,400 points. The grading scale was recentered in 1995 to adjust for the declining average test scores over time, due at least in part to the increasing numbers of students taking the exam. Specifically, the median student individual test scores for the verbal and mathematics sections had fallen below the target median score of 500. According to the conversion tables provided by the College Board on its website, a verbal or quantitative reasoning score of 420 prior to score recentering translates to a renormed score of 500, while a math score of 470 prior to score recentering translates to a renormed score of 500. Additional content-based tests, referred to collectively as SAT II or SAT Subject Tests, are taken by a much smaller percentage of students and are beyond the scope of this entry. In March 2014, the College Board announced a major redesign of the SAT for students taking it beginning in the spring of 2016. The exam will go back to a 1,600-point scale, with the essay becoming optional. The SAT faces competition from another widely recognized standardized college admissions test, the ACT, which is shorter and contains fewer but more advanced mathematics questions than does the SAT and also has an optional rather than a mandatory essay section. Increasingly, students faced with the choice of which exam to take for college admissions opt to take both. While historically more high school students annually have taken the SAT than the ACT, that changed in 2012, when 1.666 million students took the ACT and 1.664 million students took the SAT, according to the National Center for Education Statistics. Substantial state-level variation exists in the number of students taking the ACT versus the SAT, driven largely by the public university admissions requirements of the state and the state’s proportion of high school students intending to apply to elite, out-of-state colleges. According to the 1996 data compiled by Melissa Clark, Jesse Rothstein, and Diane Whitmore Schanzenbach, states such as Massachusetts, Virginia, and Pennsylvania had SAT participation rates of 60% to more than 75%,

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but ACT participation rates were well under 20%. In contrast, states such as Kansas, Illinois, and Tennessee showed SAT participation rates below 20% and ACT participation rates nearing 70% to 80%. More recent SAT data from the National Center for Education Statistics indicate that SAT participation rate differences among states persist even as the practice of taking both the SAT and the ACT is on the rise. In addition to market competition from the ACT, the SAT has been affected by a trend among many colleges and universities to give applicants the option of whether to take the SAT. This trend gained significant momentum when the then president of the University of California system, Richard Atkinson, criticized higher education’s reliance on the SAT results for college admissions. In particular, drawing on research conducted by Saul Geiser and Roger Studley, eventually published in 2002, he stated in a now famous speech to the American College of Education that content-specific achievement measures such as the SAT Subject Tests rather than measures of developed reasoning skills such as SAT verbal and math scores have been shown to be better predictors of college performance. In the wake of this criticism, a long list of colleges and universities made the SAT optional for applicants, relying instead on high school grade point average (GPA), high school course rigor, and interviews or essays to make admissions decision. That said, nearly half of high school students continue to take the SAT exam, with another large percentage taking the ACT as an alternative or an additional exam; and according to Kaplan Test Prep statistics, the vast majority of college and university admissions policies continue to require applicants to submit either a SAT or an ACT exam score. This continued reliance on the SAT or ACT supports a large and growing test preparation industry, estimated at $1 billion in 2010, according to the market research firm Outsell, Inc.

SAT and the Economics of Education The bulk of the economics of education literature incorporating SAT scores follows a theoretical framework known as the education production function, either explicitly or implicitly, to place the educational process in an input-output context. The idea of understanding educational processes from an inputoutput framework dates back to the 1966 Equality of Educational Opportunity Study, also known as the Coleman Report, which was commissioned by the

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U.S. Department of Health, Education, and Welfare. In these models, a series of inputs to the education process, including student ability and school quality, are combined in given proportions to produce the output of student learning. Because of its widespread use by the high school student population, SAT exam scores represent a primary measure used in empirical analyses of the economics of education. Depending on the level of education being modeled (i.e., secondary vs. postsecondary), SAT scores may be a proxy for student quality and represent an input to the production process, or they may be a proxy for student learning and represent an output of the production process. Furthermore, depending on the level of aggregation of the outcome of interest, student-level or mean high school or college SAT scores may be used. SAT scores are used to explain or predict college enrollment propensities, college major choice, college persistence, and college academic outcomes. In general, empirical studies document a statistically significant positive relationship between SAT scores and college GPA, even when controlling for high school grades, high school and neighborhood characteristics, and characteristics such as race, gender, and family income, which are, themselves, highly predictive of SAT scores at the student level. However, recent estimates of the magnitude of the incremental explanatory power of the SAT in college GPA models are often modest—in the range of 3% to 5%. In addition to college academic performance, SAT scores are linked empirically to college major choice and college persistence. A related literature within the economics of education field uses the SAT scores of a student relative to his or her peers, along with other measures of own and peer high school achievement, to identify and quantify the presence of peer effects on college performance. Empirical evidence linking peer SAT to own performance is mixed, with models identifying peer effects more often from high school GPA than from the SAT, as detailed in a paper by Griffith and Kevin Rask. One analysis identifying significant SAT peer effects found that a one-unit increase in a student’s classmates’ average academic rating, equivalent to roughly a 70-point increase in the combined math and verbal SAT score and a 4-point increase in high school GPA, associates with a 0.22 increase in own student GPA, although this effect is only significant for male students and male peers. Aggregated to the school or school district level, mean SAT scores have been used as a measure of

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secondary school quality, although more recent studies often rely on educational value-added measures that evaluate student progress rather than student achievement at a given point in time. Empirically, school quality as measured by the SAT is a weak predictor of home prices and property values, according to a 2008 paper by Norman H. Sedgley, Nancy A. Williams, and Frederick W. Derrick. Mean SAT is also a commonly accepted measure of college selectivity and/or the quality of the student body. Most notably, it is a significant component used to calculate the ubiquitous U.S. News & World Report’s college rankings, which themselves have been linked to the success of an institution’s admission process and pricing policy. A wide range of empirical analyses draw on SAT-based measures of aggregate student quality or institutional selectivity as an outcome or as an important control variable. Some of these studies link mean SAT to alumni giving, high school quality, and labor market success, although there is some disagreement regarding the strength of the relationship with the latter.

SAT and Selection Bias A significant problem associated with the use of SAT scores at the individual or aggregate level to measure student or school quality results from the nonrandomness of the test takers. In general, states with higher participation rates boast lower mean scores, indicating that state mean SAT scores provide an overestimate of latent or unobservable mean state student ability in the form of developed reasoning skills. The magnitude of the bias, according to recent research, is modest in states where the SAT predominates but substantial in states where the ACT predominates. At the school level, the fact that stronger high schools exhibit higher rates of SAT taking and higher SAT scores offsets the negative bias associated with stronger students opting to take the test, yielding a relatively modest selectivity bias from within-school and across-school test-taking selection. Given the predominance of SAT-optional admissions policies and the documented evidence that weaker SAT students are more likely to withhold SAT scores, selection bias exists when using aggregated SAT scores as a measure of college quality as well. Finally, at the individual level, test-taking propensity varies positively with academic achievement, which itself varies with race, family income, and family educational attainment.

SAT, Race, and Gender A common criticism of the SAT is that it favors nonminority test takers. Evidence indicates that minority test takers perform more poorly on the SAT than do their nonminority counterparts, even controlling for other sociodemographic characteristics. However, when examined at the item level rather than at the test level, evidence across a number of studies indicates that White students outperform minority students on easy questions, while the opposite is true on the more difficult questions. This differential performance is claimed to perpetuate the racial bias of the exam because race-blind student performance on “experimental questions” assesses the statistical validity of those questions and ultimately dictates their inclusion or exclusion in future SATs. The alleged racial bias of the SAT exam is a primary component in the debate over affirmative action in higher education admissions. In response, researchers have suggested a number of modifications to the SAT questions, testing methodologies, and grading system to minimize racial bias in the test. The SAT has been criticized for gender bias well. Despite the fact that women generally receive higher grades in high school and college than do their male peers, mean female performance on the both the math and the critical reading portions of the SAT lags male performance, with the gap larger for the math section. Critics point to gender differences in test anxiety, self-confidence, and demographics as possible sources of the score gap. Carlena K. Ficano See also Benefits of Primary and Secondary Education; College Rankings; Education Production Functions and Productivity; Peer Effects; Selection Bias; Teacher Performance Assessment

Further Readings Atkinson, R. C. (2005). College admissions and the SAT: A personal perspective. Observer, 18(5), 15–22. Clark, M., Rothstein, J. M., & Schanzenbach, D. W. (2009). Selection bias in college admissions test scores. Economics of Education Review, 28, 295–307. Geiser, S., & Santelices, M. V. (2007). Validity of highschool grades in predicting student success beyond the freshman year: High-school record vs. standardized tests as indicators of four-year college outcomes (CSHE Research Paper No. 6.07). Berkeley: University of California Berkeley Center for Studies in Higher Education.

School Boards Hanushek, E. A. (1979). Conceptual and empirical issues in the estimation of educational production functions. Journal of Human Resources, 14(3), 351–388. Kidder, W. C., & Rosner, J. (2002). How the SAT creates built-in-headwinds: An educational and legal analysis of disparate impact. Santa Clara Law Review, 43(1), 130–212. Monks, J., & Ehrenberg, R. G. (1999). The impact of U.S. News & World Report college rankings on admissions outcomes and pricing policies at selective private institutions (NBER Working Paper No. 7227). Cambridge, MA: National Bureau of Economic Research. Rothstein, J. M. (2004). College performance predictions and the SAT. Journal of Econometrics, 121, 297–317.

SCHOOL BOARDS A school board is an elected or appointed group entrusted by the community with the responsibility of the academic and fiscal health of a school district or system. Many scholars have described school boards as the essence of representative governance in a democracy. According to the National School Boards Association (NSBA), there are 14,000 school boards governing school systems in the United States, with responsibility for expenditures of $600 billion per year. This entry describes the powers, roles, and responsibilities of school boards; details the characteristics of board members and methods of becoming board members; briefly highlights the history of school boards within the United States; and explains the current debate surrounding the future of locally elected school boards.

Powers, Roles, and Responsibilities of School Boards The Constitution of the United States does not specifically mention education. The federal government is limited to only the powers expressed or implied in the Constitution; all other powers are reserved for the states. Therefore, the federal government plays a subordinate role to the states in public education. Most state constitutions contain a mandate to provide for a system of public schools; thus, school boards derive their power and authority from the state. School boards, then, are state-created agencies; so, consequently, board members are state officials. They establish policies and regulations in compliance with federal and state laws that govern schools.

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Schools boards may exist at both the state and the local district levels. State boards of education are the governing and policy-making body for the statewide education system and consist of lay members who govern the long-term overall needs of the public education system within their state. Commonly, the board coordinates with the state’s department of education as well as with an elected or appointed chief or state school administrator who implements and administers legislation and policies within the system under the oversight of the state board. As of 2012, 33% of state board members were appointed by their governor, 28% were appointed by their governor and then confirmed by the legislature, and 20% were elected. The remaining methods of board selection include a mixture of elections and appointment by local government officials, appointment by the legislature, and joint appointments made by both the governor and the legislature. Local school boards (also known as school committees, school directors, boards of education, or trustees) are elected or appointed. Their primary functions include setting fiscal, personnel, instructional, and student-related policies. Local school boards are empowered to construct a vision and create an atmosphere that establishes trust and provides accountability in the school district. Generally, school boards have the authority to • hire and fire the district superintendent and set policy for hiring other personnel; • negotiate contracts with employee unions; • direct the development and adoption of policy; • levy property taxes through a majority vote of the board or by placing a request before the voters from within the district; • establish and account for curricular goals; • establish budget priorities, including approving financial reports and the annual budget; • oversee facilities-related issues; and • close or construct schools.

Methods, Membership, Contributions, and Wages Methods

Policies and guidelines for becoming a member of either a state or a local school board are established by each state. Each state also establishes its own qualifications and procedures for becoming a candidate for the school board. While the vast majority of school board members are elected, several states and

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School Boards

cities have some or all of their local school board members appointed, often by the governor or the mayor of the city. According to the NSBA, as of 2010, approximately 90% of school board members were elected. Campaigns for a school board seat are generally small, localized events funded by the candidate. In a 2009 NSBA survey, 73.9% of elected board members reported that their campaign cost less than $1,000 in their most recent election, while 87% said that they spent less than $5,000. Nationally, 44% of board members called their most recent election “very easy,” while just 5.8% described it as “very difficult.” Membership

As of 2010, school board members were 80.7% White, 12.3% African American, and 3.1% Hispanic, according to the NSBA. In large districts (those with 15,000 or more students), 21.8% of the school board members surveyed were African American, and 6% were Latino. School board members are more educated than the average American, with 75% of members holding at a minimum a bachelor’s degree. Contributions and Wages

The majority of board members representing small school districts spend fewer than 15 hours per week on board-related matters, according to the NSBA. Most of these board members receive no salary, with a small percentage earning less than $5,000 per year. The majority of large school districts pay board members a salary of $10,000 or more per year, while members contribute between 15 and 40 hours per month to district work. Some school districts provide board members with a benefits package that includes health, dental, and vision coverage in addition to or instead of a wage.

History Local school boards have been a fundamental piece of the U.S. public education system since the colonial era. In 1647, the Massachusetts Bay Colony legislated that towns create and sustain local schools. Support for local schools governed by the local community spread throughout the colonies. Originally, school governance occurred during town meetings. As the complexity of administering schools grew, so did the governance structure. Members of the local community governed the schools, hired the

schoolmaster, ensured that schools were built, and maintained and addressed all other related school issues. By the early 1800s, school committees had developed into ongoing governing establishments separated from the rest of the local government. Massachusetts formally established the system of school committees, requiring each town to elect an independent school committee to take “the general charge and superintendence” of all the public schools of the town. Over time, this model spread to the rest of the nation, allowing local citizens to vote for representatives who sat on these committees or boards that were to develop and govern their public schools. During the Progressive Era, many political activists joined in efforts to reform the public education system in the United States. Specifically, they wanted to “take education out of politics” by turning what were debated as political issues into matters for professional administrative discretion by educators. Activists and reformers argued that elected school leaders and other public officials were advancing their own special interests at the expense of students’ education. Because of apparent municipal political corruption, patronage, and expansive political machines, schools and their governing boards were separated from general governance structures and placed under the separate, stronger control of local, single-purpose education governments. School board elections were established to be on a separate date from elections for elected offices and ballot measures for other branches of government, in an attempt to limit political influence from outside interest groups, political actors, and partisan politics. According to James Cibulka, this governance structure was designed to shield professional educators from politics while giving them the authority to establish and maintain a professional bureaucracy that ensured equity, accountability, and efficiency. Along with the electoral and governance changes, the Progressive Era brought with it the centralization of power in the hands of an education expert who served as the chief executive over the schooling system. School boards appointed a chief executive or superintendent for his educational expertise and experience. The board then granted the superintendent significant authority to administer and manage schools. The school superintendent was seen as an education expert who was guided by board policies and state laws as well as his own professional expertise.

School Boards

The politically insulated position of schools, school boards, and the education system has given rise to concerns that school systems are not apolitical but instead consumed by unmanageable and ineffectual politics. The education system has become more complex, requiring more expertise and leadership by local school boards. In 1983, the report A Nation at Risk discussed the state of America’s schools and called for a host of reforms based on the negative direction in which the authors believed public education was headed. This report drew the nation’s attention toward many issues and deficiencies in the American education system that had been neglected or ignored by the public at large. This led some to argue that the Progressive Era reforms that were designed to take politics out of education had instead eliminated accountability and efficiency from education. Since the publication of A Nation at Risk, there has been an increase in education policy making and funding at the federal and state levels, with initiatives such as the Goals 2000: Educate America Act, signed into law in 1994; the No Child Left Behind Act of 2001; and the Race to the Top grant competition begun in 2009. These policies had the effect of limiting the authority and sovereignty of the locally elected school boards, which were now required to carry out policies put in place by Congress and the state legislature. Subsequent to these federal and state policy changes, there has been a growing movement to eliminate oversight responsibility from single-purpose educational governance boards by replacing them with general-purpose governing actors such as mayors or governors. This trend in education reform has been growing rapidly and has stirred debate about the efficacy of locally elected school boards in general.

Current Debate About the Future of Locally Elected School Boards Critiques of School Boards

There are a variety of opinions regarding the effectiveness of locally elected school boards as both educational policy-making and oversight entities. Critics of these boards claim that they have outlasted their utility. The criticism has grown in intensity since the publication of A Nation at Risk and the subsequent development, expansion, and reporting of standardized testing. Consequent to these developments, political leaders began to recognize education as essential to the future of American

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global competitiveness. Education has increasingly become a leading electoral issue, and as a result, state and municipal leaders have attempted to increase their own direct control over the school systems within their jurisdiction. As a result, there have been increased takeovers of local school districts by state authorities or mayors of districts deemed to be “failing” on the basis of current state accountability rubrics for school success. Many critics of school boards assert that education has grown too complex for policy decisions to be made and system operations to be run by lay people at the local level. One potential solution to this issue is to separate governance from operations, allowing local schools more operational independence and freeing the boards to focus solely on governance. The scholar Frederick Hess, in discussing urban school districts, maintains that locally elected school boards are ineffective due to four specific flaws in the local school governance model. First, there is a general lack of voter attention to school district affairs, leading to narrow-interest groups having too much influence over the boards. Second, voter apathy leads to increased power for those special-interest groups that do participate in campaigns and elections. Third, the boards lack proper training to effectively address their responsibilities as board members, causing them to rely on district staff and the superintendent for guidance and direction. Finally, due to the single-purpose design and political insulation of the local school board, there is a lack of coordination and resource sharing with other municipal programs. Others disapprove of boards composed largely of White middle-class and predominately male members who do not adequately represent the diversity of the community, and therefore believe that the boards should be reformed to become more reflective of the community. Furthermore, critics argue that given the increase in external interventions in school districts as a result of the increased federal role in education, especially under the Individuals with Disabilities Education Act, the No Child Left Behind Act of 2001, and the Race to the Top grant competition begun in 2009, local school boards composed of lay people have become antiquated. Proponents of School Boards

Proponents claim that locally elected school boards continue to be the epitome of representative democracy and are an essential component of public

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school governance. The NSBA argues that there are five main reasons to continue to support locally elected school boards. First, they represent the communities’ values and beliefs. Second, education is the boards’ only issue, therefore it is always the priority. Third, the board sets standards for achievement based on community values, state laws, and federal laws. Locally elected boards can move resources from one area to another to ensure student success. Fourth, the board is assessable and accountable to the electorate. Finally, the board is responsible for ensuring efficient allocations and expenditures of tax dollars. Supporters of school boards see them as the sole fiscal agent for the district, responsible for all local school financial decisions, which they should make without state or federal influence limiting their authority. Furthermore, with more than $600 billion spent per year on public elementary and secondary schools in the United States, proponents argue that is better for those dollars to be controlled by a locally responsive, democratically elected board with education as its sole priority. In sum, while there is a long-standing tradition of locally elected school boards in American education, the debate over whether or not they should continue in their current capacity, or at all, continues. Dominic J. Brewer and Michelle Hall See also No Child Left Behind Act; Property Taxes; Race to the Top; School District Budgets; School District Wealth

Further Readings Danzberger, J. P. (1994). Governing the nation’s schools: The case for restructuring local school boards. Phi Delta Kappan, 75(5), 67–73. Hess, F. M. (2002). School boards at the dawn of the 21st century: Conditions and challenges of district governance. Alexandria, VA: National School Boards Association. Hess, F. M. (2010). Weighing the case for school boards today and tomorrow. Phi Delta Kappa, 91(6), 15–19. Howell, W. G. (2005). Besieged: School boards and the future of education politics. Washington, DC: Brookings Institution Press. Land, D. (2002). Local school boards under review: Their role and effectiveness in relation to students’ academic achievement. Review of Educational Research, 72(2), 229–278. Spring, J. (1998). Conflict of interests: The politics of American education. Blacklick, OH: McGraw-Hill.

SCHOOL BOARDS, SCHOOL DISTRICTS, AND COLLECTIVE BARGAINING As in any organization, school district personnel can be roughly categorized as either labor or management. Labor comprises teachers, nurses, librarians, guidance counselors, aides, bus drivers, cafeteria workers, and custodians; management, strictly speaking, is the school board, but in practice, it also includes district administrators such as the superintendent and executive cabinet members. (In this entry, the former category will be referred to collectively as “district employees” unless a particular type of employee is specified.) District employees generally operate under a set of work rules that dictate wages, hours, duties, safety, grievance procedures, and other working conditions. (As described in this entry, which working conditions are governed by rules, and how those rules are determined, varies by district and state.) For teachers, these rules specify class size and teaching assignment policies; the number of school days per year and hours per day; procedures for layoffs, transfers, and evaluations; and salaries, benefits, and retirement provisions. In districts that collectively bargain over work rules, employees organize themselves into labor unions—typically, teachers, counselors, and other instructional staff whose positions require a teaching credential form a union of “certificated staff,” and employees such as food service workers and paraprofessionals join a union of “classified staff.” Each union negotiates specific work rules with the district, and after bargaining is complete, labor and management enter into a binding contract, or a collective bargaining agreement (CBA). This entry will answer three questions related to the role of school districts in collective bargaining: (1) When do they bargain? (2) What provisions do they bargain? (3) What is the actual process of collective bargaining?

To Bargain or Not to Bargain? Whether or not work rules are negotiated (as opposed to their being decided on solely by the district) depends on two things: (1) whether local employees choose to form a union and (2) whether the district recognizes that union as an official employee bargaining unit. The former is the choice

School Boards, School Districts, and Collective Bargaining

of each employee group. Teachers in nearly every district in America are organized into a local professional association. These associations offer everything from professional development to insurance to scholarships and grants; their goal is to advocate for the teaching profession while providing their members with resources and acting as a conduit for communications with district leaders. An association becomes a union, responsible for negotiating work rules and entering into a contract with the school district, only if its members vote to become one. The specific conditions under which unionization occurs are determined by state law, but it generally requires a majority vote of employees and periodic recertification by union members. Whether district leaders must, may, or cannot recognize a professional association as a bargaining unit is also determined by state law. In 30 states plus the District of Columbia, if employees want to form a union and negotiate a CBA, the district must recognize them as such. In these mandatorybargaining states, as of 2008, an average of 75% of districts have a CBA with their local teachers’ union. Fifteen states leave the decision of recognition to the district, either explicitly or because state law does not directly address bargaining in education; in these states, an average of 23% of districts and local teachers’ unions have CBAs. Five states prohibit teachers from unionizing. In districts where employees can’t, or choose not to, exercise their bargaining rights, they can still enter into a “meet-and-confer agreement.” Unlike binding CBAs, where disputes must be settled by outside arbitration, meet-and-confer agreements are nonbinding memoranda of understanding. Disputes are settled locally, and the district can override the agreement in the event of a conflict. Whereas the scope and negotiation schedule of a CBA are fixed, a meet-and-confer agreement can be discussed and altered at any time, and the contents are not limited to certain provisions. Finally, it is possible that employees and district leaders have neither a contract nor any other type of formal agreement— nationwide, an average of 36% of districts in each state have no specific agreement on wages, benefits, and other working conditions.

To Bargain Over What, and When? Bargaining laws play a large part in how districts and unions negotiate work rules: The laws not only determine whether unions are legal and whether

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districts must recognize them but also what is contained within the contracts. The “scope of bargaining,” as defined by the law, is what must, may, or cannot be part of any binding contract. Wages, for example, must be negotiated in 32 states plus the District of Columbia and are a permissible bargaining subject in the remaining 13 states in which bargaining is legal. No state explicitly prohibits including wages in CBAs. In comparison, teacher transfer rules are a mandatory bargaining subject in only 5 states and a permissible subject in 33, and in 8 states, districts and unions may not include it within a CBA (meaning that the district has sole discretion over the issue, even if it has a contract with the local union over other work rules). The scope of bargaining varies widely across state lines. California, Nevada, Ohio, and Oregon have a particularly wide scope of bargaining explicitly codified in state law. Other states, notably Wisconsin and Indiana, have recent laws that narrow the scope of bargaining to essentially wages only while prohibiting most or all other subjects. The variation in the scope of bargaining is a big reason why contracts look dissimilar from district to district. CBAs in California districts differ dramatically from those in Wisconsin because in California, district leaders must negotiate with local teachers’ unions over leave time, work hours, assignments and transfers, layoffs, evaluation procedures, class size, and grievance procedures should the union wish to do so. In Wisconsin, unions are not allowed to negotiate over any of those items (or any other subjects not directly related to compensation). But while state bargaining laws limit the content of CBAs (and also stipulate how often the entire contract must be renegotiated—usually once every 3 years), they do not solely define how contracts are negotiated and what is contained within them. Local factors also define the scope of bargaining and the district’s role, and therefore, CBAs within a single state can vary widely from one another. Four of these factors are precedent, personnel, procedures, and politics.

Negotiating the Contract (and Beyond It) The first local factor that affects collective bargaining is precedent. Contracts are historical documents. Like many legal agreements, their contents are difficult to modify once created, and provisions tend to be amended and expanded rather than replaced. If a district’s first CBA was limited in scope, its current

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contract often reflects the simplicity of the original. These “thin” contracts tend to be found not just in states with a narrow legal scope of bargaining but also in districts where employees initially trusted the local district management to make reasonable decisions regarding work rules or where district leaders believed that they could work with teachers in good faith to create mutually agreeable district policies. If contracts started out encompassing more subjects, even if those subjects were only addressed with a sentence or two in the original documents, then decades of subsequent negotiations can create thick documents with multiple specific contingencies and conditions. (Larger districts also tend to have longer, more complicated contracts.) Negotiating procedures also influence collective bargaining results. Some procedures are set by labor law. For example, negotiations must occur in private and can only be open to the official representatives of both sides. But locally determined procedures can have a great impact on negotiations and the outcomes of bargaining. While formal negotiations must be held only during closed-door sessions, union or district leaders may choose to call open meetings or informal information sessions to discuss a variety of issues. Furthermore, the contracts themselves often contain clauses that serve to limit formal negotiating. Many CBAs require that district leaders create committees responsible for decisions related to, for example, teacher retirement, health and welfare benefits, or salary credits for professional development. Representatives of both labor and management must serve on these committees. (The contract can also grant the union consultation rights with the superintendent, over topics of their discretion.) While the decisions made by the committees are nonbinding, they serve two purposes. First, committee members might “prenegotiate” certain topics so that both sides are already in agreement before they enter into private bargaining sessions. Second, committees can minimize what is codified within the contract by expanding what is determined outside of it. Another example of procedures defining negotiations is the bargaining schedule. While state law dictates the frequency with which the entire contract is open for negotiation, the CBA itself stipulates which handful of provisions are automatically reopened yearly and which are not. In most cases, only wages, benefits, and an additional provision (sometimes chosen by the union and sometimes specified by the contract) may be reopened each year. This restricts just how much a contract can change from year to year.

The personnel who negotiate the contract also play a large part in the outcomes of negotiations. On the union side, organizations typically select a negotiating committee: some combination of the union president (previously elected by its membership), executive director, other elected union leaders, and/ or other appointed or specially elected members. The outcomes of bargaining are sensitive to who represents the union. If union members are not satisfied with their working conditions or feel that the district is making only self-interested decisions, they will choose negotiators who will be more aggressive (and sometimes more antagonistic) during bargaining. If union members are generally satisfied or trust that the district is willing to negotiate in good faith and make concessions when necessary, they will select a negotiating team that is less uncompromising and more collaborative, and the resulting contract will not change much from the original. In some districts, union leaders also request that a representative of the state association, of which the local union is an affiliate, participate in negotiations. Unlike the local negotiators, who are first and foremost current teachers or other district employees who have received limited training in collective bargaining (also provided by the state association), the state representatives are skilled negotiators with a great deal of experience. As such, sometimes the results of negotiations are particularly reflective of the priorities that the state association sets for its affiliates. Some see these outside negotiators as more likely to use politically tinged bargaining strategies or “play hardball” because they do not have to bear the same consequences of a soured relationship with management as do local union negotiators. On the district side, the school board most often designates district administrators—usually the head of human resources and other human resources personnel, and sometimes the superintendent as well— to negotiate on its behalf. Boards may also select one board member to sit on the negotiating team. (State administrators’ or school boards’ associations usually provide limited training for negotiators, although this training is typically not as extensive as that provided by the employee associations.) Some unions see school board elections as an opportunity to influence contract negotiations by electing board members who support, or are at least open to, the union’s interests. To that end, a union might endorse a particular candidate, donate money to his or her campaign, and/or mobilize union members to

School Boards, School Districts, and Collective Bargaining

canvass and vote. Unions will also recruit candidates to run during elections. Finally, there is a certain degree of politics that goes into negotiating the contract, and beyond it. Not only is there much outside the scope of the CBA that determines employees’ working conditions, but also bargained items such as salaries and benefits are severely constrained due to budgetary limitations. Many nonnegotiated decisions, such as reductions in force, principal hiring, and curriculum selection, are equally if not more important to teachers than what is contained in the contract. District leaders could make these decisions unilaterally, or they might offer the local union input into these decisions in return for concessions in negotiations. Or the district might be receptive to the union position regarding decisions both inside and outside the scope of the contract because of considerations such as a union-friendly public or local media, due to personal relationships with union leaders, or because they themselves are former or even current union members. Similarly, union members may trade expensive negotiated items such as raises for greater autonomy when it comes to professional decision making. (Interestingly, unions that district leaders report as “strong,” or effective in protecting their interests, don’t necessarily have “strong” CBAs.) Conversely, unions may be oppositional during negotiations because they feel that they are powerless outside the bargaining process, rendered so by district leaders who do not wish to collaborate with them informally or perhaps by public opinion that does not support union activities. Research on the actual process of bargaining is virtually nonexistent because negotiations take place behind closed doors. There is limited work on the outcomes of bargaining: As a whole, it indicates that when teachers initially unionized in the 1960s, they achieved favorable policy outcomes with respect to compensation, transfer and assignment rules, and other working conditions. Unionized districts are less likely than nonunionized ones to offer performancebased salaries, are more likely to reward experience and education, and tend to have salary schedules that offer higher returns the longer a teacher remains in the district. Studies that examined the effect of union strength on salaries (rather than comparing unionized with nonunionized districts) had mixed results—some concluded that strong unions have contracts that favor spending on teachers, but other studies disagree. Recent work also suggests that union strength, and district demographics, affects

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how much a contract restricts administrator actions with respect to teacher transfers. Finally, while unionization led to smaller class sizes, a reduction in nonteaching duties, more paid preparation time, and greater teacher control over the school calendar, the relationship between union strength and working conditions is unclear. While these studies look at the outcomes of bargaining, others ask how bargaining outcomes affect district practices. In a national survey, only 6% of 1,400 superintendents reported that their district’s CBA inhibited their effectiveness. Superintendents and administrators also describe finding ways to work around CBAs by pursuing actions that are not strictly prohibited, developing strong working relationships with union leaders so that they may mutually work around contract language, and negotiating for clauses that give them discretion to make decisions that are in the best interests of schools and students. Dara B. Zeehandelaar See also Local Control; Policy Analysis in Education; Reduction in Force; Salary Schedule; Teachers’ Unions and Collective Bargaining

Further Readings Alsbury, T. L. (Ed.). (2008). The future of school board governance. Lanham, MD: Rowman & Littlefield. Bjork, L. G., & Kowalski, T. J. (Eds.). (2005). The contemporary superintendent. Thousand Oaks, CA: Corwin Press. Hannaway, J., & Rotherham, A. (Eds.). (2006). Collective bargaining in education. Cambridge, MA: Harvard University Press. Hess, F. M. (1999). Spinning wheels: The politics of urban school reform. Washington, DC: Brookings Institution Press. Howell, W. G. (Ed.). (2005). Besieged: School boards and the future of education politics. Washington, DC: Brookings Institution Press. Loveless, T. (Ed.). (2000). Conflicting missions? Teachers unions and educational reform. Washington, DC: Brookings Institution Press. Moe, T. M. (2006). Political control and the power of the agent. Journal of Law, Economics, & Organization, 22(1), 1–29. Moe, T. M. (2011). Special interest: Teachers unions and America’s public schools. Washington, DC: Brookings Institution Press. Petersen, G. J., & Fusarelli, L. D. (Eds.). (2005). Politics of leadership: Superintendents and school boards in changing times. Charlotte, NC: Information Age.

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School District Budgets

Zeigler, L. H., Jennings, M. K., & Peak, G. W. (1974). Governing American schools: Political interaction in local school districts. North Scituate, MA: Duxbury Press.

serve students appropriately, meet state and federal mandates, and do so at an affordable price.

Balancing the Budget Equation The budget equation is most simply expressed as

SCHOOL DISTRICT BUDGETS School district budgets are financial plans for operating the district. They make explicit what the district expects to spend and to receive in revenue for the upcoming year and for several years in the future. In developing their budgets, districts face many different constraints that shape and inhibit what the district can do: fiscal, legal, programmatic, political, and public expectations. As a result, the final budget will represent a compromise between the educational goals of the district and the fiscal and political environment in which it operates. Prior to the economic recession beginning around 2007, school districts faced a more predictable and relatively stable economic environment. However, since that time, the old and well-understood rules regarding revenue expectations and expenditure growth have changed dramatically. The new fiscal reality in which they are now operating has severely altered district budgeting practices: • There is less revenue or revenue growth for districts due to strong resistance at local and state levels to tax increases and a slow economic recovery that weakens the primary tax bases that support education. • Expenditures are increasing at a greater rate, and many are largely outside the districts’ control without substantial changes to district operating practices; examples include district contributions for employee pensions that are mandated by the state and rising energy costs. • Left unchecked, the unbalanced budget conditions result in a serious structural imbalance between revenues and expenditures. • To balance their budgets under these conditions will require districts to cut back on existing programs, reduce staff, and shrink instructional and support services for students. • In the meantime, there is likely to be no letup in state and federal student achievement mandates. The net outcome for districts is that difficult adjustments will be required to achieve budgets that

Expenditures = Revenues.

That is, the budget must be balanced. By law in most states, school districts must have a budget in which planned revenues equal or exceed planned expenditures. However, it is not uncommon in developing preliminary budgets to have higher expenditure estimates than anticipated revenues. In this case, adjustments must be made to bring the budget into balance. In reaching the balance, districts need to look on both sides of the budget equation to consider both increasing revenues and decreasing expenditures. Each component of the equation presents unique challenges to closing the budget gap.

Revenue Constraints School districts face a number of constraining factors regarding their revenues from local, state, and federal sources. While specific types of revenues for districts vary across states, it is only at the local level that districts have some degree of control over the revenue they receive. The property tax, the primary revenue source for school districts in most states, is vulnerable on several fronts. The local school board may face strong and vocal opposition from taxpayer groups and may be reluctant to raise taxes in a down economy. The base for the tax, the assessed value of property in the district, may not be increasing as it had in prior years; in fact, a number of districts have seen their assessed value decrease as a result of successful reassessment appeals from property owners. Local income tax revenues, if available to districts, can shrink with a declining local economy. Likewise, with interest rates down, earnings on investments, an alternative source of local revenue in better times, do not contribute much to the local revenue stream. Delinquencies, for both property and income taxes, tend to grow in challenging economic times and can further reduce local revenues for current budgets. District revenues are also constrained by state actions. Some states have imposed limitations on what their districts can levy in terms of tax rates or revenue increases from local taxes. More directly, a number of states have reduced the amount of state aid provided to districts, in some cases severely. The state actions stem from a number of causes: lagging

School District Cash Flow

state tax collections due to the poor economy, a decision not to replace federal stimulus funds from the American Recovery and Reinvestment Act of 2009 that were used to replace state funds, an unwillingness to raise state taxes to fund education, or ideological or political beliefs that funding for education should be reduced to improve districts’ efficiency. Often, these actions affect not just 1 year but are extended over multiple years with zero increases or continued lower growth in state funding for K-12 education.

Expenditure Constraints The expenditure side of the budget equation is where school districts have their greatest ability to balance the budget through reductions. However, even this power is limited in significant ways through state and federally mandated programs required to be provided by the district, often without accompanying revenues, termed unfunded mandates. A key concern in this category is school districts’ pension contributions; the levels of pension obligations across most states have increased substantially in recent years and are not sustainable over the long run. Additional mandated costs are Social Security payments, health care, special education, and, in most states, both brick-and-mortar and cyber charter schools. There are other key expenditure areas that are not mandated but represent important aspects of an appropriate and effective educational program. These include expenditure components such as salaries, utilities, technology, supplies and equipment, and facilities. From a program perspective, these nonmandated expenditures are used to provide regular instruction, vocational education, instructional support, operations, and administrative services. Since these nonmandated expenditures are, to varying degrees, ultimately under district control or influence, they are subject to fewer constraints than other areas of the budget. Consequently, from a budgeting perspective, these are the primary areas where reductions are possible to balance the budget. School districts can reduce spending on professional, administrative, and support staff salaries through attrition, furloughs, and layoffs. In general, this means reducing academic opportunities for students, student services, or both. Reductions in academic opportunities can mean higher class sizes, fewer electives, limited Advanced Placement classes, and limited choices of world languages. Cuts to student services would limit library services, guidance

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services, psychological services, nursing services, and intervention programs that respond to the academic, social, and emotional needs of students.

Dealing With Budget Constraints School district budgets face constraints from both revenue limitations and mandatory and growing expenditures. To balance the budget within these multiple constraints, first, mandatory expenditure growth must be supported by new revenues. If insufficient revenues remain to fund nonmandatory expenditures, then they must be reduced to a level that balances the budget. William Hartman See also Budgeting Approaches; Expenditures and Revenues, Current Trends of; Fiscal Environment; Local Control; Property Taxes; Reduction in Force; Unfunded Mandates

Further Readings Hartman, W. T. (1999). School district budgeting (2nd ed.). Lanham, MD: Scarecrow Education. Ramsey, R. D. (2001). Fiscal fitness for school administrators: How to stretch resources and do even more with less. Thousand Oaks, CA: Corwin Press. Thompson, D. C., & Wood, R. C. (2005). Money and schools (3rd ed.). Larchmont, NY: Eye on Education.

SCHOOL DISTRICT CASH FLOW Critical to the success of any organization is the ability to pay its bills on time. This includes both payments to employees for their services (wages and salaries) as well as payments to vendors for goods and services rendered. Although school districts establish budgets that balance projected revenues with projected expenditures, it is typically the case that the receipt of revenues does not match the timing of the planned expenditures. In fact, expenditures tend to be relatively uniform throughout the fiscal year because most school district expenditures pay for labor, which is paid on a regular basis (the one exception might be for teachers who work and are paid on a school-year basis rather than a calendar-year basis). On the other hand, revenues often are bunched at specific times, such as when property taxes are collected, when state aid payments are made, or even when federal government categorical funds are received. Thus, a significant challenge

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School District Cash Flow

facing school business administrators is managing the cash flow. This entry describes the ways in which school districts can manage the cash flow over a fiscal year.

Variation in Receipts and Spending The single largest expense for school districts is for personnel. Teachers, administrators, and other certificated staff, along with classified staff such as clerical workers, custodians, maintenance workers, groundskeepers, food service workers, and bus drivers—whether paid on a salary or hourly basis— work year-round and expect to be paid at regular intervals (monthly, bimonthly, or possibly weekly). There are other expenditures—food, utilities, consumables for offices, and custodial operations—that also have relatively uniform expenditure demands. Finally, there are some significant expenditures that come at certain times in the year—for example, textbooks, other instructional materials, and an increasing number of computers for students are often purchased at the beginning of a school year or in bulk at other various times. The receipt of revenues tends to vary more and depends on a number of factors, including the relative share of district revenue from local versus state sources, the amount of federal program money received, and, in some instances, even the fiscal condition of the state—in hard times, some states have deferred the last school district aid payment to the next fiscal year to balance their own budgets. This means that each school district, while having a relatively similar expenditure pattern, has a vastly different pattern of revenue receipts. Managing the cash flow of the district becomes critical to ensuring that bills are paid on time. Some examples may be helpful. A district that is property wealthy and receives most of its revenue from property tax receipts may have few points in time when revenues are received. Many states mandate property tax collections once or twice a year. As a result, the district coffers grow dramatically as the due date approaches and then are drawn down until the next due date. School districts that receive most of their revenue from state aid are subject to the payment schedule established by the state. These schedules vary from state to state, and generally parallel expenditure patterns more closely, but some unevenness is still likely to occur based on the timing of state revenue receipts and the impact of other local revenues on the district’s cash position.

Managing the District’s Cash Position This variation in the timing of revenues and expenditures requires school business administrators to be certain to have adequate cash on hand to pay the bills, and it also suggests that when the available cash exceeds the anticipated expenditures, those funds be invested in some sort of safe, interestbearing account. Each of these functions is discussed below. Investing Cash

When district cash flow is positive—that is, it has more cash than it needs, safe investment vehicles are needed. The challenge facing school money managers is ensuring the value of the principal (i.e., not investing in instruments that might lose money) while maximizing the return on the investment. Investment of cash can be for periods as short as overnight or as long as several months, although in the latter case, the district will want to consider a layered approach, with some cash invested in longer term instruments that offer a higher yield and other cash in instruments that are more liquid. The district also needs to have cash liquidity for unexpected expenses and emergencies, which also drives the investment strategy. In many states, intermediate education agencies such as county school districts have investment pools that allow school districts to invest their cash. The larger pool can often invest in higher yield instruments and can take advantage of differences in the timing of revenue needs of school districts. Borrowing Cash

It is equally likely that a school district will need more cash than it has on hand and will need to borrow. The approach most frequently used for shortterm borrowing by school districts is tax revenue anticipation notes (TRANs). Banks are generally very willing to lend money to school districts—and other public agencies—at low rates for short terms. As the name makes clear, TRANs are backed by the district’s anticipated tax revenues, making the loan very safe for the bank. In instances where TRANs are not available, districts must seek cash from other places, often participating in multidistrict investment pools as described earlier.

Conclusion Managing a school district’s cash flow is one of the most important aspects of school business

School District Wealth

administration. Like any individual household, school districts must pay their bills on time. Moreover, because most of a school district’s expenditures are for people, it is critical that they be paid on time. The cash flow challenge facing school districts is to accurately estimate inflows and outflows of cash so that enough money is available to meet current needs. Lawrence O. Picus See also Budgeting Approaches; Education Spending; School District Budgets

Further Readings Guthrie, J. W., Hart, C., Ray, J. R., Candoli, C., & Hack, W. G. (2008). Modern school business administration: A planning approach. New York, NY: Pearson Education. Hartman, W. (2002). School district budgeting (2nd ed.). Reston, VA: Association of School Business Officers International.

SCHOOL DISTRICT WEALTH

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property wealth, the result was either relatively less revenue for local schools or relatively higher tax rates for residents. In California, parents argued in a lawsuit against the state initiated in 1968 (Serrano v. Priest) that the resulting disparities in revenue across districts were unconstitutional because low-wealth districts could not afford to provide the same quality of education as high-wealth districts. Over the decade that followed, similar school finance equity cases were brought in several other states. As a result of these equity cases, legislatures across the country adopted state aid formulas that use state funds to compensate for the variation in district wealth. One way to measure whether the resulting distribution of revenue is equitable is with a wealth neutrality score, which indicates the extent to which district revenue per pupil is correlated with district wealth. Positive scores mean that wealthier districts have more revenue than poorer districts, while negative scores mean that poorer districts have more revenue than wealthier districts. A funding system that is “wealth neutral” would have a score close to zero.

Measuring Wealth Almost all state school finance equalization formulas require some measure of a school district’s fiscal capacity—that is, a measure of a district’s ability to raise funds for schools locally. This is commonly referred to as a district’s wealth. In keeping with the use of the property tax as the most common local source of school revenue, district wealth is typically measured by the assessed property valuation per pupil, although some states also use alternative measures in conjunction with property values. In addition to playing a key role in state funding formulas, district wealth is often used in state-level measures of school finance equity. This entry first explores this connection between district wealth and equity in school funding, then highlights some of the concerns about using property values as the primary measure of district wealth. The entry concludes with a discussion of some of the alternative measures that states have adopted.

District Wealth and Funding Equity Up until the early 1970s, almost all school district revenue came from local property taxes. The amount of revenue in a given district was thus a function of the assessed value of property and the local property tax rate. In areas with relatively low

In most states, and in the majority of the school finance literature, district wealth is measured by the assessed property valuation per pupil. However, some have argued that this is a poor measure of fiscal capacity. One objection to using assessed property valuation alone is that many local jurisdictions raise revenue through sources other than property, such as income and sales taxes. Even in the absence of other local taxes, income is likely to be a better measure of ability to pay; for example, all federal programs that equalize aid across states use per capita income to measure state wealth, regardless of the tax mix within states. Using assessed property values in state equalization aid formulas also raises concerns about equity and need. There may be little correlation between property wealth and household income; that is, rich families may not live in property-rich districts, nor do poor families always live in property-poor districts. This contradicts a key premise of the California court case Serrano v. Priest, and other equity court cases, that a property-based funding system discriminates against districts with higher shares of poor and minority students. Research has shown that at the time of Serrano, although districts in California with lower assessed values did, indeed,

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School Finance Equity Statistics

have higher tax rates and lower revenue per pupil, there was no corresponding relationship between district revenue and family income or race; the distribution of revenue was fairly equal across different income groups, and Black students were, on average, from districts with somewhat higher revenue. Thus, while equalization formulas that compensate districts for low assessed value may be breaking the connection between property wealth and revenue, they are not necessarily allocating aid to districts with higher needs.

Alternative Measures In part because of such concerns, a handful of states use alternative measures of district fiscal capacity in their state aid policies. The most common alternative is to combine property values with a measure of local income. For example, Connecticut determines district wealth by the average of the property tax base and household income, while Maryland uses the sum of net taxable income and weighted values of different types of property. A small number of states go further, taking into account additional sources of local revenue, such as in Virginia, where a district’s ability to pay is measured with a composite index that combines property values, adjusted gross income, and taxable retail sales. Tennessee has perhaps the most complex approach to determining district-revenue-raising capacity, incorporating the sales tax base, property tax base, per capita personal income, share of property that is residential and farm, and ratio of student population to total population. Although these alternatives provide a more comprehensive picture of a district’s ability to pay for educational services, most states have been reluctant to add even more complexity to school funding systems that are often already multilayered and complicated. Thus, it is likely that assessed property valuation will continue to be the dominant measure of district wealth across the majority of states. Jennifer Imazeki See also District Power Equalizing; Educational Equity; Equalization Models; Fiscal Neutrality; Guaranteed Tax Base; Property Taxes; School Finance Equity Statistics; Serrano v. Priest

Further Readings Dembowski, F. L., Green, M., & Camerino, J. (1982). Methodological issues in the use of income in the

allocation of state aid. Journal of Education Finance, 8(1), 73–92. Odden, A. (1977). Alternative measures of school district wealth. Journal of Education Finance, 2(3), 356–379. Sonstelie, J., Brunner, E., & Ardon, K. (2000). For better or worse: School finance reform in California. San Francisco, CA: Public Policy Institute of California.

SCHOOL FINANCE EQUITY STATISTICS Employing statistics to measure equity in school financing has been important to public education in the United States since at least the middle of the 19th century, when public provision and financing of elementary and, later, secondary education became common. Historically, schools have been financed primarily by states and localities (mostly school districts), with state shares of that financing increasing and local shares decreasing since the middle of the 20th century. While local financing is almost entirely derived from taxation of residential and commercial property, which is unequally available across districts, state financing is usually generated from state sales and income taxes, and has aimed to equalize the resources available to a state’s students. This goal, to equalize resources, motivates the desire to measure the degree of school finance equity in order to assess how close state legislatures are to achieving their objectives. Moreover, beginning in the 1970s, state courts became involved in lawsuits that challenged the degree of school finance equity, which in turn spurred researchers to systematically develop sophisticated ways to quantify the degree of equity (or inequity). In the early 1980s, Robert Berne and Leanna Stiefel developed a comprehensive framework that helped identify the values associated with quantitative measures of school finance equity. This entry describes the Berne-Stiefel framework for identifying the values inherent in measures of school finance equity and discusses various measures of horizontal equity, vertical equity, and taxpayer equity. It concludes by highlighting school finance research that utilizes these measures. Although a number of measures have been created since this time, the Berne-Stiefel framework continues to provide a useful tool for organizing quantitative analysis of school finance equity. Before this framework was widely accepted, analysts used many different and often isolated measures (e.g., the

School Finance Equity Statistics

maximum vs. minimum spending per student in a state or the tax rate in one school district compared with that of another), with little concern about representing the inherent multidimensional aspects and viewpoints that a comprehensive assessment of school finance equity requires. The Berne-Stiefel framework borrowed from traditional measures of income inequality and tax equity, made clear the multidimensional nature of equity, and made explicit the several steps necessary to interpret these measures in the context of school financing. The framework is built around the answer to four questions: (1) who is the subject of the analysis (e.g., students or taxpayers), (2) what resources are the focus of analysis (e.g., spending, revenues, teacher resources, building quality, curriculum richness, test scores), (3) how are the resources evaluated (most commonly through horizontal equity or vertical equity), and (4) how much do the resources differ (characterized by the many equity statistical measures, e.g., the coefficient of variation or the federal range ratio). In the time since the introduction of this framework, other researchers have created measures of inequality at the high end of distributions and measures of adequacy to address equity issues that developed after the 1980s.

Horizontal Equity Beginning with the most common measures, those that focus on students and spending, horizontal equity represents how “equally situated” students are treated, with perfect equality indicating no differences in spending per student. The term equally situated students refers to students with the same educational (or demographic) characteristics, such as all students in general education settings or all poor students. For example, analysts might measure the variation in spending per student across equally poor students living in different school districts (e.g., those in large cities vs. those in the suburbs) or in different regions in a country. Although over a dozen statistical measures are available to quantify the degree of horizontal equity, in practice only six are used on a regular basis. These are differentiated by several characteristics, all of which are described here in terms of spending per student. The most important characteristics are (a) whether all students are counted in the measure, (b) whether equity improves when spending is transferred from a school or district with higher spending per student to a school or district with

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lower spending per student (called mean preserving), (c) whether equity can be divided in a way that indicates the amount of equity between districts within a state and the amount of equity across states, (d) whether equity improves when all spending per student increases by the same absolute amount (called absolute inequality aversion), and (e) whether equity improves when all spending per student increases by an equal percentage (called relative inequality aversion). Generally, characteristics (a) through (d) are desirable in a measure, but (e) is not. Equal percentage increases could result from inflation, and percentage increases add greater amounts of spending to schools and districts with higher per-student spending than to schools and districts with lower per-student spending. Three horizontal equity measures fail to include all students but are used frequently nonetheless. These are the range (the difference between highest and lowest observation), the federal range ratio (the observation at the 95th percentile divided by that at the 5th percentile), and the McLoone index (the ratio of total spending received by students below the median to total spending those students would obtain if at the median). None of these measures is mean preserving, and only the federal range ratio and the McLoone index exhibit absolute inequality aversion; for that reason, these latter two are more often employed than the simple range. A value of 0 represents perfect equity for the range and the federal range ratio, while a value of 1 represents perfect equity for the McLoone index. The most commonly used measures are those that include all students. This group is composed of the coefficient of variation (standard deviation/mean), the Gini coefficient (calculated from the Lorenz curve), and the Theil index (details on the formula for this can be found in The Measurement of Equity in School Finance by Berne and Stiefel). All are mean preserving and exhibit absolute inequality aversion. Additionally, the Gini and Theil indices can be separated into within/between parts, representing the amounts of inequity within a state and the amount between states. A value of 0 represents perfect equity for all these measures. More recently, Deborah Verstegen proposed a new statistical measure, the “angle of inequity,” which focuses on the upper half of the spending distribution. To compute the value of the angle of inequity, one first calculates the McLoone index, then calculates a similar measure for the top of the distribution, and then combines the two. According

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to this measure, perfect horizontal equity is achieved at equal angles (a value of 1), which represents an equal amount of dollars for the upper and lower parts of the distribution. Since the late 1980s, the goals of school finance reform have shifted to include both equity and adequacy, where adequacy is concerned with ensuring that all students have access to some basic level of resources deemed necessary to achieve state performance standards. While this is a slightly different concept from equity, it is related to horizontal equity. The Odden-Picus Adequacy Index (OPAI), developed by Allan Odden and Lawrence O. Picus, is mathematically similar to the McLoone index, but rather than the median level of spending, it uses an adequate level of spending as the benchmark for calculations. Specifically, first an “adequate” per-student spending level is identified; second, the percentage of students/districts with per-student spending at or above that level is calculated; third, the ratio of spending for students/districts below the adequate level to what spending would be if these students/districts were spending at the adequate level is calculated and multiplied by the percentage of students/districts below the adequate level; and finally, the index is created by these two numbers (i.e., the percent spending above and the ratio calculated in Step 3). An OPAI of 1 indicates that the system is providing adequate funding for all students.

Vertical Equity Vertical equity, an alternative to horizontal equity, measures the degree to which “differently situated” students are treated in a manner that appropriately addresses their differences. Differently situated refers to student characteristics that make learning harder or easier. For example, students who have learning disabilities, who speak only a language other than English, or who do not read proficiently by middle school are differently situated compared with students who have none of these characteristics. Such differently situated students “need” more spending to achieve equivalent performance standards or to be treated fairly. Vertical equity measures quantify the magnitude of the additional resources targeted to these students. Two types of statistics measure vertical equity. The first uses coefficients from bivariate or multivariate regression estimations, with per-student spending as the dependent variable and the percentage of students in the differently situated group(s) as

the independent variable(s). For example, one might regress spending per student on the percentage of students with learning disabilities (perhaps controlling for other student characteristics such as gender or race, or even other vertical equity characteristics, e.g., poverty). A positive coefficient on percent learning disabled would indicate some degree of vertical equity (the differently situated students receive more), but the exact size of the coefficient needed for perfect vertical equity would not be identified by the size of the regression coefficient. Instead, further analyses by researchers or policymakers are required to determine the amount of additional spending (the size of the regression coefficient) these students require to meet performance standards or to be treated fairly. The second vertical equity statistic is a weighted horizontal equity measure. Students in the differently situated groups are given weights that are multiples of a general education student weight (which is set at 1), where the multiples are the amount of additional spending needed by the differently situated students to achieve performance standards or fairness. For example, a weight of 1.4 would mean that the differently situated students need 40% more spending than general education students to perform at standard levels. A new spending per weighted student is calculated, and the horizontal equity measures are then computed using weightedstudent spending. These measures are interpreted in the same way as horizontal equity measures, with 0 (or 1) corresponding to perfect vertical equity and increasing (decreasing) values corresponding to increasing inequity. One of the main challenges in developing a weighted vertical equity measure is determining the exact weight that a particular type of student should receive. Each of the vertical measures differs slightly in the information it provides. Regression-based measures indicate how much more is being provided to differently situated students, while weighted dispersion measures indicate the distance from vertical equity, provided the weights are appropriate.

Taxpayer Equity Taxpayer equity is important in school finance in the United States not only because public education is publicly financed, primarily from tax revenues, but especially because the system of provision and finance is decentralized, with large roles for local school districts. The local financing also results in

School Finance Equity Statistics

homeowners’ knowledge and awareness of their tax bills, which are tightly tied to their school districts. This system often leads to citizens choosing their residential locations based, in part, on the quality and cost (in taxes) of public schools (a phenomenon known as Tiebout sorting). Taxpayer equity measures are often focused on the relationship between spending per student and the local property value per student because property taxes are the usual source of local finance. Most commonly, regressions of spending per student on property value per student (sometimes controlling for other determinants of spending, e.g., the percentage of learning-disabled students or the median income of residents) measure equity, with perfect equity indicated by 0 or a statistically insignificant coefficient and increasing inequity indicated by larger, positive regression coefficients. In one case, taxpayer equity statistics are tied to a particular feature of a state’s school finance formulas aimed partly at helping taxpayers. Under so-called guaranteed tax base formulas, the state’s goal is to implement equal spending for equal tax effort, so that all taxpayers are able to fund the same spending per student if districts levy the same tax rates. To do this, states provide the difference between the actual property tax base per student and a guaranteed tax base per student. A measure of this concept of taxpayer equity would be the correlation between per-student spending and tax rates, with higher positive correlations indicating higher taxpayer equity.

Research on School Finance Equity A number of noteworthy studies have used measures of school finance equity to make important points. One of the earliest is James Wyckoff’s measurement of student equity across the states, in which he focused on instructional expenditures per student and used the coefficient of variation, Gini coefficient, and Theil coefficient as statistics of equity. He found that from 1980 to 1987, equity improved in a majority of states according to most of the measures, with a median improvement of about 11%. Furthermore, when states were disaggregated by whether there was a legal challenge to their finance systems, he found that among all states where funding systems were upheld, equity improved; in states where funding systems were overturned, the majority experienced decreases in equity; and among states with pending cases, the majority experienced increases in equity. While these results might seem

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counterintuitive, Wyckoff offers two potential explanations: First, the threat of litigation may have induced some states to equalize expenditures, and second, those states where finance systems were overturned tended to have poorer records of equity to begin with. In addition to Wyckoff’s study of between-state school finance equity, a number of important studies have examined within-state equity of school finance. Such studies were particularly concerned with evaluating the success of school finance reforms that took place nationwide beginning in the 1970s. In one of the most widely cited papers on this topic, Sheila Murray, William Evans, and Robert Schwab used four different equity measures (the Gini coefficient, Theil index, coefficient of variation, and log federal range ratio) and found that over the period 1972–1992, court-ordered finance reform improved within-state spending equality by 19% to 34%, depending on the measure. In an update of this study, Sean Corcoran and Evans found that between 2000 and 2004, there had been an 11% to 24% decrease in within-state spending equality. Furthermore, using the Theil index to decompose spending equality into its between and within components, Corcoran and Evans found that 60% to 70% of the variation occurred between states and that improvements in between-state equality explained most of the increases in equity nationally. Measures of taxpayer equity have likewise been used to evaluate the success of court cases in achieving school spending objectives. In a famous example, California passed Proposition 13 (1978), which was largely seen as a reaction to the 1971 Serrano v. Priest court decision, which overturned California’s school finance system. Proposition 13 shifted the burden of school finance almost entirely onto the state and essentially required that the ability to fund public education be independent of a school district’s taxable wealth. To study whether the Serrano court case and Proposition 13 had had the desired effect, Thomas Downes examined the correlation between total per-student expenditures and various measures of per-student taxable wealth, using correlations and bivariate regression coefficients. According to these measures, he found slight increases in taxpayer equity, as the correlations and bivariate regression coefficients between per-student taxable wealth and per-student expenditures declined in the postSerrano period. In addition to their use in scholarly research, these statistics have been presented as evidence of inequity

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School Finance Litigation

(by the plaintiffs) or as evidence of improved equity (by the state defendants) in many of the more than 40 state court cases dealing with school finance over the past four decades. Such cases are difficult to cite specifically since they are not published in easily obtained sources, but at least in the initial wave of school finance court cases, arguments rested on the correlations between per-student property wealth and per-student spending and on the range of perstudent spending across districts. As analysts become more sophisticated, measures such as the coefficient of variation and regression coefficient were increasingly introduced as evidence. Although school finance reform is not nearly as prominent today as it was during the 1970s and 1980s, equity statistics continue to play an important role in policy, for instance, to evaluate new funding formulas. For example, in an effort to assess whether New York City’s “fair student funding” (FSF) formula was directing resources to differently situated students as intended, NYC’s Independent Budget Office (IBO) ran a series of regressions with FSF allocation per student as the dependent variable and indicators of poverty, performance below standards, English Language Learners (ELL), and special education as the independent variables. The regression coefficients, indicating the degree of vertical equity that actually existed, were then compared with the weights in the formulas to ascertain any divergences. Briefly, the IBO found that as of the 2011–2012 academic year, certain groups of students, including low-achieving middle school students, elementary and high school ELL students, and some groups of high school special education students, were being funded below their formula weights.

Conclusion Equity measures continue to be important in the evaluation of policies and school finance systems. The use of such measures may take on particular importance in the years following the Great Recession as federal aid expires and states are left to determine where best to allocate scarce resources. Furthermore, since assessed property values lag home values, assessed values were just beginning to decline in 2013, and may not rise again for several years, so that in the coming years school districts will face less revenue from property taxation. Equity measures, then, will be an important tool for examining the impact of the 2008 financial crisis on school finance and, in particular, in determining

whether any of the ensuing reductions in tax revenues undid the improvements in equity that the country witnessed in the wake of school finance reform. Leanna Stiefel and Sarah Cordes See also Horizontal Equity; Special Education Finance; Vertical Equity; Weighted Student Funding

Further Readings Berne, R., & Stiefel, L. (1984). The measurement of equity in school finance: Conceptual, methodological, and empirical dimensions. Baltimore, MD: Johns Hopkins University Press. Corcoran, S. P., & Evans, W. N. (2008). Equity, adequacy, and the evolving state role in education finance. In H. F. Ladd & E. B. Fiske (Eds.), Handbook of research in education finance and policy (pp. 332–356). New York, NY: Routledge. Downes, T. A. (1992). Evaluating the impact of school finance reform on the provision of public education: The California case. National Tax Journal, 45, 405–419. Murray, S. E., Evans, W. N., & Schwab, R. M. (1998). Education-finance reform and the distribution of education resources. American Economic Review, 88(4), 789–812. Subramanian, S. (2013, April). Is it getting fairer? Examining five years of school allocations under fair student funding. New York, NY: New York City Independent Budget Office. Retrieved from http://www .ibo.nyc.ny.us/iboreports/fsf2013.pdf Verstegen, D. A. (1996). Concepts and measures of fiscal inequality: A new approach and effects for five states. Journal of Education Finance, 22(2), 145–160. Wyckoff, J. (1992). The intrastate equality of public primary and secondary education resources in the U.S., 1980-87. Economics of Education Review, 11(1), 19–30.

SCHOOL FINANCE LITIGATION Legal challenges over inequities and/or inadequacies in state funding for public education have dramatically shaped the study and the practice of education finance in recent years. Although this type of litigation began in the late 1960s with a number of cases in the federal courts, for the past 40 years, school finance litigation has played out almost exclusively in the state courts. This entry provides an overview

School Finance Litigation

of school finance litigation in state courts; the reasons why most of the state courts have been willing to consider these suits, even though the federal courts would not; and the extent to which these court orders have been successful in promoting significant reforms.

A State Rather Than Federal Issue Constitutional challenges to state systems for funding public education have been litigated in the state courts of 45 of the 50 states since 1973. The prime reason for this extensive litigation is the inherent inequity in state education finance systems, which historically have been based largely on local property taxes. This means that children who live in districts with low wealth and low property values—as most low-income and most minority students do— will have substantially less money available to meet their educational needs. A legal challenge to Texas’s education finance system, Rodriguez v. San Antonio Independent School District, reached the U.S. Supreme Court in 1973. The Rodriguez plaintiffs lived in Edgewood, a low-income district in the San Antonio metropolitan area with approximately 90% of students Mexican American and 6% African American. The Edgewood district’s property values were so low that even though its residents taxed themselves at a substantially higher rate than did the residents of the neighboring, largely Anglo district, they were able to spend only about half the amount per capita as the neighboring affluent district did on their students, even though students in the Edgewood district clearly had greater needs. The Supreme Court agreed that Texas’s school finance system was inequitable, but nevertheless, it denied the plaintiffs’ claim, primarily because it held that education is not a “fundamental interest” under the federal constitution. Because of the Supreme Court’s ruling in Rodriguez, future plaintiffs had to seek fiscal equity relief in the state courts rather than in the federal courts. Historically, the state courts had not been pacesetters in constitutional civil rights issues, but to the surprise of most observers, their response to the fiscal equity cases turned out to be largely affirmative and innovative. Shortly after the U.S. Supreme Court issued its decision in Rodriguez, the California Supreme Court held that although the U.S. Supreme Court had now held that education is not a fundamental right under the federal constitution, it nevertheless was a fundamental right under the California

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constitution (Serrano v. Priest, 1976). Soon thereafter, courts in states like New Jersey, Connecticut, and West Virginia also declared their state education finance systems unconstitutional.

Remedy Problems in the Early State Court Cases Although the plaintiffs prevailed in most of the initial state court litigation, difficulties at the remedy stage of these early cases appeared to dissuade other state courts from requiring states to reform their finance systems in ways that would address plaintiffs’ arguments. In some states, despite rulings in plaintiffs’ favor that meant more state aid for lowincome school districts, most of this financial benefit was used to cap or lower local property taxes rather than to provide more resources to the schools. This was the case in Connecticut, for instance, after the state supreme court ruled in Horton v. Meskill in 1977 that the state’s heavy reliance on property taxes to fund schools was unconstitutional. In other states where the courts had deferred to the legislature to devise a method for eliminating the inequities, courts found themselves enmeshed in prolonged litigation to compel the legislature to take action and/or enact meaningful solutions. In New Jersey, for example, 3 years after the state supreme court had found the state’s school funding system unconstitutional in Robinson v. Cahill (1973), the court had been involved in no less than five follow-up compliance lawsuits. In other states, the courts simply ordered the states to equalize the amount of percapita funds available to each school district, leading to highly unsatisfactory results. The Serrano litigation in California provides a good example of this approach. There, the court held that funding disparities among school districts (apart from categorical special needs programs) must be reduced to “insignificant differences,” which it defined as “amounts considerably less than $100 per pupil” (Serrano v. Priest, 1976). This seemingly simple solution actually resulted in a “leveling down” and dramatic reduction of educational expenditures. California, which ranked fifth in the nation in per-pupil spending in 1964–1965, fell to 42nd by 1994–1995. One factor in this was likely the constitutional cap on local property taxes, known as Proposition 13, that voters approved in 1978; many commentators thought that Proposition 13 was enacted because of the Serrano order.

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School Finance Litigation

Despite an initial spurt of proplaintiff decisions in the mid-1970s, as of 1988, 15 of the state supreme courts had denied any relief to the plaintiffs, compared with the seven states in which the plaintiffs had prevailed. Specifically, the defendants prevailed during those years in cases litigated in Arizona, Colorado, Georgia, Idaho, Illinois, Michigan, Montana, Maryland, New York, North Carolina, Ohio, Oklahoma, Oregon, Pennsylvania, and South Carolina. Plaintiff victories occurred during those years in Arkansas, California, Connecticut, New Jersey, Washington, West Virginia, and Wyoming.

A Dramatic Turnaround in Plaintiffs’ Success More recently, however, there has been a dramatic reversal in the outcomes of the state court litigation: Since 1989, the highest courts in 23 states have issued decisions affirming or enforcing students’ right to an adequate education. These court decisions were in Alaska, Arizona, Arkansas, Connecticut, Idaho, Kansas, Kentucky, Maryland, Massachusetts, Montana, New Hampshire, New Jersey, New Mexico, New York, North Carolina, Ohio, South Carolina, Tennessee, Texas, Vermont, Washington, West Virginia, and Wyoming. In 14 other states—Alabama, Arizona, Colorado, Florida, Illinois, Indiana, Louisiana, Missouri, Nebraska, Oklahoma, Oregon, Pennsylvania, Rhode Island, and South Dakota—the courts have upheld the defendants’ positions. Eleven of these 14 cases in which the defendants won (all except Alabama, Rhode Island, and South Dakota) were decided on the grounds of justiciability and separation of powers, and the highest state courts did not consider in any detail the evidence presented at trial or the scope of the constitutional right at issue. From Equity to Adequacy

This dramatic turnaround in judicial outcomes appears to be related to a major shift in legal strategy by the plaintiffs’ attorneys. At the end of the 1980s, civil rights lawyers changed their focus from equal protection claims based on disparities in the level of educational funding among school districts to claims based on opportunities for a basic level of education guaranteed by specific provisions in the state constitutions. The education clauses of virtually all of the state constitutions contain language that requires the state to provide all of its students “an adequate public education” (Ga. Const. art. VIII, §1, ¶I), “a thorough and efficient education” (N.J. Const. art.

VIII, §4, ¶1), a “high quality system of free public schools” (Fla. Const. art. IX, §1), or a “sound basic education” (N.Y. Const. art. XI, §1). Most of these provisions had been incorporated into the state constitutions as part of the common school movement of the mid-19th century, which created statewide systems for public education and attempted to inculcate democratic values by bringing together under one roof students from all classes and all ethnic backgrounds. Others, especially in the New England states, date back to the 18th-century revolutionary ideals of creating a new republican citizenry that would “cherish the interests of literature and the sciences” (Mass. Const., Pt. 2, chap. 5, §2). Although the exact language in these constitutional clauses differs from state to state, the state courts that have interpreted these clauses have all emphasized that children are entitled to meaningful educational opportunities that will prepare them to be capable citizens in a democratic society and to compete for jobs in a global economy (see, e.g., Robinson v. Cahill, 1973; Campbell Cnty. Sch. Dist. v. State, 1995; Campaign for Fiscal Equity v. State, 2003; Conn. Coalition for Justice v. Rell, 2010). In these “adequacy” cases, courts focus on the substance of the education students are actually receiving in the classroom rather than on comparing the amount of funds that are available to each school district, as in the equity cases. The plaintiffs’ success in these cases resulted from the evidence they marshaled that showed widespread patterns of educational inadequacies, primarily affecting lowincome and minority students, in states throughout the country. In one poor rural school district in Arkansas, all high school mathematics courses were taught by the same unqualified mathematics teacher (Lake View Sch. Dist., No. 25 of Phillips Cnty. v. Huckabee, 2001); and in New York City, although the requirements for high school graduation included passing an examination in a laboratory science course, almost a third of the city’s high schools had no science labs (CFE v. State of New York, 2003). The Impact of Standards-Based Reforms

Another major factor behind the plaintiffs’ success in the education adequacy cases was the emergence of the standards-based education reform movement in the 1990s. The standards movement was triggered by a series of major reports in the 1980s that included the National Commission on

School Finance Litigation

Excellence in Education’s report A Nation at Risk, which warned of “a rising tide of mediocrity” in American education—a phenomenon that was said to be undermining the nation’s ability to compete in the global economy. Both the federal government and the states responded to this challenge by stressing the importance of raising academic standards and thereby setting forth clear expectations about what children should know when they graduate from high school. Virtually all states have now adopted such standards, and in most states, curricula, teacher training, graduation requirements, and examinations are expected to respond to these standards. The federal government also boosted the standards movement by requiring in the No Child Left Behind Act of 2001 (20 U.S.C. §6301) that states adopt “challenging” standards and by holding states accountable for making academic progress in relation to these standards. The standards-based reforms gave substantive content to the educational opportunities that the plaintiffs sought in the adequacy litigation. The reforms highlighted the extent to which student achievement in school districts that primarily served poor and minority students fell short of the levels of academic proficiency that the states themselves had now declared to be the definition of educational acceptability. Standards-based reform also provided the courts practical tools for developing judicially manageable approaches for dealing with complex educational issues and implementing effective remedies. The new constitutional approach also tends to invoke less political resistance at the remedial stage because, rather than raising fears of “leveling down” educational opportunities currently available to affluent students, it gives the promise of “leveling up” academic expectations for all other students. Although standards-based reforms were primarily aimed at improving the performance of the lowest achieving students, the reforms are comprehensive, and they also were expected to provide benefits to almost all students. Instead of threatening to shift money from rich districts to poor districts, therefore, the emphasis on providing all students a basic, substantive level of educational opportunity offers the possibility of enriching opportunity for all. The courts generally have interpreted these constitutional rights in robust, affirmative terms. “The concept of an adequate education emerging from state courts . . . goes well beyond a basic or minimum

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educational program that was considered the acceptable standard two decades ago” (Verstegen, 1998, pp. 51, 67). In essence, the predominant judicial approach has been to require states to ensure that schools in poor urban and rural areas have sufficient resources to provide their students a meaningful opportunity to meet the state’s own academic standards, at a time when these standards have generally been elevated in response to heightened public expectations and increased federal accountability requirements. They have ordered states to revise their education finance systems to ensure that districts with low property tax wealth will have sufficient funding to provide all of their students the opportunity for a sound basic education.

Equity and Adequacy in Recent Court Cases The courts’ intervention in education finance matters has resulted in significant increases in both the adequacy of educational funding and the equity of resource distribution in many states. In Kentucky, for example, litigation has resulted in dramatic reductions in spending disparities among school districts, as well as the redesign and reform of the entire education system. These reforms, at least when they were fully funded in the early years, resulted in a significant increase in that state’s student achievement scores. In Massachusetts, the adequacy litigation, and the reform legislation that followed it, also substantially reduced the funding gaps between rich and poor school districts. Massachusetts has seen a dramatic rise in the percentage of students achieving proficiency on state tests in recent years. After decades of litigation in New Jersey on behalf of the largely minority, low-income students in 31 urban districts, there have been significant increases in their achievement test scores. One of these districts, Union City, a 92% Latino district that is the poorest in the state, has effectively closed the achievement gap between its students and students in the rest of the state on third-grade reading and math tests, and it may be the first urban district in the United States where gains among low-income and minority students have been sustained into the middle school grades, narrowing the achievement gap at that level. These findings indicate that school finance litigation may have had a positive impact on student achievement, but the findings are largely descriptive and not causal given the difficulty of establishing causality with regard to the effect of school finance litigation on student achievement.

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Stated differently, it is empirically very challenging to plausibly attribute educational improvements to finance litigation. In other instances, strong resistance from the governor and/or the legislature has delayed or impeded the mandated reforms. In Ohio, for example, the legislature had partially responded to a series of court orders by, among other things, reducing funding inequities and improving school facilities following the state supreme court’s declaration that the state’s funding system was unconstitutional. However, the legislature’s failure to implement the judicial orders effectively and the judges’ unwillingness to confront the legislature led the state supreme court to retreat from the fray and terminate the cases before an appropriate remedy had been fully effectuated. In West Virginia, the legislature virtually ignored the courts’ extensive orders throughout the 1980s but then implemented some, more limited reforms after follow-up litigation was initiated in the mid1990s (Tomblin v. Gainer, 1995). In Alabama, after the election of new supreme court justices, the state supreme court reopened Alabama Coalition for Equity v. Spiegelman (1977), a case it had decided for the plaintiffs in 1993. After soliciting arguments from the two sides, the court dismissed the case, citing separation of powers and justiciability concerns (Ex Parte James, 2002). Since the 2008 recession, and the subsequent changes in economic conditions that have had a detrimental impact on many state budgets, adequacy litigation in the state courts has continued. The plaintiffs have prevailed in about half of the final outcomes of these postrecession rulings (Alaska, Connecticut, and Washington) and the defendants in the other half (Colorado, Indiana, and South Dakota). In addition, about a dozen new legal challenges are also pending. Consistent with the established constitutional doctrine that constitutional rights cannot be denied or deferred because of state financial constraints, all of the state courts that have issued rulings in cases involving reductions in budgets that jeopardize constitutionally required services have ruled for the plaintiffs (see, e.g., Abbott v. Burke, 2011 [ordering the governor to restore $500 million in budget cuts to the 31 plaintiff districts]; Hoke County Board of Education v. State of North Carolina, 2012 [invalidating funding reductions for constitutionally mandated preschool services]). Nevertheless, there is a tendency by judges in many states to avoid confronting the

executive and legislative branches by using technical and procedural justifications to avoid deciding these cases. Michael A. Rebell See also Adequacy; Educational Equity; San Antonio Independent School District v. Rodriguez; Serrano v. Priest

Further Readings Baker, B. D., Sciarra, D. G., & Farrie, D. (2012). Is school funding fair? A national report card (2nd ed.). Newark, NJ: Education Law Center. Retrieved from http://www .schoolfundingfairness.org/National_Report_Card.pdf Clune, W. H. (1994). The shift from equity to adequacy in school finance. Education Policy, 8, 377–379. Cremin, L. (1980). American education: The national experience 1783–1876. New York, NY: Harper & Row. Downes, T. (2003). School finance reform and school quality: Lessons from Vermont (Discussion Papers Series). Boston, MA: Tufts University, Department of Economics. Evans, W. N., Murray, S. E., & Schwab, R. M. (1999). The impact of court-mandated finance reform. In H. Ladd, R. Chalk, & J. Hansen (Eds.), Equity and adequacy in education finance: Issues and perspectives (pp. 72–98). Washington, DC: National Academies Press. Kaestle, C. (1983). Pillars of the republic: Common schools and American society 1780–1860. New York, NY: Hill & Wang. Kentucky Department of Education. (2000). Results matter: A decade of difference in Kentucky’s public schools 1990–2000 (pp. 72–87). Frankfort, KY: Author. MacInnes, G. (2009). In plain sight: Simple, difficult lessons from New Jersey’s expensive effort to close the achievement gap. New York, NY: Century Foundation Press. Obhof, L. J. (2005). DeRolph v. State and Ohio’s long road to an adequate education. Brigham Young University Education & Law Journal, 2005(1), 83–149. Rebell, M. A. (2009). Courts and kids: Pursuing educational equity through the state courts. Chicago, IL: University of Chicago Press. Rebell, M. A. (2012). Safeguarding the right to a sound basic education in times of fiscal constraint. Albany Law Review, 75, 1855. Reville, P. S., Coggins, C., Candon, J., McDermott, K., Churchill, A., & Long, B. T. (2005). Reaching capacity: A blueprint for the state role in improving low performing schools and districts. Boston, MA: Rennie Center for Education Research and Policy. Retrieved from http://www.renniecenter.org/research

School Quality and Earnings Schaur, M., & Durbin, S. (1993, June 28). “Protecting” school funding. Sacramento Bee, p. B14. Schrag, P. (2003). Final test: The battle for adequacy in America’s schools. New York, NY: New Press. Superfine, B. M. (2008). The courts and standards-based education reform. New York, NY: Oxford University Press. U.S. Department of Education, National Commission on Excellence in Education. (1983). A nation at risk: The imperative for educational reform. Washington, DC: Author. Retrieved from https://www3.nd.edu/~rbarger/ www7/nationrs.html Verstegen, D. A. (1998). Judicial analysis during the new wave of school finance litigation: The new adequacy in education. Journal of Education Finance, 24(1), 51–67.

Legal Citations Abbott v. Burke, 20 A.3d 1018 (N.J. 2011). Alabama Coalition for Equity v. Spiegelman, 713. So. 2d 937 (1977). Campaign for Fiscal Equity v. State of New York, 100 N.Y.2d 893 (2003). Campbell County Sch. Dist. v. State of Wyoming, 907 P.2d 1238, 1259 (Wyo. 1995). Connecticut Coalition for Justice v. Rell, 990 A.2d 206 (Conn. 2010). Hoke County Board of Education v. State of North Carolina, 731 S.E.2d 691 (N.C. Ct. App. 2012). Robinson v. Cahill, 303 A.2d 273 (N.J. 1973). Rodriguez v. San Antonio Independent School District, 411 U.S. 1 (1973). Serrano v. Priest, 18 Cal.3d 728 (Cal. 1976).

Website National Education Access Network (information on school finance lawsuits and decisions in each of the 50 states): www.schoolfunding.info

SCHOOL QUALITY

AND

EARNINGS

Increased schooling is associated with better outcomes along a number of dimensions. Among the outcomes that improve, on average, with each additional year of schooling are an individual’s wages, probability of being employed, health, and life expectancy. The improvements in outcomes associated with each year of schooling are referred to as the returns to schooling. This entry discusses the relationship between school quality and the returns to schooling.

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Theory suggests that increasing school quality can increase individuals’ wages both directly, by increasing the individual’s human capital, and indirectly, by increasing the total years of schooling attained by the individual. When choosing whether to attain an additional year of schooling, an individual compares the present discounted value of schooling with the present discounted value of its cost. Present value is how much we value today something to be received in the future. For example, if an individual were willing to pay $98 today to receive $100 a year from now, the present discounted value that individual places on receiving $100 a year from now is $98. If school quality increases the benefits of an additional year of schooling, then improving school quality increases the present discounted value of the additional year of schooling—which may include the increased earning potential, improved health outcomes, the consumption value of schooling, and other benefits of additional schooling—relative to the present discounted value of schooling, which includes any direct costs of attending school, such as tuition costs, and the opportunity costs of attending school, which include the wage the individual would earn by working and/or the value of the leisure the individual would have enjoyed if not in school. Therefore, if improving school quality increases the benefits of attaining an additional year of schooling, then improvements in school quality should also lead to increases in the total years of schooling attained by individuals, which in turn results in increased earnings. The remainder of this entry is organized as follows. First, the returns to education and the challenges in identifying the causal effect of schooling on earnings are discussed. The entry then reviews evidence of the relationship between school quality and earnings from cross-sectional studies based on the Decennial United States Census and survey data, from Project STAR, and from value-added estimates of teacher quality.

Estimates of the Returns to Education The consensus among economists is that for individuals living in developed countries, the average return to an additional year of high school or college is between 7% and 12%. For each additional year of secondary or postsecondary schooling an individual attains, the individual’s income increases 7% to 12% on average. Some of the returns to education may result from the fact that attainment of

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a high school diploma or a college degree provides a signal to potential employers. An employer cannot be certain of a potential employee’s productivity, and the diploma might reveal information about the individual’s productivity that would otherwise be hidden. For example, a college degree might signal to a potential employer something about an individual’s intelligence and work ethic. This increase in earnings associated with the signaling value of a diploma is sometimes referred to as the “sheepskin effect.” Although some of the returns to education are the result of sheepskin effects, evidence suggests that most of the returns to schooling can be attributed to the schooling itself. An additional year of education increases an individual’s knowledge and skills and can change an individual’s habits, and some combination of these enables the individual to command higher wages. Additional schooling may improve health and other outcomes as well.

Challenges in Identifying the Effect of Schooling on Earnings A positive correlation between years of schooling and earnings does not necessarily imply that an additional year of schooling causes earnings to be higher. Individuals who choose to complete more schooling may have higher earnings than otherwise similar individuals regardless of the number of years of schooling they choose to complete. And the returns to a year of schooling for those who choose to complete additional schooling might be higher than the returns to an individual who does not attain more schooling: If the individual who chose not to complete an additional year of schooling were compelled to do so, the returns to the additional schooling might be less than the returns to the individual who completes an additional year of schooling. Researchers have used a number of strategies to identify the causal effect of schooling on earnings. For example, Orley Ashenfelter and Alan Krueger compare the earnings of identical twins with different levels of schooling; David Card compares the earnings of individuals who grew up near a college with the earnings of otherwise similar individuals who did not grow up near a college; Joshua Angrist and Krueger use changes in compulsory schooling laws to identify the effects of schooling on earnings, comparing individuals who were born earlier in the year—and therefore more likely to drop out—with

those born later in the year; Angrist and Krueger use the Vietnam draft lottery to identify the effects of schooling on earnings, comparing individuals whose lottery numbers made them more likely to be drafted—and who therefore were more likely to stay in school to avoid the draft—with those whose lottery numbers made them less likely to be drafted. Although these and other studies may not be perfect, taken together they provide strong evidence that education has a causal effect on earnings.

Estimates of the Relationship Between School Quality and Earnings: Cross-Sectional Evidence The challenges in identifying the effects of school quality on earnings are similar to those in identifying the effect of additional schooling on earnings. Students are not distributed randomly across schools. Moreover, school quality does not have a simple definition. Without a clear definition of school quality, research has instead focused on the relationship between inputs to education—teacher salaries, the length of the school year, pupil-teacher ratios, or spending per pupil, for example—and the returns to education. There is little evidence that these inputs have a long-term effect on student test scores; if school quality is defined as the school’s ability to increase student learning as measured by standardized tests, inputs to education production may be a poor proxy for school quality. A number of studies have used cross-sectional data to estimate the effect of school quality on earnings. Both school-level measures of school quality and young adult outcomes are included in the National Longitudinal Survey of Youth, National Longitudinal Survey of Young Men, National Longitudinal Survey of Young Women, and High School and Beyond, making it possible to measure the association between schooling inputs and the returns to education. Most of the studies estimate a positive relationship between inputs to schooling and returns to education. However, the fact that these students are not randomly assigned to schools limits our ability to interpret these findings as a causal effect of schooling inputs on returns to education. The level of inputs to school production varies across states and over time. A number of studies have used data from the Decennial United States Census to estimate the relationship between schooling inputs

School Quality and Earnings

and returns to education. Card and Krueger used a two-step procedure to estimate the relationship between schooling inputs and returns to education among men, based on the state in which the men were born. First, the data for all states were pooled, and the wage returns to education were estimated for each state of birth for each decade. Returns to education were estimated for three cohorts of men: (1) those born in the 1920s, (2) those born in the 1930s, and (3) those born in the 1940s. This first step produced a separate estimate for the returns to education for each state-of-birthby-cohort cell, so that the returns to education among men born in South Carolina in the 1930s, for example, can be different from the returns among men born in South Carolina in the 1940s or in North Carolina in the 1930s. In the second step, the authors estimated the relationship between returns to education and average inputs to schooling in the state of birth for each cohort. The inputs analyzed were average class size, teacher wages, and length of term. The authors estimated a significant relationship between inputs to schooling and returns to education. In a related paper, the authors present evidence that improvements in the quality of historically Black schools relative to White schools can explain one fifth of the narrowing of the wage gap between White men and Black men between 1960 and 1980, a period over which the wage differential between White and Black men fell from 40% to 25%. One advantage of studies based on intrastate differences in inputs to schooling is that although many families with children make decisions about which neighborhood to live in partly on the basis of the quality of the neighborhood schools, few families decide which state to live in based on the quality of the state’s schools. And the state-level studies often control for fixed differences between states over time and also for fixed differences between cohorts across states. Therefore, studies based on state-level differences greatly attenuate the problem of nonrandom assignment of students to school, which is a shortcoming of studies based on survey data. Unfortunately, many shortcomings of statelevel studies have been identified. First, the returns to education estimated in these studies assume a nationwide or regionwide labor market, which research does not support. Second, the returns to education estimated in these studies are identified by individuals who are educated in one state and work

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in another, but such migration may be nonrandom and correlated with one of the variables of interest. Third, within-state changes in the resources devoted to schooling may be correlated with other state-level changes unrelated to schools that also influence the human capital and the years of education completed by children in that state. And finally, most state-level studies cannot take into account the state where an individual actually attended school, nor can they identify whether the individual attended public schools. These shortcomings undermine the credibility of estimates of the relationship between school inputs and returns to education based on state-level data.

Experimental Estimates of the Effect of School Quality on Earnings Tennessee’s Project STAR (Student/Teacher Achievement Ratio) is one of the few education interventions that come close to the “gold standard” of random assignment of students to treatment and control groups. Students entering kindergarten in the fall of 1985 were randomly assigned to a class in their school with either a high (15–30) or a low (11–20) student-teacher ratio, and teachers were randomly assigned to larger or smaller classes as well. Students remained in a small or large class through third grade. About 11,600 students and 1,300 teachers at 80 schools took part in the experiment. Some students moved between large and small classes over the course of the experiment; to compensate for the resulting effect on the study, the researchers estimated the effect of “intention to treat” on student outcomes. Students assigned to smaller classes had significantly higher scores, and the beneficial effect of a small classroom was larger for Black students and low-income students. Research based on Project STAR has found evidence that students assigned to smaller classes are more likely to complete high school, are more likely to take a college entrance exam, are less likely to be arrested for a crime, and have lower teen birth rates, although this last result is significant for White students only. The fact that students’ assignment to classrooms is random within schools has allowed a team of researchers, including Raj Chetty, John Friedman, Nathaniel Hilger, Emmanuel Saez, Diane Whitmore Schanzenbach, and Danny Yagan, to use data from Project STAR to estimate the effect of other teacher and class characteristics on student

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School Report Cards

outcomes as well. The authors found that students who were assigned to higher quality classrooms (as measured by the end-of-year test scores of students in the class) and students assigned to more experienced teachers had higher earnings at age 27 than otherwise similar students attending the same school. Significant variation in quality, as measured by wages at age 27, was estimated between classrooms within the same school, but this variation cannot necessarily be linked to observable teacher or classroom characteristics. It is likely that some of this variation among classrooms can be attributed to teachers. Chetty, Friedman, and Jonah Rockoff used “teacher valueadded,” which cannot be measured directly but which represents an individual teacher’s contribution to student learning. They found that students who had higher-value-added teachers in Grades 4–8 had better outcomes at ages 20 to 30, including lower rates of teen pregnancy, higher rates of college attendance, and higher earnings. Taken as a whole, the evidence from cross-sectional studies, from Project STAR, and from value-added estimates of teacher quality suggest that improvements in school quality lead to higher earnings. Eric Larsen See also Age-Earnings Profile; Benefits of Higher Education; Benefits of Primary and Secondary Education; Income Inequality and Educational Inequality; Race Earnings Differentials

Further Readings Angrist, J., & Krueger, A. (1991). Does compulsory school attendance affect schooling and earnings? Quarterly Journal of Economics, 106(4), 979–1014. Card, D. (1995). Using geographic variation in college proximity to estimate the return to schooling. In L. Christofides, E. Grant, & R. Swidinsky (Eds.), Aspects of labor market behaviour: Essays in honour of John Vanderkamp (pp. 201–215). Toronto, Ontario, Canada: University of Toronto Press. Card, D., & Krueger, A. (1992). Does school quality matter? Returns to education and the characteristics of public schools in the United States. Journal of Political Economy, 100(1), 1–40. Chetty, R., Friedman, J., Hilger, N., Saez, E., Schanzenbach, D., & Yagan, D. (2011). How does your kindergarten classroom affect your earnings? Evidence from Project STAR. Quarterly Journal of Economics, 126(4), 1593–1660.

SCHOOL REPORT CARDS School report cards are central to education accountability reforms. School report cards convey information about school and student performance to stakeholders across the education system. They are available at the state, district, and school levels and include educational indicators or statistics that characterize various aspects of the school. This entry begins by providing background on school report cards and their purpose, followed by a review of the information included in the report cards, with special emphasis on accountability indicators. It concludes with a discussion on the limitations of school report cards.

Background and Purpose The notion of report cards for schools is not new. They have been in existence in various forms in the United States since the 1980s. At that time, school report cards were not a federal requirement. Only a few states published them, and those that did tended to focus on reporting school input and resources. During the 1990s, a growing number of states began issuing, or requiring school districts to issue, school report cards that also included school performance such as student achievement results by grade, student attendance, and graduation. In general, these school reports were in paper format, short, user friendly, and mostly targeting parents to provide them with a general picture of their children’s schools. But in the era of increased school accountability, best represented by the No Child Left Behind Act of 2001 (NCLB), the concept of school report cards expanded. NCLB requires all states, districts, and schools to publish reports on their status and progress in achieving NCLB goals. The main purpose of school report cards is to exert external and internal pressures on schools to improve their performance. The information provided by the report cards can be used by state- and district-level personnel to monitor the schools and hold them accountable, as well as decide on how to redirect resources and provide support. Principals, teachers, and other school officials can also potentially use school report cards to identify areas of strengths and weaknesses and make informed decisions regarding school operations, teaching, and learning. Another use of the school report cards is to inform parents about the quality of schools.

School Report Cards

Providing parents with information on schools is likely to reduce the costs they might incur in searching for this information and is likely to enhance their abilities to make the appropriate decisions regarding their children’s schooling, thus altering market behavior. For example, school report card information can help parents decide where to live and purchase homes in order to enroll their children in high-performing schools. In areas where school choice options exist (e.g., charter schools, magnet schools), the information found in school report cards can potentially help parents decide whether to transfer their children and, if so, to which schools. The information found in school report cards might also empower parents and help them engage with the schools and discuss and influence matters that are important to their children’s education. Finally, school report cards can facilitate public understanding and discourse about current school and district status, as well as the education system at the state level.

Categories of Information Presented in School Report Cards Prior to NCLB, school report cards emphasized a limited number of indicators, focusing mostly on school input and resources, although in the 1990s, increasingly districts and schools incorporated indicators of school performance. The school report cards targeted mostly parents and the public. The reports tended to be short (about one page), clearly written, and included colorful graphics to quickly convey an overall picture of the school. Today, report cards are available digitally, are interactive, and provide multiple presentations of the same indicators. Furthermore, school report cards expanded their reach to policymakers, state and district officials, and educators. More important, school report cards include a wealth of data and have shifted toward outcome-oriented indicators as a result of NCLB. A 2012 review by Joan L. Herman and others show that common categories of information found in school report cards are the following: • School characteristics: These include data on student demographics, school size, average class size, teacher qualifications as defined by the state, percentage of teachers with emergency or provisional credential, percentage of classes taught by highly qualified and nonhighly qualified teachers, and teacher turnover rates.

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• School climate: It includes data on student attendance and discipline behaviors. • Student outcome: It Includes data on the percentage of students assessed; student academic achievement in math, reading, science, and history; and English language acquisition for English Language Learners. These data are presented for overall students as well as subgroups of students as required by NCLB. • School accountability indicators: These show whether a school is meeting NCLB goals and state goals for all students, as well as whether they are meeting these goals for economically disadvantaged students, students in certain racial and ethnic groups, students with disabilities, and limitedEnglish-proficient students. Many of these accountability indices are derived from student outcome data, and they classify schools into various categories based on their needs for improvement. Accompanying the accountability indicators is information on whether the school has been “in need of improvement” and the measures taken to address these problems. Although NCLB dictates what information is to be included in the school report cards, it does allow flexibility to states, districts, and schools to include additional data, as long as the state determines that the data presented are reliable. For example, some states provide data on school finances, including total and per-pupil expenditures, teacher salaries, the quality and condition of school facilities, and student enrollment in college. The district and schools also add to the base school report card information on their missions and priorities and how they are achieving each priority. Other customized information in the reports includes availability of academic and nonacademic programs (e.g., curriculum, cocurricular activities, Advanced Placement classes), student readiness for college and career (e.g., percentage of students who pass courses required for college), and learning environment (e.g., student, parent, and teacher perception of how conducive the school environment is to learning). For the information to be usable, the report cards should include some kind of a reference point on which to compare the school. This allows stakeholders to be able to make some judgment regarding the quality of school performance. Report cards nowadays compare each indicator to district and state averages, state-set standards and targets, as

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well as to the school’s past performance. Some states also compare school academic achievement with each other and with a group of schools of similar background.

Accountability Indicators Included in School Report Cards As described above, school report cards include information on multitude of indicators. Among these, school accountability indicators are fundamental to school report cards and tend to get most of the attention from policymakers and the public. Accountability indicators summarize the performance of a school relative to state and federal goals, and other schools in the state. Such indicators not only tend to emphasize academic achievement measures but also include other variables (e.g., student attendance, graduation rates) to minimize schools from “gaming” the system. Different types of accountability indicators are included in school report cards. The most common is the “adequate yearly progress” (AYP) measure. AYP is a concept that was created by Congress under the NCLB. AYP is a series of annual performance goals established by each state. Schools, local education agencies, and the state are determined to have met AYP if they meet or exceed each year’s goals (AYP targets and criteria). State exams in English language arts and mathematics (and graduation rates for high school) are used to make that determination. Schools are required to test annually at least 95% of their students (enrolled in third through eighth grades) using state assessments. Based on the assessment results, states, districts, and schools must demonstrate that all their students as well as student groups (e.g., English Language Learners, students with disabilities) are approaching proficiency as defined by the state’s annual academic performance goals. Schools are held accountable based on their AYP progress. Schools that do not meet their AYP in two consecutive years are considered to be “underperforming.” These schools initially are provided with technical assistance to help them improve. However, if they continue to underperform, then districts are required to take corrective action and/or restructure the schools. Some states that have been granted a waiver from NCLB accountability requirements, such as Florida and New Mexico, have moved away from emphasizing AYP measures in their report cards and instead emphasize their own system of school

grades to reflect school performance. School grades range from A through F, with F indicating that the school is failing. The factors used to calculate school grades and scores vary among states. Some take into account the school’s performance in the current year (e.g., percentage of students found proficient in subject areas, usually math and reading), school academic growth in the past 3 years, academic growth of highest and lowest performing students in the past 3 years, attendance rate, as well as graduation rate, and, for high school students, readiness for college. To calculate the school grade, different weights are given to the various factors, with higher weights given to the school’s current academic standing and growth than to factors addressing attendance, graduation, and college readiness. Report cards may also include additional accountability indicators developed by states. California, for example, put in place the Academic Performance Index (API) in 1999. The API is an indicator of schools’ academic gains from one year to the other. Although many of the state assessments used for determining API are the same as those for AYP, some are different. For example, the California Achievement Test as well as state science assessments are taken into account when estimating a school’s API but not when estimating its AYP. The API is used to compare how each school is performing relative to the state overall, as well as how each school is performing relative to schools that are similar in demographic characteristics (e.g., student ethnicity, student mobility, parent education, and poverty level). The index is a composite of various indicators, including individual level of proficiency in various subject areas (math, English language arts, science, and history), and dropout rates. To create the API, each subject matter indicator is given a different weight. The weighting process takes into account students’ progression from low levels of achievement to higher levels of achievement. Based on student scores, student progression, and dropout rate, total scores are computed and used to categorize all schools in the state by school level. The scale ranges from 1 to 10 (1 being low performing and 10 being high performing). Schools are also compared with the 100 most demographically similar schools and are categorized within that group. Thus, schools receive different rankings when compared with each school group and might receive a low rating in one and a high rating in another. Finally, other states, such as the state of Washington, are also developing indices for

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identifying schools that have excelled in their performance. These indices include indicators measuring student performance on state assessment in reading, writing, math, and science, as well as dropout or graduation rate. A score (ranging from 1 through 7) for each subject area is then calculated for each of the following categories: achievement of non-lowincome students, achievement of low-income students, achievement versus peers, and improvement from previous years. Equal weight is given to each subject area, and thus, it becomes easy to identify the student groups struggling the most and the subject areas in which they are performing poorly. An overall school score (average score across all the indicators) is calculated. Schools are then categorized on a 7-point scale. Schools scoring between 5.5 and 7 are considered exemplary, while those scoring below 2.49 are considered struggling. Those scoring below 1 are assigned a high priority of state improvement.

Conclusion School report cards provide a depiction of the status of the schools and how they perform on various measures of accountability. Although school report cards provide a wealth of information to various stakeholders for the purpose of holding schools accountable and improving them, rigorous research on the usability of school report cards, especially after the passage of NCLB, is limited. It is clear that state officials use the school report cards to hold districts and schools accountable. However, beyond this group of stakeholders, studies need to be conducted to examine in depth how district officials, principals, teachers, and parents use these reports. Specifically, the studies should address the extent to which these different groups of stakeholders read the reports, what the reports mean to them, and how they interpret the data and make decisions related to policies, instruction, and school choices. The limited research available has indicated several drawbacks in the overall utility of school report cards, irrespective of the type of stakeholder group. First, the information included in school report cards is extensive and is not clearly prioritized in order of importance. Many users are unlikely to digest all the numbers. This limits the extent to which users are able to make an informed decision efficiently. Many of the school report cards present numbers and an explanation of how to interpret the numbers. Yet there is not always discussion of what the results mean or how to connect various indicators to each

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other and, more important, to outcome and accountability indicators. Thus, it is not clear to some stakeholders how the report card information can be translated into meaningful actions. Furthermore, there is little analytic information that explains to the users what type of problems a school is facing and how it is addressing the problems. This type of information is very important, especially for parent users. Second, there are concerns about the reliability and validity of the measures. For example, the accountability measures (e.g., school grades, AYP, API) are based on multiple-choice state assessments. Some argue that these assessments are not good measures of higher order cognitive skills. Furthermore, states that report multiple accountability indicators might show contradictory results depending on how each measure is calculated and the test used, making it more difficult for stakeholders to make sense of the reporting. Finally, several studies have shown that because accountability indicators are the focus of the report cards, teachers have been moving away from developing student in-depth learning and have been increasingly “teaching to the tests” that underlie the accountability indicators to ensure they and their schools are meeting the accountability targets. Rita Karam See also Accountability, Standards-Based; Accountability, Types of; Adequate Yearly Progress; No Child Left Behind Act

Further Readings Blank, R. K. (1993). Developing a system of education indicators: Selecting, implementing, and reporting indicators. Educational Evaluation and Policy Analysis, 11(2), 65–80. California Department of Education. (2012). 2011–12 Academic performance index reports: Information guide. Retrieved from http://www.cde.ca.gov/ta/ac/ap/ documents/infoguide12.pdf Feldman, J., & Tung, R. (2001). Using data-based inquiry and decision making to improve instruction. ERS Spectrum, 19(3), 10–19. Herman, J. L. (2012). Indicators supporting school quality: Lessons learned from the United States. Los Angeles: University of California, National Center for Research and Evaluation, Standards, and Student Testing. Johnson, R. L. (2000). Framing the issues in the development of school profiles. Studies in Educational Evaluation, 26(2), 143–169.

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New Mexico Department of Education. (2012). New Mexico school grading technical guide. Retrieved from http://webapp2.ped.state.nm.us/SchoolData/ docs/1112/SchoolGrading/A-F_School_Grading_ Technical_Guide_2012_V2.0.pdf No Child Left Behind Act of 2001, 20 U.S.C. §§ 6301 et seq. Shavelson, R. J., McDonnell, L. M., & Oakes, J. (1991). What are educational indicators and indicator systems? (pp. 1–2). Washington, DC: ERIC Clearinghouse on Tests, Measurements, and Evaluation.

SCHOOL SIZE The issue of school size has been examined by educational researchers for nearly 50 years. The research and analyses that have been conducted span a wide range of topics and employ a variety of conceptual frameworks, disciplinary traditions, theories, and methodologies. Yet, despite the large body of research on the topic, the issue continues to be debated by scholars, policymakers, and educational leaders. This entry presents an introduction to the topic, followed by a discussion of the research regarding the relationships between school size and economies of scale, cost, organizational effectiveness, and student experiences and outcomes. The entry concludes with a discussion of implications for policy and practice.

Introduction and Background According to a 2012 report by the National Center for Education Statistics, the number of elementary and secondary schools in the United States steadily declined throughout most of the past century. There were approximately 248,000 public schools in 1930, but by 2010, that number had decreased to approximately 99,000. This represents a significant consolidation of schools, especially given the fact that student enrollment in K-12 public schools nearly doubled over this same period. Some of the increase in school size—that is, the number of students enrolled in a given school—is attributable to the consolidation of very small school districts, many of which contained only one school. Consequently, schools have, on average, increased in size. However, in more recent years, the trend has been somewhat different, as the number of public schools has increased, but at a relatively low rate. From 2000 to 2009, the number of public schools increased by

6%, while enrollment increased by 4.6%. At the same time, there has been a notable movement to divide very large high schools into smaller groups of “schools-within-a-school” in an effort to reap the benefits of both small and large schools. This initiative has been predominantly focused on large urban districts. Before discussing the research on school size, it is important to note that no consensus exists as to how a “small” or “large” school is to be defined. According to the National Center for Education Statistics, in 2010–2011, the average size of an elementary school was 480 students and the average high school size was 798 students. However, there are significant variations in school size, influenced at least in part by overall population density in a state or region. For example, the average high school size in North Dakota is 209, compared with an average of 1,500 students in Florida’s high schools. There is also variation in how researchers have classified school size. In some research studies, small schools are defined as having enrollments of 200 to 300 or less, medium schools in the range of 400 to 600 students, and large schools as having more than 800 students. While these size ranges are more common for how elementary schools have been classified, enrollments in secondary schools can be much higher and more varied, making it necessary for researchers to vary the size ranges specific to the schools under study. For example, in some school districts, high schools may have enrollments exceeding 3,000 students, while other districts in the same state or region often have no more than 500 students in a high school. Over the past 20 years, more research has focused on high school size as compared with elementary school size, and this represents a shift from earlier decades, in which research on elementary school size was more predominant.

Economies of Scale and Cost A significant portion of both the debate about school size and the focus of research has been on the question of whether or not larger schools are more efficient. Arguments in favor of larger schools often focus on the principle of economies of scale. That is, a larger school can provide at least the same level of student outcomes at a lower per-pupil operating cost. Larger schools may potentially provide more specialized courses, programs, services, or facilities at a lower per-pupil cost. However, it is also possible that some diseconomies of scale may

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exist. For example, larger schools may be associated with higher transportation, management, or security costs. Thus, the search for the optimal school size involves an analysis of how both costs and benefits change when school size changes. It is important to note that much of the research on economies of scale in education is conducted at the district level and examines district size. This is due to a historic lack of specific expenditure data at the individual school level. Thus, researchers examining school size have used proxies such as student-teacher ratios, expenditures on teacher salaries, and number and type of curricular offerings. Most cost studies have been focused on secondary schools. Researchers conducting these studies consistently express concern about the fact that incomplete or inaccurate data may affect study results. The existing few studies suggest that some cost savings are likely when comparing larger high schools with much smaller high schools (student enrollment of 400 or fewer). However, the evidence is far from conclusive. More research has been conducted on examining the returns to specialization that theoretically may occur in larger high schools. One might assume that larger high schools may be better able to offer a wider array of courses, such as Advanced Placement and specialized vocational classes at a lower perpupil cost. Research completed in the 1990s generally found that larger high schools provided a wider breadth of courses, but the effects of size were not uniform across all schools studied. Some larger schools were offering nearly the same array of courses as those offered in schools with as few as 400 students. Other scholars have raised the question of whether offering more varied types of courses is a good proxy for a better educational experience and have noted that in recent years, there has been a shift away from a wide variety of course offerings to a focus on providing a more constrained set of core academic courses.

Organizational Effectiveness Another important way in which research about school size effects has been conceptualized is through an examination of the impact of size on organizational features, such as teacher attitudes, leadership and supervision, and community factors. This body of research is typically situated in sociological theories about organizational structure and culture. Studies of teachers’ views found that teachers in smaller elementary and secondary schools have

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a more positive attitude about their responsibility for student learning and more satisfaction with the relationships among staff, including how conflicts are resolved. However, some evidence indicates that these positive attitudes may not hold for those teachers working in schools that became smaller over time due to declining enrollments compared with teachers who work in schools that are intentionally smaller by design. Larger schools affect the number of supervisory and leadership positions at a school. For example, large high schools will typically have multiple assistant principals, deans, and department heads, thereby altering the scope and nature of a principal’s duties and changing the nature of the relationship between the principal and the school staff. This may increase the likelihood of miscommunication, task duplication, or working at cross-purposes. However, existing research does not address the question of whether these dynamics alter the overall effectiveness of larger schools in comparison with smaller schools. With respect to other organizational dynamics, some research has noted that parental involvement is negatively affected by school size. In addition, a few studies have found that the sense of community identity and issues related to local control and support of schools can be negatively affected by increased school size.

Student Experiences and Outcomes Perhaps the most important relationship to examine is whether or not school size affects students’ educational experiences and outcomes. The research on the relationship between school size and student outcomes has included investigations of the impact on student achievement, graduation rates, participation in extracurricular activities, and sense of engagement with teachers and peers. The majority of research conducted on elementary school size and student achievement (as measured by test scores) found that, generally speaking, smaller elementary schools are associated with increased student achievement. In the case of secondary schools, research findings are more varied, with some studies finding significant negative relationships and others finding either nonsignificant or positive relationships. It is important to note that the research on the relationship between school size and student outcomes is correlational rather than causal. It is extremely difficult to tease out effects that are attributable only to school size and not to other traits

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of schools. Studies investigating secondary school size and achievement find that the relationship is not necessarily linear but may in fact look more like an inverted U-shaped curve. That is, very small and very large high schools are generally associated with a sizable negative relationship to student achievement. In addition, some research suggests that the benefits of smaller school size are greater for economically disadvantaged students than for their more affluent peers. Larger schools are not consistently associated with decreased achievement for economically advantaged students. Economically disadvantaged students and schools with greater diversity in the student population are generally associated with increased achievement in smaller high schools, while the achievement of more affluent students is generally not affected by smaller high school size. This inverted U-shaped curve also describes the majority of studies that examined the relationship between high school size and dropout rates. School size may also affect other aspects of a student’s experience, including opportunities for participating in extracurricular activities, and the nature of relationships and engagement with both peers and teachers. Research on participation rates in extracurricular activities has focused on secondary schools. The few studies done in this area indicate that overall participation rates in extracurricular activities decline as school size increases and that students participate in a wider variety of activities in smaller high schools. Research on student engagement and school size, while limited in number, find that smaller elementary and secondary schools are associated with students’ increased sense of connectedness and engagement with both teachers and peers.

Conclusions and Implications for Policy and Practice A prevailing assumption among school board members, educational leaders, and other policymakers is that, at least in theory, larger schools are less costly and more effective. During the past decade, however, there has been increased support for the establishment of smaller schools or the creation of smaller schools within a large school building. While there are no absolute conclusions that can be drawn from the research on school size effects, there are several types of findings that can be considered when making decisions about school size. At a minimum, studies have indicated that the presumed economies of scale, which may theoretically be accomplished by

increasing school size, might fail to materialize in practice. Research also suggests that there are a variety of school size effects that should be considered when making policy decisions about the size of elementary and secondary schools. These include issues of cost, organizational culture, student achievement, student participation, and student engagement with teachers and peers. Although research can serve to inform policy discussions and decisions about school size, it is important to note that in practice, there are often limits to the extent to which school size can be altered. The determinants of school size are related to a number of factors, some of which are not under the control of educators or policymakers. Some of these constraints include the enrollment history of the district, prior decisions made about school construction, the shifting demographics of communities, population density and housing patterns, the availability of property for building new schools, and the broader economic environment, including fluctuating property values that affect the capacity to generate needed financial resources. The literature on school size does not provide irrefutable guidance for policymakers regarding whether to close or consolidate any specific school or set of schools within a district. One size does not fit all purposes, students, and conditions. In addition, and as previously discussed, size is a relative term bounded by demographics, geography, and a host of other factors. However, there is considerable evidence that has accumulated over time indicating that increasing school size has the potential to create diseconomies of scale instead of cost savings. At a minimum, care should be taken to consider the numerous contexts and conditions unique to each situation, especially given the fact that school size effects may have differential and potentially inequitable impacts that depend on the nature of the student population being served. Margaret Plecki See also Cost-Effectiveness Analysis; District Size; Economic Efficiency; Economies of Scale; Technical Efficiency

Further Readings Lee, V., & Loeb, S. (2000). School size in Chicago elementary schools: Effects on teachers’ attitudes and students’ achievement. American Educational Research Journal, 37(1), 3–31.

School-Based Management Lee, V., & Smith, J. (1997). High school size: Which works best and for whom? Educational Evaluation and Policy Analysis, 19(3), 205–227. Leithwood, K., & Jantzi, D. (2009). A review of empirical evidence about school size effects: A policy perspective. Review of Educational Research, 79(1), 464–490. Monk, D. (1984). The conception of size and the internal allocation of school district resources. Educational Administration Quarterly, 20(1), 39–67. Stiefel, L., Berne, R., Iatarola, P., & Fruchter, N. (2000). High school size: Effects on budgets and performance in New York City. Educational Evaluation and Policy Analysis, 22(1), 27–39.

SCHOOL-BASED MANAGEMENT School-based management (SBM), also referred to as decentralized management, community control, administrative decentralization, and teacher empowerment, redistributes educational decision-making responsibilities from a central government to the school level. In the United States, SBM entails decentralizing authority from the district or county office of education to individual schools; in countries that lack intermediary units like formal school districts, such as Australia, SBM shifts authority away from the national government to local schools. SBM is most often used as an education reform tool to drive improvements in student achievement. Through SBM, decision-making responsibilities are expanded to include a variety of stakeholders—principals, teachers, parents, students, and local community members. Most site-based managed schools vest the power and authority in a school-site council, which in some instances serves as an adviser to the principal and in others serves as the final decision maker. In its simplest form, SBM involves local stakeholders in decision making related to the school’s education program, how money is spent, and who works at the school. This entry begins with the theory of action underlying SBM. Next, a brief overview of the history of SBM is provided, followed by a discussion of various types of SBM and implementation challenges. The final sections examine research on SBM outcomes and emerging issues.

SBM’s Theory of Action The theory of action underlying SBM argues that when local stakeholders are given more autonomy and flexibility, they will make decisions tailored to

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the specific needs of the students, and as a result, student performance will improve. The SBM model can also extend benefits by lending voice and power to teachers, parents, and students, which helps build strong buy-in and commitment and a school culture of continuous improvement. Additionally, it is argued that local stakeholders’ proximity to their work make them better able to affect school improvements than district or national governments that are distant from schools. Decisions will be made based on school-specific data, and school stakeholders are better able to respond quickly to policies and programs requiring fine-tuning during implementation.

History of SBM SBM drew on earlier democratic movements in the United States. The Teacher Council Movement (1909–1929) empowered teachers to serve on teacher councils, giving those who directly teach students more power in determining curricular and instructional practices. The Democratic Administration Movement (1930–1950) attempted to create stronger and more democratic public organizations. During this time, many districts had some form of SBM, where teachers, parents, community members, and even students were involved. The Community Control Movement (1965–1975) stemmed from public concerns that the needs of those in poverty were not being well served by public agencies unfamiliar with local circumstances. Many stakeholders, including minority parents and community leaders, began to participate in developing school policies. In addition to these movements to democratize education, more recently, there have been cycles of reform in the United States, where state and school districts have experimented with centralized and decentralized management in efforts to improve student performance. Empowerment was also a prominent business strategy in the 1980s and the 1990s. It involved the transfer of decision-making authority and responsibility from management to employees. When employees are given a meaningful voice in workplace decisions and given interesting work, the theory predicts that motivation and productivity will increase. Additionally, it is argued that workers’ proximity to their work makes them better able to effect work improvements than managers who are not directly involved in workers’ tasks. During a time of stiff competition, General Motors worked directly with

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the United Auto Workers union to create a network of self-directed work teams that were managed by two advisers, turning the company’s management model upside down by placing decision makers much closer to the assembly line. Today the norm in the top Fortune 500 companies is decentralized management, which has led to improvements in work practices and policies, as well as improvements in how work is coordinated with and communicated to employees.

Forms of SBM SBM can take on different forms. Research on school decentralization has distinguished between two forms of SBM: administrative decentralization and community control. The administrative decentralization model vests the locus of control away from a central administrator to a smaller field unit. In most instances, this model empowers the school principal with decision-making authority. In Florida, the MiamiDade public schools in the late 1980s adopted administrative decentralization to delegate powers to administrators and teachers. Led by the school principal, site councils (which ranged in size from 9 to 32 members) had decision-making authority over the school’s discretionary spending, 80% to 90% of the budget. Other powers included the selection of textbooks, the number of teachers, and class content and size. In Miami-Dade, SBM also led to resource efficiency as schools experimented with staffing and class size. One Miami high school increased the school’s discretionary fund from $90,000 to $125,000 by making the decision to increase the average class size by one, rather than hiring a single additional teacher. Through the savings, the school was able to increase its technology resources. The Miami-Dade school district also improved its efficiency by reducing middle management and allowing each school to report directly to the central office administrators. In part, to resolve a teachers’ strike, the Los Angeles Unified School District, like Miami-Dade, adopted an administrative decentralization model in 1993, but in Los Angeles, the focus was on empowering teachers. Decision making was pushed down to school leadership councils, which had between 6 and 16 members (depending on the level or the size of the school), and the school principal and a teacher served as cochairs of the council. Half of the council seats were reserved for teachers, with the remaining

spots divided among parents, community members, the school principal, and nonteaching school employees. Because SBM in Los Angeles came on the heels of a long and contentious teachers’ strike, it was difficult for principals and teachers to lead side by side; implementation of SBM was forced and slow. High levels of conflict between administrators and teachers also limited the impact of SBM, unlike Miami-Dade where the reform was a robust collaboration between the district and the teachers union. The changes implemented by the councils in Los Angeles were largely mundane—changes in lunch and recess schedules, granting teachers access to copying machines—and not much related to the core of schooling. The community control or local empowerment SBM model shifts the balance of power toward community groups not previously involved in school governance. In the late 1980s, a 19-day teachers’ strike in Chicago led to a crisis, the outcome of which was the imposition of community control by the state legislature on the Chicago public schools. This mandate led to the creation of school councils with authority over a broad range of powers: personnel, curriculum, and financial decisions. Under this model, an elected council of 11—6 parents, 2 teachers, 2 community representatives, and the principal—governed each school. As part of Chicago’s reform, tenure for principals was replaced with 4-year contracts controlled by the local school councils, which, in effect, placed parents and other community members (who held a majority) in charge of hiring and firing principals. Despite being touted initially as a promising though radical change, having noneducators in charge of the principalship led to mixed outcomes that pushed the principals association to complain that the effectiveness of educators had been compromised. In addition, as the Chicago reform played out over time, sustainability became difficult: Recruiting large numbers of parents and other community representatives to run for election and serve on the council was next to impossible once the reform became more established.

Implementation Challenges Shifting decision-making responsibilities to schoollevel stakeholders presents many challenges, since SBM requires stakeholders to take on new roles and responsibilities. We know from research on decentralized decision making in the private sector that

School-Based Management

school-level decision makers need real decision-making power; site councils or principals need assurance that their decisions will not be overturned by the central office or national government. Past research also suggests that decision makers need training and professional development to prepare them for their new responsibilities. While professional development in content areas is important, decision makers also need training to carry out their new responsibilities, such as skills for organizing effective meetings, bringing people to consensus, and teamwork skills. Decision makers also need access to timely and accurate information to make smart decisions. Do schools have access to their budgets in real time to monitor revenues and costs? What about interim student assessment data that could help guide teacher practice? Over the past decade, as U.S. school districts have adopted more sophisticated management information systems, access to realtime budget information and student performance data has become increasingly more common, as has the focus on using such data to guide decision making. Finally, research on decentralized management in the private sector suggests that SBM participants need to be rewarded (intrinsically or extrinsically) for their efforts; otherwise, the reform initiative will be difficult to sustain over time. Implementation of SBM also tends to change the roles and responsibilities of individuals at the school. Principals, teachers, parents, and community members may find themselves with an increase or decrease in power. For example, teachers may find themselves with increasing decision-making responsibilities beyond their classrooms to include school administrative and operational decisions. At the same time, principals may find their sphere of influence reduced as stakeholders formally not engaged in school decision making take on new roles. Another implementation challenge is that SBM plans may not go beyond a simple transfer of authority. Decentralized management, as we have learned from the private sector, also requires a redesign of entire organizations. As mentioned earlier, work teams in the private sector are created to manage decision making. Similarly, much of the work in SBM schools is delegated to horizontal (gradelevel) and vertical teacher teams, but has the education system been redesigned to support the teacher teams? Has the school schedule been modified to allocate time for teams to meet during the regular school day? Has the district office changed its role from telling schools what to do to helping schools

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do what they think is best? Finally, have school rules and regulations been modified in simple ways to reward schools for efficient use of resources? Can schools carry over their savings in utility usage from year to year, or are savings returned to the district, as schools under centralized systems must do? In summary, many of the implementation challenges with SBM depend on school and district capacity. Past research suggests that effective SBM is associated with capacity building at all levels of the school system; without this investment, the reform will only accentuate inequalities among schools and districts.

Research on SBM Outcomes Across the United States and the world, SBM practices have been implemented primarily to improve student achievement and school performance. However worthy this goal, researchers have been unsuccessful in connecting an authentic link between implementation of SBM programs and outcomes. Researchers studying the effects of SBM on test scores have come up with mixed results. SBM programs take time to affect change. Geoffrey Borman and colleagues, in a 2003 meta-analysis of 232 studies around the implementation of 29 SBM programs in the United States, found the number of years of implementation to be a significant predictor of the size of student achievement effects. The results showed minimal effects on student achievement indicators, such as test scores, until at least 8 years into implementation. In three separate studies performed in different countries, where researchers waited 8 years prior to measuring SBM effects on test scores, there were significant positive effects in two countries, Nicaragua and Mexico; however, no statistically significant effects were found in the third country, Brazil. While some prior research focused on whether SBM reforms have had an effect on student outcomes, most of the research on SBM has employed qualitative research methods to explore the conditions that support implementation of SBM. Research in this realm has delved into topics such as the influences of politics, district support, and school leadership on SBM reform. For example, as implied earlier, the specific form of SBM adopted is inextricably linked to state and district politics. Consider the influence of teachers’ unions on the form of SBM in the Los Angeles and Miami-Dade public schools. District support is another important condition for

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success: Has the district handed over to schools the real decision-making authority? Does the district’s management information system provide schools with easily accessible information on school finances and school/student performance? Has the district altered its role vis-à-vis schools from directors to helpers? In terms of school leadership, did the principal aim to distribute leadership roles across various stakeholders, or did the principal retain a veto over decisions? Did the principal create structures and processes to facilitate teamwork and commitment from a broad group of school stakeholders? In practice, the answers to these key questions have been shown to vary considerably by jurisdiction. The one final condition that emerged to support implementation of SBM was the existence of a higher level instructional guidance system that sets forth curriculum standards by grade level. In the 1980s and the 1990s, under the systemic reform movement, instructional guidance came from the states in the United States; prior to this, the guidance system had typically been the responsibility of local school districts. Helpful to SBM plans, the instructional guidance system established the curriculum standards (what would be taught); SBM schools had the authority to determine how students would be taught. Across all these studies, researchers found that when supporting conditions were present, SBM led to greater curricular and instruction innovations, buy-in and commitment, and operational efficiencies in schools as compared with SBM plans when the conditions were weak or not present at all.

Emerging Issues While SBM continues as a popular reform internationally, in the United States, SBM has largely been replaced by the charter school movement, which features a more radical form of school autonomy in exchange for greater accountability. Charter schools, which are public schools that operate independently from school districts, were created in part to break up the monopoly of public schooling by introducing new service providers into education. Charter schools may be started and run by educators, parents, community groups, and other nonprofit or for-profit organizations. The charter school, which has its own governing board, gives stakeholders at the school level greater decision-making power in exchange for accountability. Charter schools operate under a performance contract (typically for 5 years), and at the end of the period, school performance, governance, and management are evaluated

to decide whether the contract should be renewed or revoked. Vestiges of SBM are also evident in the recent case of state waivers under the No Child Left Behind Act of 2001, which offers states greater decision-making authority in exchange for higher levels of accountability tied to student performance and teacher and principal evaluations. In conclusion, SBM is a reform that has been tried often during most of the past century. In the 21st century, charter schools have become more mainstream, while district reforms built around decentralized management have faded. With the strong federal push for accountability that has taken hold across Republican and Democratic administrations alike, policymakers at lower levels of the system (states, districts) are less willing to grant schools autonomy, except in the case of autonomous charter schools that can be closed down for poor performance. Priscilla Wohlstetter and Eric W. Chan See also Charter Schools; Deregulation; Local Control; Parental Involvement; Teacher Autonomy

Further Readings Barrera-Osorio, F., Fasih, T., Patrinos, H. A., & Santibáñez, L. (2007). Decentralized decision-making in schools: The theory and evidence on school-based management. Washington, DC: World Bank. Borman, G. D., Hewes, G. M., Overman, L. T., & Brown, S. (2003). Comprehensive school reform and achievement: A meta-analysis. Review of Educational Research, 73(2), 125–230. Bruns, B., Filmer, D., & Patrinos, H. A. (2011). Making schools work. Washington, DC: World Bank. Mohrman, S. A., & Wohlstetter, P. (1994). School-based management: Organizing for high performance. San Francisco, CA: Jossey-Bass. Murphy, J., & Beck, L. (1995). School-based management as school reform: Taking stock. Thousand Oaks, CA: Corwin Press. Robertson, P. J., Wohlstetter, P., & Morhman, S. A. (1995). Generating curriculum and instructional innovations through school-based management. Educational Administration Quarterly, 31, 375–404. Rubenstein, S. A. (2000). The impact of co-management on quality performance: The case of Saturn Corporation. Industrial and Labor Relations Review, 53(2), 197–218. Winkler, D., & Gershberg, A. I. (2000). Education decentralization in Latin America: The effects on the quality of schooling. In S. J. Burki, G. E. Perry, F. Eid,

Schools, Private M. E. Freire, V. Vergara, & S. Webb (Eds.), Development in Latin America: Decentralization and accountability of the public sector (pp. 203–228). Washington, DC: World Bank. Wohlstetter, P., & McCurdy, K. (1991). The link between school decentralization and school politics. Urban Education, 25(4), 391–414. Wohlstetter, P., & Morhman, S. A. (1993). School-based management: Strategies for success (Finance Brief No. FB-02). New Brunswick, NJ: Rutgers University, Consortium for Policy Research in Education. The World Bank. (2007). What is school-based management? Washington, DC: Author.

SCHOOLS, PRIVATE Schools in the United States can be categorized as either public or nonpublic. Private schools, by definition, are nonpublic and operated by private entities. The history of private schools in the United States shows that most of them have been religious in origin. Private schools include religious schools, nonreligious or nonsectarian schools, and independent schools, which tend to be nonreligious and are run by a board of trustees. As of the 2011–2012 academic year, 68% of private schools were religious in orientation. Among these schools, 22.3% of schools are Catholic, 46.1% are run by other denominations, and 31.7% are nonsectarian. This entry focuses on private schools that serve PreK-12 education, although the scope of private schools ranges from private nursery and prekindergarten to institutions for postsecondary education. In this entry, prekindergarten enrollment is included in elementary school enrollment. Following a brief description of the emergence of private schools and funding source changes, the entry goes on to discuss legal issues related to private school education in the early era, enrollment changes in private schools, state regulation of private schools in terms of accreditation and teacher certification, and issues in public funding for private schools.

The Emergence of Private Schools in the United States The first schools in the United States started as local private entities, and the majority of the schools were founded on the basis of religious orientation by Catholic missionaries during the 17th and the 18th centuries. Some of the oldest private schools still remain, whether they later became independent,

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public, or remained the same. One of the oldest is Collegiate School, founded in 1628 in what is now New York by the Dutch West India Company and the Classis of Amsterdam. Collegiate School is no longer church directed, although it maintains a relationship with the Collegiate Churches. It remains as an independent day school and is run by a board of trustees, offering K-12 education for boys. Hopkins Grammar School was founded in Connecticut by Edward Hopkins in 1660 on the New Haven Green, was resettled in 1926 in the present location, on a hill overlooking the city, and is now called Hopkins School. It is a coeducational independent school serving 7th to 12th grades. In 1664, the private Hopkins Academy was founded in Hadley, Massachusetts, to prepare boys for college; it later became a public high school. In 1778, Phillips Academy was founded in Andover, Massachusetts. It remains an independent boarding school serving 9th- to 12th-grade students and providing boarding and day programs. Other private schools were established during the Colonial era that still thrive, retaining their academic legacy and, in some cases, their religious origins. Until the mid-19th century, there were no clear distinctions between private and public schools, and private schools often received local and state government assistance, such as tax dollars, land subsidies, or tuition aid. Meanwhile, during the mid-19th century in the northern states and the late 19th century in the southern states, the common school movement emerged and sought free elementary education for all children. As the common school movement surged, public school enrollments increased rapidly, and thus, state and local tax dollars were provided to fund mainly public schools. Consequently, it became necessary for private schools to fund themselves. Currently, the vast majority of funding sources for private schools are not public tax dollars but tuition, grants, endowments, and donations from individuals, business organizations, and religion-affiliated organizations. Thus, the differences in funding sources became an explicit distinction between private and public schools. The notion of using public funds for private schools became one of the most controversial issues spurring societal debate, which is ongoing. Moreover, the idea of providing public funds for educational voucher programs remains controversial.

State Education Laws and Parental Rights for Child Education In the early 20th century, the impact of World War I and the spirit of nationalism spurred the nation-

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wide growth of American public schools. In some states, education laws encountered a conflict with parents’ rights to direct their children’s education, including school choice. In the 1920s, a Nebraska statute banned teaching a foreign language to students until they had reached ninth grade. Despite the law, Robert Meyer, who was an instructor in a parochial school, taught a 10-year-old boy the German language. In 1923, the U.S. Supreme Court upheld Meyer’s right to teach German and parents’ right to control their children’s education in Meyer v. Nebraska. The Oregon Compulsory Education Act of 1922 mandated that all children between ages 8 and 16 years in Oregon attend a public school. In 1925, however, the U.S. Supreme Court declared the Oregon law unconstitutional on the basis of the Fourteenth Amendment, ruling in Pierce v. Society of Sisters of the Holy Names of Jesus and Mary that parents have the right to choose private schools for their children’s education. In 1927, the Supreme Court declared a Hawaiian law unconstitutional, thereby upholding parents’ right to direct their children’s education in Farrington v. Tokushige. Today, statutes in all 50 states reflect a parent’s right to choose a private school. Regarding compulsory education laws, private school attendance is considered an exception or acceptable alternative to the public school attendance mandate. In the meantime, homeschooling is another emerging alternative form of education.

Private School Enrollment The number of students enrolled in private schools in the United States increased until the mid-19th century; however, since then, private school enrollments have fluctuated. Because of the common school movement, student enrollments in private schools dramatically plummeted until the beginning of the 20th century. In the early 20th century, the impact of World War I and nationalism spurred the growth of American public schools, while some religious private schools struggled for survival. Thereafter, private school enrollments rapidly increased during and after World War II, rising to 5.9 million, or 13.9% of the total elementary and secondary school enrollment, for the school year 1959–1960. Private school enrollment increased to 6.3 million in 1965, decreased to 5.36 million in 1970, and then declined again. It reached 5.33 million in 1980 and increased again to 6.3 million in 2001 comprising 11.7% of the

total elementary and secondary school enrollment. Subsequently, it decreased, declining to 5.5 million in the fall of 2009, with the greatest drop resulting from an enrollment decrease in Catholic schools. Total private school enrollment is projected to be 5.3 million in 2021. According to Stephen Broughman and Nancy Swaim, as of 2011–2012, the student enrollment of private elementary and secondary schools was 4.5 million in 30,861 private schools across the United States. Among these schools, 44% have fewer than 50 students, and the average size is 146 students across all private schools. In 2011–2012, approximately one third of private schools (10,212) were not members of any private school association. Across all private schools, the average pupil-teacher ratio was 10.7 to 1. As of 2011–2012, 70% of private schools emphasized regular elementary and secondary education, and the rest of them variously emphasized the Montessori program, a special program emphasis, special education for students with disabilities, an alternative program, or early childhood education. Montessori schools offer instruction using Montessori teaching methods. Private schools with a special program emphasis focus on areas such as science and mathematics, performing arts, gifted and talented education, and foreign language immersion. Alternative schools provide a curriculum designed for alternative or nontraditional education. For instance, alternative schools provide preemployment skills training, computer literacy, food service classes, or a basic high school curriculum for students who have dropped out or have difficulty in traditional high school settings.

State Regulation of Private Schools The regulation of private schools remains a prerogative of state governments. State regulation of private schools varies across the country. No two states have exactly the same regulations. Rather, each state’s statutes reflect that state’s contexts, policy perspectives, and circumstances. This section briefly describes state regulation in terms of accreditation, teacher certification, and public funds used for private schools. Accreditation

Private school accreditation is not required in 19 states, whereas it is voluntary in 30 states, the District of Columbia, Puerto Rico, and the Virgin

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Islands. Nebraska, however, requires a private school to be accredited, to be approved, or to receive an exemption from requirements for private school operation. Teacher Certification

Requirements for teacher certification for private schools vary across the country. Only Louisiana, Nevada, North Dakota, and the Virgin Islands mandate that all private schools meet the teacher certification requirements. Twenty states require that specified private schools must meet teacher certification requirements. In five states—Michigan, Minnesota, Ohio, Oregon, and Pennsylvania— teacher certification for private schools is optional. Twenty-two states and the District of Columbia do not require teacher certification for private schools. Public Funds Use

The use of public funds for private schools varies across the country depending on each state’s circumstance, concerns, and characteristics. For instance, the California Constitution prohibits public support for a school controlled by any religious entity (California Constitution, art. XVI, sec. 5). In contrast, as of 2009, 27 states and the Virgin Islands permit students and teachers at all private schools access to publicly funded transportation services. While Missouri state policy prohibits private school students and teachers from access to public transportation, in another five states—Iowa, Kansas, Louisiana, New Hampshire, and North Carolina— only specified students and teachers at private schools are allowed access to publicly funded transportation. Meanwhile, 17 states allow free loaning of textbooks to private school students. State statues reflect U.S. Supreme Court decisions on the types of permissible public funds for private schools. Jeongmi Kim See also Educational Vouchers; Homeschooling; Private Contributions to Schools; Private School Associations; Schools, Religious; Tuition and Fees, K-12 Private Schools

Further Readings Brimley, V., Verstegen, D. A., & Garfield, R. R. (2012). Financing education in a climate of change (11th ed.). Upper Saddle River, NJ: Pearson Education.

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Broughman, S. P., & Swaim, N. L. (2006). Characteristics of private schools in the United States: Results from the 2003–2004 Private School Universe Survey (NCES 2006–319). Washington, DC: U.S. Department of Education, National Center for Education Statistics. Broughman, S. P., & Swaim, N. L. (2013). Characteristics of private schools in the United States: Results from the 2011–12 Private School Universe Survey (NCES 2013–316). Washington, DC: U.S. Department of Education, National Center for Education Statistics. Retrieved from http://nces.ed.gov/pubsearch Hunt, T. C., & Carper, J. C. (2003). Private schooling. In J. W. Guthrie (Ed.), Encyclopedia of education (2nd ed., Vol. 5, pp. 1922–1930). New York, NY: Macmillan Reference USA. Kaestle, C. F. (2008). Victory of the common school movement: A turning point in American education history. In Historians on America. Washington, DC: U.S. Department of State. Retrieved from http:// iipdigital.usembassy.gov/st/english/publication/ 2008/04/20080423212501eaifas0.8516133.html#ixzz2 qjLzNeUi Odden, A. R., & Picus, L. O. (2014). School finance: A policy perspective (5th ed.). New York, NY: McGraw-Hill. Snyder, T. D., & Dillow, S. A. (2013). Digest of education statistics 2012 (NCES 2014–015). Washington, DC: U.S. Department of Education, National Center for Education Statistics, Institute of Education Sciences. U.S. Department of Education, Office of Innovation and Improvement. (2009). State regulation of private schools. Washington, DC: Author. Retrieved from http:// www2.ed.gov/admins/comm/choice/regprivschl/ regprivschl.pdf

Legal Citations Farrington v. Tokushige, 273 U.S. 284 (1927). Meyer v. Nebraska, 262 U.S. 390 (1923). Pierce v. Society of Sisters of the Holy Names of Jesus and Mary, 268 U.S. 510 (1925).

SCHOOLS, RELIGIOUS In the United States, various religious denominations organize and operate elementary, middle, and high schools through parish churches or religious communities. Catholic schools are the most common, but Lutherans, Episcopalians, Mennonites, Jews, and various Fundamentalist Christian denominations have also established and run a

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significant number of religious schools. Religious schools at the K-12 level are often referred to as parochial schools, even when they are not run by a parish, but the term is not usually used when referring specifically to Jewish, Muslim, or other non-Christian religious schools. According to the National Center for Education Statistics, as of the fall of 2013, roughly 9% of all prekindergarten through 12th-grade (PreK-12) students attend private schools. Of these private school attendees, 80% attend religious schools, of which 54% attend Catholic schools. In the context of education economics and finance, religious schools are the primary providers of PreK-12 education in the United States, apart from traditional public schools. Therefore, the values, efficacies, and criticisms of these schools are often at the center of debates surrounding the development and implementation of private school choice policies (e.g., vouchers and private school tax credits). Unlike charter schools, Catholic schools and other religious schools explicitly incorporate faith-based instruction into their curricula. However, although these schools primarily serve the children belonging to a parish or congregation, they typically enroll a substantial number (and, in some cases, even a majority) of students who are not members of the congregation or even from the same religious background. The rest of this entry provides a brief history of religious schools in the United States, an overview of the research examining the effectiveness of these schools, and some criticisms of religious schools.

Brief History During the colonial period, elementary schooling in America was entirely left to communities. Since communities were often established by immigrants who shared a common background, almost all primary schools at that time were essentially parochial. Circumstances changed in the middle of the 19th century with the growth of urbanization stemming from massive waves of immigration. This influx would coincide with the growth of the Common School Movement as well as the shift to tax-supported public schools in the middle of the 19th century. Advocates of common schooling, such as Horace Mann, feared that the new wave of immigrants posed a threat to the preservation of a democratic society if these new Americans were not properly assimilated.

Although the public, common schools of the midto late 19th century were described as being nonsectarian, Protestant values were pervasive throughout the curricula. Students were often required to read and learn from the King James Version of the Bible and sing Protestant hymns. Catholics, in particular, believed that these types of requirements threatened the preservation of their beliefs and cultures, and Catholic parishes, along with religious orders, established their own schools to provide a Catholic education. As enrollment in parochial schools began to flourish in the latter part of the 19th and early part of the 20th centuries, private, religious schools faced major challenges to their survival. Despite the shift to nonsectarian, tax-supported public schools in the earlier part of the 19th century, parochial schools were often still supported by public tax dollars. However, the growth of nativism and anti-Catholic sentiments would lead to the proposal of a federal constitutional amendment, commonly referred to as the Blaine Amendment. This proposed amendment forbade the government from providing aid to any religion-based institutions, including schools. While the Blaine Amendment failed to pass at the federal level, the principle has been incorporated in a majority of state constitutions. The U.S. Supreme Court has ruled on some of the major controversies surrounding the constitutionality of religious schools and faith-based education in general. In Pierce v. Society of Sisters (1925), the Court unanimously ruled that states did not have the right to prohibit private school education by making public school attendance compulsory. The protections of religiously based education would be further defined by the Wisconsin v. Yoder (1972) ruling that the free exercise of religion overrides the rights of states to mandate compulsory education for students after eighth grade. More recently, the question of whether parents could use public tax dollars, in the form of vouchers, to send their children to private religious schools was addressed in Zelman v. Simmons-Harris (2002). In a 5–4 ruling, the Supreme Court held that private school tuition vouchers, even when used to attend religious schools, did not violate the Establishment Clause because enrolling students in religious schools was a parental choice as opposed to a state directive. However, this ruling has not prohibited voucher programs from being ruled unconstitutional at the state level. Even after the Zelman v. SimmonsHarris ruling, state courts have struck down these

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programs, citing contradictions with the state’s education law (e.g., Florida in 2006) and funding systems found to be unconstitutional (e.g., Louisiana in 2013).

Enrollment Trends and Responses The peak of religious school enrollment in the United States took place in the middle of the 20th century. According to the National Catholic Education Association, Catholic schools enrolled more than 5.2 million students in the early 1960s, but as of the 2012–2013 academic year, they enrolled slightly more than 2 million students. Some of the major factors associated with this sharp decline in enrollment include the decrease in Catholic religious vocations, the rise of urban sprawl, and the expansion of charter schools. Early Catholic schools were almost entirely staffed by religious priests, sisters, and brothers, who worked for very low wages. The decline in these vocations since the middle of the 20th century has made these schools more dependent on lay faculty and staff, leading to increases in the cost of tuition. Currently, 97% of Catholic schools’ full-time employees are lay men and women. The decrease in vocations has coincided with the general increase in urban sprawl. As families moved to the suburbs, they were less likely to enroll their children in parochial schools, because these schooling options were not as widely available outside the inner cities and suburban public schools were more appealing than those in the inner city. Inner-city parents looking for alternatives to traditional public schools now also have more options beyond parochial schools. The increase in the number of charter schools, especially those located in the inner city, has led to greater competition, which has also caused a decline in parochial school enrollments. As a result of financial pressures, many parochial schools have developed several approaches and strategies for maintaining or increasing enrollments. A primary source of revenue is the parish, diocese, or religious institution that supports the school. Donations from congregations are often used to offset tuition costs. Advocates for parochial schools often support private school vouchers and tax credits as means for offsetting tuition costs for families. Other noteworthy responses include the Diocese of Memphis’s decision to emphasize its mission of serving disadvantaged students, Catholic and non-Catholic, and soliciting donations from religious as well as nonsectarian philanthropists and foundations.

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The Diocese of Wichita has implemented a stewardship model, in which tuition is free for all families of the parish, and depends entirely on parishioner tithing to cover the costs. Cristo Rey schools, part of a national network, serve disadvantaged high school students and raise tuition through contracted workstudies, where students spend part of the school week working with local corporations and businesses to subsidize their own tuitions. Probably the most controversial response to the threat of shutting down is the decision by some parochial schools to convert to charter school status. This response requires that a parochial school shed its religious identity, but in return, it becomes eligible to apply for public charter school status and can obtain greater public funding.

School Effectiveness A substantial amount of research has been dedicated to determining the effectiveness of Catholic schools as well as other religious schools. William Jeynes has conducted a meta-analysis that includes 90 studies examining the effects of religious, charter, and traditional public schools. The majority of studies find that students who attend religious schools typically perform significantly better than their public school counterparts in terms of test score gains and graduation rates. These results, however, are not always found to be significant when controlling for students’ backgrounds and demographics. Moreover, even when controlling for students’ observable characteristics (e.g., socioeconomic status), it remains uncertain whether the effects can be attributed to these schools as opposed to other, unobserved characteristics associated with the selection into private schooling (e.g., parental motivation and involvement in the student’s education). More simply put, it may be the case that students in Catholic schools benefit from parents who value education so much that they are willing to spend tuition dollars; thus, the relatively high performance of these students may be due to parental support rather than to the effect of the schools themselves. Nevertheless, Catholic schools are typically found to have significant, positive impacts on educational attainment and future wages for urban minority students. Based on empirical evidence as well as qualitative research, James S. Coleman has argued that the successes of parochial schools are attributed to their high rates of social capital. Coleman (1987) defines social capital in an education context as “the norms, the social networks, and the relationships between adults

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and children that are of value for the child’s growing up. Social capital exists within the family, but also outside the family, in the community” (p. 36). He argues that social capital is highly predictive of educational outcomes, especially the likelihood that a student will drop out of school. Parochial schools are able to maintain high levels of social capital through the communities formed within parishes. Students whose families regularly attend religious services are typically less likely to drop out of school, and those who are religiously observant and attend parochial schools are significantly more likely to graduate. Outcomes are not as positive for comparable students attending independent private schools.

Controversies and Concerns In the context of school choice policy debates, whether or not parents should have access to vouchers or tax credits to enroll their children in Catholic or other religious schools is strongly contested. The results from public opinion polls tend to vary depending on the construction of the survey items. However, the general public tends to approve of public charter school policies at much higher rates than private school choice proposals. This evidence could be used to make the case that publicly funding private, religious education is not very politically popular. Another major conflict with regard to religious schools (and private school choice in general) is whether these schools promote or inhibit pluralism. Critics of these schools and universal choice argue that potentially increasing enrollment at these schools would undermine democratic ideals. The logic of this argument is that in a democratic society, the public should debate and discuss how tax dollars should be used to best educate students. Moreover, critics also worry that choice would lead to socioeconomic, racial, and religious segregation as a result of sorting and private schools possibly being able to discriminate when it comes to student admissions. Proponents of choice policies that include private religious schools contend that parents should have the right to educate their children how they see fit. They argue that public schools have curricula and values that could be at odds with the values of some parents and guardians. With regard to sorting and segregation, advocates often contend that parental choice overrides diversity prerogatives; they also argue that traditional public schools tend to already be very segregated.

One other debate over private religious schools is the extent to which they should be granted autonomy. Common issues include whether these schools should be required to have certified teachers for core subjects, whether students at these schools should have to participate in the same standardized testing requirements as their public school counterparts, and whether teachers need to adhere to state standards. Religious school autonomy is a major concern when constructing school choice policies. Some school choice advocates are actually leery of voucher programs that require religious schools to adhere to certain policies when accepting public funding. For example, some choice policies in the United States require that all participating private schools give standardized tests and share results. As a result, some choice advocates support the use of tuition tax credit programs over state-provided vouchers. Daniel H. Bowen and Gary Ritter See also Educational Vouchers; Homeschooling; Schools, Private; Tuition Tax Credits

Further Readings Bryk, A. S., Lee, V. E., & Holland, P. B. (1993). Catholic schools and the common good. Cambridge, MA: Harvard University Press. Coleman, J. S. (1987). Families and schools. Educational Researcher, 16(6), 32–38. Jackson, R. (2003). Should the state fund faith based schools? A review of the arguments. British Journal of Religious Education, 25(2), 89–102. Jeynes, W. H. (2012). A meta-analysis on the effects and contributions of public, public charter, and religious schools on student outcomes. Peabody Journal of Education, 87(3), 305–335. McDonald, D., & Schultz, M. M. (2013). U.S. Catholic elementary and secondary schools 2012–2013. Washington, DC: National Catholic Education Association. Meyer, P. (2007). Can Catholic schools be saved? Education Next, 7(2), 13–20. Neal, D. (1997). The effect of Catholic secondary schooling on educational attainment. Journal of Labor Economics, 15(1), 98–123. Viteritti, J. P. (1999). Choosing equality: School choice, the Constitution, and civil society. Washington, DC: Brookings Institution Press.

Legal Citations Pierce v. Society of Sisters of the Holy Names of Jesus and Mary, 268 U.S. 510 (1925).

Selection Bias Wisconsin v. Yoder, 406 U.S. 205 (1972). Zelman v. Simmons-Harris, 536 U.S. 639 (2002).

SEGMENTED LABOR MARKET See Dual Labor Markets

SELECTION BIAS Selection bias describes any threat to validity because subjects are included, treatment allocated, or outcomes measured for a nonrandom group or a nonrandom subset of the population to which a researcher wishes to generalize. This bias arises from the fact that the characteristics of participants that influence their inclusion in a study or selection into treatment may also affect the outcome being investigated. Selection bias is a central concern in empirical research in the economics of education, where researchers cannot perfectly control access to treatments of interest or measure all attributes of students or schools that affect the outcomes. This entry discusses the implications of selection bias for inference and describes three prominent types of selection bias. The final section discusses the role of research design in experimental and observational studies in limiting selection bias and its subsequent problems. In the presence of selection bias, any observed differences between a treatment group and a comparison group could be due to a treatment effect or a selection effect. The treatment effect is what a social scientist or analyst wants to understand (or identify, in the evaluation nomenclature). The selection effect describes any differences between treatment and comparison groups (ex ante or ex post) that are due to the processes or factors determining whether a subject is in the treatment or comparison group or whether the subject is included in the study at all. To understand the source and implications of selection bias, it is useful to distinguish between three different types common in education settings: (1) sampling bias, (2) attrition bias, and (3) assignment bias.

Sampling Bias/Sample Selection Bias Sampling bias occurs when study subjects are enlisted, enrolled, or measured in a way that is not

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representative of the population for which an intervention is relevant. This problem is common in the evaluation of education. For example, in attempting to understand the impact of a new curriculum for students, researchers might recruit students, teachers, or schools to employ the curriculum. The recruitment process could introduce sampling bias if students, teachers, or schools that expect to derive atypical benefit from the new curriculum are more likely to participate. Even if random assignment is used to determine which potential subjects receive the treatment curriculum and which serve as controls, the recruitment process introduces bias if the recruits are not representative of the full population. If the recruits are more likely than others to benefit from a new curriculum (e.g., because the recruits are motivated), a resultant bias might lead the researcher to falsely conclude that the curriculum imparts learning gains larger than would be observed in the absence of sample selection bias. Or it could be that the recruits are less likely to benefit from a new curriculum: Poor management or teaching not only could lead to underperformance and the need to try a new approach but also can lower the chances that any approach will be successful. If this is the selection process, the researcher might wrongly conclude that the new curriculum has no effect. In either case, the average treatment effect has not been identified for the students relevant to the curriculum and to the researchers.

Attrition Bias Attrition bias occurs when the subjects in a study are observed over time and outcomes for only a nonrandom subset of those in the treatment and/or control groups are available. If subjects for whom a full set of outcome measures are available are more (or less) likely to benefit from treatment, or are atypical members of the control group, differences between the treatment and control groups overtime could be due to treatment or selection effects. For example, imagine a researcher studying the effects of a program to improve conflict resolution skills for high school students and thereby reduce disciplinary infractions. If the program requires several sessions and the research design requires measuring outcomes overtime, the task of understanding the program effects would be made complicated if some participants withdrew before the end of the program or some in the study left the school altogether before all the outcome data could be collected. Inability to

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measure outcomes overtime for either the treated or the control group could mean that those who remain are subjects with atypical rates of disciplinary problems, and this may not be independent of membership in the treatment or control group. In this case, the researcher will not be able to ascertain whether changes in subsequent rates of disciplinary infractions for those in the treatment and the comparison group are due to the benefits of the program itself or the related processes that determine who completes the program and who remains in the study setting.

Assignment, Heterogeneity, or Omitted Variables Bias Assignment bias describes situations in which members of a treatment group are ex ante different from members of a comparison group in ways that cannot be measured by the researcher. This type of bias is often viewed as the principal type of selection bias and is increasingly becoming synonymous with the general term. Assignment bias occurs when those receiving an intervention have characteristics the researcher cannot observe that themselves affect the outcomes of interest. In the presence of such heterogeneity, the observed differences in outcomes could be due to observed differences in treatment or to these unobserved factors. A classic example in education settings is the empirical problem of identifying the earnings effects of education. Research on this question relies on observational data, and the researcher has no role in determining the level of education of subjects. Even when conditioning on observable characteristics, differences between subsequent earnings of those with higher levels of education and those with less education can be due to the effect of education on earnings or to a selection effect resulting from processes leading those with atypical levels of ability, connections, foresight, or other unmeasured factors to choose higher levels of education. Since these other factors are not measured, any role they have in determining earnings could be falsely attributed to education. The result is a bias in the estimate of the impact of education itself on earnings by confounding the relationship being studied with the effects of these other, omitted variables. A necessary task in research design is to limit threats from selection bias. In an ideal setting, a researcher with complete control of the subjects, setting, and treatment can carry out a study where the expected selection effects are zero. This would require random selection of subjects from the

population of interest, random assignment of the treatment to the subjects, perfect compliance on the part of treatment and control subjects, protection from extraneous influences, and no (or entirely random) attrition. This ideal is generally not possible in education settings, where the subjects are students, teachers, or schools with objectives, constraints, and agency that are naturally independent of the researcher’s interests. Nonetheless, researchers in education are increasingly utilizing randomization as a means to define treatment and comparison groups when studying interventions over which they have control. Furthermore, in observational settings research designs that make use of natural experiments and transparent variation in assignment to “treatment” are ever more common and valued. Important examples of methods that can help limit selection bias in observational studies include regression-discontinuity designs and instrumental variables regression, among others. While employing sound principles of design constitutes a necessary defense, potential threats to inference due to selection bias are an inherent hazard in empirical research. Researchers and readers alike need to be aware of the sources and consequences of these threats and thoughtful about the ways the research setting and design and can limit them. Dave E. Marcotte See also Credential Effect; Instrumental Variables; Randomized Control Trials; Regression-Discontinuity Design

Further Readings Murnane, R. J., & Willett, J. B. (2011). Methods matter: Improving causal inference in educational and social science research. New York, NY: Oxford University Press. Wooldridge, J. (2010). Econometric analysis of crosssection and panel data (2nd ed.). Cambridge: MIT Press. Wooldridge, J. (2013). Introductory econometrics: A modern approach (5th ed.). Mason, OH: South-Western.

SERRANO V. PRIEST The Supreme Court of California’s 1971 ruling in Serrano v. Priest, commonly referred to as Serrano I, represents the first major legal decision by a state Supreme Court declaring a state’s education funding

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system to be unconstitutional. In Serrano I, the state supreme court ruled that the state’s school funding system violated the equal protection clause of the Fourteenth Amendment of the U.S. Constitution as well as the equal protection clause of the California Constitution. More specifically, the Serrano I decision recognized that a student’s right to a public education in the United States is a fundamental interest that cannot be based on wealth. The California Supreme Court found no compelling state purpose justifying the state’s spending differences between its wealthier and poorer school districts and therefore deemed unconstitutional the state’s education funding system. This entry discusses one of the leading state-level school finance litigation cases of the early 1970s and how it challenged the existence of significant school funding inequities created by the U.S. tradition of disproportionately relying on local property taxes as the primary source of revenue for public schools.

Serrano I and Serrano II Rulings On August 30, 1971, the California Supreme Court in Serrano v. Priest ruled that the state’s public school financing system, which made district-level spending a direct function of a district’s wealth, was unconstitutional. The plaintiffs in the class action lawsuit, who were several Los Angeles public school students and their parents, brought two legal causes of action in their suit against the defendants—the California state and county officials charged with administering and financing the state’s public school system. The two primary causes of action were as follows: (1) based on the state’s education funding system, educational opportunities for students attending public schools in poorer public school districts were substantially inferior to those for students studying in wealthier public schools and (2) the state education funding system allowed parents in certain school districts to pay at a higher tax rate compared with taxpayers in other school districts while receiving the same or lesser educational opportunities. Fundamental to understanding the constitutional issues in the Serrano case is an understanding of the state’s education funding system, in which local property taxes provided more than 90% of the state’s school financing. The local property tax has long been the primary source of funding for schools. Legal challenges, such as Serrano, brought under the equal protection clause of state constitutions have attempted to reduce variations

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across public schools in per-pupil spending and ultimately weaken the direct link between local property tax wealth and public school spending levels. School districts in California have widely varying levels of assessed property values, and districts with higher property values are able to tax residents at a lower rate to raise the same amount of money. While local public school districts continue to receive significant revenue from property taxes in California, it is due directly to the Serrano I, the subsequent Serrano II decisions and enactment of Proposition 13 that the property tax has essentially become a state tax that is levied both more equitably and more uniformly across public school districts. The Supreme Court of California arrived at its decision based on the fact that students in wealthier communities attended schools with higher funding levels than did children in poorer communities. For example, during the 1968–1969 school year, the poorer Baldwin Park Unified School District spent $577.49 per child compared with the wealthier Beverly Hills Unified School District, which spent $1,231.72 per child to educate its students. The state supreme court recognized that the significant funding differences in per-pupil expenditures were directly related to a particular district’s property tax base. Furthermore, the court detailed in its decision that these property tax disparities resulted in inequalities in expenditure, or spending levels per student, since public school districts with higher property values could generate more funding with lower property tax rates. At the time, the court indicated that there was no mechanism in place for reducing funding disparities between wealthier and poorer public school districts. In 1972, 1 year after the Serrano I decision, the California State Legislature passed Senate Bill 90, which established a system of revenue limits, to address these disparities. The revenue limit funding formula determined the amount of state and local revenue schools could receive for general purposes and restricted revenue growth in wealthier school districts. The state supreme court found the state’s education funding system unconstitutional based on two legal considerations. First, public education in California, as in most states, is considered a fundamental right; and second, a public school district’s wealth, often measured in terms of its property tax base, is constitutionally a suspect classification, where the “funding scheme invidiously discriminated against the poor because it made the quality of a child’s education a function of the wealth of his parents and neighbors” (Serrano v. Priest,

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p. 1244). Instead, the court used a strict-scrutiny constitutional review approach and rejected the state’s governmental interest argument that relating per-pupil expenditures to the assessed property value of a school district is necessary as a means to maintain local control. Two years after the 1971 Serrano I ruling, the U.S. Supreme Court held in San Antonio Independent School District v. Rodriguez (1973) that the U.S. Constitution’s Equal Protection Clause does not apply to the nation’s public schools. In essence, the legal impact of the Rodriguez mandate was that it prevented any litigants from using the Equal Protection Clause of the Fourteenth Amendment as a legal means for school finance reform. Five years after striking down the state’s education funding system as unconstitutional, the Supreme Court of California revisited the legal issue of the validity of using the state constitution’s equal protection clause as a means of school finance reform. In its 1976 ruling, commonly referred to as Serrano II, the state supreme court held that the school finance reform legislation passed in response to the Serrano I decision was inappropriate. The court in Serrano II charged the state legislature with the legal mandate that both the state’s property tax rates and perpupil expenditures be equalized. More specifically, the court directed the state legislature to equalize property tax rates and per-pupil expenditures and ensure that, by 1980, the difference in base revenue limit funding per pupil be less than $100. This range of per-pupil funding, often referred to as the Serrano band, would increase to $300 by the year 2000 as a means to adjust to inflation.

Impact of Serrano on School Finance Instead of equalizing district spending commensurate with that in high-wealth public school districts, the California State Legislature began equalizing public school funding levels down to the spending levels of low-wealth public school districts. In 1978, California voters passed Proposition 13, which limited property tax rates to 1% of the cash value of real property. A major impact of Proposition 13 was that it led to significantly lower spending levels by California’s public schools. The state increased its support of public schools to offset the loss of property tax revenue from Proposition 13, which further shifted school funding away from local districts. It is important to recognize that the Serrano I and II legal rulings did not find the use of property

taxes as a means to finance public schools unconstitutional per se. States can use local property taxes to assist the financing of public schools. Rather, state education funding systems that rely disproportionately on local property taxes to finance public schools have an increased risk of being considered unconstitutional because such school funding systems have higher inequities compared with school funding systems that equalize access to school resources. As a result of the Serrano rulings, plaintiffs in nearly half of the states in the country filed lawsuits seeking reform of their states’ education funding systems. The legal significance of the Serrano decision is evident in the resulting challenges to state school finance systems based on these states’ overreliance on local property taxes and the wide disparities in taxable property among public school districts. Kevin P. Brady See also Property Taxes; San Antonio Independent School District v. Rodriguez; School Finance Litigation

Further Readings Fischel, W. A. (1989). Did Serrano cause Proposition 13? National Tax Journal, 42(4), 465–473. Greenbaum, N. W. (1971). Serrano v. Priest: Implications for educational equality. Harvard Educational Review, 41(4), 501–534.

Legal Citations San Antonio Independent School District v. Rodriguez, 411 U.S. 1 (1973). Serrano v. Priest, 96 Cal. Rptr. 601 (Cal. 1971), 135 Ca; Rptr. 345 (Cal. 1976) cert. denied, 432 U.S. 907 (1977).

SERVICE CONSOLIDATION Service consolidation describes the transfer of responsibility for one or more services (e.g., business services, technology administration, human resources) from a local education agency (LEA) to another public sector organization (e.g., other LEAs, educational service agencies, municipalities). As K-12 school districts face uncertain revenue streams, policymakers (e.g., legislators, school board members) consider alternative service delivery models such as service consolidation, with the goal of reduced spending by capitalizing on economies of scale.

Service Consolidation

This entry describes service consolidation from its earliest educational application (i.e., a one-room schoolhouse) to examples from today’s complex school organizations. In addition to reviewing practical applications, the entry goes on to draw on production theory and provides examples where economies of scale do and do not exist. It ends with examples of the motivating factors and potential risks associated with multiple districts consolidating services to a single entity. Historically, K-12 school districts in the United States operated as self-sufficient service organizations, maintaining local control over the provision of all services, quality expectations, and the allocation of resources such as staff, facilities, and funding. For example, students in one-room schoolhouses depended on the teacher to not only provide instruction but also to gather wood, repair broken desks, and sweep the floor. Schools still need services such as heating, repairs, and cleaning, but now school districts have set up departments to handle these operational responsibilities, and many school systems mirror the complex organizational structure of large corporations. While maintaining responsibility for service provision within the LEA is one service model, two alternative models transfer this responsibility outside the LEA: service consolidation and privatization (i.e., outsourcing). As described in this entry, service consolidation differs from privatization in that the responsibility and fee structure(s) remain within the public sector rather than allocating the funds to a private entity. Finally, service consolidation provides some of the same benefits and challenges as the consolidation of entire districts while avoiding many of the economic costs and political challenges faced when merging entire school districts. While smaller districts may not have the resources to fund and staff an entire department, especially in rural communities, the need for the provision of these services remains. One alternative is service consolidation, also referred to as shared services or service collaboration.

Theoretical Implications Production Theory and Scale Economies

Economists have long studied the parallels between manufacturing and educational organizations. Both production theory and the concept of scale economies provide a theoretical foundation for K-12 service consolidation. At its core, production

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theory describes the factors (e.g., processes, costs, materials) related to the production of goods or delivery of services. With K-12 service consolidation, three relevant production factors are at play. In his classic book Principles of Economics, first published in 1890, Alfred Marshall (1961) described “the chief advantages of production [or service delivery] on a large scale as economies of skill, economies of machinery, and economies of supplies” (p. 278). In other words, by consolidating services, LEAs might enjoy the scaling effect of access to improved skill levels (e.g., having the services of a certified public accountant rather than a bookkeeper), efficient scheduling and use of shared machinery (e.g., snow plows, lawn equipment, computer servers), and bulk purchases of supplies for lower prices than a single school or school district would pay. At its core, the first criterion necessary to claim the existence of economies of scale is a reduction in the average cost per unit as the quantity of units produced or serviced increases. For example, when consolidating multiple districts’ lawn services under a single entity’s responsibility, the average cost per acre is expected to be reduced, while increasing the number of acres maintained. The second required criterion to recognize economies of scale is that the service or production quality must remain consistent with earlier output, even after any increase in production quantities. Using the previous example, reducing average per-acre spending by mowing lawns every 2 weeks rather than the prior schedule of every week may reduce spending, but the result is a lawn quality inconsistent with earlier service levels, thus voiding any claims of economies of scale. Since many factors apart from consolidation may influence service spending (e.g., regional wage variances, supplies, equipment), mathematical models called cost functions can be used to estimate the impact of changes in various production factors on spending. In other words, cost functions mathematically relate the cost of factors of production (e.g., inputs such as salaries and supplies) to identified outputs (e.g., cost per acre). However, Henry Levin of Columbia University and Patrick McEwan of Wellesley College suggest that many of these cost functions ignore nonfinancial costs, yet these nonfinancial factors remain an important component of an overall cost model. For example, if a district does not provide bus transportation, this cost in terms of both dollars and time falls to the students and/ or parents since they must now provide transportation at their own expense. In other words, when

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measuring any potential cost savings associated with service consolidation, it is critical that nonfinancial factors be included in the analysis, given that these costs may be merely reallocated to another entity rather than being eliminated.

Resource Allocation While research of school district internal resource allocation is limited, the resource allocation research that does exist primarily focuses on the distribution of federal, state, and local revenue rather than the internal distribution of those funds once received by the LEA. However, recent studies measure and compare spending differences between LEAs that consolidate services and a county-level educational service agency, with this line of research continuing to evolve. Practical Applications

As previously defined, service consolidation differs from outsourcing in that the responsibility and fee structure(s) remain within the public sector rather than moving to a private enterprise. In addition, service consolidation provides some of the same benefits as the consolidation of entire districts while avoiding many of the economic costs and political challenges faced when merging entire school districts. For school districts to consolidate, the constituents in all the affected districts must agree to the consolidation. While the desire to maintain local control remains one of the largest obstacles to passing district consolidation proposals, other factors come into play, especially when one district holds more property wealth or has a higher socioeconomic status than the other. Service consolidation, on the other hand, can be unilaterally implemented by schools boards, with no voter input. These arrangements are typically business agreements, much like private service contracts, where service-level expectations (i.e., quality) and service expense items are defined. Interestingly, where service consolidations exist, anecdotal evidence shows little opposition to this model from labor unions or the community. Motivating Factors to Consolidate Services

Rural school districts have long recognized the need to provide instructional services to a small pool of students. While the demand for Advanced Placement courses may exist for a small number of students within a rural district, the supply of teachers to offer Advanced Placement courses may be

limited. To address this issue, rural districts may form a consortium of districts where one district provides the desired Advanced Placement course and students from multiple rural districts participate remotely via the Internet. Noninstructional services may also provide opportunities for service consolidation. For example, many small districts employ a bookkeeper to manage school finances. Typically, however, the bookkeeper is not a professional accountant trained in the complexities of school finance. Anecdotal evidence suggests that one of the primary factors motivating the consolidation of the accounting function of multiple smaller school districts to a single location is an increasing number of negative financial audit findings. To be clear, this does not suggest that the bookkeeper embezzled or misappropriated funds, but rather, given the increasing complexity of public school finance and public accounting rules, as well as the increasing levels of accountability for state and federal programs, the ability to secure access to a certified public accountant reduces a school district’s risk of future negative audit findings. Negative audit findings may reflect poorly on the school district, cause declines in credit ratings that affect future short-term borrowing, and raise community concerns when the district asks voters to approve a bond proposal. In situations such as the consolidation of business office responsibilities to a single centralized entity, the potential improvement of service quality may offset the desire for potential financial savings. A second noninstructional service ripe for consolidation might be student transportation. In a county with multiple local districts, it is not uncommon for buses from multiple districts to travel similar routes. This is especially prevalent in urban and suburban districts, where bus routes frequently cross district boundaries. A countywide bus program may pose scheduling challenges, but looking at how local public bus systems are scheduled may help determine the most efficient school bus schedules. Another advantage of consolidating transportation is improvement in maintenance service; using a uniform make and model bus allows for a common supply of parts and may minimize bus downtime due to mechanical failure. Furthermore, consolidating bus maintenance service to a single location for multiple districts may require fewer mechanics, or in the alternative, mechanics could focus on preventative maintenance services rather than reacting to mechanical emergencies.

Social Capital

Although not necessarily a motivating factor, the nature of a service plays a role in deciding whether that service is suitable for consolidation. For example, given existing communication technologies (i.e., Internet, VPNs [Virtual Path Number]), consolidating multiple districts’ accounting and payroll services to a single location essentially requires availability of staffing capacity with the service provider. While each district’s collective bargaining agreements may add a level of complexity to the planning and implementation, if capacity exists with the provider, and especially if the provider is already delivering that same service to other districts, there are minimal obstacles to consolidating these services. On the other hand, services such as the operation and maintenance of school district facilities may require workers to go to schools in person. However, scheduling software that helps coordinate and manage resources (e.g., staff, equipment) may provide an opportunity for consolidation in these areas.

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turnover at the centralized location could potentially disrupt operations in all of the participating districts. This obstacle is not one that should be taken lightly since it is a common occurrence. District administrators bear the responsibility to insist on detailed contingency plans as part of the written service consolidation agreement, since the provision of the affected service may have far-reaching negative effects, such as disrupting payroll services and causing late payments to employees. Finally, service consolidation conversations continue around the country, with recent talks occurring in Idaho, Illinois, Maryland, Michigan, Minnesota, and Texas, as school leaders wrestle with service delivery options that maximize the value of each educational dollar. Thomas A. DeLuca See also Contracting for Services; Cost-Effectiveness Analysis; Economic Cost; Intergovernmental Fiscal Relationships; Local Control; Portfolio Districts

Municipal Service Consolidations

When school districts consider service consolidation, especially for noninstructional services, they may find it helpful to review the experience of municipalities that have consolidated services such as 911 call centers or other public safety functions. Some of the claims, challenges, and benefits of municipal service consolidation appear to correlate with school districts’ noninstructional service consolidation efforts. A related service consolidation model may exist between municipalities and school districts. In addition to services, school districts and municipalities can share large equipment, such as that used for construction and snow plowing. While one entity may own the equipment, fee-based sharing can generate additional revenue, especially when the equipment is idle and both entities have a formal agreement related to the equipment’s operation and maintenance. Implications of Service Consolidation

While reduced per-pupil spending is a common expectation of service consolidation, these reductions may not always be realized. However, nonfinancial factors may offset any lack of savings, such as the increased value received through improved service quality. On the other hand, consolidating services from multiple local districts to a single location is not without its risks and obstacles. For example, staff

Further Readings Carrizales, T. J., Melitski, J., & Schwester, R. W. (2010). Targeting opportunities for shared police services. Public Performance & Management Review, 34(2), 251–267. doi:10.2753/PMR1530–9576340206 Duncombe, W. D., & Yinger, J. M. (2007). Does school district consolidation cut costs? Education Finance and Policy, 2(4), 341–375. Levin, H. M., & McEwan, P. J. (2001). Cost-effectiveness analysis: Methods and applications (2nd ed.). Thousand Oaks, CA: Sage. Marshall, A. (1961). Principles of economics. New York, NY: Macmillan for the Royal Economic Society. Tholkes, R. J., & Sederberg, C. H. (1990). Economies of scale and rural schools. Research in Rural Education, 7, 9–15.

SHEEPSKIN EFFECT See Credential Effect

SOCIAL CAPITAL Social capital refers to how individuals use social networks or the relationships between people to foster advantage and activate resources that

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facilitate achievement. As a theory, social capital is used across disciplines including political science, economics, and sociology. In education, social capital is primarily used to explain inequalities among and between groups, such as differences in test scores or college enrollment rates. Prevailing logic suggests that the more social capital one has, the greater is one’s ability to achieve well-being, or the attainment of one’s life needs and aspirations. This entry considers the major definitions of social capital, as well as major empirical evidence and theoretical developments in social capital theory.

What Is Social Capital? Understanding social capital involves recognizing the construct of capital more broadly and how it may lead to better life outcomes, particularly when it comes to addressing inequity. The notion of capital implies that one can invest in certain resources and thus bring about returns on that investment. For example, in an economic sense, individuals and families can use economic capital, such as money in a savings account, to achieve certain ends for improving life, including paying for food and housing, hiring a lawyer, or paying for college. When this idea of capital is applied to social and cultural contexts, the resources that accrue advantages to individuals and families need not be physical or financial. These resources, which can be actively invested in and mobilized as in the economic context, exist within the social relationships between people. In terms of social capital, investing in social relations and actively reaping benefits from it is what moves these relationships beyond just social interaction and into a capital context. The intentional use of a social relationship to enhance one’s wellbeing transforms these interactions into capital. For example, in the economic sense, capital can be used to pay for college, whereas with social capital, a parent’s alumni status from a particular university can be used to mobilize the relevant social networks to bring advantage to a student during the admissions process. Social capital exists within the relations between people, not just with individuals themselves. It is used to connect and strengthen relationships between individuals and groups to achieve explicit intended outcomes. In the case of education, social relationships are intended to gain further access to opportunity that is believed to enhance the pursuit of well-being. Social capital can either bond or bridge—that is, it can reinforce connections between people who

have similar characteristics or facilitate connections between people with diverse backgrounds, respectively. The size, quality, and function of one’s social network have all been used to describe social capital and its effectiveness at achieving ends.

Contrasting Conceptualizations Reviewing and defining social capital as a concept entails tracing its intellectual history. The concept of social capital in educational research is particularly influenced by James Coleman’s and Pierre Bourdieu’s writings from the 1980s. These theories attempted to explain how connections with others brought benefits, including educational ones, to individuals and families. While there are similarities between their conceptualization of social capital, important distinctions exist. Coleman and Social Control

Coleman’s model of social capital emphasizes the level of trust and obligations between people, information channels, and norms and sanctions; this form of social capital is considered to be of a functionalist tradition. Coleman conceived trust and obligations as a “credit slip”—if someone performs a favor, he or she will be owed in the future. The reciprocal element further supports the metaphor of capital—investing actions in others will result in future benefits. Providing or acquiring information from others is also an important social function. Social capital also represents the channels by which individuals acquire necessary information. Finally, a social group’s norms and sanctions will encourage or constrain actions. These ideas suggest that social capital acts as a form of social control among communities. A group with more trust among its members will accomplish more than a group with less. As information flows through this group, positive social behavior is maintained. In education, parents in a neighborhood would have a responsibility to promote behaviors that are important for their child’s success, such as through sharing information important to schooling. This form also reiterates the idea that social capital is a collective good and productive in that it brings about desirable ends. Bourdieu and Social Reproduction

The Coleman form of social capital contrasts with Bourdieu’s model, which emphasizes social reproduction. Bourdieu proposed that social capital is the

Social Capital

amount and quality of social networks in addition to the networks themselves. His writings on social capital fit in with a full model of capital that explains social, and in particular educational, inequality. In this way, social capital is intimately related to economic capital, or the financial resources individuals have, and cultural capital, or the cultural resources and behaviors typically received from the family. Bourdieu’s cultural capital is sometimes used concurrently with social capital in research frameworks; however, they are distinct forms, with cultural capital representing cultural artifacts or behaviors and social capital existing in the relationships and networks between people. Bourdieu suggested that under certain conditions, social capital could be mobilized or converted into economic capital. Coleman’s view saw social capital as related more to producing human capital, or the skills and abilities of individuals. Furthermore, Bourdieu’s model of social capital acknowledges social reproduction as opposed to social control. Social reproduction is the process by which hierarchies and inequalities persist from generation to generation. Social capital is an explanation not just for how relationships confer advantage but also for how these relationships maintain a status quo of inequalities, particularly in the case of schooling. The education system rewards and values forms of capital held by the upper class, or those in dominant positions. Given the advantages deriving from the upper class’s social and cultural resources, which are more closely aligned with the dominant structures of society, the distribution system of social capital is inherently reproductive. Bourdieu’s theory maintains that certain forms of social capital, or certain social relations, are more advantageous in traditional school settings than those possessed by members of middle-class, workingclass, and poor-social-class groups. The selection of criteria for achievement that is more associated with the dominant class is rewarded. Norms and behaviors are not enforced solely to maintain levels of trust but also to further advantage the upper classes. Social capital is thought of as a way for institutions to maintain norms among groups as well as distribute resources among those with the most access.

Social Capital: Empirical and Policy Examples Quantitative and qualitative studies in education have explored the influence of social capital on educational outcomes and students’ experience.

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Common outcomes studied in quantitative work include test scores, grade point average, high school dropout or completion status, and college enrollment. Social capital is operationalized in a variety of ways. Family measures, such as family structure, parental education and expectations, and indicators of parents’ involvement in the school and the child’s work are one common set of independent variables. The size and nature of peer networks are also used to symbolize social capital. Many of these studies generally find positive relationships between social capital indicators and educational achievement or attainment indicators. Qualitative inquiry also examines the role of families and parents in students’ social capital development through the use of case studies and conventional methods of interviews, focus groups, and observations. Studies include those that investigate how parents interact with school administrators and environments or how students navigate school processes, such as calling on peer networks during the college application process. Social capital can also be seen in a practical way, as many educational interventions have a theory of social capital undergirding their implementation. For example, college preparation programs demonstrate one way a social capital framework can be applied to current policies and programs. If some students lack access to the social capital or networks that supply relevant college information, then participating in a college preparation program, such as Upward Bound, can address this gap through activities such as mentorship, college visits, or information sessions. Thus, many educational programs are used to supply disadvantaged students with networks and resources believed to be absent; this line of thinking has been criticized or expanded on in works following Coleman and Bourdieu.

Criticisms and Expansions of Social Capital Theory Although social capital became a widely used theory in educational research, criticisms emerged regarding its applications and implications. Some have observed that social capital is proposed as an allencompassing or “cure-all” explanation for social inequalities—youth do not persist throughout schooling because they lack social capital and, therefore, should be given social capital to succeed—at the expense of a more nuanced theory. Coleman’s model is met with confusion as the sources of social capital are sometimes conflated with the benefits or resources. For example, a college education could be

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seen both as a benefit of having strong social capital and as representing an individual’s amount of social capital in that affiliation with a college and other educated people brings about an advantageous network. This conceptual uncertainty makes using the theory in empirical work more difficult. The directionality between social capital and educational outcomes becomes blurry as well: Does more social capital facilitate academic achievement, or does educational success bring about more social capital? Social capital, along with the related theory of cultural capital, has also been associated with deficit frameworks in education, according to some scholars. Deficit models attribute underachievement to students as individuals as well as their cultural, racial, familial, gender, or class backgrounds. Youths who have low test scores or drop out of high school are seen as simply lacking some resource such as social capital. Portraying marginalized individuals in this way may ignore issues of power and social structures as well as the positive contributions of students’ home cultures. This model also obscures the reproductive nature of social capital. Ignoring how differences among social classes are maintained across generations limits the effectiveness of educational attainment for addressing social inequity and overall well-being. For example, many forms of social capital, such as access to information about college admissions criteria through the college’s alumni network, are passed down from parent to child and are therefore withheld from students who do not have such access. Without a more nuanced conceptualization of social capital as applied to education, the theory is less associated with the attainability of social justice, equity, and well-being. For these reasons, matters such as historical context and power dynamics are necessary for an appropriate application of social capital theory to long-standing issues in education, such as equity and opportunity. Other uses of social capital address this limitation by augmenting the theory with considerations of the interaction between marginalized youth and institutions. Ricardo Stanton-Salazar’s notion of institutional agents—or individuals who have the capacity to transmit institutional resources and opportunities to others—emerged from and contributed to social capital theory. Examples of institutional agents are teachers, guidance counselors, or older peers. Stanton-Salazar’s framework not only emphasizes the structural barriers that keep youths

from accessing key institutional agents but also acknowledges the potential for youths’ agency to acquire social capital. The concept of funds of knowledge, or the skills and knowledge of underrepresented students’ families that are accessible through social networks, is another extension of the theory. When integrated with the traditional theory discussed above, the notion of funds of knowledge moves social capital from a dichotomy of haves and have-nots to a spectrum of varied forms of capital, with some more valuable than others in certain contexts, but with all of them having the potential to be mobilized and invested. For example, although a first-generation student may be thought of in a traditional, deficit-minded social capital framework as lacking resources amenable to college enrollment, a meaningful relationship with an institutional agent along with strong knowledge of the student’s native cultural capital can help mitigate these challenges. These concepts reflect an extension of the theory where the voice and agency of nondominant social classes can become more valued within educational institutions. The provision of knowledge, resources, and relationships to underperforming student groups, in conjunction with the valuing of their own capital and the context of their struggle for equity, can lead to the transformation of educational institutions. Concepts such as funds of knowledge have attempted to address the analytical shortcomings of social capital theory, in particular its conceptualization of low-income youths and students of color. Social capital, when applied properly, has the promise to transform schooling into a more equitable space that contributes to well-being for those who have experienced difficulty pursuing it. Whereas the foundational writings of social capital have been thought to promote deficit orientations, more contemporary applications of social capital attempt to address the limitations of the early theoretical models. As the concept of social capital develops theoretically overtime through advances in research and practice, it is poised to offer more meaningful interpretations of how it can promote equal opportunity in areas such as academic achievement, parental involvement, and college access and success. Alan G. Green and Jenna R. Sablan

Socioeconomic Status and Education See also Access to Education; Achievement Gap; Cultural Capital; Educational Equity; Human Capital; Parental Involvement; Peer Effects; Socioeconomic Status and Education

Further Readings Bourdieu, P. (1973). Cultural reproduction and social reproduction. In R. Brown (Ed.), Knowledge, education, and cultural change: Papers in the sociology of education (pp. 71–112). London, UK: Tavistock. Bourdieu, P. (1986). The forms of capital. In J. G. Richardson (Ed.), Handbook of theory and research for the sociology of education (pp. 241–258). New York, NY: Greenwood Press. Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94, S95–S120. Dika, S. L., & Singh, K. (2002). Applications of social capital in educational literature: A critical synthesis. Review of Educational Research, 72, 31–60. Portes, A. (1998). Social capital: Its origins and applications in modern sociology. Annual Review of Sociology, 24, 1–24. Stanton-Salazar, R. D. (1997). A social capital framework for understanding the socialization of racial minority youth. Harvard Educational Review, Spring, 1–40.

SOCIOECONOMIC STATUS AND EDUCATION Although the concept of socioeconomic status (SES) is addressed more widely outside economics than within it, an expansive body of research across disciplines documents the educational utility of SES. Specifically within the field of economics of education, SES is a key input to the education production function often applied to consider how various resources measured among students, within classrooms, and across schools and communities can affect student outcomes. What interests economists of education is to go into the “black box” of education and determine how SES inputs matter in terms of affecting traditional student outcomes such as educational achievement or attainment and workplace performance. Outputs linked to SES in the education production function have, however, also included physical and mental health and, more recently, socioemotional noncognitive skills. The sections that follow include discussions of how SES is

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measured, how SES is contextualized, what inequalities arise in education based on SES, and broadening the notion of SES.

Measuring SES Although researchers often disagree about how best to operationalize SES, it is typically considered as a three-part construct used to group people with similar economic, educational, and occupational characteristics. Income is the most commonly measured economic component of SES. Income as SES is extremely prominent in the discussion of SES: It is what commonly guides school finance. That is, the number of economically disadvantaged students (often measured by students/families receiving some sort of governmental assistance—e.g., receiving free or reduced-price lunches) in a school is commonly used to measure school need. Schools with lower SES students tend to receive additional revenue from state and federal governments as a way of offsetting lower local funding sources. As a side note, however, measures like free or reduced-price lunch only serve as a proxy for lower SES. Rarely do school districts have precise figures concerning family incomes to make these calculations. Therefore, calculating SES often comes with many measurement challenges. As a second way to measure SES, education and degree status undergird employable skills and are often coupled with income. Occupational prestige is generally the third component of SES because it entails social position recognized by others. It is often the case that in large-scale survey datasets of families (e.g., the Early Childhood Longitudinal Study), a composite measure of SES is created based on all three components (income, education, and occupation). These are not readily available in administrative data. All of these different indicators of SES, however, capture a different dimension of the resources available to a person and operate through different mechanisms to affect outcomes. Test score gaps between low- and high-resource children are much more pronounced, for example, when SES is measured by income rather than by the educational attainment of parents. SES and social class are related terms often used interchangeably, although they are not synonymous. SES connotes the materialist prospects of upward mobility, while social class suggests a much more rigidly structured inborn class status. Accordingly, the predominant narrative on social class draws

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on gradient approaches that focus on the effects of relative SES across three more or less distinguishable social class groups: (1) the upper class, (2) the middle and working classes, and (3) the lower class. Yet the diversity in contemporary America has erased some of what were once more obvious if not impermeable markers of social class. Religious affiliation, voting patterns, and even skin color are much less clearly the essential class indicators they once were, making more fine-grained analyses of SES increasingly important, even as growing economic inequality appears to be widening the distance separating the rungs on the proverbial ladder toward upward social mobility.

more time to read to their children at home, and more energy to devote to parenting. Other research suggests that high-SES parents employ what has been called a strategy of “concerted cultivation” to instill in their children a distinct sense of entitlement that facilitates upward mobility. Parents’ participation in social networks that include the parents of other similarly resourced school children may make available crucial information about school policies, teachers, and many of the students’ peers, empowering families to channel their resources as effectively as possible into children’s educational success.

Contextualizing SES

When examining SES at the classroom level, research in economics upholds the importance of classroom effects, which materializes as the influence of one student on the outcomes of other students in the same room. Parents, educators, and researchers have long believed that peer quality is one of the most important determinants of student outcomes. Given how influential family SES is on achievement, there may be a spillover effect onto others in the same classroom based on individual SES levels. Having answers to the influence of SES as a classroom input would inform the debates on school choice, busing, and tracking. The Coleman Report was the first major national study to demonstrate that a student’s achievement is more highly related to the SES characteristics of other students in the school than any other school characteristic. To evaluate peer effects, this report focused on the composition of Black and White students in the classroom as a primary correlate that could affect a student’s achievement. From this analysis, the authors reported higher achievement levels for low-SES Black students who attended middle-class schools, thereby suggesting that family background of students is a significant factor in the achievement of their peers. Along the same lines, Eric Hanushek attempted to determine a relationship between the varying proportions of Black students in a classroom (as a proxy of SES) and the subsequent effect on student achievement. Here again, the socioeconomic composition of the classroom proved to be a critical determinant of educational outcomes. More recently, Patrick McEwan examined various classroom-level measures including average classroom parental levels of educational attainment and average classroom family income. He found that these measures predicted differences

Research discipline aside, SES may be the most widely used contextual variable in research on educational and life success. For decades, student performance and SES have been veritably bound together in research on the measure and production of subject matter cognition and social mobility. Myriad studies confirm that SES is among the most powerful predictors of student test score performance, years of completed schooling, and human flourishing. How SES affects these outcomes is the focus of this subsection. Family SES

Neoclassical economists originally introduced human capital theory to explain how parents are uniquely positioned to invest their own socioeconomic resources in the skills development of their children, fostering in them tastes and preferences for schooling. The widely read Coleman Report of 1966 marked the onset of a large body of studies adhering closely to human capital theory in investigations of family SES—all of which showed the influence of SES on educational outcomes. Early childhood parenting practices and communication styles, for example, matter greatly and are patterned along SES lines. A lack of school-specific knowledge is often what differentiates high- and low-SES parents in their approaches to raising children. According to one well-known study, by age 3 the children of professionals had vocabularies of twice as many words as the children of parents receiving social assistance, which, in turn, bolstered advantaged children’s IQ. High-SES families have more money to invest in their children’s human capital through the purchase of learning materials,

Classroom SES

Socioeconomic Status and Education

in reading and math achievement scores. Michael Gottfried relied on a sample of urban students in a large U.S. district to examine the relationship between peer SES and student achievement. In this study, peer SES was determined by the number of students receiving free or reduced-priced lunch. The findings indicated that a greater number of students receiving free or reduced-priced lunch led to lower classroom achievement. Of all the possible peer effects examined in this study (SES, peer behavior, gender, English Language Learners, special education), the effect sizes were the largest for peer SES. Neighborhood SES

Some argue that an overemphasis on family SES has led to not enough focus on the neighborhood and community settings in which children increasingly live in concentrated wealth or poverty. While children are enveloped within families and spend a substantial portion of their waking hours in schools and classrooms, they also reside in neighborhoods often segregated by SES and basic elements of social capital, including trust, successful cooperation among community members, and networks of civic engagement. In the language of economists, such “externalities” are bidirectional and reciprocal, moving from family to community and from community to family. For example, while family SES is strongly associated with the starting point of children’s achievement in kindergarten, neighborhood SES has been shown to be even more strongly associated with achievement progress after children enter formal schooling. Indeed, SES may produce differential effects across family, school, and community domains and across stages of children’s development. Gottfried examined measures of neighborhood SES and its relationship to individual students’ absence from school. Using U.S. Census Bureau data, he linked the schooling records of individual students in an East Coast school district to home residential characteristics. Therefore, he defined neighborhood SES along multiple dimensions, including percentage of residential block at or below poverty, average residential block income, median age of residents on the residential block, average household size, percentage of the block that is owner occupied, and percentage of block residents who are Black. The findings indicated that when students lived in higher SES neighborhoods, they were more likely to attend school. These findings were distinguishable

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across student gender lines, race/ethnicity, and SES. In short, neighborhood disparities in property value, community resources, environmental health, and crime exacerbate achievement/attainment disparities between high- and low-SES children.

Inequality and SES It has been well documented that the variation in student educational outcomes can be attributed to several key socioeconomic measures, including parents’ annual income, education, and occupational status. Since the 1980s, stagnating or increasing rates of poverty alongside widening income inequality have exacerbated the gaps in educational achievement and attainment. The gap between rich and poor children’s math and reading scores is now substantially wider than it was 30 years ago. Moreover, many prominent social scientists have shown that the correlation between SES and race is inevitably linked to diminished access to quality education for underrepresented minorities and therefore to patterned racial inequality in educational outcomes. Only 15% of White children under the age of 18 were living in low-SES households in 2005 compared with almost one third of all Black and Hispanic children. As early as kindergarten, gaps in achievement by class and race already approximate 1 year of learning in both mathematics and reading, and these gaps tend to persist as children continue through school. By the fourth grade, low-SES students eligible for school lunch subsidies score approximately 1 year below the national math score average. These differences persist through eighth grade. Recent research shows that trends in the test scores of low- and high-income children parallel trends in income itself, such that income-based gaps in test scores are now twice as large as test score gaps between Blacks and Whites. Attainment gaps persist at the secondary and postsecondary levels. Low-SES and historically underrepresented minority students are much more likely to drop out of school and experience decreased health and occupational status than their high-SES counterparts. These findings prefigure similar SES and racial gaps in educational attainment at the college level, where low-income and minority students are considerably less likely to receive a bachelor’s degree or higher 5 years after entering college.

From SES to Wealth While researchers have focused extensively on SES (which is often assumed to capture the full range of

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Socioeconomic Status and Education

educationally relevant family resources), broader measures of wealth are rarely operationalized empirically in considerations of important educational and life outcomes. There are a few exceptions, but it still holds true that wealth—variously defined as assets, net worth, and ownership of financial products—is broadly overlooked in research in the economics of education. This is a striking oversight given that emergent sociological research shows that wealth has a large and measurable impact, above and beyond SES, on a child’s future success. Even after parental education, occupation, and income are controlled, parental wealth bolsters test score performance among children of ages 5 to 14. Wealth is also a strong predictor of years of completed schooling, college enrollment, and college attainment. Wealth may affect educational outcomes through a variety of mechanisms, in part depending on the sources of wealth and the age of the child. In young adulthood, for example, wealth may become an especially critical factor in shaping one’s socioemotional learning and subsequent educational trajectory. One way in which wealth may affect outcomes is by providing a safety net during times of income instability, underscoring the importance of liquid forms of wealth. It could also be that the returns to wealth are just as influential through their effect on children’s socioemotional attributes that may derive from the presence of distinct and visible manifestations of wealth throughout the course of family life. The socioemotional returns to wealth, however, are yet to be investigated in research. Disparities in wealth are far greater than disparities in annual income and other conventional measures of SES. In 2009, the median wealth of White households was 19 times that of Black households and 15 times that of Hispanic households. Moreover, wealth differences persist at every income level. Even though the wealthiest 10% of households in America experienced a loss in assets beginning in 2005, their share of overall wealth rose subsequently. Those in the top 10% were relatively less affected by the bursting of the housing market bubble in late 2007, and the Great Recession that followed, than the 90% positioned on the class rungs below. Indeed, the United States has the most unequal distribution of wealth among the Western democracies. By not accounting for a family’s wealth, researchers are overlooking an important factor that provides advantages including long-term financial security and social prestige, which may contribute to student development, social mobility, and overall well-being.

Although focusing on SES is necessary, it has not proven sufficient for meeting the educational challenges of the 21st century. Michael Gottfried and Robert Ream See also Education Production Functions and Productivity; Peer Effects

Further Readings Brooks-Gunn, J., & Duncan, G. J. (1997). The effects of poverty on children: The future of children. Journal Issue: Children and Poverty, 7(2), 55–71. Gamoran, A. (2001). American schooling and educational inequality: A forecast for the 21st century. Sociology of Education, 74, 135–153. Hanushek, E. A., & Rivkin, S. G. (2006). School quality and the Black-White achievement gap (NBER Working Paper No. W12651). Cambridge, MA: National Bureau of Economic Research. Kochar, R., Fry, R., & Taylor, P. (2011). Twenty to one: Wealth gaps rise to record highs between Whites, Blacks and Hispanics. Washington, DC: Pew Research Center. Levin, H., Belfield, C., Muennig, P., & Rouse, C. (2007). The costs and benefits of an excellent education for all of America’s children (Vol. 9). New York, NY: Columbia University Teachers College Press. Mazumder, B. (2005). Fortunate sons: New estimates of intergenerational mobility in the United States using social security earnings data. Review of Economics and Statistics, 87(2), 235–255. Mishra, S. K. (2007). A brief history of production functions (Working Paper Series). Rochester, NY: Social Science Research Network. Retrieved from http://ssrn. com/abstract=1020577 National Center for Education Statistics. (2006). Digest of educational statistics: 2006. Washington, DC: U.S. Department of Education. National Center for Education Statistics. (2009). National Assessment of Education Progress (NAEP), 2009 main NAEP mathematics and reading assessments. Washington, DC: U.S. Department of Education. Orr, A. (2003). Black-White differences in achievement: The importance of wealth. Sociology of Education, 76, 281–304. Pfeffer, F. (2008). Persistent inequality in educational attainment and its institutional context. European Sociological Review, 24(5), 543–565. Ream, R. K. (2005). Uprooting children: Mobility, social capital, and Mexican American underachievement. El Paso, TX: LFB Scholarly. Reardon, S. F. (2011). The widening academic achievement gap between the rich and the poor: New evidence and

Special Education Finance possible explanations. In G. J. Duncan & R. J. Murnane (Eds.), Whither opportunity: Rising inequality, schools, and children’s life chances (pp. 91–116). New York, NY: Russell Sage Foundation. Rothstein, R. (2004). Class and schools: Using social, economic, and educational reform to close the BlackWhite achievement gap. Washington, DC: Economic Policy Institute. Rumberger, R. W. (1983). Dropping out of high school: The influence of race, sex, and family background. American Educational Research Journal, 20(2), 199–220. Sirin, S. R. (2005). Socioeconomic status and academic achievement: A meta-analytic review of research. Review of Educational Research, 75(3), 417–453. Wilson, W. J. (2009). More than just race: Being Black and poor in the inner city (issues of our time). New York, NY: W. W. Norton. Wolff, E. N. (Ed.). (2006). International perspectives on household wealth. Cheltenham, UK: Edward Elgar. Yeung, W., & Conley, D. (2008). Black-White achievement gap and family wealth. Child Development, 79, 303–324.

SPECIAL EDUCATION FINANCE Special education consists of specialized instructional and related services provided to students with disabilities who are determined to be eligible under the federal Individuals with Disabilities Education Act (IDEA). This law dates back to 1975, when the original federal Education for All Handicapped Children Act was passed by the U.S. Congress. Renamed the IDEA in the 1990 reauthorization, the law provides a legal entitlement to educational services for children from birth through 2 years (under Part C of the statute) and of ages 3 through 21 years (under Part B). Under the IDEA, states and local education agencies are required to provide each eligible child with a disability a free appropriate public education and develop an individualized education program, a comprehensive service plan based on the child’s educational needs as determined by a team of educators, parents, and medical professionals. Furthermore, the least restrictive environment provision of the IDEA requires that children with disabilities be served to the maximum extent appropriate with children who are not disabled. Because of the specialized services received by children with disabilities, there are important funding implications for schools, school districts, states, and the federal

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government. This entry provides information on the magnitude of special education spending in the United States and estimates of per-pupil spending on students with disabilities relative to their nondisabled counterparts, and discusses how these spending patterns have changed over time. It also includes a description of how the federal and state governments distribute revenues to support special education services across the nation.

Special Education Spending In 2009–2010, 13% of public school children in the United States received special education services, and the latest comprehensive study of special education expenditures in 1999–2000 by Jay Chambers, Thomas B. Parrish, and Jenifer J. Harr indicates that the nation spent on average about two times as much (1.93) on a special education student as on a general (nonspecial) education student (see Table 1). This spending ratio was estimated based on an extensive and comprehensive set of surveys of districts, schools, and teachers, all of which were designed to gather information about programs and services provided to random samples of students who were eligible for special education services across the United States. From these surveys, Chambers and his colleagues estimated that during 1999–2000, $12,639 was spent to educate the average special education student (including general and other education services), while $6,556 was expended on the average general education student. The spending ratio is the total amount expended to educate the average special education student relative to that spent on the average general education student (in the case of the 1999–2000 spending ratio, 1.93, it is $12,639 divided by $6,556). Roughly two thirds (about 64%, or $8,080) of the $12,639 total spent per special education student was spent on special education instructional or related services. The remainder of this total was expended on a combination of general education services ($4,394) and other special needs programs ($165) for special education students who were also receiving services for low-income or English learners. Table 1 summarizes all of these key data and calculations to estimate the spending ratio and relative amounts of spending on special education services and students. In Table 1, it is important to distinguish between special education spending (i.e., dollars expended on special education instructional and related services)

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Table 1 Total, Per Pupil, and Relative Spending on Special Education Students, 1999–2000 Spending on Special Education Students

Total Dollars Average Per-Pupil Percentage (Billions) Expenditures (in Dollars) of Total

Spending on special education students A

Special education spending

50.0

8,080

64

B

General education spending

27.3

4,394

35

C

Other special programs (for low-income or English learners)

1.0

165

1

D

Expenditure to educate special education students

78.3

12,639

100

Relative spending on special education students E

Expenditure to educate a general education student

6,556

F = D/E

Ratio of spending ratio on special education students

1.93

G

Total expenditure on elementary and secondary education in the United States

360.6

H = 100 × D/G Percentage of total elementary and secondary spending to educate special education students

22

Source: Data taken from Chambers, Parrish, and Harr (2004, appendix B, table B-2).

and total spending necessary to educate a special education child. Total spending necessary to educate a special education child includes special education instructional and related services as well as general education instructional services devoted to serving special education students. The spending ratio (1.93) reflects the relative amount of additional dollars spent in total on a child in special education over and above what is commonly spent to educate a general education student. This same study suggested further that about 22% of all spending in elementary and secondary schools in the United States in the 1999–2000 school year was to educate students with disabilities. Approximately $50 billion of the $78.3 billion (64%) spent in total on students in special education was used for special education instructional and related services. While the latest figures suggest that the expenditure ratio for the average special education student in the United States is about 1.93, it is important to recognize that the ratio varies significantly across different categories of special education students. Table 2 presents estimates of spending ratios for

12 categories of students with disabilities compared with the average general (nonspecial) education student. These spending ratios vary from a low of 1.6 and 1.7 for students identified with specific learning disabilities and those with speech/language impairments, respectively, who together accounted for almost 60% of all special education students in 1999–2000, to 3.5 for students with multiple disabilities, who accounted for 2% of special education students.

Changes in Special Education Spending Over Time Since the late 1960s, four major studies of special education spending have been conducted by the Office of Special Education Programs in the U.S. Department of Education. The ratio of the average per-student spending on a special education student to a regular education student ranged from 1.92 in the late 1960s, prior to the passage of the Education for All Handicapped Children Act of 1975, to 2.17 in the late 1970s, to 2.28 in the late 1980s, and finally to 1.93 in 1999–2000. Total spending to

Special Education Finance

Table 2

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Spending Ratio and Percent Identification Rates by Disability Category

Student Category

Special Education Weight Using General Education Students as Comparison Group

Percentage of Special Education Students, 2009–2010

General Education Student (Comparison Group)

1.0

Specific Learning Disability (SLD)

1.6

37

Speech/Language Impairment (SLI)

1.7

22

Emotional Disturbance (ED)

2.2

6

Mental Retardation (MR)

2.3

7

Orthopedic Impairment (OI)

2.3

1

Other Health Impairment (OHI)

2.0

11

Autism (AUT)

2.9

6

Hearing Impairment/Deafness (HI/D)

2.4

2

Multiple Disabilities (MD)

3.1

2

Traumatic Brain Injury (TBI)

2.5

1

Visual Impairment/Blindness (VI/B)

2.9

1

Preschool (PRE) 2

2.0

5

Average Special Education Student

1.9

100

Source: Data taken from Chambers, Shkolnik, and Perez (2003, appendix B1).

educate special education students increased from about 16.6% of total spending in 1977–1978 to 21.7% in 1999–2000 (a 30% increase), while the percentage of students identified for special education increased from 8.3 to 13.2 (a more than 50% increase) during that same time period. Based on data from the National Center for Education Statistics, the special education identification rate stood at 13.1% of total enrollment in 2009–2010, a slight decline from 1999–2000 and from the high point of 13.8% in 2004–2005. Based on these analyses, it seems reasonable to conclude that, for the most part, increases in total special education spending are due to increases in the number of students identified as eligible for the program. Changes in the spending ratios over time may have resulted from several factors, including increases in the percentages of students identified for special education, declines in the extent to which students are served in segregated or separate placements (e.g., special classes or schools that exclusively serve students with disabilities), and advances in medical sciences that have reduced mortality among children with severe disabilities. The greatest increase in the

number of students identified for special education has been among students with less severe disabilities, who may cost less to educate than other special education students, leading to lower spending ratios. For example, the proportion of special education students with specific learning disabilities has almost doubled since 1975, from about 22% to 37% of the population, and this disability category on average has lower total spending ratios relative to other disability categories. Segregated or separate placements often require higher expenditures to support, and these have declined over time, which would in turn lower the ratio. On the other hand, improvements in health care may have resulted in the survival of greater numbers of children with severe disabilities, who are more expensive to serve.

Components of Special Education Spending Based on the latest comprehensive data available, from 1999–2000, more than 60% of special education spending was used to support instructional programs for school-age children (6–21 years) with disabilities within public schools, while 11% was

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Table 3

Allocation of Special Education Spending

Description of Special Education Services

Percentage of Total Special Education Spending

School-age (6–21 years) programs operated within public schools

61

School-age (6–21 years) programs operated outside public schools

11

Preschool (ages 3–5 years) programs operated within public schools

8

Preschool (ages 3–5 years) programs operated outside public schools

1

Other instructional programs for students who are in hospital or homebounda and summer schoolb

2

Home-to-school transportation servicesc

7

Administration and support services

10

Source: Data taken from Chambers, Parrish, and Harr (2004). a. Hospital or homebound students with disabilities include those who have special health problems, temporary illnesses, or injuries that prevent their attendance in school and are therefore provided with instructional services at home, in the hospital, or at another site as determined by the school district. b. Summer school in this instance refers to special education services provided during the summer months to students with disabilities. c. These include special services designed to transport students with disabilities to and from school plus the provision of the necessary support services (e.g., transportation aids) required to permit them to be transported on a regular school bus.

expended on nonpublic school programs for schoolage children (see Table 3). Preschool programs (for ages 3–5 years) for special education accounted for about 9% of the dollars, with almost 8 out of every 9 dollars spent on public school programs (see Table 3). Because of special health problems, temporary illnesses, or injuries, some students with disabilities are provided instructional services either at home or in hospital settings, and other students with disabilities also require additional services during the summer months. These two groups combined account for about 2% of expenditures. In addition, in some instances, students with disabilities require either special home-to-school transportation services or additional supports (e.g., paraprofessionals) to assist them while riding the regular school bus for home-to-school transportation, and these services account for 7% of special education spending and served about 14% of the students with disabilities. The remaining 10% was for administrative and support services. Special education services are supported by funds from a combination of federal, state, and local sources. Since fiscal year 2000, the federal funding has provided anywhere from about 7% to as much as 17% of the funds to support special education

spending, depending on the year, with the rest split between the state and local sources. Federal Funding

Prior to 1997, federal funds were distributed to states on a per-student basis up to a 12% cap on the identification rate. To respond to concerns regarding the overidentification of special education students, Congress enacted a new mechanism for distributing funds that was based on the total residential population in the age-group of special education students served within the state. Once the federal IDEA Part B revenues reached $4.9 billion (which happened in fiscal year 2000), federal revenues above the $4.9 billion mark were distributed based on a total census of the school-age population and not specifically on the number or percentage of students identified as eligible for special education. This approach is commonly referred to as a censusbased formula. Under the new formula, 85% of the additional revenues are allocated based on the total residential population, while 15% of the additional revenues are determined on the basis of the percentage of students living in poverty relative to other states.

Special Education Finance

The poverty provision of the formula was created based on the assumption that the need for special education services within a state is at least in part correlated with student poverty levels. When the federal funding of IDEA was first initiated in 1975, the law had specified that the federal government was authorized to support up to 40% of the average per-pupil expenditure (APPE) in public elementary and secondary schools. As discussed previously, the United States spent roughly two times on a special education student what it spent on a general education student in 1999–2000. Thus, using the APPE as a rough metric, a special education student should cost about two times the APPE. Or stated another way, the APPE represents a rough approximation of the incremental cost of serving a special education student. To some, the 40% provision of the law was considered to be a promise or commitment of the amount of funding to be provided by the federal government. But this level of funding, often referred to as full funding in the literature, has never been fully realized. According to Harr and Parrish, the federal government allocation has increased from 7.3% of APPE in 1996–1997 to 17% in 2008. With the infusion of funds from the American Recovery and Reinvestment Act, this percentage reached a peak of 34% in 2009, but this allocation ended in 2011. The most recent amendments to the IDEA law (1997 and 2004) emphasized and encouraged states to implement placement neutral funding formulas, which are intended to reduce the incentives for serving students in segregated settings (meaning placement away from general education students). Through these provisions, the federal government is becoming more proactive to ensure that students are served in the least restrictive environment as prescribed by the IDEA. State Funding

Based on a 2011 survey of all 50 state departments of education by Deborah A. Verstegen and Robert C. Knoeppel, state aid for special education uses one or a combination of four basic methods for allocating dollars to local education agencies: 1. Per-pupil funding or student weighted funding 2. Cost reimbursement

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Funding is distributed directly to school districts or to intermediate educational units or agencies, which act as consortiums for services and funding in concert with local school districts. Some states have also implemented programs of catastrophic aid in combination with regular funding formulas to address the needs of very high-cost special education students who can have a significant, if not devastating, impact on the budgets of local education agencies. Table 4 lists how many and which states used various approaches to funding special education. It is important to note that federal and, particularly, state funding formulas for special education services have changed and continue to change over time on a state-by-state basis to adapt to shifting policy and financial incentives. Per-Pupil Funding or Student Weights

According to the 2011 survey of state education departments, 20 states reported using a weighting system that recognizes the excess cost of programs and services above and beyond general education. This approach uses weights much like spending ratios to allocate additional dollars for special education. For example, a funding weight of 1.6 applied to a group of students would drive 60% more to a special education student relative to a general education student with no disabilities. In some instances, these weights are simply applied to the total count

Table 4

State Allocation Policies for Special Education

Allocation Mechanism (Number of States)

State

Per pupil/ Weighting (20)

AZ, FL, GA, HI, IA, KS, KY, LA, MD, MO, NY, OH, OK, OR, SC, TN, TX, UT, WA, WV

Cost reimbursement (8)

AR, IN, ME, MI, MN, NE, VT, WY

Unit based (6)

AL, DE, ID, MS, NV, VA

Census based (9)

CA, ID, IL, MA, NJ, NC, ND, NM, PA

Other (16)a

AL, AR, CA, CO, CT, ID, IL, MD, MN, MT, NH, NY, ND, OR, SD, WA

3. Unit-based funding

Source: Chambers et al. (2012, table 2.2, p. 32).

4. Census-based funding

a. Multiple methods are used in some states.

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of special education students, regardless of disability category. Other states use differential weights for students with more or less severe disability conditions (based on the student’s disability category or other criteria such as hours of service). To reduce incentives to overidentify students for special education, some states may place caps on the identification rates in their funding formulas. Cost Reimbursement and Unit-Based Funding of Special Education

Eight states reported using a cost reimbursement method to support special education services. Such reimbursement mechanisms usually define eligible cost categories and establish a percentage of the expenditures that will be reimbursed by the state. Seven states currently use this approach. In addition, six states use instructional unit approaches that pay for the number of teachers required, generally based on need or the number of students served per teacher. Census-Based Funding of Special Education

According to the survey, nine states employed a census-based funding system that provides funding based on total enrollment (a census) of all students in the district. By breaking the link between funding and the number of students eligible to be served by the special education program, the census-based approach eliminates incentives for overidentification. This approach to funding assumes that there is relatively little variation in the real incidence rates of students with disabilities across districts. Other Approaches to Funding Special Education

Sixteen states report “other” funding approaches that may be used in combination or singularly. For example, as of 2013, Alaska provided a block grant to districts to fund students in certain programs, including vocational education and programs for gifted and talented students and bicultural or bilingual students. Illinois and several other states use additional types of funding for special education, such as reimbursing school districts for the cost of personnel and allocating funds for special education students placed in preschool and private schools. Catastrophic Aid

In smaller districts, the presence of even a small number of very severely involved children can be financially devastating to a district’s budget. For

example, Chambers, Yael Kidron, and Angeline K. Spain reported that the total expenditure to educate a special education student above the 95th percentile of per-pupil special education expenditure was, on average, more than five to six times the expenditure to educate a general education student. At the 99th percentile, this multiplier was well in excess of eight times. Some states have developed a mechanism to manage this risk with what are commonly referred to as catastrophic aid or risk management programs, which are designed to provide state funding to support services for very high-cost special education students. They define excess cost usually in terms of cost ratios: the relative expenditure required to educate the target student relative to the average cost of a general education student. In some instances, states will use the base level of funding for the average student in the overall school funding formula and specify that any student who costs above a certain threshold or a multiple of the foundation amount is eligible for catastrophic aid. Often, the state will require that some minimum portion of the total cost of the eligible children is paid by the local school district to reduce the incentives for districts to overidentify such children. Amounts above this minimum threshold would then be supported by state funding. To support these types of provisions in states, the federal government in the most recent reauthorization of the IDEA permitted states to set aside up to 10% of IDEA funds to establish support for such risk pools and cost sharing for high-cost students. How School Districts Are Permitted to Use State Special Education Funds

There are differences in the requirements across states in whether and how funds generated through the special education funding formula are used. According to Eileen Ahearn, more than half of the states (56%) require that funds generated for special education students be used for special education services only. Six states (12%) require funds to be used for special education services or in support of response to intervention (RTI). RTI is an approach to providing assistance to children who are exhibiting learning difficulties. RTI operates through frequent monitoring of progress and the provision of increasingly intensive research-based interventions to ameliorate learning difficulties. By providing at-risk students with intensive interventions, this approach

Special Education Finance

becomes an alternative to immediate identification of a child as being eligible for special education. Ten states currently permit districts to use funds generated through the special education formula for services linked to any public education program.

Conclusion As a nation, the United States spends, on average, about twice as much to educate a student with a disability as a nondisabled student. In fact, more than 20% of all elementary and secondary school spending goes toward educating students with disabilities, who account for just over 13% of the total enrollment. It is important to recognize that students with disabilities are not a homogeneous population but rather represent a diverse population of students, from those with specific learning disabilities, who generally exhibit only mild to moderate disabilities, to students with multiple disabilities, who exhibit more severe conditions with a lot of special needs. And the additional spending to educate these diverse populations ranges from a low of about 60% more (for learning-disabled students) to well over 150% more (for students with multiple disabilities) than is spent to educate the average nondisabled student. Changes in special education spending over time have resulted from several factors, including increases in the percentages of students identified, declines in the percentages of students served in segregated placements, and advances in medical sciences that have reduced mortality among disabled children. Since fiscal year 2000, federal funding has provided anywhere from about 7% to as much as 17% of the funds to support special education spending, depending on the year, while the rest is split between state and local sources of revenue. States support special education through a variety of funding mechanisms, including student-weighted funding (used in 20 states), cost reimbursement (used in eight states), unit/classroom/teacher-based funding (used in six states), and, finally, census-based funding (used in nine states). Some states combine census-based funding with catastrophic aid, which directs funding on a case-by-case basis to help districts serve very high-cost students, who could have a devastating impact on local budgets. Jay G. Chambers See also Access to Education; Categorical Grants; Cost of Education; Economic Cost; Education Finance; Individuals with Disabilities Education Act; Parental

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Involvement; Pupil Weights; Risk Factors, Students; Unfunded Mandates; Vertical Equity; Weighted Student Funding

Further Readings Ahearn, E. (2010). Financing special education: State funding formulas. Alexandria, VA: Project Forum, National Association of State Directors of Special Education. Chaikind, S., Danielson, L. C., & Brauen, M. L. (1993). What do we know about the costs of special education? A selected review. Journal of Special Education, 26(4), 344–370. Chambers, J. G., Kidron, Y., & Spain, A. K. (2004, May). Characteristics of high-expenditure students with disabilities, 1999–2000 (Report No. 8). Palo Alto, CA: American Institutes for Research. Chambers, J. G., Levin, J., Wang, A., Verstegen, D., Jordan, T., & Baker, B. (2012, September 24). Study of a new method of funding for public schools in Nevada (Submitted by the Nevada Legislative Counsel Bureau). San Mateo, CA: American Institutes for Research. Retrieved from http://www.air.org/focus-area/education/ index.cfm?fa=viewContent&content_id=2671&id=5 Chambers, J. G., Parrish, T. B., & Harr, J. J. (2004, January). What are we spending on special education services in the United States, 1999–2000? (Updated Report No. 1). Palo Alto, CA: American Institutes for Research. Retrieved from http://csef.air.org/pub_seep_ national.php Chambers, J. G., Perez, M., Harr, J., & Shkolnik, J. (2005). Special education spending estimates from 1999–2000. Journal of Special Education Leadership, 18(1), 5–13. Chambers, J. G., Shkolnik, J., & Perez, M. (2003, June). Total expenditures for students with disabilities, 1999–2000: Spending variation by disability (Report No. 5). Palo Alto, CA: American Institutes for Research. Retrieved from http://csef.air.org/publications/seep/ national/Final_SEEP_Report_5.PDF National Center for Education Statistics. (n.d.). Students with disabilities: Fast facts. Washington, DC: U.S. Department of Education. Retrieved from http://nces. ed.gov/fastfacts/display.asp?id=64 Parrish, T., & Harr, J. (2011). Fiscal policy and funding for special education. In J. M. Kauffman & D. P. Hallahan (Eds.), Handbook of special education (pp. 363–377). New York, NY: Routledge. Retrieved from http://www .taylorandfrancis.com/books/details/9780415800723/ Verstegen, D. A., & Knoeppel, R. C. (2011, May). Financing education into the next decade: A 50-state survey of finance policies and programs. Paper presented at the National Education Finance Conference, Tampa, FL.

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Spillover Effects

SPILLOVER EFFECTS Spillover effects, known by the economic term externalities, occur when an individual or entity engages in an activity and as a by-product affects a bystander. The effect on the bystander, either positive or negative, is a spillover effect. One example of positive spillover effects is when a person receives a flu shot to decrease the chance of getting the flu. By being less likely to catch the flu, a person will be less likely to give the flu to those around them. Therefore, it can be concluded that getting a flu shot improves the health outcomes of other people in addition to the person receiving the flu shot. Hence, spillover effects are effects from an activity that extend beyond the intended individuals or entities. When self-interested individuals fail to take the spillover effects into account, the resultant amount of good produced and consumed does not maximize the society’s welfare. To conduct a full cost-andbenefit analysis of any activity requires analysis of its effect on the intended individuals, or the private effects, as well as an analysis of its spillover effects. This entry provides examples of spillover effects, particularly from consumption and production of education. A graph illustrates how the market fails to allocate resources efficiently in the presence of spillover effects. A few remedies to achieve better allocations are discussed. Last, this entry draws attention to spillover effects coming from intervention programs.

Price of education

An example of an activity that imposes benefits on bystanders is education. By obtaining an education, a person can become more productive and have higher earnings. Beyond these private benefits, literature has documented a number of education’s spillover effects. For instance, maternal education has been found to improve infant health and health practices of mothers. Having exposure to classmates with college-educated mothers improves educational outcomes of students. In the workplace, educated workers are found to increase the productivity of less educated workers. Additionally, a more educated populace is associated with a more civically engaged citizenry, a safer community, and better diffusion of ideas and technology, which stimulates economic growth. There is also evidence of spillover effects in the production of education. For example, it has been shown that students have larger achievement gains if their teachers have high-quality colleagues. One example of negative spillover effects in the classroom is when a classmate acts up in class. The direct effect of this activity is a student not learning. The spillover effects occur because by acting up the student disturbs his or her classmates and incites more disruption. Thus, by acting up, the student reduces his or her learning and the learning of the student’s classmates. In the presence of spillover effects, the market outcome is not the outcome that maximizes society’s welfare. The problem can be illustrated using Figure 1.

Supply (Value of private cost)

Social value (Private benefits and spillover effects) Demand (Value of private benefits)

QM

Figure 1

Supply and Demand of Education

QO

Quantity of education

Stafford Loans

The vertical axis measures the benefit and cost of education, while the horizontal axis measures the quantity of education. The demand curve represents the relationship between the price of education and the amount of education that consumers are willing and able to purchase at that given price. The supply curve represents the relationship between the price of education and the quantity of education that sellers are willing and able to supply at that given price. While the demand curve reflects the private value of the education consumption of buyers, the supply curve reflects the private cost of the education production of sellers. Self-interested consumers only consider the private benefit of getting an education. In the absence of the spillover effects, the market quantity QM would be optimal. However, as discussed above, there is an additional benefit to getting an education besides the private benefit. The values of the positive spillover effects and the private benefit combined are the social value, which is higher than the private benefit. With the presence of spillover effects, QM is less than the optimal QO that maximizes society’s welfare. However, in the presence of negative spillover effects, such as in the case of gasoline production, where there are undesirable environmental impacts from refining of crude oil, the social cost will be greater than that of the private cost. This happens because companies do not take into account the negative spillover effects when they consider their cost. In this case, the market quantity is greater than the optimal quantity. In the presence of spillover effects, there are remedies to move society closer to the optimal outcome. To discourage activities that have negative spillover effects, such as refining gasoline, the government can tax the activity or forbid it. To encourage activities that have positive spillover effects, such as education and flu shots, the government can subsidize them or make them mandatory. States often have compulsory school attendance laws that require students to attend schools. In the case of teachers, practices such as team teaching and mentorship can have spillover effects. In the case of students, practices such as tracking can have spillover effects. Last, when implementing an intervention program, other types of spillover effects may arise besides the type discussed above. Consider an educational intervention program. The effects on the treated students may affect the untreated students through changing the behavior of teachers and through interaction between the eligible and the ineligible students, such as the sharing of

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educational resources. For example, the presence of a bilingual education program has been shown to affect native students who are not served by the program. The effects of the bilingual education program are not restricted to the students the program serves. In conclusion, a complete cost-and-benefit analysis requires accounting for spillover effects. Sa A. Bui See also Education and Civic Engagement; Education and Crime; External Social Benefits and Costs; Peer Effects

Further Readings Angelucci, M., & Di Maro, V. (2010). Program evaluation and spillover effects. Washington, DC: Inter-American Development Bank. Retrieved from http://www.iadb. org/en/publications/publication-detail,7101.html?dctype =All&dclanguage=en&id=8579%20#.Umq3_nDrwyM Bifulco, R., Fletcher, J. M., & Ross, S. L. (2011). The effect of classmate characteristics on post-secondary outcomes: Evidence from the Add Health. American Economic Journal: Economic Policy, 3(1), 25–53. Chin, A., Daysal, N. M., & Imberman, S. A. (2012). Impact of bilingual education programs on limited English proficient students and their peers: Regression discontinuity evidence from Texas (NBER Working Paper 18197). Cambridge, MA: National Bureau of Economic Research. Retrieved from http://www.nber. org/papers/w18197 Jackson, C. K., & Bruegmann, E. (2009). Teaching students and teaching each other: The importance of peer learning for teachers. American Economic Journal: Applied Economics, 1(4), 85–108.

STAFFORD LOANS The federal government operates three main student loan programs for postsecondary education: (1) Stafford loans, (2) PLUS loans, and (3) Perkins loans. Stafford loans, named after former Republican senator Robert T. Stafford of Vermont, are the federal government’s largest loan program, accounting for approximately 80% of annual student loan volume. Undergraduate and graduate students are eligible to take out these loans, up to a maximum limit set by Congress. Stafford loans are financed via two different mechanisms: (1) direct loans (DLs) or (2) guaranteed student loans (GSLs). DLs are those that the U.S. Department of Education issues directly to students. GSLs are issued through third-party

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Stafford Loans

lenders (e.g., banks, credit unions, etc.) and are backed by the full faith and credit of the federal government. The GSL financing mechanism is designed to encourage private financial institutions to participate in the student loan market by insuring investors against default risks. This entry discusses the concept of loan guarantees, describes current Stafford loan terms and conditions, and identifies key policy changes affecting the Stafford loan program.

Loan Guarantees The federal government routinely provides loan guarantees in several sectors of the economy, including housing, small-business administration, community development, transportation, and education. The underlying principle in loan guarantees is the assumption that private financial institutions are unwilling or unable to provide loans to potential borrowers because of perceived risks of default and/ or due to limited collateral (i.e., lenders are unwilling to provide “unsecured” debt). When this occurs, individuals will underinvest in certain goods and services that are deemed to be socially beneficial. In the case of student financial aid, many students have no credit history, and there is no collateral to be leveraged in the event of default, making student loans a risky investment from the perspective of a private financial institution. In the absence of governmental intervention, the student loan market would only serve the nation’s most creditworthy students, resulting in underinvestment in human capital. Because of this market failure, the federal government intervenes by reimbursing lenders for any loans that go unpaid. This guarantee is designed as an incentive to encourage more lenders to participate in student loan markets, which, in turn, should reduce the credit constraints many students face when paying for college. With these loan guarantees, financial institutions issue student loans, and the federal government insures these loans against default risk. An alternative financing mechanism is to have the federal government be the direct provider of student loans, rather than a third-party investor. Under this model, the federal government originates, administers, and services the loan portfolio via the William D. Ford Federal Direct Loan Program. There is a long-standing policy debate about which financing mechanism is the most cost-effective. A 2010 Congressional Budget Office report estimates that shifting to DLs as the sole federal program would save approximately $68 billion over 10 years.

Stafford Loan Terms and Conditions Stafford loans are available to undergraduate and graduate students who attend accredited institutions at least part-time and have completed the Free Application for Federal Student Aid. Subsidized Stafford loans are restricted to students who have demonstrated financial need; unsubsidized loans are available regardless of financial need. Unsubsidized loans accrue interest while the borrower is enrolled in school, while the federal government pays the interest on subsidized loans while the borrower is enrolled in school. Over the lifetime of the loan, undergraduate borrowers can borrow a maximum of $23,000—this number is higher for independent undergraduates and for graduate students. Congress sets the interest rates for Stafford loans; since 2006, this has been a fixed rate (between 3.4% and 6.8%), but the loans have also carried a floating rate (based on the 3-month Treasury rate plus a fixed spread).

Key Policy Changes in Student Loans The 1965 Higher Education Act established the federal government’s first GSL program, which was later named the Federal Family Education Loan Program (FFELP). In 1972, Congress created the Student Loan Marketing Association (Sallie Mae) as a government-sponsored enterprise designed to increase the supply of GSLs. For nearly 30 years, the federal government’s loan policy included only GSLs, but the 1993 Student Loan Reform Act established the first DL program to compete with guaranteed loans. Borrowers could take out Stafford Loans regardless of whether they were in the FFELP or DL programs. The two programs operated together until the 2010 Health Care and Education Reconciliation Act, which required all new federal student loans (including Stafford Loans) to be made through the DL program. After 2010, the federal government would no longer provide GSLs; however, since guaranteed loans have been available for several decades, millions of borrowers are still repaying their loans through GSL programs today. Of the $998 billion total outstanding federal student loan debt in 2013, approximately 40% is in FFELP loans, and the remainder is in DLs. The policy shift toward DL is not necessarily a permanent one, as federal aid policymakers still debate the merits of operating only one student loan financing mechanism. There is ongoing controversy about the role former FFELP providers (e.g., Sallie Mae) play in this new aid system, which no longer allows them to originate

State Education Agencies

federally guaranteed loans. Regardless of that longterm policy outcome, Stafford loans continue to play a central role in financing postsecondary education in the United States. Nicholas W. Hillman See also Higher Education Finance; Public-Private Partnerships in Education; Student Financial Aid; Student Loans

Further Readings Congressional Budget Office. (2010, March). Costs and policy options for federal student loan programs. Washington, DC: Author. Retrieved from http://www .cbo.gov/publication/25051 Dynarski, S., & Scott-Clayton, J. (2013). Financial aid policy: Lessons from research. The Future of Children, 23(1), 67–91. Retrieved from http://futureofchildren.org/ futureofchildren/publications/docs/23_01_04.pdf Edmiston, K., Brooks, L., & Shepelwich, S. (2013). Student loans: Overview and issues (Research Working Paper No. 12–05). Kansas City, MO: Federal Reserve Bank of Kansas City. Lucas, D., & Moore, D. (2010). Costs and policy options for federal student loan programs. Washington, DC: Congressional Budget Office. Lucas, D., & Moore, D. (2010). Guaranteed versus direct lending: The case of student loans. In D. Lucas (Ed.), Measuring and managing federal financial risk (pp. 163–205). Chicago, IL: University of Chicago Press.

STATE EDUCATION AGENCIES State education agencies (SEAs) are the state-level entities tasked with carrying out both state- and federal-level policy work related to education, as well as the compliance and regulatory functions of the state and federal governments. Education is primarily a state function, established as such by both practice and law. However, with the expansion of the U.S. Department of Education and federal spending in education, there exists a challenging balance between the state government’s authority and the federal influence on education. For SEA officials, this often necessitates navigating difficult waters between balancing state laws and mandates (which can derive from various sources within the state— the state board of education, the executive and legislative branches, the lead education official in the state, etc.) and federal requirements. Additionally,

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although states provide the bulk of funding for public schools, the federal government provides substantial funding through various programs. Of these, the most commonly known are the Title I program, which provides block grants to states for use at the local level to assist low-income youths, and programs funded under the Individuals with Disabilities Education Act (IDEA). This federal money does not come without strings, however; there are myriad requirements in terms of data collection, reporting, accountability, and supports that states commit to providing in return for these funds. This entry includes a short review of the role of the SEA, a discussion of the balance between state and federal control, and an overview of some shifts under way in SEAs.

Brief History of the Role of SEAs SEAs became part of the educational landscape in the 1800s, with their role expanding as compulsory education became the norm in the early 1900s. Although local education agencies were developing the requirements for compulsory education as well as the educational offerings, there was variation in local ability and capacity to provide quality education to all students. Over time, this central issue in education—inequality in educational offerings—has become an important driving force in the increasing power of SEAs. Some of this power came from regulatory oversight—the ability to monitor and ensure compliance with regulations and rules. In recent years, however, this has shifted to a focus on the quality of education being provided to students and what the SEA can do to ensure a higher standard of education. There have been several landmarks in the role of the SEA. The first was the passage of the Elementary and Secondary Education Act (ESEA) in 1965. This not only was the beginning of Title I funding but also, at its inception, included Title V funds, which were intended to help strengthen state departments of education by providing targeted funds to increase state capacity. (The funding for this program was discontinued in 1981.) This influx of federal funds into SEAs was helpful, but it also created an imbalance: Staff who are partially funded by federal funds outnumber those funded only by state funds. For example, in a study using 2007–2008 data in seven states, the federally funded share of central staff positions was between 40% and 50%. This can make it difficult for states to develop and implement

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their own reform agenda in an aligned and coherent manner. In 1983, the National Commission on Excellence in Education released their report A Nation at Risk. This report was part of the impetus for many types of change, including a renewed focus on reforms such as developing standards and implementing new types of accountability systems. This work was largely in the purview of SEAs at that time, even though many SEAs were not necessarily equipped with the appropriate level of staffing or training to perform such work. The ESEA was reauthorized in 2001 with the passage of the No Child Left Behind Act (NCLB). This required states to adopt standards, implement state assessments, and develop specific accountability systems in order to receive those federal funds. This again created a need for change in the SEAs, as the SEAs had to enact these requirements but had to do so under the guidance and review of the federal Department of Education as well as within their own state-based legislative and policy environments. Finally, in recent history, the Obama administration offered the Race to the Top competitions beginning in 2009, in which states could compete for grants by demonstrating their ability to enact a series of significant reforms, including new types of accountability, assessments, and teacher evaluations. Thirty-four states changed their laws in the hope of winning these funds. The Race to the Top competition was followed by the announcement of the ESEA Flexibility program, a series of waivers from key provisions of NCLB that states could obtain by committing to certain types of reforms. This has put SEAs squarely in the role of negotiating change and flexibility with the federal government while at the same time managing the balance of pressures from the state itself. When state-led reform directions conflict with federal reform directions, SEAs are at the crossroads.

Balancing Federal and State Influence and Control Over Education According to the National Center for Education Statistics, based on fiscal year 2010, 13% of elementary and secondary education funding for local districts comes from the federal government, with the remaining portion coming from state sources (43%) and local sources (44%). One of the key roles of

SEAs is to administer the local education aid formulas in order to distribute state funding to local districts and to monitor and audit the use of that funding. While state and local sources make up a great deal of the funding for education, that 13% from the federal government has substantial power to shape the ways in which education is delivered in the states. Some of this influence has happened through NCLB since its passage in 2001. NCLB enacted a series of requirements that have dramatically changed the ways in which SEAs do business. For example, each state was required to adopt standards and conduct annual testing in specific subjects, determine “adequate yearly progress” for schools and districts based on whether or not students in those schools and districts were reaching proficiency targets, produce annual report cards, and ensure that each teacher was “highly qualified.” The expectation under NCLB was that 100% of students would be proficient by 2014, and annual proficiency targets were set accordingly. At the SEA level, this led to a massive expansion in assessment and accountability, which in turn has led to a gradual shift in the typical profile of SEA staff members. As data systems grow and become more complex, and testing for high-stakes purposes increases, states need staff who understand measurement and psychometrics, quantitative methods, and the application of those theoretical constructs to education policy, enacted in the real-time environment of an annual cycle. NCLB has reshaped the work of SEAs and the work of the education field, even though it directly relates only to a small portion of education funding. Schools and districts care deeply about the status they earn on their report cards; research has found that the “labeling” feature of accountability, rather than sanctions, can be its most powerful feature. One response in many schools and districts has been to narrow the curriculum and teach the tested subjects (reading and mathematics) more thoroughly in order to raise proficiency levels in those subjects. Another strategy has been to focus on “bubble kids”—students who are very near the cutoff score for proficiency—in order to get them over the threshold and have them counted toward those annual proficiency targets. Hence, this relatively small influx of federal money has had a significant impact on the behaviors of both the SEA and the local districts. At the time of writing, this tension between state and federal control is resurfacing. In lieu of

State Education Agencies

reauthorizing the ESEA Act, the U.S. Department of Education began issuing waivers to key provisions of NCLB, to provide states some relief from the “100% proficient” requirement. Although the waiver package was wide ranging, one component of it was related to standards and assessments. States needed to show that they had adopted “college and career ready standards” and “college and career ready assessments,” and one option they could select was to adopt the Common Core State Standards (a set of standards developed by a consortium of states) and join one of two assessment consortiums (the Smarter Balanced Assessment Consortium or the Partnership for Assessment of Readiness for College and Careers). Many states had already participated in the Common Core State Standards (CCSS) initiative, helping to write standards that would be used by multiple states, and they had either adopted those standards or were in the process of adopting them at the time of the waivers. Therefore, many states chose the CCSS as their college-and-career-ready standards, and many also joined one of the assessment consortia, in the hope of gaining efficiencies in test development, which is an expensive and timeconsuming process. However, concerns have arisen in many states (Michigan, Florida, Ohio, Utah, Alabama, Wisconsin, and Indiana, and possibly others) over the balance of federal involvement in state education affairs. By 2014, states had begun to question or reject the CCSS and the associated assessments. The tension created by this is that many states adopted these standards as early as 2010 and have been working toward implementing them for several years, with similar time devoted to assessment development. Moreover, for many states, the federal waivers to NCLB are tied to career-and-collegeready standards and assessments, so any retreat from the Common Core could put their waivers in jeopardy. Although it is recognized that states have control over education, the mechanics of exerting that control and the challenges of aligning that control with federal requirements to receive Title I funds create a difficult environment for many SEAs as they attempt to find a middle ground that preserves their federal funding while continuing to privilege the role of the state in education. Additional tensions were introduced when eight California school districts applied for a 1-year district waiver from NCLB in exchange for locally developed plans that aligned with the principles of ESEA flexibility. This waiver

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request was approved but created additional questions regarding the role of the SEA in the relationship between local districts and the federal government.

Moving Beyond Compliance Currently, some SEAs are attempting to change the discussion and broaden their efforts, turning their focus away from compliance alone to a broader view of the SEA as focused on technical assistance and performance management. Decades of compliance have not led to conclusively higher student achievement. SEAs are beginning to embrace the fact that compliance is not the goal; rather, compliance should be the given, and the work should continue from there. The challenge that SEAs face as they make this transition, however, is that the SEA is actually responsible for ensuring compliance, and ensuring compliance can be a time-consuming and resource-heavy task. This is why states have begun to employ strategies such as project management and process improvement, as well as leveraging technological solutions, to help them make compliance more standardized and less time intensive. It is important to note that there is no uniform SEA approach. However, there are common discussions happening about how the SEA can effectively influence student achievement and what that needs to look like in terms of the work itself, the organization of the agency, and the focus of efforts. Moving beyond compliance requires several things that are a challenge for SEAs, but one that many SEAs around the country are embracing. First, an SEA that wishes to move beyond compliance needs mechanisms in place that allow it to focus on student achievement goals, results, or outcomes and to measure progress against those goals. This requires that SEAs have the ability to articulate and measure these goals and outcomes with data and metrics beyond yearly summative assessment scores. SEAs need to employ individuals with strong dataanalytic skills and the ability to think creatively about how to measure progress toward a goal such as closing achievement gaps. One strategy that a number of states have begun to use is to partner with the nonprofit U.S. Education Delivery Institute and use a “delivery” approach, which the organization defines on its website as “a systematic process by which education leaders can drive progress and deliver results.” Nine states currently use this model for their K-12 systems.

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Moving beyond compliance also requires that states shift their thinking from a “gotcha” mode to a support mode. Rather than primarily investigating how and to what extent districts have spent funds in a way that complies with federal and state regulations or have completed certain tasks, the SEA has to approach districts and schools with a mind-set of “How can we support the work you are doing to focus on improvement?” SEAs need to work on ways in which states can reduce regulatory burden and support innovation, incentivize districts and schools, develop systems capacity that includes data and planning systems, and provide interventions that can help turn around persistently low-achieving schools. SEAs are investigating how to leverage funding sources in new ways. Traditionally, funding has been very specifically targeted—Title I funds have certain purposes, IDEA funds have certain purposes, and state and local funds have certain purposes. This has led to more compliance work, as districts attempt to expend funds in ways that follow the federal cost principles (necessary and reasonable, allowable, allocable). With new guidance from the federal government and a renewed urgency to make sense of how the funding can work to fit with initiatives that support student improvement, SEAs are beginning to make inroads into how to leverage funds in more innovative manners. One continued tension in all of this, however, is the “supplement, not supplant” provision of Title I. Title I funds are not meant to replace or substitute for state or local funds but instead to provide additional supports. This means that states and their local districts are required to show that Title I funds are not used to provide services that would otherwise be provided but rather to enhance and add to programming and offerings. SEAs need to be well versed in all of the requirements for leveraging federal, state, and local funds together and should also present coherent guidance to their local districts. Finally, the U.S. Department of Education is similarly signaling a move away from compliance to technical assistance and a results-driven focus. The ESEA Flexibility waivers focus heavily on technical assistance and supports and far less on compliance than has been the case under NCLB. The Office of Special Education Programs has also been rolling out substantial new guidance around IDEA funds and work, asking states to develop statewide Systemic Improvement Plans and specifically directing states to begin focusing on technical

assistance and to move compliance into more of a background role.

The Need for Strategic Policy Evaluation and Research at SEAs Embedded in all of this is the need for SEAs to have responsive and readily available strategic policy evaluation and research initiatives. To provide actionable information at the time when a policy decision is being made or options are being evaluated, SEAs need dedicated human capital resources to be able to leverage the massive statewide longitudinal data systems and other forms of information and provide policymakers with rapid-response answers. Several states, including Massachusetts, Tennessee, and Michigan, have begun to build capacity internally as well as through external partners to provide a range of research and evaluation keyed to the SEA’s priority policy areas. Another strategy being pursued by 12 SEAs around the country is to partner with the Harvard Strategic Data Project. This project places data strategists who have high-level quantitative and policy analysis skills in fellowships at SEAs and school districts around the nation, to increase capacity within the agencies as well as assist states and local districts in obtaining actionable information. Venessa A. Keesler See also Accountability, Types of; Capacity Building of Organizations; Common Core State Standards; Elementary and Secondary Education Act; No Child Left Behind Act

Further Readings Brown, C. G., Hess, F. M., Lautzenheiser, D. K., & Owen, I. (2011, July). State education agencies as agents of change: What it will take for the states to step up on education reform [Online]. Retrieved from http://www .activate-ed.org/sites/default/files/resources/ StateEducationAgenciesasAgentsofChangepdf.pdf Federal Education Budget Project. (2013, June 30). School finance: Federal, state, and local K-12 school finance overview. Washington, DC: New America Foundation. Retrieved from http://febp.newamerica.net/backgroundanalysis/school-finance Gross, B., Jochim, A., Hill, P., Murphy, P., & Redding, S. (2013, May). The SEA of the future: Leveraging performance management to support school improvement. Seattle, WA: CRPE. Retrieved from http:// www.crpe.org/publications/sea-future-leveragingperformance-management-support-school-improvement

State Education Codes Hemelt, S. W. (2011). Performance effects of failure to make adequate yearly progress (AYP): Evidence from a regression discontinuity framework. Economics of Education Review, 30(4), 702–723. Junge, M., & Krvaric, S. (2011, October 25). The supplement not supplant conundrum (Rick Hess: Straight Up) [Weblog post]. Education Week. Retrieved from http://blogs.edweek.org/edweek/rick_hess_ straight_up/2011/10/the_supplement_not_supplant_ conundrum.html Louis, K. S., Leithwood, K., Wahlstrom, K. L., Anderson, S. E., Michelin, M., Mascall, B., . . . Moore, S. (2010, July). Learning from leadership: Investigating the links to improved student learning. New York, NY: Wallace Foundation. Retrieved from http://www. wallacefoundation.org/knowledge-center/schoolleadership/key-research/Pages/Investigating-the-Links-toImproved-Student-Learning.aspx Murphy, P., & Ouijdani, M. (2011, August). State capacity for school improvement: A first look at agency resources. Washington, DC: CRPE. Retrieved from http://www.crpe.org/sites/default/files/pub_states_ statecap_Aug11_0.pdf Rouse, C. E., Hannaway, J., Goldhaber, D., & Figlio, D. (2013). Feeling the Florida heat? How low-performing schools respond to voucher and accountability pressure. American Economic Journal: Economic Policy, 5(2), 251–281. Timar, T. B. (1997). The institutional role of state education departments: A historical perspective. American Journal of Education, 105(3), 231–260.

Websites Strategic Data Project: http://www.gse.harvard.edu/sdp/ U.S. Education Delivery Institute: http://www.delivery institute.org/

STATE EDUCATION CODES The constitutions of all 50 states contain language obligating state governments to provide public education. In response to this constitutional charge, legislatures have established state education codes that specify the set of functions required for the state to fulfill its duty of providing public education. State education codes are compilations of state laws regarding education, and they are changed with the passage of new legislation: adding new mandates, amending existing ones, or repealing outdated provisions. Over the years, education codes have expanded to reflect new federal and state laws

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as well as court decisions. This entry provides a description of the education functions often included in state education codes, the agencies authorized in education codes to carry out these functions, and examples of some of the different approaches taken by states in their education codes to fulfill the constitutional mandate to provide public education. Functions specified in state education codes often include structure and organization, namely, how schools and school districts are organized; finance and business services such as revenue generation, resource allocation, and facilities planning/management; personnel, including teacher licensure, hiring, allocation, evaluation, firing, and training and professional development; and education programs, including development and adoption of state standards and instructional materials, and testing/ assessment. Education codes might also include requirements around student matriculation and graduation requirements, interventions or sanctions for failing schools, and the minimum days or hours for the school year. In addition to specifying the required functions of a state education system, state education codes often authorize the creation of different agencies and bodies to carry out the key functions and outline the powers and responsibilities of each agency. Some examples include state boards of education (SBEs), departments of education, chief state school officers, state superintendents, local superintendents, and local school boards. The structures and processes dictated by state education codes vary across states, with some dictating a more “top-down” approach, vesting the majority of control in the governor’s office or SBE, for example, while others rely on local districts to determine key functions of education such as curriculum, staffing, and resource allocation. Looking across education codes nationally, key differences emerge in several areas, as outlined below. The Composition of the SBE. Most state education codes have explicit rules on the composition of the SBE, starting with whether members are elected or appointed and, if appointed, by whom. Furthermore, education codes often specify that the SBE is required to include a variety of constituents, such as representatives from different regions of the state, political parties, genders, or institutions. For example, the Alaska education code requires that its SBE include representatives from the four judicial districts as well as one student advisor and one military

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advisor, whereas the New Jersey education code requires that the board be composed of at least three women and specifies that no two members may be appointed from the same county. Statutory Control. State education codes often vest powers over different functions at either the state or the local level. For example, in Idaho, the education code grants the SBE power over curriculum, textbooks, and district boundaries, while in Minnesota, the SBE was abolished in 1999 by the legislature and its responsibilities were transferred to the state education commissioner. Collective bargaining is often addressed in education codes, with different scopes of bargaining required or permitted, while in five states, collective bargaining is prohibited. Fragmentation of State Agencies. State education codes, in specifying which institution has responsibility over which functions, determine whether powers at the state level should to be concentrated in a single body or distributed across multiple bodies. For example, the Iowa education code gives substantial power to nonstandard bodies such as the department of management, board of examiners, area education agencies, and a teacher licensing board, while in several states, the SBE oversees these functions. Uniformity of Districts. Education codes, in addition to dictating the structure and organization of a state’s education system, often specify different district arrangements depending on district population, size, and location. For example, in Mississippi, there are consolidated school districts, county school districts, and municipal school districts. Some superintendents and local school board members are elected, while others are appointed, and in some states, different rules govern districts that reach a certain minimum population level (e.g., school districts with fewer than 300,000 residents in Oregon have different rules from those with 300,000 or more residents). Political Integration. Another area addressed by state education codes is whether education-related elections at the state and local levels are synchronized with the rest of the political system. This can include whether the elections for the board, chief state school officers, and superintendents follow the general election cycle and whether elections are on a partisan or nonpartisan ballot. For example, in

Oklahoma, local school board elections follow a different cycle from major elections, and in California, the elections for chief state school officers and local school board members are nonpartisan and follow a different election cycle. Span of State Control. State education codes also specify whether the authority of the department of education or SBE extends beyond K-12, for example, having prerogative over prekindergarten and/or higher education. In addition, as per the education codes of some states, SBEs can develop and/or adopt teacher licensure rules, standards, curricula, instructional materials, teacher evaluation requirements and procedures, graduation requirements, and so on. For example, in Nebraska, the department of education has leadership and support responsibilities for the state’s system of early childhood, primary, secondary, and postsecondary education and carries out its duties on behalf of Nebraska students in public, private, and nonpublic school systems. The comprehensiveness of state education codes varies across states, as does their prescriptiveness. California and Texas have two of the most exhaustive: California’s Education Code consists of approximately 500 chapters and more than 1,250 separate articles, which themselves also contain numerous subarticles. Texas’s state code consists of nearly 3,600 separate articles. In comparison, Florida’s state code consists of about 14 chapters, 60 articles, and related subarticles. North Carolina’s education code is split into five sections consisting of approximately 300 policies. Some interpret lengthy education codes as being overregulation of the work of districts and schools to provide public education. Although specific provisions in state education codes are at times repealed, the general trend has been toward legislative sprawl rather than reduction in regulations. Joanna Smith, Fatima Capinpin, and Hovanes Gasparian See also Evolution in Authority Over U.S. Schools; School Boards; State Education Agencies

Further Readings Bitensky, S. H. (1991). Theoretical foundations for a right to education under the U.S. Constitution: A beginning to the end of the national education crisis. Northwestern University Law Review, 86(3), 550.

Student Financial Aid Brewer, D., & Smith, J. (2008). A framework for understanding educational governance: The case of California. Education Finance and Policy, 3(1), 20–40. Hubsch, A. W. (1989). Education and self-government: The right to education under state constitutional law. Journal of Law and Education, 18, 93–140. James, T. (1991). State authority and the politics of educational change. Review of Research in Education, 17, 169–224.

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$10 billion in student grants. Higher educational institutions ($43 billion), employers, and foundations ($14 billion) also provide significant merit- and need-based aid to students. This entry focuses on need- and merit-based grant programs and reviews the research literature on how this aid affects both college attendance and completion.

Financial Aid and Attendance

STUDENT FINANCIAL AID Higher education provides an opportunity for economic mobility to low-income families. Throughout the past three decades, the returns to college have increased, leading to a surge in demand for higher education. Yet despite the increasing return to higher education, low-income individuals still lag in their opportunity to attend college, and the disparity in college attendance between low- and high-income students continues to be large. College affordability is one potential explanation for the existing gaps between low- and high-income students. College tuition has increased dramatically over the past three decades, and students who either do not attend college or drop out of college frequently cite concerns with affordability. Financial aid policies are the most prominent way that state and federal governments increase college access for low-income families. Financial aid in the broadest sense includes any transfer or subsidy given to students to defer the costs of college. In this broad sense, financial aid includes federal policies such as the Pell grant, Federal WorkStudy Program, federal student loan programs, educational tax credits and strategic programs such as the Federal Academic Competitiveness Grants, SMART (Science and Mathematics Access to Retain Talent) grants, and the GI Bill. In the 2011–2012 school year, the federal government awarded almost $48 billion in grant programs, with most of that in the form of Pell grants ($34 billion). Total expenditure on work-study ($1 billion), subsidized student loans ($110 billion), and educational tax credits ($20 billion) accounted for an additional $131 billion in spending in 2011–2012. States also have aid programs that use both meritand need-based criteria. States may sponsor college savings programs or may provide additional appropriations that may indirectly provide financial aid. In the 2011–2012 school year, states awarded nearly

The earliest research on financial aid found that it did account for an increase in college attendance by students from low-income and middle-class families. In researching the effects of need-based aid policies, researchers need to account for several confounding variables correlated with whether students receive federal need-based grants and how much they receive. For instance, students who are academically prepared are more likely to attend selective and expensive colleges, so the size of aid awards may be larger for students with strong academic credentials than for others. Besides the inherent problems in simple comparisons, data availability was also problematic, making it difficult to make comparisons in the cases where selection bias could be adequately controlled for. Researchers have begun to use experimental and quasi-experimental methods to identify the impacts of financial aid. One such quasi-experimental study, on the Pell Grant Program, did not find any significant positive effect on enrollment. Thomas Kane demonstrated that enrollment among eligible students grew more slowly than enrollment for other students after the Pell Grant Program began in the early 1970s. Other researchers reached similar conclusions. Other natural experiments, such as changes in aid awards, have been used to study the effects of need-based aid programs. Susan Dynarski compared students before and after the removal of a Social Security program that provided tuition benefits to students and found significant effects on both college access and completion rates. While there was a trend of reduced college attendance during this period, there was an even greater reduction in college attendance for students where the father was deceased. Other researchers using quasi-experimental approaches to study need-based aid programs found that Cal Grant awards in California increased college attendance and made students more likely to choose private schools, where they would get a higher award, and that a Florida grant

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program for low-income students made students more likely to attend a public college. Studies of a merit-based aid program that provides free instate tuition, the Georgia HOPE Scholarship, found increases both in enrollment and in students choosing to attend colleges in Georgia rather than in another state. The evidence from these studies shows a mixed picture on the effectiveness of aid, with some more recent causal analyses suggesting positive effects on enrollment from both merit-based and need-based aid.

Financial Aid and College Retention and Completion Research on the effects of aid on college outcomes, including graduation and retention rates, is less advanced than research on its effects on college attendance. The key problem in estimating the impact of financial aid on retention is the endogeneity of attendance. As outlined in the previous section, merit- and need-based aid likely affected enrollment. In some cases, such as in Florida, the change in enrollment was large. There are two effects: (1) the effect of the aid on students who would have attended college in the absence of aid (i.e., how does continued aid improve college success) and (2) the effect of the aid on the cumulative careers of students who would not have attended in the absence of the aid award. However, most of the studies examine the outcomes of the overall population and are unable to distinguish between the two effects. Distinguishing between the two effects could potentially inform and improve policy design. There is some evidence that need-based aid improves degree completion. Dynarski found that eliminating the Social Security death benefit reduced retention as well as attendance. Among students with a deceased parent, the results indicated that increasing aid by $1,000 would improve retention by 3.6 percentage points. Using discontinuities in the Pell formulae caused by small differences in family size and the number of children in college, Eric Bettinger found that Pell grants increase students’ persistence rates during their first year in college. His work suggested that increasing Pell grant awards by $1,000 leads to a 3% increase in persistence during students’ initial year in college. Another study by Bettinger, looking at a change in eligibility for Ohio’s need-based grant program, found that students were 2 percentage

points more likely to persist after their first year in college when they benefited from that change. In examining Florida’s need-based grants, Ben Castleman and Bridget Long also examine the impact of aid on various measures of student success. In this way, they can illustrate important markers of student success. They found that the award increased college attendance rates by 3.2 percentage points. By the end of the first year of college, persistence rates were 4.3 percentage points higher, and ultimately, the probability of having a degree within 6 years increased by 4.6 percentage points. The increase of 4.6 percentage points represents a 22% increase in college completion. The research described earlier focuses on programs that estimate the overall impact of a program on college success. The Wisconsin Scholars Grant, a $3,500 need-based grant given to students who are already enrolled in college, is perhaps the only such program that conducts its application process after students have already begun attending classes. The award augments students’ existing aid award and was randomly assigned. Sara Goldrick-Rab and colleagues used this randomization to estimate the impact. Since students did not learn of the award until they entered school, the results do not conflate the attendance/persistence margins. Goldrick-Rab and colleagues were able to estimate the impact of aid on students who would have been in college even without the award. They found that the award increased four-year graduation rates by 4.5 percentage points, representing a 29% increase in college completion for this population. Most of the other studies measuring the impact of aid on college retention and completion are merit-based programs. A study by Dynarski found an increase in persistence and completion rates by students who receive merit aid in Georgia and Arkansas. However, in both programs, renewal of students’ financial aid awards depended on academic success and progress in school, and it is unclear what role these conditions played in the observed effects. Other work in Georgia suggests that some of the intermediate outcomes may lag. In particular, during students’ first year in college, they are less likely to take a full course load. There is some suggestion that this lack of course-taking is students’ way of gaming the interim achievement requirements. Students are only evaluated on their progress after they have crossed a threshold, and research suggests that students take easier schedules during the terms in which their credit accrual triggers an interim review.

Student Financial Aid

There is other significant research that suggests that credit accumulation is positively affected by aid programs. One study of a merit-based award targeted at low-income adults in Louisiana found that, after three semesters, the $2,000 award had effects on both persistence and credit accumulation for the students who randomly received it. Another study explored interim academic requirements in evaluating the impacts of West Virginia’s PROMISE (Providing Real Opportunities for Maximizing In-State Student Excellence) scholarship. Results showed that the academic requirements incentivized students to continue acquiring credits. Students are much more likely to take full loads and progress in their majors.

Conclusion Student financial aid continues to be an important determinant of low-income students’ attendance and success in college. As this entry has illustrated, need- and merit-based awards affect college attendance, college choice, and ultimately college outcomes. While early studies presented mixed results, much of the recent work that utilizes experimental and quasi-experimental methods finds more positive results. Both state and federal need- and merit-based financial aid produces significant, positive impacts on student outcomes. There are a range of other issues related to financial aid, including student loans and work-study programs, which have received significant attention in recent years. In the coming years, these other programs will receive continued attention. There are two other useful directions that the literature on financial aid has taken that deserve mention. First, there has been continued emphasis on the complexity of the financial aid processes. One study that examined the impact that simplification of financial aid applications has on student attendance and persistence found that the measured effects are large, representing nearly a 30% improvement in college attendance. It may be that the salience of aid programs depends on the complexity surrounding students’ access to the program. Second, some studies examine the relationships between various financial aid strategies. Research that examines how institutional aid is affected by Pell awards found evidence that institutions adjust their financial aid awards so that they capture portions of the Pell grant allotted to students. There is also evidence that in some cases financial aid increases as

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some schools strive to attract talented low-income students. An examination of how state and federal need-based programs interacted, particularly during the most recent recessions, found evidence that state programs actively crowd out, or weaken, federal programs, thereby reducing any potential impact of the programs. To the extent that the incidence of grant programs is compromised, the estimated impacts of student aid could be underestimated in existing studies. Eric Bettinger Author’s Note: This entry draws partly on Bettinger, E. (2012). Financial aid: A blunt instrument for increasing degree attainment. In A. P. Kelly & M. Schneider (Eds.), Getting to graduation: The completion agenda in higher education (pp. 157–174). Baltimore, MD: Johns Hopkins University Press.

See also College Completion; Federal Work-Study Program; Student Loans; Tuition and Fees, Higher Education

Further Readings Bettinger, E. (2004). How financial aid affects persistence. In C. M. Hoxby (Ed.), College choices: The economics of where to go, when to go, and how to pay for it (pp. 207–238). Chicago, IL: University of Chicago Press and National Bureau of Economic Research. Bettinger, E. (2012). Financial aid: A blunt instrument for increasing degree attainment. In A. P. Kelly & M. Schneider (Eds.), Getting to graduation: The completion agenda in higher education (pp. 157–174). Baltimore, MD: Johns Hopkins University Press. Bettinger, E., Long, B., Oreopoulos, P., & Sanbonmatsu, L. (2012). The role of simplification and information: Evidence from the FAFSA experiment. Quarterly Journal of Economics, 127(3), 1205–1242. Brock, T., & Richburg-Hayes, L. (2006). Paying for persistence: Early results of a Louisiana Scholarship Program for low-income parents attending community college. New York, NY: MDRC. Castleman, B., & Long, B. (2013). Looking beyond enrollment: The causal effect of need-based grants on college access, persistence, and graduation [Harvard Mimeo]. Retrieved from http://scholar.harvard.edu/files/ btl/files/castleman_long_-_looking_beyond_enrollment_ -_fsag_paper_7-31-13.pdf Cornwell, C., Mustard, D., & Sridhar, D. (2006). The enrollment effects of merit-based financial aid: Evidence from Georgia’s HOPE Program. Journal of Labor Economics, 24(4), 761–786.

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Dynarski, S. (2000). Hope for whom? Financial aid for the middle class and its impact on college attendance (NBER Working Paper No. 7756). Cambridge, MA: National Bureau of Economic Research. Dynarski, S. (2003). Does aid matter? Measuring the effect of student aid on college attendance and completion. American Economic Review, 93(1), 279–288. Goldrick-Rab, S., Harris, D., & Trostel, P. (2009). Why financial aid matters (or does not) for college success: Toward a new interdisciplinary perspective. In J. C. Smart (Ed.), Higher education: Handbook of theory and research (Vol. 24, pp. 389–426). New York, NY: Springer Science and Business Media B.V. Hansen, L. (1983). The impact of student financial aid on access. In J. Froomkin (Ed.), The crisis in higher education (pp. 84–96). New York, NY: Academy of Political Science. Kane, T. (2003). A quasi experimental estimate of the impact of financial aid on college-going (NBER Working Paper No. 9703). Retrieved from http://www.nber.org/ papers/w9703 Scott-Clayton, J. (2010). On money and motivation: A quasi-experimental analysis of financial incentives for college achievement. Journal of Human Resources, 46(3), 614–646.

STUDENT INCENTIVES Recently, economists have proposed providing financial incentives for students to improve their academic performance. These are usually provided in the form of cash or prizes given to students for high performance on exams or meeting other academic benchmarks. This strategy for improving student outcomes derives from the simple idea that if you pay someone to do something, they will be more likely to do it. Outside of education, this has been shown to be the case both in the laboratory and in real life. Thus, one might expect this rule to apply to students as well. Nonetheless, the empirical evidence on the effectiveness of student incentives has been mixed. Thus, whether financial incentives can be effectively used to improve student performance remains a key open question in the economics of education. This entry provides background on how financial incentives have been used to influence student behavior and summarizes the research on student incentives. First, it discusses experimental and quasiexperimental evidence on the effectiveness of student incentives in primary and secondary schools.

Research in developing countries focuses on incentives to induce students to attend school, while research in the United States has focused on incentives designed to improve achievement both through attaching financial rewards to outputs such as test scores and to inputs such as the number of books a student reads. The entry then discusses incentives in U.S. higher education. Most of these incentives operate through the financial aid system for needy students or through merit aid grants provided by some states. The entry ends with a brief discussion of how behavioral economics has started to inform the design of student incentives.

Student Financial Incentives in K-12 Education Teachers and schools often use some form of incentive to induce students to improve performance. Teachers often provide students with recognition awards (i.e., a gold star on a test) or small prizes (i.e., a pizza party or special field trip) rarely exceeding a few dollars in value. In the past few years, however, economists have conducted a number of experiments that test whether high-value awards can serve as effective tools for improving student outcomes. Incentives to Attend School

A large problem in developing countries is a lack of regular attendance of students in school. Often children—particularly those beyond primary school age—are expected to provide labor income for the family or to assist with household chores instead of attending school. Furthermore, in some cultures, a preference for boys leads to larger investments in male children’s education than in females’ education. Hence, some countries have established programs to incentivize families to send their children to school for longer periods. One of the most well known, and well studied, of these programs is PROGRESA (Programa de Educación, Salud y Alimentación, or the Education, Health, and Nutrition Program) in Mexico that provided cash to poor families in exchange for their children regularly attending school. Research by Jere Behrman, Piyali Sengupta, and Petra Todd along with separate work by T. Paul Schultz show that the program increased enrollment in school and educational attainment. It also generated spillover effects on younger siblings who were not eligible for the payments. Another program in Colombia provided vouchers that helped

Student Incentives

low-income students pay for private school. Joshua Angrist and coauthors found in 2002 that the vouchers led to improvements in educational attainment, time in school, and test scores, though it is unclear how much of that is due to the incentive effect of the voucher or differential productivity between private and public schools. Incentives Linked to Outputs From Education Production

In countries where low school attendance is less of a problem, research has focused on the role of providing incentives linked directly to outcomes, particularly achievement tests, grades, and educational attainment. Typically, these studies have utilized randomized experiments since few states and school districts have implemented these programs on their own. In general, the results have been mixed. One experiment by Michael Kremer, Edward Miguel, and Rachel Thornton paid girls in Kenya for exam performance and found large improvements with spillover effects on boys. In another example, Joshua Angrist and Victor Lavy studied a program in Israel that paid low-achieving students if they passed a graduation exam and also found large positive effects for girls (which further led to increased college enrollment), but not for boys. On the other hand, the evidence on incentives that pay students for test performance in the United States has been more mixed. In a small school district in Ohio, Eric Bettinger randomly provided awards of up to $100 to elementary students for test performance. He found large increases in math but no effect on reading, social science, or science exams. Roland Fryer has conducted similar experiments in Chicago and New York, with the experiment in Chicago involving cash awards for grades and that in New York for test scores. The use of large districts allow for substantially larger samples and multiple contexts. Even so, Fryer has not found confirmatory evidence of positive effects, though he cannot fully rule out modest positive impacts. Finally, C. Kirabo Jackson studied a program in Texas that gave awards to students and teachers for scoring well on Advanced Placement exams. He found the awards to be effective at improving Advanced Placement scores and that they increased both college going and grades when enrolled in college. If students become used to receiving short-term gains from education, a substantial worry is that they will no longer be motivated by the potential

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long-term gains and personal enrichment provided by education. This is often described as a loss of intrinsic motivation due to the increase in extrinsic motivation generated by the financial incentives. Unfortunately, for the time being, there is little empirical evidence on whether financial incentives to students have this negative long-term impact. Nonetheless, Fryer asks students about their intrinsic motivation in his study and finds little impact from the incentives. Incentives Linked to Inputs in Education Production

While most incentive schemes involve paying students for their performance on an exam or grades (outputs), some researchers have investigated the potential for paying students to do tasks that are believed to improve learning, or inputs into education production. One concern with providing awards for outcomes is that some students may be incentivized to get the award but would not know how to achieve their goals. Incentivizing inputs such as reading books, solving math problems, or writing essays has the potential to work around this problem. To date, however, evidence on the impacts of providing input incentives is thin. One key study by Fryer paid students in Dallas, Texas, $2 for every book they read for up to $20 in total. While he found no effect overall, there were large, positive impacts on performance on the English exam by students other than English Language Learners, which were offset by negative effects for English Language Learners on that exam. Since English Language Learners tend to read books in Spanish, Fryer speculates that this could have reduced their English achievement.

Student Financial Incentives in Higher Education Impacts of Financial Aid and Scholarships on College Attendance and Success

In the United States, public funds are used to support college education through direct aid to students and subsidies to universities. Most of this student aid comes in the form of grants and low-interest loans to students with low incomes, collectively called financial aid. The purpose of this aid is to induce low-income persons to attend college and increase college attendance. The availability of grants, or payments to students to offset tuition and other costs of attendance,

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has had tremendous impacts on college attendance. A number of researchers using research designs that allow for causal interpretations of results have found that the provision of financial aid grants increases college attendance, persistence, and attainment, showing these grants to be effective incentives for students to advance their education. Our understanding of the impacts of loans, on the other hand, is more limited. At best, there is suggestive evidence that expansions of student loan availability led to higher enrollment, as did the reduction of credit constraints brought on by the housing price boom in the mid-2000s. Some states have instituted grants based on a student’s performance in high school instead of, or in addition to, financial need. As with financial aid, there is substantial evidence that these programs also increase enrollment. Nonetheless, there are some caveats. For example, research by Chris Cornwell, Kyung Hee Lee, and David Mustard on Geogia’s HOPE program has shown that much of the increase in enrollment there is due to students switching from out-of-state to in-state schools. A number of these programs also institute requirements for students to maintain certain enrollment statuses and grade point average (GPA) minimums to maintain their scholarships. Thus, they provide a potential incentive to not just induce students to enroll in college but to also improve performance while enrolled. In this case, the evidence is mixed. The merit aid programs in Georgia, Arkansas, and West Virginia appear to have increased persistence in college, with additional evidence of higher GPAs in West Virginia. However, in Georgia, there were also indicators that recipients took longer to earn a degree and strategically selected courses to make it easier to meet GPA requirements. Furthermore, an experiment in Ontario, Canada, by Joshua Angrist, Tyler Williams, and Philip Oreopoulos found evidence that students targeted the GPA thresholds set by the experiment, but this did not lead to an overall increase in GPA.

The Potential for Behavioral Economics to Inform Student Incentives The last portion of this entry will discuss the increasing realization by education economists that behavioral economics plays a large role in student incentives. Behavioral economics is a field of economics that studies behaviors of people that diverge from what standard economic models predict. For

example, behavioral economics seeks to explain why people tend to stay with a default option even when the cost to switch is negligible and why people sometimes forgo opportunities to get “free” money. In terms of student incentives, there is some evidence that these seemingly unimportant aspects of incentive design actually have large impacts on outcomes. To test some aspects of behavioral theory, Steven Levitt, John List, Susanne Neckermann, and Sally Sadoff conducted an experiment in three school districts outside Chicago. In each experiment, they provided students with cash rewards for performing well on an exam. The students only knew about the rewards immediately prior to the exam, so the researchers were able to see responses in terms of test-taking effort but not learning effort. Furthermore, they randomly assigned some students to receive the reward 1 month later or to receive a prize of equal value instead of cash. Rewards given immediately after the exam were effective while delayed rewards were not, and for younger students, prizes were more cost-effective. The impact of providing these students prizes worth $3 was equivalent to the impact of cash awards between $10 and $20. Behavioral economics also has a potentially large role to play in the use of incentives in higher education. One way is through potentially improving student loan programs. A recent study by Brian Cadena and Benjamin Keys shows that, potentially due to an aversion to taking on loans or in an attempt to control their future spending, as many as one in six students offered interest-free loans turn them down. This could indicate that student loans may not provide as large an incentive for increasing college attendance and persistence as one might hope.

Conclusion Student incentives have the potential to generate substantial improvements in education outcomes. Nonetheless, the current empirical evidence on their effectiveness is mixed and specific to particular situations. Incentives to encourage students to attend primary and secondary schools in developing countries and colleges in developed countries have been highly effective. Financial incentives to improve the performance of students while enrolled have been shown to be effective in some cases but not in others. Nonetheless, there is little evidence that such incentives negatively affect student outcomes in the

Student Loans

short run. Even so, long-run impacts of these incentives are yet to be studied in depth. Scott A. Imberman See also Behavioral Economics; Pay for Performance; Quasi-Experimental Methods; Student Financial Aid; Student Loans

Further Readings Angrist, J., Bettinger, E., Bloom, E., King, E., & Kremer, M. (2002). Vouchers for private schooling in Colombia: Evidence from a randomized natural experiment. American Economic Review, 92(5), 1535–1558. Angrist, J., & Lavy, V. (2009). The effects of high stakes high school achievement awards: Evidence from a randomized trial. American Economic Review, 99(4), 1384–1414. Angrist, J., Oreopoulos, P., & Williams, T. (2010). When opportunity knocks, who answers? New evidence on college achievement awards (Working Paper No. 16643). Cambridge, MA: National Bureau of Economic Research. Behrman, J., Sengupta, P., & Todd, P. (2005). Progressing through PROGRESA: An impact assessment of a school subsidy experiment. Economic Development and Cultural Change, 54(1), 237–275. Bettinger, E. (2012). Paying to learn: The effect of financial incentives on elementary school test scores. Review of Economics and Statistics, 94(3), 686–698. Cadena, B., & Keys, B. (2013). Can self-control explain avoiding free money? Evidence from interest-free student loans. Review of Economics and Statistics, 95(4), 1117–1129. Cornwell, C., Lee, K., & Mustard, D. (2005). Student responses to merit scholarship retention rules. Journal of Human Resources, 40(4), 895–917. Dynarski, S., & Scott-Clayton, J. (2013). Financial aid policy: Lessons from research. Future of Children, 23(1), 67–91. Fryer, R. (2011). Financial incentives and student achievement: Evidence from randomized trials. Quarterly Journal of Economics, 126(4), 1755–1798. Gneezy, U., Meier, S., & Rey-Biel, P. (2011). When and why incentives (don’t) work to modify behavior. Journal of Economic Perspectives, 25(4), 191–209. Jackson, C. K. (2010). A little now for a lot later: A look at a Texas Advanced Placement incentive program. Journal of Human Resources, 45(3), 591–639. Kremer, M., Miguel, E., & Thornton, R. (2009). Incentives to learn. Review of Economics and Statistics, 91(3), 437–456.

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Levitt, S., List, J., Neckermann, S., & Sadoff, S. (2012). The behavioralist goes to school: Leveraging behavioral economics to improve educational performance (Working Paper No. 18165). Cambridge, MA: National Bureau of Economic Research. Schultz, P. (2004). School subsidies for the poor: Evaluating the Mexican PROGRESA poverty program. Journal of Development Economics, 74(2), 199–250.

STUDENT LOANS Student loans are a financial instrument that can be used to borrow money for educational expenses, typically at a postsecondary level, such as tuition and fees, books, and living expenses. Education loans are a means to finance human capital investments and can aid students’ attempts to acquire knowledge and skills by providing money to pay for college. Benefits of college can include not only private rewards, such as higher earnings and better health, but also social benefits, such as reduced crime and more civic participation. This entry will cover how typical student loans work and the decisions to borrow, lend, and repay, and it will conclude with an overview of the education credit market.

How Typical Student Loans Work Borrowers initially receive funds from lenders, and in return, they are required to remit a stream of future payments. Loans typically come with a cost, as borrowers pay interest (a charge by the creditor for the use of funds) on the borrowed money. The amount that students are able to borrow and the interest rate charged can depend on the cost of attendance, the level of other financial resources available to the student, and the lender, among other factors. Most student loans are installment loans, where debt obligations are repaid in a fixed number of regular monthly payments over a 10- to 25-year time period. Examples of alternative repayment plans are incomebased or income-contingent repayment, where remittance amounts vary with debtors’ incomes.

Decisions to Borrow In the absence of personal or other financial resources to completely cover costs, many students and their families need to borrow in order to attend college. In the United States, estimates indicate that more than half of the students borrow education loans

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annually. The total and per-student amount of student loan borrowing increased in the United States in the 2000s, and loans also comprise an increasing portion of students’ college financing strategies. The decision of whether and how much to borrow is connected to the decision of whether to attend college. An individual would be expected to enter college if her present value of expected benefits, such as an earnings premium associated with attending college, exceeds the present value of the costs, including tuition, fees, and foregone earnings. In other words, a prospective student should compare the positives she anticipates to gain by attending college against the negatives and attend if the former outweighs the latter. Higher earnings are a significant expected benefit to college, as research consistently demonstrates that graduating from college is associated with relatively positive job market outcomes. For example, the U.S. Bureau of Labor Statistics estimates that in 2012, bachelor’s degree holders had nearly half the unemployment rate and more than 1½ times the weekly earnings of those that completed no further education beyond high school. Students’ calculations of costs and benefits of entering college are far from straightforward, however. Students must calculate benefits among a number of uncertainties, such as macroeconomic conditions, and with heterogeneous returns to education across college types, college majors, careers, and student abilities. For example, students may have difficulty estimating their probability of program completion or the expected earnings differences across fields. Moreover, costs such as foregone earnings may be difficult to precisely predict, and when deciding to borrow, students must be able to understand relatively complicated financial concepts related to student loans. In periods with high unemployment or with low college wage premiums, a number of concerns can arise related to student borrowing beyond just the costs of default. High levels of student debt can burden students and be an economic drag. Government estimates indicate that outstanding student loan debt in the United States is approaching $1 trillion in 2013, making it the second largest sector of debt in the country behind housing. Students with a great deal of debt may reduce consumption, have reduced access to the credit market, or delay purchases of wealth-building assets such as houses. High debt burdens, moreover, may influence students’ postcollege career choices. For example, when faced with high debt, students may be more likely to choose

higher salary jobs, such as in the finance industry, instead of lower paying jobs, such as in the nonprofit or education sectors, that may be more closely associated with serving the “public interest.”

Decisions to Lend In loan transactions, lenders provide borrowers money in exchange for future repayments of the amount borrowed plus extra charges. A key issue related to student loans is the price (e.g., interest rate and fees) to charge for educational credit. In addition to covering capital, information, and processing costs, the price of credit in other contexts, such as housing finance or payment cards, is often related to the risk of borrower default. Education loans have a number of characteristics that would be expected to lead to high risk-based prices. This, however, may conflict with public goals of encouraging access to education, which is one reason why interest rates in government-sponsored loan programs are frequently maintained at a relatively low level. Educational loans are typically not secured by physical assets. In the event of default on a loan secured by collateral, the lender will take ownership of the asset placed against the debt, such as a house or automobile; sell it off; and close the loan. Collateral helps lower prices of debt by reducing the costs associated with default. Consider, however, if a student loan borrower defaults on debt obligations. There is typically no collateral for the lender to repossess, since the assets in the transaction are the increased skills of the borrower. A second distinguishing factor of student lending is that student-borrowers often have thin credit histories. In the absence of collateral to secure the debt, lenders would look to a record of creditworthiness, such as a high credit score, in order to motivate the underwriting of the loan. Many students have not yet had the opportunity to establish a strong credit profile. Borrowers’ probability of default is difficult to estimate without signals of creditworthiness based on prior behavior, leading to increased risk and therefore higher prices. Adding to the challenge, lenders need to forecast students’ ability to repay in the future, after students attend college and therefore have higher, though uncertain and heterogeneous, expected earnings potential. An increasingly common practice because of many students’ sparse credit histories is to require cosigners on student loans. A cosigner will normally have adequate credit history, income, and assets to support the student’s

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borrowing requests and will have to assume repayment responsibility if the student defaults.

Decisions to Repay Decisions to repay student loans will depend on borrowers’ ability to repay, along with the costs and benefits of default. In the event of an income or asset shock, such as a job loss or drop in house prices, borrowers may not have the ability to service their student loan debt. When faced with financial constraints, moreover, borrowers may use available resources to fund consumption or pay down other debt that preserves liquidity. Reflective of the recent difficult economic conditions, the U.S. Department of Education reports a 2010 default rate on federal program loans of more than 9%, about twice the lowest annual rate of the previous decade. Costs of loan default can be substantial. Default limits future access to, and raises prices in, the credit market because of a damaged credit profile, impairing borrowers’ ability to finance future asset purchases. As well, the government can garnish borrowers’ wages and tax returns if borrowers default on certain loan program obligations. Because borrowers do not have to relinquish houses, cars, or other physical assets placed against debt obligations, fears that student loan borrowers will engage in strategic default have nevertheless motivated policy decisions. An approach to address concerns about strategic default behavior and lenders’ lack of recovery in the event of a default has been to prevent student loans from being expunged or reduced through bankruptcy. Borrowers in the United States cannot typically purge themselves of either federal or private education debt obligations through bankruptcy, except in cases of undue hardship. This increases the amount of expected recovery by the lender in the event of default, potentially lowering prices. However, critics of the bankruptcy protection argue that it prevents struggling borrowers from financially rebuilding in the event of a hardship.

The Educational Credit Market There are two primary types of student loans in the United States: (1) federal loans and (2) nonfederal loans. Nonfederal loans include those originated by private lenders, as well as loans from state and postsecondary institutions programs. In the United States, federal loans comprise approximately 80% of loan disbursements in the 2000s. Examples of

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federal loan programs include the Robert T. Stafford Student Loan Program, the Parent Loans for Undergraduates Program, and the Federal Perkins Loan Program. Along with other federal student financial aid programs such as Pell grants, the largest federal education loan programs are authorized by Title IV of the Higher Education Act of 1965 and subsequent amendments. From the early 1990s until 2010, most federal loan programs were available through two different delivery mechanisms in the United States: (1) the William D. Ford Federal Direct Loan Program (“Direct Loan”) and (2) the Federal Family Education Loan (FFEL) Program. New originations under the FFEL program were discontinued in 2010, leaving the federal government as the remaining provider of most federal loans through the direct loan program. Though terms and borrower eligibility rules under the two programs were equivalent, the source of funds differed. The federal government is the lender and provider of funds under the direct loan program, whereas private lenders, such as banks and some schools, financed loans under the FFEL program. Under FFEL, in addition to public guarantees on the amount owed, private lenders received subsidies from the federal government to maintain a federally mandated interest rate level and to cover expenses associated with loan origination. Federal loan programs in the United States have many distinguishing features as compared with private lender loans. Most borrowers qualify for federal student loan programs as long as they attend an eligible institution, and rates are typically constant across all types of borrowers, such that interest rates do not vary with expected default risk. Interest rates in federal loan programs are generally subsidized by the government and therefore offered at a lower rate than can be found from private lenders. Some programs have extra benefits for students who demonstrate financial need. In addition, some programs allow students to avoid accruing interest while in school and during grace periods, and students can also often postpone or forbear government loan obligations during times of enrollment or hardship. Some researchers contend that government subsidies and guarantees lead to an overextension of educational credit. A reason used to justify government involvement in the student loan market, however, is to address the problem of social underinvestment in education. Many believe that public benefits such as reduced crime and more civic participation result in social returns to education that exceed private

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returns. Consequently, some individuals may not invest in their education at a socially optimal level without a public subsidy. Private employers, moreover, tend to underinvest in generalized training for their employees. An imperfect capital market, where students are unable to borrow uncollateralized loan money against future earnings, contributes to the social underinvestment problem. Human capital investments are not as easy to finance as physical capital investments, since educational credit is generally not secured by collateral and because productivity depends on borrower cooperation. Therefore, a robust student loan market potentially improves efficiency by increasing the supply of highly skilled workers. Credit constraints are often used to explain gaps in college enrollment between families with high and low incomes. Students from high-income families are more likely to be able to rely on family endowments to defray college costs. Students from low-income families, however, are expected to have fewer private resources to pay for expenses and therefore attend college. Access to student loans, therefore, can play an important role in encouraging college attendance across income levels in an environment where students must finance at least a portion of their schooling. It should be noted that some researchers find evidence of a competing, but not necessarily mutually exclusive, interpretation of college-going differences among income classes. Under this theory, since long-term income factors that influence students’ cognitive and noncognitive development contribute to college attendance decisions, the availability of educational credit may not be sufficient to solve the problem. Rajeev Darolia See also Federal Perkins Loan Program; Higher Education Finance; Stafford Loans; Student Financial Aid; Tuition and Fees, Higher Education

Further Readings Avery, C., & Turner, S. (2012). Student loans: Do college students borrow too much—or not enough? Journal of Economic Perspectives, 26(1), 165–192. Baum, S., & Payea, K. (2012). Trends in student aid. New York, NY: College Board Advocacy & Policy Center. Campaigne, D. A., & Hossler, D. (1998). How do loans affect the educational decisions of students? Access, aspirations, college choice, and persistence. In R. Fossey & M. Bateman (Eds.), Condemning students to debt:

College loans and public policy (pp. 85–104). New York, NY: Teachers College Press. Dynarski, M. (1994). Who defaults on student loans? Findings from the National Postsecondary Student Aid Study. Economics of Education Review, 13(1), 55–68. Heller, D. E. (2008). The impact of loans on student access. In S. Baum, M. McPherson, & P. Steele (Eds.), The effectiveness of student aid policies: What the research tells us (pp. 39–68). New York, NY: College Board. Lochner, L. J., & Monge-Naranjo, A. (2011). The nature of credit constraints and human capital. American Economic Review, 101(6), 2487–2529. Rothstein, J., & Rouse, C. (2011). Constrained after college: Student loans and early career occupational choices. Journal of Public Economics, 95(1–2), 149–163.

STUDENT MOBILITY The term student mobility is most often used to refer to the movement of students from one school to another. Students change schools for a variety of reasons, and these can be used to distinguish between two main types of mobility: (1) structural mobility and (2) nonstructural mobility. Structural mobility refers to moves that occur due to the structure of the school system, such as when students advance from elementary school to middle school or when they change schools due to redistricting. Nonstructural mobility refers to moves that occur for other reasons, such as residential mobility or parents seeking out better schools for their children. Residential mobility can be precipitated by positive or negative life events (e.g., job promotion, job loss, marriage, divorce, buying a house, or eviction) and can also be the result of parents moving to jurisdictions that share their preferences for school quality (the process by which consumers move to communities that share their preferences for government services and taxes is known as Tiebout sorting). Parents may also seek out different schools without changing residences, for example, by transferring their children to private or charter schools. Most research studies on student mobility focus on nonstructural, rather than structural, mobility, and some definitions of student mobility explicitly exclude moves that are due to grade promotion. Student mobility is of interest for several reasons: First, it is relatively common in the United States. Second, research suggests that it is associated with worse academic outcomes for students, though the

Student Mobility

findings vary across different types of moves. Finally, student mobility has implications for education policy; while many polices aim to decrease student mobility or ameliorate its negative effects, other policies, such as school choice, are premised on increasing student mobility, at least temporarily, to facilitate the movement of students to higher quality schools. The remainder of this entry discusses the frequency of student mobility, the relationship between student mobility and academic outcomes, and the implications of student mobility for education policy.

Frequency of Student Mobility Student mobility is relatively common in the United States. For example, a recent Government Accountability Office report found that 13% of students in Grades K-8 had changed schools four or more times, and a report by the National Research Council and Institute of Medicine found that in 1998, 34% of 4th graders, 21% of 8th graders, and 10% of 12th graders had experienced at least one school move in the previous 2 years. While there is little information about the exact reasons why students change schools, mobile students are more likely to come from disadvantaged backgrounds. Black and Hispanic students are more likely to change schools than White students, and students from lower socioeconomic status (SES) families are more likely to move than those from higher SES families. The relationship between student mobility and SES suggests that many moves may be related to issues that are more likely to be faced by lower SES families. School officials interviewed by the Government Accountability Office cited job loss, foreclosure, eviction, and family instability as reasons for student mobility.

Student Mobility and Academic Outcomes Because students have diverse reasons for changing schools, student mobility could, in theory, be related to higher or lower academic outcomes. Student mobility is typically expected to cause lower achievement because moving could disrupt children’s relationships with teachers and peers and interrupt the continuity of instruction. However, student mobility could cause higher student achievement if students move from lower quality to higher quality schools, and any disruptive effects of the move are offset by gains from improved school quality. Student mobility could also be correlated with lower achievement if it signaled the presence of other disruptive

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factors, like family or personal problems, which affect achievement. Most of the empirical evidence on the relationship between student mobility and academic outcomes comes from studies that examined nonstructural student mobility. This research suggests that nonstructural student mobility is associated with a range of worse academic outcomes, such as lower test scores, increased grade retention, and lower rates of high school graduation. However, it is not clear to what extent this association represents a causal relationship, since mobile students could differ from nonmobile students in other ways that affect achievement. Studies usually deal with this problem by examining whether the relationship between student mobility and achievement remains after controlling for other variables, such as premobility achievement or family income. Yet because studies can only account for the variables they observe in the data, there is always the possibility that unobservable factors—like job loss, divorce, or eviction—could confound the relationship between student mobility and achievement. Consistent with the notion that mobile students would likely have lower achievement even if they had not changed schools, numerous studies have shown that controlling for students’ background characteristics substantially reduces the estimated effect of mobility on achievement. For example, Karl L. Alexander, Doris Entwisle, and Susan L. Dauber found that mobility no longer had a significant effect on achievement after the inclusion of these controls, and Judy Temple and Arthur J. Reynolds found that these controls explained half of the achievement gap between mobile and nonmobile students. However, even after controlling for background characteristics, several studies still found statistically significant, negative effects of mobility on achievement. For example, Reynolds, Chin-Chih Chen, and Janette E. Herbers conducted a meta-analysis of 16 studies that included controls for premobility achievement and background characteristics and found that student mobility had a statistically significant relationship with lower student achievement and a greater likelihood of dropping out of school. The relationship between student mobility and achievement varies by the type of moves. For example, the meta-analysis by Reynolds, Chen, and Herbers found that higher numbers of moves were significantly associated with worse academic outcomes, and Mariesa Herrmann found that nonstructural student mobility during the summer had smaller negative effects on student achievement

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than mobility during the school year. This could be because certain types of moves (e.g., more frequent moves and moves during the middle of the school year) are more disruptive than others or because the frequency and timing of moves is associated with other factors like family or personal problems that affect achievement. The effect of student mobility on achievement depends not only on whether moves cause disruption but also whether students move to a better or worse school. Eric A. Hanushek, John F. Kain, and Steven G. Rivkin investigated the relationship between student mobility and school quality by examining the relationship between the locations of moves and postmobility student achievement. They argued that the differences between the effects of these moves could reflect changes in school quality and found that moves to a different district within the same region significantly increased achievement but moves within district and moves to a new district in a new region did not. The literature on school choice and charter schools also provides evidence that student mobility has heterogeneous effects on achievement. There is limited literature on the effects of structural mobility on achievement, and findings from this literature are mixed. Jonah Rockoff and Benjamin Lockwood compared students from elementary schools with different grade configurations (K-5, K-6, and K-8) and found that students who moved to middle schools had lower achievement than students who remained in the K-8 schools. Marisa de la Torre and Julia Gwynne found that school closures had negative effects on student achievement in the year prior to closure (the year closure was announced). However, in the years after the move, they found no effect from school closures on achievement or on the freshman on-track indicator, which uses course credits earned in ninth grade to determine how likely students are to graduate within 4 years. They also found that the effect of structural mobility due to school closure depended on the characteristics of the receiving school: Students who moved to new schools with higher achievement performed better than those who moved to new schools with lower achievement. In addition to affecting the achievement of mobile students, student mobility could also affect the achievement of students’ peers, for example, because teachers need to help new students catch up. In their study of student mobility, Hanushek, Kain, and Rivkin examined peer effects by comparing cohorts

who attended the same school and grade. They found that cohorts with higher student turnover had lower achievement than those with lower student turnover. However, it is worth noting that the larger peer effects literature—some of which exploits natural experiments that induced student mobility and the resulting changes in peer composition—has generally found mixed evidence of peer effects.

Implications for Education Policy Student mobility has several implications for education policy. First, it may reduce the effectiveness of some school-based interventions (e.g., the adoption of new curricula), since these interventions can only affect students who remain in the same school. Second, there are a number of policies that districts and schools could implement to reduce mobility. For example, Russell W. Rumberger noted that schools and districts could limit redistricting and provide supports (e.g., transportation) to enable students who change residences to remain in the same school. Third, schools and districts could implement policies to mitigate the negative effects of mobility on achievement. The National Research Council and Institute of Medicine highlighted several approaches, like providing distance learning to the children of migrant workers, developing intake procedures to assess students’ needs, and expanding community schools that provide students with supports such as health services and counseling. Finally, not all educational policies attempt to reduce student mobility; some policies encourage student mobility to facilitate the movement of students from lower to higher quality schools. For example, school accountability or school choice policies (e.g., vouchers or charter schools) use the availability of student mobility to motivate school improvement. These policies could increase student mobility as students move from lower performing to higher performing schools. Mariesa Ann Herrmann See also Charter Schools; Educational Vouchers; Peer Effects; Tiebout Sorting

Further Readings Alexander, K., Entwisle, D., & Dauber, S. (1996). Children in motion: School transfers and elementary school performance. Journal of Educational Research, 90, 3–12. Government Accountability Office. (2010). K-12 Education: Many challenges arise in educating students who change

Supplemental Educational Services schools frequently (United States Government Accountability Office Report to Congressional Requesters No. GAO-11–40). Washington, DC: Author. Hanushek, E. A., Kain, J. F., & Rivkin, S. G. (2004). Disruption versus Tiebout improvement: The costs and benefits of switching schools. Journal of Public Economics, 88, 1721–1746. Herrmann, M. A. (2012). One size fits all? The effect of curriculum standardization on student achievement. In Three essays on the economics of education (Unpublished doctoral dissertation). Columbia University, New York. Retrieved from http://hdl.handle. net/10022/AC:P:13197 de La Torre, M., & Gwynne, J. (2009). When schools close: Effects on displaced students in Chicago Public Schools. Chicago, IL: University of Chicago Consortium on Chicago School Research. National Research Council and Institute of Medicine. (2010). Student mobility: Exploring the impact of frequent moves on achievement: Summary of a workshop. Washington, DC: National Academies Press. Reynolds, A. J., Chen, C.-C., & Herbers, J. E. (2009, June). School mobility and educational success: A research synthesis and evidence on prevention. Paper presented at the Workshop on the Impact of Mobility and Change on the Lives of Young Children, Schools, and Neighborhoods, The National Academies, Washington, DC. Retrieved from http://fcd-us.org/sites/default/files/ ReynoldsSchoolMobilityAndEducationalSuccess.pdf Rockoff, J., & Lockwood, B. (2010). Stuck in the middle: Impacts of grade configuration in public schools. Journal of Public Economics, 94, 1051–1061. Rumberger, R. W. (2002). Student mobility and academic achievement. Champaign, IL: ERIC Clearinghouse on Elementary and Early Childhood Education. Temple, J., & Reynolds, A. (1999). School mobility and achievement: Longitudinal findings from an urban cohort. Journal of School Psychology, 37(4), 355–377.

SUPPLEMENTAL EDUCATIONAL SERVICES Supplemental educational services (SES) are a major federal intervention introduced by the No Child Left Behind Act of 2001 (NCLB, Pub. L. No. 107110), which reauthorized the 1965 Elementary and Secondary Education Act. The SES program is nested in the law’s Title I program, the largest categorical federal education program in the United States. The NCLB requires schools to offer SES if they receive Title I funding and fail to meet targets for student

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performance for three or more consecutive years. Eligible students at these schools can receive tutoring through the SES program. This entry provides an overview of the rationale for SES, describes the major guidelines for the program, and considers research on its implementation and effectiveness. It concludes with a look toward the future of SES. The theory of action of SES is rooted in marketbased reforms, specifically that market strategies of choice and competition are a necessary condition for improving the quality of services while reducing costs. The underlying intent of the program appears directed toward avoiding undue costs and/ or barriers for providers of tutoring services and limiting the role of government in regulating these providers. One example of this is the program’s flexibility around how providers demonstrate effects. Providers can use any assessment, as opposed to the standardized assessments required of public agencies under NCLB.

Guidelines No new federal monies were allocated to support the delivery or management of SES. NCLB lays out criteria and guidelines for state and local educational agencies in approving SES providers, arranging for their services, and managing contracts and financial systems. School districts with eligible schools are obligated to set aside 20% of their federal Title I funding for SES and to measure provider effectiveness in increasing student achievement. Under SES, tutoring must be provided outside of the school day, and states are urged to promote expansive choice in registering nonprofit, for-profit, faith-based, and community organizations. In the White House proposal for NCLB, SES, or “extra tutoring,” is described as a “consequence” or “corrective action” for schools that fail to make adequate yearly progress for disadvantaged students. These services typically include tutoring and remediation in reading and mathematics. Providers may include public or private (nonprofit or for-profit) organizations, such as public, charter, and private schools; educational service agencies; higher education institutions; faith-based and community-based organizations; and private businesses. According to the law, the content and educational practices of SES should be aligned with the state’s academic content standards and applicable federal, state, and local health, safety, and civil rights laws [Title I, Part A, § 1116(e) (12)(B)(i)] and should be based on high-quality

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research evidence of their effectiveness in increasing student academic achievement [Title I, Part A, § 1116(e)(12)(C)]. In fact, the law requires states to withdraw approval from SES providers that fail for 2 years to increase student academic achievement. Participation in SES is voluntary among eligible students. Under NCLB, eligible students are those from low-income families who attend schools that failed to meet the law’s targets for three or more consecutive years. School districts usually use participation in the free and reduced-price lunch program to determine whether students are from low-income families. Some school districts use additional criteria, such as student performance and/or grade level, to determine eligibility. School districts are required to notify families of their children’s eligibility and the availability of approved SES providers.

Implementation Research As SES expanded tutoring opportunities for lowincome students, a substantial number of diverse organizations have entered the market to compete for available SES funds. Since the enactment of the law, the flux in the SES vendor market has been considerable. Many smaller organizations enter and leave after attracting few students, while others have rapidly expanded their share of students served. However, as the market matured, a number of national and international education companies have started to dominate the market, particularly online providers. Online providers, such as Educate Online, provide tutoring services to students remotely via computer and the Internet. Some school districts, such as Chicago Public Schools, have also operated their own SES programs—though as this is conditional on the district making adequate yearly progress, district roles as providers also vary from year to year. The substantial year-to-year fluctuations complicate state and local educational agency efforts to comply with NCLB requirements to identify organizations that provide services consistent with state and local instructional programs and to withdraw approval from providers that fail to increase student academic achievement for 2 years. In theory, parents and students should be holding SES vendors accountable through their choices of providers. They ostensibly use information distributed by school districts and SES providers to identify the best provider to meet the children’s needs. Students who become aware of their eligibility may choose to register for SES with a specific SES provider, and SES providers invoice the school district

for the number of hours these students attend, up to a maximum per-student dollar allocation. However, the service agreement between a district and its SES providers is, effectively, a cost-reimbursement contract, albeit one with no performance contingencies. In addition, only state educational agencies, not districts, have the authority to approve SES providers and establish program criteria, such as an acceptable student-tutor ratio. Districts have to establish additional criteria (preferably before registration for services opens) to determine which eligible students will get access to services if more students are expected to sign up for SES than there are funds available to serve them. But not all students decide to follow through in attending with a chosen provider even if they are eligible and given the opportunity to register for SES. And some stop attending before their total SES dollar allocation is expended. Differences exist between SES-eligible students who register and attend SES and those who do not. The percentage of days absent in the previous school year is one of the most consistent, negative predictors of registration for SES and for SES attendance across districts. Research also suggests that students more frequently absent during the regular school day, for example, students are more likely to forgo an afterschool option. One of the most consistent, positive predictors of SES registration and attendance is whether the SES-eligible students attended SES in the previous school year.

Effectiveness Research Rigorous research on SES tutoring under NCLB has found few statistically significant, positive average impacts of these tutoring programs on student achievement. However, existing research has identified the conditions under which SES tutoring is likely to be effective. For example, one study employed a regression discontinuity design to look at the impact of offering tutoring to third- through eighth-grade students. The study found no statistically significant impact of participating in out-of-school-time tutoring on student achievement in reading or mathematics. However, across the districts studied, students received an average of just 21 hours of outof-school-time tutoring over the school year. Other research suggests that reaching some minimum threshold of tutoring hours (i.e., approximately 40 or more hours according to the current evidence base) is critical to producing measurable effects on students’ achievement.

Supplemental Educational Services

Related Research The features of SES that are key to its effectiveness—activities and resources used in instruction, the nature of interactions between students and providers, and institutional and structural elements that influence tutoring practices—are among the least visible to states and school districts. That said, afterschool tutoring programs have long been in operation, including federally funded programs, and there is a large body of research on their implementation and effects, including studies specifically focused on SES. Afterschool programs that provide multiple kinds of learning activities, are aligned with schoolday curriculum, employ qualified staff, and have small tutor-student ratios (no greater than 1:3) are consistently identified as exemplifying best practices. Since SES began, school districts have been under pressure to comply with federal requirements to make SES available to as many eligible students as funding allows and to assess vendor effectiveness in increasing the achievement of participating students. Some school district accountability and evaluation units attempt to measure program effectiveness and, in some cases, SES provider efficacy. However, both district staff and researchers face numerous challenges to properly evaluating student- and vendor-level SES effects. One of the biggest challenges is that who gets tutored in SES programs and for how long is influenced by a variety of factors, including student and family characteristics and program administration.

Limitations of Research The fact that SES takes place outside of the regular school classroom and that instructional practices are known to vary significantly—not only between providers but also within the same provider depending on the setting and specific tutor—further challenges state and local agency efforts to acquire knowledge of SES content and effectiveness. Differences between the eligible students who register and attend SES tutoring and those who do not can be adjusted when estimating SES effects. However, if those registering and attending differ in ways that are not observed or measured and that relate to student achievement—in the level of encouragement they receive from regular school-day teachers, for example—generating accurate estimates of SES effects can instead prove very difficult. Rigorous research on provider-level effects is lacking. Some states and districts rely only on

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information self-reported by providers or from datagathering efforts such as parent satisfaction rates from voluntarily completed surveys (with very low and selective participation). Other districts attempt to make use of the data they collect on student SES attendance and provider invoices for operating SES to evaluate its effectiveness. More research is needed on traditionally underserved students (e.g., English Language Learners and students with disabilities) who have been found to register and participate in SES at high rates.

Future of the Program As of 2013, the future of SES remained uncertain. Waivers to NCLB granted by the U.S. Department of Education to most states and to a group of California districts enable school systems to opt out of the requirement that certain schools offer SES. Federal funding to affected school districts is being reallocated for other purposes, including for teacher professional development and for districts’ own afterschool tutoring. Patricia E. Burch See also Adequate Yearly Progress; Markets, Theory of; No Child Left Behind Act

Further Readings Burch, P. (2009). Hidden markets: The new education privatization (The Critical Social Thought Series). New York, NY: Routledge. Deke, J., Dragoset, L., Bogen, K., & Gill, B. (2012). Impacts of Title I supplemental educational services on student achievement (NCEE Rep No. 2012–4053). Jessup, MD: National Center for Education Evaluation and Regional Assistance. Good, A., Burch, P., Stewart, M., Acosta, R., & Heinrich, C. (2014). Instruction matters: Lessons from a mixed method evaluation of out-of-school time tutoring under No Child Left Behind. Teachers College Record, 116(3). Retrieved from http://www.tcrecord.org/library/abstract .asp?contentid=17351 Heinrich, C., & Burch, P. (2011). The implementation and effectiveness of supplemental educational services. Washington, DC: National Education Policy Center. Heinrich, C., & Nisar, H. (2013). The efficacy of private sector providers in improving public educational outcomes. American Educational Research Journal, 50(5), 856–894. doi:10.3102/0002831213486334

T In addition, the principle suggests that fairness in taxation encompasses reasonableness of tax burdens and equitable distribution among taxpayers of varying means. A high-quality measure of tax burden considers not just taxes billed but, more important, the impact of taxes actually paid on the taxpayer’s economic well-being. Evaluation of tax burden is aided by measuring the tax burden fully to include identification and quantification of the intersection of taxpayer circumstances and public policies that reduces the burden for some taxpayers while increasing the burden for others. Although viewing the sum of all taxes paid provides superior insight into the overall level and distribution of tax burdens, consideration of individual taxes is necessary. The local property tax is of special relevance due to its importance in financing local public services and schools. This entry identifies components of tax burden and explains how the intersection of public policies and taxpayers’ specific situations determines who pays taxes and how much tax they pay. Particular attention is paid to factors that reduce tax burden for some taxpayers, while increasing tax burden for others. The remainder of the entry considers tax burden evaluation and presents standards against which tax burden may be appraised.

TAX BURDEN Tax burden is the claim of taxes on ability to pay. Taxes are levied on the value of taxable items such as income and property and market exchanges, including purchases of goods and services. Taxes are a major component of government revenue, distinguished from all other sources by being compulsory. Governments require payment of taxes to redistribute income and wealth and to pay for programs for which voluntary payments are unlikely to produce sufficient funding. Public education is an excellent example of a function for which revenues from nontax sources would be inadequate. Although some people willingly pay tuition to private elementary and secondary schools, the majority of households would not have the means to finance their children’s educations. Policymakers understand the value of a well-educated citizenry. Thus, education is considered a merit good, which means the benefits of providing wide access justify financing with taxation. Because payment of taxes is compelled without any promise of commensurate benefit levels, the amount of taxes removed from private hands through taxation and the allocation of tax shares are essentially public policy concerns. The ability-to-pay principle provides guidance in shaping high-quality tax systems. The ability standard informs us that citizens ought to support government in accordance with their respective capacities to assume the economic burden of taxation.

Sources of Tax Burden All taxes share a common approach. First, a base against which taxes will be assessed is decided. 723

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Popular tax bases include earned income, capital gains, real property such as buildings and land, personal property such as vehicles and business equipment, and purchases of goods and services. Second, a value is assigned to the tax base. Third, a tax rate is determined and applied against valuation to arrive at the tax due. Finally, the taxpayer pays the tax bill. Taxation seems simple enough until we consider all the twists and turns that contribute to the accumulation and eventual allocation of tax burdens. Table 1 outlines the process through which tax burdens emerge. Policies and Processes of Taxation

The first phase of taxation is enacting public policies that specify what will be taxed (the tax base) and how and when the tax base will be

Table 1 Phase 1

2

3

valued for taxation. Tax base composition varies greatly, even for seemingly similar tax bases. The individual income tax provides an informative example that highlights the diversity of tax policies. The federal government, many states, and some larger local governments tax individual income. Emphasis is usually placed on earned income, which includes wages and interest, among other sources. Capital gains may be included in taxable income or segregated and taxed differently. Some states tax only unearned income, which includes dividends, interest, and capital gains. The timing of when a base is valued for taxation and when the tax is imposed affects tax bills. In some cases, tax base valuation is current and taxation occurs in the same period. For example, taxes on purchases are levied at the time of the

Estimating Tax Burden Step

Data Required

Information Sources

1

Tax base definition

Tax laws

2

Method(s) for valuing tax base

Tax laws

3

Valuation of tax base

Measured by the taxing jurisdiction using established criteria

4

Whether taxable value may be reduced

Policymakers enact exemptions, deductions, and other reductions in taxable value

5

Taxpayer eligibility for reduction(s) in taxable value

Tax laws

6

Tax base value net of reductions

Compute allowable decreases; deduct from total taxable value

7

Rate(s) of taxation

Policy decision

8

Preliminary estimate of taxes due

Multiply tax base valuation by applicable tax rate(s)

9

Taxpayer eligibility for credit(s) against taxes due

Calculate amount of each tax credit and sum

10

Net taxes due

Subtract credits from taxes due

11

Treatment of credit value that exceeds taxes Refundable tax credits may increase due income, subject to limitations

12

Taxes shifted to other taxpayers

Determine whether shifting is probable; estimate tax savings

13

Taxes shifted onto taxpayer

Estimate income reduction and added costs

14

Adjusted taxes

Modify taxes paid by taxes shifted to others and taxes shifted onto taxpayer

15

Tax burden estimate

Express adjusted taxes as percentage of income or alternative capacity measure

Tax Burden

sale on a tax base value simultaneously established by the price paid. Income taxes, which are computed on the basis of the previous year’s income and due by April of the new tax year, are comparatively current and align the determination of tax base value and tax payment. At the other end of the spectrum, annual property taxes are based on valuations that lag behind market value to varying degrees, even in the same town. Local governments value properties when they are sold or improved, or when a community-wide revaluation is conducted. Differences in when properties are assessed produce dissimilar valuations and tax bills for very similar properties. In some states, property tax limitation measures constrain the growth of tax bases and limit increases in taxable value of individual properties. Whether due to limitations or failure to revalue property regularly, inaccurate valuations produce distortions in the allocation of tax shares across properties and tax burdens borne by property owners. Because the timeliness of valuations differs greatly, locally assessed valuations are not comparable. States often develop estimates of market-based “full” value and use state valuation to compare tax burdens and distribute state aid for education. Tax Breaks and Tax Burdens

The next phase of tax burden analysis is review of whether taxable value may be reduced and, if so, under what conditions. The federal government and most states permit taxpayers to use exemptions, deductions, and business losses to decrease taxable income. At the local level of government, property tax valuation is often reduced through dollar or percentage exemptions granted to qualifying taxpayers, for example, elderly homeowners. Even after tax liability is computed, tax credits may reduce taxes due. The federal government and some states offer tax credits for child care, energy conservation investments, and job creation, among others. An earned income credit, which is designed to increase the incomes of workers of limited means, may be partially or wholly refundable. A fully refundable credit pays the tax filer the credit amount that exceeds taxes due. Tax Shifting and Exportation

Some taxpayers who are legally obligated to pay tax manage to get someone else to pay some or all of

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the tax, a process called “tax shifting.” For example, landlords may charge higher rents to acquire the funds to pay their property and income tax bills. Businesses shift tax payment to consumers as lower wages, reduced fringe benefits, and higher prices. A complete estimate of tax burden encompasses the monetary effects of tax shifting because the weight of taxation rests on the person whose resources are reduced. Although it is impossible to determine how much tax is shifted and who truly pays, considering the likelihood and effects of tax shifting improves estimates of tax burden. When interstate or interlocal comparisons of tax burdens are analyzed, a specialized form of tax shifting called “tax exportation” should be considered. Tax exportation occurs when taxes levied in one political jurisdiction are paid by someone who lives elsewhere. For example, property taxes on vacation property and lodging taxes are frequently paid by travelers. Per capita taxes can be misleading when a community has a sizable business sector. Taxes are paid by residents and by businesses, which are likely to export some tax. Nonresidents who work in the town may pay sales taxes and may have taxes shifted to them by employers. The combination of tax shifting and exportation increases some tax burdens while reducing others.

Evaluating Tax Burden Evaluations of the burdens imposed by taxation and the equity of the distribution of those burdens are guided by the ability-to-pay principle from public finance. The ability standard informs us that taxpayers are obliged to support government in accordance with their respective capacities to assume the burden of taxation. Taxes are considered to be fairly distributed when the least able in society are relieved of taxpaying responsibility or bear only a small tax burden and higher income households bear more tax burden. Vertical equity considers the distribution of tax burdens from the least to the most able. Implicit in the ability standard is the expectation that taxpayers with similar means will pay similar taxes, a goal known as horizontal equity. Analyzing tax incidence, which is the claim of taxes on people with varying abilities to pay, requires estimation of both tax shifting and exporting. The distribution of tax burden shares is changed fundamentally by public policies that reduce burdens for some taxpayers while increasing burden for others. Policies that provide preferential treatment

726

Tax Elasticity

to selected taxpayers are called “tax expenditures.” Forgiving a portion of tax due has the same net budgetary effect as spending those dollars. Tax losses necessitate a higher tax rate, a reduction in service levels, or some combination of strategies to bring revenues and planned spending into balance. When programmatic benefits are reduced, former recipients face increased financial burden not unlike a tax. When tax breaks benefit the higher income taxpayer more than the lower income taxpayer, vertical equity is reduced. Horizontal equity is compromised when taxpayers of similar means pay different taxes because only one is eligible for tax breaks. For example, one taxpayer may rent, while the other owns a home and is able to reduce taxable income with mortgage interest and property tax deductions. To ensure reasonable tax burden levels and to accurately gauge who pays, particular attention must be paid to identifying and quantifying effects of preferential public policies. Despite the importance of considering the sum of taxes to fully evaluate tax burdens, particular taxes merit special attention. The local property tax necessarily receives substantial scrutiny. First, property taxes finance the majority of local spending for general purpose government functions and education. Second, property taxes are highly visible and unpopular and often become the target of limitation measures that jeopardize funding for schools. Third, property taxes do not meet the ability standard. Property taxes tend to impose a heavy burden on low- and middle-income households, many of whom pay this tax as part of rents. Fourth, property wealth varies widely across communities and is not always adequate to finance needed spending. For example, some school districts are able to raise tax dollars easily, while others must carry significant tax burdens to pay for schools. In some rural and agricultural regions, property wealth is so low that even very high tax rates fail to produce sufficient revenue. Left unaddressed, differences in local wealth produce unequal educational opportunities and contribute to widely differing and, in some cases, intolerable property tax burdens. Fortunately, states often provide substantial funding for local schools to ensure adequate learning opportunities for all children, regardless of where they reside; lessen heavy property tax burdens; and mitigate interdistrict disparities in taxpayer burden. Well-designed state aid programs ease the burden of property taxation and improve its allocation. In closing, measuring and monitoring tax burdens can help us answer important questions about the

relationship between tax policies and the distributions of opportunities and incomes in society. Given many citizens’ distaste for taxation, maintaining reasonable tax burdens that are fairly allocated may help preserve access to the tax dollars considered necessary for operating quality schools. Josephine M. LaPlante See also Ability-to-Pay and Benefit Principles; Horizontal Equity; Progressive Tax and Regressive Tax; Tax Limits; Vertical Equity

Further Readings Baker, B. D., Green, P., & Richards, C. E. (2008). Financing education systems. Upper Saddle River, NJ: Pearson Education. Burman, L. E., & Slemrod, J. (2013). Taxes in America: What everyone needs to know. New York, NY: Oxford University Press. Rosen, H. S., & Gayer, T. (2010). Public finance (Rev. 9th ed.). New York, NY: McGraw-Hill. Slemrod, J. (2008). Taxing ourselves: A citizen’s guide to the debate over taxes (4th ed.). Cambridge: MIT Press.

TAX ELASTICITY Tax elasticity is a concept that compares how the yield of a tax responds to incremental changes in income. If receipts from a tax change faster than income, the yield is said to be elastic. If tax receipts do not change as fast as income, the tax is inelastic. Mathematically, the elasticity of a tax is computed as the percent change in tax receipts divided by the percent change in income. Thus, an elastic tax is one where this calculation leads to a total greater than 1, and an inelastic tax yields a measure less than 1. A tax elasticity of unity, or one, indicates that the yield of the tax changes at the same rate as income. This entry describes the elasticity of major sources of taxation—property, income, and sales taxes— and how that affects the governmental units levying those particular taxes.

Elasticity and Tax Yield Performance of a tax on the concept of elasticity is important to its successful implementation. Taxes that are too elastic are resented. A common indicator of this problem is consumer purchasing power, where, despite income growth, consumers have

Tax Elasticity

gained little or no advantage on the consumer price index. Similarly, inelastic taxes can result in unfair benefit or disadvantage to taxpayers. For example, taxpayers with rapidly escalating incomes will not see a commensurate increase in sales or property tax rates. Furthermore, while there is a common belief that elasticity of unity is fair, it may not be fair at all given different income levels. The sales tax provides a classic example. As income declines, an increasingly larger proportion of income goes toward purchasing basics such as food, clothing, and housing, while the sales tax rate does not change in response. Any of the three conditions relating to elasticity results poses problems for government. If elasticity is significantly present in a tax, government is seen to be taking advantage of the tax and creating a burden on taxpayers. If inelasticity is present, policymakers are faced with raising tax rates to combat the effect of growth in the costs of government. If elasticity of unity is observed, critics will complain about tax unfairness to lower income taxpayers. The challenge is to create taxes that respond to the challenging condition where the need for services may increase sharply at the very time when tax revenues may be decreasing or, alternatively, revenues may be growing rapidly due to favorable economic conditions but are nonetheless subject to criticism that they are preventing taxpayers from getting ahead of the tax curve. Elasticity describes one perspective on tax behavior, whereas stability of yield examines the same tax behavior more from the viewpoint of government in need of revenue. A stable tax is one that demonstrates constancy of yield, despite changes in the economy. As might be anticipated, a stable tax, such as property tax, will not respond immediately to economic shifts. Under these conditions, a tax may be favored or disfavored by different audiences, depending on whose needs are being considered. Taxpayers want to see a lack of elasticity in inflationary times and immediate elasticity during times of economic hardship. Stability interests government in that government’s revenue needs almost never decline regardless of boom-and-bust economic conditions. The difference, of course, is that government must be concerned with both elasticity and stability, since it must placate its patrons while satisfying its own revenue needs.

Elasticity of Individual Taxes Each of the three major sources of taxation—property, income, and sales—has a different measure of

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elasticity. Moreover, changes in the base of each tax can affect the relative elasticity of that tax. Property Taxes

Data on the elasticity of the property tax are mixed. For most tax experts, the intermediate conclusion has been that it shows an elasticity of unity. A few studies have suggested that it may be slightly inelastic, but there is an argument to be made that data have not been reliable due to the number of tax units that have experienced tax limitation movements, a phenomenon that could affect the ability of the property tax to keep pace with changes in income. Similarly, many exceptions built into various states’ property tax laws such as the so-called circuit breakers, which limit or reduce property taxes for certain individuals (e.g., low-income, elderly, or disabled property owners), and homestead exemptions have an impact on these data by reducing tax revenue in ways that are not commensurately reflected in the income data against which elasticity is measured. As a general rule, however, the property tax has not been shown to be either highly elastic or inelastic, leaving the tax in a relatively neutral position on this dimension. Income Tax

Income taxes are generally highly elastic. Progressive tax rates are fundamentally elastic, since revenues will increase faster than incomes so long as income growth is sufficient enough to push taxpayers into successively higher tax brackets. During periods of normal or high inflation, elasticity is evident. During periods of economic downturn, however, a different set of problems may be encountered in that the stability of the income tax is weakened to a greater extent by changes in taxpayers’ economic condition than is true of the property tax, for example. Since both federal and state governments generally depend on the income tax for general fund revenues that pay for a host of government programs, periods of significant economic recession produce lagging income tax receipts, which in turn severely restricts services. Sales Tax

Like other taxes, the sales tax has its own set of peculiar characteristics relating to elasticity. There is some disagreement about whether the sales tax is elastic. One school of thought suggests that the sales tax is elastic because it can be shown that tax revenue has had a steeper rate of increase than income over

728

Tax Incidence

the same period of time. Another school of thought argues that this is true for undesirable reasons in that revenue growth is a function of inflation driving up the price of goods, while incomes have not always kept pace. Critics also point out the recent propensity of revenue-hungry governments to raise the sales tax rates, a reality that has been seen in many states in recent years. Still a third perspective on sales tax elasticity has been advanced, arguing that people buy more goods and services when their incomes are robust. Finally, the sales tax has inelastic tendencies to the extent to which individuals shift spending from goods (which are taxed) to services (which are generally not taxed), meaning that consumption on taxable goods grows more slowly than income. Given a fixed sales tax rate that would lead to estimates of an inelastic tax, how these four factors interact depends on economic conditions and sales tax rates, along with factors such as consumer confidence.

Conclusion The elasticity of a tax is critical to the amount of revenue a government generates. Elastic taxes are particularly good at raising more revenue in good economic times, but in times of economic downturns, these often produce less revenue than desired. Inelastic taxes offer more revenue stability but are more challenging for individuals to pay in hard times. By changing the base on which a tax is levied, the elasticity of that tax may change as well. For example, many states exempt food purchased from grocery stores from sales taxation. Consequently, as incomes fluctuate, the percentage of household consumption directed toward unprepared foods may also shift, affecting sales tax revenues. The use of income tax deductions and property tax exemptions and circuit breaker tax relief programs can also affect the elasticity of a tax. Lawrence O. Picus See also Expenditures and Revenues, Current Trends of; Property Taxes

Further Readings Due, J. F., & Mikesell, J. L. (1994). Sales taxation: State and local structure and administration (2nd ed.). Washington, DC: Urban Institute Press. Monk, D. H., & Brent, B. O. (1997). Raising money for schools: A guide to the property tax. Thousand Oaks, CA: Corwin Press.

Websites Government Finance Officers Association: http://www.gfoa .org/ Tax Foundation: http://www.taxfoundation.org

TAX INCIDENCE An important issue in taxation is who actually pays for a tax. While it seems straightforward that the owner of real property pays the property tax, and owners of automobiles pay a personal property tax on motor vehicles, often individuals are able to shift taxes to others as part of the good or service they are selling or providing. Understanding who “really” pays a tax is called tax incidence, and the ability to shift taxes to others varies with the tax. If tax systems are concerned about horizontality and verticality, they must also be concerned about who pays the tax in the end, because this ultimately relates to vertical equity. The terms tax impact and tax incidence, along with a related concept of tax shifting, are used to shed light on this issue. The impact of a tax falls on the one who formally makes a tax payment. Tax incidence, in contrast, refers to those on whom the burden of paying the tax falls in reality. Tax shifting occurs when the person on whom the tax impact falls shifts the tax incidence to someone else. Tax incidence concerns arise with certain types of taxes because one person may be seen as paying the tax (impact), while others actually pay the tax (incidence). An example will help clarify these points. The owners of apartment buildings must pay property taxes to the government and in turn receive income tax deductions. Thus, the tax impact falls on the owners because they write the checks to the government. Of course, they do not want to pay the property tax, but they desire the deduction to gross income that goes along with it. Thus, the owners want to shift as much of the tax burden as is possible to the tenants. The owners, therefore, include as much of the tax as possible in the form of higher rent charged, thereby shifting some, or all, of the tax incidence to the tenants. In contrast, the income tax is not susceptible to tax shifting, because taxes on income are taxes on individuals. In this example, owners of the apartment building might benefit twice by receiving the income tax deduction on the

Tax Incidence

property taxes paid, while shifting the tax burden to the tenants. The following sections describe the incidence of the three major sources of taxation: property, income, and sales taxes.

Incidence of the Property Tax It seems reasonable to believe that owners of identical properties paying identical tax rates pay the same amount of property taxes. However, several problems arise that cast doubt on that belief. It is difficult to identify taxpayers in exactly equal circumstances for several reasons. One reason is that outward appearances may be deceiving, as in the case of a mortgaged property versus a debt-free title. A similar case can be made for retired people struggling to meet taxes and maintenance costs. Another reason is that property taxes can be shifted, particularly as in the case of renters who may end up paying a tax, while the owner takes a double tax benefit by deducting taxes as an expense in addition to not actually paying the tax. The problem, of course, is that the property is unrelated to income from which taxes are eventually paid. If horizontal equity performance were thus difficult to test, it becomes clear that vertical equity performance is in serious question, although devices such as property tax circuit breakers (which protect low-income taxpayers from property tax “overload” when property taxes exceed a certain percentage of their income) have introduced some degree of verticality to the system. However, these devices make it even more difficult to confidently assess equity of property taxes on this criterion. Little progress is likely on this front because of the disconnectedness between the property tax and income, making equity issues (incidence and impact) difficult to disentangle cleanly.

Incidence of the Income Tax Given the frequent criticism of property taxes on the criterion of equity, many reformers have turned to the income tax in search of a more equitable method of exacting revenue from taxpayers. The search for a more equitable tax has been perennial, as each tax has shown itself to have less-than-perfect scores on several of the many characteristics of desirable tax systems. With the tremendous yield of the income tax and promotion by advocates of this tax, income as a basis for taxation has been closely evaluated for its equity performance.

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While the property tax has shown mixed behavior on the benefit principle, there is little doubt that the income tax is disconnected from any expectation of receiving direct benefits from the tax, particularly at the federal level. In part, this is because income taxes at the state and federal levels are viewed as swallowed up by some distant treasury. At the same time, the progressive rate structure, first built into the federal system and then adopted into many state tax codes, aids in denying the benefit principle because progressivity holds the concept of ability to pay as its fundamental root. Some people do not like progressive taxation because they argue that it is a disincentive for personal industry in a capitalist society. The current progressive rate structure is based on marginality, the concept that the last income dollar is less needed by a taxpayer than the first dollar. If ability to pay were a valid criterion for tax systems, design of the income tax theoretically fares well. Of particular interest is performance on horizontal and vertical equity dimensions. This is inextricably entwined with how well the tax does on the impact and incidence criteria in that a tax that can be shifted may not be very equitable. The progressive structure of the tax contributes favorably to evaluation on these criteria, as the unequal tax burden among income classes is intended to weigh against higher income groups by forcing them to pay a larger percentage of income in support of government. Horizontality is aided because everyone, regardless of income, pays the same tax rate on the first dollar earned, while rates increase only when income brackets threshold are exceeded. Verticality is thus aided by the exact same design, wherein lower income groups avoid the tax burden paid by higher income groups. At the same time, the concept of adjusted gross income further aids verticality, because it is an attempt to mitigate income inequality on a personal level by subtracting subsistence allowances for dependents and other approved expenses. This performance is strengthened by evaluation of the impact and incidence of the tax, wherein most observers agree that impact and incidence are identical in that income tax cannot be shifted. On the basis of structure alone, the income tax seems to fare well on horizontal and vertical dimensions. Operationally, however, critics are prone to question its performance as a result of features commonly built into the income tax code. Progressivity, for example, can be hindered by other well-intentioned policies such as tax-exempt instruments that

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Tax Limits

make it possible for very wealthy people to earn taxfree income that may result in less tax due than is true for low-income wage earners. Similarly, taking advantage of tax deductions, such as personal business expenses or home mortgage interest, requires a taxpayer to first qualify for itemizing deductions when filing an income tax return, thereby likely disqualifying truly low-income people from the advantages of these vertical aids. Likewise, the criteria relating to progressivity and neutrality is somewhat in conflict if it is accepted that neutrality leaves everyone exactly positioned after tax than before the tax: In other words, a delicate balancing act is involved to ensure that progressivity does not leave the wealthy person even slightly less wealthy or the poor person even slightly more wealthy.

Incidence of the Sales Tax Determining the incidence of the sales tax appears to be very clear. Generally, sales taxes are collected from retailers on the basis of total gross sales. Yet as anyone who has made a store purchase knows, the sales tax is simply passed on to the end consumer in the form of an additional payment above the price of the good itself. Interestingly, the incidence of sales taxes is not quite that simple. Because sales tax rates vary across states and often among local municipal or county governments within a state, there may be important “border” effects that shift the incidence to retailers. Take, for example, the states of Oregon and Washington. Oregon does not have a sales tax, whereas Washington does. On the surface, for Washington residents living in close proximity to the Oregon border, it pays to purchase many goods by crossing the state line. However, it is possible that to improve their own sales, Washington retailers offer a lower price to compensate for the impact of the sales tax. Determining how much lower prices must be is complex, and retailers need to include factors such as convenience and the cost of driving longer distances, so the discounts offered may not match the cost of the sales tax, but to some extent, if not completely, some of the burden or incidence of the sales tax may be shifted to retailers.

Conclusions Understanding who really pays a tax is important in assessing the equity of any tax. The foregoing discussion shows that individual homeowners may have difficulty shifting property taxes, but that owners of rental units may be able to shift some or all of their

property tax burden to their tenants, potentially benefiting twice as they can also deduct the property tax payments as a cost of doing business. In this case, it is likely that the incidence of the property tax is shared between the owner and the tenant. Income taxes and sales taxes are generally harder to shift to others, although in some border areas where there are substantial differences in sales tax rates, retailers in the higher tax area may charge lower prices to mitigate the effect of the higher sales tax rate, thus sharing the tax burden with their customers. Lawrence O. Picus See also Tax Elasticity; Tax Yield

Further Readings Due, J. F., & Mikesell, J. L. (1994). Sales taxation: State and local structure and administration (2nd ed.). Washington, DC: Urban Institute Press. Monk, D. H., & Brent, B. O. (1997). Raising money for schools: A guide to the property tax. Thousand Oaks, CA: Corwin Press.

Websites Government Finance Officers Association: http://www.gfoa .org/ Tax Foundation: http://www.taxfoundation.org

TAX LIMITS While the preponderance of tax and expenditure limits (TELs) currently in place date back to either the “tax revolt” of the late 1970s and early 1980s or to a second minirevolt in the early 1990s, the combination of rapidly escalating real estate prices prior to the Great Recession and continued growth in property tax bills during the Great Recession led to numerous recent efforts to control the growth of state and local revenues. Since 2004, Taxpayers’ Bills of Rights (TABORs) have been promoted in 30 states. While none of these efforts have been successful, and the model for these proposals, Colorado’s TABOR, was rolled back in 2005, this action at the state level signifies the continued prominence of TELs in the policy debate. TABORs, as well as their predecessors, could affect elementary and secondary education because they could constrain the ability of local governments to raise revenues and limit the capability of state

Tax Limits

governments to provide aid to local school districts. However, there is no universal agreement about the likely impact of TEL-induced constraints. Many supporters of the 1970s-era limitations believed that these limits would lead to increased efficiency in governments; that is, that governments would cut waste without reducing service levels. On the other hand, analysts of TELs have argued that these policies could lead to reductions in student outcomes that are far larger than might be expected given the changes in spending. Knowing how tax and expenditure limits vary and understanding what is known (and not known) about these limits is thus of continued relevance to state and local policymakers. This entry first describes what tax and expenditure limits are and then summarizes what is known about the impact of limits on the mix of revenue sources and the level of revenues of local school districts. It also reviews what is known about the impact of limits on the distribution of spending across districts and the impact of limits on the quality of education provided.

What Are Tax and Expenditure Limits? TELs vary primarily in their restrictiveness. The most common form of TELs prior to the 1978 passage in California of Proposition 13 was a limit on specific property tax rates. For any local jurisdiction, such as a fiscally independent school district, the property tax revenues of that jurisdiction are determined by the following relationship: Property tax revenue = (Tax rate levied by the jurisdiction) × (Taxable value of property).

Total tax payments made by the residents of any jurisdiction equal the revenues of that jurisdiction and any payments made to overlying jurisdictions. As a result, limits on specific tax rates, that is, setting a maximum tax rate that specific local jurisdictions can levy, may not reduce the property tax payments of residents of those jurisdictions. This follows, in part, because spending responsibilities could be shifted from the jurisdictions constrained by the specific limit to overlying jurisdictions that are not constrained and, in part, because the limits do not control the growth in the taxable value of property in the jurisdiction, also known as the assessed value of property. Starting with Proposition 13, TELs either limited both property tax rates and growth in the assessed value of property or directly limited the growth in

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property tax revenues. A few, such as Colorado’s TABOR, were even more restrictive because they either limited overall revenue growth from all sources or limited expenditure growth. Researchers have tended to classify a limit as binding if the limit constrains overall revenue growth from all sources, constrains overall expenditure growth, or combines a limit on the growth in the assessed value of property with a limit on the overall property tax rate. However, the extent to which a limit constrains the ability of local governments to raise revenues or make expenditures depends not only on the type of limit but also on how restrictive it is. For instance, a TEL that limits annual revenue growth to 1% is much more likely to constrain local governments than would be a TEL that limits growth to 10%. Furthermore, a limit that permits voters to override the constraint, such as Proposition 2½ in Massachusetts, is less binding than is a limit that cannot be overridden, such as Proposition 13 in California.

Impact of TELs on Revenue and Spending The early research literature on the effects of TELs indicated that TELs slowed the growth of property tax revenues, but did not significantly slow the growth of total revenues received by all local governments. Most estimates indicated that TELs had little or no effect on total revenues of local governments. The impact of TELs was muted by three changes in the fiscal systems in states with local limitations. First, increased state aid, funded by increased state taxes, compensated for the decline in local revenues. Second, overrides, in the states that permit overrides, permitted voters in some localities to allow postlimitation spending to continue along the path it would have followed in the absence of the limit. Third, in localities constrained by TELs, there was an increased reliance on other local taxes and user fees. However, recent work on utilization of fees suggests that this third avenue has been little used by school districts, with the magnitude of fee revenues being only slightly higher in districts facing TELs. More recent work focusing on the impact of limits on school districts indicates that, on net, TELs had almost no effect on total spending on public education because increases in direct and indirect spending by state governments compensated for reduced local own-source revenue. Not surprisingly, the presence of a limit on state government revenues or expenditures reduced the extent to which state

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Tax Limits

spending can compensate for reductions in local own-source revenue. Some limited evidence on the distributional effects of TELs is offered by analysis of withincounty variation in spending by local governments from all counties in the United States. On average, TELs increased the within-county variation in education expenditures. The more binding the limit was, the more within-county inequality grew after the TEL. Given that TELs were more likely to bind in initially low-spending districts, TELs might have led to greater disparities in spending across districts within a state because state aid has typically not fully compensated for the impact of the constraints. The impact of TELs on affluent suburban fringe counties also appears to differ from the average impact of these limits. For these counties, more binding limits reduced variation in education expenditures across school districts and had no impact on the within-county variation in general expenditures. Why these counties look different from all others is not clear, though differential ability to pass overrides and differential access to nontax revenues are two possible explanations.

Do Limits Affect Student Performance? According to case studies of some of the earliest TELs, government officials responded by first making cuts in capital expenditures and in areas of current expenditure that these officials felt were peripheral. Nevertheless, these case studies consistently showed that residents of the studied states perceived a drop in the quality of publicly provided services. But, given the nature of the cuts that were made, it was not clear that this perception reflected reality. These case studies offer very imperfect evidence of the impact of TELs on education quality because, given their timing, the results could not be used to draw any conclusions about the long-run effects of TELs. Only by examining student outcomes directly, and by determining how these outcomes had changed relative to the prelimit baseline, could researchers ascertain the effect of limits. In other words, the effects of TELs could only be isolated by looking across the states or by examining the long-run experience in a state in which a limit was passed and no major changes in the school finance system had occurred. Papers that have taken these approaches have generated relatively consistent information about the effects of limits. David Figlio, using a national crosssection of student-level data, found that revenue and

expenditure limits significantly reduced 10th-grade performance in mathematics, reading, science, and social studies. Thomas Downes, Richard Dye, and Therese McGuire used variation generated by the imposition of property tax limits on some, but not all, school districts in Illinois to implement the second approach mentioned above and concluded that, in the short term, the limits led to slower growth in the performance of third graders on a standardized test of mathematics but did not do so for eighth graders or for either third graders or eighth graders in reading. Downes and Figlio combined the two approaches to determine how the TELs of the late 1970s and early 1980s affected the distribution of student performance in states in which limits were imposed and how student performance has changed in these states relative to student performance in states in which no limits were imposed. Their results confirmed the earlier cross-sectional finding that TELs reduced mathematics test scores by 1% to 7%, depending on model specification, as well as the finding for Illinois of no observable impact of TELs on reading scores. Since the students in their analysis were of high school age, it is sensible to believe that high school mathematics differences may be more attributable to differences in schooling than are high school reading differences, and therefore, TELs generally have a stronger effect on mathematics than on reading. Information on the distributional effects of limits is very limited. Researchers have generally found that TELs have more negative effects on student performance in economically disadvantaged localities, but these estimates lack the precision to support robust conclusions. Thus, while this finding is plausible since it is consistent with the fact that TELs are more likely to bind lower income communities, the impact of limits on the cross-community distribution of student performance remains an open question. Finally, after the passage of Proposition 2½ in Massachusetts, the share of the potential student population served by the public schools was lower in districts in which more initial cuts were necessary when the limits were first imposed. This finding supports the conclusion that higher dropout rates or more switching from the public to the private sector could result from binding TELs.

Reconciling Impact of TELs With the “Money Does Not Matter” Literature One lingering challenge facing researchers analyzing the impact of TELs is reconciling the evidence

Tax Limits

that suggests that these limits led to small reductions in school spending and significant reductions in student performance. Even researchers who disagree with the conclusion that increases in education spending do not translate into increases in student achievement face a conundrum: The most-cited research that seems to establish that “dollars matter” indicates that the marginal value of a dollar is small. Thus, the post-TEL declines in performance seem to be inconsistent with what are, at most, small post-TEL declines in spending. Some of the explanations for why the marginal product of spending is small may offer insight into why small cuts in spending could lead to what seem to be disproportionate cuts in performance. For example, dollars might appear to not matter if those dollars were being captured as union or administrative rents, with no additional productive inputs being provided. Here, the term rents refers to the extraordinary returns that accrue to the individuals currently in control of resources because of their ability to direct new resources to expenditure categories that most benefit them. If rent seeking is an explanation for the small gains associated with increases in expenditures, relatively large reductions in outcomes could result when equal-sized reductions in resources are imposed, as with TELs. In general, if reductions in spending do not change (or increase) the premium to a less productive input, then outcomes should fall, just as outcomes would not be expected to rise as spending increases. Consider, for instance, administrative rents. Reductions in spending would be more likely to be borne by instruction, rather than administration, just as increases in spending would be more likely to accrue to administration, rather than instruction. If instructional spending matters for student achievement, then this logic would imply that reduced spending, leading to reduced instructional spending, would have impacts on student outcomes that are larger in magnitude than would similarly sized spending increases. In the case of teacher union rents, if reductions in spending increase the relative salary of experienced teachers in a district because starting salaries are lowered, the school district may end up holding on to its more experienced teachers while running off its less experienced teachers and reducing its ability to attract high-quality teachers from the outside. Such behavior could explain the finding that limitations substantially reduced the average qualifications of new entrants into a state’s teaching force.

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Asymmetry in the impact of TELs could also result because, even if schools in the districts affected by the limits are capable of reducing waste and maintaining the prelimitation level of student performance, the limits typically provide no explicit incentive to administrators of these districts to eliminate waste. Strategic behavior of school districts in response to the imposition of a limit, with the intent of encouraging voters to override the limit, could thus explain the declines in performance that are observed. Short-run analyses of limits imposed in a subset of a state’s communities appear to contradict the administrator rents story, with affected school districts reducing administrative spending more than instructional spending and with performance unaffected. Competition might explain why this occurred in school districts with localized tax limitations, while school districts in states with statewide limitations made relatively large cuts in instruction. When limits are not universal, some school districts will be affected while others will not be, making it possible that the unaffected school districts put competitive pressure on the affected districts. The plausibility of this explanation is buttressed by research that finds evidence of competition among school districts only in those counties in which school districts are subject to TELs and that suggests that the discipline provided by competition may disappear when the limits move closer to being universal. The magnitude of private responses to constraints provides additional indirect evidence on the limited scope of competitive pressure as a disciplining mechanism. The central lesson from research on private schooling and private contributions is that private responses to constraints tend to be small. Research also suggests that tax limitations may reduce student outcomes because communities constrained by TELs become more heterogeneous over time, mainly because of a reduction in the extent to which high-income households sort themselves into high-income communities. If educating a more heterogeneous population is more costly, then one might expect that fiscally constrained school districts would experience reduced outcomes for any given level of spending. Similarly, research indicates that, in the wake of TELs, students, especially those of high ability, leave the public sector for the private sector, in all likelihood, because of concerns about the quality of the public schools. Such sector switching would cause mean performance levels in the public sector to fall and would mean that the remaining students in the public sector would be costlier to educate.

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Tax Yield

While none of the explanations given above has conclusively been shown to be the explanation for the negative relationship between TELs and student outcomes, the accumulated evidence establishes the plausibility of student outcomes falling more dramatically than would be expected given the magnitude of the reductions in spending induced by TELs. In addition, the evidence on the impact of TELs on student performance establishes that a central desire of supporters of limits—that they would receive reductions in taxes but not face appreciable reductions in student outcomes— appears to have not been realized. Since TELs provide no explicit requirements to spend money in a particular way, there is no reason to believe that adding an additional constraint would necessarily eliminate waste and inefficiency even if the system was inefficient or wasteful at the outset. If waste exists, the evidence does not suggest that tax limitation measures reduce this waste or, at least, that they reduce waste without hurting student performance. Finally, a growing body of research looking at how state and local governments respond to downturns suggests that the existence of fiscal constraints could serve to exacerbate the impact of downturns on education spending, both by limiting the ability of localities to respond to state aid cuts and by shifting local revenue away from a stable source, the property tax, to less stable sources. Thomas Downes See also Private Contributions to Schools; Property Taxes; School Finance Litigation; Serrano v. Priest; Tax Burden

Further Readings Brunner, E., & Imazeki, J. (2005). Fiscal stress and voluntary contributions to public schools. In W. C. Fowler (Ed.), Developments in school finance: 2004 (pp. 39–54). Washington, DC: National Center for Education Statistics. Courant, P., Gramlich, E., & Rubinfeld, D. (1983). Why voters support tax limitations: The Michigan case. National Tax Journal, 38, 1–20. Downes, T. A., Dye, R. F., & McGuire, T. J. (1998). Do limits matter? Evidence on the effects of tax limitations on student performance. Journal of Urban Economics, 43, 401–417. Downes, T. A., & Figlio, D. N. (2000). School finance reforms, tax limits, and student performance: Do

reforms level-up or dumb down? [Mimeo]. Boston, MA: Tufts University. Dye, R. F., & McGuire, T. J. (1997). The effect of property tax limitation measures on local government fiscal behavior. Journal of Public Economics, 66, 469–487. Dye, R. F., McGuire, T. J., & McMillen, D. P. (2005). Are property tax limitations more binding over time? National Tax Journal, 58, 215–225. Figlio, D. N. (1997). Did the “Tax Revolt” reduce school performance? Journal of Public Economics, 65, 245–269. McGuire, T. J. (1999). Proposition 13 and its offspring: For good or for evil? National Tax Journal, 52, 129–138. McGuire, T. J., & Rueben, K. (2006, May). The Colorado revenue limit: The economic effects of TABOR. State Tax Notes, 40(6). Mullins, D. R. (2004). Tax and expenditure limitations and the fiscal response of local government: Asymmetric intra-local fiscal effects. Public Budgeting and Finance, 24, 111–147. Mullins, D. R., & Wallin, B. A. (2004). Tax and expenditure limitations: Introduction and overview. Public Budgeting and Finance, 24, 2–15. Shadbegian, R. J. (2003). Did the property tax revolt affect local public education? Evidence from panel data. Public Finance Review, 31, 91–121.

TAX YIELD Tax yield refers to the amount of revenue raised by any particular tax. Principles of taxation suggest that a good or fair tax should have a broad base (the object that is taxed) and a low rate to minimize inefficiencies created by the payment of the tax on economic behavior. The yield from any tax is a function of the rate of taxation times the base. The yield of any tax will therefore change as the rate or the base (or both) move up or down. As a result, efforts to exempt certain portions of a tax base from taxation (e.g., income tax deductions, sales tax exemptions, or homestead exemptions to property value subject to property taxation) reduce the yield of the tax. This entry describes the general characteristics of taxation systems and the impact of base and rate on the yield of the most common forms of taxation for education in the United States.

Tax Base The term tax base refers to any object against which a proposed tax could be levied. Three major tax bases exist—wealth, income, and consumption.

Tax Yield

Wealth

Wealth has been the first and most important basis for taxation. Real property is the most important form of wealth that is taxed, as real estate in the form of land and improvements on land suggests wealth in a very visible fashion. However, wealth could also be defined to include such things as personal property or money in the form of cash or stocks or bonds. Personal property includes any number of physical items not otherwise described as real property and is sometimes referred to as tangible wealth. Negotiable instruments such as currency, certificates of deposit, or stocks and bonds are often referred to as intangible wealth. Thus, wealth in its various forms is best represented by possessions, since people who hold such items do so as a consequence of financial stability and prosperity. The advantage of taxes on wealth such as property taxes is that the base can’t move to other jurisdictions, and generally, its value fluctuates relatively little in the short term. Yet there is another side to wealth as a basis for taxation that must be noted. Although it is true that intangible wealth represents the cash position of an individual, other forms of wealth may not be a ready asset and might not even accurately represent a person’s financial state. For example, ownership of land could be interpreted differently when looking at income realized from the land. A farmer owning hundreds or thousands of acres might make a very poor living after the cost of real estate taxes and the expense of machinery and crop production. In fact, a farmer could regard land as a liability if it could not be sold in a stagnant market, particularly if the land were mortgaged. Similarly, a retired person’s complete ownership of a home could give the appearance of a great asset, even though the person might be struggling to meet the cost of taxes and home maintenance. Likewise, buying a home and owning it outright are two different matters, since a mortgage represents a liability against the homeowner’s wealth. Other types of tangible wealth fail to accurately indicate one’s wealth because some are easy to hide and can even misrepresent one’s personal worth. Intangible wealth can also be misleading. For example, it is easy to hide wealth in tax-sheltered investments; in contrast, intangibles may not be a vigorous asset if the economy is struggling. Obviously, intangible wealth can take any of several forms that could be the basis of taxation and is not therefore a

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flawless reflection of true wealth. Nevertheless, the high visibility of some kinds of wealth has made it the backbone of tax systems, particularly at the local level. As seen earlier, this has been especially true for schools. Income

Although it might be argued that every kind of wealth was income at the time it was accumulated, income as a basis for taxation is generally limited to changes in current financial position. Because people think in terms of annual income as a basis for taxation, income most often refers to wages, salary, bonuses, dividends, interest payments, and other earned or unearned income represented by cash payments. Income tax laws allow taxpayers to adjust gross income by special deductions and credits that reduce tax liability. While the conceptual underpinnings of deductions may be subject to question, the belief that it costs money to produce income legitimizes deductions from gross income to produce a net amount on which taxes should be paid. The federal government has largely captured income as a basis for taxation, with 41 states also taxing income to a lesser extent. Even some local units of government, such as cities, levy income taxes. Although schools seldom have direct access to an income tax base, state and federal revenue from this source is used to provide aid to schools. Consumption

A third basis for taxation is consumption, which is more commonly referred to as retail sales. Sales taxes actually take several forms, depending on whether a tax is general or specialized. General sales taxes are levied on almost every retail transaction at the state and local levels. While each state has its own laws that determine what is taxed and what is exempt from general sales tax, taxing consumption is a significant revenue source for intermediate and local governmental units. A general sales tax taxes consumption of goods and services under the theory that the tax is voluntary in that consumers willingly choose to pay when they decide to make a purchase. Exemptions tend to be of three kinds. One kind recognizes that not all purchases are voluntary, especially when considering the impact of tax on lower income groups. For this reason, some states exempt food and medicine in the belief that these items are not purchased voluntarily. Another exemption may

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stem from efforts to stimulate the economy or from how “retail” is defined. For example, a state may charge sales tax to a citizen buying lumber but may exempt the same item when used by a contractor building a house. Third, the completed house may be exempt from sales tax given the goal of promoting home ownership or encouraging job growth in the construction industry. A specialized consumption tax is often called an excise tax. Many examples of excise taxes exist and include taxes on items such as motor fuels, car tires, liquor, minerals extracted from the earth and similar types of objects singled out for taxation. In some cases, social goals may be part of the decision to levy excise taxes. For example, there is a general consensus that the use of alcohol and tobacco adds nothing helpful to society and that these items should be singled out and heavily taxed. Likewise, taxes on gambling constitute a type of selective tax that targets an undesirable social behavior. In these cases, the logic of levying taxes is to discourage the behavior and simultaneously generate significant revenue. In a somewhat different vein, other excise taxes link fuel and car tires to a fund for road maintenance and repair. States have often used taxes on fuel and minerals and gaming, while the federal government has long engaged in steep taxes on many kinds of commodities in lieu of enacting a general sales tax. Regardless of how decisions to implement the sales tax are made, this basis for taxation yields substantial revenue for states and, in some cases, local governments. When legally permissible, cities and counties sometimes ask voters to add a retail sales tax onto the existing sales tax of the state. For example, a community in a state with a 6% retail sales tax might pass a half-cent county sales tax and another half-cent city sales tax, making a total of 7% total sales tax. If that community experienced total sales of $100 million in a year, the state would gather $6 million in revenue, while the county and city would each capture an additional $500,000. In sum, general and specialized sales taxes have become a significant basis for taxation. Again, although schools seldom have direct access to this basis, sales tax often provides a significant part of the money sent to schools in the form of general aid, especially at the state level.

Tax Rates One of the reasons that these three tax bases have persisted over time is that each generates a

significant annual tax yield. A tax should generate a satisfactory amount of revenue to justify its existence. Whereas a grossly unfair tax yielding vast revenues would not be justified, a tax regarded as completely fair would not be worth pursuing if it yielded few dollars. In effect, a tax can be judged in part by whether it deserves to be levied on the basis of yield. Two terms useful in evaluating a tax on the criterion of yield are elasticity and stability. Elasticity is discussed in another entry in this volume. Stability of revenue is considered here. The earlier discussion of federal, state, and local tax systems gave clear indication of the tremendous revenue yielded by taxing wealth. Although wealth taxes take different forms at each level of government, the property tax at the local level supports the concept of wealth as a powerful tax base. Most property taxes are collected by local governments. The federal government collects virtually no property taxes, and states collect a relatively small share of the total. The property tax has proven to be a stable tax. Because it is not linked to income, changes in the economy brought about by income shifts are very slow to show up in property tax data. Short of disastrous conditions, the stability of the property tax generally results in smooth revenue streams for government. Stability, coupled with yield, has been the primary force for using the property tax. Income taxes provide the bulk of revenue for the federal government and most of the states. It may be correctly observed that the tremendous yield of the income tax is due entirely to conditions relating to the structure of the tax itself. A good tax system was argued earlier to exhibit high yield, combined with other properties such as elasticity and stability. The income tax has been so tightly structured that its yield has been great, particularly through the use of devices such as payroll withholding of taxes owed, thereby greatly reducing people’s ability to escape tax liability. This reality has been greatly aided by the progressive structure of the tax, particularly at the federal level. Federal income tax code follows two kinds of logic, wherein tax revenues increase as income rises and, by virtue of a progressive rate structure, the tax rises again with each successively higher income level. Many states similarly have progressive income tax systems either by creating their own tax brackets or by treating state income tax liability as a percentage of an individual’s federal income tax liability.

Teacher Autonomy

The yield from income taxes is less stable than that of the property tax, owing to fluctuations in incomes over time. When the economy is strong, income tax receipts tend to rise, whereas during economic downturns, revenue from income taxes stagnates or even declines. This has caused substantial fluctuation in the ability of states to meet their financial obligations in times of recession, often when state expenditures are most needed to help individuals weather the impact of the recession through assistance such as unemployment payments and public assistance. The sales tax is used most heavily at the state level. High sales tax yields result because individuals need to purchase goods and services, and the taxation of virtually all retail transactions yields revenue from purchases large and small, resulting in a high aggregate yield. In contrast to the highly stable property tax and the moderately stable income tax, the sales tax shows considerable instability for several reasons. The first reason is that sales of many goods indeed track the economy, as people put off major purchases until consumer confidence rises. In this scenario, sales tax revenue may drop sharply if people are not buying automobiles, televisions, refrigerators, and other major consumer goods. Second, consumers watching their expenses will be cautious even in areas of subsistence, buying hamburger instead of steak or buying generic labels instead of more costly name brands. While these may seem like small differences, millions of consumers saving pennies results in many sales tax dollars lost at the cash register. Third, consumer pessimism may stimulate growth in savings accounts. While this may seem to be a desirable goal, a negative result is lack of consumer activity. As a result, sales tax receipts demonstrate significant volatility.

Conclusion The yield of a tax is critical to the ability of governments to fund their operations. Taxes with broad bases and stable yields offer many advantages as they enable governments to plan over the long term. Less stable taxes tend to generate substantial revenues in times of economic growth but stagnate or decline when the economy is not doing as well. This can be problematic, for it is during times of economic recession that the demand for many government services may be highest. For education, local school districts typically rely most heavily on property taxes, while

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states use sales and income taxes to support their operations, including the provision of assistance to school districts. Income taxes are the largest source of revenue for the federal government. Lawrence O. Picus See also Tax Elasticity; Tax Incidence

Further Reading Odden, A. R., & Picus, L. O. (2013). School finance: A policy perspective (5th ed.). New York, NY: McGraw-Hill.

Websites Government Finance Officers Association: http://www.gfoa .org/ Tax Foundation: http://www.taxfoundation.org

TEACHER AUTONOMY Teacher autonomy refers to the degree of control that teachers have over their instruction and other classroom responsibilities. Teacher autonomy is inextricably linked to the nature of teachers’ work, which has potential implications for teachers’ effectiveness, the desirability of teaching as a career, and, in turn, the quality of the teacher workforce. However, there is no consensus on whether more or less autonomy is optimal for teacher effectiveness. While teachers are typically viewed as having considerable discretion in the manner in which they conduct their classes, some observers perceive potential threats to this freedom, particularly in the form of recent education reforms. Other analysts comment that teachers are given too much autonomy and argue that their instructional practices should be more tightly controlled. This entry discusses how the unique organization of schools gives rise to the debate over teacher autonomy, outlines the measurement of teacher autonomy, reviews potential constraints on autonomy, summarizes the arguments for and against teacher autonomy, and reviews the existing empirical evidence on the effects of teacher autonomy.

The Organization of Schools Schools are unique as organizations. Most organizations are described as “tightly coupled” systems, meaning that there is clear coordination between the

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elements of an organization, such as the technology and the task of the organization. Karl Weick outlines four characteristics of tightly coupled systems: (1) there are rules; (2) there is agreement on what those rules are; (3) there is a system of inspection to see if compliance occurs; and (4) there is feedback designed to improve compliance. While administrative services in education, such as human resources, transportation, and food services, are designed as tightly coupled systems, the core technology of schools—instruction—is not. Theoretically, at least, all four of Weick’s characteristics of a tightly coupled organization are ambiguous in schools’ classroom instruction. First, there are no specific rules of teaching; that is, there are no generally accepted steps, protocols, or guidelines for classroom instruction. Since there are no specific rules of teaching, the remaining characteristics are irrelevant; if there are no rules, there cannot be agreement on rules. While principal observation and evaluation of teachers may be considered as a form of inspection, these checks are subjective and occur so infrequently in most schools that they are far from a system that checks for compliance. Principals typically only observe a teacher’s classes once or twice per school year and rarely stay for the entire class period. Last, principals as supervisors are expected to provide feedback to teachers to improve their instruction, not necessarily to comply with a set of rules. Schools are therefore often characterized as “loosely coupled” organizations. The lack of coordination between schools’ technology and task is evidenced by the fact that teachers have traditionally had substantial freedom in deciding how to instruct their classes, with few checks on the nature or the quality of their work. The nature of schools’ core technology gives rise to this unique organization. Schools’ primary task is the education of its students. Traditional organizational analysis portrays schools as applying a technology—teachers—to a raw material—students. Yet this metaphor is limited in its usefulness since students are not a uniform raw material, and furthermore, applying a uniform technology to all students will not result in the same result every time. In addition, unlike other professions such as medicine and law, there are no widely accepted, standard procedures for practicing teaching. A host of differences between students, including cognitive ability and prior experience, complicate the application of standard teaching procedures. Instead, teaching is

heavily context dependent, with instruction varying based on the content, student, and level of education. The coordination of schools’ technology and task is further complicated by the fact that the task of educating students is a multifaceted function that includes the development of cognitive ability, interpersonal skills, technological aptitude, moral conduct, and civic duty.

Measuring Teacher Autonomy Teachers encounter a multitude of decision points in carrying out their responsibilities in the classroom. They must decide what topics to teach and what topics not to teach, how fast or slow to teach them, and how to teach them. These decisions hold clear implications for the effectiveness of a teacher, especially as measured by students’ achievement. Teacher autonomy is a difficult construct to operationalize in one measure because of its multifaceted nature and because it is a concept that is relative to other classrooms and schools. The Schools and Staffing Survey (SASS), administered by the National Center for Education Statistics, has included survey items to measure teacher autonomy in the classroom in seven administrations from 1987–1988 to 2011–2012. Teachers were asked to report the extent of control they have in their classroom over six areas: (1) selecting textbooks and other instructional materials, (2) selecting content topics and skills to be taught, (3) selecting teaching techniques, (4) disciplining students, (5) determining the amount of homework to be assigned, and (6) evaluating and grading students. In the four administrations of the SASS between 1993 and 2007, teachers reported the lowest levels of autonomy in selecting textbooks and selecting topics and skills to be taught. The average reported level of control in the other four categories was roughly equal.

Potential Constraints Teachers’ autonomy in making classroom decisions can be restricted from both inside and outside the school. School administrators may choose to set school policies concerning student discipline, textbooks, homework, or assessment. Principals can also use frequent observations of teachers’ classrooms and evaluations of teachers as tools of monitoring their work. District and state administrators, on the other hand, may attempt to control

Teacher Autonomy

teachers’ work by instituting curriculum pacing guides, grade and subject standards, scripted lessons, prepackaged curriculum, and uniform professional development. Academic standards and standardized testing, as mandated by the federal No Child Left Behind Act of 2001, have also been viewed as levers of controlling teachers’ work. While intended largely to increase student performance, narrow achievement gaps, and hold schools accountable, these policies have also been criticized for encouraging and instituting policies that limit teachers’ autonomy in the classroom. Proponents of increased teacher autonomy argue that standardized testing restricts teachers’ freedom by proscribing curriculum, standardizing instruction, and incentivizing teaching to the test.

Control Versus Autonomy There are two basic viewpoints in the debate on teacher autonomy. One side argues that schools are too loosely organized with not enough control over the work that teachers are entrusted to carry out. The other side argues that schools are highly bureaucratic organizations that overly regulate teachers’ work and stifle the flexibility teachers need to most effectively do their jobs. Arguments for Control

A theoretical argument against increased teacher autonomy originates from principal-agent theory. According to this theory, agents (i.e., teachers) are contracted by principals (in this case, school districts) to meet their objectives of educating public school students most efficiently. A potential problem arises since teachers may act differently than what the education authority intended due to different utility functions. In other words, teachers may act in ways that benefit themselves and not the principal. To control such deviations, the state or district may impose certain controls on teachers’ work. One concern of opponents of increased teacher autonomy is that when curriculum decisions are left to teachers, students may be exposed to a weak curriculum. Instead of abiding by some common curriculum detailing what should be taught at each grade, autonomous teachers may have discretion over what to teach their classes. In fact, the National Commission on Excellence in Education pointed to weak curriculum as one of the main causes of

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declining achievement in its 1983 report A Nation at Risk. As a result, the need for a common curriculum was one of the major driving forces behind the standards-based movement over the past two decades. Another argument put forth by opponents of increased teacher autonomy is that education needs uniformity. The concern here is that if curriculum, standards, and instruction are left to teachers’ discretion, the result will be different educational experiences for students not only from school to school but even from class to class within schools. Some teachers may set low expectations for their students, while others will set high expectations. A potential lack of uniformity is problematic for at least two reasons. First, educational inequalities may become exacerbated if students from historically disadvantaged groups do not have access to the same types of educational experiences as their more advantaged peers. Second, a severe lack of uniformity in education would create great disparities in the value of high school diplomas as perceived by employers and postsecondary education institutions. Building on the previous points, by controlling certain aspects of teachers’ work, namely the standards, curriculum, and instruction that teachers deliver, states and districts may protect learners from harm and ensure equal access to a quality education. Arguments for Increased Autonomy

Proponents of increasing teacher autonomy offer at least five reasons for doing so. First, they argue that increased teacher autonomy will strengthen the profession of teaching. Although teaching possesses several of the features commonly cited as necessary to qualify as a profession, including a specialized training, required certification, and membership in professional associations, some observers question whether teaching qualifies as a profession. Another important element of a profession is the autonomy to make decisions, so increased autonomy may elevate the professional status of teaching which may, in turn, attract more and higher quality teachers. Under this scenario, teachers who are better educated, more creative, and more committed to their work will seek careers in teaching. Increasing the overall quality of the teaching force will increase the quality of education and improve educational outcomes. In addition, increased teacher autonomy will increase

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teachers’ satisfaction and commitment to their profession, which will lead to greater rates of teacher retention. A second argument for increased teacher autonomy is that it permits more flexibility in teachers’ work. The increasing diversity of student achievement and backgrounds in most classes requires teachers to possess a variety of pedagogical tools to effectively teach all students. Increased autonomy allows for teachers to differentiate their instruction to accommodate the various levels and learning styles of students within their classrooms. A third argument for increased teacher autonomy arises from differences between schools. Different regions within the same country have different histories, different values, and different expectations for schools. Increased teacher autonomy allows for curriculum and instruction to be differentiated at the lowest of levels—that of the classroom. Another argument for increased teacher autonomy counters the principal-agent problem. Since the agent has superior knowledge of the client due to proximity and familiarity, the agent should make decisions on what is best for the client. In other words, the teacher knows the needs of the student better than the school principal, district administrator, or state policymaker. While a policy may be well intentioned, it can be misinformed and not work in all situations. Teacher autonomy permits the discretion of the teacher to apply the appropriate remedy. Finally, proponents of teacher autonomy argue that limiting teachers’ freedom in the classroom will stifle innovation. If teachers are not given flexibility to experiment with topics and instructional methods, it is argued, they will not be able to search for a better way of educating students. This is especially relevant in the case of difficult-to-educate populations of students, such as special education students and English Language Learners.

Research There is little research on the effects of teacher autonomy, in part due to the difficulties of accurately measuring the concept. Furthermore, there are difficulties in measuring increased control of teachers’ work since the constraints mentioned above are not typically intended to control teachers but to improve educational outcomes. Teachers’ decreased autonomy in the classroom is a mediating factor that is difficult to ascertain except with intensive

observation of classroom instruction. Proponents of increased teacher control generally point to support from organizational theory and occupational research studies. While it is difficult to draw a link from teacher autonomy to student outcomes, a number of studies have examined the correlates of teacher autonomy on intermediate outcomes. Higher levels of teacher autonomy have been associated with greater teacher job satisfaction, greater levels of teacher commitment, higher rates of teacher retention, and greater school effectiveness, flexibility, and differentiation of instruction. Using SASS data, Richard Ingersoll found that teachers who reported greater autonomy in their classrooms also reported lower levels of conflict between teachers and students, between teachers, and between teachers and principals. Also using SASS, Ingersoll and Henry May found that a low level of classroom autonomy was the factor most associated with the retention of math teachers but was not a significant predictor of science teacher retention, while Donald Boyd and colleagues have found similar results using New York City data.

Summary Teachers’ classroom responsibilities include many decisions that can plausibly affect the achievement and other outcomes of their students. The degree of autonomy extended to teachers in these decisions is a fundamental aspect of the nature of teachers’ work and plays an integral role in the perception of teaching as a profession. While teachers have traditionally had wide discretion over the decisions they make in their classrooms, there is wide disagreement over whether this autonomy should be increased or decreased. Dominic J. Brewer and Matthew Duque See also Educational Innovation; Principal-Agent Problem; Teacher Supply

Further Readings Ingersoll, R. (2003). Who controls teachers’ work? Power and accountability in America’s schools. Cambridge, MA: Harvard University Press. Ingersoll, R., & May, H. (2012). The magnitude, destinations, and determinants of mathematics and science teacher turnover. Educational Evaluation and Policy Analysis, 34(4), 435–464.

Teacher Compensation Weick, K. (1976). Educational organizations as loosely coupled systems. Administrative Science Quarterly, 21(1), 1–19. Weick, K. (1982). Administering education in loosely coupled schools. Phi Delta Kappan, 63(10), 673–676.

TEACHER CERTIFICATION See Licensure and Certification

TEACHER COMPENSATION During the 2009–2010 school year, U.S. public schools spent $214 billion for salaries and $74 billion for benefits for instructional personnel. These compensation payments account for 55% of K-12 current expenditures and 90% of instructional costs. Given the large share of K-12 costs that arise from educator compensation, even small gains in efficiency can yield large social dividends. Concern over school performance and teacher quality is stimulating interest in more efficient and performance-oriented teacher compensation systems. Congress has also provided an impetus for experiments with performance pay for teachers through its Teacher Incentive Fund grants, which since 2006 have awarded $1.6 billion to states and school districts. In addition, implementation of performance pay was encouraged by the U.S. Department of Education when it awarded $4 billion in grants to states as part of the recent Race to the Top program to be used for broad-based school reform initiatives. A number of large urban districts, most notably Denver, also have taken important steps in pay reform. This entry examines evidence on the overall level of teacher compensation in the United States, the structure of teacher pay, and reforms designed around performance-based pay.

Relative Teacher Pay There is a contentious debate about the overall level of teacher salaries and whether teachers as a group are “underpaid.” Assertions that teachers are underpaid are commonplace. One factor motivating these assertions is a belief that reduced labor market barriers for women (and the resulting higher earnings opportunities) have lowered the quality of the supply of public school teachers. This “crowding

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thesis” holds that, prior to advances in civil rights and antidiscrimination legislation during the 1960s and 1970s, barriers to entry in other professions crowded well-educated and academically talented women into K-12 teaching positions. As these barriers declined in recent decades, women of higher academic ability are thought to have left teaching for more remunerative careers. The above argument highlights what economists call “pull” factors—higher returns to skills in sectors outside of K-12 education lured academically talented would-be teachers away. But there are “push” factors as well, such as salary compression and a consequent reduced return to academic skills in public K-12 schools. Carolyn M. Hoxby and Andrew Leigh developed a statistical model designed to identify push and pull factors while accounting for the growth in supply of high-ability female college graduates from the early 1960s to 2000. They find that salary compression within teaching—likely due to the coincident rise in teacher collective bargaining—was a bigger factor in pushing out top-tier women. More generally, comparisons of salaries and benefits between teachers and other professionals present empirical challenges, and establishing whether teachers are “underpaid” is difficult to determine. It is often assumed that annual salary is the relevant price that an individual uses to determine whether to teach or pursue an alternative career. Yet differences in other forms of pecuniary and nonpecuniary compensation between teaching and other careers are large. The most obvious comparability problem concerns the much shorter annual work hours in teaching as compared with other professions. Teachers enjoy a shorter work week (hourwise) on site and work fewer weeks per year (a typical contract is roughly 185 days or 37 weeks). Data from the Bureau of Labor Statistics suggest that the net result is that public school teachers work roughly 700 fewer hours per year on site or only two thirds of the annual hours worked by a typical private sector manager or professional. (Of course, teachers and other professionals work at home as well. However, hours of work at home are more difficult to measure in survey data. For reasons noted below, an hour of home work may be more attractive for teachers than an hour of on-site work.) Teaching thus tends to attract individuals who value short and predictable hours of work on site and long summer vacations. Women with young children, or who plan to have children, fit that

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description. In fact, census data show that teachers have more children than do other college-educated women in the workforce. Thus, it is not surprising that teaching is a female-dominated occupation and, unlike many other professions, increasingly so. Between 1961 and 2007, the female share of public school teachers has increased from 69% to 76%. Total compensation includes both salaries and benefits. On average, fringe benefits (primarily health insurance and retirement benefits) are a larger share of total compensation for teachers than for private sector managers and professionals. A recent study suggests that the generous retirement plans of teachers (if priced correctly) give a large boost to their relative compensation.

Quantity Versus Quality Trade-Offs The pay of public school teachers is set by school districts. It is not market based as in most other professions. For example, it is very difficult for a school

district to deviate from the stated salary schedule in order to recruit or retain a particularly effective teacher, whereas in the private sector, firms bid up wages for the most skilled workers. Given this, a question arises as to how school districts have chosen to trade off the level of teacher pay with staffing ratios (i.e., the ratio of students to teachers). When spending per student rises by 5%, other things being equal, school administrators can raise teacher pay by 5% and hold staffing ratios constant, hold teacher pay constant and lower staffing ratios by 5%, or do any combination of the two that adds up to 5%. Figure 1 presents data for U.S. staff and enrollments in public schools, indexed to fall to 1980 levels. It shows a clear dip in enrollments by the mid-1980s and a subsequent rise as children in the baby boom echo, or those born to the baby boom generation, entered the school system. It also shows the decline in teacher employment associated with the 2008 recession. Nonetheless, the gap between enrollment and employment growth is notable.

160

Nonteachers

Growth in Real Spending Per Student = 2.1% 150

140 Teachers 130 Avg Tch Salary = $57,253 120

Student Enrollment

Avg Tch Salary = $46,351

110

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Source: Michael Podgursky, with data from the National Center for Education Statistics.

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Figure 1 Teacher and Nonteacher Employment Growth: 1980–2010

Teacher Compensation

Between 1980 and 2010, enrollments grew by 21%, but teacher employment grew by twice that rate and nonteacher employment faster still. Over the same period, inflation-adjusted spending per student grew on average by 2.1% per year. If the staffing ratio had been held constant at the level in 1980, real teacher compensation could have grown by 85% over this period and would have produced an average 2010 teacher salary of $85,861. In fact, real teacher salaries grew by only 24% over this period, and the average public school teacher salary in 2010 was $57,523. This is not a phenomenon unique to the United States. Darius Lakdawalla presents data showing that student-teacher ratios have been falling since at least the 1950s in all the major industrial nations. This human resource policy on the part of school districts is hard to understand in light of complaints about low teacher pay and “teacher shortages.” Some researchers have proposed economic explanations for this strong preference for lower staffing ratios. Flyer and Rosen argue that individualized attention by school staff and parents at home are substitutes. Therefore, as family incomes and the market value of female earnings rise, households substitute schooling for mothers’ time. They examine state-level data and find that declines in the student-teacher ratio are positively associated with increases in the labor force participation of women. Alternatively, Lakdawalla finds an explanation in K-12 education production. Rising skill premia economy-wide have increased the opportunity wage of academically skilled teachers, that is, the amount academically skilled teachers could earn in other work. However, the productivity of academically skilled teachers in the K-12 education sector has not risen at the same rate as productivity in the private sector. Thus, schools rationally substitute less skilled teachers for more skilled teachers and quantity over quality, since the value of an academically talented teacher is simply not as great in K-12 education. Other explanations for the decrease in staffing ratios note the rise of collective bargaining. Other things being equal, teacher unions may favor smaller class sizes and thus larger membership rolls.

The Structure of Teacher Compensation Whatever the overall average level of teacher pay, an important question concerns the structure of pay— which teacher characteristics are rewarded and which are not. In fact, salary schedules are a nearly universal

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feature of public school districts. Under these schedules, base pay for teachers is determined by years of teaching experience and education credentials or graduate credit hours. Table 1 provides an example of a salary schedule, in this case, for Chicago public school teachers. The rows and columns refer to years of experience and levels of teacher education, respectively. The cells give the salary. (In this case, “pension pick up” refers to the fact that the district pays the teacher contribution to the pension plan.) Schedules such as this can be found in public school districts across the United States. The number of rows or columns may vary, as well as the returns to a master’s degree or a year of experience, but the general structure is common across the K-12 landscape. Teacher salary schedules are commonly referred to as “single salary schedules,” a term reflecting the fact that prior to World War II, many districts had separate (and lower) schedules for elementary versus high school teachers. Since elementary school teachers were nearly all women, whereas high school teachers were mostly men, early struggles for a single salary schedule were seen by some commentators as an important part of feminist struggles for pay equity. Eventually, the unification of schedules for elementary and secondary school teachers was embraced by teacher unions and embedded in collective bargaining agreements and, in some cases, state legislation. Salary schedules might have some efficiency rationale if the factors rewarded—teacher experience and graduate education—were strong predictors of teacher productivity. However, studies that examine the relationship between these teacher characteristics and student achievement growth find no support for a positive effect of teacher graduate degrees. Teacher experience has a positive effect during the first few years of employment, but additional experience gains largely disappear after that. Use of these types of salary schedules to pay teachers contrasts with the situation in most other professions. In medicine, the pay for doctors and nurses varies by specialty. Even within the same hospital or HMO (health maintenance organization), pay will differ by specialty field. In higher education, there are large differences in pay between faculty members by field. Faculty pay structures in most higher education institutions are flexible. Starting pay is usually market driven, and institutions will often match counteroffers for more senior faculty whom they wish to retain. Merit- or performancebased pay is commonplace.

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Extended Pay Bi-weekly 1818.00 1912.46 2014.75 2109.21 2203.60 2298.10 2376.76 2471.24 2565.67 2660.10 2762.41 2856.85 2941.70 2983.30 3021.76 47,268 49,724 52,384 54,839 57,294 59,751 61,796 64,252 66,707 69,163 71,823 74,278 76,484 77,566 78,566

Annual

Pens pick up 3,309 3,481 3,667 3,839 4,011 4,183 4,326 4,498 4,670 4,841 5,028 5,199 5,354 5,430 5,500

57,583 60,210 63,057 65,684 68,312 70,938 73,129 75,756 78,384 81,010 83,856 86,484 89,053 90,210 91,280

Total Comp

50,577 53,205 56,050 58,678 61,304 63,933 66,121 68,750 71,377 74,004 76,850 79,477 81,838 82,995 84,065

Total Comp 50,542 52,998 55,657 58,114 60,568 63,025 65,069 67,526 69,981 72,436 75,096 77,552 79,856 80,937 81,937

Annual 54,080 56,707 59,553 62,182 64,808 67,436 69,624 72,253 74,879 77,507 80,353 82,981 85,446 86,603 87,673

Total Comp

Lane V - Master's plus 45 Semester Hours of Approved Graduate Credit Step Extended Pay Annual Pens Total Comp Bi-weekly pick up Gross 1 2132.80 55,453 3,882 59,335 2 2227.25 57,908 4,054 61,962 3 2329.54 60,568 4,240 64,808 4 2424.03 63,025 4,412 67,436 5 2518.44 65,480 4,584 70,063 6 2612.87 67,935 4,755 72,690 7 2691.57 69,981 4,899 74,879 8 2786.02 72,437 5,071 77,507 9 2880.47 74,892 5,242 80,135 10 2974.91 77,348 5,414 82,762 11 3077.24 80,008 5,601 85,609 12 3171.64 82,463 5,772 88,235 13 3265.84 84,912 5,944 90,856 14 3307.44 85,994 6,020 92,013 15 3345.90 86,994 6,090 93,083

Step Extended Pay Bi-weekly 1 1943.93 2 2038.37 3 2140.66 4 2235.14 5 2329.54 6 2424.03 7 2502.67 8 2597.15 9 2691.57 10 2786.01 11 2888.32 12 2982.78 13 3071.37 14 3112.97 15 3151.43

Pens pick up 3,538 3,710 3,896 4,068 4,240 4,412 4,555 4,727 4,899 5,071 5,257 5,429 5,590 5,666 5,736

2010-2011 SCHOOL YEAR Lane II - Master's Degree

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Step

Extended Pay Bi-weekly Gross 2195.80 2290.24 2392.53 2486.93 2581.40 2675.87 2754.54 2848.98 2943.42 3037.86 3140.17 3234.63 3330.70 3372.30 3410.76

57,091 59,546 62,206 64,660 67,116 69,573 71,618 74,074 76,529 78,984 81,644 84,100 86,598 87,680 88,680

Annual

*New Step 14 (FY09) - Teachers with 14 years. **New Step 15 (FY11) - Teachers with 20 years. ***New Step 16 (FY12) - Teachers with 25 years

THE COLUMN ENTITLED "TOTAL COMP" REFLECTS THE TOTAL COMPENSATION PAID (ANNUAL SALARY PLUS PENSION PICKUP) AND IS ROUNDED TO THE NEAREST DOLLAR.

THE COLUMN ENTITLED "PENSION PICKUP" (ARTICLE 36-2.1) WHICH HAS BEEN ROUNDED TO THE NEAREST DOLLAR IS THE AMOUNT OF PENSION PAID BY THE BOARD OF EDUCATION CALCULATED AT SEVEN PERCENT OF SALARY.

3,996 4,168 4,354 4,526 4,698 4,870 5,013 5,185 5,357 5,529 5,715 5,887 6,062 6,138 6,208

61,087 63,715 66,560 69,186 71,815 74,443 76,631 79,259 81,886 84,513 87,360 89,987 92,660 93,817 94,887

Pens pick up Total Comp

Lane VI - Doctorate Degree (PH.D or ED.D)

Lane III - Master's plus 15 Semester Hours of Approved Graduate Credit Step Extended Pay Annual Pens pick up Total Comp Bi-weekly 1 2006.90 52,179 3,653 55,832 2 2101.34 54,635 3,824 58,459 3 2203.59 57,293 4,011 61,304 4 2298.05 59,749 4,182 63,932 5 2392.54 62,206 4,354 66,561 6 2486.95 64,661 4,526 69,187 7 2565.66 66,707 4,669 71,377 8 2660.10 69,163 4,841 74,004 9 2754.56 71,619 5,013 76,632 10 2848.97 74,073 5,185 79,258 11 2951.31 76,734 5,371 82,105 12 3045.73 79,189 5,543 84,732 13 3136.20 81,541 5,708 87,249 14 3177.80 82,623 5,784 88,406 83,623 5,854 89,476 15 3216.26

THE COLUMN ENTITLED "ANNUAL" REFLECTS THE ANNUAL SALARY ROUNDED TO THE NEAREST DOLLAR. IT IS BASED UPON 203 DAYS OF PAY WHICH INCLUDES 193 TEACHER ATTENDANCE DAYS (INCLUDING HOLIDAYS AND 10 EARNED VACATION DAYS) VACATION IS EARNED IN ACCORDANCE WITH ARTICLE 43-1 OF THE COLLECTIVE BARGAINING AGREEMENT

THE COLUMN ENTITLED EXTENDED PAY BI-WEEKLY GROSS IS BASED ON 26 EQUAL PAY PERIODS.

Lane IV - Master's plus 30 Semester Hours of Approved Graduate Credit Step Extended Pay Annual Pens pick up Bi-weekly Gross 1 2069.86 53,816 3,767 2 2164.29 56,271 3,939 3 2266.59 58,931 4,125 4 2361.05 61,387 4,297 5 2455.48 63,843 4,469 6 2549.90 66,297 4,641 7 2628.63 68,345 4,784 8 2723.06 70,800 4,956 9 2817.52 73,256 5,128 10 2911.95 75,711 5,300 11 3014.25 78,370 5,486 12 3108.68 80,826 5,658 13 3201.04 83,227 5,826 14 3242.64 84,309 5,902 85,309 5,972 15 3281.10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Step

FY11 (4%) Lane I - Bachelor's Degree

FULL-TIME TEACHER SALARY SCHEDULE -38.6 WEEK POSITIONS - 6.25 Hour Day

Chicago Public Schools 2010–2011: Teacher Salary Schedule

Source: National Council on Teacher Quality (http://www.nctq.org/docs/Chicago.pdf).

Table 1

Teacher Compensation

Public charter and private schools report using salary schedules as well, although they are not as common as in traditional public schools, and evidence suggests that other factors tend to play a larger role in salary determination. In addition, teachers do not have tenure in either charter or private schools, so what looks like a return to experience may reflect higher quality as less productive teachers are weeded out over time. Three consequences of the rigid salary schedules in education are of particular concern. All derive from the simple economics maxim “You can’t repeal the law of supply and demand.” By this, economists mean that if price is not allowed to clear the market, something else will. In the case of teaching, that “something else” is often quality. One problem is that the single salary schedule suppresses pay differentials by field within school districts. All teachers with the same experience and education level earn the same base pay, meaning that a second-grade teacher will earn the same pay as a high school chemistry teacher. Consistent with the major differences in human capital investments across teaching fields (e.g., elementary education vs. secondary physical science), potential earnings in a nonteaching job differ significantly across fields as well. Available evidence shows much greater recruitment difficulties and more “out of area” teaching in some fields (e.g., science, math, special education) compared with others (e.g., elementary education, social studies). In such a case, quality rather than price is clearing the market. A second problem with the single salary schedule is that it suppresses compensating wage differentials by schools within districts. In larger urban districts, dozens or even hundreds of schools are covered by the same salary schedule. The working environments for teachers often vary greatly between these schools. Some may even be dangerous places to work, whereas other schools are more pleasant and attractive. Often teachers in the less desirable schools will be able to use their seniority to transfer to a more pleasant school in the same district, or they may simply quit at a higher rate. The result is that students in high-poverty schools, on average, have less experienced (and less educated) teachers. Because the salary schedule assigns lower pay to teachers with less experience within a school district, and inexperienced teachers disproportionately teach in high-poverty schools, an unintended consequence of a districtwide salary schedule is lower spending per student in the highest poverty schools within a

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district. In sum, if you want to equalize teacher quality across schools in a district, you need to disequalize pay. If you equalize pay with a salary schedule, you disequalize quality. A third consequence of single salary schedules is the equalization of pay regardless of teacher effectiveness. A consistent finding in the teacher value-added literature is that there is considerable variation in teacher effectiveness within districts and even within schools. The single salary schedule suppresses pay differences between more effective and less effective teachers (however defined). Rewarding more effective teachers on the basis of performance is thought likely to increase student achievement for two reasons. The first is a motivation effect. Incumbent teachers would have an incentive to work harder to raise whatever performance measure is rewarded. Second, over the longer term, performance pay would exert a positive selection effect. It would draw teachers into the workforce who are relatively more effective at meeting the performance targets and would help retain such teachers as well. Equalizing teacher pay among teachers who differ in effectiveness thus tends to lower the overall quality and performance of the teaching workforce. The costs associated with teacher salary schedules are exacerbated by two other features of K-12 human resource policy: (1) tenure and (2) the size of wage-setting units (i.e., districts). Even if experience per se does not raise a teacher’s effectiveness, in principle, a seniority-based wage structure might be efficient if less effective teachers are weeded out over time through contract nonrenewal. However, personnel policies in traditional public schools are not likely to produce such an effect. Teachers in traditional public school districts (unlike charter or private school teachers) receive automatic contract renewal (tenure) after 2 to 5 years on the job. Once a teacher has received tenure, it is very difficult to dismiss him or her for poor job performance. Thus, the presence of teacher tenure laws and collective bargaining, which further hampers dismissal of lowperforming teachers, makes the economic costs associated with single salary schedules greater. Another factor that increases the cost of rigid district salary schedules is the size of wage-setting units. Other things being equal, the larger the size of the unit, the greater the economic cost of rigid salary schedules. The wage-setting unit in private and charter schools is typically the school, whereas in traditional public schools, wage setting is at the district level. In fact, most personnel policy concerning

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teachers—the level and structure of teacher pay, benefits, and recruiting—is centralized at the district level in traditional public schools. This makes the market for teachers less flexible and less competitive than if pay were set at the school level. Another consequence of having large wage setting-units is that the wage-setting process becomes more bureaucratic and thus less amenable to merit or market adjustments.

Deferred Compensation Pensions have long been an important part of compensation for teachers in public schools. Available evidence suggests that retirement benefits as a percentage of earnings are much larger for teachers than for private sector professionals. In addition, the incentive effects regarding the timing of retirement are very large, and evidence suggests that educators are highly responsive to them.

Conclusion Salaries and benefits for educators account for the majority of school spending. Accountability pressures and a growing awareness of the importance of teacher quality’s effect on student achievement are causing many school leaders to rethink how they are spending nearly $300 billion annually for the compensation of instructional personnel. Given the efficiency costs of rigid salary schedules and growing pressure on schools to raise performance, it is not surprising that interest in market- and performancebased pay is increasing. Federal grant programs also have encouraged states and school districts to experiment with performance pay. Performanceand market-based pay systems are much more common in charter schools and are expanding with the charter school base. Michael Podgursky and Cory Koedel See also Cost Accounting; Cost of Education; Pay for Performance; Teacher Effectiveness; Teacher Pensions; Teacher Supply

Further Readings Ballou, D., & Podgursky, M. (1997). Teacher pay and teacher quality. Kalamazoo, MI: W. E. Upjohn Institute for Employment Research. Ballou, D., Springer, M., McCaffrey, D., Lockwood, J., Stecher, B., Hamilton, L., & Pepper, M. (2012). POINT/ CounterPOINT: The view from the trenches of

education policy research. Education Finance and Policy, 7, 170–202. Corcoran, S., Evans, W., & Schwab, R. (2004). Women, the labor market, and the declining relative quality of teachers. Journal of Policy Analysis and Management, 23, 449–470. Hanushek, E., & Rivkin, S. (1997). Understanding the 20th century growth in U.S. school spending. Journal of Human Resources, 32, 35–68. Hoxby, C., & Leigh, A. (2004). Pulled away or pushed out? Explaining the decline in teacher aptitude in the United States. American Economic Review, 94, 236–240. Kershaw, J., & McKean, R. (1962). Teacher shortages and salary schedules (A RAND Research Memorandum). Santa Monica, CA: RAND Corporation. Lakdawalla, D. (2006). The economics of teacher quality. Journal of Law and Economics, 49, 285–329. Podgursky, M. (2010). Teacher compensation and collective bargaining. In E. A. Hanushek, S. Machin, & L. Woessmann (Eds.), Handbook of the economics of education (Vol. 3, pp. 279–313). Amsterdam, Netherlands: North-Holland. Podgursky, M., & Springer, M. (2011). Teacher compensation systems in the United States K-12 public school system. National Tax Journal, 65, 165–192.

Websites Current salary schedules for more than 100 of the largest school districts in the U.S. can be accessed at the National Council on Teacher Quality website at www .nctq.org/districtPolicy/contractDatabaseLanding.do Data on public school teachers’ salaries and demographics can be accessed at National Center for Education Statistics website at http://nces.ed.gov/programs/ digest/2012menu_tables.asp

TEACHER EFFECTIVENESS Teacher effectiveness is generally regarded as the key schooling input determining student success. Recent federal efforts in the United States to hold schools and teachers accountable for student achievement through the No Child Left Behind Act of 2001 and the Race to the Top competition begun in 2009 have resulted in concerted efforts on the part of states, districts, and schools to develop measures of teacher performance in addition to measures of school performance. Conceptually, teacher quality is a complex construct with many dimensions. For example, teachers may help students acquire both cognitive and

Teacher Effectiveness

noncognitive skills. Cognitive skills refer to subject matter knowledge, comprehension, and reasoning abilities. Noncognitive skills refer to skills that are largely behavioral and attitudinal, such as paying attention; completing homework assignments in a timely, neat, and organized manner; and presenting a pleasing affect. Such skills might prove very useful in a student’s success both in school and in later life on the job or in other postschooling endeavors. While it is widely recognized that good teaching is a difficult thing to measure by any objective onesize-fits-all standard, there are a number of proxies and measures that have the potential to capture some aspects of a teacher’s effectiveness. They generally fall into one of the following categories, namely 1. teacher characteristics; 2. measures based on student outcomes; 3. “process”-based measures, such as classroom observations or portfolio evidence of work; and 4. student perceptions.

Measures in each of these categories have the potential to contribute to our understanding of a teacher’s merit. It is difficult, however, if not impossible to pin down a precise and valid estimate of a particular teacher’s effectiveness because there is no measure that can serve as a “gold standard” against which to judge other measures. This entry discusses aspects of teacher characteristics and performance that have been used in efforts to determine whether teachers are effective.

Teacher Characteristics and Student Achievement Research by Steven Rivkin, Eric Hanushek, and John Kain, among others, indicates that teachers have strong effects on student achievement, although the variance in teacher effectiveness is not easily accounted for by observable differences in teacher attributes. Nevertheless, a variety of human capital and ability measures have been associated to some degree with teacher effectiveness. Prior to the widespread application of standardized testing regimes, research studies focusing on teacher effectiveness generally examined the relationship between certain teacher characteristics and student achievement using longitudinal datasets constructed from linked student and teacher samples. Some studies, such as those by Mark Fetler and by Brian Rowan, Richard Correnti, and Robert J. Miller, found small

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positive correlations between teacher experience and student achievement, although the effects are seemingly largest in the early years of a teacher’s career. The impact of other human capital measures has been less clear. In the case of teacher certification, some researchers have found small positive effects, while others have found no effect. Also difficult to link to effectiveness are advanced degrees, due to their varied nature. Evidence indicates that master’s degrees in specific subjects are correlated with effective teaching in those subjects. Dan Goldhaber and Dominic J. Brewer found a weak positive association between high school achievement gains in mathematics and having teachers with a master’s degree in mathematics versus teachers with out-ofsubject degrees. Research by David H. Monk and by Rowan, Correnti, and Miller, however, found no evidence of a positive impact of advanced degrees on achievement. In addition, Ronald G. Ehrenberg and Brewer have found that measures capturing academic “ability”—for example, the selectivity of a teacher’s undergraduate institution and scores of verbal ability on college entrance examinations—have generally been positively correlated with teaching effectiveness. More recently, administrative datasets combining student-level standardized test scores and links to teachers have provided the support for additional studies. Goldhaber and Emily Anthony found that certification through the National Board for Professional Teaching Standards, an advanced program for experienced teachers, was linked to effectiveness, although this was primarily due to the characteristics of the teachers who chose to take part in the certification. Research on alternative certification programs shows mixed results depending on the certification program. Studies have found positive links to student achievement in studies of certain programs, such as Teach for America, while other programs, such as the New York City Teaching Fellows program, do not appear to produce teachers with an advantage or disadvantage with respect to traditional certification programs. Apart from these types of studies that investigate associations between teacher characteristics and student achievement, a vast literature on the effectiveness of various curricular or pedagogical approaches exists, but those are outside the scope of this entry. Another issue, and one that is relatively underresearched, is the impact of matching certain teacher characteristics to certain student characteristics. Thomas Dee, for example, found that the racial

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matching of students to teachers could have a positive impact on achievement.

Measuring Teacher Effectiveness Using Student Outcomes The use of student test scores to evaluate teacher performance—pioneered largely by William Sanders in Tennessee—has proliferated in recent years due to the now almost universal implementation of standardized testing across the United States and the general policy push toward judging schools and educators on the basis of learning outcomes. A number of statistical procedures have been developed to produce teacher performance scores or rankings based on the test scores of their students. Generally, these scores are based on student test score improvement during the time period in which they are exposed to their teachers. The simplest way to compute such a measure is to average the difference between test scores at the beginning and end of the exposure period for all students in a teacher’s class. Such a measure is preferable to simply averaging student test scores at the end of the time period, because some teachers might have started out with high-performing students and others with low-performing students. If teachers were evaluated on the test scores of their students at only one point in time, those with highperforming students would seem more effective than others even if they provided very little in the way of good instruction. The most popular approaches to computing teacher scores based on student growth therefore use “value-added models” or “growth models” that compute teacher performance scores based on regression techniques that control in some way for students’ achievement in a prior year and often for other student characteristics, as well, such as special needs, English Language Learner status, race/ethnicity, family income, and gender. Thus, value-added models have a powerful advantage—namely, the ability to approximate teachers’ contributions to their students’ learning through direct and comparable measure of student growth based on standardized tests. There are three essential challenges that such models must surmount, however, to produce good measures of the performance of individual teachers: (1) endogeneity bias, (2) measurement error, and (3) small sample sizes. Endogeniety bias refers to a situation in which causality is erroneously ascribed to a particular factor

in a model. For example, a model may indicate that teachers are responsible for their students’ learning, when, in fact, parents are responsible for that learning and those same parents chose the teachers to whom their children are assigned. In statistical terms, endogeneity bias can therefore occur when unobserved factors related to student achievement are correlated with the assignment of students to particular teachers. In other words, if students are nonrandomly assigned to teachers in ways that can’t be observed directly from the data, some teachers might look better or worse than they actually are. For example, if certain teachers in a school are always given students with the highest potential for learning growth, they will likely appear to be the best teachers in the school even if their teaching is no different from that of others. To avoid this type of bias in the measures, therefore, teachers must either be randomly assigned to students or the statistician must be able to control for the factors that determine classroom assignment. If, in fact, students are nonrandomly assigned to teachers—but on the basis of something that we can observe, such as prior test scores—then we can control for this factor, and endogeneity bias of this type will be largely mitigated. Evidence from work by Daniel Aaronson, Lisa Barrow, and Sander suggests that a great deal of randomness generally exists in classroom assignment, but Steven Dieterle, Cassandra Guarino, Mark Reckase, and Jeffrey Wooldridge find that a nontrivial amount of sorting on the basis of prior test scores does occur in practice. Measurement error in test scores poses an additional problem because students may post inaccurate learning gains if the tests do not adequately measure their progress. Therefore, their teachers will be judged on the basis of student achievement measures that are subject to error. Measurement error is particularly troublesome if students with different characteristics have different amounts of error in their test scores. A further problem related to precision is that of small sample sizes. Class sizes are quite variable, and teachers with smaller classes will have fewer student test scores contributing information to their effectiveness scores. When small amounts of data are available to compute a statistical measure, the measure is less precise. This can result in a fair amount of misclassification of teachers as high or low performing. One way to increase the numbers of students contributing to a teacher’s performance measure is to use multiple years of data. However,

Teacher Effectiveness

this precludes the computation of performance measures on new teachers, and it can result in different amounts of precision for teachers with different amounts of experience. Some studies have provided evidence suggesting that the potential for bias in teacher performance measures based on value-added specifications may be fairly low. Thomas Kane and Douglas Staiger compared experimental value-added estimates for a subset of Los Angeles teachers with earlier nonexperimental estimates for those same teachers and found that they were similar. Raj Chetty, John Friedman, and Jonah Rockoff found that student achievement responded in expected ways to the entry and exit of teachers with differing value-added measures to a school. Studies by Brian Jacob and Lars Lefgren and by Douglas Harris and Tim Sass have found that value-added measures are correlated with subjective evaluations by principals. On the other hand, studies that examine the intertemporal stability of value-added estimates, such as those of Aaronson, Barrow, and Sander, find a fair amount of variation from year to year, leading one to question how well these estimates capture a teacher’s competence. Jesse Rothstein has devised falsification tests that challenge the validity of value-added-based measures of teacher performance in North Carolina, although research by Goldhaber and Duncan Chaplin and by Josh Kinsler has shown that such tests can be misleading. Although concerns remain whether such measures may be capable of evaluating all teachers fairly, their use is growing, and research is gradually compiling more reliable information about their strengths and limitations.

Process Measures of Teacher Effectiveness As the use of value-added measures proliferates, the use of classroom observations—a process-oriented measure rather than an outcome-oriented measure— is increasing as a tool to evaluate teachers. A number of observation rubrics have been developed to aid observers in coding and rating teaching behaviors. Some rubrics, such as the Framework for Teaching developed by Charlotte Danielson, are fairly generic in that they apply equally to classes in different subject areas—for example, math and English language arts. Others, such as the Mathematical Quality of Instruction developed by Heather Hill, are subject specific. The use of classroom observations as a means of evaluating teachers poses some challenges. First,

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despite the detail and guidance furnished by rubrics, the ratings might be fairly subjective and may vary depending on who is doing the rating. In the Measures of Effective Teaching (MET) project sponsored by the Bill & Melinda Gates Foundation, ratings of video-taped teaching lessons for more than 1,000 teachers in Grades 4–8 by trained individuals using several different rubrics were analyzed. They found that achieving high reliabilities was somewhat challenging, as the interrater reliability, the reliability from rubric to rubric, and even that from lesson to lesson, holding the rubric and rater constant, tended to be rather low. They found that averaging over multiple lessons was needed to achieve fairly acceptable reliabilities. Second, one might question whether observations successfully pick up the transmission of cognitive skills. One might imagine, for example, a teacher who designs very effective assignments for his or her students but fails to provide a visibly engaging classroom experience for students. It might be the case that observations are better at picking up classroom dynamics, including student engagement and attentiveness than they are at discerning whether students are learning the subject material. This might be especially true at the secondary level, where evaluators such as principals and assistant principals might themselves lack the subject matter expertise to judge the extent to which cognitive skills are being learned. Nevertheless, it should be noted that the transmission of noncognitive skills might be of great value to students and their future job prospects. In any case, research has generally found positive but relatively low correlations between classroom observations and value-added measures of teacher performance (e.g., the correlation between the Framework for Teaching and underlying value-added measure in mathematics was .18 in a recent MET study), which may stem in part from the imprecision of both types of measures and in part from the possibility that they pick up different aspects of teacher effectiveness. A third issue is that classroom observations require a fair amount of training on the part of evaluators and can be time consuming and expensive to implement. Currently, many schools and districts are relying on school administrators to spend a larger share of their time conducting observations than ever before. On the positive side, classroom observations provide teachers with feedback regarding particular elements of their pedagogy—an advantage that many teachers may regard as valuable and that does not

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typically result from metrics based on student test score results.

Student Perceptions of Teacher Effectiveness Student evaluations are another potential source of information on teacher effectiveness. The MET project included evaluations from students in Grades 4–9 obtained using the Tripod Survey developed by Ronald Ferguson. The surveys ask students for feedback on multiple aspects of teaching, such as clarity and control. The MET study found that student evaluations were positively correlated (with correlations of around .2) with value-added measures of teacher performance. Although student feedback would likely prove valuable to teachers, the MET project found, not surprisingly, that high school students were more likely than younger students to discriminate among teachers. It is likely that such measures are most useful when students are old enough to provide relatively mature assessments of their teachers.

Issues in Implementing Systems to Determine Teacher Effectiveness Patrick McGuinn reported that more than 40 states required annual teacher evaluations, and the vast majority of those incorporated student performance measures in the teacher evaluations where feasible (i.e., when standardized tests relating to a teacher’s subject matter were available). There are, however, numerous challenges involved in implementing teacher evaluations systems that incorporate many of the elements described above. To compute value-added measures of teacher performance, data systems must accurately link students’ test scores to the teachers who taught them. In some states, such linkages did not exist prior to the Race to the Top competition. Adding these data elements to administrative data systems can sometimes take time, and not all states are in a full state of readiness to construct the teacher measures. Related to the mechanics of linking, there is the issue of correctly assessing the degree of exposure students have had to particular teachers. Many students are highly mobile, and data systems do not always track movements within the school year. Moreover, standardized testing periods are sometimes scheduled in the middle of the school year, making it difficult to attribute achievement growth from one year to the next to one particular teacher. If the data are sufficiently fine-grained, one can

model exposure to specific teachers using variables that record the fraction of the year that the student is taught by the teacher. A further consideration is the statistical methodology used to model teacher performance. A plethora of methodological options exist, and research by Guarino, Reckase, and Wooldridge has shown that different methods can result in different evaluations for the same teachers as well as different amounts of imprecision in the estimates. Further concerns center on the use of student test scores for high-stakes rewards and sanctions. One potentially negative consequence may be that teachers will teach to the test, resulting in a narrowing of the curriculum. However, if tests are of sufficiently high quality, this may be less of a concern. The use of test scores to reward or sanction teachers also increases incentives for teachers to cheat. Additionally, there is a concern that the public release of teacher evaluation measures might have a negative impact on teacher morale—in Los Angeles and New York City, where the media have reported on data linking teachers to their students’ test performance, teachers have reported morale problems. Such problems could have negative effects on the teacher labor market in the long run. Despite the many controversies surrounding issues of evaluation and the imposition of greater accountability on teachers, it is clear that both the national dialogue regarding teacher effectiveness as well as policy implementation has changed rapidly in recent years. The actions of many states indicate that a view is emerging that a combination of measures should be assembled in the process of evaluating teachers, and no one measure should constitute the basis for an overall evaluation, particularly one that would lead to punitive actions or other types of high stakes. Cassandra Guarino See also Accountability, Types of; Performance Evaluation Systems; Race to the Top; Teacher Evaluation; Teacher Value-Added Measures

Further Readings Aaronson, D., Barrow, L., & Sander, W. (2007). Teachers and student achievement in the Chicago Public High Schools. Journal of Labor Economics, 25(1), 95–135. Chetty, R., Freidman, J., Hilger, N., Saez, E., Schanzenbach, D., & Yagan, D. (2010). How does your kindergarten classroom affect your earnings? Evidence

Teacher Effectiveness from Project STAR (NBER Working Paper No. 16381). Cambridge, MA: National Bureau of Economic Research. Chetty, R., Freidman, J., & Rockoff, J. (2011). The longterm impacts of teachers: Teacher value-added and student outcomes in adulthood (NBER Working Paper No. 17699). Cambridge, MA: National Bureau of Economic Research. Decker, P. T., Mayer, D. P., & Glazerman, S. (2004). The effects of Teach for America on students: Findings from a national evaluation (Mathematica Policy Research Report No. 8792–8750). New York, NY: Mathematica Policy Research. Dee, T. (2004). Teachers, race, and student achievement in a randomized experiment. Review of Economics and Statistics, 86(1), 195–210. Dieterle, S., Guarino, C. M., Reckase, M. D., & Wooldridge, J. (2012). How do principals assign students to teachers? Finding evidence in administrative data and the implications for value-added (IZA Discussion Paper No. 7112). Retrieved from http://ssrn .com/abstract=2199795 Ehrenberg, R., & Brewer, D. (1994). Do school and teacher characteristics matter? Evidence from high school and beyond. Economics of Education Review, 13(1), 1–17. Figlio, D. N. (2006). Testing, crime and punishment. Journal of Public of Economics, 90(4–5), 837–851. Goldhaber, D., & Anthony, E. (2007). Can teacher quality effectively be assessed? National Board certification as a signal of effective teaching. Review of Economics and Statistics, 89(1), 134–150. Guarino, C., Reckase, M., & Wooldridge, J. (2012). Can value-added measures of teacher performance be trusted? (Working Paper No. 18). East Lansing: Michigan State University Education Policy Center. Retrieved from http://education.msu.edu/epc/ publications/documents/WP18Guarino-ReckaseWooldridge-2012-Can-Value-Added-Measures-ofTeacher-Performance-Be-T_000.pdf Harris, D., & Sass, T. (2009). What makes for a good teacher and who can tell? (Working Paper). Washington, DC: Urban Institute, National Center for the Analysis of Longitudinal Data in Education Research (CALDER). Retrieved from http://www.urban.org/ publications/1001431.html Harris, D., Sass, T., & Semykina, A. (2010). Value-added models and the measurement of teacher productivity (Working Paper). Washington, DC: Urban Institute, National Center for the Analysis of Longitudinal Data in Education Research (CALDER). Retrieved from http://www.urban.org/publications/1001508.html Heckman, J., & Rubinstein, Y. (2001). The importance of noncognitive skills: Lessons from the GED Testing Program. American Economic Review, 91(2), 145–149.

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Jacob, B., & Lefgren, L. (2008). Can principals identify effective teachers? Evidence on subjective performance evaluation in education. Journal of Labor Economics, 26(1), 101–136. Kane, T., & Cantrell, S., & Associates. (2010). Learning about teaching: Initial findings from the Measures of Effective Teaching project. Seattle, WA: Bill & Melinda Gates Foundation. Kane, T., McCaffrey, D. F., Miller, T., & Staiger, D. (2013). Ensuring fair and reliable measures of effective teaching: Culminating findings from the MET project’s three year study. Seattle, WA: Bill & Melinda Gates Foundation. Kane, T., McCaffrey, D. F., Miller, T., & Staiger, D. (2013). Have we identified effective teachers? Validating measures of effective teaching using random assignment. Seattle, WA: Bill & Melinda Gates Foundation. Kane, T., & Staiger, D. (2008). Estimating teacher impacts on student achievement: An experimental evaluation (Working Paper No. 14607). Cambridge, MA: National Bureau of Economic Research. Kane, T., Staiger, D., & Associates. (2012). Gathering feedback for teaching: Combining high quality observations with student surveys and achievement gains (MET Project Research Paper). Seattle, WA: Bill & Melinda Gates Foundation. Koedel, C., & Betts, J. (2011). Does student sorting invalidate value-added models of teacher effectiveness? An extended analysis of the Rothstein critique. Education Finance and Policy, 6(1), 18–42. McCaffrey, D., Lockwood, J. R., Louis, T., & Hamilton, L. (2004). Models for value-added models of teacher effects. Journal of Educational and Behavioral Statistics, 29(1), 67–101. McGuinn, P. (2012). The state of teacher evaluation reform: State education agency capacity and the implementation of new teacher-evaluation systems. Washington, DC: Center for American Progress. Monk, D. H. (1994). Subject area preparation of secondary math and science teachers and student achievement. Economics of Education Review, 13(2), 125–145. Rice, J. K. (2003). Teacher quality: Understanding the effectiveness of teacher attributes. Washington, DC: Economic Policy Institute. Rivkin, S. G., Hanushek, E., & Kain, J. F. (2005). Teachers, schools and academic achievement. Econometrica, 73(2), 415–458. Rothstein, J. (2010). Teacher quality in educational production: Tracking, decay, and student achievement. Quarterly Journal of Economics, 125(1), 175–214. Rowan, B., Correnti, R., & Miller, R. (2002). What largescale survey research tells us about teacher effects on student achievement: Insights from the prospects study of elementary schools. Teachers College Record, 100(8), 1525–1567.

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Sanders, W., & Horn, S. (1994). The Tennessee ValueAdded Assessment System (TVAAS): Mixed-model methodology in educational assessment. Journal of Personnel in Education, 8, 299–311. Wayne, A., & Youngs, P. (2003). Teacher characteristics and student achievement gains: A review. Review of Educational Research, 73(1), 89–122.

TEACHER EVALUATION Teacher evaluation systems are designed to measure the quality of instruction and contributions of teachers to the school learning environment. This entry covers the policy relevance of these systems in the United States with a focus on two important types of component measures used in many teacher evaluation systems—(1) those based on teacher practices and (2) those based on student achievement growth. It also discusses other measures, methods of summarizing results across these different component measures to obtain final evaluation scores and the future of teacher evaluation systems.

Policy Background Teacher evaluation is important because compared with other schooling inputs, teachers have the greatest influence on student achievement, and student achievement, in turn, is strongly correlated with many adult outcomes. For evaluation systems to support improving the teacher workforce, they must be able to identify high and low performers with a high degree of validity and reliability. Teacher evaluation systems often combine several different measures to form a single summative performance rating. Thus, the quality of the component measures and the weights attached to them are critical to a valid and reliable evaluation outcome. Robert Marzano and colleagues described educators’ theories about best practices for teacher supervision and evaluation in the United States since the early 19th century. These theories typically called for observations of teacher practice and conferences between the supervisor and the teacher. Notably, as early as 1924, one educator argued for including measures of student ability and student progress in the teacher’s evaluation. However, those espousing teacher observation and postobservation conferences did not offer any structured protocols to guide the measurement of teacher instructional practices during the observation until 1980, when

a general framework for a seven-step lesson was developed, and this model was put forth as the standard for judging the teacher’s practice. Then, in 1996, the book Enhancing Professional Practice: A Framework for Teaching by Charlotte Danielson defined four rating levels for very specific items organized into four domains of teaching practice. Since then, several more observation rating systems have been developed to measure instructional practices. Although the evolution of theories about teacher evaluation practice is documented, the field lacks systematic information on the evaluation practices actually used in schools since the early 19th century or even those used currently. Similarly, the field lacks systematic information on the extent to which evaluation outcomes affect tenure decisions, pay, professional development, and retention. The available information is based on small and selected samples. Existing evidence suggests that many districts do evaluate teachers fairly regularly before they receive tenure; however, it is not clear how many are actually denied tenure due to poor performance. Since the 1930s, teacher pay in most districts has been based primarily on a “single salary schedule,” which means pay is based mostly on experience and education (though extra pay is often given for teachers willing to take on additional responsibilities such as coaching sports or extracurricular activities). Professional development opportunities are common, but it is not clear how those opportunities are driven by evaluation results, especially for tenured teachers who are often only evaluated once every few years. According to a RAND study in 1984, even in districts that were reported to have “highly developed systems,” evaluations were done inconsistently and evaluators received inadequate training. A more recent and influential report on this topic completed in 2008, The Widget Effect, examined teacher evaluation practices and outcomes in 12 districts in four states before they had implemented new more rigorous teacher evaluation systems (discussed below). In most evaluation systems covered in The Widget Effect, administrators based their evaluations on one or two classroom observations and often had little training on the observation instrument. These evaluation systems did not distinguish excellent teachers from average teachers, and fewer than 1% of teachers were rated as unsatisfactory. With federal support, many states and school districts have taken steps in recent years toward adopting new, more rigorous teacher evaluation systems. These systems share several features that

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differentiate them from previous teacher evaluation practices, including the use of more than two performance categories (thereby permitting distinctions among very-high-performing teachers, satisfactory teachers, and very-low-performing teachers), the inclusion of student achievement growth as an evaluation component, more extensive training of observers on observational rubrics, and the use of evaluation results for both professional development and career-related decisions. If a teacher evaluation system provides valid and reliable information about practice quality and effectiveness, that information can support actions by teachers and their employers to improve the quality of the teacher workforce. For example, teachers and their supervisors can identify professional development needs, and teachers can obtain professional development that addresses those needs. School districts can identify which teachers should be given tenure, allow high-performing teachers to transfer to a school of their choice, and offer incentives to retain high-performing teachers. Incentives can include linking the salary scale to performance levels or tying special bonuses to high performance. Districts may also link strong evaluation results with access to greater responsibilities (e.g., serving as a mentor, trainer, or supervisor of other teachers) designed to extend the exemplary teacher’s “reach” to more students or to extra roles chosen by teachers (e.g., supervising student clubs or activities). The roles and responsibilities typically offer extra pay and may lead to career advancement. Teachers with weak evaluation results over multiple years may face losing their position. The new teacher evaluation systems developed in recent years typically combine measures of teacher practice, primarily observational measures based on a rubric, and measures of student growth, for example, based on statistical models of student achievement growth on state summative assessments. In some instances, they also include student perceptions of teacher practice based on student survey data. The Measures of Effective Teaching (MET) study of 3,000 teachers in six urban school districts found that all three types of measures contribute to a more accurate prediction of a teacher’s later effectiveness.

Practice Measures Measures of teacher professional practice are generally based on a rubric that uses teacher observations in the classroom and other evidence such as lesson

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plans and student work to rate performance on a variety of dimensions that typically include preparation, instruction, and classroom management. Supervisors may also consider the teacher’s contributions to the work of teacher teams or to school operations in rating teacher practice. Several proprietary teacher professional practice rubrics are available for use nationally, including the widely used Danielson Framework for Teaching and a number of others such as Marzano’s Teacher Evaluation Model and the Mid-continent Research for Education and Learning Teacher Evaluation System. Some states and school districts have developed their own teacher practice rubrics. Many rubrics are aligned with the model professional teaching standards that were developed by the Interstate Teacher Assessment and Support Consortium of the Council of Chief State School Officers. Two important features of teacher professional practice measures are validity (whether they measure what they claim to measure) and reliability (the consistency of scores across observers, observation occasions, and contexts). Attaining high levels of validity and reliability of teacher practice measures is a major challenge. Teacher evaluation systems have been based on rubrics in the past, but administrators typically applied the rubric without extensive or regular training, leaving items open to individual interpretations. How the rubrics are used can affect the overall validity and reliability of these measures. Published or proprietary rubrics generally have a manual and training and certification procedures that can be purchased to ensure that their evaluation systems are well implemented. The MET study examined the reliability of observation systems (based on different raters, occasions, and teacher selection of lessons) and concluded that four observations using four different observers produced ratings with high reliability; additional raters improved reliability by negligible amounts. Compared with other measures used in teacher evaluations, teacher professional practice measures have specific items that support feedback to improve practice and identify professional development needs. However, the usefulness of this feedback for improving teaching quality depends on the extent to which higher scores on observational practice measures correlate with improvements in student achievement and the extent to which these correlations relate to causal impacts. The correlations between the overall teacher observation rubric score and a teacher’s “value-added” score, or the average

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test score gain for his or her students, adjusted for differences across classrooms in student characteristics, are positive but not strong. Improving the strength of these relationships could require new or different observation items that are more closely related to student achievement growth or student assessments that more accurately measure student achievement growth. Furthermore, research is needed to understand how specific teacher practices in different content areas and grade levels relate to student achievement growth. In addition, since correlation is not the same as causation, any correlations identified will need to be investigated to see if they can be used to improve the ways in which teachers are provided with professional development. For example, teacher collaboration may be a strong predictor of student achievement growth, but this relationship may be driven more by the impacts of teachers’ interactions with students rather than by their interactions with each other. Also, a lack of a statistically significant correlation should not be interpreted to mean that a practice is unimportant. For example, lesson plans are likely very important, but if almost all teachers implement them, there may not be a statistically significant correlation between lesson plans and student outcomes in most datasets. One way to use teacher evaluations is for voluntary professional certification (e.g., certification by the National Board for Professional Teaching Standards, or NBPTS). Some research has found that teachers who achieve voluntary professional certification have higher student achievement gains in the following years than those who fail to achieve certification, though the research is mixed. For NBPTS certification, teachers submit a portfolio that includes videos of their teaching, examples of student work showing growth and achievement, and reflections and analysis of the teaching samples that demonstrate a strong command of content and the ability to design learning experiences to advance student achievement. Portfolios are evaluated by peers using rigorous standards.

Student Achievement Growth Measures Teacher evaluation systems can also include measures of student achievement growth derived from student test scores obtained near the beginning and the end of the period the students are with the teacher. Such measures are important because an essential goal of teaching is to improve student

achievement, which can have long-term consequences for many adult outcomes. Some observers raise concerns that the student achievement growth measures focus narrowly on test scores and ignore aspects of social-emotional development that contribute to positive education and employment outcomes, such as persistence, motivation, character, agreeableness, and selfcontrol. Most grades and subjects are not covered by growth measures because relevant assessment data are not available. Nevertheless, value-added scores for individual teachers are about as reliable as the performance assessments used for high-stakes decisions in other fields, such as health care and real estate, and studies on the use of value-added measures indicate that they increase the validity of personnel decisions about teachers. Finally, some observers question whether these measures provide valid estimates of teacher contributions to student achievement. However, simulation results and empirical evidence suggest that the bias may be quite small. Measures of student achievement growth based on statewide summative assessments use year-toyear changes in the test scores of a teacher’s students. Value-added measures estimate the teacher’s contribution to student achievement growth. Commonly used measures include value-added models used by the Value-Added Research Center run by Rob Meyer at the University of Wisconsin and the Education Value-Added Assessment System of William Sanders. Student growth percentiles of Damian Betebenner use the year-to-year changes in the student’s percentile in the test score distribution to estimate whether, compared with initially similarly scoring peers, the student performed better or worse. These measures vary in the statistical model used and in the control variables included. All growth models control for prior test scores in some way. The Value-Added Research Center models also control for other student demographic characteristics, while the Education Value-Added Assessment System and the Student growth percentiles models do not (though both could be modified to do so). Using multiple years of student growth data for each teacher reduces the statistical error of teacher valueadded estimates, but remaining statistical error rates should be considered when these measures are used for high-stakes purposes. All these models require complicated statistical modeling; therefore, state and district education agencies often use outside vendors to obtain

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the student growth percentiles or value-added estimates. The time required (often as much as 6 months) delays the final evaluation results well into the following school year. These measures require at least two assessment points; in many states, this means that growth measures can only be calculated for teachers of English and mathematics in fourth through eighth grades. Growth measures can be extended to teachers of more grades and subjects by administering baseline and summative assessments in more grades and subjects. Another approach to measuring student growth for teacher evaluation involves teachers setting goals for student achievement growth over the school year and assessing the proportion of students who attain that goal. Teachers might measure student growth based on the difference between fall and spring standardized assessments or teacher-developed assessments (e.g., measures of music performance) and set a goal for growth or for student achievement levels at the end of the year that takes into account knowledge of students’ starting points. These student learning objectives or student growth objectives avoid the statistical complexity of value-added scores and can provide a student growth measure where no other is possible, but they may not ensure valid comparisons across teachers.

Other Measures Some states and districts are broadening their teacher evaluation measures to include ratings based on student surveys. Student input has the advantage of daily observations of the teacher; on the other hand, teachers raise concerns that student ratings might not be valid, and in high-stakes situations, it might introduce bias. However, the MET study, which used student surveys to assess student perceptions of the classroom instructional environment, found that the student survey measure was more reliable than observations and value-added scores. The student survey measure also improved the prediction of teacher effectiveness more than using only the observation and value-added score. Teachers can also influence noncognitive skills, such as persistence, self-control, cooperation, and agreeableness, although these skills can be difficult to measure. Some research shows that various measures of noncognitive skills, such as student absences, suspensions, grade progression, and student grades during the eighth and ninth grades, are related to later college attendance and earnings even

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after controlling for student cognitive skills and that teacher effects on cognitive and noncognitive skills are only weakly correlated. This suggests that including teachers’ contribution to noncognitive skills in teacher evaluations would improve the validity of overall measures of teacher effectiveness (defined as contributing to positive adult outcomes).

Summary Measures Many states and school districts combine the component measures for teacher evaluation into a single summary rating to use in making career decisions, including tenure. Measures can be combined in multiple ways. One approach is to use a weighted average of the component measures. Many systems use weights based on stakeholder input; to the extent that these weights are inversely related to the component measure standard deviations, each component will influence the summary measure similarly. Another approach essentially transforms each component measure into a binary rating and then combines those binary ratings. An example of this approach would be to identify teachers as effective if both their school growth and individual observation scores were above certain cutoff points.

Future of Teacher Evaluation Systems New teacher evaluation systems that include more standardized observations of teacher practice, measures of student achievement growth, and support for improving professional practice are still in the early implementation stages in many states. The new systems’ ability to more clearly distinguish teachers based on effectiveness has not been firmly established. Initial information from Florida, Pennsylvania, and Tennessee, which have implemented such systems, suggests that a large proportion of teachers with low student achievement growth scores received high observation ratings, suggesting that further improvements in these systems are possible. States and districts are experiencing growing pains as they implement the new systems, and many features could change over the next several years in response to implementation challenges, new information, and other education policy changes. Different measures of teacher practice, such as content area–specific measures and measures aligned with the Common Core State Standards, may be introduced. Likewise, teacher observations may be done more frequently, by more observers, and for

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shorter periods as districts absorb results from the MET study and related findings, or observation frequency may vary according to the previous year’s effectiveness level as administrators balance teacher evaluation with their other responsibilities. More measures of student growth and achievement may be introduced to tap multiple academic and socialemotional outcomes across all grade levels. The introduction of common English and mathematics assessments developed by the Partnership for Assessment of Readiness for College and Careers and the Smarter Balanced Assessment Consortium may improve the validity and reliability of student achievement growth measures in these subjects. Finally, school districts may begin to include student surveys of the instructional environment to improve the overall validity and reliability of teacher evaluation measures. Christine Ross and Duncan Chaplin See also Accountability, Types of; Elementary and Secondary Education Act; National Board Certification for Teachers; Salary Schedule; Teacher Training and Preparation; Validity

Further Readings Betebenner, D. (2011). A technical overview of the student growth percentile methodology: Student growth percentiles and percentile growth projections/trajectories. Dover, NH: National Center for the Improvement of Educational Assessment. Retrieved from http://www .state.nj.us/education/AchieveNJ/teacher/ SGPTechnicalOverview.pdf Chetty, R., Friedman, J. N., & Rockoff, J. E. (2011). The long-term impacts of teachers: Teacher value-added and student outcomes in adulthood (Working Paper No. 17699). Cambridge, MA: National Bureau of Economic Research. Gill, B., Bruch, J., & Booker, K. (2013). Using alternative student growth measures for evaluating teacher performance: What the literature says (REL 2013–002). Washington, DC: U.S. Department of Education, Institute of Education Sciences, Regional Education Laboratory Central. Retrieved from http://ies.ed.gov/ ncee/edlabs Glazerman, S., Loeb, S., Goldhaber, D., Staiger, D., Raudenbush, S., & Whitehurst, G. (2010). Evaluating teachers: The important role of value-added. Washington, DC: Brookings Brown Center on Education Policy. Goldhaber, D., & Chaplin, D. (2012, May 11). Assessing the ”Rothstein test“: Does it really mean teacher value-added

models are biased? Paper presented at the Association for Public Policy Analysis and Management Annual Fall Research Conference, Washington, DC and the National Bureau of Economic Research, Cambridge, MA. Harris, D. N. (2011). Value-added measures in education: What every educator needs to know. Boston, MA: Harvard Education Press. Kane, T. J., McCaffrey, D. F., Miller, T., & Staiger, D. O. (2013). Have we identified effective teachers? Validating measures of effective teaching using random assignment (Measures of Effective Teaching project Research Paper). Seattle, WA: Bill & Melinda Gates Foundation. Retrieved from www.metproject.org Marzano, R. J., Frontier, T., & Livingston, D. (2011). Effective supervision: Supporting the art and science of teaching. Alexandria, VA: ASCD. Meyer, R. H. (1999). The production of mathematics skills in high school: What works? In S. Mayer & P. Peterson (Eds.), Earning and learning: How schools matter (pp. 168–204). Washington, DC: Brookings Institution Press. Rivkin, S. G., Hanushek, E. A., & Kain, J. F. (2005). Teachers, schools, and academic achievement. Econometrica, 73(2), 417–458. Rothstein, J. (2010). Teacher quality in educational production: Tracking, decay, and student achievement. Quarterly Journal of Economics, 125(1), 175–214. Schochet, P. Z., & Chiang, H. S. (2013). What are error rates for classifying teacher and school performance using value-added models? Journal of Educational and Behavioral Statistics, 38(2), 142–171. Weisberg, D., Sexton, S., Mulhern, J., & Keeling, D. (2009). The widget effect: Our national failure to acknowledge and act on differences in teacher effectiveness (2nd ed.). Washington, DC: New Teacher Project. Retrieved from widgeteffect.org Wise, A. E., Darling-Hammond, L., McLaughlin, M. W., & Bernstein, H. T. (1984). Teacher evaluation: A study of effective practices. Santa Monica, CA: RAND Corporation.

TEACHER EXPERIENCE Economists of education have long been interested in whether and how teacher experience “matters” for school productivity. In part, this interest was driven by data availability: Teacher experience was one of few school resource measures available for estimating education production functions (along with teacher salaries, degree attainment, and spending per pupil). But teacher experience is important to researchers for at least two other reasons. First,

Teacher Experience

a long-established finding in labor economics is that worker productivity increases with experience, usually peaking at some point midcareer. Considerable attention has been given to estimating the analogous “returns” to experience in the classroom. Second, teacher salaries, which account for a substantial share of educational costs, are typically tied to years of experience. Understanding how teacher experience relates to educational outcomes is thus an important step toward the efficient allocation of scarce resources. This entry begins by describing the level of experience in the teacher workforce and how it is distributed across states, districts, and schools. It then briefly reviews evidence on the importance of teacher experience to student outcomes. Finally, it closes by highlighting key questions for future research. The experience profile of the teacher workforce has varied over time. According to the National Center for Education Statistics, the average public school teacher in 2007–2008 had 13 years of experience, and nearly half (47%) had been teaching for fewer than 10 years. The latter reflects a greening of the teaching pool, as novices replaced a wave of retiring baby boomers: From 1993–1994 to 2007–2008, the share of public school teachers with fewer than 3 years of experience increased from less than 10% to 13%; at the other end of the spectrum, the share of teachers with at least 10 years of experience decreased from 65% to 53%. National averages conceal variation in experience across states, districts, and schools. For example, rapidly growing states like Arizona tend to have more recently hired, inexperienced teachers, while slow-growth states like South Dakota tend to have more experienced teachers. (In 2007–2008, the average teacher in these two states had 11 and 15.5 years of experience, respectively; 28% of Arizona teachers had fewer than 4 years of experience compared with 15% in South Dakota.) Teacher experience tends to be higher, on average, in traditional public schools (13.1 years) than in private (11.6 years) and charter schools (7.5 years), and numerous studies find inexperienced teachers disproportionately employed by economically disadvantaged and racially isolated schools and districts. In their study of teachers in New York State, Hamilton Lankford, Susanna Loeb, and James Wyckoff reported that 11.8% of poor students in the state were taught by first-year teachers, versus 9.8% of nonpoor students. Similarly, 9.9% of non-White students had novice teachers, versus 6.7% of White students. Charles Clotfelter,

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Helen Ladd, and Jacob Vigdor found similar patterns in North Carolina. The uneven distribution of teacher experience has equity consequences, as research finds it is one of the few observed teacher characteristics associated with student achievement. In these studies, economists seek to estimate the “return” to experience—that is, how much learning or educational outcomes change, on average, when students are taught by a more experienced teacher. Measuring the causal impact of experience is difficult, however, because teachers are not randomly allocated across classrooms. As noted, experienced teachers are more likely to be found in educationally advantaged settings, so that researchers must distinguish causal impacts from the correlation that arises due to teacher sorting. It may also be that effective teachers are less (or more) likely to leave the profession than less effective teachers, such that the observed returns to experience reflect a change in the composition of teachers. These empirical issues are a more serious concern in studies that rely on cross-sectional data, or relate aggregate outcomes at the district or school level to average teacher experience. Stronger research designs use longitudinal data on individual students matched to their teachers to isolate the impact of experience. The most convincing estimates of the return to experience find that the average teacher becomes more effective at increasing student achievement in each of her first several years but does not continue to improve beyond that point. In his review of the literature, Dan Goldhaber concludes that the greatest returns to experience are in the first 3 to 5 years of a teacher’s career. Though the bulk of the literature finds small or insignificant returns to experience after the initial years, emerging research suggests that the models used in previous studies may be overly restrictive and that there may be greater returns to higher levels of teacher experience than was previously thought. There are a number of understudied questions related to the role of teacher experience in school productivity. First, teacher experience may matter more for some populations of students than for others. Little is known about how the importance of experience varies across students. Second, the vast majority of studies on teacher experience examine its impact on achievement as measured by test scores. The value of these estimates may be limited by the properties of the test used, and the impact of experience may go beyond its direct effect on instructional quality. For example, experienced

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teachers may have multiple roles within the school or may improve the effectiveness of less experienced teachers through peer effects. Finally, we know little about why teacher experience matters—that is, what it is that experienced teachers do that makes them more productive. New and emerging data on teacher practices may help identify key mechanisms behind these effects. Sean P. Corcoran and Emilyn Ruble Whitesell See also Education Production Functions and Productivity; Salary Schedule; Teacher Supply; Teacher Value-Added Measures

Further Readings Clotfelter, C. T., Ladd, H. F., & Vigdor, J. L. (2007). Teacher credentials and student achievement: Longitudinal analysis with student fixed effects. Economics of Education Review, 26, 673–682. Goldhaber, D. (2008). Teachers matter, but effective teacher quality policies are elusive. In H. F. Ladd & E. B. Fiske (Eds.), Handbook of research in education finance and policy (pp. 146–165). New York, NY: Routledge. Hanushek, E. A., & Rivkin, S. G. (2006). Teacher quality. In E. A. Hanushek & F. Welch (Eds.), Handbook of the economics of education (Vol. 2, pp. 1051–1078). Amsterdam, Netherlands: Elsevier. Lankford, H., Loeb, S., & Wyckoff, J. (2002). Teacher sorting and the plight of urban schools: A descriptive analysis. Educational Evaluation and Policy Analysis, 24, 37–62.

TEACHER INTELLIGENCE The relationship between teacher characteristics and student achievement is an important focus of educational research and policy making. The 1966 report Equality of Educational Opportunity, better known as the Coleman Report, was one of the first attempts to parse out the causes and consequences of achievement disparities among student groups. Although controversy over the methods used in the report exists, the report documented an important pattern: Teachers’ verbal aptitude, or verbal intelligence, was strongly correlated with student achievement. The authors of the report measured teachers’ verbal aptitude with a short assessment at the end of a survey, and teachers’ results were highly correlated with the academic performance of their students. Since publication of the report, researchers,

policymakers, and district officials have tried to measure teacher intelligence using a variety of proxies: college entrance exams (SAT, ACT, etc.), college selectivity, licensure exams covering both content and pedagogical knowledge, undergraduate major, and so on. However, to evaluate the relationship between something as broadly and variedly defined as teacher intelligence, it is important to delve into the latent construct, or meaning, of the term intelligence as well as discuss the different ways it has been measured and used. This entry will cover the basic difficulties in defining and measuring intelligence and discuss how the concept of intelligence has been used in research on teacher quality. The definition and measurement of intelligence is a tenuous and controversial task. Not only is there disagreement about the definition of intelligence and the measures used to capture it, but there are also many who do not think a generalizable form of intelligence exists, let alone that it can be measured. Intelligence has typically been seen as a hierarchical construct. The most basic level contains contextualized domain-specific knowledge (e.g., 10th-grade physics), and at the highest level, it is a decontextualized general ability (often referred to as “intelligence quotient” or g factor). Ideally, a single assessment would produce an accurate measure of intelligence that correlates with individuals’ labor productivity, credit worthiness, and so on. However, intelligence cannot be fully captured by a single measure, nor can it be measured without error. Measures of intelligence, while imperfect, take many forms—for example, measures of broad abilities such as verbal, quantitative, and spatial reasoning—and can serve to predict future outcomes. For example, admission offices in colleges and universities around the world rely on measures of broad abilities (e.g., SAT, Graduate Record Exam, Law School Admission Test) in deciding among which applicants to admit for the following school year. Domain-specific measures of intelligence, such as the Medical College Admission Test, cannot be generalized in the same way as measures of broad abilities. The Medical College Admission Test, which assesses verbal, quantitative, and spatial reasoning, may predict success in medical school. But unlike a board exam, it does not measure the specific knowledge required to be a doctor. Certain kinds of licensing exams, such as those for doctors, lawyers, and teachers, are used as screens to keep unqualified individuals from entering professions or other uniform communities. The exams

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used as screens are most often domain-specific measures of intelligence. In other cases, the score on an intelligence measure is used as a signal for future success. Both domain-specific exams, such as the Praxis teacher licensing exam, and tests of broad abilities, such as the Graduate Record Examination general test and the Medical College Admission Test, are often used to predict success in postsecondary education and job performance.

Research Findings Research has shown that teachers are the most important school-based factor in student achievement. For example, in a study by Eric A. Hanushek, John F. Kain, and Steven G. Rivkin, students with a teacher in the 85th percentile of teacher quality, as measured by growth in student achievement, outperformed students with a teacher at the median of teacher quality by 0.22 standard deviations. Other research on the relationship between teacher ability or intelligence and student achievement has used performance on college admission tests, attendance at a selective college, course taking and degrees, and certification status. Teacher Testing

Research that began in the 1960s on teacher intelligence initially relied on measures of teachers’ verbal intelligence, while the more recent research focuses on teacher licensure exams, which are measures of domain-specific knowledge. These assessments measure different skills, which may have a different relationship with student achievement. Researchers using the short assessment at the end of the Coleman survey found a positive association between teacher verbal ability and student achievement. Other extant research has also found that teachers who had higher SAT scores and attended more selective undergraduate institutions had larger gains in student achievement than teachers with lower SAT scores and those who went to schools with lower average selectivity. More recently, researchers have begun to use results on teacher licensure exams such as the Praxis and student-level longitudinal data to study the relationship between teacher characteristics and student achievement. While there is a small, positive relationship between whether a teacher has passed the Praxis and student math scores, there is no relationship between passing the Praxis and students’ English scores. The research on certification exams

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shows that they are at best a weak signal of future teacher quality. The exams were established as a screen, not a signal; however, they do not serve as a strong signal or screen in terms of a teacher’s ability to raise students’ test scores. Furthermore, the extant research suggests that other factors, beyond teachers’ certification exam scores, are important predictors of success in the classroom. For example, research demonstrates that Black teachers in North Carolina had a statistically significant positive effect on Black students, regardless of their Praxis scores. Although principals often have access to prospective teachers’ various proxies of intelligence (e.g., college grade point average, certification exam scores), recent research documents that this information is not used during the hiring process. Instead, principals often assume that the credentialing process screens out individuals unfit to enter the teaching profession. A conflict in the hiring process may well exist if the professional screens used by many states and districts are unrelated to student achievement. Furthermore, unless a more unified approach is taken, the relationship between teacher licensure exams and student achievement will be heterogeneous across states. Existing research suggests that not only have teacher licensure exams failed to improve teacher quality, but they can also actually keep potentially strong teachers from entering the profession. Individuals may be deterred by the cost of studying for the exams and the presence of measurement error in the exams. Furthermore, licensure exams may also keep minority teachers out of the profession, since they are more likely to fail the exam. This is problematic since, regardless of their test scores on licensure exams, minority teachers often produce larger achievement gains for minority students than do White teachers. Other researchers have found that the quality of teachers, as measured by high school class ranking, college admission tests, and degree type, has declined over the past few decades, with the most notable changes at the tails of the quality distribution.

Conclusion A recent wave of federal policies and programs, such as the Race to the Top grant competition and waivers to requirements of the No Child Left Behind Act of 2001, as well as the economic circumstances faced by most schools and districts, require administrators and policymakers to make informed, objective

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decisions about the future teacher labor market. Administrators and policymakers do not, however, have the coherent and reliable sources of information necessary to make these decisions. The extant research does show a loose relationship between student achievement and teachers’ grades, test scores, university selectivity, and other observable factors. It also shows that teachers have a large impact on student learning. It would benefit policymakers and other educational stakeholders, along with the research community, to create an objective, unified measure of teacher ability that more strongly predicted an individual’s future success as a classroom teacher. Andrew McEachin Author’s Note: This entry draws on McEachin, A., & Brewer, D. J. (2013). Teacher Intelligence: What is it and why do we care? In J. Hattie & E. M. Anderman (Eds.), The international guide to student achievement (pp. 254–256). New York, NY: Routledge.

See also Accountability, Standards-Based; Teacher Effectiveness; Teacher Experience; Teacher Supply; Teacher Training and Preparation

Further Readings Angrist, J. D., & Guryan, J. (2008). Does teacher testing raise teacher quality? Evidence from state certification requirements. Economics of Education Review, 27, 483–503. Boyd, D., Grossman, P., Lankford, H., Loeb, S., & Wyckoff, J. (2006). How changes in entry requirements alter the teacher workforce and affect student achievement. Education Finance and Policy, 1(2), 176–216. Buddin, R., & Zamarro, G. (2009). Teacher qualifications and student achievement in urban elementary schools. Journal of Urban Economics, 66(2), 103–115. Clotfelter, C. T., Ladd, H. F., & Vigdor, J. L. (2007). How and why do teacher credentials matter for student achievement? (NBER Working Paper No. 12828). Retrieved from http://files.eric.ed.gov/fulltext/ ED509655.pdf Coleman, J. S., Campbell, E. Q., Hobson, C. J., McPartland, J., Mood, A. M., Weinfeld, F. D., & York, R. L. (1966). Equality of educational opportunity. Washington, DC: Department of Health, Education, and Welfare. Corcoran, S. P., Evans, W. N., & Schwab, R. M. (2004). Women, the labor market, and the declining relative quality of teachers. Journal of Policy Analysis and Management, 23(3), 449–470.

Ehrenberg, R. G., & Brewer, D. J. (1994). Do school and teacher characteristics matter? Evidence from high school and beyond. Economics of Education Review, 13(1), 1–17. Ehrenberg, R. G., & Brewer, D. J. (1995). Did teachers’ verbal ability and race matter in the 1960s? Coleman revisited. Economics of Education Review, 14(1), 1–21. Goldhaber, D. (2007). Everyone’s doing it, but what does teacher testing tell us about teacher effectiveness? Journal of Human Resources, 42(4), 765–794. Goldhaber, D., & Hansen, M. (2010). Race, gender, and teacher testing: How informative a tool is teacher licensure testing? American Educational Research Journal, 47(1), 218–251. Pelayo, I., & Brewer, D. J. (2010). Teacher quality in education production. In P. Peterson, E. Baker, & B. McGaw (Eds.), International encyclopedia of education (pp. 438–442). New York, NY: Elsevier. Rivkin, S. G., Hanushek, E. A., & Kain, J. F. (2005). Teachers, schools, and academic achievement. Econometrica, 73(2), 417–458. Santibanez, L. (2006). Why we should care if teachers get A’s: Teachers test scores and student achievement in Mexico. Economics of Education Review, 25, 510–520. Shavelson, R. J., & Huang, L. (2003). Responding responsibly to the frenzy to assess learning in higher education. Change, 35(1), 10–19. Wayne, A. J., & Youngs, P. (2003). Teacher characteristics and student achievement gains: A review. Review of Educational Research, 73(1), 89–122.

TEACHER PENSIONS Part of the compensation of professional employees, whether in the public or private sector, takes the form of employer contributions for retirement. From the end of World War II until the early 1970s, public and private sector professionals had similar types of retirement plans. However, due to increased federal regulation of private sector plans and growing worker mobility, the way that retirement benefits are structured in the public and private sectors began to diverge. At present, most private sector professionals are in plans in which employers make contributions to a retirement account owned by employees. Such plans are called defined-contribution (DC) plans. In contrast, public sector professionals, including public educators, are nearly universally enrolled in defined-benefit (DB) plans. In DB plans, employees are guaranteed a specified monthly pension at retirement payable until death. This benefit is funded by

Teacher Pensions

the assets in a large pooled fund representing the pooled contributions of employers and employees. The employers are obligated to make sure that there are enough assets in the fund to pay for the pension liabilities that have accumulated. Paying for educator pension plans, and keeping them solvent, is a large and growing expense for school districts and state governments. Robert Costrell and Michael Podgursky show that employer costs for public pensions have risen sharply over the past decade—from 10.5% of salaries in 2004 to 16.7% today. By contrast, retirement benefit costs for employers in the private sector over the same time period have been nearly flat at about 10.5% of salaries. These figures do not include worker contributions, which for educators are often 5% to 10% or more of salary. Nor do they include retiree health insurance costs, which can be substantial for many school districts given that most teachers retire prior to becoming eligible for Medicare (typically at age 65). In this entry, we describe briefly how these plans work, the powerful incentives they create for work and retirement and how these incentives shape workforce behavior, and some reform options being considered by many states.

How Teacher Pension Plans Work Most educator retirement plans are administered at the state level, although a few municipal plans remain (e.g., New York City, Chicago, St. Louis). Nearly all of these plans, whether state or municipal, use a formula such as the following to determine the annual benefit the teacher receives at retirement: B = F ∗ YOS ∗ FAS.

In the equation, B represents the annual benefit, F is a formula factor, which is usually between 1.5% and 2.5%, YOS indicates years of service in the system, and FAS is the teacher’s final average salary, commonly calculated as the average of the final (highest) few years of earnings. In many plans, the annuity payments are increased over time using cost-of-living adjustments, which are meant to maintain the spending power of the annuity in the face of inflation. Each plan has its own rules that determine retirement eligibility. Once a teacher becomes eligible for retirement, he or she can begin collecting the pension. Eligibility is based on some combination of age and/or years of system service. In Missouri, for example, a teacher is eligible for a full pension if she

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has 30 years of service, has reached age 60 with at least 5 years of service, or if her age added to her service years totals at least 80 (“rule of 80”). Many states also have rules that permit a teacher to retire with reduced benefits at a younger age, or with fewer service years. In Missouri, the early-retirement provision is called “25 and out.” It allows a teacher to retire and begin collecting benefits immediately, at any age, once he or she has worked for 25 years in the system. Like similar provisions in other state plans, there is a benefit penalty to retirement via 25 and out. However, even with the penalty, the 25-and-out provision is still actuarially beneficial for teachers who wish to leave the profession prior to meeting other retirement eligibility threshold in that the reduction in the annual benefit is not enough to offset the overall gain in expected lifetime benefits collected. Teachers are not automatically vested in their pensions when they start working. It typically takes 3 to 5 years for vesting, although Kathryn M. Doherty, Sandi Jacobs, and Trisha M. Madden report that 13 states now require 10 years of service for new teachers to be vested—up from 9 years in 2008. Matters are further complicated by the fact that about 30% of public school teachers are not covered by Social Security. All these complicated rules regarding the calculation of the annuity, which depends on age and/ or experience rules concerning retirement, vesting, cost-of-living adjustments, and so forth, vary from state to state and, on the face of it, make cross-state comparisons of the plans difficult. For example, California teachers can retire at age 60 and earn 2% for every year of service. Final average salary is based on the highest single year of earnings. In Missouri, teachers can retire when the sum of age plus experience equals 80 or more and earn 2.5% for each year of service, and the final average salary is based on the highest 5 years of salary. How can we tell which pension plan is more generous? Tools from the larger financial economics literature allow us to compute comparable measures of the value of retirement benefits as they accrue over a teacher’s work life in different plans. Pension wealth is a simple measure of the cash value of a pension at any point in a worker’s career, in present discounted value (discounted to a particular point in time). Discounting is an important concept when one considers the value of educator pension benefits because benefit collection occurs in the future, and income in the future is less valuable than income today.

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is that teachers who exit the system before the peak in Figure 1 collect fewer annuity payments. To see this, note that the teacher who quits after 5 years must wait until age 60 to collect her pension, but an otherwise similar teacher who works continuously, will be eligible to retire with full benefits under the rule of 80 when she is 52 years old with 28 years of system service. That is, the full-career teacher will be eligible to collect pension payments for eight additional years relative to the early exiter. Economists describe the payoff structure shown in Figure 1 as backloaded. It reflects the very powerful pull and push incentives that are built into these plans. At the front end of a teaching career, the plan exerts a strong retention effect that encourages teachers to stay in the profession until they are eligible to collect a pension. Past this retirement date, however, pension wealth actually falls. This is due to the fact that if the teacher does not retire and collect a pension, the benefits are lost—pension benefits cannot be collected while working, nor can unused pension wealth be passed on to children or heirs. Costrell and Podgursky show that the highly backloaded pattern of pension wealth accrual shown in Figure 1 for Missouri is typical of plans in other states and municipalities. Indeed, it is a direct mathematical consequence of the types of rules built into these systems. In addition to encouraging retirements within a narrow career window, another consequence of

Figure 1 shows the evolution of pension wealth accrual over time for a representative midcareer teacher in Missouri who began her career at the age of 24—the modal age for beginning teachers in the state. Vesting in Missouri takes 5 years. Thus, the teacher does not accrue any pension wealth until she has completed her fifth year of service. At the completion of her fifth year, the teacher is entitled to collect a pension on retirement. Her benefit is calculated using the formula shown above. In Missouri, the formula factor is 2.5%, and the final average salary is calculated based on the highest 3 years of earnings. If the teacher quits immediately after her fifth year of work, for example, her annuity payment on retirement would equal 12.5% (0.025 * 5) of her average earnings across her three highest paid years. As per the retirement eligibility rules, she would be eligible to begin collecting payments on her 60th birthday. Notice from the figure that pension wealth accrues very slowly for teachers in the early years. There are two main reasons for this. Continuing with our teacher who exits after 5 years as an example, one reason is that the final average salary calculation is held fixed until annuity collection. So for the teacher who quits after her fifth year, the annuity she collects at age 60 is based on her salary in her late 20s, unadjusted for inflation or life cycle pay increases (note that cost-of-living adjustments are not made until after retirement). A second reason $300,000

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Figure 1 Teacher Pension Wealth Over the Work Life of a Typical Missouri Teacher (Discounted to Age 24) Source: Calculations done by using Missouri pension plan rules.

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this backloading is that it creates severe penalties for mobility, even within teaching. This is because the benefit formula, and retirement rules, depend on system service, not teaching service. Educators who move from state to state over a career will have much less pension wealth than an educator who works an entire career in a single plan. These harsh penalties for mobility built into these plans have raised concern as the educated labor force, including teachers, has become more mobile.

Labor Force Effects and Enhancements Research finds that teachers are highly responsive to the retirement timing incentives built into these systems and that educators tend to retire at levels of age or experience where pension wealth is maximized. For teachers and educational administrators, this means retirement at relatively young ages—typically in the mid- to late 50s. Costrell and Joshua McGee, for example, show that teacher retirements in Arkansas tend to spike in the years in which pension wealth is maximized. Studies of DB plans outside of education report similar findings. These early-retirement incentives are sometimes justified as cost savings for school districts, since a high-salary senior teacher is replaced by a new hire earning a lower entry-level salary. However, Cory Koedel, Podgursky, and Shishan Shi question the extent to which there are real cost savings, noting that retiring teachers continue to collect their pensions while not working but must still be replaced in the classroom. Their calculations suggest that the incentives for early retirement raise total system costs. The research literature on labor force quality effects is much more limited. Koedel, Podgursky, and Shi find no net effect on workforce quality, as measured by teacher effects on student test scores, from the pull and push incentives shown in Figure 1. During the 1990s boom in the stock market, most teacher retirement plans enhanced benefits, often substantially. These enhancements have led to sharply rising contribution costs for educators, school districts, and state governments. Koedel, Shawn Ni, and Podgursky find that the increase in contribution costs has almost certainly made new teachers worse off, despite their enhanced pensions.

Options for Reform In the face of rising costs, some states are considering alternatives to their DB retirement plans for teachers. These include DC plans, which are similar

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to the 401k plans typically received by private sector professionals, and cash balance plans, which produce smooth accrual of pension wealth over a teaching career and pay out as an annuity. A key feature that distinguishes these alternatives from current DB plans is that benefits are tied directly to contributions. This reduces opportunities for school personnel to “game the system” by seeking legislative changes in pension plan benefit rules to permit earlier retirement, increasing benefit payments, or boosting final average salary or service years in various ways (e.g., providing supplemental boosts in salary just before retirement or including unused sick or vacation days in service years). Tying benefits directly to contributions helps keep overall system benefits in line with costs and eliminates penalties for educator mobility. Some states have also adopted hybrid plans, which combine a less generous DB plan with a DC plan. Some reforms give new teachers a choice between a more mobile DC benefit and a traditional DB plan. Finally, 16 states let charter schools choose whether or not to participate in the state pension plan. In most cases, charter schools choose not to participate. The weak financial condition of many state pension plans ensures that pension reform will continue to be at the forefront of policy discussions. Because such a large share of the total resources currently devoted to educator compensation is tied up in DB pension systems, the reform process has the potential to meaningfully affect the educator labor market along a number of dimensions, including how teachers are recruited and retained and when they retire. Current reform efforts offer an opportunity to redesign educator retirement benefit systems for a 21st-century workforce. Michael Podgursky and Cory Koedel See also Cost Accounting; Cost of Education; Teacher Compensation; Teacher Effectiveness; Teacher Supply

Further Readings Costrell, R., & McGee, J. (2010). Teacher pension incentives, retirement behavior, and potential for reform in Arkansas. Education Finance and Policy, 5, 492–518. Costrell, R., & Podgursky, M. (2009). Peaks, cliffs and valleys: The peculiar incentives in teacher retirement systems and their consequences for school staffing. Education Finance and Policy, 4, 175–211. Costrell, R., & Podgursky, M. (2010). Distribution of benefits in teacher retirement systems and their

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implications for mobility. Education Finance and Policy, 5, 519–557. Doherty, K. M., Jacob, S., & Madden, T. M. (2012). No one benefits: How teacher benefit systems are failing both teachers and taxpayers. Washington, DC: National Council on Teacher Quality. Friedberg, L., & Turner, S. (2010). Labor market effects of pensions and implications for teachers. Education Finance and Policy, 5, 463–491. Hansen, J. (2010). An introduction to teacher retirement benefits. Education Finance and Policy, 5, 402–437. Koedel, C., Ni, S., & Podgursky, M. (2014). Who benefits from pension enhancements? Education Finance and Policy. Advance online publication. doi:10.1162/ EDFP_a_00128 Koedel, C., Podgursky, M., & Shi, S. (2013). Teacher pension systems, the composition of the teaching workforce, and teacher quality. Journal of Policy Analysis and Management, 32(3), 574–596. Lazear, E., & Gibbs, M. (2009). Personnel economics in practice. New York, NY: Wiley. Olberg, A., & Podgursky, M. (2011). Charting a new course to retirement: How charter schools handle teacher pensions. Washington, DC: Fordham Institute.

TEACHER PERFORMANCE ASSESSMENT Education research has clearly established the importance of teacher quality in raising student achievement, and policymakers have responded by elevating the goal of improving teacher quality. However, traditional teacher qualifications, such as certification, have proved poor indicators of teacher effectiveness. Accordingly, researchers, policymakers, and practitioners have sought to develop new strategies for evaluating teacher competence, both for teachers first entering schools and those already practicing in schools. The assessment of preservice teachers is particularly important as novice teachers are, on average, less effective than their more experienced colleagues. Teacher education programs are the most logical point of assessing preservice teachers as they serve as the primary gateway to the profession. Teacher performance assessments seek to measure the knowledge, skills, and competencies of beginning teachers and allow credential boards further insight into the effectiveness of potential teachers. Although teacher education programs have used student teaching performance to determine candidates’ readiness for

their own classrooms for almost a century, recent legislation in many states now requires candidates to pass a standardized summative assessment of teaching performance to earn their teaching credentials. This entry describes the edTPA, the first nationally available research- and standards-based support and assessment program to measure teacher candidate performance.

History In the early 21st century, the federal government and states proposed sweeping changes in teacher education, including teacher assessment. At the time, states relied on written licensure tests to document teachers’ readiness to enter K-12 classrooms. California took the lead in the development of preservice teacher performance assessment by requiring that, beginning in 2008, all teacher education programs include a summative assessment of teaching performance in preservice teacher preparation. The success of one of the three teaching performance assessment models approved by California and the growing demand for a national teacher performance assessment led to the formation of the Stanford Center for Assessment, Learning and Equity national teacher performance assessment, the edTPA. Since 2008, 28 states and the District of Columbia have joined the Teacher Performance Assessment Consortium, either formally adopting or considering adopting the edTPA for statewide use to license new teachers or to approve teacher preparation programs. The edTPA was field tested for 2 years with 12,000 teacher candidates to determine its validity and became fully operational at the end of 2013.

Design The edTPA is intended to be comparable with entrylevel licensing exams in other professions such as medicine, architecture, and law. The design of the national teacher performance assessment program was based on several assessments, including those of the National Board for Professional Teaching Standards, the Interstate New Teacher Assessment and Support Consortium, and the Performance Assessment for California Teachers. The program is designed to achieve five goals: 1. Improve student outcomes 2. Improve the information base guiding improvement of teacher preparation programs

Teacher Supply

3. Strengthen the information base for the accreditation and evaluation of program effectiveness

affects teacher recruitment to the profession should also be examined, especially since most of the cost is passed on to the future teacher candidates.

4. Be used in combination with other measures as a requirement for licensure 5. Guide professional development for teachers across the career continuum

All teacher education candidates complete the edTPA toward the end of their program when they are immersed in student teaching. Teacher candidates submit materials from a subject-specific segment of three to five lessons in one unit taught to one class of students. Materials include video of instruction, lesson plans, samples of student work, analyses of student learning, and candidate reflections. The edTPA is available for early childhood, elementary, middle childhood, and secondary levels in a full complement of subjects, including English language arts, mathematics, history, science, physical education, technology, visual arts, and business. The edTPA evaluates teachers on three tasks— planning, instruction, and assessment—using 15 rubrics to analyze performance on the tasks. The instruction task requires candidates to video subjectspecific pedagogical approaches. The edTPA has a rubric that explicitly assesses classroom environment and integrates the Common Core State Standards and Next Generation Science Standards that have been adopted by many states. The assessment task requires candidates to submit three representative samples of student work from one assessment. The rubrics for all three edTPA tasks include how well teachers structure academic language development and reflect on their practice to improve student performance. Finally, the edTPA rubrics are scored on a 5-point scale, with 3 considered a passing score. In all three assessments, candidate performance is scored by trained assessors using rubrics that describe levels of performance relative to each task.

Future Prospects The scaling up of this teacher performance assessment has led some teacher educators to voice concerns that assessment of future teachers, once a local endeavor, will become standardized. Because this assessment is still in its infancy, it is critical that educators study the correlation between the edTPA and inservice teacher effectiveness. The cost-effectiveness of this massive program and the degree to which it

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Frederick Freking See also National Board Certification for Teachers; Teacher Effectiveness; Teacher Evaluation; Teacher Training and Preparation

Further Readings Campbell, D., Melenyzer, B., Nettles, D., & Wyman, R. (1999). Portfolio and performance assessment in teacher education. Boston, MA: Allyn & Bacon. Hill, D., Hansen, D., & Stumbo, C. (2011). Policy considerations for states participating in the teacher performance assessment consortium (TPAC). Washington, DC: Council of Chief State School Officers. Pecheone, R., & Chung, R. (2006). Evidence in teacher education: The performance assessment for California teachers (PACT). Journal of Teacher Education, 57, 22–36. Wei, R. C., & Pecheone, R. L. (2010). Assessment for learning in preservice teacher education: Performancebased assessments. In M. M. Kennedy (Ed.), Teacher assessment and the quest for teacher quality: A handbook (pp. 69–132). San Francisco, CA: JosseyBass. Retrieved from https://scale.stanford.edu/system/ files/WeiPecheone.pdf

TEACHER SUPPLY Teacher supply is concerned with entry into, exit from, and movement within the teacher labor market—that is, who chooses to teach (and where), who remains in teaching, and what factors influence these choices. Policymakers and education economists are intensely interested in this topic, as teacher effectiveness has been found to be one of the most important school inputs into students’ learning outcomes. This entry provides an introduction to research on teacher supply. It begins by noting the importance of teacher labor markets for education policy and describing the general approaches to studying this topic. It then briefly reviews the evidence on long-run trends in the quantity and quality of teachers and factors affecting the supply of teachers. Finally, it closes by describing recent policies intended to improve teacher supply. Staffing schools with high-quality teachers is an immense, ongoing challenge. According to the

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National Center for Education Statistics, there were 3.6 million PreK-12 teachers nationwide in 2009, with annual turnover rates of 16% (8% switching schools and 8% leaving the profession). Turnover rates tend to be particularly high among novice (25%), charter school (24%), and private school teachers (21%), as well as teachers in disadvantaged schools. Such turnover is costly, financially and educationally. An understanding of what motivates graduates to enter and stay in teaching, or to seek and accept employment in a particular school or district, is necessary for crafting policies to obtain desired levels of human capital in teaching. Teacher supply research approaches the topic from “macro” and “micro” perspectives. Macrolevel studies define the teacher labor market broadly, examining long-run movements into and out of the profession, aggregate shortages and turnover rates, and factors contributing to these phenomena. Microlevel studies define teacher labor markets more narrowly and address the supply of teachers to specific schools, districts, or fields. Teacher supply has quantity and quality dimensions; most studies are focused on the supply of teachers of a given quality or level of qualifications.

Trends in the Supply of Teachers The past 60 years have witnessed dramatic shifts in the quantity and quality of teachers. In response to the baby boom, there was a sharp increase in the demand for teachers in the 1950s–1960s. Between 1955 and 1970, the number of public school teachers nearly doubled, from 1.1 to 2.1 million. Enrollment declined as baby boomers exited the K-12 system, but the size of the teaching force remained roughly constant. This led to a weakened demand for new teachers, an aging of the workforce, and a decline in relative salaries. The size of the teacher workforce has steadily increased since 1990, though the recent recession brought localized reductions in force. There is also evidence of a long-term decline in the quality of graduates entering the teaching profession. Sean Corcoran, William Evans, and Robert Schwab documented a decline in the quality of new teachers between 1960 and 2000, as measured by math and verbal skills, driven by a sharp drop in the percentage of high-achieving women choosing to teach. This decline corresponded with significant changes in the labor market for women, as highachieving women gained access to professions traditionally held by men.

Factors Affecting the Supply of Teachers Economics has much to say about factors affecting teacher supply. Potential (and existing) teachers weigh the benefits of entering (or remaining in) the profession against the costs of doing so. Benefits are pecuniary, such as salary and benefits, and nonpecuniary, such as the intrinsic reward of educating children, job security, and having summer months “off.” Costs include the foregone earnings collegeeducated professionals could earn in other fields (their “opportunity cost”) and explicit costs associated with education, training, and licensing. To the extent teacher training is not transferable to other fields and the candidate is uncertain about her future career plans, there is additional risk associated with this investment. Factors that theoretically affect teacher supply have been empirically tested in many settings. With respect to the pecuniary benefits of teaching, the noted long-run decline in teacher quality has been linked to relative growth in the earnings of collegeeducated women outside of teaching and the teacher salary schedule, which has not historically rewarded high performers to the same extent as other professions. Peter Dolton’s review of the literature concludes that teacher supply is highly responsive to salaries, and many studies have found a relationship between salary offers and average teacher quality. Nonpecuniary factors are particularly important to teacher supply. Working conditions influence initial entry into the profession, and additional challenges associated with teaching in disadvantaged schools make it difficult for these schools to attract and retain teachers. Research finds that teachers have a preference for teaching close to where they grew up, or in similar districts, compounding the challenge of staffing high-need, urban schools with quality teachers. Fortunately, there is some evidence that bonus pay targeted to teachers in hard-to-staff positions and schools can improve recruitment and decrease turnover in disadvantaged schools.

Future Directions: Policies to Improve Teacher Supply Many reforms underway in public education are designed to improve the supply of teachers. Most prominently, the structure of teacher compensation is changing, as states and districts experiment with pay that aims to reward high performance. While this approach may make teaching more attractive to academically talented graduates with high-outside-wage

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opportunities, it also increases the risk associated with teaching, given more variable earnings. These reforms are relatively new, however, making it difficult to gauge their success. An alternative approach to improving the supply of teachers is the reduction of barriers to entry through paring back or eliminating traditional certification requirements. The theory is that lower entry costs will increase supply, particularly among high-achieving graduates deterred by traditional licensing. It remains to be seen what effect these reforms will have on aggregate teacher supply and quality, although there is evidence that alternative routes (e.g., the Teach for America program) have been effective in filling shortages in disadvantaged schools.

licensure programs, future teachers usually complete content courses, but little or no coursework in methods or student teaching prior to working as full-time teachers; after they begin teaching full-time, they typically complete methods courses. This entry provides an overview of the structure and organization of teacher training and preparation in the United States. Then, it reviews research on the longterm outcomes of various approaches to teacher education. Finally, it concludes by considering implications of research for (a) the practice of teacher training and preparation and (b) future scholarship in this area.

Sean P. Corcoran and Emilyn Ruble Whitesell

In the United States, more than 1,300 public and private colleges and universities offer teacher preparation programs; the number of school districts, state agencies, and private organizations that offer preparation programs is unknown. Colleges and universities with teacher preparation programs generally offer one or more of the following types of programs: early elementary generalist (e.g., Grades PreK-3); elementary generalist (e.g., Grades K-6 or Grades 1–8); secondary (e.g., Grades 6–12) mathematics, English language arts (ELA), science, history, social studies, and/or world languages; and middle grades generalist (e.g., Grades 5–8). College and university teacher preparation programs typically consist of one or more of the following types of degree programs: bachelor’s degree (4 years), master’s degree (1–2 years), postbaccalaureate (1–2 years), and bachelor’s degree plus 1 year (5 years). The number of teaching candidates who graduate each year from college- or university-based preparation programs in the United States ranges from 25 or fewer in small liberal arts colleges and elite universities to nearly 1,000 in a few large public institutions. In general, highly selective colleges and universities prepare fewer teachers (or none at all), while less selective institutions generally prepare hundreds of teaching candidates each year. However, several selective and highly selective research universities prepare 100 or more teaching candidates each year, including Stanford University; Teachers College, Columbia University; Michigan State University; and University of Wisconsin, Madison. In general, elementary and middle grades preparation programs differ significantly from secondary programs. The latter primarily emphasize

See also Hedonic Wage Models; Teacher Compensation; Teacher Experience

Further Readings Corcoran, S. P., Evans, W. N., & Schwab, R. M. (2004). Women, the labor market, and the declining relative quality of teachers. Journal of Policy Analysis and Management, 23(3), 449–470. Dolton, P. J. (2006). Teacher supply. In E. A. Hanushek & F. Welch (Eds.), Handbook of the economics of education (Vol. 2, pp. 1079–1161). Amsterdam, Netherlands: Elsevier. Murnane, R. J., & Steele, J. L. (2007). What is the problem? The challenge of providing effective teachers for all children. Future of Children, 17(1), 15–43.

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In the United States, the following types of entities provide teacher training and preparation for elementary and secondary teachers: colleges, universities, school districts, state agencies, and private organizations. Teacher training and preparation usually consists of a combination of university-level content courses, university-level methods courses, and student teaching placements. In traditional university-based preparation programs, future teachers typically complete content courses, methods courses, and student teaching placements prior to working as full-time teachers of record. In alternative teacher

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coursework in content areas (e.g., mathematics, English, or science), content-specific pedagogy (methods), and some additional courses (e.g., special education, social foundations of education, multicultural education). On the other hand, elementary and middle grades programs include pedagogy (methods) courses in multiple content areas and other education courses. Furthermore, within the same state, the structure of elementary, middle grades, and secondary preparation programs can vary based on state policies or individual decisions by institutions. Teacher preparation programs and states also vary with regard to features of and requirements for clinical student teaching placements. As of 2011–2012, of the 50 states, 41 required 5 to 18 weeks of student teaching (Education Week, 2013). Furthermore, preparation programs within states typically vary with regard to aspects of student teaching program such as oversight of the selection of the cooperating teacher, requirements for cooperating teacher experience, stability of cooperating teachers’ participation, amount of contact between program faculty and field supervisors, number of required supervisory observations, explicit links between coursework and field experience, and number of courses that have required field experiences attached to them. Alternate-route-to-licensure programs have grown significantly in the United States over the past 15 years. Between 1985–1986 and 1997–1998, fewer than 10,000 individuals a year were licensed by alternate routes. Beginning in 1998–1999, the number of teachers licensed through alternate routes exceeded 10,000 and climbed steadily so that by 2004–2005, approximately 50,000 teachers (about 33% of all teachers hired that year) entered through alternative licensure programs. As of 2013–2014, such programs were offered by local school districts, county school districts, state agencies, private organizations, and institutions of higher education. In many jurisdictions, such programs enable teaching candidates to begin working as full-time teachers of record while they meet requirements for standard teaching licenses. In New York City and Houston, for example, alternate-route candidates must earn standard licenses within their first 2 years of teaching. California, New Jersey, and Texas have some of the oldest alternate-route-to-licensure programs in the United States and account for high percentages of all alternatively licensed teachers. For example, in 2005, these states issued alternative licenses

to nearly 50% of all teachers in the United States licensed through alternative routes. About 40% of New Jersey’s new teachers (almost 24,000 teachers) were licensed through alternate routes, as well as approximately 33% of new teachers in California and Texas.

Research on Teacher Training and Preparation Several research studies have examined issues involving the design, practices, and short-term outcomes of teacher preparation. For example, there are many studies across virtually all K-12 subject areas that have examined how teaching candidates’ experiences in individual courses or student teaching seem to be associated with changes in their beliefs or instructional practices while they are student teachers. In addition, a growing number of studies have compared teachers trained at universities with teachers who enter the profession through alternative licensure programs in terms of their effectiveness (as measured by impact on student learning). At the same time, very few studies have investigated the long-term outcomes of various teacher preparation approaches, structures, or designs for practicing teachers or K-12 students. This requires longitudinal research that takes place over 2 or more years, and this is more challenging to do than shortterm studies focused on individual courses, student teaching experiences, or impacts on student learning during a limited time period. Outside the United States, researchers in Germany were among the first to employ longitudinal designs in studying teacher preparation. In addition, Niels Brouwer built on the work of several German scholars in an extensive study in the Netherlands. These researchers all found that beginning teachers’ instructional practices were more strongly influenced by their school contexts and their teaching experiences during their first years of full-time teaching than by preservice preparation. Their results were similar to findings from U.S. studies of teacher socialization conducted in the 1970s, 1980s, and 1990s. More recently, studies have shown that preservice preparation can affect beginning teachers’ instructional practices and their effectiveness.

Qualitative Studies of Teacher Preparation In a qualitative study, Hilda Borko, Dominic Peressini, and colleagues collected data on six secondary mathematics candidates in two reformoriented undergraduate preparation programs in

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the United States as they went through teacher education and subsequently worked as full-time teachers for 2 years; these programs focused on preparing secondary mathematics teachers to focus on students’ mathematical reasoning, conceptual understanding, and problem solving. The authors used a situated perspective that focused on (a) the role of teacher professional identity in learning to teach and (b) the compatibility of goals and visions across various teacher preparation contexts (i.e., university courses, student teaching, full-time teaching). They found that despite the challenges posed by school norms and accountability demands, as second-year teachers, the participants in their study were able to teach for conceptual understanding in secondary mathematics while responding to other pressures. In another qualitative study, Pamela Grossman and colleagues investigated the experiences and writing instructional practices of 10 elementary and secondary ELA teachers as they completed master’s degree programs at a university in the northwestern United States and worked for 2 years as full-time teachers. In this study, the authors considered ways in which the candidates’ student teaching placements reinforced what they learned in their ELA methods courses. Grossman and colleagues concluded that in their second year of teaching the teachers in their study began to regularly use the pedagogical tools emphasized in their preparation program.

Quantitative Studies of Teacher Preparation In a large-scale study known as the New York City Pathways study, Donald Boyd, Pamela Grossman, Hamilton Lankford, Susanna Loeb, and James Wyckoff examined how elementary teaching candidates’ preparation program experiences were related to their effectiveness as first- and second-year teachers. They found that a few aspects of teacher preparation across university-based and alternative licensure programs seemed to contribute to mathematics learning gains for the students of first-year teachers. These included being supervised during student teaching, the congruence between the school context in which the student taught and their current school context, and being required to complete a capstone project (e.g., portfolio, action research). In addition, Boyd and colleagues reported that having taken mathematics methods courses was associated with teaching effectiveness among second-year teachers but not first-year teachers.

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This study was one of the first U.S. studies to use large-scale data to follow teachers through teacher preparation and into their second year of teaching. It included all the university-based preparation programs and alternative licensure programs that provide elementary teachers to New York City schools, including the alternative licensure programs Teach for America (TFA) and the New York City Teaching Fellows (NYCTF) program. Instead of simply trying to determine whether university-based programs are effective and the TFA and NYCTF programs are ineffective (or vice versa), it examined the characteristics of university courses, student teaching experiences, and other program aspects that were common across university programs (and sometimes common across university programs and TFA and/ or NYCTF) to see which characteristics were associated with teacher effectiveness. Drawing on the same NYC dataset as Boyd and colleagues, Ronfeldt reported an association between field placement characteristics in teacher preparation programs and beginning teacher retention and effectiveness. In particular, he compared novice teachers who had student teaching placements in easy-to-staff schools, defined as schools with low teacher turnover, with novices who had placements in difficultto-staff schools. Ronfeldt found that novices with student teaching placements in easy-to-staff schools were more effective than other novices when they became teachers of record (i.e., when they became full-time beginning teachers). In addition, during their first 5 years of teaching, novices who had done their student teaching in easyto-staff schools were more likely to remain in the schools where they started working as teachers of record, even if these were difficult-to-staff schools. Ronfeldt suggests that easy-to-staff schools may have better working conditions, which could refer to strong principal leadership and support, more experienced faculty, and more collegial and productive working relations among staff. These conditions may foster productive teacher learning communities, in which student teachers learn useful instructional strategies from more experienced faculty. Another large-scale study of teacher preparation took place in the Netherlands and was published in 2005 by Brouwer and Fred Korthagen. The study focused on 148 teaching candidates in elementary and secondary preparation programs at a single Dutch university. The researchers followed the teaching candidates through teacher preparation and into their second year of teaching. They collected survey data

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from all 148 teaching candidates and from 128 cooperating teachers and 31 university supervisors who worked with them. In addition, they collected interview and observation data from a subsample of 12 teaching candidates as well as interview data from 17 cooperating teachers and 12 university supervisors. The authors reported that in their first year of teaching, most beginning teachers in the survey sample focused on teacher-centered instructional practices and strict disciplinary strategies that were inconsistent with (a) their personal preferences and (b) those recommended by their preparation programs. By their second year, though, most of the teachers were using the types of instructional practices that had been emphasized in their courses and student teaching experiences. These included sequencing instructional activities in an inductive order, placing students in active learning roles, and relating to students in productive ways. While school contextual influences on second-year teachers’ instruction were more numerous and powerful than those from preservice preparation, the authors concluded that the teachers’ practices emphasized in their preservice programs went through a quiet period during the teachers’ entry into the profession and then reemerged in their second year of teaching.

Implications for Teacher Training and Preparation Taken together, the results of the studies by Borko, Peressini, and colleagues; Grossman and colleagues; Boyd and colleagues; Ronfeldt; and Brouwer and Korthagen have several implications for the practice of teacher training and preparation. First, these studies indicate the importance for teaching candidates of experiencing consistent messages regarding effective teaching across multiple contexts (i.e., university courses, student teaching, and full-time teaching). When candidates experience consistent messages, they are more likely as second-year teachers to use the types of instructional practices emphasized in teacher preparation. Second, the studies by Ronfeldt and by Boyd and colleagues suggest that a few aspects of student teaching can affect beginning teacher outcomes. That is, having one’s student teaching placement in a school with productive working conditions is associated with higher levels of teacher retention. In addition, being supervised during student teaching, being required to complete a portfolio or action research project, and experiencing a sense of

alignment between one’s student teaching placement and the school where one begins full-time teaching are associated with a teacher’s ability to promote student math achievement.

Implications for Research on Teacher Preparation The results of these studies also have several implications for efforts to build a more unified field of inquiry in the area of teacher preparation research. First, these studies suggest that future research needs to be longitudinal. In some cases, it could follow final-year teaching candidates into their second and third years of teaching (for up to 3 or 4 years), while in other cases, it could follow individuals from the point when they apply to or enroll in a preparation program until they complete the program and begin teaching. Second, these studies indicate that future research should be large scale. That is, to investigate effects of certain program characteristics or program experiences, studies should include samples of, for example, 200 elementary teaching candidates, 150 secondary mathematics candidates, or 100 special education teaching candidates. Third, research also should include a range of teacher preparation programs or pathways including preparation programs at research universities, large teaching institutions, private liberal arts colleges, and alternative licensure programs. Fourth, future studies should bring together researchers in areas such as economics of education, sociology of education, and educational policy with subject-specific experts in areas such as curriculum, instruction, special education, and gifted and talented education. Finally, future research should have multiple ways of measuring competence or effectiveness at different stages (e.g., final year of preparation, first year of teaching, fifth year of teaching). In sum, economists have a key role to play in collaborating with other scholars to build a stronger research base on the effects of university-based teacher preparation programs and alternative licensure programs. Future studies that take account of these implications have the potential to contribute in meaningful ways to such a research base. Peter Youngs and Iwan Syahril See also Licensure and Certification; National Board Certification for Teachers; Teacher Effectiveness; Teacher Supply

Teacher Value-Added Measures

Further Readings Borko, H., Peressini, D., Romagnano, L., Knuth, E., Willis-Yorker, C., Wooley, C., . . . Masarik, K. (2000). Teacher education does matter: A situative view of learning to teach secondary mathematics. Educational Psychologist, 35(3), 193–206. Boyd, D., Grossman, P., Lankford, H., Loeb, S., & Wyckoff, J. (2009). Teacher preparation and student achievement. Educational Evaluation and Policy Analysis, 31(4), 416–440. Brouwer, N., & Korthagen, F. (2005). Can teacher education make a difference? American Educational Research Journal, 42(1), 153–224. Cochran-Smith, M., Feiman-Nemser, S., McIntyre, D. J., & Demers, K. E. (Eds.). (2008). Handbook of research on teacher education: Enduring questions in changing contexts. New York, NY: Routledge/Taylor & Francis Group and the Association of Teacher Educators. Cochran-Smith, M., & Zeichner, K. (Eds.). (2005). Studying teacher education: The report of the AERA panel on research and teacher education. Washington, DC: American Educational Research Association. Darling-Hammond, L., & Bransford, J. (2006). Preparing teachers for a changing world: What teachers should learn and be able to do. San Francisco, CA: Jossey-Bass. Education Week. (2013). Code of conduct: Safety, discipline, and school climate. Quality counts 2013. Washington, DC: Editorial Projects in Education. Feistritzer, C. E., & Haar, C. K. (2008). Alternate routes to teaching. Upper Saddle River, NJ: Pearson/Merrill/ Prentice Hall. Grossman, P., Hammerness, K. M., McDonald, M., & Ronfeldt, M. (2008). Constructing coherence: Structural predictors of perceptions of coherence in NYC teacher education programs. Journal of Teacher Education, 59(4), 273–287. Grossman, P. L., Valencia, S. W., Evans, K., Thompson, C., Martin, S., & Place, S. (2000). Transitions into teaching: Learning to teach writing in teacher education and beyond. Journal of Literacy Research, 32(4), 631–662. Peressini, D., Borko, H., Romagnano, L., Knuth, E., & Willis, C. (2004). A conceptual framework for learning to teach secondary mathematics: A situative perspective. Educational Studies in Mathematics, 56(1), 67–96. Ronfeldt, M. (2012). Where should student teachers learn to teach? Effects of field placement school characteristics on teacher retention and effectiveness. Educational Evaluation and Policy Analysis, 34(1), 3–26. Youngs, P., & Grogan, E. (2013). Preparing teachers of mathematics in the USA. In J. Schwille, L. Ingvarson, & R. Holdgreve-Resendez (Eds.), TEDS-M encyclopedia: A guide to teacher education context, structure, and

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quality assurance in seventeen TEDS-M countries (pp. 67–85). East Lansing: Michigan State University.

TEACHER VALUE-ADDED MEASURES Students spend almost all of their time in schools under the direction of a teacher, so in debates about the most important school inputs, it comes as little surprise that many argue teachers are the most important input. Recent research measuring teacher performance with value-added measures has helped quantify teacher importance by showing the vast differences in performance between the most and least effective teachers. For a single year, having one of the best teachers is associated with an 8 percentile point increase in achievement over what it would have been with one of the least effective teachers, and these differences may add up over time if there is a sequence of high value-added teachers. This evidence about teacher performance has struck a chord with policymakers, yielding a veritable revolution in teacher personnel policies. In the past, teachers were given substantial autonomy over their work; accountability was fairly weak partly because it was considered difficult to measure teacher performance. The core problem is that student outcomes are most closely associated with factors outside the control of schools, especially family background and specific parenting behaviors such as how often parents read to their children at home. Most of the BlackWhite test score gap, for example, exists the first day children enter kindergarten. Therefore, if the goal is to measure how much teachers contribute to student learning, then performance measures somehow have to separate the contributions of families from those of schools generally and of specific teachers within those schools. Value-added measures address this selection bias problem by taking into account the prior achievement of students before they enter the classroom. This entry provides both an intuitive explanation of teacher value-added measures and a statistical explanation. It also summarizes empirical evidence about the statistical qualities of the measures.

Intuitive Explanation of Value-Added Measures When trying to measure anything, it is important to start by clearly describing the construct or question

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to be answered. In this case, the goal is to hold workers (specifically, teachers) accountable for what they control. In most professions, this is accomplished through informal and subjective observations of behavior by supervisors. This is also true in education, as policymakers have widely accepted classroom observations of teacher practice as one performance measure. Focusing on actual teacher practices and behaviors automatically (though still imperfectly) accounts for differences in the student population, school context, performance of other colleagues, and other factors that are outside of each teacher’s control. The second general approach to measuring employee performance is to measure actual output and attribute that to specific individuals. This may be preferable, because practices and behaviors may be only loosely related to output. In this case, the output is student outcomes, and statistical procedures are used to separate what teachers contribute from other factors affecting student results. This is not an easy task. Suppose that policymakers decided to evaluate teachers based strictly on a snapshot of student test scores at the end of the school year (sometimes called achievement status or attainment). Teachers would immediately, and rightly, object to this idea because student academic ability varies widely, and some teachers are much more likely to start the school year with low-performing students. If the goal is to hold teachers accountable for what they control, then it would be patently unfair to hold teachers accountable for what students learned before they entered that teacher’s classroom. This problem also leads to an obvious solution: Account for where students start by subtracting student scores from the previous year from this year’s score. This yields a measure of student growth, the first step toward value-added measures. Certainly, it is much more reasonable to attribute how much a student learns or grows in a given year to that year’s teacher than it is to attribute to the teacher the students’ end-of-year attainment, which encompasses everything that has influenced students over the course of their lives. While growth measures represent a substantial improvement over attainment, they still have weaknesses, however. First, schools and classrooms differ in the resources they have to work with. Significant inequities in funding and other resources persist, largely outside the control of schools. One possible way to account for these weaknesses is simply to compare student growth with that in similar classrooms and schools. We

could imagine one group of classrooms that has few disadvantaged students and low school resources, another group with half disadvantaged students and low school resources, and so on. This simple categorization would divide classrooms into many different groups, and we can make more fair comparisons within each classroom type.

Statistical Interpretation of Value-Added Measures The above intuition can be translated into a useful statistical framework that is the basis for actual value-added measures. First, it is useful to note why the above grouping approach is insufficient. The more detailed and fair we want the comparisons to be, the more groups we need; and the more groups we have, the fewer the comparisons we can make within the group. So if we start thinking about all the factors outside the control of this year’s teachers that influence student outcomes, and if we want to distinguish schools with a class size of 15 from those with 16, and so on, then we will have possibly hundreds of groups, each with a very small sample size. To address this new problem, it is useful to reframe the reason why we placed classrooms into groups to start with. Intuitively, it is easy to see why it would be more reasonable to compare teachers with others who teach students with similar initial test scores, in schools with similar resources levels, and so on. In statistical terms, this allows for a reasonable prediction of subsequent student achievement. If teachers produce outcomes above what is predicted or expected, then it is reasonable to say they are “high performing.” Once we recognize that the goal is to come up with a reasonable prediction, it becomes possible to account for the whole range of factors outside the control of teachers, such as class size and the other factors mentioned earlier. Teachers who are at a disadvantage on these measures end up with lower predicted achievement for their students. This approach allows accounting for a wide range of factors outside the control of schools simultaneously, while still allowing each comparison to be based on a large number of other teachers. It is difficult to measure everything that affects student outcomes that is outside the control of teachers, but this is a substantial improvement over attainment, simple growth measures, or even comparing growth in each classroom to others of the same classroom type.

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Researchers account for all of these various factors in developing the prediction using a statistical tool called regression analysis. In its simplest form, regression is similar to a correlation between two variables, but regression allows researchers to include many different independent variables and measure their quantitative association with the variable of interest. In this case, the typical approach is to estimate the relationship between end-of-year achievement and the following independent variables: last year’s achievement, indicators of student income levels, class size, special education status, and so on. Regression analysis produces estimates of the relationship between each variable and the variable of interest while holding constant the other factors. For example, an analysis might yield an estimate suggesting that reducing class size by one student on average is associated with a 1-percentile-point increase in test scores, holding constant student poverty. The regression estimates yield an equation that associates student test scores with these various other factors. The next step is to predict achievement for each student by plugging into the estimated regression equation the appropriate values of each independent variable for each student. Again, students with lower achievement and income and larger classes will have lower predicted achievement. The difference between the actual value of the variable of interest and its predicted value is called the residual. The average of all the residuals for students of a given teacher yields an estimate of that teacher’s value-added measure. In reality, the situation is more complicated than this, and researchers use random or fixed-effect estimation, but these nuances are beyond the scope of this entry. If the average actual end-of-year score is above the average predicted value for all students learning under a given teacher, then this teacher has above-average value-added measures. Decisions about which independent variables to include in value-added regression analyses are sometimes controversial. Given the focus on student growth noted earlier, including prior achievement is essential. Class size and student poverty indicators are also almost always included, but most other potential variables, such as student absences, are at least partly within the control of the teacher or at least the school. The most controversial variable, however, is student race. While this is obviously not within the control of the teacher, the effect of accounting for race is to allow teachers with more

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minority students to attain higher value-added scores than other teachers, even without generating more learning. This is because minority students tend to learn less each year, even after accounting for other differences such as poverty. The reasons for this are unknown, although one theory is that minority students read less during the summer, which makes them appear to learn less during the school year. Whatever the reasons, accounting for race leads to a perception that minority students are being held to lower expectations. Proponents of including race variables, in contrast, argue that if minority students learn less for reasons unrelated to teachers, then accounting for race in the value-added calculations is necessary to obtain better estimates of teacher performance. As a practical matter, including race has a relatively small influence on teacher value-added estimates.

Evidence on Statistical Properties A key issue with all performance measures, including value added, is their accuracy. For researchers, this means that measures should be valid and reliable. Validity refers to the degree to which something measures what it claims to measure, at least on average. Reliability refers to the degree to which the measure is consistent when repeated. A measure could be valid on average, but inconsistent when repeated. Conversely, a measure could be highly reliable but invalid—that is, it could consistently provide the same invalid information. Validity is closely related to the idea of bias. A common criticism of value-added measures is that some teachers are at a disadvantage because they are assigned students who are more difficult to educate, even after the value-added measures account for students’ prior achievement scores—what researchers call selection bias. No matter how many times we calculate value-added measures for these teachers, this form of bias means the results will still be invalid. The above concern about the use of status/ snapshot measures really reflects a concern about validity and bias that value-added measures are intended to address. Serious consequences arise when measures used for personnel evaluation are not valid and reliable: They increase classification errors—the placement of teachers into incorrect performance categories. In a growing number of states, where value-added measures play a key evaluation role, teachers misclassified as unsatisfactory may lose their jobs.

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The evidence on the validity and reliability of value-added measure is evolving. One widely publicized study, which created a statistical test of the validity of value-added measures, initially found reason for concern. Several subsequent studies suggest that value-added measures are probably reasonably valid on average. Another pair of studies, one of which was the Gates Foundation’s Measures of Effective Teaching (MET) project, randomly assigned students to teachers and found that selection bias was only a small problem, although there is debate about the interpretation of the result. Recent research also suggests that high value-added teachers also seem to generate more positive long-term benefits for students, including higher earnings. It is important to note, however, that even if these conclusions are right, they provide evidence about whether value-added measures are valid on the average across large numbers of teachers. They could still be—and apparently are—invalid for specific subgroups of teachers. For example, several studies point out that almost all the evidence about validity is based on studies in elementary schools and provide evidence that typical value-added measures are biased in middle school and high school. Another study suggests that teachers whose students start off with very high achievement will receive lower performance ratings than they deserve because of the “test ceiling,” which refers to the fact that a test will not measure how much a student has learned if the questions are insufficiently difficult. Valueadded measures are probably also highly sensitive to the context of teachers’ classrooms, including the behavioral issues they face in the classroom and the school culture. The assumptions underlying valueadded models have also been shown to be false, and violations of the assumptions may influence different teachers in different ways. One example is that the effect of each measured input seems to vary by age, contradicting one of the assumptions of value-added measures. The bottom line is that a measure cannot be considered valid if it is heavily influenced by factors such as these that are outside the control of teachers. So these findings raise important concerns. While the validity of test-based performance measures improves when shifting from status/snapshots to growth/value-added measures, this comes with a sacrifice of reduced reliability. The highest possible reliability measure is 1.0, which means that the performance measure does not change at all over time. At the other extreme, reliability of zero means that any given measure of teacher performance tells

us nothing about what the measure will be for the same teacher the next time. When creating student tests, for example, designers usually set a standard of at least .9. The MET study reports reliability for teacher value-added measures of about .3 to .5 when 3 years of data are used. To put this in perspective, one study finds that only 28% to 50% of teachers ranked in the top fifth on value-added measures for 1 year were still ranked in the top fifth in the subsequent year, and 4% to 15% of teachers switched from the top fifth to the bottom fifth. Critics of value-added measures might stop right here and argue that the limited reliability argues against using value-added measures at all. Such critiques are based partly on the standards for personnel evaluation set by the American Educational Research Association and the American Psychological Association. But these standards focus on each measure separately and do not consider the alternatives. The common practice of paying teachers based on their years of experience and degrees, for example, is hard to justify given the weak relationship between these measures and teacher performance. A single classroom observation of a teacher has lower reliability than value-added measures, but a combination of four classroom observations yields a reliability of about .65. The validity of classroom observation measures is less clear. The MET study involved highly trained observers who had passed an exam demonstrating their skill; this level of training is unlikely in everyday school settings. Older evidence suggests that classroom observations can be influenced by factors unrelated to performance such as age and race. Also, it seems likely that classroom context will affect the measures just as it appears to with value-added measures. It may be difficult to make valid comparisons between, for example, the classroom management of a teacher who has severely emotionally impaired students, subject to more frequent disruptions, and a teacher with students who are less disruptive. Overall, it is difficult to assess value-added measures without comparing them with the alternatives and without considering how the measures will be used. As a general rule, the stakes attached to any given measure should be roughly proportional to its validity and reliability.

Conclusion Value-added measure, as a statistical approach, has contributed a great deal to our understanding

Teachers’ Unions and Collective Bargaining

of teaching. Most important, the approach has allowed researchers to quantify how much teacher effectiveness varies within and across schools. The result of these findings has been that policymakers now want to use the methodology to come up with better ways to measure teacher effectiveness and to use the measures to make high-stakes personnel decisions. Indeed, many policymakers have embraced value-added measures as a way to measure teacher performance and create more aggressive and explicit accountability. The use of value-added measures for holding individual teachers accountable have grown significantly in recent years due to the rapid expansion of standardized testing of students resulting from the No Child Left Behind Act of 2001, as well as federal programs encouraging the use of value-added measures to evaluate teachers, such as the George W. Bush administration’s Teacher Incentive Fund and the Obama administration’s much larger Race to the Top grant competition. While much has been learned about the validity and reliability of value-added measures, much less is known about the use of these measures in accountability systems or their effects on teaching and learning. Important questions going forward include the following: Does accountability based on valueadded measures lead to more distortions in instruction (e.g., teaching to the test)? Do these evaluation systems reduce morale and job satisfaction of teachers, driving some away, or does stronger accountability such as merit pay based on value-added (and other) measures attract more of the most able young people into the profession? While the value-added measures for individual teachers represent the core of these policies, the logic behind them will also be used in the years to come to answer these important research questions. Douglas N. Harris See also No Child Left Behind Act; Pay for Performance; Race to the Top; Teacher Effectiveness

Further Readings Bill & Melinda Gates Foundation. (2012). Gathering feedback for teaching. Seattle, WA: Author. Chetty, R., Friedman, J. N., & Rockoff, J. E. (2013). Measuring the impacts of teachers II: Teacher valueadded and student outcomes in adulthood (NBER Working Papers 19424). Cambridge, MA: National Bureau of Economic Research.

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Clotfelter, C., Ladd, H., & Vigdor, J. (2006). Teacherstudent matching and the assessment of teacher effectiveness. Journal of Human Resources, 41(4), 778–820. Goldhaber, D. (2007). Everyone’s doing it, but what does teacher testing tells us about teacher effectiveness? Journal of Human Resources, 42(4), 767–794. Harris, D. N. (2011). Value-added measures in education: What every educator needs to know. Cambridge, MA: Harvard Education Press. Koedel, C., & Betts, J. (2011). Does student sorting invalidate value-added models of teacher effectiveness? An extended analysis of the Rothstein critique. Association for Education Finance and Policy, 6(1), 18–42. Rivkin, S. G., Hansuhek, E. A., & Cain, J. F. (2005). Teachers, schools and academic achievement. Econometrica, 73(2), 417–458. Rockoff, J. E. (2004). The impact of individual teachers on students’ achievement: Evidence from panel data. American Economic Review, 94(2), 247–252. Rothstein, J. (2010). Teacher quality in educational production: Tracking, decay, and student achievement. Quarterly Journal of Economics, 125(1), 175–214.

TEACHERS’ UNIONS AND COLLECTIVE BARGAINING Collective bargaining entails a process of negotiations between a union and an employer over employees’ working conditions and/or wages. In K-12 education in the United States, teachers’ unions negotiate with local school districts or school boards over the contents of collective bargaining agreements (CBAs)—the contract put in place for all district teachers between the union and the district. Local teachers’ unions and school districts are the key actors in these collective bargaining negotiations. Teachers’ unions have been important actors in U.S. education policy and governance since the early 1900s. They are key actors at all levels of education governance—federal, state, and, above all, at the local school district level. The two main teachers’ unions—the National Education Association and the American Federation of Teachers—have active affiliates in every state and in virtually every school district in the country and together maintain a membership of approximately 4.7 million education professionals. The unions’ influence comes in part from their size, but it also comes from their ability

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to command considerable resources to endorse and provide support for politicians and lobby for policies at the state and federal levels. However, unions’ main influence over policy occurs at the local district level, through their rights to collectively bargain on behalf of their members. Currently, districts are required to bargain with teachers in 36 states, and another 6 states have legislation that allows teacher collective bargaining. Even in the 5 states in which districts are prohibited from bargaining collectively with teachers’ unions, at least one of the two main unions have active chapters and continue to organize teachers. Negotiations between school districts (often the school boards or their representatives) and teachers’ unions result in CBAs that enshrine multiple aspects of district policy into a binding contract negotiated approximately every 3 years. CBAs dictate the majority of district and school policy surrounding teachers’ work, including work rules that govern teachers’ compensation (salaries and benefits); teachers’ working hours and yearly schedule; the time teachers have to prepare for instruction; the size of the classes they teach; the ways in which teachers are evaluated, assigned, and transferred within the district; how grievances are processed; and myriad other details of school and district operation. Although unions are the subject of much controversy, and recently, of a great deal of legislative activity, there is as yet little known about the role teachers’ unions play in shaping education policy or the impacts teachers’ unions have on important education outcomes. This entry provides an overview of teachers’ unions in the United States, specifically focusing on their role in shaping school district policy. It first reviews the three ways local teachers’ unions can shape school district policy. Next, it discusses the union’s role as collective bargaining unit and highlights the theoretical reasons why CBAs may affect district and school outcomes. Last, it briefly reviews the extant research on the role of teachers’ unions in district policy setting and the impact of CBAs on outcomes of importance.

The Role of Teachers’ Unions in School Districts Local teachers’ unions participate in school district policy making in multiple ways. Underlying all of them is the fact that teachers’ unions are labor organizations structured to protect the rights and interests of their members. The three key functions

played by teachers’ unions in the school district policy process are the representation of members as (1) a professional association, (2) a political organization, and (3) a collective bargaining unit. The remainder of this section briefly explores each role. Teachers’ Unions as Professional Associations

Teachers’ unions are professional associations. They collect dues from their members to be used in the service of their members through efforts to improve teachers’ status locally and across the country. At the district level, unions can work to foster policy change and reform both on their own and in collaboration with stakeholders including school districts, parents, and other community groups. Unions often work to propose reforms, policies, and strategies for addressing issues related to teachers’ work, ranging from school governance to instruction and teacher evaluation. In some districts, union leaders work with district administrators to develop solutions to problems in ways that meet the needs of teachers and of students. In their role as professional associations, unions also support teachers being affected by district reforms and policies. In part, unions do this by helping teachers understand their contractual rights. In addition, unions can provide teachers with technical assistance, knowledge, and professional development opportunities to help them become better teachers; understand reforms; and develop strategies for how to succeed within the district and new policy contexts. Teachers’ Unions as Political Organizations

Although much of the literature discusses political activity as in the realm of state and national unions, local teachers’ unions also wield political influence in the local arena. District teachers’ unions hold political power in a number of ways. First, teachers’ unions can use the dues they collect from members as they see fit to promote the interests of their teachers—often via investing in the political process. Second, and related, they influence teachers’ and the public’s views on proposed and enacted district policies via media and other forms of endorsing or denouncing policies and reforms. Third, teachers’ unions can directly influence the makeup of the school board by rallying the public and mobilizing voters in local school board election to vote for favored candidates. Fourth, teachers’ unions can influence the school boards’

Teachers’ Unions and Collective Bargaining

actions by lobbying school board members to act in accordance with union preferences and engaging in other special interest group tactics. Last, as a result of unions’ influence on school board makeup and actions, teachers’ unions can affect who holds the role of district superintendent and often the actions the superintendent can take. Teachers’ Unions as Collective Bargaining Units

Although district teachers’ unions play important roles as professional association and political organizations, local unions are primarily collective bargaining units. Operationally, this is the most important role for local unions in local district policy. At least every 3 years, teachers’ unions sit with superintendents and district administrators to negotiate the policies and regulations that are included in districts’ CBAs or contracts. This is the main activity of local teachers’ unions, and research has shown that this is the activity that local union members find the most important. As discussed above, the CBAs unions negotiate with school districts, and boards contain within them a plethora of discretionary local policy. The remainder of this entry focuses on the ways in which unions can affect district policy through their role as collective bargaining agents.

Theoretical Impacts of CBAs CBAs are themselves local district policy. The contracts negotiated between administrators and unions contain within them regulations that dictate a wide range of school and district operations. These include education policy in the areas of grievance procedures, evaluations, compensation, class size and hours, school day and year schedule, leaves of absence, association rights, supplementary classroom personnel, job security, teacher safety, student discipline, professional development, meetings, noninstructional duties, duty-free periods, assignment, transfer and vacancy policies, and more. Although the specific content of CBAs varies widely across districts and states, nearly all contracts contain sections devoted to the majority of the above-mentioned topics. Given the wide range of policies set forth in CBAs, these contracts are often viewed as the most important policy documents affecting school and district operations. There are two hypothesized functions that CBAs serve. The first potential role CBAs play is to benefit teachers and schools by providing protections for teachers from arbitrary and subjective decision making and by instituting policies that improve the

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working conditions of teachers. Teaching is a difficult and important job, and CBAs are thought to provide some guarantees that make the job more pleasant and manageable. This includes ensuring that staffing decisions aren’t made for arbitrary or unfair reasons, ensuring that the data included in evaluations reflect a teachers’ own performance, and providing teachers with duty-free lunches, manageable class sizes, and fair wages. On the other hand, CBAs may serve as constraints to district autonomy and flexibility. To this end, CBAs may restrict administrators’ abilities to implement necessary policies and reforms and reduce administrator discretion over schools and districts to a great extent in ways that harm districts and students and cause schools and districts to operate inefficiently. This includes requiring administrators to make staffing assignments based on seniority rather than quality, fit, or need; making it difficult for administrators to evaluate teachers based on available data; requiring administrators to balance class size before staffing and enrollment is settled or to take specific actions if class sizes are exceeded; and constraining administrators from paying teachers based on quality or merit as opposed to seniority and education credits. If CBAs truly do enhance working conditions and protect teachers from unfair practices, then they will improve district and school productivity by attracting and retaining teachers, especially to schools and districts that are considered less pleasant places for work (urban, low-income, low-achievement districts). In contrast, if CBAs reduce efficiency, then they will harm school and district operations as well as student outcomes.

Empirical Impact of CBAs There has been relatively little academic attention given to carefully describing the kinds of policies set in CBAs and examining how they may affect district and school operations and outcomes. It has been shown that the contents of CBAs vary widely across districts and states. In addition, although there is a great deal of discussion about the restrictiveness of CBAs and media attention has focused on the ways a number of specific policies set within CBAs may constrain district operations, research has shown that contracts include both substantial flexibilities as well as restrictions. Together, the extant research on CBAs highlights the dual nature of contracts described above, noting that many aspects of contracts restrict district policy making even as

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other elements of CBAs provide administrators and board members with flexibilities and teachers with protections that enhance their professional working conditions. However, research has shown that CBAs change over time in ways that appear to become slightly more restrictive and do not seem to make teachers’ working conditions more professional. In addition, urban and larger districts tend to have more restrictive contracts overall. Only a few studies have explored the relationship between contracts and outcomes, including teacher working conditions, compensation, and student achievement. It is clear from the research that CBAs contain within them many provisions that serve to enhance teachers’ working conditions and that class sizes (an important working condition) are indeed smaller in unionized districts. However, the most recent research in California shows that districts with stronger contracts do not have lower student-teacher ratios. In addition, research has shown that although unionized districts pay teachers more, on average, than do nonunionized school districts, stronger CBAs are not associated with greater spending on teachers’ wages (although stronger contracts are associated with higher education spending overall). The research that has examined the relationship between CBAs and achievement has found that (a) districts with stronger CBAs have higher overall spending and (b) more restrictive contracts are found in districts with lower student achievement, lower graduation, and lower proficiency rates. However, as yet there is no causal evidence linking the degree of contract restrictiveness to decreased achievement growth, suggesting that it is still an open question whether contract restrictiveness leads to lower student achievement outcomes, or whether districts with lower student achievement negotiate contracts with more protections for teachers to improve their working conditions to attract and retain teachers. In addition, studies have confirmed the role of teachers’ unions as strong political actors, with various studies highlighting the ways in which teachers’ unions engage in lobbying, fundraising, and spending, as well as expending both fiscal and human capital toward school board elections. Moreover, evidence also exists that this activity matters for election outcomes. In addition, research has shown that the union’s role as political organization is tied to its role as a collective bargaining unit in that more politically powerful unions negotiate stronger CBAs. Katharine O. Strunk

See also Local Control; Policy Analysis in Education; Reduction in Force; Salary Schedule; School Boards; School Boards, School Districts, and Collective Bargaining

Further Readings Eberts, R. W., & Stone, J. A. (1984). Unions and the public schools: The effect of collective bargaining on American education. Lexington, MA: Lexington Books. Hannaway, J., & Rotherham, A. (Eds.). (2006). Collective bargaining in education: Negotiating change in today’s schools. Cambridge, MA: Harvard Education Press. Hess, F. M., & Leal, D. L. (2005). School house politics: Expenditures, interests, and competition in school board elections. In W. G. Howell (Ed.), Besieged: School boards and the future of education politics (pp. 228–253). Washington, DC: Brookings Institution Press. Johnson, S. M., & Donaldson, M. L. (2006). The effects of collective bargaining on teacher quality. In J. Hannaway & A. J. Rotherham (Eds.), Collective bargaining in education: Negotiating change in today’s schools (pp. 111–140). Cambridge, MA: Harvard Education Press. Kerchner, C. T., & Koppich, J. E. (Eds.). (1993). A union of professionals: Labor relations and education reform. New York, NY: Teachers College Press. Loveless, T. (Ed.). (2000). Conflicting mission? Teachers’ unions and educational reform. Washington, DC: Brookings Institution Press. McDonnell, L., & Pascal, A. (1979). Organized teachers in American schools (RAND Report R-2407-NIE). Santa Monica, CA: RAND Corporation. Moe, T. (2011). Special interest: Teachers unions and America’s public schools. Washington, DC: Brookings Institution Press. Strunk, K. O. (2011). Are teachers’ unions really to blame? Collective bargaining agreements and their relationships with district resource allocation and student performance in California. Education Finance and Policy, 6(3), 354–398. Strunk, K. O., & Grissom, J. A. (2010). Do strong unions shape district policies? Collective bargaining, teacher contract restrictiveness, and the political power of teachers’ unions. Educational Evaluation and Policy Analysis, 32(3), 389–406.

TECHNICAL EFFICIENCY Technical efficiency is a useful tool for benchmarking school performance. Schools are technically efficient when their resources—including personnel, supplies, and computers—result in best practice educational

Technical Efficiency

outcomes, such as improvement in standardized test scores and graduation rates of their students. Alternatively, they are identified as technically efficient when their observed educational outcomes are achieved with best practice use of their resources. A practical advantage of technical efficiency as a benchmarking tool is that it does not require information on prices and can use observed data to identify best practice. Technical efficiency describes the relationship between the physical inputs, such as personnel, required to produce quantitative outputs, such as graduation rates, passing rates on standardized tests, absenteeism rates, and improvements in test scores, while accounting for student and school characteristics. It can thus serve as a tool in determining school accountability as required by No Child Left Behind, for example. This entry is organized as follows: It begins by introducing more formal definitions and models of what is meant by technical efficiency and then turns to alternative approaches to measuring or estimating technical efficiency, especially in the education context. A technical discussion is included at the end of the entry.

Definition Probably, the definition most closely related to the current understanding of what is meant by technical efficiency is that associated with Michael J. Farrell. Farrell defines efficiency in terms of production, in which inputs, such as teachers, administrators, and supplies, are used to produce quantitative outputs, such as improvements in test scores. A school is deemed technically efficient if it is not possible to reduce inputs (teachers and other school resources) and still produce its observed level of outputs (test scores, graduation rates, etc.). This is referred to as input-based technical efficiency. Alternatively, a school is deemed to be technically efficient if it is not possible to increase outputs without increasing the observed level of inputs. This is output-based technical efficiency. There have been many extensions of these measures of technical efficiency, which include identifying scale efficiency (which identifies deviations from constant returns to scale), changes in efficiency over time, and the relationship to productivity change, among others, which are beyond the scope of this entry.

Models and Estimation This section provides an overview of several approaches to estimating technical efficiency.

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It begins with what may be called nonfrontier approaches, then turns to frontier approaches which fall generally into two categories: (1) nonparametric, usually associated with data envelopment analysis (DEA) or activity analysis, and (2) parametric frontier approaches, which are generally econometric. Nonfrontier Models

Perhaps the earliest estimates of technical performance are found in the educational production function literature, which typically specifies a single measure of educational output as the dependent variable, which is regressed on independent variables, which include inputs to the educational process under evaluation. Estimation was usually by ordinary least squares regression, which provides a conditional mean estimate of output rather than the maximum feasible output. Eric A. Hanushek has provided a review and meta-analysis of many of these studies and finds that there is no consistent evidence that increased resources improve output. This suggests that there is considerable technical inefficiency, which is best captured using frontier methods. Nonparametric Frontier Methods

Farrell provided the basic underpinnings of the nonparametric approach by using mathematical programming techniques to estimate a best practice frontier for U.S. agriculture relative to which individual states could be compared and ranked with respect to technical efficiency. This approach was formalized and applied in the education context by Abraham Charnes, William W. Cooper, and Edward Rhodes, and following them it came to be known as DEA. They used linear programming techniques to estimate the best practice frontier as piecewise linear segments connecting the observations in the sample that are best practice, that is, those with the highest levels of educational outputs given educational resources or those with the least levels of inputs given educational outputs. The linear programming problem estimates the distance to the frontier for the individual observations in the sample, providing the estimates of technical efficiency. The solution to the linear programming problem also provides information concerning the peer observations forming the area on the frontier with which the observation under evaluation is compared. This information is useful as a type of benchmarking.

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This approach is nonparametric—no specification of a functional form or for the distribution of efficiency is required to estimate the best practice frontier. Rather, the frontier is estimated subject to basic production theoretic axioms, including convexity in the DEA formulation. In its original formulation, it is also deterministic—all variables are assumed to be measured without error, and all deviations from the best practice frontier are interpreted as inefficiency. Thus, this approach is sensitive to outliers and measurement error—especially if errors occur in the observations determining the frontier, which could result in biased estimates for the entire sample. On the other hand, it avoids specification error, other than that due to omitted variables. DEA approaches readily allow for multiple inputs and outputs, which is useful for assessment in the educational context but introduces the curse of the dimensionality problem, unless the number of observations is suitably increased. The DEA approach imposes convexity when estimating the best practice frontier; this restriction was relaxed and dubbed the Free Disposal Hull (FDH) model, resulting in a “tighter” best practice frontier formed as a stair-step frontier enveloping the data. Again, no specification of functional form is required, but sensitivity to outliers and measurement error remains. Several issues soon arose as these estimators were generalized and applied. When researchers sought to explain the sources of inefficiency identified in their samples, it became clear that production of education services was not completely under the control of school administrators, for example. Rather, there exist environmental factors or inputs to the educational process, including peer effects, socioeconomic characteristics, previously acquired knowledge, parental inputs, and so on that affect educational outcomes but are not under school control. Several authors proposed modifications of the linear programming problems to treat the two types of input differently, by scaling only on the discretionary inputs, treating environmental inputs as fixed factors or as suggested by John Ruggiero, limiting comparison of individual observations to those with similar environmental characteristics. Others chose to use a two-stage approach in which technical efficiency is estimated using DEA or FDH including only discretionary school inputs and the resulting estimates are regressed on the nondiscretionary factors. This twostage approach was criticized by Leopold Simar and Paul W. Wilson, who proposed a modified approach

that included bootstrapping to mitigate the serial correlation among the technical efficiency scores. Until recently, hypothesis testing was limited to nonparametric tests by the seeming lack of statistical underpinnings. This was redressed by Simar and Wilson as well as Rajiv D. Banker, among others. Recent work summarized in Cinzia Daraio and Simar introduced robust nonparametric estimators of frontiers, including alpha-quantile frontiers and order-m frontiers, which reduce the sensitivity to outliers, as well as conditional frontier models to estimate the effect of environmental variables on technical efficiency.

Parametric Frontier Methods Stochastic frontier analysis is probably the most familiar parametric frontier method used to estimate technical efficiency. It is a regression approach that is characterized by a composed error term. This incorporates both random error, which as usual is assumed to be normally distributed, and a second error term that is designed to capture inefficiency and is “one sided.” The composed error is thus asymmetric. The researcher must specify a specific parametric form for the relationship between educational resources and educational outcomes, as well as specifying a distribution for the inefficiency term, both of which introduce possible specification error. The relationship between resources and outcomes is specified typically as a single-output production function or as a distance function that allows for multiple outputs. Another regression approach that has been employed to estimate technical efficiency is corrected ordinary least squares (COLS). Ordinary least squares is used to estimate a production function or distance function, in which the intercept is adjusted until all deviations from the frontier are of the same sign, to capture inefficiency, much like DEA. In contrast to DEA, the frontier in COLS is determined by the average rather than best practice, and the inefficiency in COLS is normally distributed. Availability of panel data has facilitated both nonparametric and parametric frontier estimation of technical efficiency.

Conclusions Assessing the performance of the educational sector is important, driven in part by limited resources but also because of the key role education plays in building human capital and in technical progress, and growth. Frontier methods are available to assess

Technical Efficiency

performance, but specification and measurement of the relevant educational resources and outcomes remain a major challenge.

Technical Details Farrell illustrates technical efficiency with a simple two-dimensional diagram of what he refers to as an efficient isoquant, which consists of the best practice inputs required to produce a given level of output. Suppose we have two school inputs (in practice we may have more), say teachers (which we call Input 1) and administrators (Input 2), which are used to produce math test scores of 75%, which we call y. Then, the isoquant consists of all the best practice combinations of teachers and administrators that can produce math test scores of 75%, as Figure 1 illustrates. In the figure, the curve yy represents the efficient isoquant. Suppose we observe a school that employs the combination of teachers and administrators observed at Point A, 7 that is, this school is using more than the minimum required teachers and administrators to produce output level yy, our 75% math test scores. Farrell proposes that we measure the degree of technical inefficiency of operating at A by the largest proportional reduction in both inputs that will still result in a passing rate of 75%, as represented by the best practice isoquant yy. In Figure 1, that proportional reduction moves us along the ray 0A (which preserves the ratio of administrators to teachers that we observe at A) down to the combination of teachers and administrators observed at Point A´ on the isoquant. The ratio 0A´/0A, which is less than 1, gives the factor by which we can reduce the numbers

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of administrators and teachers at A to reach the bundle A´ on the isoquant. This is Farrell’s measure of input-based technical efficiency, which is often referred to as Fi(y, x). The F is for Farrell, the i refers to the fact that we are reducing inputs and (x, y) tells us that it depends on the observed inputs (x) and outputs (y). In the Farrell context, all combinations of our inputs on the isoquant, like the bundle A´, would be technically efficient with values of technical efficiency equal to 1. Farell also shows that this measure of technical efficiency may be considered as a component of cost efficiency, as illustrated in Figure 2. Here the prices of our inputs (in our case, salaries of teachers and administrators) and their associated costs are introduced as the downward sloping, parallel lines, where the slopes capture the relative prices of Input 1 (teachers) and Input 2 (administrators). Here the technical efficiency measure for the school at A is equivalent to the ratio of the cost at A´ to the cost at A, that is, by removing technical efficiency 0A´/0A, costs are reduced by that same factor. The combination of teachers and administrators at Point B represents the point at which costs are minimized given the relative prices of the inputs and the target output yy. The ratio of the minimum cost associated with the input bundle B (which is the same as the costs at bundle A˝) and the observed costs at A is the Farrell measure of cost efficiency, which is equivalent to the ratio of 0A˝ to 0A. The gap between 0A˝ and 0A´ is what Farrell calls allocative efficiency or cost inefficiency due to hiring a mix of teachers and administrators that is not at the lowest cost to achieve the target output given the input prices.

Input 2

Input 2 y

y

A

A

B A´ A´

A˝

L(y)

y

y 0

Figure 1

Input 1

Farrell Measure of Input-Based Technical Efficiency

0

Figure 2

Input 1

Cost Efficiency

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In sum, Farrell’s cost efficiency decomposes multiplicatively into technical and allocative efficiency. In this example, 0A˝/0A = 0A´/0A • 0A˝/0A´.

(1) A formal, mathematical definition of input technical efficiency is given as Fi(y, x) = max{λ : x/λ ∈ L(y)},

(2) where λ is the scaling factor and L(y) is the set of all inputs that can produce the given output level y. The boundary of that set is the efficient isoquant, yy in Figures 1 and 2. Farrell also points out that technical efficiency may be defined from an output-increasing point of view, which is shown in Figure 3. Here the input quantities, x (which we have been assuming are teachers and administrators), are taken as given, and the best practice technology is defined in terms of all possible output quantities, y, that can be produced with those inputs. If we limit ourselves to two outputs, say test scores of 75% in math (Output 1) and test scores of 75% in reading (Output 2), Figure 3 illustrates the output set that is referred to as P(x). Here, the idea is to expand the observed outputs (as at A) while holding their ratio constant as much as possible in order to reach the boundary of the output set—in our example at B. Thus, the school observed at A is technically inefficient, which would be measured as the ratio 0B/0A. Note that now we have more than one output, which would be typical of production in the education sector. In the figure, there are only two outputs, but in principle there could be more.

Output 2

B

The technical efficiency measure in this case would compare the observed outputs at A with the maximum feasible proportional expansion of those observed outputs, labeled B in the figure. The formal definition of this output-based measure of technical efficiency is Fo(y, x) = min{θ : y/θ ∈ P(x)},

(3) where θ is the scaling factor that projects the observed outputs to the boundary of the output set, P(x). Shawna Grosskopf See also Allocative Efficiency; Economic Cost; Economic Efficiency

Further Readings Banker, R. E. (1996). Hypothesis testing using data envelopment analysis. Journal of Productivity Analysis, 7, 139–160. Charnes, A., Cooper, W. W., & Rhodes, E. (1981). Evaluating program and managerial efficiency: An application of data envelopment analysis to program Follow Through. Management Science, 27, 668–697. Daraio, C., & Simar, L. (2007). Advanced robust and nonparametric methods in efficiency analysis. New York, NY: Springer. Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, Series A, General, 120(Pt. 3), 25–281. Hanushek, E. A. (1986). The economics of schooling, production and efficiency in public schools. Journal of Economic Literature, 24, 1141–1177. Ruggiero, J. (2004). Performance evaluation in education: Modeling educational production. In W. W. Cooper, L. M. Seiford, & J. Zhu (Eds.), Handbook on data envelopment analysis (pp. 323–348). Norwell, MA: Kluwer Academic. Simar, L., & Wilson, P. W. (2008). Statistical inference in nonparametric frontier models: Recent developments and perspectives. In H. O. Fried, C. A. K. Lovell, & S. S. Schmidt (Eds.), The measurement of productive efficiency and productivity growth (pp. 421–521). New York, NY: Oxford University Press.

A P(x) 0

Figure 3

Output 1

Farrell Measure of Output-Based Technical Efficiency

THEORY

OF THE

FIRM

The theory of the firm is a basic economic concept that helps describe what motivates firms to

Theory of the Firm

act within the context of two basic markets: (1) the market in which they sell products and services and (2) the market in which they purchase the resource inputs for production. This theory assumes that all firms are motivated by profit, either its maximization or a determinable allowable amount. Thus, the theory of the firm explains why firms seek to minimize cash expenses in the face of the prices they are able to command in the competitive market where they compete for limited consumer income. This entry describes how the theory applies in the changing economic environment facing K-12 public schools. What follows is a discussion of a few select changes taking place that affect these schools in relation to the principles of the theory of the firm. Next, attention is paid to a finite portion of the theory of the firm and how it, in particular, is likely to inform school operations in the future. Finally, the interaction between freedom, education, and markets is discussed with an overview of a contemporary school reform movement, charter schools.

Changes Facing K-12 Publicly Funded Education The market for elementary and secondary education in the United States, like that of many American industries in the past 40 years (e.g., transportation, banking, and health care), is undergoing rapid changes that are partially related to the government’s response to the convergence of the for-profit and nonprofit sectors. Education, as a public good that has far-reaching benefits to a nation’s population, such as higher voter participation and lower crime rates, has attracted a public debate focused on efficiency in production with the finite resources available to achieve this greater common good. Historically a feature of education, this melding of the public and private sectors has now become a mature, quasi-public good calling for innovation. The core problem of publicly funded American education, widely thought of in terms of K-12, is that increasing amounts of funding go into the provision of education, but those investments do not seem to yield improvements in student performance. To complicate matters, as desirable as performance increases are, the outcome measures have varied greatly over the past two decades. This seems to leave us with one of two options: (1) fix the processes that yield the outcome, such as financial, labor, technology resources, and curriculum or (2) change the measurement criteria. The No Child Left Behind Act

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of 2001 changed the rules by increasing the focus on standardized test performance; so part of the difficulty of winning at the game of education is that the determinants of success are a moving target. This change begets the need to continuously evaluate, measure, assess, and adjust the process of delivering K-12 education funded by the American public. The debate is further complicated by the fact that education, although a mature industry, does not exhibit some of the characteristics of industries that have undergone market-induced change. Specifically, in the long run, with entry and exit of firms, generally only the most differentiated and productive firms, or schools in this case, can survive. Increasingly, we are seeing more school closures in major metropolitan school districts. But, in general, as guaranteed and regulated local monopolies, to a great extent this degree of natural extinction has not applied to education. Over the past 20 or so years in education, however, an increase in market-based reform and competition has begun to seriously threaten those incumbent school monopolies. These programs are commonly labeled as “school choice” and fall under categories such as charter schools, magnet schools, and voucher programs. These reforms were, and continue to be, supported in application by followers of the economist Milton Friedman and in research by theory of the firm advocates such as the disciples of Ronald Coase.

Fundamentals of the Theory of the Firm When viewed through the lens of economics, the education debate arises from the theory of the firm’s focus on the needs for productive and allocative efficiency. This means that school operators are increasingly required to use smaller amounts of resources, such as teacher labor, land, and fixed capital infrastructure, and yet be able to produce higher quality outputs, in this case highly educated students, while eliminating exclusivity from a market that demands this socially critical service. More and more, both the data and sentiments of some of the consumer population are demanding more privatization of education under the belief that firms operate more successfully at achieving both cost minimization and quality improvements when free from external, especially governmental, constraints. Thus, it would appear that the traditional microeconomic approaches applicable to the theory of the firm

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are now more salient than ever in assisting school operators to remain competitive, viable, and increasingly successful. There are certain parties who believe that spending more money for education will yield better results. Some research may support this, but other research indicates that there are negative returns to marginal spending in education. Using root cause analysis and relating it to the field of economics, then, the question is one of scarce resources being demanded to offer maximum utility to consumers. One concept that may be used to solve this inputoutput problem is the theory of the firm and its implications for controllable management principles. The basis of the theory of the firm revolves around the principle of controlling transaction costs. If it can, a firm will execute a series of transactions for less than it would cost to do so in the market. When those internal costs are continually lesser, the firm grows as it benefits from its ability to produce more inexpensively vis-à-vis the market. The theory asserts that external factors that influence the market—inefficiencies such as taxes, regulation, and so on—increase costs and make firms grow. Charter schools and other “schools of choice” may be less encumbered by outside regulation and thus should be able to produce better achieving students when unhindered. Consequently, if these schools are unable to achieve profitability, then they are subject to exiting the market. This outcome might have positive economic ramifications. But one must also consider the impacts that a school closing could have on the community it served.

Freedom and Education For nearly half a century, the work of Friedman has applied to many industries and increasingly to education. According to both his theories and research, the effects of decreasing regulation and empowering schools to operate freely help increase competition. The increase in competition threatens the unincentivized traditional monopoly school and forces it to attempt to innovate in order to survive. The argument for freedom is bilaterally applicable to both producers and consumers. However, freedom does not seem to reign with respect to most K-12 American schools. On the producer side, schools are stressed by unions requiring artificially enforced wages and benefits, the federal government changes while states vacillate in reporting requirements and outcome measurements, politicians are influenced

by political business cycles that seek demonstrable change before curricular change’s effects can be witnessed. These uncontrollable external variables, as Coase’s work suggests, increase the external transaction costs in the market and enable incumbent schools to get larger and demand more funding. Meanwhile, teacher pay increases as do class sizes and classroom resources, but sadly, student learning often does not improve. On the consumer side, students are often eligible to attend only their neighborhood public school unless their families can afford their tax allocation as well as private school tuition—a luxury not available to a majority of households. If freedom reigned, consumers would be able to self-selectively allocate their portion of household income and rationally pick among competitive educational options. If free, producers would be able to hire from a pool of the best available talent, implement a curriculum that they believe best served their unique clients’ needs, and change the input mix, including labor, in accordance with the demands of their purchasing population. Although perfectly efficient markets may never be attainable in education, the largess of the public school and its attempts to satisfy standardized measures give schools more of an assembly-line, one-size-fits-all attitude that often does not meet the needs of its diverse consumer base. If freedom yields efficiency, this structure of locally regulated, tax-based school monopolies, and excessive constraints that cloud consumer and producer freedom, might benefit from more freedom for better results. The theory of the firm, then, is an approach that treats schools as organisms within their environments. If the schools can be presumed as analogous to a living entity, then history is replete with examples of how freedom yields greater utility. For simplicity, let’s represent the firm model as a mobile ice-cream cart vendor. The vendor operator is unfettered in his motions and able to pursue the clients he believes enable his desire to earn profit for himself. If the operator finds himself in an environment where there are too few customers or competition is too great, his mobility enables him to seamlessly transport his operation to an environment where his services are more highly valued. Likewise, the frozen treat–seeking buyer can determine whether her marginal benefits are exceeded in purchasing the good from the vendor at his costs, including profits. If not, he or she is free to consume elsewhere. This bilateral source of

Theory of the Firm

mobility and freedom is aligned well with Charles Tiebout’s philosophies on how movement within environments and the corresponding freedom can yield greater value in economic transactions. One argument that must be taken into account is the fact that markets do fail periodically. We have seen this recently in America even in the highly regulated market of financial institutions. Given the agreed-on greater public value that arises from K-12 education, can we risk to the market the possibility of customers not having reasonable vendor choices within reach? In short, how reliable is freedom in marketplaces, and can we depend on it unchecked within the context of American education? How far back might we throttle that and leave chance to private institutions?

The Options for School Choice The theory of the firm seems to propagate the notion that market-based freedom applied to elementary and secondary school education markets may yield productive and allocative efficiency, which may assist in helping schools produce higher achieving students. With this theoretical underpinning, the markets have responded by creating an array of school choice alternatives, programs such as tax credits, school vouchers, and charter schools. Of the three, the last, charter schools, a movement that is perceived to have the most embedded freedom and traces its roots back to the early 1990s, seems to enjoy the bulk of the attention. But is this mechanism truly representative of the theory of the firm and public sentiment? The body of research is still evolving but seems to indicate that, while charter schools remain popular, the public still prefers smaller class sizes as the primary mechanism to improve student performance. In the context of the theory of the firm, this seems to be counterintuitive. Smaller class sizes would imply increasing the resources—physical, labor, and financial—needed to run schools that already exist and are operating suboptimally and would not guarantee significant improvements in student performance outcomes. Thus, sentiment seems to support the notion of bigger schools with smaller classes and more teachers, which is likely to perpetuate the existing problem of underperformance. So if the theory of the firm is to apply to the market for elementary and secondary education in the United States, it would appear that the growth of external market alternatives would arise in concert

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with the increases in cost structure and resources to existing public schools. So what comes next? There are two options: either (a) new school choice alternatives that are both aligned with economic theory and attractive to the public’s desire for greater freedom of choice or (b) a recognition that current school choice alternatives aligned to the theory of the firm, such as charter schools, are able to assuage economic theorists, demonstrate a pattern of improved academic achievement in certain student populations, and satisfy the public’s desire for greater expressions of freedom in elementary and secondary education. It appears that this market, however mature, will still be undergoing radical change for the foreseeable future. Alvin Kamienski See also Administrative Spending; Budgeting Approaches; Charter Schools; Education Finance; Fiscal Environment; Markets, Theory of; No Child Left Behind Act; Public Good

Further Readings Alexander, K. L. (1997). Public schools and the public good. Social Forces, 76(1), 1–30. Bushaw, W. J., & Gallup, A. M. (2008). Americans speak out: Are educators and policy makers listening? The 40th annual Phi Delta Kappa/Gallup Poll of the public’s attitudes toward the public schools. Phi Delta Kappan, 90(1), 9–20. Retrieved from http://www.pdkmembers .org/members_online/publications/Archive/pdf/ k0809pol.pdf Bushaw, W. J., & Lopez, S. J. (2013). The 45th annual PDK/Gallup Poll of the public’s attitudes toward the public schools: Which way do we go? Retrieved from http://pdkintl.org/noindex/213_PDKGallup.pdf Carpenter, D. M., & Kafer, K. (2012). A history of private school choice. Peabody Journal of Education, 87, 336–350. Chitester, B. (Producer). (1980). Free to choose [DVD]. Retrieved from http://www.freetochoose.tv/program. php?id=ftc1980_6&series=ftc80 Coase, R. H. (1988). The nature of the firm: Meaning. Journal of Law, Economics, & Organization, 4(1), 19–32. Dee, T. S., Jacob, B., & Shwartz, N. L. (2013). The effects of NCLB on school resources and practices. Educational Evaluation and Policy Analysis, 35(2), 252–279. Friedman, M., & Friedman, R. D. (1980). Free to choose. San Diego, CA: Harvest Books. Hanushek, E. A. (1996). Making schools work. Washington, DC: Brookings Institution Press.

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Hanushek, E. A., Rivkin, S. G., & Taylor, L. L. (1996). The identification of school resource effects. Education Economics, 4(2), 105–125. Kamienski, A. K., & Hicks, A. O. (2013, November). Thriving in turbulent times: The financial health of social service organizations, 2008–2010. Paper presented at the annual meeting of the Annual Conference of the Association for Research on Nonprofit Organizations and Voluntary Action, Hartford, CT. Laitsch, D. (2013). Smacked by the invisible hand: The wrong debate at the wrong time with the wrong people. Journal of Curriculum Studies, 45(1), 16–27. Moe, T. M. (2001). Schools, vouchers, and the American public. Washington, DC: Brookings Institution Press. Rose, L. C., & Gallup, A. M. (2007). The 39th annual Phi Delta Kappa/Gallup Poll of the public’s attitudes toward the public schools. Phi Delta Kappan, 89(1), 33–45. Tiebout, C. M. (1956). A pure theory of local expenditures. Journal of Political Economy, 64(5), 416–424. Tiebout, C. M. (1961). Economic theory of fiscal decentralization. In National Bureau of Economic Research (Eds.), Public finances needs, sources, utilization (pp. 79–96). Princeton, NJ: Princeton University Press.

TIEBOUT SORTING Tiebout sorting refers to the sorting of households into neighborhoods and communities according to their willingness and ability to pay for local public goods (tax-supported amenities and services—such as K-12 education—provided to residents of a local jurisdiction). In theory, sorting results in an efficient provision of public goods as local governments compete to provide desired services at a price residents are willing to pay (via housing costs and property taxes), and households choose communities that fit their preferences for public services, housing, and taxes. This entry introduces the economist Charles Tiebout’s hypothesis about the effects of residential sorting and its implications for policy. It lays out key assumptions and predictions of the theory and highlights recent empirical tests of these. It concludes by describing how recent education policies do and do not promote Tiebout “effects.” The term itself refers to Tiebout’s seminal 1956 paper, which challenged the conventional view in economics that individuals will not voluntarily reveal their preferences for public goods, leading to an underprovision of those goods in the free market

(known as the “free rider problem”). Tiebout argued that a system of local governments and property taxes creates a “market” for local public goods, as households with similar tastes and abilities to pay for these goods choose to locate in the same communities. Households effectively reveal their preferences through mobility, or by “voting with their feet.”

Implications of Tiebout Sorting for the Economics of Education Tiebout’s hypothesis has a number of implications for the provision of public goods such as education. First, local governments have an incentive to provide residents’ desired quantity and quality of services at the least cost, as failure to do so would lead to a loss of residents (and their tax dollars) to other communities. Second, local provision results in a better match between households and public goods expenditures than would arise under a centralized system. Third, households will stratify across communities according to tastes and ability to pay for public services, resulting in the segregation by characteristics associated with demand (e.g., income, socioeconomic status, and number of children). Fourth, there will be as much inequality in expenditures on public goods as there is variation in tastes and willingness to pay for them. Fifth, when the local supply of housing is fixed, the quantity and quality of public services (and their tax cost) will be capitalized into housing prices. Sixth, because of the link between taxes, public service quality, and property values, homeowners have a strong incentive to monitor the performance and productivity of their local governments. The Tiebout model has been instrumental in helping public finance and education economists analyze local government productivity and the optimal provision of public goods. On the one hand, sorting has the potential to encourage competition between districts, incentivize quality, and minimize costs. On the other hand, a purely local system of public education can be highly unequal, with spending and quality functions of local income, property wealth, and tastes. The model illustrates an efficiency-equity trade-off in which the beneficial effects of Tiebout sorting are weighed against its unequal distribution of services and segregating effects. Distribution becomes increasingly relevant in the presence of “interjurisdictional spillovers”—that is, when the provision of a public good in one community affects the well-being of another. Education arguably exhibits these spillovers, as citizens have an interest in the

Title I

education of others in their broader labor market area, state, or nation.

Assumptions and Tests of the Tiebout Model Tiebout’s model relies on strict assumptions that even he acknowledged were unrealistic. These assumptions, however, may hold to an approximation in some contexts and are often used as a benchmark against which real-world settings are compared. For example, a key condition for Tiebout sorting is a large number of jurisdictions from which households choose. This assumption is more likely to hold in metropolitan areas than in large cities or rural towns. In the same way, one might expect to see greater Tiebout sorting (and effects) in states like New Jersey, which has 590 local school districts, than in Florida, which has only 67. Even large jurisdictions experience a form of Tiebout sorting, as households sort into neighborhoods within communities that satisfy their demands for public services. Other assumptions of the model include the absence of constraints on residential choice (e.g., moving/commuting costs or employment) and perfect information about the quality and cost of public services. Again, neither assumption is strictly realistic, but research finds that households do consider public goods—such as the quality of local schools— when making residential choices. A number of careful empirical studies have found that housing units vary in market value according to differences in nearby school quality and property taxes. For example, in a well-known study, Sandra Black compared housing prices across attendance boundaries within the same school district and found that parents were willing to pay 2.5% more for 5% higher average test scores. Such behavior is prima facie evidence that Tiebout sorting exists. The competitive effects of sorting were examined in a prominent study by Caroline Hoxby, who compared test outcomes and expenditures across school districts that—for historical reasons—were exposed to varying amounts of “Tiebout competition.” She found that students in districts where the conditions for Tiebout sorting were greatest had better outcomes per dollar of expenditure (indicating greater productivity/efficiency) than those in districts facing less sorting pressure.

Conclusion Over time, public education in the United States has moved away from Tiebout’s vision of local public

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good provision and toward greater centralization at the state level. While this shift has resulted in greater resource equity, Tiebout’s model predicts a corresponding loss of efficiency. Equalization can raise dissatisfaction with the level of spending and reduce overall support for public education (perhaps increasing private school enrollment); additionally, quality and productivity may suffer as incentives for investing in and monitoring public schools are diminished. One of the arguments for recent educational reforms such as intradistrict choice and school accountability is that these initiatives restore some of the competitive pressures offered by Tiebout sorting but lost under centralized provision. Sean P. Corcoran See also Median Voter Model; Property Taxes; Public Choice Economics

Further Readings Black, S. E. (1999). Do better schools matter? Parental valuation of elementary education. Quarterly Journal of Economics, 114, 577–599. Fischel, W. A. (2001). The homevoter hypothesis: How home values influence local government taxation, school finance, and land-use policies. Cambridge, MA: Harvard University Press. Hoxby, C. M. (2000). Does competition among public schools benefit students and taxpayers? American Economic Review, 90, 1209–1238. Tiebout, C. M. (1956). A pure theory of local expenditures. Journal of Political Economy, 64, 416–424.

TITLE I Title I, Part A of the Elementary and Secondary Education Act (ESEA), provides federal funds to schools and school districts for the education of disadvantaged students, prekindergarten through high school. The largest federal program of aid to elementary and secondary education, with appropriations of $14.5 billion in fiscal year (FY) 2012–2013, Title I finances supplemental educational and related services for low-achieving and other students attending high-poverty schools. Nearly all school districts and about two thirds of schools in the United States receive Title I funds. Although Title I represents less than 3% of total revenues for elementary and secondary education, regulations and guidance associated with the law have profoundly shaped

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Title I

the direction of education policy over the past two decades. This entry describes how Title I funds are allocated, the distribution and use of Title I funds, and the effects of Title I on education policy.

provided an additional $534 million for these school improvement activities in FY2013.

Funding Formula

School districts must distribute their Title I funds to schools with high concentrations of low-income students, typically using data from the free or reducedprice lunch program. A school is eligible for Title I funds if its attendance area has a poverty rate that is at least equal to the district average poverty rate or 35% (whichever is less). Districts may concentrate their Title I funds on their highest poverty schools and limit school eligibility to a poverty level that is higher than the districtwide average. They must, however, fund all schools with 75% or more disadvantaged students before funding schools with lower poverty rates. Districts may give schools different amounts of Title I funds per low-income student as long as schools with higher poverty rates receive higher allocations per low-income student than schools with lower poverty rates. If the district serves schools with poverty rates below 35%, it must ensure that any school with a poverty rate above 35% receives a per-pupil Title I allocation that is at least 125% of the districtwide allocation per lowincome student. School districts must also use Title I funds to provide academic enrichment services to eligible children enrolled in private schools. Schools in which children from low-income families make up at least 40% of enrollment may use Title I funds, along with other federal, state, and local funds, for “schoolwide programs” that serve all children in the school. Title I schools with less than this 40% threshold, or that choose not to operate a schoolwide program, must offer “targeted assistance” to students who are failing, or most at risk of failing, to meet academic standards. Schools may use Title I funds for preschool, kindergarten, and extended-time programs; instructional staff, materials, and equipment; professional development; student support; transportation; and administration. Title I programs must use instructional strategies based on scientifically based research and implement parental involvement activities. To ensure that Title I funds are used to provide services that augment the regular services normally provided by a school district, districts must meet three fiscal requirements related to the expenditure of their regular state and local funds. First, they must maintain their expenditures for public education

The federal government distributes Title I, Part A, grants through four statutory formulas that are based primarily on poverty estimates calculated by the U.S. Census Bureau. These poverty measures are based on family income, size, and composition. 1. Basic Grants (45% of the total allocation) provide funds to school districts in which the number of children in poverty is at least 10 and exceeds 2% of the district’s school-age population. 2. Concentration Grants (9% of the total allocation) flow to school districts where the number of children in poverty exceeds 6,500 or 15% of the total school-age population. 3. Targeted Grants (23% of the total allocation) uses a weighted-child formula, in which children from low-income families count as more than one child for the purposes of funding, to allocate larger amounts per pupil to districts with higher numbers or percentages of poor children. Districts are eligible for these grants if the number of schoolchildren counted in the formula (without application of the weights) is at least 10 and at least 5% of their school-age population. 4. Education Finance Incentive Grants (23% of the total allocation) distribute funds to states with (a) higher fiscal effort, measured as a state’s financial support for education relative to its per capita income and (b) greater fiscal equity, measured as the degree to which per-pupil education expenditures (weighted for the number of Title I formula children) vary across school districts within a state. District eligibility is the same as for Targeted Grants. All four formulas also incorporate a state per-pupil expenditure factor that serves as a proxy for cost-ofeducation differences across states—state minimum allocations that provide larger allocations to small states and hold harmless provisions. States may reserve up to 1% of total Title I, Part A, allocations for state-level administrative activities, and they must reserve 4% of Title I, Part A, allocations for competitive subgrants to local school districts for school improvement programs. Title I

Distribution and Use by School Districts and Schools

Title I

from state and local funds from one year to the next, or what ESEA refers to as “maintenance of effort.” Districts cannot reduce their own spending for public education and replace those funds with federal funds. Second, at the school level, districts must ensure that each Title I school receives its fair share of resources from state and local funds (“comparability”). Third, at the individual student level, districts must ensure that Title I–funded services provided to students participating in Title I do not replace or supplant services that districts would ordinarily provide to all its students (“supplement, not supplant”). The most recent study of the distribution and use of Title I funds was conducted as part of a national evaluation of the implementation of the No Child Left Behind Act of 2001 (the 2001 reauthorization of ESEA) and was based on data from the 2004–2005 school year. The researchers found that the actual distribution of Title I funds reflected the distribution of low-income students that the program targets. For example, the highest poverty districts, which educated 49% of poor children, received 52% of Title I funds. Conversely, the lowest poverty districts, which educated 7% of poor children, received only 6% of Title I funds. Title I funds were somewhat more targeted at the school level. Fifty-four percent of the nation’s public schools received Title I funding in 2004–2005. High-poverty schools, defined as those with 50% or more low-income students, received 76% of Title I funds, more than their share of all low-income students (63%). About 6% of Title I funds were allocated to low-poverty schools, schools that were generally located in low-poverty school districts. Districts have historically chosen to concentrate Title I funding at the elementary school level. As a result, elementary schools received 76% of the school allocations although they served only 57% of low-income students, while high schools received 10% of Title I funds and enrolled 22% of all lowincome students. Schoolwide programs accounted for 56% of all Title I schools and 70% of Title I funds allocated to schools, while targeted assistance programs accounted for the remaining 44% of all Title I schools and 30% of Title I funds. The study also showed that Title I funds were used to purchase additional educational services as intended. In 2004–2005, districts and schools spent their Title I funds primarily (73%) for instruction. They spent another 18% on professional development, student support staff, instructional support staff, and parent

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involvement activities. The remaining 10% was used for school- and district-level administration, facilities, and other support, such as student transportation. Seventy-one percent of the Title I funds spent on personnel at the school level were used to employ teachers, and 17% were used for teacher aides. The addition of Title I–funded staff resulted in a 7% increase in the average number of teachers, a 24% increase in the number of teacher aides, and a 3% increase in noninstructional staff in an averagesize Title I school of 500 students.

Title I and Education Policy Receipt of Title I funds by states, school districts, and schools is contingent on their acceptance of the statutory provisions, regulations, and nonregulatory guidance that direct the implementation of the law. Over the course of the past 45 years, these “strings” attached to Title I grants have not only directed the allocation and use of Title I funds but have also significantly shaped education policy for all students. The ESEA of 1965 was enacted as part of President Johnson’s War on Poverty. Title I was designed initially to provide funding for educationally disadvantaged students rather than to provide specific compensatory services to them. Proponents of the law argued that federal aid, targeted to schools and school districts with concentrations of economically disadvantaged students, could help break the cycle of poverty by funding additional educational services for these students. At this time, unequal educational opportunity was attributed to a lack of resources, not to the quality of programs and services provided to these students. By focusing on the allocation rather than the use of Title I grants, federal policymakers also deferred to the United States’s long tradition of local control over the content of educational programs. Changes to Title I in its early years tightened the targeting provisions of the law by adding the comparability, maintenance of effort, and supplement not supplant provisions, as well as concentration grants for districts with high percentages of socioeconomically disadvantaged students. The changes also held school districts fiscally accountable for their expenditure of Title I funds. The 1978 amendments to ESEA eased some regulations in response to complaints by program administrators that the provisions were administratively burdensome, uncoordinated, and inflexible. The substance of Title I

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programs, however, remained in the hands of state and local governments. Beginning in the mid-1980s, the national discourse on education shifted from equal educational opportunity to educational excellence, and with this, it shifted from an emphasis on educational inputs to an emphasis on academic standards and educational outcomes. The reauthorization of Title I in 1988, in 1994 (as part of the Improving America’s Schools Act), and in 2001 (as part of the No Child Left Behind Act of 2001) played a major role in redirecting the focus of education policy from fiscal accountability to the quality and outcomes of education provided to poor students. With the 1988 reauthorization of ESEA, Title I held states accountable for the first time for the educational performance of their Title I schools. States were required to define expected academic growth for Title I students, identify Title I schools that did not meet these goals, and establish procedures for assisting these schools. However, states were free to establish their own academic performance standards for Title I students, and non–Title I schools and students were not subject to these provisions. To address concerns that Title I students were being held to different and lower standards than their more advantaged peers, the Improving America’s Schools Act of 1994 required states to establish challenging content and performance standards in at least reading and mathematics, to implement assessments that measured all students’ performance against these standards in at least three grade spans, to hold schools and school districts accountable for the achievement of all students, and to take other steps to encourage school districts to reform their curriculum and instructional practices. These requirements led all states to develop academic standards, assessments, performance reporting, and, in most cases, consequences for low performance and to include nearly all students in these policies. States, however, differed in the content and performance standards they established for students and schools, the measures they included in their accountability systems, and the consequences for schools. The No Child Left Behind Act of 2001 was designed, in part, to address this variability in state policy by standardizing state assessment and accountability policies. The law expanded the mandated assessment of reading and mathematics to Grades 3–8, required states to assess students in science in at least three grade spans, and limited testing exemptions for students with special needs

and English Language Learners. Accountability requirements became more prescriptive. States had to establish a single, statewide accountability system that would be applied to all schools, regardless of Title I status. Schools and school districts were held accountable for the performance of students grouped by race/ethnicity, socioeconomic status, and special educational needs, as well as for the overall student population. States had to set annual performance goals that would result in all students meeting state standards by 2013–2014. The law specified an increasingly punitive set of consequences for Title I schools that did not meet these annual goals and required states to create a statewide system of support for Title I schools in need of improvement. Beginning in 2010, schools that received Title I school improvement grants had to implement one of four federally designated school turnaround models. As the 2013–2014 deadline for 100% proficiency approached, the number of Title I schools requiring technical assistance or subject to sanctions outpaced the capacity of states and school districts to provide the needed services. In late 2011, the U.S. Department of Education announced a program of administrative waivers to address the states’ concerns. The waivers loosened some of the accountability provisions of Title I, including the requirement that all students be deemed “proficient” in reading and math by the end of the 2013–2014 school year. States are now allowed to establish new testing and accountability systems that set “achievable but ambitious” achievement goals and are given greater flexibility in identifying and designing interventions for failing schools. The Obama administration used these waivers to further its education agenda by requiring states that received the waivers to adopt “college- and career-ready” standards, such as the Common Core State Standards approved by 46 states, and to enact teacher evaluation systems based in part on student performance.

Future Directions Title I now both funds and defines equal educational opportunity for educationally disadvantaged students. Over time, presidents and the Congress have used Title I to advance their visions of a quality education for these children and their classmates. As policymakers consider forthcoming reauthorizations of ESEA, they face three ongoing policy issues: (1) Is Title I funding adequate? (2) Is the allocation of Title I funds equitable? and (3) What is the appropriate

Tracking in Education

balance of control over the substance and delivery of education between the federal government, on the one hand, and states and local school districts, on the other? First, while Title I appropriations increased 40% between FY2002 (the year that ESEA was last reauthorized) and FY2009 to $14.5 billion, appropriations were not increased in subsequent years. Proponents of Title I argue that this level of funding is insufficient to meet the educational needs of high-poverty schools and that appropriations for the program fall nearly $10 billion short of the amount authorized in the law. A second issue concerns how equitably Title I funds are allocated to and within school districts. Critics charge that (a) in spite of greater targeting, the allocation formulas direct funds to low-poverty schools located in low-poverty school districts at the expense of higher poverty schools in high-poverty communities; (b) the inclusion of the state per-pupil expenditure factor in the allocation formulas favors higher spending and wealthier states; and (c) flawed measures of comparability fail to equalize state and local spending between Title I and non–Title I schools. Finally, reauthorization raises the age-old question about the appropriate role of the federal government in education. While Title I has driven states to adopt academic standards and shaped state accountability policy, federal law forbids U.S. Department of Education agencies from mandating or directing the content of academic standards, assessments, or instructional materials. How much further, in what direction, and through what kinds of incentives can or should the federal government shape the content and quality of education for disadvantaged students? Margaret E. Goertz See also Adequate Yearly Progress; Categorical Grants; Elementary and Secondary Education Act; Intergovernmental Fiscal Relationships; No Child Left Behind Act; Supplemental Educational Services; Weighted Student Funding

Further Readings Chambers, J. G., Lam, I., Mahitivanichcha, K., Esra, P., Shambaugh, L., & Stullich, S. (2009). State and local implementation of the No Child Left Behind Act: Vol. VI. Targeting and uses of federal education funds. Washington, DC: U.S. Department of Education, Office of Planning, Evaluation and Policy Development, Policy and Program Studies Service.

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Cross, C. T. (2004). Political education: National policy comes of age. New York, NY: Teachers College Press. Debray, E. H., McDermott, K. A., & Wohlstetter, P. (Eds.). (2005). Federalism reconsidered: The case of the No Child Left Behind Act. Peabody Journal of Education, 80(2), 1–188. Jennings, J. F. (2000). Title I: Its legislative history and its promise. Phi Delta Kappan, 81(9), 516–522. Kaestle, C. F., & Lodewick, A. E. (Eds.). (2007). To educate a nation: Federal and national strategies of school reform. Lawrence: University Press of Kansas.

TRACKING

IN

EDUCATION

Tracking is the often controversial practice of sorting students based on their observed ability or academic achievement into homogeneous groups with the aim of allowing teachers to tailor instructional style and content to students’ needs and abilities. This broad definition encompasses a wide range of educational practices, including differentiated instruction within skills-heterogeneous classrooms (a practice that is common in elementary schools in the United States), between-class sorting within skills-heterogeneous schools (a practice that is common in U.S. high schools), and between-school sorting (a practice that is common at the secondary school level in Europe and Asia). Each of these practices is predicated on the hypothesis that homogeneous learning environments and specialized instruction increase educational efficiency. At the same time, each of these practices carry risks related to the expansion of educational, economic, and social inequality if students tracked in low-status educational environments suffer negative educational consequences. As Jeannie Oakes demonstrates, these risks may be particularly pronounced if race, gender, family characteristics, or other nonacademic criteria influence track placement decisions. After providing a theoretical framework for understanding different types of tracking systems, this entry briefly introduces the research literature on within-class, between-class, and between-school tracking systems. It discusses empirical evidence regarding the effects of tracking for the distribution of student achievement, since this question has been a particular focus in the economics of education. It also provides a historical context regarding the development of tracking systems and the psychosocial consequences of tracking. While within-class, between-class, and betweenschool tracking systems have common goals and risks, their consequences for the distribution of

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educational outcomes may vary considerably. Sociologists identify four key dimensions on which tracking systems differ: 1. Inclusiveness, or the extent to which a tracking system makes high-level coursework available to students 2. Selectivity, or the extent to which tracking produces homogeneous learning environments 3. Electivity, or the extent to which students can choose their own classroom placements 4. Scope, or the extent to which classroom placements in one academic subject are associated with classroom placements in other academic subjects

The consequences of academic tracking may vary considerably with these four dimensions. For example, many U.S. high schools moved from a system in which counselors assigned students to overarching curricular tracks to a system in which students could choose their tracks on a course-bycourse basis. In making this shift, educators hypothesized that increasing track electivity and decreasing track scope would help counteract the social stigma associated with low-track placement and make it more possible for students to move between tracks over the course of their academic careers. In this framework, within-class, between-class, and between-school tracking systems differ most importantly in terms of scope. In within-school tracking systems, educators may sort and re-sort students for different subject areas. For example, many U.S. elementary schools track students into reading groups but offer ungrouped instruction for the rest of the academic day. Between-school tracking systems, on the other hand, are relatively absolute. Once a student is placed in an academically selective German Gymnasium, for example, she is quite unlikely to take a course in a less academically selective vocational Hauptschule. Reasoning that the extent to which tracking decisions correspond with differences in students’ educational experiences is a function of track scope, Adam Gamoran argues that tracking systems with wider scope are more likely to exacerbate educational inequalities than tracking systems with relatively narrow scope.

Within-Classroom Grouping In the United States and elsewhere, elementary schools typically operate on a “common school”

model, enrolling all youths who live within the school’s geographic catchment. As a result, elementary school teachers often face challenges associated with educating students with diverse interests, educational backgrounds, and skills. Within-classroom differentiation strategies approach these challenges by creating relatively skills-homogeneous small instructional groups within larger classrooms. These strategies are quite common in the United States, where elementary educators often sort students based on their reading skills for literacy instruction. Nationally representative data collected in 2002 and 2003 indicate that teachers use within-classroom skill grouping in 42% of kindergarten and 72% of first-grade classrooms. Several studies indicate that elementary school students who are placed in high-skills groups experience more rapid achievement gains than peers who are placed in low-skills groups. These results typically hold after controlling for a wide range of background factors, including prior achievement and teacher assessments of student skills and behavior. If these findings are unbiased reflections of the causal effect of within-classroom grouping, they suggest that these strategies increase educational inequalities but do little to improve average educational achievement. However, this research literature is subject to two important threats to internal validity. First, since schools track students based on both observable and unobservable characteristics, unmeasured variable biases hamper efforts to estimate the effects of within-classroom differentiation and other tracking strategies. Furthermore, even if analysts could fully account for the factors that predict track placement, selection biases likely confound estimates of these effects of track placement. In many educational settings, very little overlap exists between the distribution of achievement for students placed in high and low tracks. These empirical challenges are common to much of the literature surrounding the effects of tracking on the distribution of educational outcomes. A handful of recent studies have drawn on propensity score approaches to address these methodological challenges. Rather than comparing students in high-skills groups with their peers in low-skills groups, these studies compare students in classrooms that utilize skills grouping with peers in classrooms that do not. While this approach does little to address potential omitted variable biases, it does provide a solution to the “common support” problem that emerges when selection regimes render

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the populations in high-track groups relatively distinct from the populations in low-track groups. These analyses typically indicate that high-grouped students gain reading skills more rapidly than comparable students in nongrouped classrooms, while low-grouped students gain fewer reading skills than comparable students in nongrouped classrooms. However, in a series of articles, Guanglei Hong and colleagues demonstrate that the effects of withinclassroom grouping are in large part a function of the amount of instructional time available to students. In classes where teachers spent more than an hour daily on reading instruction, within-class grouping has a positive effect on student literacy skills, as well as on students’ socioemotional development and behavior. Furthermore, many of these positive grouping effects are stronger for students at the bottom and the middle of the achievement distribution than for students at the top of the achievement distribution. These studies provide broad externally valid data on within-class grouping in the context of U.S. elementary schools. However, important questions remain regarding the true effects of within-class grouping on the distribution of achievement.

Between-Classroom Tracking Compared with the literature on within-class grouping, the literature on between-classroom tracking is relatively large and well developed. As is the case with research on within-classroom tracking, much of this research is based on longitudinal studies of nationally representative samples of U.S. high school students collected by the National Center for Educational Statistics. Accordingly, many of the same challenges regarding omitted variable bias, selection biases, and the definition of the treatment and counterfactual that appear in the literature surrounding within-class grouping recur in the literature on between-class tracking. Research in this empirical tradition generally indicates that between-class tracking has little to no effect on mean academic achievement. However, several studies indicate that this form of tracking exacerbates educational inequality. For example, Laura Argys, Daniel Rees, and Dominic J. Brewer estimate odds of student track placement based on a vector of student and school characteristics (including student gender, race/ethnicity, socioeconomic status, prior achievement, and school size and demographics) for a representative sample of students enrolled in U.S.

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high schools in the early 1990s. Their analyses use classroom teacher survey data to categorize tracks, comparing students in classrooms in which teachers describe student skills as “above average,” “average,” and “below average,” with students in classrooms that teachers describe as “heterogeneous.” Using the results of this model to correct for endogeneity in the track selection process, Argys, Rees, and Brewer then estimate the effect of track placement on student achievement. They find that tracking has modest positive effects on mean levels of student achievement, such that a typical student placed in a heterogeneous class would score approximately a percentage point lower on a 10th-grade measure of mathematical achievement than the sample mean. However, the gains associated with between-class tracking are not distributed evenly. Students placed in high-track classes score significantly higher than they might have had they been placed in untracked classes, while students placed in low-track classes score significantly worse. Furthermore, these analyses indicate that these track placement effects do not vary substantially with student ability. In sum, these analyses indicate that tracking benefits students who are placed in high-track classes and hurts students who are placed in low-track classes. Several other studies—including related regression-based analyses of nationally representative data as well as studies of particular school districts— have reached similar conclusions. However, as David Figlio and Marianne Page point out, none of these approaches accounts for potential biases arising from selection into tracked classes based on unmeasured student characteristics. To address this empirical challenge, Figlio and Page investigate achievement outcomes for students who enroll in tracked and untracked schools by student prior achievement levels. Regression analyses indicate that attending tracked schools has no effect for students at any point in the test score distribution. However, these estimates may be biased if students sort into tracked schools based on unmeasured characteristics. To address this potential bias, the authors use state- and county-level indicators as instruments for school-level tracking. The resulting analyses indicate that tracking may have positive achievement effects for students at the bottom of the achievement distribution and no effect for moderate- and high-performing students. While these findings are sharply at odds with the rest of the literature on within-school tracking in U.S. high schools, recent evidence from Dallas elementary schools using a

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similar empirical technique provides further evidence to suggest that tracking may have positive effects even for students in low-status tracks. Experimental data might help resolve this empirical debate regarding the distributional consequences of tracking. While it has been several decades since a large-scale experiment regarding the effects of tracking has been undertaken in the United States or elsewhere in the developed world, Esther Duflo, Pascaline Dupas, and Michael Kremer present relevant data from tracked and untracked elementary schools in Kenya. This study, which compares students in 61 Kenyan schools in which students are randomly assigned to first-grade classes with students in 60 schools in which students are assigned based on prior achievement, indicates that tracking has significant positive effects on student achievement. While students benefit on average from exposure to highachieving peers, this analysis indicates that tracking benefits students across the achievement distribution since it allows teachers to teach at their level.

Between-School Tracking In contrast to the United States and Canada, where elementary and secondary schools typically enroll a skills-heterogeneous student body and most tracking occurs either within classrooms or between classrooms, several European and Asian educational systems have tracking systems that allocate students to hierarchically organized schools based on their prior academic performance. The German system is perhaps the most well known of these between-school tracking systems. In Germany, students are assigned to 4 years of untracked schools known as Grundschule based on their geographic location beginning at approximately age 6. At approximately age 10, German students compete for admissions to tracked secondary schools, with the most academically high-achieving students enrolling in Gymnasien and other students enrolling in less selective academically oriented Realschule or vocationally oriented Hauptschule. Many other European and Asian education systems have similar between-school tracking systems, although the timing of these tracking decisions varies considerably from country to country. In a 2006 study, Eric Hanushek and Ludger Woessmann took advantage of this variation in the timing of betweenschool tracking decisions to estimate the effects of tracking on the distribution of student achievement. Since no countries track before 4th grade, but many track before 10th grade, nation-level changes in

achievement between 4th and 10th grades provide a way to observe the effects of tracking. This difference-in-difference approach reveals little evidence of a tracking effect on mean achievement, but it reveals a meaningful evidence of a tracking effect on inequality. While inequality declines between 4th and 10th grades in 9 of 10 countries that don’t track before 10th grade, it increases in 7 of 9 countries that do track. In a later paper, Woessmann and colleagues expanded this analysis to consider the link between tracking and intergenerational mobility. Consistent with the above findings, this paper indicates that the link between family background and achievement is stronger for students in countries that sort students into tracked schools relatively early in their educational career. Subsequent analyses have raised important questions about the extent to which these findings are robust to alternative specifications. Nonetheless, taken at face value, they indicate that between-school tracking broadens educational and social inequality. Other analyses have taken advantage of policy changes within countries to estimate the consequences of between-school tracking practices on student achievement. Beginning in the early 1960s, several counties in the United Kingdom began to eliminate test-based placement for students of ages 10 or 11 with a comprehensive system of skillsheterogeneous secondary schools. Similar changes occurred in Sweden in the early 1990s. Consistent with much of the literature discussed above, analyses of these within-country shifts in tracking policies indicate that between-school sorting practices boost achievement and other outcomes for students placed in high-status schools at the expense of students who are placed in lower status schools. However, as is the case elsewhere in the tracking literature, questions persist regarding the extent to which unmeasured selection effects bias these findings.

Discussion Two common threads run through the empirical literature on tracking and its consequences for the distribution of educational achievement. The first of these threads is a clear and persistent association between tracking practices and educational inequality. Whether the sorting occurs within classrooms, between classrooms, or between schools, tracking decisions are associated with higher scores at the top of the achievement distribution and lower scores at the bottom of the achievement distribution.

Tragedy of the Commons

Since affluent and other socially advantaged students tend to cluster at the top of the achievement distribution, it is not surprising that this literature also provides some evidence to suggest that tracking strengthens the link between family backgrounds and educational outcomes. These findings are discouraging, particularly since tracking practices aim to improve achievement for students across the achievement distribution by allowing teachers to adjust the content and method of their instruction to student needs. However, consistent with Gamoran’s theory regarding the link between track scope and educational inequality, there is some evidence to suggest that within-classroom grouping is less closely associated with educational inequality than tracking systems that sort students between classrooms and schools. Furthermore, the second thread emerging from the literature on tracking raises an important question about the extent to which the available literature provides reliable evidence regarding the causal effects of tracking. Since tracking systems are designed to sort students among educational environments to improve the match between students and the instruction to which they are exposed, any attempt to assess the consequences of tracking must fully account for selection into track placements. While researchers have used a broad array of strategies in an attempt to model selection—including regression, value-added, propensity matching, difference-in-difference, and instrumental variable approaches—selection bias remains an important threat to validity throughout the literature on tracking. In general, the studies that most convincingly address the threat of selection bias return results that are most consistent with the theory underlying tracking. The experimental evidence that Duflo and colleagues present from Kenya is particularly important in this regard. This study indicates that elementary school students who are randomly assigned to schools with tracked classrooms outperform peers who are randomly assigned to untracked schools. However, too little rigorous experimental or quasiexperimental evidence exists in this area to draw strong conclusions regarding the effects of tracking. Recent shifts in educational practice and policy in U.S. middle and high schools may provide new opportunities to test the effects of tracking excluding unmeasured selection. In particular, many U.S. middle schools have begun to detrack middle school mathematics classes in an effort to enroll all students in algebra before they transition to high school. There is some evidence to suggest that this move has

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substantially detracked middle school mathematics classrooms. New data collection and analysis efforts aim to use this shift to provide new insights into the consequences of various forms of tracking for educational efficiency and equity. Further research is also needed to better understand the mechanisms through which tracking influences achievement and achievement inequalities. Thurston Domina See also Income Inequality and Educational Inequality; Selection Bias; Technical Efficiency

Further Readings Argys, L. M., Rees, D. I., & Brewer, D. J. (1996). Detracking America’s schools: Equity at zero cost? Journal of Policy Analysis and Management, 15(4), 623–645. Betts, J. R. (2011). The economics of tracking in education. In E. A. Hanushek, S. Machin, & L. Woessmann (Eds.), Handbook of the economics of education (Vol. 3, pp. 341–381). Amsterdam, Netherlands: Elsevier. Duflo, E., Dupas, P., & Kremer, M. (2011). Peer effects and the impact of tracking: Evidence from a randomized evaluation in Kenya. American Economic Review, 101(5), 1739–1774. Figlio, D. N., & Page, M. E. (2002). School choice and the distributional effects of ability tracking: Does separation increase inequality? Journal of Urban Economics, 51(3), 497–514. Gamoran, A. (1992). The variable effects of high school tracking. American Sociological Review, 57(6), 812–828. Hanushek, E. A., & Woessmann, L. (2006). Does educational tracking affect performance and inequality? Difference-in-differences evidence across countries. Economic Journal, 116(510), C63–C76. Hong, G., Corter, C., Hong, Y., & Pelletier, J. (2012). Differential effects of literacy instruction time and homogeneous ability grouping in kindergarten classrooms: Who will benefit? Who will suffer? Educational Evaluation and Policy Analysis, 34(1), 69–88. Oakes, J. (2005). Keeping track: How schools structure inequality. New Haven, CT: Yale University Press.

TRAGEDY

OF THE

COMMONS

The tragedy of the commons is a concept used in the fields of economics and politics to describe a particular social dilemma requiring social coordination

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to solve. The term comes from the title of an article by the ecologist Garrett Hardin in which he identified a class of social dilemmas using the metaphor of herders’ choices over their use of pastures when such lands are commonly owned. Specifically, he argued that individual herders have an incentive to overgraze their sheep on the lands owned in common. Thus, if individuals are left to do as they please, the commons will eventually be ruined, contrary to the desires of those in the community (and hence, Hardin’s use of the term tragedy). The concept is typically employed to describe dilemmas where some naturally occurring resource that is not privately owned—be it grazing land, fish in the ocean, or the atmosphere’s ozone layer—winds up being overexploited in the absence of a social mechanism that limits its use. The tragedy of the commons, thus, represents one type of a more general category of social problems where choices at the individual level may be rational, while the resulting social outcome is not. Hardin’s parable concretely spells out the general features of this problem, and it has become exceptionally useful in fostering recognition of a wide range of social problems, as well as in identifying their solutions. This entry provides a brief history of the tragedy of the commons (referred to here as the commons problem, or CP), discusses the range of problems to which the concept is applicable, and then examines solutions to it.

Background The parable told in Hardin’s article is one where a group of herders overgraze a commonly owned pasture (“the commons”) in their community that provides food for their sheep; in the process of doing so, they destroy their own livelihood. Hardin identified the problem as one where the herders’ incentives led each to add to his flock so as to increase his own personal wealth. Yet each additional sheep reduces the ability of the commons to restore its productive capacity, and thus, the sheep wind up with less to eat. Despite this, and despite even understanding the eventual consequences of his actions, no individual herder has an incentive to reduce the size of his herd—in fact just the opposite—and eventually the commons is ruined. Hardin’s metaphor was intended to illustrate an entire class of social problems that share the key features contained in this story. Most commonly, it is used today to describe the dilemma faced when

individuals have unregulated access to a finite resource and is typically associated with environmental problems. The general argument is that open access to a valuable resource will lead to the resource becoming overexploited because the benefits of using it accrue to the individual, whereas others besides the user suffer the associated costs. In economic terms, the problem is one where the private marginal cost of exploiting the resource is below the social marginal cost of usage—using the resource creates a negative externality, meaning it imposes costs on others not using the resource. Avoiding the destruction of the commons depends on how successfully the community, or more generally society, is able to curtail individuals’ use or consumption of it. Hardin’s commonsense, concrete illustration of the CP is one that for centuries has engaged philosophers, economists, and observers of public life. Aristotle made what may be the first recorded observation of it: “What is common to many is taken least care of for all men have greater regard for what is their own than for what they possess in common with others” (Mankiw, 2009, p. 233). In an early-19th-century essay, William Foster Lloyd drew attention to the specific dilemma faced by villagers owning a common pasture and the tendency for them to overgraze it. In 1968, Hardin retold Lloyd’s tale while also reducing it to its essential features and then generalizing it to an entire class of problems. Describing Hardin’s CP and evaluating solutions to it has now become standard fare in introductory economics courses, where the CP is described as one occurring when goods or services are both nonexcludable (there is open access to them) and rival (using the good or service reduces the benefit others derive from it).

Applications The tragedy of the commons as a metaphor is now widely recognized as applicable to a range of problems that are a part of our social life. Some of these are more obvious analogies to the grazing example. For instance, some species of animals can be considered common resources. Fish, whales, clams, oysters, deer and other game, rhinoceros, and elephants are all but a few of the animals that have economic value for which there is open access. Yet although people have an incentive to kill these animals, doing so reduces their availability for everyone else. The result is often the excessive depletion of animal stocks that can lead to the collapse of their

Tragedy of the Commons

population. A similar story can be told about forest resources such as firewood, where harvesting on public lands may occur at a rate that exceeds the ability of the forest to replenish its wood stock. Water, too, can suffer from the CP, for instance, when it exists in an aquifer or a river from which all can draw—although in the case of a river, the cost of overexploiting it rises as one moves downstream, thus introducing a geographical (and often international) dimension to the problem, as well as an uneven distribution of the costs and benefits associated with the CP. Another illustration of the CP is roadway congestion. Here, one can think of roads as a commons to which all have unfettered access. But if the roads are congested, then using them inflicts costs on others by slowing down travel on them. Despite this, individuals have strong incentives to further congest roads, which without some coordinated response can (and does) lead to a massive overuse of roads. The commons can also refer to the atmosphere, which is an open resource because it provides us with valuable services—it screens harmful ultraviolet light, it provides us with fresh oxygen to breathe, and it moderates heat, thereby making the earth’s temperature one capable of sustaining human life. People use these services when they burn fossil fuels (thereby emitting carbon dioxide into the atmosphere), emit pollutants such as sulfur dioxide into the air, or release chlorofluorocarbons. While the cost that accrues to the individual engaged in these activities is lower than the benefit they themselves derive from it, the social costs—which includes the cost to everyone affected by climate change, acid rain, or ozone depletion—are often higher. In education, one may consider the example where peer effects are important and schools lacked the ability to exclude students as potentially creating a tragedy of the commons. In this example, schools that enjoyed strong peer effects might find themselves overrun with enrollments, which might dilute or degrade the school’s initial strengths arising from its strong peer effects. Theoretically, then, the gain to a new student from enrolling in a school with stronger peer effects may be overshadowed by the effect of that student weakening the school’s overall peer effect. This hypothetical example can and is used to justify educational practices that separate students by ability level, either within or between schools, although the empirical evidence for it is mixed. The CP may also occur as a result of government action, such as the practice of creating

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highways to which people have free access. Hardin himself has described a particular financial crisis in the United States as the result of a “commons” created by the government. In his telling, the Federal Savings and Loan Insurance Corporation created a common pool of money for financial institutions to draw on should they run into financial difficulty. Institutions subsequently undertook riskier investments and eventually wound up drawing deeply on this common pool of resources that had been created for them at the expense of taxpayers. This example, however, is best characterized as a social problem that shares some features of the CP, but not all, and broadens the scope of his original characterization of the CP in a way that makes it less helpful to analyze. In economics, the CP is typically viewed as a particular problem within a wider class of social dilemmas giving rise to collective action problems.

Solutions Hardin argued that the simple existence of a common resource does not necessarily mean there will be a CP. Rather, whether a CP exists will depend on the number of people who want to use the resource, the rate at which they do so, and the ability of the resource to replenish itself. When it comes to fish in the ocean, for instance, the rate at which humans extracted fish for most of human history was matched by the rate at which fish multiplied. Only recently have the number of fishers and the ability of each to extract fish grown so much that today many fish populations around the world are plummeting. The Nobel Prize–winning political economist Elinor Ostrom has spearheaded efforts to argue that CPs are not as prevalent nor as difficult to solve as Hardin and others contend. In many instances, resource users themselves can cooperate to conserve the resources for their mutual benefit. Led by Ostrom, many researchers have been theorizing and empirically investing when self-organizing and governing communities are likely to solve the CPs they face. Many, including Hardin, have also pointed out that smaller communities can solve the CP through informal mechanisms or norms; Hardin, in fact, conjectured that below a population size of 150, communities can use their ability to shame one another to shape members’ actions. Many other social scientists have investigated the conditions under which individuals are more likely to engage

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in prosocial, as opposed to strictly selfish, behaviors, which would help solve the CP. Those CPs, too, with defined boundaries—where members frequently interact with one another, where easy rules of thumbs can be followed, where it is easy to monitor the actions of community members as well as those of the monitors, and where disputes among community members over the commons can be resolved at low cost—are all associated with a greater ability to solve CPs in their community. Yet in most cases, social scientists conclude that one should not rely on individual morality or norms as a means of policing the commons because this solution rewards the selfish and punishes the unselfish. In most cases, communities and nations need a collective mechanism to solve their CPs. If one considers the example of overgrazing, a range of potential solutions become apparent. The community could regulate the number of sheep that herders could own, it could limit the number of sheep per herder permitted on the commons, it could charge herders for access to the commons, it could divide the land so that each farmer had his own private pasture, or it could sell the pasture to one member of the community and let that member allow others access in exchange for a payment. The enclosure movement in England in the 1600s, when much of England’s common grazing land was privatized, is frequently described as an example of this last solution to a CP—an illustration of the fact that in practice, solutions to CPs are heavily influenced by power relations within that society. Extrapolating from the specific example above, solutions to CPs typically fall into the category of governmental regulations, where governments can limit access to a good via regulation (e.g., establishing hunting seasons), taxes (e.g., requiring fishermen to purchase fishing permits), or by numbers (e.g., limiting the clams one can take from the beach or the amount of pollution an industry can emit into the air). Alternatively, governments can find ways to charge for using the commons, such as the increasingly common practice of charging those who use roads, release pollutants into the air (emissions charges), or emit carbon (carbon taxes). Finally, governments (or private citizens) can seek to privatize a commons. Fish farming can be seen as a limited solution to the problem of overfishing, managed game reserves as a solution to the loss of wildlife, and even rhinoceros farms as a solution to the problem of a declining rhino population.

Which solution is best, or even will work, depends on specific features of the CP. When it comes to government regulation, the effectiveness will depend primarily on a government’s ability to enforce its regulations. Governments may lack the resources to track down and punish offenders, or doing so may be nearly impossible, as in the case of villagers cutting down trees for firewood or burning land to farm it. The same problem confronts governments seeking to charge or tax citizens for their use of common resources. In many instances, economic prosperity can help solve a nation’s various CPs: With growth, renewable natural resources tend to become less economically valuable, citizens demand better management of their country’s natural resources, and technological solutions to the CP—such as electronic monitoring of road usage or smokestack emissions—become more affordable and feasible. With technological change, privatization as a solution to CPs has new potential, as is the case now with the ability to charge users for their road usage. However, the most vexing CPs are the ones for which privatization has little salience. In the case of overfishing and climate change (a result of the overdumping of carbon dioxide into the atmosphere), government regulation of some sort is the only feasible solution, although in these cases, the global nature of the problem raises difficult political problems. Increasingly, economists and policy analysts argue for tradable permits in pollution or carbon emissions as a way to simulate the results that privatizing the commons would achieve. Katherine Baird See also Behavioral Economics; Education and Civic Engagement; Opportunity Costs; Principal-Agent Problem; Public Choice Economics; Public Good

Further Readings Dunber, S., & Levitt, S. (2008, April 20). Not-so-free-ride. The New York Times. Retrieved from http://www .nytimes.com/2008/04/20/magazine/20wwlnfreakonomics-t.html?pagewanted&=all&_r&=0 Hardin, G. (1968). The tragedy of the commons. Science, 162(3859), 1243–1248. Hardin, G. (2008). The tragedy of the commons. In D. Henderson (Ed.), The concise encyclopedia of economics (2nd ed.). Indianapolis, IN: Library of Economics and Liberty.

Transaction Cost Economics Mankiw, N. (2009). Principles of economics. Mason, OH: South-Western Cengage Learning. Ostrom, E., Burger, J., Field, B., Norgaard, R., & Policansky, D. (1999). Revisiting the commons: Local lessons, global challenges. Science, 284, 278–282.

TRANSACTION COST ECONOMICS Transaction costs can be defined as the costs of specifying, reaching agreement on, and enforcing contracts. The purpose of contracts, in turn, is to facilitate exchange (the vehicle for generating gains from trade between two or more parties). In contrast to the traditional (neoclassical) economics treatment of transactions, typically between anonymous buyers and sellers, in which no costs of transactions are recognized and all transactions somehow take place at a market-clearing price, transaction cost economics emphasizes that in reality these costs are large and indeed can lead to market failure. Indeed, at the macroeconomic level, transaction costs can include the costs of the many political and economic organizations that provide the stable social environments within which transactions take place. This entry explains how transaction costs might be measured and why they may be so important, especially in the analysis of education and the efficiency of any type of educational institution. The importance of the specialized approach to economic analysis known as transaction costs has been recognized three times through the award of the Nobel Memorial Prize in Economic Sciences: once in 1993 to Douglass North, another in 2009 to Elinor Ostrom and Oliver Williamson, and still another in 2010 to Peter Diamond, Dale Mortensen, and Christopher Pissarides for their research on the application of transaction costs to job searches. The research for which the Nobel Prize was awarded did not deal with education, but transaction cost economics can be applied to multiple areas of education research.

Measurement How large are these transaction costs relative to the size of the economy as a whole? Identifying those sectors of the economy that they thought supplied most of the ingredients of transactions, in the early 1980s North and John Wallis used input-output tables of the U.S. economy to show that transaction costs could well constitute more

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than 40% of the total GDP (gross domestic product) of the United States. They also showed that the importance of transaction costs in GDP had been growing over time. More recent input-output tables, moreover, show that this trend has very much continued. Transaction costs would seem especially prominent in the education sector of almost any economy. For example, the 1992 Benchmark Input Output table of the United States showed that more than 50% of the output of the education and social services sector was purchased from other sectors of the economy (especially real estate services, other business and professional services, maintenance and repair services, printing and publishing, and communications) and that well over 90% of the sector’s value added derived from the salaries of teachers and administrators. All such transactions involve either formal or informal contracts and therefore entail transaction costs in specifying and enforcing these contracts. The measurement issues making for high transaction costs in education derive from the multiple valued attributes that are characteristic of both the input and the output sides of education. On the input side, for every teacher, there are many different dimensions that contribute to effective learning (e.g., time, enthusiasm for the subject, pitching the material to the level and interests of the students, ability to cater to variations in student quality, accuracy, fairness, and promptness of evaluation). Each one of these dimensions is hard to measure, and weighing them so as to come up to an overall evaluation is likely to be subjective and controversial, thus making it extremely difficult ex ante to arrive at comprehensive evaluations of the services offered by individual teachers. Behind the teacher, in turn, is teacher training and the need to evaluate the cost-effectiveness of different training systems. Moreover, the effectiveness of the individual teacher is just one of many contributors to student learning. Likewise, on the output side, the effectiveness of learning also depends on the textbooks and other materials utilized, the curriculum offered by the school, and the quality of the school’s environment for learning. The output of schooling, moreover, that individuals and society as a whole benefit from includes not just the specific skills learned, such as reading and writing, foreign languages, mathematics, science, music, and art, but also citizenship and social norms that contribute to the stable institutional environments in which

800

Transaction Cost Economics

cooperation can flourish and transaction costs are minimized.

Transaction Costs in the Provision of Education As the economy grows and globalization proceeds, the division of labor and specializations grow more than in proportion to the numbers of students, contributing to the increasing breadth of the curricula at any level of education and the need for coordination among these different levels and specialized areas of education. The major contributions of Oliver Williamson to transaction cost economics were (a) to explain which transactions take place within firms (or public agencies) and which take place between different firms or agencies, or outside them but between individuals, and (b) to explain the distinct organizational structures that arise within the large firms and corporations (or public agencies) that have come to dominate in most economies. Because of the many different specializations required for teaching all the subjects taught in most schools, and because of economies of scale and scope in organizing production, most teaching is accomplished not individually (between one student and one teacher) but rather in large units. With schools and universities of varying size and varying breadth, clearly transaction cost economics is just as applicable for distinguishing what is done within schools and universities from what is done between them and the rest of the economy, and then explaining the structure whereby their various departments and divisions are organized. There can be advantages in managing classroom performance, textbooks and materials, and classroom maintenance, as well as teacher-student and teacher-teacher relations at a decentralized level, where the most appropriate incentives for each teacher or other contributor to the educational process can be put into effect, performance can be most easily monitored, and any necessary penalties can be imposed. Nevertheless, due to economies of scale and scope in management, contract enforcement, dispute resolution, and so on, the benefits of centralized and vertically integrated systems arising from the lower transaction costs of arriving at and, especially, enforcing these contracts (without having to go to lawyers and courts to resolve disputes) can far outweigh the aforementioned disadvantages. In any case, the desire to lower the transaction costs of educational production and management

leads to the search for optimal regulations and structures for different types of providers, for example, at the higher education level for religious universities, secular nonprofit private universities, public universities, and for-profit private universities. Needless to say, the means of minimizing these transaction costs and maximizing the cost-effectiveness of education may vary among these rather different types of universities. Enforcement of contracts within education providers and between these providers and their customers (both private and public) requires ex post evaluation of not only the quantity and quality of each teacher’s effort but also the effectiveness of the school and its curriculum in delivering the multidimensional characteristics desired by individual students, their families, and the community or society as a whole. The transaction costs in carrying out such evaluations are very high and often require multiple means. For example, in evaluating teacher performance, one may make use of student test scores and ratings by students, departments, administrators, and perhaps even parents. Each such approach can be subject to bias arising from opportunities to practice opportunism by one or more of the parties involved. For example, in some contexts, administrators may be bribed by parents to have their children admitted to good schools, and teachers may be convinced to give higher grades to certain students. When teachers are evaluated by the test score performance of their students, teachers may be induced to teach to the tests instead of addressing other important educational objectives. Schools, in turn, may prepare their curricula and other documentation to satisfy the preferences of members of the teams evaluating them. Evaluations at all these levels are complicated by the fact that the effects of learning can only be fully realized over the entire lifetimes of individual learners and some of the benefits are external to the learners. Enforcement, of course, goes well beyond evaluation. It involves the disciplining of teachers and administrators who are deemed not to be of sufficient quality or not to be putting in sufficient effort. This can be done by firing workers, but in some situations, the costs of firing teachers (involving lawyers, court costs, time involved in hearings, etc.) can be prohibitively high, so that the more realistic alternatives might be denial of promotions or salary increases or, especially within large institutions, assigning unfavorable teaching schedules or locations.

Tuition and Fees, Higher Education

Given the complexity of the educational production function and the costs of monitoring and enforcing contracts, it is not surprising that transaction cost economics lie behind some of the important issues that researchers have examined in studies exploring how to raise the quality of education. These include studies on boosting the role of parents, improving school budgeting practices, and developing better means of contracting for the services of teachers and those of textbook and computerized learning suppliers. Jeffrey B. Nugent See also Accountability, Standards-Based; Centralization Versus Decentralization; Contracting for Services; Deregulation; Economic Efficiency

Further Readings Das, J., Dercon, S., Habyarimana, J., Krishnan, P., Muralidharan, K., & Sunararaman, V. (2013). School inputs, household substitution, and test scores. American Economic Journal: Applied Economics, 5(2), 29–57. Fan, X., & Chen, M. (2001). Parental involvement and students’ academic achievement: A meta-analysis. Educational Psychology Review, 13(1), 1–22. Kremer, M., Chaudhury, N., Rogers, F. H., Muralidharan, K., & Hammer, J. (2005). Teacher absence in India: A snapshot. Journal of the European Economic Association, 3, 658–667. Levin, H. M., & Belfield, C. R. (2001). Families as contractual partners in education. UCLA Law Review, 49(Pt. 6), 1799–1824. Vegas, E., & De Laat, J. (2003). Do differences in teacher contracts affect student performance? Evidence from Togo. Washington, DC: World Bank. Retrieved from http://www-wds.worldbank.org/servlet/WDSContent Server/WDSP/IB/2003/10/24/000160016_20031024103 517/additional/310436360_20050276095003.pdf

TUITION AND FEES, HIGHER EDUCATION The terms tuition, college costs, and expenditures per student are often used interchangeably, but this is not accurate. This entry begins with more precise definitions for these terms as well as of two other often confused terms: net tuition and tuition discount rates. With the definitions established, the entry discusses why tuition levels in both public

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and private higher education have risen at rates far above the rate of inflation in the United States over the past 30 years, why net tuition levels have increased at slower rates and, in some sectors, have fallen for some students, and how various categories of expenditures per student have changed in recent years. The entry concludes with a discussion about the changes that are occurring that may dramatically influence tuition levels and expenditures per student in the future.

Definitions Tuition, or more precisely tuition and fees, are the posted prices that academic institutions charge students to attend their institutions. College costs include tuition and fees, as well as the average cost charged to students for dining, housing, books, and other incidental expenses. College costs differ depending on whether students commute to college or live away from home, and the distance of the college from their homes. Both tuition and college cost figures are often quoted for full-time students; this ignores the fact that many American students attend college part time. Omitted from published measures of college costs are the opportunity costs (primarily foregone earnings) that college students incur while attending college. The level of tuition charged to undergraduate students at public higher institutions is typically much lower for state residents than it is for out-ofstate students. How state residency is defined differs across states; for example, some states allow any student who has been enrolled for a year to be classified as a resident student, while most are much stricter in defining residency. In most states, the difference between in-state and out-of-state tuition has grown in recent years, as public institutions try to offset reductions in state support while trying to limit the tuition increases faced by resident students. Also, in response to reductions in state support, public universities, especially flagship public research universities, are increasingly charging differential tuition to students based on the field they are studying and/or their year (lower or upper division) in the program. Students often pay less than the posted level of tuition and fees because of the availability of grant aid provided by federal and state governments, the institution itself, and other private and public sources. Students and their families may also receive federal tax credits and tax deductions, which further

802

Tuition and Fees, Higher Education

either public funds or private donations rather than borrowing. Finally, the tuition discount rate is the share of each dollar of undergraduate tuition revenue that an institution takes in that is returned to undergraduate students in the form of institutional grant aid. At the very wealthiest private academic institutions, most of the grant aid is funded out of endowment income. However, for the vast majority of private nonprofit academic institutions, grant aid is mostly funded out of unrestricted sources of revenue in the operating budget of the institutions, much of which is from tuition dollars “recycled” to some students in the form of aid. Public higher education institutions typically have fewer resources to provide institutional grant aid, and again except for the wealthiest, these resources come from recycled tuition dollars. Some states mandate that a share of tuition revenue be used for grant aid, but others explicitly limit or prohibit public institutions from doing so. Historical data on how tuition discount rates have varied over time are available only for private academic institutions.

reduces the cost of attendance. The net tuition that a student pays to attend an institution is the posted level of tuition less all of the grants, the tax credits, and the value of the tax deductions received by the student and his or her family. The net tuition levels faced by different students at an institution will vary because the federal grants and tax credits a student is eligible to receive vary with family income; because grant aid programs offered by states are based, depending on the state, on family income and on merit (as measured by test scores or grade point averages); and because an institution’s provision of grant aid from its own revenues may be based on a student’s financial need, merit, athletic ability, and other factors. An academic institution’s expenditure per student may substantially exceed the tuition it charges its students because of subsidies that it is able to provide to students from income generated from its endowment; from giving by individuals, corporations, foundations, and others that is used to support the current operations of the institution; as well as from the subsidies it receives from state governments. While the latter accrue primarily at state-supported institutions that receive direct appropriations from state governments, in some states, private institutions also receive support from the state government. As Gordon Winston has emphasized, what does not show up in measures of current expenditures per student is the implicit value of the services provided to students by the buildings in which they are educated that were financed with

Table 1

Why Tuition Keeps Rising Table 1 presents data on the average values of published undergraduate tuition and fees by institution type in the United States in 2012–2013, along with the percent change in these values from the previous year. The average tuition levels at public 2-year and 4-year institutions for in-state students were

Average Published Tuition and Fees in 2012–2013

Institution Type

2012–2013 ($)

% Change From 2011–2012

Public 2-year in-state

3,131

5.8

Public 4-year in-state

8,655

4.8

Public 4-year out-of-state

21,706

4.2

Private nonprofit 4-year

29,056

4.2

For-profit

15,172

3.0

Public doctoral in-state

9,539

4.5

Public master’s in-state

7,606

5.5

Public bachelor’s in-state

6,718

4.4

Private doctoral

35,660

4.2

Private master’s

25,997

4.4

Private bachelor’s

27,482

4.0

Source: Data taken from College Board (2012, table 1A, p. 10).

Tuition and Fees, Higher Education

inflation average tuition levels have increased over the recent 30-year period. On average (the average of the three-decade percent increases), over the 30 years, tuition levels increased annually by 3.3% more than the rate of inflation at private nonprofit 4-year colleges, by 4.3% more at public 4-year colleges, and by 3.5% more at public 2-year colleges. Put another way, during the 30-year period, the cumulative increases in tuition after adjusting for inflation were 267%, 357%, and 282%, respectively, in the three sectors. The forces that have caused tuition levels to rise by so much more than the rate of inflation over the past 30 years have been widely discussed. In the private nonprofit academic sector, it is largely because like other nonprofit organizations, private higher education institutions have but one objective—to be as good as they can in every dimension of their activities. They want to have the best instructional and research facilities, to attract the best students and faculty, and to provide the best quality education and support services that they can. All of these things take resources, and for all but the wealthiest private institutions, undergraduate tuition revenue is the major source of revenue that supports undergraduate education. During most of the period, increasing tuition was an easy way to generate more revenue because in a world in which students and their families believe that college quality is associated with tuition levels, institutions only had limited concern that increasing tuition levels would reduce their applications and enrollments. Furthermore, an institution could always use a share of the additional tuition revenue that its tuition increases generate to provide grant aid to students who otherwise would not be able to afford to attend the institution or to students whom they especially wanted to attract (e.g., top-scoring students). If needed, they could also use grant aid to

$3,131 and $8,655, respectively, that year. The average tuition level for out-of-state students at public 4-year institutions was $21,706, which was somewhat lower than the comparable average tuition of $29,056 charged at private nonprofit 4-year institutions. The average tuition at private for-profit institutions was $15,172. These averages mask the considerable variation in tuition levels that occurs in each type of institution. For example, in 2012–2013, while 13.6% of students attended private nonprofit 4-year institutions that charged tuition levels of less than $15,000, another 15.8% were at institutions charging tuition levels of $42,000 or more (College Board, 2012, figure 2, p. 12). Data are also presented in this table on how, within public and private higher education, average undergraduate tuition varies with the highest degrees offered by the institution. In public higher education, average undergraduate tuitions are higher at institutions with doctoral programs than they are at institutions whose highest degrees offered are master’s degrees, which in turn are higher than the average undergraduate tuitions charged at institutions that offer primarily undergraduate degree programs. In private higher education, undergraduate tuition is again the highest at the doctoral institutions, reflecting both the higher average salary levels that faculty are paid at doctoral institutions as well as the larger fraction of students at these institutions studying in expensive science- and technology-related fields. Average tuition levels at the different types of institutions increased between 2011–2012 and 2012–2013 by 3% to 5.8%. These figures were considerably higher than the rate of increase of inflation, as measured by the consumer price index of 2.1% during the calendar year 2012. In fact, average tuition levels have increased by substantially more than the rate of inflation for many years. Table 2 presents data on how much more than the rate of

Table 2

803

Annual Percent Changes in Inflation-Adjusted Average Tuition and Fees 1982–1983 to 2012–2013 1982–1983 to 1992–1993 (%)

1992–1993 to 2002–2003 (%)

2002–2003 to 2012–2013 (%)

Cumulative Percent Change

Private nonprofit 4-year

4.6

3.0

2.4

267

Public 4-year

4.6

3.2

5.2

357

Public 2-year

5.1

1.6

3.9

282

Type of Institution

Source: Data taken from College Board (2012, figures 4 and 5, p. 14).

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Tuition and Fees, Higher Education

reduce their net tuition levels enough to attract more students. At the most selective private nonprofit institutions, which provide grant aid to undergraduate students based only on financial need, during the 30-year period the numbers of students seeking admission kept getting larger and larger, as students and their families intuitively understood that where students enrolled in college mattered almost as much as whether they attended college. With few exceptions, most academic studies suggest that their intuition was correct; other factors held constant, students who attend selective private academic institutions both receive higher postcollege earnings and have better chances of attending high-quality graduate institutions than their otherwise similar counterparts who attend less selective institutions. With the long lines of students seeking admission to them, the selective private academic institutions faced very little market pressure to restrict their tuition increases. Their tuition increases provided a cover for the less selective private academic institutions to raise their tuitions. If these institutions faced problems filling all of their slots or enrolling the caliber of students that they desired, they could give back a portion of their tuition increases in the form of grant aid to try to achieve their desired enrollment levels and student body composition. Pressure to increase per-student expenditures, which put further pressure on tuition, also came from the U.S. News & World Report annual ranking of colleges and universities, by now the gold standard for undergraduate program rankings. Research conducted by Ronald Ehrenberg and James Monks suggests that when an institution improves in the U.S. News & World Report rankings, other factors held constant, it sees its number of applicants increase, it can admit a smaller fraction of its applicants, a greater fraction of its admitted applicants enroll at the institution, its entering students’ test scores increase, and it can attract its class of students with fewer grant aid dollars. In contrast, when it falls in the rankings, the converse happens. A major component in the U.S. News & World Report ranking is the institution’s per-student expenditures; other factors held constant, if an institution’s expenditures fell relative to its competitors, it would cause the institution to fall in the rankings. Hence, this puts pressure on an institution to increase its expenditures and thus its tuition level.

Historically, especially at selective liberal arts colleges, the belief has been that the essence of a highquality education is small class sizes and substantial interaction between faculty and undergraduate students. This makes it difficult for these colleges to achieve any productivity gains in educating students and to reduce their educational costs. Finally, the growth of technology can lead to improvements in the quality of higher education. Many students are educated, for example, in tiered classrooms with Internet connections and videoconferencing capabilities that allow speakers from remote locations. Class-required readings are available to students electronically. All this technology comes at a cost, including the cost of the highly skilled workforce that has replaced the lower skilled clerical employees. While technology offers the promise of holding down academic institutions’ costs in the future, to date it has been a driver of increased costs and tuition levels. All of these factors influence tuition increases in public higher education as well, although tuition setting is more complicated in public higher education because in some states governors and legislatures have more direct control over tuition levels. However, another important factor affecting tuition levels in public higher education is the level of state support that institutions receive. In private higher education, tuition increases are always accompanied by increases in per-student expenditures in real terms. In contrast, in public higher education, tuition increases often are associated with reductions in per-student expenditures in real terms. In fiscal year 1987, state educational appropriations per full-time equivalent (FTE) student averaged $8,497 (as measured in 2012 dollars). By fiscal year 2012, after the Great Recession, the comparable figure was $5,906 (SHEE0, 2013, figure 3). Although in many years during the period total state appropriations for their public academic institutions increased, these increases on balance were not sufficient to keep up with the declining appropriations in other years and with their public institutions’ growing enrollments. Over the 25-year period, real state appropriations per FTE student fell by an average of about 1.45% a year. Put simply, a major driver of tuition increases in public higher education has been the inability of state governments to maintain the historic levels of support for their public academic institutions and the efforts by public higher education institutions to offset these cuts to maintain their per-student expenditures.

Tuition and Fees, Higher Education

What About Net Tuition Changes? Changes in the average tuition levels faced by students overstate the average net tuition cost that students face because of increases in federal, state, and institutional grant aid as well as the availability of federal education tax credits and income tax deductions for educational expenses. Table 3 presents data from the College Board that contrast changes in published inflation-adjusted average tuition and fees and average net tuition and fees over the 1992–1993 to 2012–2013 period for public 2-year and 4-year colleges and for private nonprofit 4-year colleges. While average public 2-year college tuition levels increased by 72% over the period, the average net tuition that students paid at 2-year colleges actually declined during the period, largely due to substantial increases in the generosity of the Federal Pell Grant Program, which provides grant aid to students from lower income families, and the increasing share of students at the 2-year colleges coming from lower income families. Average net tuition did increase for students attending public and private nonprofit 4-year colleges during the period. But in each case, the percentage of increase in the average net tuition was less than half the increase in the average published tuition levels. The increase in tuition rates dramatically overstates the growth in net tuition costs to students. Average net tuition increases are only averages. Federal grant aid programs are based on students’ financial need. Eligibility for state grant aid programs varies across states; some states have

Table 3

805

need-based programs, some have merit-based programs, and some have both. Some institutional grant aid programs are purely need based, but the majority of private institutions’ aid programs have need- and merit-based components. The net tuition level that any individual student faces at an institution varies widely across individuals. In an effort to provide better information to potential students, the federal government now requires that academic institutions have “net price calculators” on their web pages so that potential students can learn what the likely true cost of their attendance will be. The usefulness of these calculators depends on whether the grant aid decisions that an institution makes can be accurately summarized when an individual student provides the requested set of data (family income, family size, test scores, grade point averages, and any other information that might influence grant aid amounts) to the institution.

Tuition Discount Rates At a small number of wealthy private colleges and universities, institutional grant aid to undergraduate students is funded largely from endowment resources. However, the vast majority of private colleges and universities fund grant aid almost totally out of their general operating budgets, and the major source of unrestricted revenues for these institutions is undergraduate tuition revenue. The National Association of College and University Business Officers (NACUBO) reports that in the fall of 1990, the average tuition discount rate for full-time

Changes in Inflation-Adjusted Published Average Tuition and Fees and Average Net Tuition and Fees, 1992–1993 to 2012–2013

Type of Institution

Average Net Tuitiona

Average Tuition 1992–1993 ($)

2012–2013 ($)

% Change

1992–1993 ($)

2012–2013 ($)

% Change

Public 2-year

1,820

3,130

72

490

−1,220

Negative

Public 4-year

3,810

8,660

127

1,920

2,912

52

17,040

29,060

71

10,010

13,380

34

Private nonprofit 4-year

Source: Data taken from College Board (2012, figure 9, p. 19; figure 10, p. 20); the numbers in this table were rounded to the nearest $10 in the publication. a. Average net tuition refers to tuition, less the average grant aid and federal education tax credits and deductions for all students.

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Tuition and Fees, Higher Education

first-year students at private colleges and universities was 26.4%; by the fall of 2012, this figure is estimated to have risen to 45%. Increasing tuition discount rates puts pressure on the operating budgets of private colleges and universities and contributes to the pressure to raise the posted tuition levels. Why has this dramatic increase in tuition discount rates occurred? Since around 1980, the average tuition levels at private colleges and universities have risen dramatically not only relative to inflation but also relative to family income levels. For example, at Cornell University, tuition was roughly 28% of median family income in 1980; by 2011, it had risen to more than 65% of median family income. If an institution awards grant aid based only on a student’s financial need, when tuition increases more than the increase in students’ family incomes, students already receiving grant aid will see their aid packages increase and more students will become eligible for grant aid. As a result, the share of students receiving grant aid will increase. So even if the institution’s grant aid policy parameters do not change over time, the institution’s tuition discount rate will increase. A host of other factors have added to the increase in tuition discount rates. First, from 1990 to 2008, average endowment returns were higher than expected. Most institutions aim to spend a specified percentage, often 4% to 5% of the average of their endowment value, over a specified period of time, often 12 quarters. With the high rates of endowment growth, spending as a percentage of the current value of the endowment often was much lower than the specified percentage of the average value over a number of periods, and the low current spending rates put pressure on the institutions to spend more. So the wealthiest institutions enhanced their grant aid programs, while institutions with smaller endowments tried to compete to the best of their ability. Second, data on the number of students coming from lower income families, as measured by Pell grant recipients, began to become publicly available, and it became evident that despite the need-blind admissions and need-based grant aid programs of the wealthy selective private colleges and universities, they had very few Pell grant recipients attending their institutions. This put pressure on the institutions to enhance their efforts to attract more lower income students, which added to their financial aid costs. This pressure was increased when the U.S. Senate Finance Committee began an investigation

of the endowment spending rules of wealthy institutions in 2007–2008 because of the belief that they were not spending enough from their endowments and that increased spending could be used to moderate tuition increases and/or to enhance grant aid programs. Facing an implicit threat of regulations governing how much they should spend from their endowments, the wealthiest institutions responded by dramatically enhancing their grant aid programs, with several of them eliminating all loans from their aid programs, and the other institutions again followed to the best of their ability. Almost immediately, and unexpectedly, the Great Recession began, and family assets and income levels were reduced, which put further pressure on grant aid budgets. Other, less selective private 4-year academic institutions also faced increasing competition from lower priced public institutions. Although public in-state tuition levels were increasing at higher rates than private tuition levels (as seen in Table 2), because the public tuition levels were initially so much lower, the difference between the average posted tuition levels in public and private 4-year institutions actually was increasing. For example, Table 3 shows that the difference between average private 4-year and average public 4-year tuition levels in 1992–1993 was $13,320 (measured in 2012 dollars); by 2012–2013, this difference had grown to $20,400. This put pressure on the less selective private academic institutions to increase their grant aid and offer larger tuition discounts, both to fill all their slots and to best achieve their desired class compositions in terms of student selectivity and other characteristics. Put simply, market forces have resulted in a very difficult financial situation for these institutions. Indeed NACUBO reported that in 2011–2012, the average change in net tuition revenue received by private academic institutions was actually negative, due to enrollment declines and increased tuition discount rates.

Expenditures per Student Table 4 presents data tabulated and cleaned by the Delta Cost Project at the American Institutes for Research on expenditures per FTE student in 2010–2011 by institutional type and for a number of categories of expenditures. In the table, doctoral institutions are those that offer a wide range of 4-year undergraduate and graduate degrees, including doctoral degrees. Master’s institutions offer a wide range of 4-year undergraduate and master’s level graduate degrees, bachelor’s institutions offer primarily

Tuition and Fees, Higher Education

the higher salaries paid to faculty at these institutions and the larger number of advanced classes with relatively small student sizes. Instructional expenditures per FTE student are lowest at the public 2-year institutions, reflecting the lower faculty salaries paid there and the heavy reliance on the use of nontenure-track part-time faculty. Table 5 displays the cumulative percent changes in real terms in the various categories of expenditures

4-year undergraduate degrees, and 2-year institutions are those that for the most part offer 2-year degree and certificate programs. Total educational and general expenditures per FTE student were highest at the private doctoral institutions and lowest at the public 2-year institutions. Instructional expenditures, which are for faculty salaries and benefits and for other activities directly related to instruction, are highest at the private doctoral institutions, reflecting

Table 4

807

Expenditures per Full-Time Equivalent Student: 2010–2011 Private Doctoral ($)

Instruction

Private Master’s ($)

Private Bachelor’s ($)

Public Doctoral ($)

Public Master’s ($)

Public 2-Year ($)

20,032

7,232

8,423

10,189

6,355

4,805

Student services

3,482

2,820

3,919

1,395

1,442

1,184

Academic support

5,663

1,738

2,099

2,943

1,541

919

Institutional support

6,857

3,862

5,024

2,508

2,048

1,684

Operations/ maintenance

4,020

1,421

2,095

1,804

1,372

1,042

50,919

22,121

27,031

28,325

15,036

11,373

TEGa

Source: Data taken from Hulburt and Kirshstein (2013, figure 1 and supplementary tables). a. TEG represents total education and general expenditures, which includes public service expenditures, research expenditures, and net scholarships and fellowships, which are not separately listed in this table. Research expenditures are separately budgeted and are largely funded by external grants and contracts. Excluded from these expenditure figures are expenditures on auxiliary enterprises such as hospitals and other operations, which are not included in TEG.

Table 5

Cumulative Percentage of Real Changes in Expenditures per Full-Time Equivalent Student Academic Year 2000–2010 Private Doctoral ($)

Private Master’s ($)

Private Bachelor’s ($)

Public Doctoral ($)

Public Master’s ($)

Public 2-Year ($)

Instruction

19.9

9.8

10.8

8.4

4.7

−10.7

Student services

34.1

24.5

27.1

16.9

14.3

−4.9

Academic support

29.1

12.3

13.9

12.1

2.5

−13.6

Institutional support

21.5

12.1

4.1

12.1

2.2

−8.2

Operations/ maintenance

35.7

5.5

9.2

−0.5

−2.2

−7.9

Source: Data taken from Hulburt and Kirshstein (2013, figure 1, p. 2).

808

Tuition and Fees, Higher Education

per FTE student over the 2000–2010 period. For each private higher education institution category, instructional expenditures increased at a slower rate than expenditures on student services and academic support, and for the doctoral institutions also, instructional expenditures increased at a slower rate than expenditures on institutional support. Why did the direct costs of instruction lose out in the competition for funds with other expenditure categories? Student service expenditures include the cost of admissions, registrar activities, and activities whose primary purpose is to contribute to students’ emotional and physical well-being and to their development outside the classroom. Examples include student organizations, tutoring support outside the classroom, and intramural athletics. Student health services and intercollegiate athletics are sometimes included in this category if they are not operated as self-supporting auxiliary enterprises. While some critics of higher education view these expenditures as “frills” that make no direct contribution to students’ persistence in college and graduation rates, Douglas Webber and Ronald Ehrenberg suggest that these expenditures positively influence persistence and graduation rates and that their effects are largest at institutions that enroll large shares of students coming from disadvantaged educational and economic backgrounds, based on their scores on entrance exams and whether they are receiving Pell grants. With efforts to expand enrollments and graduation rates for students from such backgrounds taking place, institutions’ efforts to increase investments in these areas may be prudent. Academic support expenditures include libraries, museums, and academic computing. The more rapid growth of expenditures in this category occurred in part because technology has often been adopted by academic institutions to enhance student learning and to provide students with tools that they will need to compete in the job market, even if these adoptions increase costs. Another factor that contributes to cost increases in this category is the increase in the cost of library books, journals, and other forms of media and the proliferation of electronic journals, which have added to library costs. Institutional support expenditures include legal, finance, audit, human resources, budget, alumni affairs and development, audit and risk management, and public relations costs. Some of these cost increases are due to a proliferation of government regulations, reporting requirements, and caps on recoveries of indirect costs on federal research

grants. Others are due to the fact that it takes spending money to make money (e.g., alumni affairs and development). Other increases are simply due to the fact that in flush times institutions did not pay much attention to their nonacademic cost structures. However, after the financial meltdown and recession that started in 2007, institutions have looked much more closely at their nonacademic costs and have begun to figure out ways to behave more efficiently. Indeed, the Delta Cost Project reports that between 2009 and 2010, institutional support expenditures per FTE student fell in real terms at each of the private and public categories of academic institutions. At public institutions, student service expenditures grew at more rapid rates (or, in the case of the 2-year colleges, fell at a slower rate) than instructional expenditures during the decade. The decline in each expenditure category at the public 2-year institutions reflects the financial problems faced by state and local governments due to the Great Recession and the limited ability of these institutions to raise tuition levels, because a large fraction of their students come from low-income families. As a result, their tuition levels increased by smaller percentages than did those of their public 4-year counterparts over the past two decades, as shown in Table 2. While private colleges and universities were able to expand funding for operations and maintenance expenditures during the decade, public institutions were unable to do so, and the biggest cutbacks occurred at the public 2-year institutions. It is always easier to postpone maintenance expenditures rather than cutting staff positions and/or raising tuition. But such postponements lead to maintenance backlogs and problems that may accumulate quite quickly.

Looking to the Future The financial models of both private and public higher education are breaking down, and the landscape faced by academic institutions is changing rapidly. A highly educated workforce is key to economic prosperity, and pressure is likely to grow on institutions to increase enrollments and increase first-year persistence and graduation rates. Stagnant real-income levels of families and growing debt burdens of students will likely enhance the pressures that both public and private institutions face to hold down tuition levels and/or to increase grant aid. Put in the starkest possible terms, increasingly institutions must take steps to reduce their educational and administrative costs.

Tuition and Fees, Higher Education

Academic institutions need to figure out ways to use technology to simultaneously reduce instructional costs and improve educational outcomes. The impact that massive open online courses will have is uncertain, but work by the National Center for Academic Transformation and by the Carnegie Mellon Open Learning Initiative suggests that many introductory classes can be redesigned to use technology to promote active learning, enhance course persistence, and reduce costs. Institutions also could share academic resources with competitors to reduce costs and expand educational outcomes. This could be done through online learning or, when two or more institutions are geographically close to each other, by allowing students from any one institution to take classes at all the others. Given the large numbers of students who begin their education at 2-year institutions, efforts should be made to improve the ability of students to transfer from 2-year institutions to 4-year institutions, and more generally to transfer credits across institutions. With the help of management consulting firms, large research universities have begun to substantially reduce their administrative cost structures through reducing the layers of administration and increasing the numbers of direct reports that each administrator supervises, by centralizing procurement and limiting purchasing to preferred vendors to achieve price discounts, and by moving more fully to electronic purchasing and to reorganizing the delivery of support services. Some public higher education systems, such as the State University of New York, share administrative resources systemwide and/or by pairing individual institutions. Private institutions are learning that cooperating with competitors to reduce administrative costs to achieve similar cost saving is a win-win situation. The growing for-profit higher education sector, where currently about 10% of all American college students have been educated, has been among the leaders in employing technology to reduce educational costs. It also has been the sector that has most quickly abandoned the traditional model of staffing classrooms with tenured and tenure-track faculty. The growing financial pressures faced by public and private nonprofit academic institutions will likely continue to lead to the use of more contingent (parttime and full-time non-tenure-track) faculty at these institutions as a way of slowing the growth of their costs. Ronald G. Ehrenberg

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See also Baumol’s Cost Disease; Benefits of Higher Education; College Rankings; Faculty in American Higher Education; Pell Grants; University Endowments

Further Readings Archibald, R. R., & Feldman, D. H. (2011). Why does college cost so much? New York, NY: Oxford University Press. Bowen, W. G. (2013). Higher education in the Digital Age. Princeton, NJ: Princeton University Press. Brewer, D. J., Eric, R. E., & Ehrenberg, R. G. (1999). Does it pay to attend an elite private college? Cross-cohort evidence on the effects of college type on earnings. Journal of Human Resources, 34(1), 104–123. College Board. (2012). Trends in college pricing 2012. New York, NY: Author. Retrieved from http://trends .collegeboard.org/sites/default/files/college-pricing-2012full-report_0.pdf Dale, S., & Krueger, A. B. (2002). Estimating the payoff to attending a more selective college: An application of selection on observables and unobservables. Quarterly Journal of Economics, 117(4), 1491–1527. Dale, S., & Krueger, A. B. (2011). Estimating the returns to college selectivity over the career using administrative earnings data (NBER Working Paper 17159). Cambridge, MA: National Bureau of Economic Research. Davis, N. P. (2013, June). Demand drives discount rates. Business Officer, 46(11). Retrieved from www.nacubo. org/Business_Officer_Magazine/Magazine_Archives/ June_2013/Demand_Drives_Discount_Rates .html Ehrenberg, R. G. (2002). Tuition rising: Why college costs so much. Cambridge, MA: Harvard University Press. Ehrenberg, R. G. (2006). The perfect storm and the privatization of public higher education. Change, 38(1), 46–53. Ehrenberg, R. G. (2010). The economics of tuition and fees in American higher education. In B. McGraw, P. Peterson, & E. Baker (Eds.), The international encyclopedia of education (Vol. 2, 3rd ed., pp. 2229–2234). Oxford, UK: Elsevier. Ehrenberg, R. G. (2012). American higher education in transition. Journal of Economic Perspectives, 26(1), 193–216. Ehrenberg, R. G. (2013). Is the Golden Age of the private research university over? Change, 45(3), 16–23. Hulburt, S., & Kirshstein, R. J. (2013). Spending: Where does the money go? Washington, DC: American Institute of Research. Retrieved from http://www .deltacostproject.org/sites/default/files/products/DeltaSpending-Trends-Production.pdf

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Kiley, K. (2012, November 5). Other people’s money. Inside Higher Ed. Retrieved from http://www.insidehighered .com/news/2012/11/05/use-public-tuition-financial-aidlikely-become-political-issue-many-states Monks, J., & Ehrenberg, R. G. (1999). U.S. News & World Report’s college rankings: Why do they matter. Change, 31(6), 42–51. State Higher Education Executive Officers. (2013). State higher education finance FY2012. Boulder, CO: Author. Retrieved from http://www.sheeo.org/sites/default/files/ publications/SHEF-FY12.pdf Webber, D. A., & Ehrenberg, R. G. (2010). Do expenditures other than instructional expenditures affect graduation and persistence rates in American higher education? Economics of Education Review, 29(6), 947–958. Winston, G. C. (1999). Subsidies, hierarchy and peers: The awkward economics of higher education. Journal of Economic Perspectives, 13(1), 13–36.

TUITION AND FEES, K-12 PRIVATE SCHOOLS The global trend, especially in developed countries, is toward the provision of free public K-12 education, with no, or minimal, fees charged to students or their families. By contrast, almost all private schools, regardless of mission or affiliation, depend on pupil tuition and fees, variously known also as admission or school fees, to provide a significant source of their operating funds. The nature and extent of private school tuition and fees vary considerably, as does the variety of private K-12 schools. Tuition and fees, generally, are the highest at schools offering specialemphasis programs, somewhat lower at independent schools, and usually lowest in sectarian private schools. However, the unique nature of each school ensures that this is, at best, a general observation; exceptions are commonplace. And while private school fees were once treated as a unitary expense, current practice commonly sees tuition and enumerated fees listed as separate and distinct charges in a manner similar to what is found in postsecondary education. This entry discusses the general background of the different types of private schools, the differentiation of tuition and fees, and the various efforts to reduce the burden of these charges on enrolling families. Understanding private school tuition and fees requires some awareness of the range of school types. The U.S. Department of Education’s National

Center for Education Statistics categorizes private schools into several broad typologies, which effectively mirror the patterns found in most other countries. In general, private schools are categorized as nonsectarian and sectarian. Within these categories, additional distinctions are commonly made. Nonsectarian schools mostly refer to what are termed independent schools, which are usually highly competitive college preparatory schools, as well as specialized-focus schools and special education private schools. Sectarian schools can be subclassified by denomination and also governance. The National Center for Education Statistics separates Catholic schools in the United States from other sectarian schools as they represent the single most significant denominational system of schools. These can be further subdivided into schools operated by dioceses, schools run by individual parishes, and independent schools operated with a Catholic focus. The prevalence of private education varies considerably geographically. In the United States, some 30,000 private schools educate more than 4.5 million students annually, or about 10% of the school-age population. These schools almost universally operate on a fee-for-service basis and, with the exception of a growing number of predominantly evangelical Christian schools, tend to be concentrated in metropolitan areas. Patterns in other countries vary because of differences in history and geography. For example, in Ireland a preexisting system of sectarian schools ensures that more than 90% of Irish schoolchildren attend private schools, mostly affiliated with the Catholic Church. The prevalence of private schools internationally often depends on the extent of K-12 schooling before the organization of modern systems of public schools. In some countries, like the United States and the United Kingdom, private schools may serve as privately funded adjuncts to extensive public school systems. By contrast, in countries that previously developed widespread private schooling, such schools may be integrated into, and funded as, part of the nation’s comprehensive system of K-12 education. This phenomenon is especially prominent in countries with a strong tradition of Catholic schools predating the establishment of public education.

Private School Tuition and Fees Tuition

Tuition is commonly defined as payment for the direct costs of educational instruction. Tuition

Tuition and Fees, K-12 Private Schools

typically covers expenses such as teacher salaries and benefits, administration and support staff, textbooks and instructional materials, technology, and the daily upkeep of the school. Because private schools also receive regular funding from other sources, such as annual giving, fundraising, endowment earnings, and outside organizations, tuition may not fully reflect each student’s share of the total cost of his or her education, but it does represent a prorated amount for that child’s share of the tuition-funded portion of the program. Tuition rates can vary by grade levels, as the need for specialized teachers, materials, or smaller class sizes may affect per-pupil costs. It is also important to note that private school budgets are made with a specific anticipated tuition revenue in mind. The reliance of private schools on tuition makes enrollment management a necessity for private school administrators. Too few students or too many requiring financial aid may leave the school without the revenues needed to operate. Too many tuition-paying students, by contrast, may improve the annual budget but may affect the school’s mission by imperiling promises of small class sizes and personal attention. Fees

Fees represent a more complex issue in terms of the charges for a private school education. By simple process of elimination, fees generally are those charges for private school attendance not comprising the core educational mission. As in higher education, there are numerous reasons for the charging of fees separately from tuition. Perhaps the most common is to account for independent and selfsufficient internal service funds for special purposes, such as athletics or student organizations. Although private schools may not be under the strictures of fund accounting in the way their public counterparts are, private accreditation and institutional accounting standards may create a preference for segregating some revenues for specific operational purposes or for long-term investments. Many private schools charge additional fees for long-term capital needs and for building their permanent endowment fund. Additionally, accounting for fees as separate from tuition can allow private schools, in their advertising, to mask the full cost of attendance and minimize the appearance of tuition increases. A second reason for the establishment of private school fee schedules is to differentiate costs that may

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be variable, or even optional, depending on each student’s circumstances. For example, private boarding schools charge considerable fees, often half the total cost of enrollment, for room and board. Yet these schools will commonly also enroll local students in their day classes. Some schools may even fully operate day and boarding divisions. In such circumstances it would be unfair, even abusive, to charge full room and board fees to students who live at home and, at most, eat five meals a week on campus. Likewise, in private schools that operate their own transportation service, no justification would exist to charge this fee to students living within walking distance of the school or outside the school’s transportation zone. Additionally, sectarian schools often enroll children from different faiths. The establishment of fee schedules may allow interfaith families to avoid paying fees to support purely denominational functions.

Substitution and Abatement of Private School Tuition and Fees Private school tuition and fees can represent a significant cost to families and a substantial barrier to the enrollment of otherwise qualified students. The National Association of Independent Schools estimates the average full cost of attendance in a member school in 2012 at $20,000 for a regular K-12 day program and double that figure, $40,000, for a boarding program. Some policymakers and educational reformers see improving access to private K-12 schools as an essential element of public education reform. Lowering financial barriers to attendance is an essential element of such efforts. Finally, some foreign countries, and even a handful of U.S. states, including Vermont, often need to find ways to utilize existing, and even dominant, private school inventories to meet the public demand to educate all children. In such cases, students may be enrolled in private schools, with the state paying part or, more commonly, all of the tuition and fees. In response to these considerations, both private K-12 schools and governments have responded with various efforts to reduce the direct costs of private education, including financial aid programs, vouchers, and tax credits. Financial Aid

As private school fees and tuition continue to increase, financial aid, once uncommon, has become standard. The National Association of Independent Schools estimates that the average American

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independent school pupil received more than $3,000 in financial aid in 2012. Some figures place the percentage of American private school students receiving some amount of financial aid at perhaps 90%. Nor is financial aid solely an issue in pricey, independent college preparatory schools. Sectarian schools often have a strong commitment to facilitating attendance, especially for children belonging to a specific denomination or congregation. Catholic schools in the United States and elsewhere have a particularly strong tradition of enabling indigent Catholic children to receive a Catholic education regardless of their families’ ability to pay. Indeed, some Catholic schools, especially in the developing world, may be run entirely on a charitable basis. Unlike higher education, need-based financial aid for private school students in the United States is not generally available from public sources (see below). Consequently, most private school financial aid must come from the institution itself or from outside private sources. A traditional source of financial aid is endowed scholarship funds, where the revenue from dedicated endowments is used to provide full or partial scholarships to qualified students. Many schools address their need for increased tuition and fees by encouraging, and even campaigning for, increased donations to support their general scholarship fund. Perhaps the most common alternative is to provide direct aid in the form of assistance, which, in effect, amounts to a rebate of the total tuition costs to a family. Under such a scheme, the total tuition and fees represent a nominal figure, perhaps affordable to only the school’s most affluent enrollees, while the majority of the school’s enrollments are charged a lesser amount based on family ability to pay, with the difference accounted for as “scholarship.” A long-standing and modified version of this approach is often used to partially or fully fund the tuition and fees of the children of school staff as a perquisite of their employment. Public Funding

A growing source of support for private school enrollment in the United States and elsewhere have been various public schemes designed to facilitate greater private school participation by K-12 students traditionally served by public schools. Barriers to such efforts include political resistance by supporters of public education and legal concerns such as the First Amendment’s separation of church and state in America. One method attempted was the

development of tuition tax credits pioneered in the United States in places such as Minnesota. While upheld as constitutional by the Supreme Court, individual tuition tax credits have been generally found to be insufficient to empower private school attendance. Because the credits presume that the family has sufficient income to pay the initial tuition and also sufficient state tax obligations to deduct the credit from, these tax credits are generally seen more as a tax refund for well-to-do families with children in private schools. A somewhat more operable version is to allow wealthy private and corporate donors a tax credit for donations to support vouchers and scholarships for the enrollment of less affluent students. Perhaps the most common form of public aid for private school tuition and fees are vouchers whereby families are able to apply public educational grants to enroll in nonpublic schools. These schemes are common in many countries and have been gaining in popularity in the United States. Conceptually, vouchers are used to pay part or all of a private school’s tuition and fees for students who are qualified on the basis of some criterion, such as attending a failing and/or overcrowded public school, having a disability, or falling below a certain standard of family wealth and income. Of significance to this issue is the possibility that some vouchers may be designated to fully pay tuition, while leaving fees to the child’s family. In some countries and states, vouchers are even used as an enrollment management technique for public schools. In some parts of New England, states have long subsidized private school attendance in areas with a low population and long-established private schools as an alternative to building a competing and low-viability public school locally. In the Netherlands, public education authorities use a “floating voucher” to meet the variable demand for public educational services. When unexpected increases in demand for school capacity occur, the voucher amount is increased as an incentive to move additional children into private schools. When the demand wanes, the voucher’s value is decreased. Voucher schemes are problematic as means of meeting private school tuition and fees. First and foremost, the success of voucher schemes depends on the availability of an adequate inventory of local private schools. In metropolitan areas, this may not be a problem, but in areas with lower population densities, the availability of a voucher may mean little if there are no qualified recipient schools. Hence,

Tuition Tax Credits

while vouchers may work well in Ireland, Canada, and the Netherlands, they may have less utility in the more remote regions of the United States and Australia. Added to this is the need for available seats. Well-attended private schools may see no reason to provide space for possibly less capable and lower funded voucher students over full-paying pupils. This can be an especially acute problem in the United States, where voucher programs, when they exist, often require schools to consider the public voucher as payment in full for private school tuition and fees. While some less expensive and selective sectarian schools may accept these vouchers for some qualified students, most highly selective and high-cost independent schools may refuse voucher enrollments altogether. Luke M. Cornelius See also Educational Vouchers; Fund Accounting; Schools, Private; Schools, Religious

Further Readings Baker, B. D., Green, P., & Richards, C. E. (2008). Financing education systems. Upper Saddle River, NJ: Pearson Education. Benveniste, L., Carnoy, M., & Rothstein, R. (2003). All else equal: Are public and private schools different? New York, NY: Routledge. Brimley, V., Verstegen, D. A., & Garfield, R. G. (2012). Financing education in a climate of change. Upper Saddle River, NJ: Pearson Education. Broughman, S. P., & Swaim, N. L. (2013). Characteristics of private schools in the United States: Results from the 2011–12 private school survey. Washington, DC: U.S. Department of Education, Center for Education Statistics. Case, A. G. (2006). Operating an independent school. Lanham, MD: Rowman & Littlefield Education. Cox, B., Weiler, S. C., & Cornelius, L. M. (2013). The costs of education. Lancaster, PA: ProActive.

TUITION TAX CREDITS Tuition tax credits are a form of state income tax relief designed to provide tuition assistance for students choosing a private school alternative to their assigned public school. While similar to school vouchers in many respects, the funding and administration mechanisms of tuition tax credits distinguish them as a separate form of private school choice.

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There are two main types of tuition tax credit programs: (1) tax credit scholarships and (2) individual tax credits or deductions. Eligibility for both types of tuition tax credit programs is typically determined by family income, a student’s special needs, or the performance of the student’s current public school. Despite the connotation of the term scholarship, students do not have to demonstrate strong academic ability to qualify for a tax credit scholarship. Depending on the particular regulations of a given program, tuition tax credits can also be used to pay for transportation costs, tutors, school supplies, books, and computers, provided they have been approved as legitimate educational expenses. Participation in tuition tax credit programs is associated with modest improvements in student achievement outcomes in both math and reading. Additionally, fiscal analyses of tuition tax credit programs have revealed significant savings as a result of these programs. This entry includes a brief history of tuition tax credits, an overview of the different tax credit programs, and discussions of their impact on student achievement and fiscal impact. The first tuition tax credit program for K-12 education was created in 1997 in Arizona. Ten years after the establishment of this program, similar programs had been created in five states, providing private school scholarships to more than 92,500 children. The development of new programs and expansion of existing ones has continued. Three tax credit scholarship programs expanded, and four new initiatives were established in 2012 alone. A public opinion poll conducted across five states in May 2012 showed that a majority of voters support scholarship tax credit programs. Tuition tax credit programs are similar to educational vouchers in that they make resources available for students to enroll in private schools and thereby expand parental school choice. They differ from vouchers, however, in their funding method and administration. Unlike voucher programs, which use general government revenues to fund the vouchers and tend to be directly administered by government, tuition tax credits induce private contributions, which nonprofit organizations receive and convert into “scholarships” for students to attend private schools or for reimbursing individual families that self-finance private schooling for their children. Both federal and state judges have affirmed the constitutionality of tuition tax credits in the face of claims that they violate the separation of church and state. Some of the early initiatives, particularly the large tax credit scholarship program

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in Florida, were established because supporters believed that they were on stronger constitutional footing than vouchers, due to their indirect funding mechanism. Although little research has been conducted on tuition tax credit programs, participants have demonstrated some academic achievement benefits as a result of these programs. The available research indicates that the performance of students in tax credit scholarship programs on standardized tests tends to be similar to or better than the performance of their public school peers. Achievement gains could be the result of any number of factors, such as providing access to a more diverse range of schools, thus increasing the chance of parents matching students to the learning environment that is most effective for them. It could equally be attributed to safer school environments, more effective curriculum or pedagogy, or higher quality teachers in the private schools students switch into with their scholarships. The size of these positive impacts may even be underestimated. This is because additional research indicates that tuition tax credit programs have positive indirect impacts on student performance in neighboring traditional public schools, due to pressure from competition, suggesting a general increase in student test scores across both school sectors as a result of tuition tax credit programs. In addition to improving student outcomes, tuition tax credit programs have a positive fiscal impact because of the lower state expenditures on education resulting from fewer students in public schools. These programs are typically designed to reduce state expenditures on education to a greater degree than the reduction in tax revenue from the credits given to contributors.

Tax Credit Scholarships Tax credit scholarship programs allow taxpayers to receive a full or partial state income tax credit for donating to nonprofit organizations that award private school scholarships. In the 2012–2013 school year, there were 14 scholarship tax credit programs in operation across 11 states: Arizona, Florida, Georgia, Indiana, Iowa, Louisiana, New Hampshire, Oklahoma, Pennsylvania, Rhode Island, and Virginia. These programs served approximately 148,300 students, awarding an average scholarship amount of $2,534. Individual state laws establish the eligibility rules both for donors to receive tax credits and for

students to receive scholarships. Some programs limit eligibility for the credits to corporations. Other programs restrict eligibility to individual donors, and some include both types. Donations must not be directed to a specific student or private school but must simply go into a pot of funds to generate scholarships for participating students. All existing tax credit scholarship programs have a cap on the total amount of credits that can be provided in a given year, so that there is a ceiling on the amount of revenue that will be lost to the state. The highest state cap is $229 million in Florida, representing about 0.3% of overall state spending. The lowest state cap is $3.5 million in Oklahoma, approximately 0.05% of overall state spending. It is a common misperception that eligibility for a tax credit scholarship is determined by individual student performance, student aptitude, or teacher recommendations. It is not. The Georgia Scholarship Tax Credit Program has universal eligibility. The other 13 programs determine eligibility based on measures of student disadvantage. Means-tested programs give preference to students from low- and middle-income families when granting scholarships. In 2012–2013, 11 of the 14 total tax credit scholarship programs in the United States were means tested. Other initiatives, referred to as failing schools programs, are designed to give preference to students attending public schools that have been designated “persistently failing” or “in need of improvement” by state officials. Such designations can be based on the school’s performance in a statewide letter grade accountability system, for example, with private school scholarships offered to students in schools graded D or F. Oklahoma’s Equal Opportunity Education Scholarship Program is a failing school tax credit scholarship program. Finally, special needs scholarship programs are designed specifically for students with one or more disabilities that affect their learning. Special needs scholarships are intended to permit such students to access a private school with the resources and services to address their special education needs. Lexie’s Law, enacted in Arizona in 2009, is an example of a corporate tax credit scholarship program that provides private school scholarships to children with disabilities. Opponents of tax credit scholarship programs have brought legal challenges against these initiatives, questioning their constitutionality because students use potential tax dollars to attend faith-based schools. Although at least some secular private

Tuition Tax Credits

schools participate in all of the tax credit scholarship initiatives, most of the students who enroll in these programs use their scholarships to attend religious schools, raising concerns that the programs violate the Establishment Clause of the First Amendment to the U.S. Constitution, which prohibits the government establishment of religion. Most states also have clauses in their own constitutions that prohibit the establishment of religion or the government funding of religious organizations. Every legal challenge to tax credit scholarships to date, except one, has been rejected, either by the trial court or on appeal. The exception is the challenge to the New Hampshire Education Tax Credit Program, a corporate tax credit program enacted in 2012, which has been ruled unconstitutional by a trial court. An appeal of the decision is pending. State and federal judges have overwhelmingly ruled that tuition tax credit programs are constitutional because, in their opinion, they meet a threepronged test of constitutionality. For a government program that results in religious organizations receiving public money to be constitutional, the program must (1) have a valid secular purpose and (2) not give preference to religious organizations over nonreligious ones, and (3) any funds directed toward religious schools must end up there as a result of the individual decisions of private citizens, not of the state. In the view of most federal and state courts, tuition tax credit programs satisfy all three of these criteria.

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Indiana, Louisiana, Minnesota, and Wisconsin— will operate individual tuition tax credit and tax deduction programs in 2013–2014. Most of these programs offer annual financial benefits of $250 to $1,000, too small to genuinely affect most families’ choices of where to send their child to school. As a result, some school choice advocacy organizations and scholars do not classify individual tuition tax credit/deduction arrangements as private school choice programs.

Impacts on Student Performance Studies of the impacts of tax credit scholarship programs have considered their direct and indirect effects on student outcomes. A 2011 study by David Figlio of the direct effects of using a scholarship to attend a private school shows positive results: Participation is associated with modest improvements in mathematics and reading scores on standardized tests, relative to public school students who applied for the program but were not able to participate. The indirect effect of tax credit scholarship programs also appears to be positive. A 2011 study by Figlio and Hart of public school responses to private school competition brought on by a tax credit scholarship program found that students in public schools that face a greater threat of enrollment losses due to a tax credit scholarship program improve their test scores more than students in schools that face a lesser threat.

Fiscal Impacts Individual Tuition Tax Credits/Deductions Individual tax credits or deductions differ from tax credit scholarships in that they do not use the services of a scholarship-granting organization. Instead, families are reimbursed directly for approved educational expenditures associated with their children. Specifically, individual tax credits or deductions allow families to receive state income tax relief to cover certain educational expenses, such as private school tuition, books, tutors, computers, or transportation costs. The key difference between tax credit and tax deduction programs is that tax deductions reduce taxable income, whereas tax credits provide a dollar-for-dollar reduction in a person’s tax liability. While the value of a tax deduction rises with the taxpayer’s income, a tax credit has the same value for all taxpayers and is thus more commonly used as a way to distribute subsidies through the tax system. Seven states—Alabama, Iowa, Illinois,

Tuition tax credit programs are designed to save the state money. Program participants typically receive only a portion of the state-allocated amount to fund their education, so each student choosing to attend a private school through a tuition tax credit program generates a net savings for the state. The magnitude of these potential taxpayer savings depends on four key variables: 1. Public school per-pupil state funding 2. The value of the tuition tax credit 3. Enrollment in the tuition tax credit program 4. The proportion of tuition tax credit students who would have otherwise attended public schools

Regarding the first two variables, so long as the value of the tuition tax credit is lower than the perpupil state funding amount for public schools, this

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gap generates potential taxpayer savings. Regarding the third variable, as enrollment in the tuition tax credit program increases, the value of the state savings rises too. The final variable that is needed to calculate a program’s net fiscal impact is the percentage of program participants who would have otherwise attended a public school. Gold standard studies of low-income school voucher programs across the nation can help us generate credible estimates of this figure. Approximately 10% of students in these studies still attended private schools without the assistance of a voucher, implying that approximately 90% of tuition tax credit scholarship recipients would otherwise attend public schools. Taking these four variables into account, the Florida legislature has estimated that the nation’s largest program, the Florida tax credit scholarship, saves the state approximately $36.2 million per year, or 0.05% of the state’s total budget. Patrick J. Wolf and Anna J. Egalite See also Educational Vouchers; Privatization and Marketization; Schools, Private; Schools, Religious

Further Readings Figlio, D. N. (2011). Evaluation of the Florida tax credit scholarship program: Participation, compliance and test scores in 2009–10 (Unpublished manuscript). Retrieved from http://www.floridaschoolchoice.org/pdf/FTC_ Research_2009–10_report.pdf Figlio, D. N., & Hart, C. M. D. (2011). Does competition improve public schools? New evidence from the Florida

tax-credit scholarship program. Education Next, 11(1), 74–80. Glenn, M., & Swindler, R. (2013). School choice now: The power of educational choice. Washington, DC: Alliance for School Choice. Retrieved from http://s3.amazonaws .com/assets.allianceforschoolchoice.com/admin_assets/ uploads/132/Types%20of%20School%20Choice%20 Programs.pdf Office of Program Policy Analysis & Government Accountability. (2008). The corporate income tax credit scholarship program saves state dollars (No. 08–68). Tallahassee, FL: OPPAGA of the Florida Legislature. Retrieved from http://www.oppaga.state.fl.us/reports/ pdf/0868rpt.pdf Office of Program Policy Analysis & Government Accountability. (2010). Florida tax credit scholarship program fiscal year 2008–09 fiscal impact. Tallahassee, FL: OPPAGA of the Florida Legislature. Retrieved from http://www.floridaschoolchoice.org/information/ctc/files/ OPPAGA_March_2010_Report.pdf Wolf, P. J., Komer, R. J., & McShane, M. Q. (2013, January 18–21). Blame it on politics: The (non-) effect of anti-aid amendments on private school choice programs in the U.S. States. Paper presented at the Second Annual International Academic Conference on School Choice and Reform, Ft. Lauderdale, FL.

TWO OR THREE TIER FUNDING PROGRAMS See Equalization Models

U The term unfunded mandate refers to a directive that is not linked to an adequate provision of resources by a higher level of government to facilitate compliance with the directive. When there is inadequate funding by the higher level of government, the lower level of government must use revenues generated at the local level or shift funding priorities among the services provided locally. In essence, unfunded mandates create a mismatch between expenditures and supporting revenues. The phenomenon of unfunded mandates has become mostly relevant with the expansion of the role of the federal government in the latter part of the 20th century and with the increased power of the purse at the federal level. Political and public reaction to unfunded mandates has closely followed. Although much of the public criticism has focused on mandates that the federal government imposes on both state and local levels, mandates flowing from state governments to local governments (including local education agencies) are also influential. In most states, the majority of primary and secondary education expenditures stem from the local district level. Districts are subject to numerous mandates from federal and state levels, requiring districts and schools to comply with those mandates for many reasons, some of which include qualifying for additional funds or avoiding financial or nonfinancial sanctions. Concomitantly, local districts depend on state and federal sources of revenue. The level of dependency on state aid varies among school districts. Some school districts have a broad tax base that can generate enough revenue to offset unfunded and underfunded mandates with little

UNFUNDED MANDATES This entry defines and addresses the issue of unfunded mandates. It explains what constitutes a mandate in the world of education and the concerns associated with how mandates are funded and who pays for them. The entry concludes with a discussion of the fiscal and economic impacts of unfunded mandates on education. A mandate is a provision handed down by a higher level of government by statute, administrative regulation, or court order—individually or in some combination—that requires a specific procedure, process, or service to be fulfilled by the lower governmental agency or institution. An education mandate may dictate direction for a variety of issues, including teacher pay and teacher quality, length of educational time, transportation, equal access to learning opportunities, and mandatory learning goals and assessments. Mandates are often directives that require specific programs or services to be implemented by the local government agency. An example might be a state law requiring school districts to maintain a set of certain professional development standards for teachers. Conversely, a mandate may prohibit the local agency or organization from conducting an activity. An example would be a federal law prohibiting the maintenance of facilities inaccessible to Americans with disabilities. Local government agencies such as school districts strive to comply with state and federal mandates in order to qualify for funding and to avoid sanctions or penalties. 817

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burden. However, other more rural or less economically enriched areas will struggle to meet the needs of students and provide additional unfunded mandated services. Clearly, the existence of unfunded mandates may have profound effects on both the adequacy and the equity of education funding. The extent to which the state imposes specific educational requirements on districts, but the concomitant amount of state aid does not follow, may certainly raise questions about the degree to which the state is meeting its particular constitutional duty to provide thorough and efficient education to children. Additionally, the degree to which unfunded mandates result in greater fiscal burdens among less wealthy districts may raise serious equity questions.

Unfunded Mandates, Underfunded Mandates, and Mandates With Negative Funding Consequences Mandates that impose costs on school districts and other local public agencies can be separated into three categories: (1) unfunded mandates, (2) underfunded mandates, and (3) mandates with negative funding consequences. Unfunded Mandates

An unfunded mandate is characterized as a directive to produce a service or program that is fully funded by the local government agency. In this situation, the higher authority may never have intended to assist with the additional cost of the service, or it may have neglected to account for additional costs borne at the local level. An example might be the implementation of a statemandated teacher evaluation system (mandated either by statute or by regulation by a state department of public instruction). If the mandated system represents a fundamental change from the previous evaluation process, there may be significant costs associated with implementation and maintenance of the new system (e.g., teacher and evaluator training, documentation requirements, ongoing professional development, etc.). If no categorical funding sources are granted by the state to defray these costs, and if the general funding formula dollars are not increased accordingly, the additional costs must be borne locally and financed with revenues generated by local taxes. In this example, the mandate is imposed but the funding does not follow.

Underfunded Mandates

An underfunded mandate is characterized by having some portion of the cost of the additional good or service defrayed through intergovernmental revenue support. A state may, for example, require local districts to provide public transportation of students who live outside a certain radius from a school to and from that school. There may be a supplementary funding formula that provides state aid to districts as a percentage of estimated per-pupil transportation costs. Here, the mandating level of government (the state) and the local district that actually provides the service share the additional mandated costs. Some of the more costly elements of educating students may be thought of as underfunded mandates. For example, state laws setting maximum class sizes, state regulations for minimum teacher salaries, and the federal Individuals with Disabilities Education Act could be considered underfunded mandates. The ability of a school district to supplement the funding for an underfunded mandate varies among districts. Mandates With Negative Funding Consequences

Mandates with negative funding consequences are those in which school districts or local agencies that fail to comply risk the loss of revenue support from the higher level of government, and in some cases, they may be required to return funds to the issuing authority. The federal government, for example, through Title IX of the Education Amendments of 1972, mandates that school districts provide a degree of equity in the funding of girls’ and boys’ competitive athletic programs. The failure to comply could potentially result in the loss of federal funding, according to the statutory language regulating the mandate. Also, federal Title I funds for disadvantaged students may not be used to pay for services, staff, programs, or materials that would otherwise have been paid for by state or local funds. If a school violates this requirement, any misused money must be reimbursed to the federal government. Numerous federal and state mandates increase school costs and require funding solutions provided by the local tax base. The next section addresses the impact of unfunded mandates at the local level.

Overall Impact of Unfunded Mandates The issue of unfunded mandates has received considerable attention in the popular press, and to some extent, policymakers have responded. Congress and some state legislatures have enacted laws that aim

University Endowments

to remedy the problem of unfunded mandates. The federal Unfunded Mandates Reform Act of 1995 (UMRA), Pub. L. 104-4, though certainly not prohibiting unfunded mandates from the federal government, creates informed decision making that considers the cost to local entities associated with federal legislation. Arguably, the UMRA has minimally affected federal unfunded mandates. In fact, not all laws meet the UMRA definition of a mandate to be identified and reviewed under the act’s provisions. The UMRA defines a mandate as any law or regulation imposed on a state, local, or tribal government or a private entity that would remove or reduce funding previously authorized for existing mandates and would result in a local governmental private entity incurring more than $100 million in costs during a certain year. Some hotly debated laws that have created budgetary concerns, such as the Social Security Amendments of 1965, which created Medicaid, and the No Child Left Behind Act of 2001, do not contain mandates under the UMRA definition, so they cannot be reviewed or scrutinized as unfunded mandates. Additionally, many state legislatures have instituted measures to curb unfunded mandates by requiring new bills to include fiscal impact statements, or fiscal notes, to provide an estimated cost of proposed legislation and by requiring state agencies to reimburse local governments and school districts for any additional costs incurred locally. The reimbursement of mandate costs is only required in 17 states, and the success rate of local levels of government receiving repayment is low. However, the existence of a reimbursement clause has proven effective in slowing the rate at which unfunded mandates are passed, resulting in a trend toward lowering the financial burden on schools and other local entities by providing some new money for operating costs while slowing the influx of new financial burdens from new mandates. The existence of unfunded mandates has implications for the economics and financing of education. Local education agencies are legally bound to fully comply with many types of unfunded mandates even without full or adequate provision of funding. From a macroeconomic perspective, education must compete with other public services for scarce public resources. Yet in a time of increased accountability, districts and schools face higher expectations. From a microeconomic perspective, districts are forced to make critical decisions about the provision of educational services, including teacher salaries, professional development, transportation, and academic programs, given the increasingly austere levels of

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financial support. A school may have to reduce instructional staff due to budgetary constraints, leading to increased class sizes and a potential reduction in educational effectiveness. If a drop in effectiveness translates into decreases in test scores, then a school may face further costly sanctions. Clearly, these mandates affect education funding adequacy. Furthermore, fiscal equity may also be affected, as the compliance requirements may be constant across local communities though the ability to meet the financial burden placed by the mandate varies naturally across regions based on various economic and demographic characteristics of the communities and the students being served. To relieve the fiscal pressure of unfunded mandates, school officials may endeavor to educate citizens about their real and potential effects. The existence of mandates has the potential to increase local taxes and to affect public services, including education. Grassroots efforts that call attention to costly mandates with no supplemental funding may potentially slow the passage of new mandates and, perhaps, even result in rescinding of outdated mandates that require funding yet no longer serve a purpose locally. Jeffrey Maiden and Stephen Ballard See also Adequacy; Education Spending; Evolution in Authority Over U.S. Schools; Expenditures and Revenues, Current Trends of; Tax Burden

Further Readings Beckett-Camarata, J. (2004). Identifying and coping with fiscal emergencies in Ohio local governments. International Journal of Public Administration, 27(8–9), 615–630. Galle, B. (2008). Federal fairness to state taxpayers: Irrationality, unfunded mandates, and the “SALT.” Michigan Law Review, 106(5), 805–852. Kelly, J. (1994). Unfunded mandates the view from the states. Public Administration Review, 54(4), 405–408. Pendell, M. J. (2008). How far is too far? The spending clause, the Tenth Amendment, and the education state’s battle against unfunded mandates. Albany Law Review, 71(2), 519–543.

UNIVERSITY ENDOWMENTS College and university endowments represent a significant source of revenue for institutions of higher education, especially in the United States.

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Recent estimates place the total asset value of American endowments for higher education in excess of $400 billion, with more than 70 institutions individually claiming endowments in excess of $1 billion. According to the National Association of College and University Business Officers and the Commonfund Study of Endowments, some 850 colleges and universities in the United States and Canada maintain institutional endowment funds with an average balance in the tens of millions of dollars under management. The purpose of these funds is to allow the institution to permanently invest the principal to generate revenues in the forms of interest, dividends, distributions, and capital gains that can be used to further the goals of the college or university. Since endowments only generate returns through investment, their active management is an issue of significant concern to institutional leaders. This entry covers the origin and development of university endowments. It then discusses the modern management of such funds, including types of endowments, managerial approaches, and legal concerns. Finally, the entry concludes with some social and political considerations regarding institutional endowments.

Origins and Evolution Endowments from wealthy benefactors, religious denominations, civic and philanthropic organizations, and governments are at the core of the establishment of many, if not most, Western colleges and universities prior to the 20th century. Each of the constituent colleges of Oxford and Cambridge Universities were founded by the benevolence of private donors, often owing their inception to a single benefactor. Nearly all private, nonprofit colleges and universities in both Colonial America and in the United States of the 19th century likewise owe their founding to charitable gifts from individuals and organizations. Early American colleges were often established and endowed by religious denominations, while later in the 19th century, wealthy philanthropists, usually Gilded Age industrialists, founded the great private research universities that often bore their names, such as Vanderbilt, Johns Hopkins, Carnegie-Mellon, Stanford, and Chicago (Rockefeller). Nor has the endowing of universities been restricted to private benefactors. Many early public institutions owed their establishment to early endowments of public lands, such as those provided by the Northwest Land Ordinances. Most significant

were the Morrill Land-Grant Acts of 1862, 1890, and 1994, which ceded enormous amounts of federal land to, mostly, public colleges and universities. These public endowments put institutions, at least initially, in the business of managing valuable, and often profitable, lands. The rents and sale of these lands not only permitted the initial establishment and operation of the so-called land-grant colleges but also funded their permanent endowment funds. Endowments are not static foundational legacies. Institutional endowments grow almost continually from both investment, appreciation of assets, and ongoing donations both great and small. When permitted by laws, policies, and donor restrictions, colleges and universities may reinvest the proceeds of their endowed investments to allow their principal to grow and provide a greater base from which to support their intended purposes. Likewise, as the value of investments increase, institutions can either use these capital gains to grow the principal of the endowment or utilize the net gain as a source of funding for its programs. Additionally, colleges and universities are constantly working for the growth of their endowments by actively seeking new donations from alumni, philanthropists, corporate donors, and other groups and organizations. In this effort, public and nonprofit institutions are aided by federal and state tax policies that generally regard charitable contributions to educational institutions as tax deductible for the donors and tax exempt for the recipients. Largescale donations can often be solicited in return for the naming of a program, building, institute, specialty school or college, or other campus feature for the benefactor. Likewise, increasingly important sources of endowments have been bequests from not only wealthy benefactors but also alumni, friends of the school, and even former faculty. It has been a long-established tradition that contributing to the permanent endowment of a college or university bestows on the donor a measure of immortality.

Characteristics of Endowments Jessica Bellfleur and the U.S. Government Accountability Office define endowments as gift agreements that are intended to provide a stable base for the long-term funding of specific college and university programs. Although these agreements generally define endowment monies as university funds, the use of these funds can be subject to conditions imposed by the donors. Although colleges and

University Endowments

universities are free to refuse these funds and their associated terms, in practice, this is generally applied only to donations associated with some controversial donor or purpose. Institutions are obligated to closely and carefully account for thousands, even millions, of individual gifts to ensure that they are not only managed profitably but also used only for the purposes intended by the donors.

Types of Endowment In general, endowment gifts can be classified as one of three types. The most common, and most difficult to manage, is the restricted true endowment. This represents a gift intended for a specific purpose where the principal is to be retained permanently by the institution and only the revenue off this principal can be expended. This type of perpetual endowment can see the terms of the donation enforced for endless centuries. This can create serious issues when the purpose of the gift ceases to exist or becomes obsolete. For some common purposes, like the institution’s general scholarship fund, this is rarely a problem. A second type of endowment gift is referred to as a term endowment. This type of endowment allows donors to dictate the terms for the use of their gift for a specific period of time, perhaps 50 or 100 years, after which the principal can either be expended by the college or added to its general endowment. This type of endowment, often preferred and encouraged by skilled managers, avoids the problems associated with obsolete true endowments. A third common endowment type is the board-designated quasi-endowment. This is where an institution essentially endows itself by setting aside certain funds, often results of some nonrecurring windfall not needed for current operations, so that the principal can be invested to grow the overall endowment and be set aside for some future major expenditure, such as a major building project. Of course, the only restrictions on the allocation and use of such funds are those the institution imposes on itself legally or by policy.

Endowment Management It is a generally accepted convention that college and university endowments should be invested and managed conservatively, in a manner similar to trusts and pension funds. This is not only because highly speculative investments risk the principal, and the function for which it was donated, but also because significant losses in an institution’s managed assets

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may have a negative effect on future donations. Studies by National Association of College and University Business Officers and the Commonfund Study of Endowments show that in American colleges and universities, about 15% of all endowment dollars are invested in domestic equities, while 6% are in foreign stocks. Fully half of all institutional endowments are placed in various strategic investments such as real estate, natural resources, hedge funds, venture capital funds, derivatives, debt, and similar financial products. The remainder is typically placed in fixed-income assets and more liquid holdings. The management of these various investments is a matter of considerable complexity. Smaller institutional endowments are often managed by outside professionals and firms based on strategies agreed on with institutional leaders and boards. Such arrangements are usually matters of necessity, as lesser endowed institutions do not have the resources to hire highly skilled and experienced fund managers. At the same time, this necessitates that institutions must give up some control over the dayto-day investment decisions involving their funds. Additionally, such arrangements usually involve the payment of considerable outside management fees, including outside consultants and auditors, which can significantly affect the return from these investments. This can cause concerns with both campus constituencies and donors who object to the diversion of revenues meant for educational purposes to private profits. The challenges of managing larger funds are even more problematic. It is generally inadvisable to turn large, especially billion-dollar, endowments over completely to third-party financial managers. In such cases, the potential for catastrophic losses due to a lack of close oversight and the amounts paid out as management fees can be considerable. It should be noted that the amounts of some university endowments can constitute significant components of the national economy and financial markets in their own right. For years, the leading university endowment fund in the United States has been that of Harvard University, which exceeds $30 billion. Placed in context, Harvard’s endowment has a greater worth than the holdings of more than 75% of the Fortune 500 corporations as well as that of many sovereign nations. The preferred model for large fund management is generally for the university, or some affiliate such as a foundation, to employ its own investment

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manager, and staff, so as to retain more control and revenue for the institution. This model, however, has become increasingly challenging. Such large university endowments can often equal, or exceed, the wealth of private equity funds and even commercial banks. Highly successful endowment managers, who essentially perform the work of private sector fund managers, are constantly under pressure to move to the private sector for substantially greater compensation. This places universities in the uncomfortable position of either constantly replacing their endowment leadership or being forced to provide professional compensation far in excess of that considered reasonable for even other highly paid institutional employees such as presidents, coaches, and renowned research faculty.

Legal Issues in Endowment Management There is also a significant legal aspect to the management of college and university endowments. True endowments, as noted, are theoretically perpetual in both their principal and their terms. This can create an acute problem with obsolete endowments known as the “dead hand effect,” such as requiring an expenditure on a particular building long since lost to fire or natural disaster. An attempt to deal with this was the Uniform Management of Institutional Funds Act of 1972, a model state law that was later revised and is now known as the Uniform Prudent Management of Institutional Funds Act. To date, 47 U.S. states have adopted some form of this act as a means of ameliorating unreasonable or impossible bequests. The act allows for institutions to petition the original grantor or, in the absence of the original grantor, allows the courts to seek a modification or release of the terms of the original gift. Courts will enforce that the endowment must still be reserved for institutional, educational, or charitable purposes and may require that the funds be utilized for modern purposes that remain close to their original intent. This is perhaps one of the most difficult aspects of endowment management, especially as it can entail considerable legal costs. Institutions must also constantly be careful of the misuse of not only funds but also endowed land and property given for specific purposes. When there is a perception of the misuse of such endowed assets, institutions can expect to be challenged by heirs and descendants of the original benefactor seeking to reclaim former family wealth. Recommended

approaches include convincing donors to consider term endowments and having institutional counsel involved in crafting larger agreements with benefactors that include terms allowing the conversion of their gifts to similar purposes in the event their original purpose becomes impractical.

Social and Political Concerns With Institutional Endowments On the surface, it may appear that college and university endowments share much in common with private investments such as private equity or hedge funds. However, there are significant differences between them, and not just in terms of their mission and purposes. Politically, large university endowments, especially when held by public institutions, can become targets during economically challenging periods. Policymakers may question why colleges and universities are requesting tuition increases and more public financial aid for their students while simultaneously holding hundreds of millions, or even billions, in managed assets. These questions may persist even in the face of legally binding donor restrictions. For public universities, one common solution to these challenges is to establish and designate a separate, institutionally affiliated nonprofit foundation to collect and manage endowment funds. Even private, nonprofit colleges are not immune to such political challenges. U.S. states, such as Massachusetts, perhaps recognizing the economic magnitude of top institutional endowments in the investment sector, have proposed extending financial taxes and regulations to them. Another challenge can be found in the social priorities of various campus and external constituents. In recent decades, it has become common for politically and socially active students, faculty, community members, alumni, and even donors to demand university endowments to divest from certain controversial regions and investments. This strategy was visible and, arguably, successful in forcing Harvard and other universities in the 1970s and 1980s to divest from companies located in or doing business in apartheid-era South Africa. Recently, popular calls for university divestment have targeted investments in tobacco, weapons and defense manufacturing, coal, fossil fuels, and highly polluting industries. The argument can be made that universities should not seek to promote the social good while simultaneously investing in socially questionable activities. The counterargument is that such investments,

U.S. Department of Education

especially from oversized endowments such as Harvard’s, serve to place universities in a position, often as voting members of corporate boards, to influence and ameliorate corporate decision making. Luke M. Cornelius See also External Social Benefits and Costs; Markets, Theory of

Further Readings

those historical functions with the No Child Left Behind Act of 2001 (NCLB) and has continued to grow with recent programs such as the Race to the Top grant competition. This entry reviews the history and structure of ED, then details ED’s major functions and responsibilities and discusses how its role has fundamentally changed since the passage of NCLB increased its influence over the nation’s education system.

History and Structure of ED

Bellfleur, J. L. (2010). College and university endowments: Case studies and tax issues. Hauppage, NY: Nova Science. Davidson, H. A. (1971). Investing college endowment funds: A comparison of internal and external management. Financial Analysts Journal, 27, 69–74. Dimmock, S. G. (2012). Background risk and university endowment funds. Review of Economics and Statistics, 94(3), 789–799. Helms, L., Henkin, A. B., & Murray, K. (2005). Playing by the rules: Restricted endowment assets in colleges and universities. Nonprofit Management & Leadership, 15(3), 341–356. National Association of College and University Business Officers and Commonfund Study of Endowments. (2012). 2012 NACUBO-Commonfund study of endowment results. Washington, DC: Author. Retrieved from http://www.nacubo.org/Research/NACUBOCommonfund_Study_of_Endowments/Public_NCSE_ Tables.html Weisbrod, B. J., Ballou, J. P., & Asch, E. D. (2011). Mission and money: Understanding the university. Cambridge, UK: Cambridge University Press.

U.S. DEPARTMENT

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EDUCATION

The U.S. Department of Education (ED) is a cabinetlevel department headed by the secretary of education whose mission, according to the department’s website, is to “promote student achievement and preparation for global competitiveness by fostering educational excellence and ensuring equal access.” ED’s historical primary functions are to administer and coordinate federal assistance to education, assist states and localities in their provision of education, gather data on the state of U.S. education, focus attention on pressing educational issues and challenges, and ensure equity and equal access to education. However, the federal role in education administered through ED began to expand beyond

The ED, in its current form as a cabinet-level department, was established in 1980 under President Jimmy Carter. The creation of ED consolidated the education functions of the preceding Department of Health, Education and Welfare with functions of multiple other federal agencies. The Carter Administration created ED to concentrate federal oversight of education in one entity that could ensure equal access to education; improve the quality of education in the states; centralize federal research, evaluation and data gathering on education; and coordinate the administration and management of federal education programs. ED is currently organized into offices, including the Office for Civil Rights; the Institute of Education Sciences; the Office of Legislative and Congressional Affairs; the Office of Planning, Evaluation and Policy Development; the Office of Federal Student Aid; the Office of Postsecondary Education; and the Office of Elementary and Secondary Education.

Roles and Responsibilities Under the U.S. Constitution, the federal government has neither the power nor the responsibility to provide education. That responsibility lies with individual states. In this decentralized structure, ED does not play a direct role in educating students or defining curricula. It does not create or accredit schools, colleges, or universities. Instead, ED’s historical roles have been to support state and local education agencies in their provision of education, to conduct the legislative agenda and regulatory responsibilities of the executive branch, and to carry out research and gather data on education. In recent years, the federal government has extended beyond these roles and used its authority to produce substantial changes in the U.S. education policy landscape. Without direct control over schools, ED provides various kinds of assistance to states, localities, and, in the case of postsecondary students, individuals.

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This assistance takes the form of federal financial assistance, gathering and distributing data and research on education, and identifying and focusing attention on pressing national education policy concerns. ED is responsible for administering federal assistance to education. In 2011, federal funds constituted about 12% of total revenue for public elementary and secondary schools, with the remainder roughly split between local and state sources. The primary source of federal funding comes through Title I of the Elementary and Secondary Education Act of 1965, through which the federal government provides funds for children of low-income families ($14.7 billion in 2011). In addition, ED provides federal funds under the Individuals with Disabilities Education Act and coordinates federal special education funding ($12 billion in 2011). ED administers federal postsecondary aid, including Pell grants and subsidized loans, which in 2011 constituted more than 48% ($38 billion in 2011) of ED’s appropriations. Beyond financial assistance, ED also supports state and local education agencies with technical assistance. Through the Institute of Education Sciences—which includes the National Center for Education Statistics and the National Center for Education Evaluation and Regional Assistance— the department collects data, conducts and reviews research, and provides technical assistance to state and local education agencies. ED serves as the primary executive office for dealing with education policy through legislation and regulation. Through multiple offices, ED’s programs highlight education issues, initiatives, and priorities of the executive branch of government. The Office of Legislative and Congressional Affairs directs ED’s legislative agenda and liaises with Congress in pursuit of that agenda. The Office of Civil Rights directs policy and regulations to ensure equal access to educational opportunity, conducts civil rights investigations, and takes enforcement actions to ensure equity and equal access to education. In 2002, Congress reauthorized the Elementary and Secondary Education Act of 1965 by passing NCLB, which tied receipt of Title I funding to requirements for student assessment, data gathering,

and reporting, as well as to consequences to schools for poor performance on student assessments. ED is the primary office for regulating and enforcing these NCLB requirements. As a result of NCLB, there were substantial changes to the operation of elementary and secondary schools in the states. NCLB has been the subject of much debate and is frequently regarded as a broad extension of the federal role in education. In addition, ED’s role in providing states with waivers from NCLB requirements in exchange for specific education policy changes further reflects ED’s increased influence over education in state and local agencies. Since the Great Recession began in 2008, federal programs such as the Race to The Top, federal funding sources including the American Recovery and Reinvestment Act of 2009, and federal School Improvement Grants have been administered by ED and provided billions of dollars to state and local agencies. These programs and funds have come with particular education policy requirements attached to them. ED’s change in focus from inputs (dollars into the education system) to outcomes (assessment and test scores for students) has delineated a new era for the federal role in education that exchanges accountability and compliance at the state and local levels for funding at the federal level. The policies that have shifted ED’s focus from inputs to outcomes have arguably extended ED’s influence over the U.S. education system, particularly for the operation of elementary and secondary schools. Nat Malkus See also Elementary and Secondary Education Act; Evolution in Authority Over U.S. Schools; Individuals with Disabilities Education Act; National Assessment of Educational Progress; National Center for Education Statistics; No Child Left Behind Act; Race to the Top; Title I

Further Readings U.S. Department of Education, Office of Communications and Outreach. (2010). Overview of the U.S. Department of Education. Washington, DC: Author.

V Internal validity is described in terms of content validity, criterion-related validity, and construct validity.

VALIDITY Validity is an overall judgment or criterion that is used to evaluate how well measures in an instrument or a test are used for the purpose of research or a test. In conducting a study, a researcher needs to determine research questions to answer and, then, ways in which to measure the key constructs that will enable these questions to be answered. In social science research, including research on the economics of education, a measure is a standard unit that is used to estimate the degree of something. Researchers draw conclusions on the basis of a research instrument that includes measures for concepts or variables. If the measures for concepts or variables are not appropriate, the conclusions from that research will not be valid. Therefore, the quality of measures is critical in obtaining fair research results. Validity is the extent to which measures in an instrument or a test measure what they are supposed to measure. Social scientists have largely agreed that validity is indispensable to quality research, although definitions and interpretations of validity of measures are broad and comprehensive. Validity of measures is also called validity, measurement validity, or validity evidence. In this entry, validity of measures is used interchangeably with the term validity. In describing validity of measures, this entry briefly introduces types of variables and values for those variables, and then, it describes validity focusing on internal validity and external validity.

Types of Variables and Accordant Values for Measuring Variables Validity of measures refers to the goodness of fit between an operational definition of a concept or a variable and the concept or the variable that is supposed to be measured. Reliability, on the other hand, relates to the consistency or stability of a measure. To answer research questions, researchers select variables to examine. A variable is something that can have any one of a set of values, such as race or ethnicity, gender, test scores, attitudes, or intelligence. Also, a variable can be observable or unobservable. An observable variable can be a categorical variable, an ordinal variable, an interval variable, or a continuous variable. A categorical variable has categorical values. An ordinal variable has ordered values. An interval variable has ordered values with equal intervals or distances. A continuous variable has numeric values. These observable variables have objectively measurable values. Gender is a categorical variable that has two categorical values: (1) male and (2) female. For a categorical variable, values for each category are mutually exclusive. For instance, a category for males in gender includes any men but does not include any women. Education is an ordinal variable that has ordered values, such as kindergarten, elementary, secondary, and postsecondary education levels. Income is 825

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Validity

a continuous variable that has numeric or continuous values. Conversely, a variable can be unobservable, such as emotion, self-esteem, or intelligence. Many variables in education research are not directly observable. Measuring an unobservable variable, such as emotion or attitude, is complicated, and often, it is difficult to accurately measure the unobservable variable compared with measuring an observable variable. Most unobservable variables are concepts and can be measured only by inference. Inference can be true or false. To make an accurate inference, the measures for a concept or an unobservable variable must be valid. Pertaining to research questions and objectives, researchers determine what types of variables they will include and explore. For instance, if a researcher is interested in examining whether or not there is a gender difference in eighth-grade students’ mathematics achievement, the researcher will select two observable variables such as gender and test scores. The measures for gender are male and female. These measures are concrete and can be used validly. On the other hand, if a researcher is interested in determining how attitudes are related to eighth-grade students’ mathematics achievement, the researcher should have valid measures for attitudes, and attitudes are not directly observable. To measure unobservable variables, researchers operationally define the variables and construct measures to estimate those unobservable variables in order to solve research questions.

Internal Validity Internal validity refers to how well a test or an instrument yields valid results in the research context that was administered. Internal validity is described here in terms of content validity, criterion-related validity, and construct validity. Content Validity

In a test or an instrument for research, content validity is related to whether the test or an instrument includes content that is supposed to be measured. In particular, content validity is critical for a student achievement test because the purpose of an achievement test is to measure how well students have learned the content. For instance, if a teacher wants to test eighth-grade student learning in algebra, the measures in the test should be related to instructional objectives. The questions in the test must be relevant to the content that was taught by the teacher. If a test covers only the content that

was taught by a teacher and does not include any questions for content that was not taught, the test seems to have content validity. However, even if the test seems valid, there is no guarantee that the test includes all possible questions in the content of algebra domain to measure student learning in algebra. Put another way, if a classroom assessment does not cover all content in state academic standards, the content validity of the classroom assessment is limited. Furthermore, if a teacher wants to measure student aptitudes in algebra, it is even more difficult to evaluate content validity. Also, content validity usually depends on an expert’s judgment. Therefore, content validity is used not without limitation. Criterion-Related Validity

While content validity more often depends on experts’ judgment, criterion-related validity is a quantitative approach used to measure a validity coefficient, usually a correlation coefficient. A correlation coefficient tells how strongly two variables are related. The range of correlation coefficients is between 0 and 1. If a correlation coefficient between two variables is zero, there is no correlation between the two variables. If a correlation coefficient is .70 or higher but less than 1, it is considered that there is a substantial correlation between the two variables. Criterion-related validity includes predictive validity and concurrent criterion-related validity. Predictive validity refers to a correlation coefficient between a measure and a future criterion that is predicted by the measure. For example, the ACT and SAT are openly used to predict whether or not a candidate will be successful in college, and there has been some predictive validity found between these tests and academic performance in college. Concurrent criterionrelated validity is relevant to computing a correlation coefficient between two measures that have different scales but use the same criterion. If a new test has an accepted criterion, and the test results from the new test are highly correlated with the test results from a well-established test, the new test has concurrent criterion-related validity. However, criterion-related validity also has limitations. Most concepts are abstract, and no appropriate criterion measures are developed for many concepts. For instance, a criterion variable for self-esteem is not known. Construct Validity

Construct validity involves whether or not an instrument or a test actually measures concepts that

Validity

are supposed to be measured. In particular, construct validity is important for an instrument or a test that includes measures that were not measured previously. According to Samuel Messick, construct validity is based on the degree to which evidence supports the interpretation of, or meaning of, test scores. Evidence can include content-related evidence and criterion-related evidence. Observable variables such as weights can be directly measured by using scales. In contrast, unobservable variables such as self-esteem, intelligence, or creativity cannot be directly measured; rather, these unobservable variables are often measured through inference. Inference can be wrong if the measures for instrument are not valid. To measure concepts or unobservable variables, researchers make operational definitions of variables in the context of their research to obtain the best fit with the concept. An operational definition of a variable is very specific, and it shows how the variable will be measured. The clearer an operational definition for a variable, the better the measures of the variable capture the characteristics of the concept; thus, construct validity increases. For instance, if a researcher wants to examine whether or not students’ self-esteem is related to mathematics achievement, the researcher needs an operational definition for what self-esteem means and then to know how to measure it. The instrument should only include questions that relate to self-esteem and should not include any questions that relate to students’ reading comprehension ability or cultural knowledge. For one concept, however, multiple operational definitions can be formulated by researchers, and consequently, multiple or inconsistent outcomes can be generated measuring the same concept. The interpretations from particular research results that are based on an operational definition may be valid only in that particular research context and not in others. Therefore, construct validity has limitations.

External Validity External validity is related to the generalizability of certain research results. External validity depends on whether the research results can be generalized beyond a particular research situation. In other words, research has external validity if the research results from a researcher can be replicated by other researchers in different situations, times, and places. For instance, when a researcher in California analyzes data that have representative samples to find

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out the relationship between students’ socioeconomic status and their academic achievement, the research can be replicated by other researchers who analyze the same variable based on other representative samples in different states and in different times. If researchers in different states can draw the same conclusion that students’ socioeconomic status is related to student achievement, the research results from different researchers in various places can be generalized and the research has external validity. To obtain external validity, researchers use random sampling to collect representative samples. Nonetheless, it should be cautioned that neither do any data include the whole population nor do any data have perfect measures without any measurement errors.

Conclusion Validity of measures is one of the most important conditions for quality of an instrument or a test. Validity depends on whether the measures for an instrument or a test make sense and are appropriately used to measure what is supposed to be measured. The types of aforementioned validity are not mutually exclusive. Rather, those types of validity are somewhat mutually compensated. Also, validity of measures is comprehensive, and several different approaches have been used to determine validity, including internal validity and external validity. A test or an instrument is not in itself valid or invalid. Rather, validity is determined by inferences that are supported by evidence from an instrument or a test that has validated measures. Validity also relies on the usefulness or appropriateness of inferences on the basis of an instrument that covers what is supposed to be measured. For instance, if a teacher is interested in eighth-grade student achievement in reading comprehension, the teacher should test eighth-graders’ reading comprehension skills, not mathematics skills, nor other grades’ reading comprehension skills. Therefore, whether information resulting from a test or instrument is valid depends on whether the test or instrument is appropriately used to make correct inferences based on the test or the measures of instrument. Jeongmi Kim See also Instrumental Variables; Measurement Error; National Datasets in Education; Omitted Variable Bias; Reliability

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Vertical Equity

Further Readings Keeves, J. P. (Ed.). (1997). Educational research, methodology, and measurement: An international handbook (2nd ed.). New York, NY: Pergamon Press. Kubiszyn, T., & Borich, G. (2007). Educational testing and measurement: Classroom application and practice (8th ed.). Danvers, MA: Wiley. Messick, S. (1995). Validity of psychological assessment. American Psychologist, 50, 741–749. Singleton, R. A., & Straits, B. (2005). Approaches to social research (4th ed.). New York, NY: Oxford University Press. Vierra, A., Pollock, J., & Golez, F. (1998). Reading educational research (3rd ed.). Upper Saddle River, NJ: Prentice Hall. Vogt, W. P. (2007). Quantitative research methods for professionals. Boston, MA: Allyn & Bacon.

VALUE-ADDED MODEL See Teacher Value-Added Measures

VERTICAL EQUITY In education finance, vertical equity is defined by the notion that unequals should receive appropriately unequal treatments. Furthermore, vertical equity proposes that certain populations in a school system need more revenue depending on the qualification of these populations; hence, vertical equity has implications for K-12 funding mechanisms. This entry outlines the origin and evolution of the term vertical equity and its use in school finance as a methodological tool. Next, it relates vertical equity to economics by providing a conceptual framework for the term, thereby helping inform research, legislation, and policy in relation to public expenditures in education. The entry then outlines the limitations of vertical equity and concludes with some remarks regarding its utility.

Origins of Vertical Equity The use of vertical equity as a term in finance originated in relation to the study of taxation policy and tax equity. Vertical equity began to emerge as a concept in education finance in the late 1970s through the research of Robert Berne and Leanna Stiefel. In their early work on equity in school finance, Berne

and Stiefel provided a definition of vertical equity (unequal treatment of unequals) and proposed that children with additional educational needs, such as English Language Learners and students with disabilities, should receive unequal treatment in the form of larger revenues per pupil. Vertical equity, as described in this early work, would help address school finance equity–based legal challenges by providing students with additional educational needs with more funding to deliver an equivalent education to all students. For example, an English Language Learner in a public school may require more funding to receive an equal education because he or she is not proficient in English. Conceptually, vertical equity would suggest that differences in funding are necessary for a school to educate all of its students based on the number of students with additional educational needs, the cost to educate these students, the specific needs of students in the district, and the size of the district. This means that as vertical equity increases, the operating revenue for specific populations should increase as well, and a school would receive increased funding as the number of students with additional educational needs increases.

Function of Vertical Equity Vertical equity evolved from a measurement of education finance at the state level (i.e., horizontal equity) to a measure of education finance within districts and schools. As a concept, it evolved from the need to measure how equitable school finance is from the perspective of the consumer. Current research uses bivariate and multiple regression analysis as well as correlational analysis to assess vertical equity as a relationship between objects and variables of interest. Research on vertical equity also includes analysis using a combination of student count and univariate dispersion methods that measure different student needs in a school system. In short, for school funding to be considered vertically equitable, a school or school district with higher populations of students with additional educational needs should receive more funding. In an effort to provide a more equitable and adequate education to students with additional educational needs, states have modified their funding formulas and adjusted per-pupil revenue based on the socioeconomic composition of a district. But, as has been contested through litigation, this in itself has not been enough to ensure that all populations will be adequately educated. Clearly,

Vocational Education

however, vertical equity has played a significant role in how equity is defined in law and policy.

Limitations of Vertical Equity Although vertical equity is able to measure funding for students at the school level, there are constraints on its utility due to measurement limitations and operationalization of equitability. In theory, vertical equity measures changes in funding for students with additional educational needs. This means that vertical equity must have a definitive measurement metric for it to be accurately assessed. On the whole, this has not been the case. Vertical equity is measured by using descriptive analysis of the variation among per-pupil revenues after an adjustment for the vertical equity measure, ratios of per-pupil revenues for two groups, bivariate correlations or regressions between per-pupil revenues, or selected vertical equity characteristics of districts. There are many limitations to this type of measurement. First, metrics used to assess vertical equity do not account for the effect of multidimensional needs of the student and the district. When examined through the lens of policy, this looks like the current system whereby states allocate money for various student groups in lump sums instead of accurately assessing the needs of particular student groups within the school or district. A second major limitation is that there are no specific target measurements that definitely point to vertical equity attainment. It has been theorized that ratio analysis can measure the unequal treatment of unequals, but there is no definitive way to measure how large differences in funding between various student populations should be. The current statistical analysis measurements (e.g., correlation and regression analysis) can assess whether states are allocating more money or if districts are receiving more funding but cannot assess the adequacy of this amount. Also, questions arise about which populations are considered to have greater needs. This second limitation can be summed up with two questions: (1) Who is considered unequal? and (2) Who determines if these groups are treated unequally? Cost studies have attempted to provide some resolution, but as revenue for public education shrinks, a state’s ability to apply the actual funding levels determined by these cost studies becomes more difficult.

Conclusion Vertical equity is a concept in education finance derived from the need to measure education funding

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not only at the state level but also at the district and school levels. It provides a framework for the statistical measure of unequal groups and the funding applied to these groups. As funding for students with additional educational needs increases in proportion to funding for students without these needs, vertical equity will also increase. Vertical equity, although an important concept, still suffers from two significant limitations. First, while vertical equity can assess changes in funding levels, it does not measure whether the funding is sufficient to provide an appropriate education to students with additional educational needs. Second, it is subject to variations in application in that states determine the amount of funding necessary to educate students with additional educational needs. This may lead to underfunding and decreases in vertical equity, resulting in litigation necessary to determine the equitability of funding in the school districts. Oscar Jimenez-Castellanos and David Martinez See also Education Finance; Educational Equity; Horizontal Equity

Further Readings Berne, R., & Stiefel, L. (1979). Concepts of equity and their relationship to state school finance plans. Journal of Education Finance, 5(2), 109–132. Berne, R., & Stiefel, L. (1994). Measuring equity at the school level: The finance perspective. Educational Evaluation and Policy Analysis, 16(4), 405–421. Berne, R., & Stiefel, L. (1999). Concepts of school finance equity: 1970 to the present. In H. Ladd, R. Chalk, & J. Hansen (Eds.), Equity and adequacy in education finance: Issues and perspectives (pp. 7–33). Washington, DC: National Academies Press. Musgrave, R. A. (1967). In defense of an income concept. Harvard Law Review, 81, 44–62. Toutkoushian, R. K., & Michael, R. S. (2007). An alternative approach to measuring horizontal and vertical equity in school funding. Journal of Education Finance, 32(4), 395–421.

VOCATIONAL EDUCATION Capital, in the economic sense, is any physical or abstract entity with productive capability. Vocational skills are a form of human capital in that the productive capability to perform a particular job resides in an individual who has gained the necessary skills and knowledge. Vocational education is formal

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Vocational Education

training in the skills required to perform a specific trade or vocation. Broadly defined, vocational education takes different forms: accredited programs provided by high schools or institutions of higher education, apprenticeship, or on-the-job training. In contrast, academic education encompasses more intellectual and less applied learning. The humanities and pure sciences are leading examples of academic education, whereas trade schools and career academies typify vocational education. In practice, however, there is considerable overlap between the two domains. Emphasis on vocational versus academic education has varied across cultures and time, and perceived deficiencies in one or the other are a persistent source of debate among educators and policymakers. The entry proceeds as follows. First, vocational education is situated in the context of human capital theory. Then, four classes of vocational education systems are introduced. This is followed by a brief sketch of research of the labor market returns to vocational versus academic training.

Vocational Human Capital Gary S. Becker’s seminal theory of human capital investment distinguished two broad types of human capital: general and firm-specific. General human capital refers to the skills and knowledge that enhance workers’ productivity in any firm and any occupation—for example, literacy, numeracy, communication, critical thinking, problem solving, and so forth. Firm-specific human capital, in contrast, characterizes the idiosyncratic skills and knowledge that enhance worker productivity at one firm. Examples of this more narrow class of human capital include the skills necessary to operate custom-built machinery, to process certain kinds of paperwork, or to navigate a firm’s chain of command. With some exceptions (e.g., where workers have limited flexibility to change jobs), workers will bear much of the cost of their general training because firms do not wish to invest in portable human capital. Firms bear at least a part of the cost of firm-specific human capital. Vocational education lies between general and firm-specific human capital because terminal, vocational training in a skilled trade can be applied in multiple firms but cannot necessarily be applied in any occupation. In much the same way that the returns to firm-specific human capital erode between jobs, vocational human capital is at risk of depreciation when workers move from one occupation to

another or when technological change allows physical capital to crowd out labor.

Systems of Vocational Education Informal systems of apprenticeship and vocational education date back thousands of years, well before the Platonic Academy formalized academic, intellectual education. The Babylonian Code of Hammurabi, one of the earliest media of recorded history, codifies circumstances under which an apprentice could prosecute his master if the latter failed to teach his craft. In antiquity, vocational training was the bedrock of knowledge transmission between generations but was not typically delivered by secondary (i.e., high school) or postsecondary (college) institutions. The modern-day coexistence of higher level academic and vocational education, both available to the masses, arose in the early 20th century when industrial economies began to value human capital as much as machinery and natural resources. Science, technology, and invention helped fuel the growth of big business. The day-to-day tasks of workers grew more complex, and accordingly, firms demanded more workers with advanced education in a range of disciplines, both intellectual and practical. This demand-side development coincided with a supply-side expansion of primary and secondary education in industrial nations, particularly in the United States. By nature, universal education systems cater to diverse student interests spanning academic and practical knowledge. The cost of vocational education is often borne by students or taxpayers rather than firms due to the portability of occupational skills across firms as well as credit constraints that hinder students from borrowing against future earnings. The following subsections introduce four major classes of preemployment vocational education: (1) tracked vocational training and/or apprenticeship, (2) blended vocational and academic education at the secondary level, (3) publicly subsidized job training programs, and (4) vocational postsecondary and tertiary programs. Vocational Tracking and Apprenticeship

The German dual system of apprenticeship is a leading example of tracked vocational training. For two out of three students in Germany, secondary schooling is part time and highly applied, engineered toward one of about 350 careers and supplemented

Vocational Education

by relevant apprenticeship training in a partnering firm. Similarly structured dual systems are found in Austria, Denmark, Switzerland, and Turkey. The touted benefits of structured apprenticeship programs are higher rates of employment and secondary schooling that is more responsive to employers’ demand for particular skills. Blended Academic and Practical Secondary Education

Formal apprenticeship programs are very limited in number and scale in the United States, and singularly vocational secondary schools are rare. Instead, the United States is characterized by one of the oldest secondary systems of academic education available to youths en masse. The U.S. high school movement dates back to New England in the early 20th century: By 1955, 80% of U.S. youths in the age-group 15 to 19 years were enrolled in academic secondary schools versus less than 20% for much of Europe. It should be noted, however, that the movement’s progression was uneven across races and regions. One concession made by supporters of African American education in the segregated Southeast was that African American schools would focus on practical skills in lieu of intellectual training. Though the emphasis of more advantaged schools was primarily academic, curricula in these schools were interwoven with practical applications as well. In the current day, U.S. secondary education remains focused on general academic training, but with overlap between academic and practical content. Career and technical education programs (CTE, organized by the National Association of State Directors of Career Technical Education Consortium) are expanding the footprint of secondary vocational education. CTE programs are organized into 16 “clusters” that include disciplines with traditionally strong academic components (e.g., business, law, science, technology, math, and engineering) as well as traditionally vocational trades (e.g., manufacturing and construction). The extent to which CTE programs will supplant or supplement academic secondary education remains to be seen. In light of growing income inequality, youth underemployment, and employer concerns about the job readiness of high school graduates, many have argued for increased emphasis on vocational training in secondary schools. Others worry that reallocating limited resources to career training

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(or, as some systems have proposed, making career coursework a graduation requirement or a substitute for academic requirements) will harm the prospects of college-bound students. Postsecondary and Tertiary Vocational Programs

The mass secondary education movement in the United States evolved naturally into a mass postsecondary education movement. The result is a large network of postsecondary education institutions differentiated by selectivity and duration. Open-access community colleges have typically specialized in vocational disciplines (but not to the exclusion of general education), offering certificates and associate’s degrees in a variety of narrowly defined occupations. Four-year colleges and universities, in contrast, tend to be more focused on academic disciplines, but not to the exclusion of applied training. In recent years, for-profit postsecondary education institutions have emerged as increasingly popular providers of vocational, career-focused education. Less common venues for postsecondary vocational schooling are public technology centers, such as the Tennessee Colleges of Applied Technology. Public Job Training Programs

Publicly funded and publicly operated job training programs are another source of vocational training. These are typically targeted to high school dropouts and other young individuals who are “at risk” of failing to transition successfully from school to work. Research has shown that programs of this nature yield little to no impact on the earnings of targeted individuals and that programs that do have some impact on earnings do not necessarily pass cost-benefit examination. Insufficient resources may be a root cause: U.S. per-capita public resources devoted to job training programs for high school dropouts and others lacking career-ready skills are a small share of the value of subsidies and grants for higher ability, college-going students. Moreover, underemployed youths compete for public resources with other out-of-work individuals, like those benefitting from the Workforce Investment Act. This act is a large federal job training program that funds job training for senior citizens, workers who are involved in mass layoffs, those transitioning off Temporary Assistance for Needy Families and General Relief, in addition to high school dropouts and at-risk youths.

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Vocational Education

Returns to Vocational Training Technical Skills, Economic Growth, and Income Inequality

The U.S. tradition of mass secondary generalskills education is credited with spurring a long period of post–World War II growth that outpaced that of European nations with traditions of separate vocational and academic tracks. But the relationship between vocational emphasis and growth is not necessarily negative. The successful industrialization of East Asia, for instance, is credited in part to preemployment vocational training. Furthermore, some scholars contend that emphasizing academic high school curricula in the United States led to overinvestment in education for students who would have been better served by a vocational track, resulting in labor market mismatch and growing income inequality around the turn of the 21st century. Since the 1980s, new technologies have helped high-productivity, high-earning workers do more (and earn more) but have supplanted the work of middle-skilled workers. This “hollowing out” of intermediate skills across all sectors of the economy—industrial, service, and agriculture sectors were all revolutionized by information technology— distinguishes the most recent wave of technological change from earlier episodes, which hollowed out the industrial sector but increased demand for intermediate and high technical skills in other sectors. Individual Returns to Vocational Training

Many studies relating adult employment and earnings to the nature of schooling suffer from omitted variable bias: Factors that push students into vocational education also affect subsequent occupational choice, productivity, and earnings. Lower cognitive ability, for instance, in systems with academic entrance examinations will correlate with vocational training and later earnings, understating the effect of vocational education on earnings. Even in systems with self-selected vocational training, higher ability students gravitate toward academic disciplines. Conventional wisdom holds that vocational education eases the school-to-work transition. Empirical research broadly supports this notion in the years immediately following schooling, despite the ability bias described above. Individuals with general education tend to have a longer, bumpier period of job search and job mismatch than individuals with narrower training suited to particular occupations. Over

the life cycle, however, research has shown that the employment of academically trained workers outpaces that of vocationally trained workers, especially in European countries with apprenticeship systems. The impact of vocational education on earnings—controlling for unobserved ability and productivity—is inconclusive and subject to omitted variable bias. Two studies described below avoid these biases by taking advantage of experimental or quasi-experimental assignment to a higher degree of secondary vocational education. Contrasting conclusions underscore the idea that some vocational systems are more effective than others. Career academies, dating back at least 35 years, represent one of the largest formal systems of vocational education in the United States. Career academies are elective high school tracks offering a mixture of academic and vocational coursework. A long-running experimental assessment of career academies finds significant and cost-effective impacts of participation on earnings and employment, more so for men than for women. In 1973, a quasi-experiment in vocational versus academic education arose naturally in Romania, when students entering vocational schools were abruptly required to have two additional years of general, academic schooling. The economists Ofer Malamud and Christian Pop-Eleches found that the reform had the expected effects on vocational training and occupational sorting but had no effect on total years of schooling or secondary attainment. More than 20 years after the reform, students who experienced more general education than their close peers exhibited no significant gains or losses in employment and earnings. Celeste K. Carruthers See also For-Profit Higher Education; Human Capital; Job Training; Tracking in Education

Further Readings Autor, D. H., Katz, L. F., & Kearney, M. S. (2008). Trends in U.S. wage inequality: Revising the revisionists. Review of Economics and Statistics, 90(2), 300–323. Becker, G. S. (1962). Investment in human capital: A theoretical analysis. Journal of Political Economy, 70(5), 9–49. Goldin, C. (2003). The human capital century. Education Next, 3(1), 73–78. Hanushek, E. A., Woessmann, L., & Zhang, L. (2011, October). General education, vocational education, and

Vocational Education labor-market outcomes over the life-cycle (NBER Working Paper No. 17504). Cambridge, MA: National Bureau of Economic Research. Katz, L. F., & Margo, R. A. (2013, February). Technical change and the relative demand for skilled labor: The United States in historical perspective (NBER Working Paper No. 18752). Cambridge, MA: National Bureau of Economic Research. Krueger, D., & Kumar, K. B. (2004). US-Europe differences in technology-driven growth: Quantifying the role of education. Journal of Monetary Economics, 51(1), 161–190. LaLonde, R., & Sullivan, D. (2010). Vocational training. In P. B. Levine & D. J. Zimmerman (Eds.), Targeting investments in children: Fighting poverty when resources

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are limited (pp. 323–249). Chicago, IL: University of Chicago Press. Malamud, O., & Pop-Eleches, C. (2010). General education versus vocational training: Evidence from an economy in transition. Review of Economics and Statistics, 92(1), 43–60. Ryan, P. (2001). The school-to-work transition: A crossnational perspective. Journal of Economic Literature, 39(1), 34–92. Tan, J., & Nam, Y. J. J. (2012). Pre-employment technical and vocational training and education: Fostering relevance, effectiveness, and efficiency. In R. Almeida, J. Behrman, & D. Robalino (Eds.), The right skills for the job? Rethinking training policies for workers (pp. 66–103). Washington, DC: World Bank.

W an additional dollar increment for every student who is poor, lacks English proficiency, requires services for disabilities, and so on. This traditional staffingbased allocation model didn’t allow schools to have flexibility in the use of their funds. In contrast, with the WSF allocation system, school leaders have some flexibility in how they apply funds and can select the mix of staff and other resources as appropriate for their schools. As such, the development of WSF is important to the economics of education in that it sets the stage for rethinking how resources are applied in public education.

WEIGHTED STUDENT FUNDING Weighted student funding (WSF), also known as student-based allocation, student-based budgeting, and fair student funding, is a means of allocating resources from districts to schools or from states to districts. Whereas most districts allocate resources by use of a staffing formula, with WSF, fixed-dollar increments are allocated out to schools by student or student type. WSF is a significant development within the field of education finance and one that continues to gain traction particularly among larger urban districts in the United States. This entry begins by describing how WSF works at the district level, why districts use it, and key implementation issues. The entry then describes the use of WSF for state allocations. WSF was first formally introduced in 1976 by Superintendent Mike Strembitsky for the Edmonton Public Schools in Alberta, Canada. The budgeting strategy was designed as a means to allocate funds to schools (in contrast to allocating staff positions) such that the school leaders could then make choices about how to use their funds. Traditional staffingbased allocation models rely almost entirely on staffing formulas that assign staff positions (vs. funds) to schools and effectively ensure that all schools are staffed in a uniform manner. For example, whereas a traditional staffing allocation system might allocate one teacher for every 22 students, one principal per school, one vice principal and one counselor for every 400 students, and so on; a WSF model would allocate a fixed-dollar amount for each student, plus

Weighting Students With Greater Needs WSF formulas hinge on the notion that funding should be apportioned according to individual student needs. The term weighted in WSF refers to the additional funds allocated to students with characteristics that require greater resources. For instance, the Boston Public Schools 2013–2014 WSF model allocated $4,981 for each secondary student, an additional $383 if the student was considered poor, and other increments for student needs as indicated in Figure 1. Using the secondary student allocation as the base, the increments for different characteristics can be converted to a percentage weight. As is indicated in Figure 1, the percentage weight for poverty is then 8%, meaning that secondary schools in Boston receive 8% more funds for a poor student than for a nonpoor student. Defining weights in terms of the percentage over base is sometimes preferred over using a specific dollar amount because a percentage weight allows policymakers to increase 835

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Weighted Student Funding

$4,981

Grades 9–12

Poverty

Weight = 1.0 or 100%

$383 Weight = 0.08 or 8%

$3,832

Low Disability

Weight = 0.77 or 77%

$5,364

Moderate Disability

Weight = 1.08 or 108%

English Language Learners $0

Figure 1

$1,648 Weight = 0.33 or 33% $1,000

$2,000

$3,000

$4,000

$5,000

$6,000

2013–2014 WSF Allocations for Secondary Students in Boston Public Schools

Source: Based on Boston Public Schools weighted student funding budget for fiscal year 2014, with weights based on author’s computations.

the base and proportionally increase student weights at the same time. There are no standard weights used for WSF, although many WSF systems apply additional weights to students with limited English proficiency, poverty (or qualifying for free or reduced-priced meals), prior student performance, disability type, and sometimes grade level, gifted status, or participation in vocational programs. While some systems further weight school characteristics (e.g., school size or magnet status), these are characteristics of the schools, not the students, and thus wouldn’t be considered part of a student-based formula.

Reasons for Shift by Districts to WSF Districts in Houston, Seattle, and Cincinnati were among the first in the United States to officially adopt WSF, and in each case, the shift was part of a plan to decentralize decisions around resource use to the school level. Since then, districts in San Francisco, Oakland, Denver, Boston, New York City, Milwaukee, Hartford, Newark, Baltimore, New Orleans, the District of Columbia, and other cities have adopted WSF as their primary means of allocating resources to schools. The reasons for implementing WSF vary but often center on equity, transparency, and school-level flexibility. With financial data emerging that enabled per-student spending calculations by school, in some districts the traditional staffing formulas prompted concerns over equity. In Denver, for instance, an advocacy group called Metro Organizations for

People published actual per-pupil spending by school in 2006, calling attention to uneven distribution of district funds under its staffing-based formula. Shortly thereafter, Denver Public Schools adopted WSF primarily as a means to ensure spending equity across all schools and to enable transparency in its budgeting process. In other districts, WSF is part of a portfolio district strategy, whereby the district’s strategy is to decentralize control of schools down to the school level, encourage differentiation among schools, and allow students to choose their school. In the portfolio model, schools are given the flexibility to apply their funds in different ways as they see fit to best serve their students. In the portfolio strategy, districts such as those in Baltimore and Hartford expect to have different school designs and offer students choice to attend their preferred school. By allocating a fixed amount per student type, the funds are considered “portable” in that the funds follow the student to whichever school is chosen by the student. Along these lines, WSF is thought to promote adoption of innovation, as individual schools have the flexibility to implement new delivery models, without requiring districtwide policy changes. Some also see WSF as aligning school-based accountability with schoolbased spending decisions.

Key Implementation Issues for WSF Districts that adopt WSF tend to phase it in over a number of years or across sets of schools and, typically, then modify the formula on a regular basis.

Weighted Student Funding

And indeed, some districts, such as those in Seattle and the District of Columbia, have abandoned it altogether, returning to a staffing-based allocation system. Generally speaking, implementation of WSF requires decisions not only about what characteristics to weight and by how much but also what portion of the district’s total funds to include in the formula and how quickly to phase it in. WSF districts tend to hold some funds out of the formula, typically for centrally managed functions or shared services (data systems, etc.). One analysis compares WSF districts on what portion of the budget is allocated as part of the weighted student formula, and on this measure, districts vary from 27% to greater than 50% with most adding funds to the studentbased formula over time. Some, including Baltimore City Public Schools, have even added funds for centrally managed services to the formula, enabling schools to purchase centrally provided services on a fixed-price basis. Another key implementation decision is whether to adjust a school’s allocation in some way for variation in salary costs. Salary costs can be higher at some schools if their staff average more years of service or higher levels of degree attainment. While a “pure” WSF model would have schools offsetting higher salary costs elsewhere in their budgets, most districts use a process of “salary averaging” such that all schools are charged only the districtwide average salary for each type of staff position. With salary averaging, schools with lower average salaries essentially subsidize schools with higher salaries. The goal for some districts is to eliminate the salary averaging over time with staff attrition. Underenrolled schools present another challenge, as very small schools might find it difficult—even with spending flexibility—to provide appropriate services on just the formula allocation. Some districts actively close underenrolled schools, while others provide the smallest schools with extra funds outside the formula. Nonformula allocations for small schools or other reasons can be controversial, as those nonformula allocations will draw some funds away from the student-based formula allocations in any constrained resource environment.

WSF at the State Level Only recently have states referred to their allocations as WSF, even though many state formulas allocate large portions of their funds via a student-based

837

formula. The term is now gaining traction as some states are consolidating what had been categorical allocations into a student-based formula such that state funds are flexible and aligned with enrollments of different student types. California’s 2013 finance overhaul, called the Local Control Funding Formula, is described as a “weighted student formula” with “local control.” In California, the Local Control Funding Formula allocates a per-pupil amount per student per grade and then augments that allocation by 20% for students in certain subgroups, including foster youths and those who lack English language skills.

Effects on Student Outcomes To date, no definitive research has directly connected WSF to effects on student outcomes, nor has much research attempted to connect the two. In fact, WSF is not intended to directly influence student learning but rather to enable other reforms that would in turn affect student learning (e.g., school-based decision making). Marguerite Roza See also Budgeting Approaches; Education Finance; Educational Equity; Pupil Weights; School-Based Management

Further Readings Education Resource Strategies. (2012, March). Weighted student funding: Why do districts decide to implement weighted student funding? Education Resource Strategies [Online]. Retrieved from http://www .erstrategies.org/library/why_districts_implement_wsf Hill, P., & McAdams, D. (2002). First steps to a level playing field: An introduction to student-based budgeting. School communities that work (An Initiative of the Annenberg Institute for School Reform at Brown University) [Online]. Retrieved from http:// annenberginstitute.org/sites/default/files/product/283/ files/SBB.pdf Miles, K., & Roza, M. (2006). Understanding studentweighted allocation as a means to greater school equity. Peabody Journal of Education, 81(3), 39–62. Policy Analysis for California Education. (2012). School finance reform: A weighted pupil formula for California [Online]. Retrieved from http://www .edpolicyinca.org/sites/default/files/school_finance_ reform.pdf Roza, M., & Simburg, S. (2013, January). Student-based allocation to enable school choice. Center on

838

Weighted Student Funding

Reinventing Public Education [Online]. Retrieved from http://www.crpe.org/sites/default/files/rr_10_sba_2013_ jan13.pdf Samuels, C. A. (2013, June). Districts experiment with “weighted” funding: Student numbers, needs drive dollars. Education Week [Online]. Retrieved from http:// www.edweek.org/ew/articles/2012/06/13/35weighted .h31.html

Snell, L. (2009, April). Weighted student formula yearbook 2009: Best practices for weighted student formula budgeting (Reason Foundation) [Online]. Retrieved from http://reason.org/files/wsf/yearbook.pdf Thomas B. Fordham Institute. (2006, June). Fund the child: Tackling inequity & antiquity in school finance [Online]. Retrieved from http://www.schoolfunding.info/ resource_center/media/Fordham_FundtheChild.pdf

Appendix A RESOURCE GUIDE Journals Major Journals in Education Economics and Finance

Education Finance and Policy The journal of the Association of Education Finance and Policy, publishing mainly economicsoriented articles on school finance and education policy issues. http://www.mitpressjournals.org/ loi/edfp Educational Evaluation and Policy Analysis Published by the American Educational Research Association, featuring a broad variety of education topics and disciplinary backgrounds, including economics. http://epa.sagepub.com/ Economics of Education Review Specialized field journal. http://www.journals.elsevier.com/economics-of-education-review/ Journal of Education Finance Specialized field journal. http://www.press.uillinois.edu/journals/jef.html Related Journals

Economics journals such as the American Economic Review, Quarterly Journal of Economics, Journal of Human Resources, Journal of Labor Economics, and Journal of Public Economics, among others, occasionally publish articles on the economics of education. Education journals such as the American Educational Research Journal, Teachers College Record, Educational Policy, and others occasionally publish articles on education finance and economics of education. The Journal of Policy Analysis and Management also regularly includes articles on education policy.

Books, Articles, and Reports There are many important books, articles, and reports in the fields of education finance and the economics of education. Here, we highlight a handful of the most important, with a brief description of each. Books

Aaron, H. J. (1975). Who pays the property tax? A new view. Washington, DC: Brookings Institution Press. An important analysis of the incidence of property taxes that continues to have implications for school finance policy today. 839

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Appendix A

Adams, J., Jr. (Ed.). (2010). Smart money: Using educational resources to accomplish ambitious learning goals. Cambridge, MA: Harvard Education Press. A summary of school finance policy issues today, this volume contains chapters that address equity, adequacy, and current approaches to public school choice. Baumol, W. (2012). The cost disease: Why computers get cheaper and health care doesn’t. New Haven, CT: Yale University Press. An overview and application of Baumol’s “cost disease” relevant to educational productivity. Becker, G. S. (1964). Human capital: A theoretical and empirical analysis, with special reference to education. Chicago, IL: University of Chicago Press. One of the major early overviews of the human capital concept, which is central to the economics of education. Berke, J. (1974). Answers to inequity: An analysis of the new school finance. New York, NY: Russell Sage Foundation. One of the first volumes to address the importance of equity in school finance. Berne, R., & Stiefel, L. (1984). The measurement of equity in school finance: Conceptual, methodological, and empirical dimensions. Baltimore, MD: Johns Hopkins University Press. A classic volume on how to measure school finance equity. It remains the standard by which the equity of state funding systems is measured today. Bransford, J., Brown, A., & Cocking, R. (1999). How people learn. Washington, DC: National Academies Press. Provides comprehensive discussion of student learning, important in understanding how resources might be allocated to produce greater student achievement. Brewer, D. J., Gates, S., & Goldman, C. (2002). In pursuit of prestige: Strategy and competition in U.S. higher education. New Brunswick, NJ: Transaction Books. Offers a research-based framework to analyze fiscal behavior of institutions of higher education. Brewer, D. J., & McEwan, P. J. (Eds.). (2010). Economics of education. Amsterdam, Netherlands: Elsevier. Covers all major topics in the economics of education, providing international perspectives that describe the origins of these subjects, their major issues and proponents, their landmark studies, and opportunities for future research. Aimed at a graduate student level. Burkhead, J. (1959). Government budgeting. New York, NY: Wiley. One of the early discussions of public budgeting processes. Burtless, G. (Ed.). (1996). Does money matter? Washington, DC: Brookings Institution Press. An excellent summary of the issues surrounding the question of whether student outcomes are related to spending levels. Chubb, J. E., & Moe, T. M. (1990). Politics, markets, and America’s schools. Washington, DC: Brookings Institution Press. An early defense of choice policy for schools, including suggestions for how choice programs could be implemented in public schools. Coons, J., Clune, W., & Sugarman, S. (1970). Private wealth and public education. Boston, MA: Harvard University Press. The authors of this volume developed the legal argument that low property wealth districts were a suspect classification and thus should be subject to strict judicial scrutiny in legal cases dealing with school finance. This argument was the basis for the Serrano v. Priest decision in California and the decisions in many other school finance cases after that.

Resource Guide

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Cubberley, E. P. (1906). School funds and their apportionment. New York, NY: Columbia University, Teachers College. The first book devoted to consideration of equity in school finance. Friedman, M. (1955). The role of government in education. New Brunswick, NJ: Rutgers University Press. Classic discussion of how government and markets provide education. Friedman, M., & Friedman, R. D. (1980). Free to choose. San Diego, CA: Harvest Books. Milton Friedman was the original proponent of school vouchers; this book describes his arguments clearly and concisely. Guthrie, J. W., Hart, C., Ray, J. R., Candoli, C., & Hack, W. G. (2008). Modern school business administration: A planning approach. New York, NY: Pearson Education. Textbook devoted to concepts of school business management and administration. Harris, D. N. (2011). Value-added measures in education: What every educator needs to know. Boston, MA: Harvard Education Press. Summarizes value-added measures, their computation, and how they can be used. Hartman, W. T. (1988). School district budgeting. Englewood Cliffs, NJ: Prentice Hall. Describes the process of budgeting for public school districts. Ladd, H. F., Chalk, R., & Hansen, J. S. (Eds.). (1999). Equity and adequacy in education finance: Issues and perspectives. Washington, DC: National Academies Press. Ladd, H. F., & Hansen, J. S. (Eds.). (1999). Making money matter: Financing America’s schools. Washington, DC: National Academies Press. These two volumes describe the school finance research of the National Academy at the end of the 20th century. Levin, H. M., & McEwan, P. J. (2001). Cost-effectiveness analysis: Methods and applications (2nd ed.). Thousand Oaks, CA: Sage. A comprehensive introduction to the importance of cost effectiveness in education and how to measure it, with applied illustrations. McPherson, M., & Schapiro, M. (1998). The student aid game: Meeting need and rewarding talent in American higher education. Princeton, NJ: Princeton University Press. Summarizes the economics of student aid at the university level. Mincer, J. (1974). Schooling, experience, and earnings. New York, NY: National Bureau of Economic Research. Landmark study outlining how to model earnings as a function of education and labor market experience. Monk, D. H., & Brent, B. O. (1997). Raising money for schools: A guide to the property tax. Thousand Oaks, CA: Corwin Press. Describes the operation of property taxes to support public schools. Musgrave, R., & Musgrave, P. (1976). Public finance in theory and practice. New York, NY: McGraw-Hill. Classic public finance text. Netzer, D. (1966). Economics of the property tax. Washington, DC: Brookings Institution Press. Major textbook on how property taxes are assessed and evaluated. Odden, A. R., & Busch, C. (1998). Financing schools for high performance: Strategies for improving the use of educational resources. San Francisco, CA: Jossey-Bass. Early description of how research-based strategies can be used to allocate resources in schools to improve learning.

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Appendix A

Odden, A. R., & Picus, L. O. (2014). School finance: A policy perspective (5th ed.). New York, NY: McGraw-Hill. Provides a comprehensive introduction to all aspects of school finance in the United States. The volume is suitable for practitioners and the lay reader. Rebell, M. A. (2009). Courts and kids: Pursuing educational equity through the state courts. Chicago, IL: University of Chicago Press. Treatise on school finance equity and litigation. Wildavsky, A. (1988). The new politics of the budgetary process. Glenview, IL: Scott Foresman. Classic book on public budgeting. Summarizes four earlier editions of The Politics of the Budgetary Process. Widely regarded as the primary basis for understanding and developing public budgets. Wise, A. (1968). Rich schools–poor schools: A study of equal educational opportunity. Chicago, IL: University of Chicago Press. Originally written as a PhD thesis at the University of Chicago, this book is widely credited with providing the rationale for challenging state school finance systems to provide more resources for low-income children. It led to a number of early school finance lawsuits and the improvement of equity across all states. Articles and Reports

Andrews, M., Duncombe, W., & Yinger, J. (2002). Revisiting economies of size in American education: Are we any closer to a consensus? Economics of Education Review, 21(3), 245–262. Discusses findings on economies of scale as they relate to school operations and student achievement. Ashenfelter, O., & Krueger, A. (1994). Estimates of the economic return to schooling from a new sample of twins. American Economic Review, 84(5), 1157–1173. A modern classic with an innovative approach to measuring the rate of return to education. Baker, B. D., Taylor, L., & Vedlitz, A. (2005). Measuring educational adequacy in public schools (Report prepared for the Texas Legislature Joint Committee on Public School Finance, The Texas School Finance Project). Summarizes the major approaches to school finance adequacy. Barnett, W. S. (2011). Effectiveness of early educational intervention. Science, 333(6045), 975–978. Provides evidence regarding the returns to investments in early education. Borman, G. D., & D’Agostino, J. V. (1996). Title I and student achievement: A meta-analysis of federal evaluation results. Educational Evaluation and Policy Analysis, 18(4), 309–326. A comprehensive summary of the effectiveness of Title I programs. Card, D., & Krueger, A. (1992). Does school quality matter? Returns to education and the characteristics of public schools in the United States. Journal of Political Economy, 100(1), 1–40. One of the most important reviews of the link between school quality and earnings. Clune, W. (1994). The shift from equity to adequacy in school finance. Educational Policy, 8(4), 376–394. The first effort to define what we mean by school finance adequacy. Coase, R. H. (1960). The problem of social cost. Journal of Law and Economics, 3(1), 1–44. A classic overview of externalities and how best to deal with them. Coleman, J. S., Campbell, E. Q., Hobson, C. J., McPartland, J., Mood, A. M., Weinfeld, F. D., & York, R. L. (1966). Equality of educational opportunity. Washington, DC: Government Printing Office, U.S. Department of Health, Education, and Welfare.

Resource Guide

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The famous Coleman Report, which was one of the first attempts to use extensive national data to examine the relationships between families, resources, and schools. Dee, T., & Jacob, B. (2009). The impact of No Child Left Behind on student achievement. Journal of Policy Analysis and Management, 30(3), 418–446. A rigorous empirical analysis of accountability policy in the United States. Gill, B. P., Timpane, P. M., Ross, K. E., Brewer, D. J., & Booker, T. K. (2007). Rhetoric vs. reality: What we know and what we need to know about vouchers and charter schools (2nd ed.). Santa Monica, CA: RAND Corporation. A comprehensive, balanced, and nontechnical overview of the extensive empirical literature on charter schools and vouchers. Glazerman, S., Loeb, S., Goldhaber, D., Staiger, D., Raudenbush, S., & Whitehurst, G. (2010). Evaluating teachers: The important role of value-added. Washington, DC: Brookings Brown Center on Education Policy. A balanced overview of issues surrounding the use of value-added measures in teacher evaluation. Goldhaber, D. D., & Brewer, D. J. (2000). Does teacher certification matter? High school teacher certification status and student achievement. Educational Evaluation and Policy Analysis, 22(2), 129–146. A widely cited example of education production function style literature using national longitudinal data—in this case, to examine the effects of teacher characteristics on student achievement. Greenwald, R., Hedges, L., & Laine, R. (1996). The effect of school resources on student achievement. Review of Educational Research, 66(3), 361–396. The first response to Eric A. Hanushek’s argument that there is no systematic link between education spending and student outcomes. The authors review the same studies and reach an alternative conclusion. Hanushek, E. A. (1986). The economics of schooling, production and efficiency in public schools. Journal of Economic Literature, 24(3), 1141–1177. A summary of the education production function concept and literature. This article is widely cited and has been updated multiple times to include studies since its original publication. Krueger, A. M. (1999). Experimental estimates of education production functions. Quarterly Journal of Economics, 114(2), 497–532. A rare example of using experimental data (in this case, from a class-size intervention in Tennessee) to attempt to estimate education production functions. McUsic, M. (1991). The use of education clauses in school finance reform litigation. Harvard Journal on Legislation, 28(2), 307–340. Develops a categorization of state constitution clauses related to education and provides a summary of litigation strategies dealing with each category. Mort, P. (1926). Equalization of educational opportunity. Journal of Educational Research, 13(2), 90–103. First exposition of the foundation program used in most school finance systems across the 50 states. Murray, S. E., Evans, W. N., & Schwab, R. M. (1998). Education-finance reform and the distribution of education resources. American Economic Review, 88(4), 789–812. Major review of the impact of school finance reforms across all 50 states. Reschovsky, A., & Imazeki, J. (2001). Achieving educational adequacy through school finance reform. Journal of Education Finance, 26(4), 373–396. Early cost function estimates for school finance and funding levels. Rivkin, S. G., Hanushek, E. A., & Kain, J. F. (2005). Teachers, schools, and academic achievement. Econometrica, 73(2), 417–458. An example of the use of state longitudinal data to disentangle the impact of teachers and schools on student achievement, with particular attention to the methodological challenges of doing so.

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Appendix A

Sanders, W. L., & Horn, S. P. (1994). The Tennessee value-added assessment system (TVAAS): Mixedmodel methodology in educational assessment. Journal of Personnel Evaluation in Education, 8(3), 299–311. Early development of value-added methods. Schultz, T. W. (1961). Investment in human capital. American Economic Review, 51, 1–17. One of the earliest major introductions of the concept of human capital in education. Spence, M. (1973). Job market signaling. Quarterly Journal of Economics, 87(3), 355–374. Classic treatise on how earnings may be related to education without necessarily increasing an individual’s productivity. Tiebout, C. (1956). A pure theory of local expenditures. Journal of Political Economy, 64(5), 416–424. Classic description of how people sort themselves into communities based in part on the provision of public goods.

Appendix B CHRONOLOGY Date(s)

Event(s)/Publication

1647

General Court of Massachusetts passed the Old Deluder Satan Act requiring every town to set up a school or pay a sum of money to a larger town to support education. Towns with at least 50 families were required to appoint a teacher for reading and writing, while towns with more than 100 families were also required to establish a secondary school. The first tax on property to support local schools was levied in Dedham, MA, in 1648. New Hampshire becomes the second state to require towns to support elementary schools. The Wealth of Nations by Adam Smith is published. Widely considered the first modern book on economics, it includes foundational concepts such as the theory of markets and division of labor. By this year, 13 of the then 23 states have constitutional provisions for provision of education, and 17 of the 23 have statutory provisions pertaining to establishment of public education. The idea of the common school, or secular, publicly funded schools that would mold students into democratic citizens, is revived, and educational planning begins to be shaped by data collection at a systematic level. The Morrill Act allowed for the establishment of land-grant colleges and universities. Educational research begins to emerge as a major field, especially with the founding of major research universities such as Johns Hopkins University (1876) and Stanford University (1891). The Principles of Economics by Alfred Marshall is published. It synthesizes ideas about supply and demand and the costs of production, among other fundamental notions in economics. Elwood Cubberly publishes School Funds and Their Apportionment, which noted the inequities in per-pupil property wealth among school districts within states. The Servicemen’s Readjustment Act, more popularly known as the G.I. Bill, provided financial assistance for education for veterans. Founding of the International Monetary Fund, which prompted collection of a common set of economic data across all IMF member countries.

1693 1776

1820

1830s/1840s

1862 Late 1800s

1890

1905 1944 1947

845

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Appendix B

Date(s)

Event(s)/Publication

1947

The Higher Education for American Democracy report, more popularly known as the Truman Report, is released. The report by the President’s Commission on Higher Education, the first commission established by a president to investigate the educational system, details the state of higher education in the United States. The report recommended dramatic changes to postsecondary education, including the formation of community colleges and increased federal spending in scholarships. Introduction of the Marshall Plan to aid European recovery after World War II. Among other things, the plan provided for statistical and technical assistance to increase productivity of European manufacturers. The rise of growth accounting as economists probe the relationship between growth in inputs and growth in outputs. Congressional passage of the Cooperative Research Act, which facilitated the federal government’s role in educational research. President Dwight D. Eisenhower establishes the Committee on Education Beyond the High School to study the needs and problems of higher education. Jacob Mincer uses the term human capital in a classic article in the Journal of Political Economy. This idea is further expanded by Theodore Schultz in a 1961 article in the American Economic Review and by Gary Becker in a 1964 book, Human Capital: A Theoretical and Empirical Analysis, With Special Reference to Education. Human capital theory posits that individuals make expensive upfront investments to enhance their productivity in hopes of a future stream of benefits. McInnis v. Shapiro and Burris v. Wilkerson court cases challenge differences in education spending resulting from unequal property tax bases. Claims by plaintiffs were denied in both cases. Start of the Perry Preschool experiment. In 2006, results showed that students in the program had overall better educational outcomes. The British scholar Mark Blaug prepared a bibliography of reports and publications in the book An Introduction to the Economics of Education. The Equality of Educational Opportunity report, also known as the Coleman Report, found that differences in school characteristics have a small effect on student achievement. More important to the study of education economics, the study used an economic production function, which models student achievement as a function of student, teacher, school, and program, as well as peer characteristics. The economic production function has become the foundation of most empirical work in the economics of education and provides one straightforward approach to estimating how combining different inputs may lead to various school outputs. The Education Resources Information Center, an online library of education research and information, sponsored by the U.S. Department of Education, is established. The economist William Baumol articulates his “cost disease” idea that education is burdened by rising costs as improvements in technology in the economy are not translated into education provision. The “cost disease” problem in education is made worse by the fact that personnel costs constitute the majority of costs in education. Arthur Wise publishes Rich Schools, Poor Schools: The Promise of Equal Educational Opportunity, which argues that education is a fundamental right protected by the Equal Protection Clause of the Fourteenth Amendment to the U.S. Constitution and that variation in expenditures per pupil that resulted from variation in property wealth is not constitutional.

1948

1950s 1954 1956 1958

Early 1960s

1963 1964 1966

1966 1966

1968

Chronology

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Date(s)

Event(s)/Publication

1968

Serrano v. Priest filed in California challenging education spending disparities on the basis of the Fourteenth Amendment’s Equal Protection Clause and the Equal Protection Clause of the California Constitution. In 1972, the California Supreme Court allowed the case to proceed to trial eventually overturning the state’s education funding on state constitutional grounds.

1970

John E. Coons, William H. Clune, and Stephen D. Sugarman publish Private Wealth and Public Education, which argues that students in low-per-pupil property wealth districts were a suspect classification and that courts should apply strict scrutiny to challenges to school funding systems.

1973

The U.S. Supreme Court rules in Rodriguez v. San Antonio that the strict scrutiny test does not apply to education finance challenges because education is not a fundamental right under the U.S. Constitution, which does not mention education.

1973

In Robinson v. Cahill, the New Jersey Supreme Court ruled that although inequalities in the New Jersey funding system did not create a suspect class, nor was there a fundamental right to education, the state’s funding system did violate the state’s education clause, which called for a thorough and efficient public education system. This led to a number of successful state school finance challenges.

1983

A Nation at Risk report by the National Commission on Excellence in Education is released. The report is widely considered seminal in education research and a call to action for federal, state, and local policymakers to address inefficient and failing public schools.

1985

The first court ruling in the Abbott v. Burke case in New Jersey, a case that ran for more than 30 years. The ruling by the New Jersey Supreme Court included more explicit remedies than court rulings in school finance cases in other states. In New Jersey, the court required that spending in the 30 poorest districts had to be at the same level as the average of the 100 highest spending districts.

1986

The economist Eric Hanushek conducted a meta-analysis and concluded that additional dollars in education would be ineffective due to wasteful spending. These findings have been challenged by other researchers who have found that expenditures do make a significant positive difference on student achievement.

1988

The first cohort of the National Education Longitudinal Studies (NELS) program tracks students beginning with their elementary or high school years and follows them over time into adulthood.

1989

The Kentucky Supreme Court rules in Rose v. Council for Better Education, the first of the “adequacy” lawsuits filed in education. The court not only held the state’s finance system to be unconstitutional but also effectively declared the entire state education system unconstitutional, requiring the state to reestablish its state department of education, create a new funding system, and develop new student accountability standards.

1991

The first charter school in the United States is formed in Minnesota.

1995

Trends in International Mathematics and Science Study (TIMSS) begin assessing math and science achievement in fourth and eighth grades every 4 years across multiple countries.

2000

Start of the Programme for International Student Assessment (PISA), which tests 15-yearolds in randomly selected schools across multiple countries every 3 years. In 2012, 65 countries participated with around 28 million students.

2001

Progress in International Reading Literacy Study (PIRLS) assesses trends in reading comprehension in fourth grade every 5 years.

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Appendix B

Date(s)

Event(s)/Publication

2002

No Child Left Behind Act (NCLB) is signed by President George W. Bush. It requires states to adopt a standards-based accountability approach and greatly accelerated the trend that several states, such as North Carolina, Oklahoma, and Texas, had already begun. Portfolio management model, which includes charter schools and traditional public schools and provides more autonomy for individual schools, is implemented in New Orleans in the aftermath of Hurricane Katrina. Commission on the Future of Higher Education releases the report A Test of Leadership: Charting the Future of U.S. Higher Education, which proposes solutions to the problems facing higher education. Race to the Top, a grant program administered by the U.S. Department of Education, is funded by the American Recovery and Reinvestment Act. It is intended to catalyze nationwide innovation and education reform, including performance-based teacher evaluation systems, Common Core State Standards, and support for “market-based” reforms. The Los Angeles Times publishes school and teacher value-added scores, marking the first time that value-added estimates were publicly made available. States are allowed to apply for waivers for the NCLB under which they implemented education reforms backed by the Obama administration in exchange for more flexible accountability requirements. In Gannon v. Kansas, the Kansas Supreme Court ruled that the level of spending for education was too low and thus unconstitutional, and it appeared to require the Legislature to provide more funding for schools. The issue of separation of powers remained a topic of discussion in Kansas as this book was written.

2005

2006

2009

2010 2011

2014

Appendix C GLOSSARY allocative efficiency Occurs when the level of output demanded by society is satisfied by the firms in the market, and it represents the optimum production and consumption point when the benefit of an extra unit of a good consumed is equal to the cost of producing it (see entry). attrition bias Occurs when subjects in a study are observed over time, and some subjects choose to discontinue their participation in an experiment. There may be systematic observed and unobserved differences between those who leave the experiment and those who remain, so that outcomes for only a nonrandom subset of those in the treatment and/or control groups are available. bootstrapping Method for assigning measures of accuracy to sample estimates, by which repeated sampling of a single dataset is done to construct an empirical distribution for the target statistic. categorical dependent variable An outcome variable that has two or more categories in no specific order and typically takes the value of 1 or 0 to indicate the different groups. chance error See random error. clearing the market See market clearing. coefficient estimate Gives an approximation of the change in the expected value of the dependent variable for a one-unit increase in the independent variable while holding all other independent variables constant. coefficient of variation Statistical measure of the dispersion of data points in a series around the mean; the ratio of the standard deviation to the mean. confounding variable A variable that obscures the effects of another. consumer surplus Difference between the consumers’ willingness to pay for a commodity and the actual price paid by them. cost function Mathematical formula used to predict the cost associated with a certain level of output. Also shorthand for a type of analysis of educational adequacy (see Adequacy and Adequacy: Cost Function Approach entries). cost-benefit analysis Way to evaluate whether the benefits exceed the costs of projects or policies. Includes assigning market values to projected benefits and costs and discounting over the appropriate time frame of the project (see entry). cost-effectiveness analysis Method of evaluating the effectiveness of interventions or determining which intervention provides the greatest improvement or benefit per dollar spent (see entry). cost-of-living adjustment (COLA) Periodic changes in wages to compensate for loss in purchasing power of money due to inflation. cross-sectional data Data collected at one point in time on a sample of individuals. data envelopment analysis Nonparametric technique of estimating production functions and evaluating the efficiency of several producers (see entry). 849

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Appendix C

decision-making unit Team of individuals within an organization that is responsible for finalizing major decisions. demand elasticity Refers to the sensitivity of demand for a good to changes in other economic variables. The most common type of demand elasticity is “price elasticity of demand,” which refers to changes in quantity demanded in response to changes in price. difference-in-differences Quasi-experimental method that compares the differences in the outcome measure for treatment and control groups before and after a policy or program is implemented (see entry). discount rate Used in a general sense to indicate the rate at which future costs or benefits are adjusted to present value. In a more specific sense, it is the interest rate charged to commercial banks and other depository institutions for loans received from the Federal Reserve Bank’s discount window. discounted utility A measure of future satisfaction in present value. dispersion The degree to which items vary from some central value. distance function Describes the distance between elements of a set. distorted markets See market distortion. dual labor markets A model distinguishing between jobs in the primary sector with high wages, status, and good working conditions and those in the secondary sector with low wages and poor working conditions (see entry). dummy variable Used in statistical analyses to sort data into mutually exclusive categories that take the value of 0 or 1. The term dummy relates to the fact that the values taken on by such variables do not indicate a meaningful measurement but rather the categories of interest. dynamic complementarity Two activities are complementary if doing more of one activity increases the returns from doing the other. economies of scale Can be defined as the per-unit decreasing cost advantages that a plant or an enterprise gains by increasing the overall size of operation (see entry). economies of scope Cost advantages that result when firms provide a variety of products rather than specializing in a single product or service (see also economies of scale). endogeneity bias Situation in which causality is erroneously ascribed to a particular factor in a model. endogenous variable Factor in causal modeling whose value is dependent on other variables. equilibrium State in which the selected interrelated variables are adjusted to each other and therefore remain static when external factors are assumed fixed. excludable A good or service that cannot be accessed by people who have not paid for it. exogenous variable Factor in causal modeling whose value is independent from the states of other variables in the system. expectations operator Value of a random variable one would expect to find if one could repeat the random variable process an infinite number of times and then take the average of the values obtained. externality Consequence of an economic activity that is experienced by unrelated third parties; externalities can be either positive or negative. factor analysis Process in which the values of observed data are expressed as functions of a number of possible causes to find which are the most important. factor prices Prices of the factors of productions when factor supply equals factor demand. The main factor prices are wage rates (factor price of labor), interest rates (factor price of capital), rents (factor price of land), and profits (factor price of technology and entrepreneurship) (see entry). federal range ratio Divides the difference between the revenue for the student at the 95th percentile and the student at the 5th percentile by the revenue for the student at the 95th percentile. The result is a measure of disparity that is not affected by inflation.

Glossary

851

fixed-effects models Controls for the unobserved differences that may result in the difference in outcomes between treatment and comparison groups and provides more unbiased estimates of causal effects than crosssectional ordinary least squares (OLS) regression (see entry). forcing variable In nonexperimental settings such as regression discontinuity designs, forcing (or running) variables are used to determine treatment assignment. It is a measure for which there is a cutoff or threshold that determines eligibility for treatment. frontier Maximum potential output of a completely efficient decision-making unit. fungibility Property of a good whose individual units are capable of mutual substitution. gainful employment Occurs when a worker gets consistent work and payment from a firm, or it can refer to simply having a paying job and being on an employer’s payroll (see entry). general equilibrium Condition in which there is economic equilibrium for all variables in a market (see Partial and General Equilibrium entry). Gini coefficient Measure of statistical dispersion intended to represent the income distribution of a nation’s residents. hedonic wage models Posit that there are compensating wage differentials, thus, jobs that are more demanding or unpleasant or those that have less desirable working conditions will command higher wages (see entry). hierarchical linear modeling (HLM) A statistical method to analyze parameters that vary at more than one level (e.g., in education, districts, schools, and pupils are often used as multiple levels in HLM). high-powered incentives Efficiency gains from a market transaction that flow directly to the parties involved in the transaction. human capital Notion that individuals’ decisions about schooling are similar to that of firms about physical capital; individuals make expensive upfront investments to enhance their productivity in hope of a future stream of benefits (see entry). income elasticity of demand Measure of the rate of change in the demand for a good in relation to the rate of change in income. independent variable Inputs in a statistical model that are the presumed “cause” of the changes observed in the dependent variable. indirect spending Goods and services that are not directly incorporated into a product being manufactured. input-output model Matrix that represents the inputs and outputs of various industries, and measures the effects of industries in the economy as a whole. instrumental variables Variables that affect the treatment status of a subject but have no other impact on the outcome, allowing estimation of the causal effect of a treatment when the treatment is not randomly assigned (see entry). internal rate of return Discount rate used in capital budgeting to project the rate of growth of an investment. item response theory Modern test theory that does not assume that items in a test are equally difficult. labor market rate of return Usually used to refer to the extent to which years of additional schooling are associated with increased labor market earnings. law of large numbers Theorem from probability theory that states that as a procedure is repeated, the average value of its outcomes will approach the value that the outcomes are theoretically expected to be. linear regression Statistical method that models the linear relationship between a dependent variable and one or more independent variables. logistic regression Statistical method used to model the relationship between a categorical dependent variable and one or more independent variables. logit models See logistic regression. low-powered incentives Incentives that are loosely related to performance.

852

Appendix C

market clearing Condition in a market in which quantities supplied are equal to quantities demanded. market distortion Intervention in a market that does not allow the market to operate freely. markets, theory of The theory that in markets the interaction between buyers and sellers leads to the exchange and allocation of goods and services and that consensus on price is the key determinant of transactions (see entry). mean imputation Replacement of missing observations of a variable in a dataset with the mean of the nonmissing values of that variable. mean preserving Change from one probability distribution to another probability distribution while leaving the mean unchanged. measurement error Difference of the true outcome from its measured value due to random or systematic error (see entry). merit good Good or service subsidized by the government regardless of a person’s ability to pay because of the assumed positive externalities. method of moments A general purpose method of estimation of population parameters based on the law of large numbers and matching the sample moments with the corresponding distribution moments. It typically produces consistent (asymptotically unbiased) estimators. Mincerian equation A function that relates earnings to investments in human capital measured using time, such as years of schooling. monopoly When a single company or group produces all or nearly all of the goods in a market. monopsony Market in which one consumer or consumer group purchases all or nearly all of the goods or services produced in the market. Monte Carlo simulation Computerized mathematical technique that allows people to account for risk in quantitative analysis and decision making. moral hazard Occurs when two parties are in an agreement, and one party has an incentive to engage in risky behavior to earn a profit before the contract ends (see entry). multiple imputation Replacement of missing observation values in a dataset with plausible values. multiple regression analysis See regression analysis. natural experiments Experiment where differences in exposure to a treatment result from changes outside the control of a researcher and allow for the study of a treatment group and a control group. natural logarithm Logarithm with a base of e, where e is a constant approximately equal to 2.718281828, usually written as ln. In the economics of education, the most common application is in estimating returns to schooling where the dependent variable is the natural log of the wage (ln wage). This is used in a regression because the predicted wage is guaranteed to be positive; it also means the estimated coefficient on years of education can be interpreted as the percentage increase in the wage for one additional year of education. new institutional economics Focuses on the legal and social framework and the institutions necessary for the optimal functioning of the economy (see entry). noise Unexplained variation as a result of error. nonexcludable A good or service for which it is not possible to exclude nonpaying consumers from accessing the good or service. nonparametric methods Statistical methods in which the data are not required to fit a normal distribution. oligopoly A market with limited competition, in which a small group of firms produce all or nearly all the goods or services. omitted variable bias Occurs when an important independent variable is excluded from an estimation model, and its exclusion causes the estimated effects of the included independent variables to be biased (see entry).

Glossary

853

opportunity costs Foregone benefits of an activity that include considerations of different uses associated with the resources (see entry). ordinary least squares Popular estimation technique in linear regression analyses that derives coefficients from minimizing the sum of squared distances between observed and predicted values (see entry). panel data Dataset with multiple observations of an entity across time. parametric method A statistical procedure that assumes that the shape and distribution of the population is normal. partial effects Amount of change in the dependent variable that is produced by an independent variable. partial equilibrium Condition of economic equilibrium that takes into consideration only one commodity or service in an isolated market (see Partial and General Equilibrium entry). peer effects Refers to the impact of classmates and schoolmates on a student’s achievement (see entry). pooled time-series cross-sectional data A statistical method in which random samples from a large population are collected independent of each other at different points of time. present value of earnings Takes into consideration the time value of money and estimates the current value of a future stream of earnings (see entry). price discrimination Occurs when similar goods are strategically sold at different prices by the same producer (see entry). principal-agent problem Arises when the principal provides incentives for the agent to act as the principal wants in order to protect the principal from information asymmetry and other risks (see entry). probability distribution A table or an equation that links each outcome of a statistical experiment with its probability of occurrence. probit model Regression in which the dependent variable is dichotomous. production function Refers to the relationship between inputs and outputs or combinations of inputs that produce a given set of outputs (see Education Production Functions and Productivity entry) production possibility frontier (PPF) Production possibility frontier is a curve representing all maximum output possibilities for two or more goods given a set of inputs. The PPF assumes that all inputs are efficiently used. productive efficiency Refers to the least costly method of production. propensity score matching Estimates an average treatment effect by first calculating a propensity score, or likelihood of selection in treatment, and then calculating the differences in outcomes for individuals in the treatment and control groups with similar propensity scores (see entry). public choice economics Branch of economics that analyzes political behavior and collective decision making using the theories and techniques of economics (see entry). public good Nonexcludable and nonrivalrous good (see entry). quantile regression Form of regression analysis that estimates coefficients based on the conditional median or other quantiles rather than the conditional mean (see entry). quasi-experimental methods Econometric techniques and approaches used to evaluate policies and programs in the absence of experimental data (see entry). random error Refers to a statistical error that can be attributed to randomness and is not considered systematic. randomized control trials (or randomized controlled trial) Experiment in which the subjects are randomly distributed into groups that either receive a treatment or serve as controls (see entry). range Difference between the largest and the smallest items in a set of numerical data. regression analysis A type of statistical analysis that accounts for the effects of variables on one another.

854

Appendix C

regression-discontinuity design Quasi-experimental method that utilizes the cutoff in assignment to a treatment to compare those above and below this point to estimate the local average treatment effect (see entry). reliability Refers to the consistency of a measure or whether consistent results are produced under similar conditions (see entry). rival A type of good that may only be possessed or consumed by a single user; using a rival good prevents its use by other possible users. running variable See forcing variable. selection bias Statistical error that causes a bias in the sampling portion of an experiment. The error causes one sampling group to be selected more often than other groups included in the experiment. This may produce an inaccurate conclusion if the selection bias is not identified. social capital Refers to the value of connections between individuals and entities (see entry). social efficiency Refers to the optimal distribution of resources in a society when all internal and external costs and benefits are taken into account. spillover effects Externalities of economic activity or processes that affect those who are not directly involved. standard deviation Statistic that indicates how tightly all the various examples are clustered around the mean in a set of data. The formula to obtain the standard deviation is the square root of the variance. stock and flow Refers to different types of variables; stock variables are a quantity measured at a specific moment in time, whereas flow variables are a quantity measured over a period of time. systematic error Refers to a statistical error that is not produced by chance or at random but rather indicates methodical inaccuracies such as measurement error. technical efficiency Occurs when a firm is producing the maximum output from the minimum amount of input with no waste in the production process (see entry). theory of the firm Encompasses several microeconomics concepts used to describe and explain the behavior of a firm in markets (see entry). time-invariant variables Variables that are unchanged over time. In fixed-effects models using panel data, the coefficients of these variables are not directly estimated; instead, they are swept away by the first difference. tragedy of the commons Occurs when a public resource is depleted by individuals who act in self-interested and rational matter, for example, overfishing (see entry). transaction cost economics Addresses the cost involved in any economic exchange and market participation (see entry). t-test Statistical hypothesis test based on a test statistic whose sampling distribution is a t-distribution. A t-test assists when comparing whether two groups have different average values. two-stage least squares (2SLS) An instrumental variable’s estimation technique in which the researcher first predicts a treatment status using an instrument and then estimates the relationship between the outcome and the predicted treatment status. univariate dispersion measures Typically refers to measures of horizontal equity that captures the variability of a variable (the distance between observed values) or the magnitude of dispersion around the average. Simply put, they measure how much variation there is across observations in the distribution of a particular variable. unmeasured variable bias See omitted variable bias. unobservables Important variables in a model that cannot be observed. validity Characterizes the extent to which a measurement procedure is capable of measuring what it is supposed to measure (see entry).

Index Entry titles and their page numbers are in bold.

Aaron, H. J., 2:839 Aaronson, Daniel, 2:748, 749 AASA. See American Association of School Administrators Abadie, Alberto, 2:596 Abatements: of private school tuition and fees, 2:811–813 for property tax reduction, 2:572, 575, 576 Abbott v. Burke, 2:656, 847 Abdulkadirog˘lu, Atila, 1:241 Ability bias, 1:68–69, 2:832 Ability-to-pay and benefit principles, 1:1–4 demand for education and, 1:193, 194 district wealth and, 2:647, 648 fairness and equity in, 1:286 fiscal disparity and, 1:344, 345 neighborhood effects and, 2:479, 480 progressive taxation and, 1:3, 2:566 property taxes and, 1:3, 4, 2:573, 574–575 redistributional goals and, 2:564 tax burden and, 2:723, 725 tax incidence and, 2:729 Tiebout sorting by, 2:786 Absenteeism, 1:81, 223, 351, 406, 2:689, 779 Academic Common Market Electronic Campus, 2:500 Academic freedom, 1:329 Academic standards. See Standards Academy federations in England, 1:120–121, 122 Access to education, 1:4–6 demand-side obstacles to, 1:194–195 food services for, 1:5, 61 improvement of, 1:5–6 Internet access and, 1:208–209 opportunity to learn and, 2:503 physical and economic aspects of, 1:4, 6–7 transportation for, 1:60, 61 See also Open enrollment and open access Access to information, innovation in, 1:289–290 Access to technology: as an education issue, 1:280–281 and the digital divide, 1:208–209

online learning and, 2:499 opportunity to learn and, 2:503 Accountability, 1:xxvi capacity building and, 1:95–96 in contracting for services, 1:169, 171 democratic, from state to state, 2:440 dysfunctional behavior and, 1:52–53, 95 essence of, 1:95 and the evolution of authority over schools, 1:315 managed by the traditional central office, 1:114 moral hazard and, 2:460 opportunity to learn and, 2:503, 504 in philanthropic activities, 2:534 public choice economics on, 2:579 school report cards for, 2:661, 662–663 teaching to the test and, 1:95, 2:663, 775 See also No Child Left Behind Act; Title I; Transparency Accountability, standards-based, 1:6–11 effects of, 1:9–10 essential components of, 1:7 finance litigation and, 2:654–655 NCLB and, 1:6, 8–10, 2:487, 488, 490 next-generation accountability and, 1:8–9, 10 opportunity to learn and, 2:503 performance evaluation systems in, 2:528 See also Common Core state standards Accountability, types of, 1:11–14 Accounting functions, service consolidation for, 2:682, 683 Accounting standards board. See Governmental Accounting Standards Board Accreditation, 1:14–17 in higher education, 1:16–17, 356, 357 of K-12 schools, 1:15–16 of online programs, 2:500 of private schools, 2:672–673 of religious schools, 2:553, 554 Acemoglu, Daron, 1:163, 324 Achievement (student) and: 855

856

Index

board election outcomes, 1:13 charter schools, 1:124, 125–126, 127 class size, 1:237–238, 239, 260, 274, 2:496, 507, 593, 597, 598, 612–613, 659–660 collective bargaining agreements, 2:777, 778 comprehensive school reform, 1:159, 160, 161 cost of education, 1:175 desegregation, 1:202, 204, 205 distance learning, 1:215 earnings, 1:18, 2:603, 604–605, 659–660 education management organizations, 1:271–272 educational vouchers, 1:294, 295, 296 environmental factors, 1:406 expenditures, 1:174, 177–178, 249–250, 268, 274, 277 extended day, 1:320 facilities, 1:403–406 finance litigation, 2:655–656 gender, 1:18, 19, 20, 2:594 homeschooling, 1:387, 388, 389 household/family income, 1:18, 397–398, 2:487, 636 infrastructure financing, 1:403–406 intellectual ability, 1:402 National Board certified teachers, 2:470 neighborhood effects, 2:479, 480, 481–483 No Child Left Behind, 2:487, 489–490, 593 online learning, 2:499 opportunity to learn, 2:503, 504 parental education level, 1:18, 397, 407 parental involvement, 1:19, 2:513 pay for performance, 2:518, 519, 521–522 peer effects, 2:523–524 performance evaluation, 2:528 principal leadership, 1:12 productive efficiency, 1:295, 387 race (see Achievement gap) Race to the Top, 2:607, 608, 609 racial matching of students to teachers, 2:747–748 reduction in force, 2:617–618 school size, 2:665–666 school-based management, 2:667, 669 social advantage, 1:189 social capital, 2:684, 685, 686 socioeconomic status (see Achievement gap) standards-based accountability, 1:9 student mobility, 2:717–718 supplemental services, 2:719, 720, 721 teacher certification, 1:253, 2:434, 437, 747 teacher characteristics, 2:747–748 teacher evaluation, 2:607, 609, 723, 754–755 teacher experience, 1:275, 2:743, 747, 757 teacher intelligence, 2:758, 759 teacher performance pay, 2:518, 519, 521–522 teacher professional development, 2:561 teacher quality, 1:12, 274–275, 328, 2:520, 746

teachers, the important influence of, 2:752, 759 Title I, 1:304, 2:790 tracking, 1:20, 2:792, 793, 794–795 tuition tax credit participation, 2:813, 814 tutoring, 2:720–721 wealth, 2:690 work-study, 1:339 See also Achievement gap; International assessments Achievement gap, 1:17–22 early childhood education for narrowing of, 1:234 ESEA for reduction of, 1:7 explanations for, 1:18–21 on the first day of kindergarten, 2:771 gender and, 1:18, 19, 20, 2:594 income inequality and, 1:397–398 NCLB for reduction of, 1:9 permanent income and, 2:531 racial/ethnic, 1:12, 17–18, 19, 20, 21, 285, 397, 2:531, 605, 606, 771 social, 1:234 socioeconomic status and, 1:18, 19, 20, 21, 2:605, 606, 687, 689 standards-based accountability and, 1:9 student mobility and, 2:717 ACT exam, 1:165, 2:615, 624, 634–635, 826 Activity-led staffing, 1:24 Ad valorem taxes, 2:511, 570. See also Property taxes ADA (average daily attendance), 1:39, 162, 201, 217, 225, 305, 351 Adams, J., Jr., 2:840 Addicted lottery players, 2:446–447 Additional year of schooling: arrest/incarceration rates and, 1:263 earnings and, 2:658 likelihood of volunteering and, 1:75 likelihood of voting and, 1:260 present value of, 2:657 rates of return for, 1:252, 373, 2:657–658 Additional year of teacher experience, 2:519, 629 Adequacy, 1:xxvi, 22–26 Brown ruling and, 1:88 cost function approach to, 1:25, 26–28, 287 defining of, 1:22–23, 26, 32 equity and, 1:13–14, 23, 27, 32, 33, 286–288 evidence-based approach to, 1:24, 25–26, 28–31, 287 expenditures, outcomes, efficiency, and, 1:250 finance litigation on, 1:23, 27, 2:654–655 fundamental questions on, 1:32 in infrastructure and facilities, 1:404, 405, 406 methods for estimating, 1:23–26 opportunity to learn and, 1:22, 2:504 professional judgment approach to, 1:23–24, 31–35, 287 pupil weighting and, 2:589 school finance equity statistics and, 2:650

Index shift in focus to, in education finance, 1:265, 267, 268 of state public education spending, 1:279 successful district approach to, 1:24–25, 35–38, 287 unfunded mandates’ impact on, 2:819 See also Cost of education; Education spending Adequacy: cost function approach, 1:25, 26–28 biases and assumptions in, 1:177–178 equity and, 1:27, 287 procedures for, 1:27 See also Adequacy Adequacy: evidence-based approach, 1:25–26, 28–31 characteristics of funding under, 1:29 equity and, 1:287 methods for, 1:29–30 in practice, 1:24, 30–31 See also Adequacy Adequacy: professional judgment approach, 1:23–24, 31–35 elements of, 1:33–34 equity and, 1:32, 33, 287 See also Adequacy Adequacy: successful school district approach, 1:24–25, 35–38 costs included in, 1:36–37 equity and, 1:287 identification of the districts in, 1:37–38 reform-initiative alternative to, 1:177 Adequate yearly progress, 1:38–41 additional measures of, for high schools, 2:488 failure to make AYP, 1:39–40, 2:488, 662, 719 lessons learned from, 1:40–41 making AYP, alternative methods for, 1:40, 2:488 making AYP, criteria for, 1:39–40 making AYP, manipulation for, 2:490 in school report cards, 2:662, 663 state agencies dramatically affected by, 2:702 state systems in conflict with, 1:40–41 supplemental services and, 2:488, 719, 721 waivers for (see Waivers for NCLB) See also No Child Left Behind Act ADM (average daily membership), 1:217, 305 Administrative decentralization, 1:116, 2:668. See also Centralization versus decentralization; School-based management Administrative spending, 1:41–44 measurement issues in, 1:42–43 65 percent solution and, 1:42–43, 44 See also Central office, role and costs of Adopt-a-school programs, 2:584–585 Adult Basic Education Program, 1:46 Adult education, 1:45–48 dropout rate calculation and, 1:221 in for-profit institutions, 1:47, 354, 357 life cycle investments model for, 1:45–46

857

postsecondary, 1:45, 47, 47 (table) for professional development, 2:559 See also Continuing education Adult Education Act, 1:46 Adult literacy, 1:75, 278, 421, 2:509 Advanced Placement (AP) courses, 1:225, 2:464, 645, 665, 682, 711 Advisory Commission on Intergovernmental Relations, 2:574 AEFP. See Association for Education Finance and Policy Affirmative action, 1:140, 296, 2:605, 636 Affordable Care Act, 2:493 Afghanistan, 1:417 Africa, 1:417, 418, 2:432, 536. See also Sub-Saharan Africa African American facilities, under separate but equal, 1:202 African Americans: and African American/White dissimilarity levels, 1:203 at charter and public schools, 1:127 at community colleges, 1:154 at Department of Defense schools, 1:197, 198 desegregation, student outcomes, and, 1:204–205 exposure of, to White students, 1:202, 203, 204 high school graduation rates for, 1:220 incarceration probabilities of, and benefits of education, 1:75 and jobs restricted to Whites, 1:227 NAEP scores for, 1:198 neighborhood effects and, 2:480, 482 philanthropic support for, 2:532 practical skills for, in segregated areas, 2:831 proportion of, in U.S. schools, 1:205 in race earnings differentials, 2:480, 601, 603 on school boards, 2:638 White enrollment rates and, 1:202 See also Blacks AFT (American Federation of Teachers), 2:775 Afterschool activities, 1:286, 320, 2:623, 720, 721. See also Extended day Age: and choice of college major, 1:130 of leaving school, 1:163 and pension plan rules, 2:761, 762, 762 (figure) of postsecondary students, 1:47, 47 (table) and rate of college enrollment, 1:137, 137 (figure) See also Age-earnings profile Age-earnings profile, 1:48–50 by education level, 1:48, 49 (table), 49 (figure) for men and women, 1:48, 49 (table), 49 (figure), 2:540 (figure), 540–541, 541 (figure) and present value of earnings, 2:540, 540 (figure), 541 (figure) Agency for International Development, U.S., 1:195 Agency theory, 1:50–53. See also Principal-agent problem

858

Index

Aggregate demand curve, 1:193. See also Demand for education Agodini, Roberto, 2:568 Agricultural Adjustment Act, 1:61 Ahearn, Eileen, 2:696 Ahlburg, Dennis A., 1:338 Aid ratio, for percentage equalizing formulas, 1:311 Alabama, 1:197 (table), 379, 2:440, 654, 656, 703, 815 Alabama Coalition for Equity v. Spiegelman (1977), 2:656 Alaska, 1:175, 404, 2:440, 654, 656, 696, 705 Alexander, Karl L., 2:717 Alexander, Nicola, 1:287 Alishjabana, A., 1:81 Allen, I. Elaine, 2:498 Allensworth, E., 1:94 Allison, G. S., 1:360 (table) Allocative efficiency, 1:53–56 definition of, 2:849 regulations and, 1:200 technical considerations in, 1:54–56, 2:781, 782 theory of the firm and, 2:783, 785 Altruism, 1:66 Alumni, in postsecondary fundraising, 2:549, 550–551, 552 America 2000 plan, 1:7. See also Goals 2000 American Association of Christian Schools, 2:554 American Association of School Administrators, 1:56–58 American Association of University Professors, 1:332 American Community Survey, 1:221, 222, 2:540 American Council on Education, 1:364, 365 American Dream Demonstration, for college savings plans, 1:145 American Education Finance Association, 1:58, 278. See also Association for Education Finance and Policy American Educational Research Association, 1:341, 2:774, 839 American Federation of Teachers (AFT), 2:775 American ideals, and globalization, 1:372 American Indian communities, 1:138, 152. See also Native Americans American Psychological Association, 2:774 American Recovery and Reinvestment Act, 1:8, 85, 105, 120, 208, 318, 424, 2:529, 607, 645, 695, 824, 848 America’s Choice (reform model), 1:160 Analysis of covariance, 1:299 ANB (average number belonging), 1:305 Anderson, Lorin W., 1:342 Andrew W. Mellon Foundation, 2:533 Andrews, M., 2:842 Andrews, Matthew, 1:218, 219 Angle of inequity, 2:649–650 Angrist, Joshua, 1:163, 241, 255, 369, 2:593, 597, 658, 711, 712 Angrist, Joshua D., 2:524

Anker, Richard, 1:227 Anna T. Jeanes Fund, 2:532 Annenberg Foundation, 2:532 Annual growth rate, compound. See Compound annual growth rate Annual measurable objectives, 1:39, 40, 2:488 Annuities and pension plans, 2:761, 762, 763. See also Teacher pensions Anthony, Emily, 2:747 Anyon, Jean, 1:189 AP (Advanced Placement) courses, 1:225, 2:464, 645, 665, 682, 711 Apprenticeship, 1:164, 343, 2:830–831, 832 Argentina, 1:118, 372 Argys, Laura, 2:793 Aristotle, 2:796 Arizona: finance litigation in, 1:404, 405, 2:654 first K-12 tuition tax credits in, 2:813 for-profit EMO schools in, 1:271 full state funding of facilities in, 1:104 local taxing allowed in, 2:440 nonprofit EMO schools in, 1:271 per-pupil expenditures in, 1:175 Race to the Top grant for, 2:608 tax credit scholarships in, 2:814 teacher experience profile in, 2:757 Arkansas: district consolidation in, 1:218 evidence-based approach to adequacy in, 1:30 finance litigation in, 1:404, 2:654 local taxing allowed in, 2:440 lottery proceeds in, 2:443 (table) pupil weighting in, 2:588 student financial aid in, 2:708, 712 teacher pensions in, 2:763 Armed Forces Qualifying Test, 2:605 Aronson, Joshua, 1:19 Arrest-education correlation, 1:263. See also Education and crime Arrow, Kenneth, 1:186 Artha, R. P., 1:81 Articles and reports in education economics and finance, 2:842–844 Arts, the, NAEP assessment of, 2:467 Ashenfelter, O., 2:842 Ashenfelter, Orley, 1:206, 409, 2:658 Asia: access to education in, 1:5 decentralization in, 1:117 IMF, WTO, and, 1:423 labor market rate of return in, 2:432 tracking in, 2:791 vocational education in, 2:832

Index Asian Americans, 2:601, 602 (table), 603, 606 Asians, 1:197, 2:480 Aspire Pubic Schools, 1:120 Assessed property valuation per pupil, 2:647 Assessment, international. see International assessments Assessment, of teachers. See Teacher evaluation; Teacher performance assessment Assessment, property tax, 2:570–575 Assessment, standardized. See Standardized tests Assessment consortia, 1:150, 2:703, 756, 764 Asset specificity, 2:486 Assignment bias, 2:678. See also Selection bias Assignment variable, 2:619–621 Associate’s degree institutions: for adult students, 1:47, 47 (table) faculty at, 1:331 (table) See also Community colleges; Two-year colleges Association for Education Finance and Policy, 1:58–59 Association for Public Policy Analysis and Management, 2:535 Association of Catholic Colleges and Universities, 2:554 Association of Christian Schools International, 2:554 Association of Classical and Christian Schools, 2:554 Association of Education Finance and Policy, 2:839 Association of School Business Officials International, 1:100 Astin, Alexander, 1:143, 261 Asymmetric information, 1:96, 2:451, 460, 484, 488, 544, 582 Asynchronous settings for learning, 1:213–214, 2:498 “At risk,” use and implications of the term, 2:624–625. See also Risk factors, students Atkinson, Richard, 2:635 Attendance counts. See Average daily attendance (ADA); Enrollment counts Attrition, in randomized control trials, 2:611–612, 613 Attrition bias, 2:677–678, 849. See also Selection bias Attrition of employees, 2:645 Augenblick, John, 1:110 Australia, 1:46, 353 (figure), 396 (figure), 2:584, 813 Austria, 1:353 (figures), 2:831 Autism, 1:400, 2:693 (table) Autonomy: in block grant programs, 1:80, 82 capacity building and, 1:94, 95, 96 centralization/decentralization and, 1:113, 114, 116–118 in charter schools, 1:21, 120, 123, 124, 269, 272, 2:440 collective bargaining and, 2:643, 777 in for-profit higher education, 1:356 globalization and, 1:372, 374 in portfolio districts, 2:538 of principals, in hiring teachers, 1:95 of religious schools, 2:676

859

restricted under ESEA, 1:304 in school-based management, 1:80, 82, 114, 2:667, 670 of teachers (see Teacher autonomy) Auxiliary services, 1:59–62 at community colleges, 1:154 scope of, 1:42, 59–60, 276 See also Contracting for services; Food services; Transportation services Average, several meanings of, 1:158 Average daily attendance (ADA), 1:39, 162, 201, 217, 225, 305, 351 Average daily membership (ADM), 1:217, 305 Average number belonging (ANB), 1:305 Average per-pupil expenditure, 2:695. See also Per-pupil expenditures Average treatment effect, 1:350, 2:483, 568, 595–596, 597, 598, 677 Avery, Christopher, 1:147, 338 AYP. See Adequate yearly progress Babylonian Code, 2:830 Baccalaureate and Beyond Longitudinal Study, 2:475–476 Bachelor’s degree institutions: adult students at, 1:47, 47 (table) expenditures per student at, 2:806–807, 807 (tables) faculty at, 1:330, 331 (table) tuition and fees at, 2:802 (table) See also Four-year colleges and universities Backloaded pension structure, 2:762–763 Bahrain, 1:197 (table) Bailey, Martha, 1:133, 138 Baker, Bruce D., 1:379, 2:695 (table), 842 Baker, E., 2:602 (table), 604 (table) Baker, Eva L., 1:343 Bakewell, Thomas, 2:552 Bakija, Jon, 2:566 Balanced-budget incidence, 2:565. See also Tax incidence Banerjee, Abhijit, 1:290 Bangladesh, 1:417, 2:433 Banker, Rajiv D., 2:780 Bargerstock, Charles, 2:616 Barnard, Henry, 1:199, 265 Barnett, W. Steven, 1:179, 180, 2:842 Barrera-Osorio, Felipe, 1:80, 81, 290 Barro, Robert J., 1:324 Barron’s Admissions Competitiveness Index Data Files, 2:473 Barron’s Profiles of American Colleges, 1:129, 146, 147 Barrow, Lisa, 1:290, 295, 2:748, 749 Barry, Daniela, 1:341, 342 Bartel, Ann, 1:324 Base and variable pay, 2:519, 521. See also Teacher compensation Base or foundational funding, 1:92, 278

860

Index

Basic adult education, 1:45, 46 Baum, Sandy, 1:68, 70 Baumol, William, 1:63, 289, 2:840, 846 Baumol’s cost disease, 1:63–64, 289, 2:846. See also Cost disease Beating-the-odds resource profiles, 1:33–34 Beatty, A., 1:81 Becker, Gary S., 1:xxv, 138–139, 186, 251, 252, 391, 2:513, 846; 2:830, 840 Beginning Postsecondary Students (dataset), 2:475, 476 Behavioral economics, 1:64–68 in savings mechanisms, 1:144 in student incentives, 2:712 theory of markets and, 2:455 Behr, Todd, 1:246 Behrman, Jere, 2:710 Belfield, Clive, 1:73–74, 75, 185, 2:555 Belgium, 1:197 (table), 353 (figure), 396 (figure) Bell, Terrel H., 2:463 Bellfleur, Jessica, 2:820 Bender, N., 1:341, 342 Benefit principle: ability-to-pay principle and, 1:1–4 as an equity principle, 1:286, 288 fair taxation and, 2:564, 566 income taxes and, 2:729 property taxes and, 2:574–575, 729 Benefit-cost analysis. See Cost-benefit analysis; External social benefits and costs Benefits, nonwage. See Nonwage benefits Benefits, spillover of, 1:264, 325 Benefits of higher education, 1:68–71 for adult students, 1:47 as an investment, 1:68, 70, 71, 139 in civic engagement, 1:70–71, 260 college choice and, 1:128–129 crime and, 1:70, 71, 263 dual enrollment and, 1:225, 226 in earnings, 1:48–49, 49 (table), 49 (figure), 68–70, 2:542 (table), 602 (table), 714 external social benefits, 1:322–323, 323 (table), 325 human capital model of, 1:391–392, 393 beyond labor market outcomes, 1:70, 322–323, 323 (table) present value of expected benefits, 2:714 social capital in, 2:685–686 student perceptions of, 1:139 See also Tertiary education Benefits of primary and secondary education, 1:72–76 for adult students, 1:46 in civic engagement, 1:72, 75, 260 conceptual models for, 1:72–73, 74, 75 crime and, 1:74–75, 263, 264 in earnings, 1:48–49, 49 (table), 49 (figure), 2:542, 542 (table)

in health, 1:74, 75–76 high school dropout and, 1:48, 74, 181, 220, 223 intergenerational benefits, 1:75–76 international comparisons of, 1:74, 2:432–433 labor market outcomes and, 1:73–74 Benhabib, Jess, 1:324 Berends, Mark, 1:20 Berinsky, Adam, 1:260 Berke, J., 2:840 Berne, Robert, 1:109, 286, 288, 390, 2:648, 828, 840 Berne-Stiefel framework, and school finance equity, 2:648–649 Best fit, line of, 1:237, 237 (figure). See also Regression line Betebenner, Damian, 2:754 Bettinger, Eric P., 1:139, 2:708, 711 Big data, and learning analytics, 1:283 Bilingual education, 1:76–79 adequacy and, 1:24 effectiveness of, 1:77–78 ESEA support for, 1:303 pedagogical approaches to, 1:77 spillover effects from, 2:699 and Title III of NCLB, 1:109, 199 See also Indigenous languages Bilingual Education Act, 1:77, 78 Bill and Melinda Gates Foundation, 1:226, 2:499, 532, 533, 536, 584, 749, 774 Birth cohort, in measuring completion rates, 1:132 Black, Sandra, 2:523, 787 Black schools and White schools, and wage differentials, 2:659. See also Race earnings differentials Blackboard (learning management system), 1:212, 2:498 Blacks: achievement gap, explanations for, 1:19, 20, 2:531 achievement gap, NAEP data for, 1:17, 18 achievement gap, on first day of kindergarten, 2:771 and Black-White dissimilarity levels, 1:203 Brown case and, 1:86–88 in California, and the Serrano case, 2:647–648 class size benefits for, 2:659 college completion rates for, 1:134 college enrollment rates of, 1:138 college selectivity and, 1:397 neighborhood SES for, 2:689 peer effects and, 2:523–524, 688 in race earnings differentials, 2:602 (table), 602–606, 604 (table) resegregation of, 1:20 segregation of, 1:86 separate but equal concept and, 1:86 student mobility of, 2:717 wealth of, 2:690 See also African Americans

Index Blaine Amendment, on funding to religious institutions, 2:674 Blaug, Mark, 2:846 Bleeker, M., 2:561 Blended learning: as an evolving trend, 1:281–282 and classroom configurations, 1:283–284 cost and funding of, 1:388 delivery systems for, 2:498–499 in distance learning, 1:214 for homeschooling, 1:389 Block grants, 1:79–83 categorical grants compared to, 1:201, 413 in deregulation initiatives, 1:201 effects of, 1:81–82 from ESEA Title I, 2:701 for funding special education, 2:696 for higher education, 1:385 pathways for, 1:80–81 types of, 1:80 Bloom, E., 2:711 Bloomberg Businessweek, college rankings by, 1:142–143 Board of Education of Oklahoma City v. Dowell (1991), 1:203 Boarding schools, 1:77, 2:464, 553, 671, 811. See also Schools, private Boardman, Anthony, 1:181 Boards of education. See School boards Bok, Derek, 1:140 Bolivia, 1:395 Bolling v. Sharpe, 1:86 Bond program tax credits, 1:85, 105 Bonds in school financing, 1:83–86 at community colleges, 1:153 strategies for passing of, 1:84–85 See also Capital financing for education; General obligation bonds Bonus pay benefits, 2:491, 521, 522 Booker, T. K., 2:843 Books in education economics and finance, 2:839–842 Bootstrapping, 1:192, 2:780, 849 Border effects, and sales tax, 2:730 Borko, Hilda, 2:768, 770 Borman, Geoffrey, 1:160, 304, 2:669, 842 Borrowing, student. See Student loans Borrowing against future income, 1:46, 161. See also Opportunity costs Borrowing on bonds. See Bonds in school financing; General obligation bonds Boston Public Schools, weighted student funding in, 2:835, 836 (figure) Bound, John, 1:133, 369 Bounded rationality, 1:65 Bourdieu, Pierre, 1:188, 189, 2:684–685

861

Bowden, A. B., 1:185 Bowen, William, 1:63, 140 Bowen’s curse, 1:63. See also Baumol’s cost disease Bowles, Erskine, 1:104 Bowles, Samuel, 1:189, 2:604 Boyd, Donald, 2:617, 740, 769, 770 Boyne, George A., 1:170 Bracket budgeting, 1:91 Brain drain, 1:374, 422 Brain gain, 1:374 Brain plasticity, 1:233–234 Brand, Jennie, 1:261 Bransford, J., 2:840 Braunstein, Andrew, 1:338 Brazil, 1:81, 117, 2:584, 669 Breakfast programs. See Meal programs Bredesen, Phil, 2:609 Brennan, Robert L., 2:624 Brennan, William, 2:633 Brent, B. O., 2:841 Breton, Theodore, 1:324 Bretton Woods conference, 1:422, 423 Breuer, K., 1:341, 342 Brewer, Dominic J., 1:55 (figure), 69, 253, 2:546, 561, 747, 793, 840, 843 Briggs v. Elliott, 1:86 Brigham, Carl, 2:634 Brill, Steven, 1:229 Britton, E., 2:561 Broad Foundation, 2:532 Broadband speeds, and the digital divide, 1:208 Broh, Beckett, 1:19 Brooks, David, 2:609 Broughman, Stephen, 2:672 Brouwer, Niels, 2:768, 769, 770 Brown, A., 2:840 Brown, Linda, 1:86 Brown, Oliver, 1:86 Brown, S., 1:160, 2:669 Brown II ruling (1955), 1:87 Brown v. Board of Education, 1:86–88 as cornerstone of social justice movement, 1:202 desegregation policies responding to, 1:87, 203, 204 financial significance of, 1:87–88 the ruling on, 1:87 transportation services affected by, 1:60 Brunner, Eric, 2:546, 547 Bruns, Barbara, 1:81, 118 Bryk, Anthony S., 1:94 Bubble kids, and the NCLB/AYP framework, 1:9, 40, 2:490, 702 Buckley, James, 1:333 Buckley Amendment, 1:333 Budgeting approaches, 1:88–92

862

Index

bracket budgeting, 1:91 capital budgeting, 1:91, 97–101 formula budgeting, 1:91–92 incremental budgeting, 1:91 line-item budgeting, 1:89, 90, 2:563 median voter model and, 2:457–459 object-oriented systems, 1:359, 360 (table), 361 opportunity cost applicability in, 2:501–502 outcomes-focused budgeting, 1:90 performance budgeting, 1:89 planning programming budgeting, 1:90 program budgeting, 1:89–90, 361, 2:562–564 program planning budgeting and evaluation system, 1:90 site-based budgeting, 1:90–91, 92, 2:438, 441 zero-based budgeting, 1:90 See also Fund accounting Building corporation, and school district debt, 1:103 Building school capacity. See Capacity building of organizations Buildings. See Facilities Bundled services, 1:358 Burch, Patricia, 1:170 Burden, tax. See Tax burden Burdick-Will, Julia, 2:483 Bureau of Labor Statistics, 1:128, 2:474, 714, 741 Bureaucracy, public choice economics on, 2:580, 581 Burke, Mary, 2:524 Burkhead, J., 2:840 Burris v. Wilkerson, 2:846 Burtless, G., 2:840 Busch, C., 2:841 Busch, Carolyn, 1:177 Buse v. Smith (1976), 1:379 Bush, George H. W., 1:7 Bush, George W., 1:401, 2:848 Bush, Vannevar, 2:477 Busing for desegregation, 1:60, 203. See also Transportation services Byrne, John, 1:143 Bystanders, effects on. See Spillover effects Cadena, Brian, 2:712 CAGR. See Compound annual growth rate California: Academic Performance Index in, 2:662 alternative teacher certification in, 2:436, 768 block grants in, 1:201 categorical aid in, 1:110, 201 charter management organizations in, 1:122 class size reduction program in, 2:535–536, 613 collective bargaining in, 2:641, 778 community college system in, 1:152, 153 education code in, 2:706 funding disparities in, 1:88

high school reform initiative in, 2:536 local education foundations in, 2:546 local taxing in, 2:440 lottery proceeds in, 2:443 (table) National Board certified teachers in, 2:470 NCLB waivers in, 2:721 nonprofit EMO schools in, 1:271 notice of teacher ineffectiveness in, 1:229 parcel tax in, 2:511–512 Proposition 13 in, 2:511, 574, 651, 653, 679, 680, 731 pupil weighting in, 2:587, 588, 837 religious schools in, 2:673 revenues for schools in, 1:314, 314 (table) school board elections in, 2:706 Social Security participation in, 2:491 special education enrollment rate in, 1:403 stakeholder levels in, 2:438–439 student financial aid in, 2:707 teacher pensions in, 2:761 teacher performance assessment in, 2:764 teacher preparation in, 2:435, 436 See also Serrano v. Priest Caliper matching, 2:568. See also Propensity score matching Callahan, Rebecca, 1:296 Cameron, Stephen, 1:366 Campbell, C., 2:537, 538 Campbell, Donald, 2:597, 619 Campbell, E. Q., 2:842 Canada: compulsory schooling laws in, 1:163 endowment funds in, 2:820 foregone earnings in, 1:353 (figures) income inequality in, 1:396 (figure) school vouchers in, 2:813 student financial aid in, 2:712 weighted student funding in, 2:835 Candoli, C., 2:841 Cap and tiers systems, 1:216 Capacity building of organizations, 1:93–97 central office as a resource for, 1:114 educational equity and, 1:288 essential building blocks of, 1:93–95 school-based management and, 2:669 Capital, human. See Human capital Capital, land, and labor in capitalism, 1:105. See also Capitalist economy Capital, social. See Social capital Capital assets as infrastructure, 1:101. See also Capital financing for education; Infrastructure financing and student achievement Capital budget, 1:91, 97–101 best practices for, 1:98–100 CIP and, 1:97, 98, 99–100, 102

Index operating budget and, 1:97–98, 99 total spending and, 1:42 See also Capital financing for education Capital financing for education, 1:101–105 bonds in, 1:102–103, 104, 105 at community colleges, 1:153 data on, 1:101, 102 infrastructure, achievement, and, 1:403–406 local, state, federal support for, 1:102–105 See also Capital budget; Infrastructure financing and student achievement Capital improvement program (CIP), 1:97, 98, 99–100, 102 Capital-investment budgeting, 1:91 Capitalist economy, 1:105–108 centralization/decentralization and, 1:107–108, 117 cultural/linguistic globalization facilitated by, 1:372 key features of, 1:106 See also Privatization and marketization Card, David, 1:206, 2:596, 658, 659, 842 Career academies, 2:830, 832 Career and technical education programs, 1:278, 428, 2:471, 588, 831. See also Vocational education Caribbean region, 1:416–417, 2:432 Carl D. Perkins Career and Technical Education Act, 1:428 Carnegie, Andrew, 2:532 Carnegie Basic Classification, 1:47, 47 (table) Carnegie Corporation, 2:467, 469, 532 Carnegie Mellon Open Learning Initiative, 2:809 Carnoy, Martin, 1:81, 2:602 (table), 603, 604, 604 (table) Carolina Abecedarian project, 1:234 Carrillo, Paul, 1:290 Carruthers, Celeste K., 1:47 (table) Carter, Jimmy, 1:90, 315, 2:823 Carter, Linnie S., 2:551 Cash balance pension plans, 2:763 Cash bonus benefits, 2:491 Cash flow: discounted, 1:210, 211 internal rate of return and, 1:414–416 in school districts, 2:645–647 Castleman, Ben, 2:708 Catastrophic aid programs, 2:695, 696, 697 Categorical dependent variable, definition of, 2:849 Categorical funding, 1:92, 108–112, 216, 2:587, 818. See also Categorical grants Categorical grants, 1:108–113 block grants compared to, 1:201, 413 impact of, 1:110–111 pupil weights and, 2:587 transformed into block grants, 1:201 types of, 1:109–110, 412 unintended consequences of, 1:111–112

863

Categorical variable, 1:241, 2:825, 849 Catholic schools: achievement gaps and, 1:20–21 associations of, 2:553–554, 555 civic engagement and, 1:261, 296 data on, 1:253, 2:671, 674, 675 effectiveness of, 2:675–676 enrollment in, declining, 2:672, 675 ESEA coverage of, 1:303 establishment of, 2:674 parents’ right to choose, 2:672 social cohesion and, 1:296 tuition at, 2:675, 812 types of, 2:810 See also Schools, religious Catholic Schools Accreditation Association, 2:553 Catlin, D., 1:184 Causal estimates and causal effects: centralization/decentralization and, 1:117, 118 on college selectivity and student outcomes, 1:146, 147, 148 cost-benefit analyses and, 1:181, 182 credential effect and, 1:186, 187 difference-in-differences for, 1:69, 239, 255 economics of education on, 1:251, 254, 255 of education and crime, 1:263 of education and health, 1:70 of education policy, 1:243 of financial aid and enrollment, 1:139 fixed-effects models and, 1:350 in IV methods, 1:240–241, 407 neighborhood effects and, 2:480, 481, 482, 483 ordinary least squares for, 2:505, 506–507 of preschool effects, 1:234 propensity score matching for, 2:567 randomized control trials for, 1:238–239, 2:611 regression discontinuity for, 1:241 of schooling and earnings, 2:658 Causality, at the heart of evaluation research in education finance, 1:350 Causality and correlation, distinctions between, 1:236–237, 238, 2:507, 611, 754, 757 CBA (cost-benefit analysis). See Cost-benefit analysis CBAs (collective bargaining agreements). See School boards, school districts, and collective bargaining; Teachers’ unions and collective bargaining CCD (Common Core of Data), 1:222, 223, 253, 388, 2:472, 473 CCSS. See Common Core State Standards CEA. See Cost-effectiveness analysis Cellini, Stephanie, 1:139 Census Bureau data: on annual spending for education, 1:276 for a comparative wage index, 1:155

864

Index

for dropout calculations, 1:221, 222 for estimating returns to education, 2:658–659 on lifetime earnings, 1:134 place-of-work areas defined for, 1:156 on poverty, 2:788 on school district finance, 1:101, 102 on state lotteries, 2:442 (table) Census-based funding, 2:589, 694–695, 695 (table), 696 Center for Research on Education Outcomes, 1:271 Center for World-Class Universities, college rankings by, 1:142 Center on Reinventing Public Education, 2:449, 537–538 Central office, role and costs of, 1:113–115. See also Administrative spending Centralization of private fundraising and distribution, 2:547 Centralization versus decentralization, 1:115–119 capitalism and, 1:107–108, 117 in continuing education models, 1:165 in intergovernmental fiscal relationships, 1:410, 411 motives for, 1:117–118 in opposing views of local control, 2:439 outcomes of, 1:118–119 See also Central office, role and costs of; School-based management Certainty preference, 1:66, 67 Certificate programs, 1:166, 355, 357, 428, 2:831. See also Credentialing programs; Licensure and certification Certification and licensure. See Licensure and certification Ceteris paribus (all other things being equal), concept of, 1:301, 2:453, 454, 517 Chalk, R., 2:841 Chambers, Jay, 1:24, 34, 2:691, 692 (table), 693 (table), 694 (table), 695 (table), 696 Chaplin, Duncan, 2:749 Charitable contributions, 1:70, 71, 125, 2:445, 531, 549, 550, 820. See also Private contributions to schools; Private fundraising in postsecondary education Charitable organizations, 1:355, 385, 2:549, 550. See also Philanthropic foundations in education Charitable property, 2:571, 572 Charnes, Abraham, 2:779 Charter management organizations, 1:119–123, 126 data on, 1:120 emerging issues for, 1:122–123 See also Charter schools; Education management organizations Charter School Growth Fund, 1:120, 2:533 Charter schools, 1:123–128 administrative spending in, 1:43 alternative educational options at, 1:398 arguments for and against, 1:124, 127

authorization and funding of, 1:124–125 capacity building and, 1:95, 96 charter authorizers for, 2:440 CMO-member schools, 1:119–123 competition from, 1:123, 124, 127, 2:456, 675 comprehensive school reform in, 1:160–161 contracting for services by, 1:169 core underlying concept for, 2:440 cyber charter schools, 1:125, 282 data on, 1:125, 126, 293, 2:440–441 decentralization tendencies in, 1:116 deregulation and, 1:200, 201 economic cost and, 1:245 as educational laboratories, 1:200 effectiveness of, 1:21, 125–126, 271, 2:614 first opening of, 1:119, 125, 2:847 government failure and, 2:580 growth and characteristics of, 1:125, 2:440–441 high-performing, characteristics of, 1:126–127 as innovation in local control, 2:440–441 lottery systems for, 1:21, 125, 2:448, 449 parent choice, behavioral economics, and, 1:67 pay for performance in, 2:520 peer effects in, 2:523 philanthropic support of, 1:120, 2:532, 533 policy analysis of, 2:535 in portfolio districts, 2:537 public-private partnerships in, 2:585 salary schedules in, 2:745 school-based management in, 1:80, 2:670 shopping centers and, 1:241 stand-alone schools, 1:120, 121 state legislation on, 1:269 students in, characteristics of, 1:127 tax-exempt debt market access for, 1:103 teacher experience in, 2:757 teacher turnover in, 2:766 theory of the firm on, 2:783, 784, 785 traditional schools compared to, 1:122, 123–124, 125, 126, 271 virtual schools compared to, 2:499 See also Charter management organizations; Educational vouchers Charter Schools Program Grants competition, 1:120 Chat feature, in distance learning, 1:215 Chauncey, Henry, 2:634 Chautauqua movement, 1:46 Cheating on tests, by administrators and staff, 1:52–53, 2:490, 750 Chen, Chin-Chih, 2:717 Chen, Stacey, 1:369 Cheng, H., 1:185 Chetty, Raj, 2:659, 660, 749 Chevalier, Arnaud, 2:531 Chicago Child-Parent Center program, 1:264

Index Chicago Longitudinal Study, 1:234 Chicago public schools, 1:52, 2:626, 668, 744 (table) Child Care and Development Fund, 1:233 Child care benefits, 2:491 Child labor: access to education and, 1:6 data on, 1:195, 2:433 foregone earnings in, 2:433 laws restricting, 1:6, 161, 162, 195, 260, 2:433 need or demand for, 1:4, 194, 195, 353 opportunity cost of, 1:194 programs to reduce, 2:615 Child rearing. See Parental involvement; Parenting styles Children and intergenerational benefits, 1:70, 75–76, 260 Children and Young Adults (dataset), 2:476 Children of color, 1:285. See also Racial/ethnic groups; Students of color Children’s Scholarship Fund, 2:533 Chile, 1:117, 293, 294, 295, 2:556–557, 584 China, 1:117–118, 374, 417 Chinese Americans, earnings of, 2:601. See also Asian Americans Choice, school. See School choice Choice architecture, 1:67 Christian school associations, 2:554 Christian schools, 2:554, 673, 810. See also Catholic schools Christofides, Constantinos, 1:246 Chubb, J. E., 2:840 Cibulka, James, 2:638 CIP (capital improvement program), 1:97, 98, 99–100, 102 Circuit breakers (tax relief), 1:286, 2:573, 575, 727, 728, 729 City and County of San Francisco v. Farrell, 2:511 Civic engagement. See Education and civic engagement Civil Rights Act, 1:315, 2:605 Civil rights and school finance litigation, 2:653, 654 Civil rights movement, 1:199 Clark, Melissa, 2:634 Class size: achievement and, 1:237–238, 2:507, 597 achievement and (STAR study), 1:239, 260, 274, 2:496, 593, 598, 612–613, 659–660 California expenditures on, 2:535–536 collective bargaining agreements on, 2:777, 778 expenditure constraints and, 2:645 facilities and, 1:406 in school-based management, 2:668 theory of the firm on, 2:784, 785 See also Student-teacher ratios Classical test theory, 2:622–623 Classroom observation. See Teacher observation Classroom supplies bought by teachers, 1:276–277 Clearing the market. See Market clearing Clery, Sue, 1:319

865

Clinical placements in teacher preparation. See Student teaching Clinton, Bill, 1:7 Closures, school, 2:538, 539, 718, 783. See also Takeovers Clotfelter, Charles, 2:757 Clune, William H., 1:23, 268, 285, 378, 2:840, 842, 847 Cluster schools, 1:204 CMOs. See Charter management organizations Coalition on the Academic Workforce, 1:332 Coase, Ronald, 2:783, 784, 842 Cocking, R., 2:840 Codes, state. See State education codes Coding and categories: in cost accounting, 1:171–172 in fund accounting, 1:359, 360 (table), 361 under GASB, 1:376 (table), 377 Coefficient estimate, 2:495, 505–506, 849 Coefficient of variation, 1:285, 312, 390, 2:649, 651, 652, 849 Cognitive and noncognitive skills: ability bias and, 1:69 in civic engagement, 1:32 college attendance decisions and, 2:716 financial literacy and, 1:340 GED® and, 1:366 human capital and, 1:393 indicators of, in performance evaluation, 2:528 race earnings differentials and, 2:601, 603 teacher effects on, 2:746–747, 749, 755 Cohen, David K., 2:520 Cohen, Jacob, 1:299 Cohen’s d, 1:297, 298 Cohen’s U3 index, 1:299 Cohen-Vogel, Lora, 2:616 Cohesiveness of society, 1:71. See also Social cohesion Cohn, Elchanan, 1:257, 352 Cohort rates, 1:220–221. See also Dropout rates COLA (cost-of-living adjustment), 2:761, 762, 849 Colclough, C., 2:432 (figure) Coleman, James, 1:253, 273, 2:464, 522, 675–676, 684, 685, 2:842 Coleman Report, 1:xxv, 12, 20, 204, 273, 2:464, 523, 635, 688, 758, 846 Collateral for a loan, 2:714 Collective bargaining. See School boards, school districts, and collective bargaining; Teachers’ unions and collective bargaining Collective decision making, public. See Public choice economics College and career ready standards and assessments, 1:225, 2:703, 790 College Board (association), 2:634 College choice, 1:128–132 amenities and, 1:141 behavioral economics and, 1:67

866

Index

college selectivity and, 1:69–70, 129, 130–131, 140, 147 enrollment management and, 1:307 factors in, 1:139–141 financial aid and, 2:707, 709 graduate school and, 1:131 market signaling and, 2:452 work-study effect on, 1:338 College completion, 1:132–133 by adult students, 1:47 college choice and, 1:131 college selectivity and, 1:133 educational inequality and, 1:398 factors in, 1:132–135 financial aid and, 2:708–709 in for-profit colleges, 2:500 work-study impact on, 1:338–339 See also College dropout; College graduation rates; Student persistence in higher education College costs. See Tuition and fees, higher education College credit accumulation, and financial aid, 2:708–709 College dropout, 1:134–136, 357, 358. See also College completion College enrollment, 1:136–141 by charter versus public school graduates, 1:126 college completion compared to, 1:132 dropout rates compared to, 1:134 enrollment management and, 1:306–309 expenditures, revenues, and, 1:318–319 family income and, 2:716 financial aid and, 2:707–708, 716 rates of, 1:137–138, 141 College financial aid. See Student financial aid College graduation rates: college choice and, 1:131 college selectivity and, 1:147, 397 faculty characteristics and, 1:332 financial aid and, 1:136, 2:708 at for-profit institutions, 2:500 student services and, 2:808 See also College completion College major: college choice and, 1:130–131 gender, minority status, and, 1:70 labor market outcomes and, 1:69, 70 SA scores linked to, 2:635 College persistence. See Student persistence in higher education College rankings, 1:142–143 college choice and, 1:129 influence and criticisms of, 1:143 methodology in, 1:142, 2:636 per-student expenditures in, 2:804 revealed preference for, 1:147

College retention, 1:147, 306, 307, 308, 309, 339, 2:708. See also College completion College savings accounts. See College savings plan mechanisms College savings plan mechanisms, 1:144–146, 2:707 College selectivity, 1:146–148 Barron’s categories of, 1:147 college choice and, 1:69, 129, 130–131, 140, 147 college completion and, 1:133 college major and, 1:130–131 Competitiveness Index of, 2:473 enrollment management and, 1:307, 308 measures of, 1:147, 2:636 open access compared to, 1:154 peer effects and, 2:524 race and, 1:397 student outcomes related to, 1:69–70, 146–147 teacher preparation and, 2:767 tuition and, 2:804, 806 Colombia, 1:118, 290, 293, 2:557, 584, 710–711 Colonialism, 1:106, 117 Colorado: charter schools in, 2:440 finance litigation in, 1:404, 2:654, 656 funding model in, 1:156 NCLB proficiency levels in, 2:489 online courses in, 1:358 Race to the Top grant for, 2:608 State Board of Education in, 2:440 successful school district approach in, 1:36, 177 taxpayers’ bill of rights in, 2:730, 731 Commission on the Future of Higher Education, 2:848 Commodity or service, education as a, 1:373 Common Core of Data (CCD), 1:222, 223, 253, 388, 2:472, 473 Common Core State Standards, 1:149–152 adequacy for meeting, 1:23, 26 assessments aligned to, 1:150 coherence and uniformity encouraged by, 1:10 costs of, 1:150–151 edTPA integrated with, 2:765 equal educational opportunities and, 1:398 federal involvement in, concerns for, 2:703 GED® exam aligned with, 1:365 guiding principles for, 1:149 international benchmarking of, 1:149, 151 Race to the Top and, 1:9, 150, 316, 2:439, 609, 848 state education agencies affected by, 2:703 teacher evaluation systems aligned with, 2:755 Common school model, and educational tracking, 2:792 Common school movement, 1:199, 265, 386, 2:570, 654, 671, 672, 674, 845 Common support problem, 2:792–793 Commons problem. See Tragedy of the commons

Index Communication technology. See Education technology; Information and communication technology; Internet Community, perception of, 1:219 Community colleges: in college choice, 1:129 competition with for-profit schools, 1:428 dropout rates at, 1:134 enrollment management in, 1:309 job training programs in, 1:428, 429 vocational disciplines in, 2:831 wage benefits for adult students in, 1:47 See also Associate’s degree institutions; Community colleges finance; Two-year colleges Community colleges finance, 1:152–155 and the 2-year college label, 1:152 expenditure characteristics, 1:153 funding sources for, 1:152–154, 383–384 private fundraising in, 2:548, 551, 552 taxation, willingness to pay, and, 1:324–325 See also Community colleges Community Control Movement, 2:667 Community facilities districts, for capital financing, 1:103 Community participation: as an investment, 2:603 in campaigning for bond measures, 1:85 in capacity building, 1:94, 96 and district consolidation, 1:218 in school block grant programs, 1:79–80, 82 in school-based management, 2:667, 668, 669, 670 in site-based budgeting, 1:91 in solving the commons problem, 2:797–798 as stakeholders, 1:84, 96, 218, 219, 2:667, 776 See also Parental involvement Community service, participation in, 1:126, 259, 261, 337, 338, 2:452. See also Education and civic engagement Comparability (Title I requirement), 2:789, 791 Comparative wage index, 1:155–156 Compensating wage differentials, 1:381–382, 2:745 Compensatory education, 1:109, 111, 199, 2:587, 588 Compensatory models, in performance evaluation, 2:529 Competency-based education, 1:46 Competency-based pay, 2:631. See also Teacher compensation Competition: adding value for competitiveness, 1:247 in capitalist economic systems, 1:106 charter schools and, 1:123, 124, 127, 2:456, 675 in contracting for services, 1:168 among districts, 2:733, 786, 787 among EMOs, 1:270 between EMOs and district-run schools, 1:269, 270, 272 between for-profit schools and community colleges, 1:428

867

freedom of choice from, 2:584 Friedman (Milton) on, 1:292–293 for i3 grants, 1:424–426 intergovernmental, 1:411 international, 1:246–247 market-based, and capacity building, 1:95 between neighboring jurisdictions, 2:572 perfect and imperfect, 2:454–455 public choice economics of, 2:580 for RTT grants (see Race to the Top) school vouchers and, 1:292–293, 294, 295 theory of the firm and, 2:783 Tiebout, 2:787 tuition tax credits and, 2:814 U.S. competitiveness, 1:315, 2:465, 466, 520, 639, 823 See also College rankings Competitiveness Index, NCES-Barron’s, 2:473 Complementarity, dynamic, 1:393, 2:850 Complementary models, in performance evaluation, 2:529 Complete College America (nonprofit organization), 2:533 Completion. See College completion; College dropout; Dropout rates; High school completion Compound annual growth rate, 1:157–158 Compound interest, 1:157 Comprehensive school reform, 1:158–161 Comprehensive School Reform Demonstration Program, 1:160 Compulsory schooling laws, 1:161–164 in Canada and Europe, 1:163–164, 260 in capitalist economies, 1:107 centralization trends and, 1:117 changes in, 1:260, 263, 2:658 demand for education and, 1:193 development of, 1:265 earnings and, 1:162, 163, 164 higher education benefits and, 1:69 homeschooling and, 1:386 Computer literacy, 1:416, 2:672 Computer science, 1:130, 283, 2:464, 478 Computer-assisted instruction, 1:185, 209 Computer-based instruction, 1:289, 290 Computers, access to, 1:208–209, 280, 281, 289–290. See also Education technology; Internet Concentration grants in Title I, 2:788, 789 Concerted cultivation strategy, 1:19, 2:688 Concurrent criterion-related validity, 2:826 Concurrent enrollment, 1:225. See also Dual enrollment Condron, Dennis, 1:19 CONFEMEN international assessment program, 1:417 Confidence intervals, 1:40, 181 Confounding variable, 1:230, 2:567, 568, 707, 849 Conjunctive models, in performance evaluation, 2:529

868

Index

Connecticut: college graduation rate in, 1:131 common schools in, 1:265 district wealth measurement in, 2:648 finance litigation in, 2:653, 654, 656 Horton v. Meskill in, 2:653 local taxing in, 2:440 per-pupil expenditures in, 1:175 PISA participation by, 1:420 Consensus on price, in theory of markets, 1:252 Consolidation: of school districts, 1:60, 217, 218–219, 2:616, 618, 664, 681, 682 of schools, 1:60, 2:664 service consolidation, 2:680–683 Consortia of districts, for service consolidation, 2:682 Constitutional challenges on education funding, 2:653–657 Constitutions, U.S. and state. See State constitutions; U.S. Constitution Construct validity, 2:826–827 Construction, infrastructure. See Facilities; Infrastructure financing and student achievement Consultants, educational, 1:276, 277 Consumer and producer surplus, 1:54–55, 55 (figure) Consumer choice, 1:65, 67, 95, 96, 292, 295. See also School choice Consumer preferences, and allocative efficiency, 1:54 Consumer surplus, definition of, 2:849. See also Consumer and producer surplus Consumption, as a basis for taxation, 2:735–736 Consumption and production, 1:55, 105, 327, 381, 2:577, 581, 698 Contagion theory, 2:482. See also Peer effects Content validity, 2:826 Contextual interactions, neighborhood influences as, 2:480, 482 Continuing education, 1:164–167. See also Adult education; Job training; Licensure and certification; Vocational education Continuous variable, 1:198, 2:619, 825, 826 Contract training programs, 1:154, 166, 167, 319 Contracting for services, 1:167–171 criteria for benefiting from, 1:167–168 with education management organizations, 1:170, 269–270, 272 impact of, 1:169, 170–171 as provision privatization, 2:557 by the traditional central office, 1:114 See also Auxiliary services; Outsourcing; Service consolidation Contracts: in agency theory, 1:50–51, 53 and assessment of property, 2:571

under collective bargaining, 2:640–643, 775–778 enforcement of, 2:800, 801 and moral hazards, 2:459–461 in new institutional economics, 2:486 for supplemental services, 2:719, 720 transaction costs in, 2:485, 799–801 Contributions, private. See Private contributions to schools; Private fundraising in postsecondary education Control groups in experiments, 1:238, 2:611–615. See also Randomized control trials Control versus autonomy, in teachers’ work, 2:739–740 Cook, Thomas, 2:619 Coons, John E., 1:285, 378, 2:840, 847 Cooper, William W., 2:779 Cooperative Research Act, 2:846 Coordinated tax base, 1:411 Copyright, 1:107, 283, 2:500. See also Intellectual property Corcoran, Sean, 2:651, 766 Cornelisz, Ilja, 2:584 Cornman, S. Q., 1:314 (table) Cornwell, Chris, 2:712 Cornwell, Christopher, 1:139 Corporate donors to education, 2:546, 549, 550. See also Philanthropic foundations in education Corporation for Enterprise Development, 1:145 Corrected ordinary least squares, 2:780 Corrections (criminal justice), public spending on, 1:262. See also Education and crime Correlated effects, 1:72 (figure), 72–73, 2:480, 482 Correlation and causality, distinctions between, 1:236– 237, 238, 2:507, 611, 754, 757 Correlation coefficients, 1:297, 298, 349, 2:826 Correnti, Richard, 2:747 Correspondence courses, 1:212, 282. See also Distance learning Corruption, 1:79, 116, 118, 170, 375, 411, 2:638 Corter, C., 2:793 Cosigners for student loans, 2:714–715 Cost, economic. See Economic cost Cost, transaction. See Transaction cost economics Cost accounting, 1:171–173 Cost adjustment strategies, 1:29, 155–156 Cost disease, 1:63, 253, 328, 2:846. See also Baumol’s cost disease Cost efficiency, 1:169, 258, 284, 2:563, 781 (figure), 781–782 Cost equalization, 1:310, 311, 312 Cost function, 1:177–178, 256, 257–258, 2:681, 849. See also Adequacy: cost function approach Cost inefficiency, 2:781 Cost of college. See Tuition and fees, higher education Cost of education, 1:173–179 determination of, 1:175–178 district size and, 1:218–219

Index expenditures across states, 1:175, 176 (figure) foregone earnings and, 1:353 (figures) psychological cost, 1:140 over time, 1:173, 174 (figure), 174–175 See also Adequacy; Foregone earnings Cost of living, 1:36, 155, 156, 178, 216, 277 Cost of living adjustment (COLA), 2:761, 762, 849 Cost overruns, 1:99 Cost reimbursement: as an allocation method, 2:695 in special education, 1:31, 2:695, 695 (table), 696, 697 in supplemental services contracts, 2:720 unfunded mandates and, 2:819 Cost spillover, 1:410–411 Cost structure, 1:284, 346, 2:785, 808, 809. See also Fiscal environment Cost versus value, and economic development, 1:247 Cost-benefit analysis, 1:179–182 cost-effectiveness analysis compared to, 1:183 definition of, 2:849 in market signaling, 2:451–452 mechanics and formula for, 1:179 Perry Preschool example for, 1:179–180 sensitivity analysis in, 1:181–182 spillover effects analysis in, 2:698, 699 See also Cost-effectiveness analysis; External social benefits and costs Cost-effectiveness analysis, 1:182–186 accountability and, 1:11–12 capacity building and, 1:93, 96 cost-benefit analysis compared to, 1:183 cost-effectiveness ratios in, 1:183, 184, 185, 2:586 definition of, 2:849 economics of education on, 1:254–255 effectiveness analysis for, 1:184 general method of, 1:183 in public-private partnerships, 2:586 See also Cost-benefit analysis Cost-effectiveness ratios, 1:183, 184, 185 Costing-out estimates. See Adequacy: cost function approach; Adequacy: evidence-based approach; Adequacy: professional judgment approach; Adequacy: successful school district approach Cost-of-living adjustment (COLA), 2:761, 762, 849 Costrell, Robert, 1:178, 2:761, 762, 763 Cost-utility analysis, 1:184, 185 Council for Advancement and Support of Education, 2:551–552 Council for the Accreditation of Education Preparation, 1:16–17 Council of Chief State School Officers, 1:149, 2:753 County entities as stakeholders, 2:439 County jurisdictions, number of in the U.S., 1:410 Cournot, Antoine Augustin, 2:454

869

Coursera (for-profit venture), 1:283, 358, 2:499 Court decisions. See School finance litigation; State education codes; Supreme Court of the United States Covariance, analysis of, 1:299 Coverdell Education Savings Accounts, 1:144, 145 Cream skimming, 1:52, 127, 2:523 Credential effect, 1:186–188. See also Sheepskin effect Credentialing programs, 1:428, 2:437. See also Certificate programs; Licensure and certification Credit enhancement, and capital financing, 1:103, 104 Credit ratings, 1:84, 85, 103, 104, 2:682 Credits, tax. See Tax credits Crime and education. See Education and crime Criterion-related validity, 2:826 Cross-border standardization, and globalization, 1:370, 374 Cross-national analyses: in adult literacy, 1:421 of centralization versus decentralization, 1:115, 116–117, 118 of citizenship education, 1:416 of computer and information literacy, 1:416 data envelopment analysis for, 1:192 in early production-function analyses, 1:274 of economic inequality between nations, 1:372 on education and per capita growth, 1:324 of foregone earnings, 1:352, 353, 353 (figures) on health indicators and secondary education, 1:74 of highest to lowest earners, 1:395 of income inequality, 1:246, 395–396 of labor market rate of return, 1:74, 252, 2:431–434 of mathematics, 1:253, 416–420, 423, 2:508, 594 of rates of return, 1:74, 373 of reading, 1:416, 417, 418–419, 423, 2:508 of school-based management, 1:81–82, 118 of science, 1:253, 416–420, 423, 2:508 See also International assessments; International datasets in education Cross-price elasticity of demand, 1:301, 302 Cross-sectional data, definition of, 2:849. See also National datasets in education Cross-sectional risk factors, 2:626 Crowding thesis, and the labor market, 2:741 Crowding-out effects, 2:586 CSPs (college savings plans). See College savings plan mechanisms CSR. See Comprehensive school reform Cuba, 1:197 (table) Cuban-origin subgroup, earnings of, 2:601 Cubberley, Ellwood, 1:249, 266, 278, 2:575, 841, 845 Cuéllar-Marchelli, Helga, 2:584 Cullen, Julie Berry, 1:240 Cultural capital, 1:188–189

870

Index

benefits of education and, 1:72 for college completion, 1:133 cultural reproduction and, 1:188–189 social capital and, 2:685, 686 Cultural deficit models, pervasive use of, 1:111–112 Cultural identity, 1:372 Cultural norms, 2:482, 485, 486, 597 Cultural reproduction, 1:188–189 Cumulative annual growth rate, 1:157. See also Compound annual growth rate Cumulative distribution functions, 2:592, 592 (figures) Current and permanent income compared, 2:530, 531 Current Population Survey, 1:221, 222 Curriculum and instruction: adequacy of, 1:22 aligned to standards, 1:7, 8, 2:490 and the central office, 1:113, 114 charter schools, EMOs, and, 1:201, 270, 2:440 guidance system for, 1:93–94 opportunity-to-learn concept and, 2:504 teacher autonomy and, 2:739, 740 Curti, Merle, 2:549 Curtis, J. W., 1:331 (table) Curtis, John, 1:330 (table) Custodial services, 1:114, 169, 361, 2:557, 646 Customer satisfaction surveys, 2:528 Cutoff of a threshold, in regression discontinuity, 1:241–243 CWI. See Comparative wage index Cyber schools, 1:126, 282, 388, 2:645. See also Virtual schools Cypers, Scott, 2:584 Czech Republic, 1:353 (figures), 396 (figure) Dadisman, Kim, 1:320 D’Agostino, Jerome, 1:304, 2:842 Dale, Stacy Berg, 1:69, 129, 148 Danielson, Charlotte, 2:749, 752, 753 Daraio, Cinzia, 2:780 Darity, William A., 1:227 Darling-Hammond, Linda, 1:267 Data envelopment analysis, 1:191–193 of administrative spending, 1:43 definition of, 2:849 method of, 1:191–192, 192 (figure) of technical efficiency, 1:191–192, 2:779–780 See also Allocative efficiency; Economic efficiency; Technical efficiency Datasets. See International datasets in education; National datasets in education Dauber, Susan L., 2:717 Davila, Alberto, 1:261 Davis v. County School Board of Prince Edward County, 1:86

Day, extended. See Extended day Dayton, John, 1:88 DD. See Difference-in-differences De facto segregation, 1:87, 379, 397 De Graaf, Nan Dirk, 1:189 De jure segregation, 1:86, 87, 202 de la Torre, Marisa, 2:718 De Soto, Hernando, 1:106 DEA. See Data envelopment analysis Dead hand effect, 2:822 Deadweight loss, 1:55 DeAngelis, Karen, 2:604 DeArmond, M., 2:537, 538 Deasy, John, 1:58 Debra P. v. Turlington (1981), 1:229 Debt instruments. See Bonds in school financing; General obligation bonds Debt ratings, 1:99 Decentralization: of central office roles, 1:114 in postsecondary fundraising, 2:551 prior to NCLB, 2:487 in the private sector, 2:668, 669 school-based management and, 1:79, 114 See also Centralization versus decentralization; Schoolbased management Decision rights, and privatization, 2:556, 557, 558 Decision-making unit, definition of, 2:850 Declarative knowledge, 1:341 Decline, calculation of annual rate of, 1:158 Deconcentration, as a type of decentralization, 1:116 Deductions, income tax. See Income tax deductions Dee, Thomas, 1:230, 241, 260, 261, 296, 2:490, 747, 843 Default, and discount rate, 1:210 Default effects, 1:66, 67 Defaults on student loans, 1:129, 336, 358, 364, 429, 2:500, 700, 714–715 Deferred compensation, 2:746. See also Teacher pensions Deferred tax provisions, and property assessment, 2:571 Deficit-based labeling of grant targets, 1:111–112 Defined-benefit and defined-contribution plans, 2:491, 492, 760, 763. See also Teacher pensions Delancey, D., 1:111 Delaware, 2:608 Delegation, as a type of decentralization, 1:116 Delivery approach, for moving beyond compliance, 2:703 Dell Foundation. See Michael and Susan Dell Foundation Delprato, Marcos, 1:260 Delta Cost Project, 2:806, 808 Demand: income elasticity of, 1:301, 302, 2:851 law of, 2:453–454 price elasticity of, 1:301–302, 413 See also Supply and demand

Index Demand curves: in allocative efficiency, 1:54, 55 (figure) elasticity and, 1:301 in the median voter model, 2:458–459 partial equilibrium and, 2:517 spillover effects and, 2:698 (figure), 699 in theory of markets, 2:453 (figure), 453–454 Demand elasticity, 1:308, 2:850. See also Price elasticity Demand for education, 1:193–195 Deming, David, 1:264 Democratic Administration Movement, 2:667 Democratic values, 1:259, 292, 293, 296, 2:654. See also Education and civic engagement Denmark, 1:116, 353 (figure), 396 (figure), 2:831 Dental insurance benefits, 2:491, 493 Department of Defense Education Activity (DoDEA), 1:196–198 Department of Defense schools, 1:196–198 Department of Education. See U.S. Department of Education Depreciation, 1:1, 100, 103, 274, 2:571 Depreciation accounting, 1:245 Deregulation, 1:199–202 costs and benefits of, 1:200–201 desegregation and, 1:201 privatization and, 2:555 Derrick, Frederick W., 2:636 Desegregation, 1:202–206 achievement gaps and, 1:20 Brown II and other cases on, 1:87, 203 Brown ruling on, 1:86–88 busing/transportation for, 1:60, 203 deregulation and, 1:201 measurement of, 1:202–203 policies and practices for, 1:203–205 student outcomes and, 1:204–205 See also Segregation (racial) Design teams, in comprehensive school reform, 1:158– 159, 160 DesJardins, Stephen L., 1:338 Developed countries: centralization trends in, 1:116 cultural dominance of, 1:372 education level linked to earnings in, 1:252 foregone earnings in, 1:352, 353 indirect costs of education in, 1:194 rate of return in, 1:373 returns to an additional year of schooling in, 2:657 student achievement in, 2:580 students flowing to, from developing countries, 1:370 See also Industrialized countries Developed countries, least, 1:194, 195 Developing countries: access to technology in, 2:499

871

benefits from primary/secondary education in, 1:73, 74, 76 centralization/decentralization in, 1:116, 117 demand for education in, 1:194 demand-side obstacles to schooling in, 1:194–195 economic development in, 1:247 education level linked to earnings in, 1:252 foregone earnings in, 1:352, 353 indirect costs of education in, 1:194 international assessments in, 1:418 opportunity cost in, 1:254 policy analysis impact on, 2:536 public-private partnerships in, 2:584 randomized experiments used in, 1:274 rates of return in, 1:74, 252, 373, 2:431–434 students flowing from, to developed countries, 1:370 women’s education in, 1:373 See also Labor market rate of return to education in developing countries Development programs in higher education, 2:548. See also Private fundraising in postsecondary education Developmental education, 1:154. See also Remedial education Devolution, as a type of decentralization, 1:116 Di Pietro, Giorgio, 1:260 Diamond, Alexis, 2:596 Diamond, Peter, 2:799 Dickieson, J., 1:343 Dickson, Lisa M, 1:137 (figure), 138 (figure) DID. See Difference-in-differences Dieterle, Steven, 2:748 Difference equations, 1:322, 323 Difference-in-differences, 1:206–208, 2:595–596 on causal impact of financial aid offer, 1:255 definition of, 2:850 overview and example of, 1:239–240, 240 (figure) randomized control trials and, 2:614 Differential tax incidence, 2:565. See also Tax burden Differentiated pay, 2:630–631. See also Teacher compensation Digital divide, 1:208–210 broadband speeds and, 1:208 as a continuing issue, 1:280, 281, 282 economic and educational impact of, 1:209 gender, ethnicity, socioeconomic status, and, 1:280 online education and, 2:499 See also Online learning Dillow, S. A., 1:314 (table) DiMaggio, Paul, 1:189 Direct and indirect costs: in cost accounting, 1:171, 172–173 in the human capital model, 1:391 in labor market rate of return, 2:431–432, 433 as obstacles to schooling, 1:195

872

Index

Direct and indirect spending, 2:731. See also Indirect spending Direct effects model, 1:72, 72 (figure), 73, 74, 75 Direct loans, federal, 1:278, 385, 2:699, 700, 715 Disabilities, individuals with. See Individuals with disabilities; Individuals with Disabilities Education Act Disadvantaged students, ESEA for, 1:302, 303, 304. See also Title I Disciplinary matters, and due process, 1:228, 229. See also Student suspensions; Teacher suspensions Disclosure requirements, in education regulations, 1:199–200 Discontinuity, regression. See Regression-discontinuity design Discount rate, 1:210–212 in the capital budgeting approach, 1:91 cost-benefit analysis and, 1:179 definition of, 2:850 future value calculation with, 1:211 human capital theory and, 1:391, 392, 393 in internal rate of return, 1:414–415 present value calculation with, 1:210–211 in present value of earnings, 2:541 for tuition, 2:802, 805–806 for value of crime reduction, 1:264 See also Internal rate of return; Present value of earnings Discounted cash flow, 1:210, 211, 414–415 Discounted utility, 1:393, 2:452, 850 Discounting: in cost-benefit analysis, 1:179, 181 in crime reduction value, 1:264 for pension plan wealth, 2:761 in present value of earnings, 2:541, 542 (table) in value of schooling, 2:657 Diseconomies of scale: in district size, 1:218, 219 fiscal environment and, 1:346 identified in estimation calculations, 1:257 models of adequacy and, 1:30, 32 per-meal costs and, 1:62 ray economies of scale and, 1:258 reasons for, 1:256 school size and, 2:664–665, 666 See also Economies of scale Dismissals. See Teacher dismissals Disparities, fiscal. See Fiscal disparity Dispersion, definition of, 2:850 Dispersion measures, 1:285, 390. See also Coefficient of variation; Gini coefficients; McLoone index; Range (dispersion); Standard deviation; Theil indices Dissimilarity index of segregation, 1:203 Distance function, 1:43, 2:780, 850

Distance learning, 1:212–215 dual enrollment offered via, 1:225 economic impacts of, 1:215 Internet delivery of, 1:212–213, 214, 2:557 privatization and, 2:557–558 student mobility and, 2:718 Distance to the nearest college, and benefits of higher education, 1:69 Distorted markets, 1:183. See also Market distortion Distributional effects, 1:52, 2:565, 593, 732. See also Quantile regression District, successful. See Adequacy: successful school district approach District of Columbia. See Washington, D.C. District power equalizing, 1:92, 215–217 guaranteed systems and, 1:215, 378–379 principles of, 1:215–216 states that use, 1:216, 378–379 See also Guaranteed tax base District size, 1:217–220 administrative spending and, 1:44 consolidation and, 1:218, 219 salary schedules and, 2:745–746 state codes on, 2:706 District superintendents. See Superintendents District uniformity, state codes on, 2:706 District wealth. See School district wealth Districtwide choice programs, 2:447, 448–449. See also School choice Dobbie, Will, 1:21 Doctoral institutions: adult students at, 1:47 (table) expenditures per student at, 2:806, 807, 807 (tables) faculty at, 1:330, 331, 331 (table) ray economies of scale in, 1:258 tuition and fees at, 2:802 (table), 803 See also Research universities DOD schools. See Department of Defense schools DoDEA (Department of Defense Education Activity), 1:196–198 Doherty, Kathryn M., 2:761 Dolfin, S., 2:561 Dolton, Peter, 2:766 Domina, Thurston, 2:592 (figures) Donald, Stephen, 1:69 Donations, private. See Private contributions to schools; Private fundraising in postsecondary education Doringer, Peter, 1:226 Downes, Thomas, 1:59, 178, 2:651, 732 Downey, Douglas, 1:19 Downward bias, for OLS estimators, 2:506 Dropout rates, 1:220–225 calculation of, 1:221–222, 223 causes of dropping out, 1:223

Index graduation rates and, 1:220, 221, 222, 223 interventions and prevention, 1:181, 185, 223–224 student mobility and, 2:717 See also College dropout; High school dropout; Risk factors, students Dropout status, changeability of, 1:221 Dropping out, risk factors for, 1:223–224, 2:624–627 Drug prices, intellectual property, and the WTO, 1:423 Dual credit, 1:225. See also Dual enrollment Dual enrollment, 1:225–226. See also Advanced Placement (AP) courses Dual labor markets, 1:226–227, 2:850 Dual-language program, in bilingual education, 1:77 Due process, 1:228–231, 2:618 Duflo, Esther, 2:794, 795 Duggan, Molly H., 2:551 Duke Endowment, 2:533 Dummy variable, 1:147, 206, 2:432, 596, 850 Duncan, Arne, 1:11, 2:607 Duncan, Greg, 2:515 Duncombe, W., 2:842 Duncombe, William, 1:23, 218, 219 Dupas, Pascaline, 2:794, 795 Dupre, Anne, 1:88 Dusseault, B., 2:537, 538 Dye, Richard, 2:732 Dynamic complementarity, 1:393, 2:850 Dynarski, Mark, 2:568 Dynarski, Susan, 1:133, 138, 139, 239, 255, 2:707, 708 Early childhood education, 1:233–236 cost-benefit analysis of, 1:179–180 crime effects and, 1:234, 263–264 datasets in, 2:473–474, 476 enrollment data for, 1:233, 235 fade-out of benefits from, 1:235 high school dropout/completion and, 1:224, 234 higher education and, 1:234 long-term impact of, 1:14, 234–235 parental interventions and, 2:515 peer effects in, 2:523–524 special education spending in, 2:694, 694 (table) Early Childhood Longitudinal Studies, 2:472, 473–474, 476 Early College High School Initiative, 1:226. See also Dual enrollment Early decision and early action programs, 1:140. See also College choice; College enrollment Early Grade Reading Assessment, 1:417 Earned income tax credits, 1:411 Earnings: college choice and, 1:128, 129 college dropout and, 1:134 college major and, 1:130, 131

873

college selectivity and, 1:69, 129, 131 and the college/work decision, 1:45–46 comparative wage index, 1:155–156 comparisons of (see Earnings comparisons, data for) compulsory schooling laws and, 1:162, 163, 164 in cost-benefit analyses, 1:180 credentials, credential effect, and, 1:186–187 crime, opportunity cost, and, 1:71, 72, 75, 263 demand for education and, 1:194 early childhood education and, 1:234 economics of education on, 1:252, 254 forgone, 1:321, 352–354, 2:433, 714, 766, 801 GED® and, 1:366, 2:542 gender and, 1:48, 49, 74, 180, 227, 352, 2:540–542, 602–604 GI Bill and, 1:369 gross domestic product and, 1:373 growth rate of, 2:540–541 Hedonic wage models, 1:156, 381–382, 2:851 high school dropout and, 1:48, 74, 181, 220, 223, 366 high school graduation and, 1:48, 49, 50, 74, 134, 181, 366, 2:466, 542, 602, 714 higher education and, 1:48–49, 68–70, 128, 129, 134, 2:466, 542 hours worked in college and, 1:339 permanent income, 2:530–531 present value, 1:139, 391, 392, 2:539–542, 853 race and, 2:601–607, 659 ratio of highest to lowest earners, 1:395 school quality and, 2:601, 603, 604, 657–660 of teachers (see Teacher compensation) See also Earnings/wage “gap” terminology; Foregone earnings; Income inequality and educational inequality Earnings comparisons, data for: by age, 1:48–50, 49 (table), 2:540 (figure), 541 (figure) by college major, 1:130 by college selectivity, 1:129 by education level, 1:49 (table), 49 (figure), 74, 128, 129, 134, 2:542 (table), 594, 602 (table), 604, 604 (table), 714 by gender, 1:49 (table), 49 (figure), 74, 180, 2:540 (figure), 541 (figure), 542 (table), 602 (table), 604 (table) for higher education faculty, 1:331 (table) by race, 2:601–606, 602 (table), 604 (table) by year, 2:602 (table), 604 (table) Earnings on investments, 2:492, 549, 644 Earnings residuals, 2:603–604 Earnings/wage “gap” terminology: by age, 1:49 by education level, 1:49, 2:466 by gender, 1:49, 227, 2:603, 604 by race, 2:659 See also Earnings; Earnings comparisons, data for

874

Index

Easton, J. Q., 1:94 Easy-to-staff schools, student teachers in, 2:769 ECE. See Early childhood education Eckstein, Otto, 1:179 Econometric methods for research in education, 1:236–244 difference-in-differences, 1:239–240, 240 fixed effects, 1:243, 274, 350–352, 2:596, 851 instrumental variables method, 1:240–241 lotteries, 1:240 natural experiments, 1:239 propensity score matching, 1:243 randomized control trials, 1:238–239 regression, 1:237–238 regression discontinuity, 1:241–243 See also Difference-in-differences; Instrumental variables; Ordinary least squares; Propensity score matching; Randomized control trials; Regression analysis; Regression-discontinuity design Economic capital, 2:684, 685 Economic cost, 1:244–245. See also Economics of education Economic development and education, 1:245–249 education as a development tool, 1:247–248 factor endowment and, 1:328 in globalization, 1:371, 373 property taxes and, 2:572–573 Economic efficiency, 1:249–251 Economic globalization, 1:371–372. See also Globalization Economic incidence, 2:564–565, 575 Economic interactions, 2:484. See also New institutional economics Economic Opportunity Act, 1:46, 337 Economic rationality, 1:64–65, 66, 67 Economics of education, 1:251–256 datasets used in, 1:253–254 empirical applications of, 1:253–255 human capital foundations of, 1:252 journals, books, articles, reports in, 2:839–844 production functions in, 1:252–253 quality improvement and, 1:254–255 rate of return and, 1:251, 252, 254 theory of markets in, 1:251–252 as a wide-ranging and vibrant field, 1:xxvi See also Education production functions and productivity Economics of Education Review (journal), 2:839 Economies of scale, 1:256–259 centralization and, 1:114 for charter management organizations, 1:120, 121, 122 in contracting for services, 1:168 criteria for, 2:682

definition of, 2:850 district size and, 1:218–219 economies of scope and, 1:257, 258 in private versus public entities, 1:270 in provision of public goods, 1:411 school size and, 2:664–665, 666 in service consolidation, 2:680–681 transaction costs and, 2:800 Economies of scope, 1:257–258, 2:800, 850 Ecuador, 1:290 Edgewood I, II, and III lawsuits, 1:378 Edison (education management organization), 1:269, 272 edTPA (teacher performance assessment), 2:764–765 Education Amendments of 1972, 2:818 Education and civic engagement, 1:259–261 cost-benefit analysis of, 1:180 data on, 1:75, 260 estimated value of, 1:323 (table) GI Bill enhancement of, 1:369 higher education and, 1:70–71, 260 for homeschooled students, 1:388 primary/secondary education and, 1:72, 75, 260 public choice economics on, 2:577 in public versus charter schools, 1:124, 126 socioeconomic status and, 2:689 voucher plans and, 1:296 Education and crime, 1:262–264 correlation between, 1:262–263 in cost-benefit analysis, 1:180, 181 data on, 1:262, 263, 264 early childhood education and, 1:234, 263–264 estimated value and, 1:323 (table) high school dropout and, 1:220 higher education and, 1:70, 71, 263 opportunity costs and, 1:71, 72, 75, 263 primary/secondary education and, 1:74–75, 263, 264 school safety and, 2:475 and street crime versus white-collar crime, 1:321 Education as cause versus education as proxy, 1:260 Education as investment. See Investment in education Education Commission of the States, 1:225, 2:560 Education Department, U.S. See U.S. Department of Education Education finance, 1:265–268 adequacy and productivity, shift in focus to, 1:265, 267, 268 basic problem of, 1:268 between-state versus within-state, 2:481 central question of, 1:22 and fiscal disparities, 1:266, 267 fiscal equity as traditional emphasis of, 1:265 journals, books, articles, reports in, 2:839–844 key challenge of, 1:267

Index proliferation of research on, 1:xxv state governments in, growth of, 1:265–266 as a wide-ranging and vibrant field, 1:xxvi See also School finance litigation Education Finance and Policy (journal), 1:58, 59, 2:839 Education Finance Incentive Grants, 2:788 Education for All goals, 2:547 Education for All Handicapped Children Act, 1:230, 399–400. See also Individuals with Disabilities Education Act Education for All movement, 1:5, 2:547 Education IRA, 1:144 Education Longitudinal Study of 2002 (ELS: 2002), 1:222, 365, 2:472, 474, 475 Education management organizations, 1:268–273 charter management organizations and, 1:119, 268 contracting for services with, 1:170, 269–270, 272 data on, 1:271, 272 effectiveness of, 1:271–272 market forces and, 1:269, 270 types of, 1:268–269 See also Charter management organizations; Charter schools Education of the Handicapped Act, 1:399. See also Individuals with Disabilities Education Act Education production functions and productivity, 1:273–276 in economics of education, 1:252–253 SAT and, 2:635 socioeconomic status in, 2:687 and teacher quality, 1:274–275 See also Production function analysis; Production theory Education Resources Information Center (ERIC), 2:846 Education savings accounts. See College savings plan mechanisms Education spending, 1:276–280 components of, 1:276 data on, 1:276, 277 inequality in, between-state and within-state, 2:481 measurement of, 1:279 See also Expenditures, on personnel; Expenditures and revenues, current trends of; Per-pupil expenditures Education technology, 1:280–284 access to, 1:280–281 blended learning with, 1:281–282, 283–284 data on, 1:280, 281, 282, 289, 291 digital divide and, 1:208–209, 280 educational costs and, 2:804, 809 innovation and, 1:289–291 mobile devices and, 1:281, 290 See also Internet; Online learning Educational and income inequality. See Income inequality and educational inequality Educational consultants, 1:172, 276, 277

875

Educational equity, 1:xxvi, 284–289 in access to technology, 1:208–209 adequacy and, 1:13–14, 27, 32, 33, 286–288 adequacy and, major difference between, 1:23 in admissions lotteries, 2:449, 450 in centralization of private contributions, 2:547 deregulation and, 1:200, 201 in distribution of board certified teachers, 2:470 district power equalizing for, 1:215–216 in early childhood interventions, 1:234 efficiency, leadership, effectiveness, and, 1:249 enrollment equity, and access to education, 1:5 in finance litigation, 2:653–654, 655 homeschooling and, 1:387 measurement of, 2:586 opportunity to learn and, 2:503, 504 and principles of equity, 1:285–286 in public-private partnerships, 2:584, 585, 586 teacher experience and, 2:757 in voucher plans, 1:292, 293, 294, 295–296 See also Equity; Finance equity; Fiscal disparity; Horizontal equity; Income inequality and educational inequality; Inequities; School finance equity statistics; School finance litigation; Vertical equity Educational Evaluation and Policy Analysis (journal), 2:839 Educational innovation, 1:289–292 deregulation and, 1:200 i3 Fund on, 1:120, 160, 424–426 limited, implications of, 1:289 in public-private partnerships, 2:583, 584, 585 in state education agencies, 2:703–704 teacher autonomy for, 2:740 Educational opportunity. See Coleman report; Equal educational opportunities; Opportunity to learn Educational Testing Service (ETS), 1:365, 2:634 Educational vouchers, 1:292–297 admissions lotteries and, 2:448 capacity building and, 1:95 countries using, 1:293, 294, 295, 296, 2:556–557, 710–711 court cases on, 1:294, 2:674–675 equity and, 1:295–296 freedom of choice and, 1:294–295, 296 government failure and, 2:580 market forces and, 1:269 neighborhood effects and, 2:482–483 outcomes evaluation for, 1:294–296, 2:557 peer effects and, 2:524–525 privatization and, 2:556, 557 productive efficiency and, 1:295 public funds for, 1:293, 294, 2:671, 674 public-private partnerships in, 2:585

876

Index

for religious and private schools, 1:294, 2:671, 674, 676, 812–813 support services linked to, 1:294 tax credits compared to, 2:676, 813–814 theory of markets and, 2:455–456 theory of the firm and, 2:783 edX online service provider, 1:283, 358, 2:499 Effect size, 1:185, 297–300 Effectiveness. See Cost-effectiveness analysis; Teacher effectiveness Efficiency: adequacy and, 1:34, 37 allocative, 1:53–56, 200, 2:781, 782, 783, 785, 849 cost, 1:169, 258, 284, 2:563, 781–782 deregulation and, 1:200 economic (see Economic efficiency) empirical measurement of, 1:191–192 equity and, 1:288 in freedom, 2:784 Kaldor, 1:249 local control development and, 2:438 Pareto, 1:54, 249 productive (see Productive efficiency) social, 1:55–56, 321, 325, 2:854 in tax systems, 1:411 technical, 1:191–192, 2:778–782, 854 of voucher plans, 1:205, 292, 293, 295 See also Data envelopment analysis; Inefficiency Efficiency frontier, 1:191, 192, 2:780 Egan, Laura, 1:353 (figures) Ehrenberg, Ronald G., 1:69, 143, 329, 339, 2:747, 804, 808 Eide, Eric, 1:69 Eisenhower, Dwight D., 2:846 El Salvador, 1:80, 81, 82 ELA. See English language arts (ELA) Elacqua, Gregory, 1:296 Elasticity, 1:300–302 calculation of, 1:301 demand elasticity, 1:308, 2:850 lotteries and, 2:446 to measure fiscal neutrality, 1:349 price elasticity, 1:301–302, 413 tax elasticity, 2:565–566, 726–728, 736 Elections and referenda: for bond funding, 1:84–85, 103, 153, 368 for education officials, 2:440 election cycle for, 2:706 for school board members, 2:637–638, 639, 640, 642, 706, 776 See also Voting Electronic classroom. See Education technology; Internet; Online learning Elementary and Secondary Education Act, 1:302–305

data on, 1:303 debates over, 1:304 federal role stimulated by, 1:277, 303–304, 315 goal of, 1:303 Goals 2000 and, 1:7–8 in the history of educational regulation, 1:199 primary aim of, 1:302 revisions of, 1:303–304 standards-based accountability and, 1:7, 8 state education agencies affected by, 2:701 students with disabilities covered by, 1:399 Title I of (see Title I) Title V of, 1:303, 315, 2:701 Title VI of, 1:199, 303 Title VII of, 1:78 Titles II, III, IV of, 1:303 waiver program (see Waivers for NCLB) See also No Child Left Behind Act; Race to the Top Eligibility rules, and regression-discontinuity design, 2:597, 619, 620, 621 ELLs. See English language learners (ELLs) ELS: 2002 (Education Longitudinal Study of 2002), 1:222, 365, 2:472, 474, 475 Elson, A., 1:184 E-mail, 1:213, 281, 356, 2:498 Emler, Nicholas, 1:72 Emmison, Michael, 1:189 EMOs. See Education management organizations Emotional disturbance category, in special education, 2:693 (table) Employment. See Earnings; Job training; Labor market outcomes; Working conditions Employment contracts and moral hazard, 2:460–461 Endogeneity bias, 2:748, 850 Endogenous sorting, 2:523. See also Sorting Endogenous variable, definition of, 2:850 Endowment effect, 1:66 Endowments: in college rankings, 1:142 factor endowment, 1:328 family endowment, 2:716 in for-profit higher education, 1:356 in higher education, 1:383, 384, 385, 2:802, 805–806, 819–823 in K-12 education, 2:811, 812 postsecondary fundraising for, 2:548, 549 in private schools, 2:671, 811, 812 Engagement, civic. See Education and civic engagement Engerman, Stanley, 1:328 Engineering, 1:130, 425, 2:477, 478, 479, 500 England, 1:74, 120–121, 122, 197 (table), 2:520. See also United Kingdom English immersion program, 1:77. See also Bilingual education

Index English language arts (ELA): CCSS for, 1:149, 150, 151 in charter schools, 1:21, 2:614 A Nation at Risk and, 2:464 NCLB focus on, 1:9, 40 in NCLB/AYP framework, 1:38, 39, 40, 41, 2:662 remedial programs for, 2:615 student incentives in, 2:711 teacher evaluation systems and, 2:755, 756 teacher preparation in, 2:769 See also Reading English language capacity, and earnings, 2:603 English language dominance, and globalization, 1:374 English language learners (ELLs): in bilingual education, 1:76, 77–79 in charter schools, 1:127 fair student funding for, 2:652 i3 grants for, 1:425 under NCLB, 2:488, 489, 790 in parcel tax districts, 2:512 resources needed for, 2:587 student incentives for, 2:711 teacher compensation and, 1:382, 2:522 vertical equity for, 2:828 weighting systems for, 1:78, 306, 2:588, 589, 836, 836 (figure) English-as-a-second-language programs, 1:78. See also Bilingual education English-only approaches under NCLB, 1:77, 78. See also Bilingual education Enhancing Education Through Technology program, 1:209 Enrollment, college. See College enrollment Enrollment, U.S., current data on: higher education (2010), 1:318 higher education (2010–2021), 1:141 K-12 (2010), 1:228, 318 K-12 (2012–2013), 1:xxv See also School size Enrollment counts, 1:305–306 average daily attendance for, 1:39, 162, 201, 217, 225, 305, 351 average daily membership for, 1:217, 305 average number belonging for, 1:305 in dropout rate calculations, 1:221 for operationalizing district size, 1:217 Enrollment management in higher education, 1:306–310 Enrollment management in private schools, 2:811, 812 Enterprise operations, expenditures to, 1:279 Entitlement categorical funding, 1:109 Entwisle, Doris, 2:717 Environmental factors (air, light, sound, water): economic cost of pollution, 1:245 estimated value of benefits from, 1:323 (table)

877

as negative externalities, 1:107 public good and, 2:581 student achievement and, 1:406 tragedy of the commons applied to, 2:796–797, 798 Epidemic theory, 2:482. See also Peer effects Equal educational opportunities: accreditation standards on, 1:16 adequacy and, 1:286–287 Brown case and, 1:87, 88 Common Core State Standards and, 1:398 educational excellence and, 2:790 ESEA goal of promoting, 1:303 fiscal neutrality and, 1:286 nutritional needs for, 1:61 Rodriguez case and, 2:633 in state constitutions, 1:378 transportation access and, 1:60 See also Access to education; Educational equity; Opportunity to learn Equal Protection Clause of the U.S. Constitution, 1:87–88, 202, 267, 378, 2:485, 632–633, 679, 680, 846, 847 Equal protection clauses of state constitutions, 1:267, 378, 2:679, 680, 847 Equal sacrifice doctrine, 1:3 Equal treatment of equals, 1:285–286, 389, 390. See also Horizontal equity Equality and equity compared, 1:285, 2:578 Equality of Educational Opportunity (report), 1:xxv, 12, 2:464, 635, 758, 846. See also Coleman Report Equalization funding formulas, 1:92, 215–216, 266 Equalization models, 1:310–313, 378–379 combined models, 1:311–312 spectrum of, 1:310 (table) See also Fiscal disparity Equally situated entities, 1:285 Equally situated students, 2:649 Equally situated taxpayers, 2:574 Equilibrium: definition of, 2:850 financial, 1:121 market, 1:381, 382, 2:453 (figure), 453–454, 517 See also Partial and general equilibrium Equilibrium price and quantity, 1:54, 55 (figure), 2:453 (figure), 454, 516–518 Equity: educational (see Educational equity) equality compared to, 1:285, 2:578 essence of, 1:284 horizontal (see Horizontal equity) inequity aversion, 1:66, 67 in infrastructure and facilities, 1:404–405 in intergovernmental fiscal relationships, 1:410, 411 local control development and, 2:438, 439 of lotteries, 2:446–447

878

Index

social justice compared to, 1:285 tax equity, 2:564, 575, 649, 650–651, 828 taxation, burden, and, 1:3 taxpayer, 2:651 vertical (see Vertical equity) Equity in school finance. See District power equalizing; Educational equity; Equalization models; Horizontal equity; School district wealth; School finance equity statistics; School finance litigation; Vertical equity; Weighted student funding E-Rate program, 1:209, 280 ERIC (Education Resources Information Center), 2:846 Errors-in-variables problem, 2:523 ESEA. See Elementary and Secondary Education Act ESEA flexibility waivers. See Waivers for NCLB Establishment Clause of the U.S. Constitution, 2:674, 815. See also Separation of church and state Estimated standard error, 1:238 Estonia, 1:353 (figures) Ethics: education, crime, and, 1:262 in experimental designs, 2:567, 595, 611, 619 and fairness in taxation, 2:564, 566 See also Educational equity; Equity Ethnic groups: access to technology and, 1:280 measurement of segregation of, 1:202–203 stratification of, in voucher plans, 1:296 See also Racial/ethnic groups Ethnic isolation, 2:480 Ethnic neighborhood composition, preferences for, 2:480 Ethnic/racial groups. See Racial/ethnic groups ETS (Educational Testing Service), 1:365, 2:634 Europe: compulsory schooling laws in, 1:164 decentralization in, 1:117 Department of Defense schools in, 1:196 international datasets for, 1:420 marketization in, 2:555 private universities in, 2:556 privately operated schools in, 1:124 strong centralized states of, 2:438 tracking in, 2:791 vocational systems in, 2:832 European Commission, 1:374 European Union, 1:372 Evangelical Christian schools, 2:554, 810 Evans, K., 2:769, 770 Evans, W. N., 2:843 Evans, William, 2:651, 766 Event dropout rates, 1:220, 222. See also Dropout rates Evidence-based approach. See Adequacy: evidence-based approach

Evolution in authority over U.S. schools, 1:313–316 data on, 1:313, 314 (tables), 315 in federal government’s role, 1:314–316 in state-local relationships, 1:313–314 Excise tax, 2:736 Excludable, definition of, 2:850 Excludable and rival goods, 2:581, 582. See also Nonrival and nonexcludable goods Exempt students, in an admissions lottery, 2:448 Exemptions, tax. See Tax exemptions Exit, freedom to, 2:556 Exogenous variable, 2:517, 850 Exogenous variation, 1:260, 2:449, 482 Expectancy theory of motivation, 2:519–520 Expectations operator, 2:595, 850 Expenditures, on personnel: at central offices, 1:114–115 at community colleges, 1:153 cost analysis and, 1:172, 183, 184 cost disease and, 1:253 at DoD schools, 1:197 in higher education, 1:153, 330–332 as the largest expense in district budgets, 2:646 local economic improvement from, 1:247 in public schools, 2:741, 746 weighted student funding and, 2:837 See also Teacher compensation Expenditures and revenues, current trends of, 1:xxv, 316–320 and cash flow, 2:645–646 and enrollment, 1:316, 317, 318, 319 expenditures, 1:xxv, 317, 318, 319, 2:645 in higher education, 1:318–319, 383 the new fiscal reality and, 2:644 revenues, 1:317, 318–319 revenues, by source, 1:313, 314, 317, 319, 2:481, 702, 824 revenues, from private contributions, 2:545–552 See also Education spending; Expenditures, on personnel Expenditures per pupil. See Per-pupil expenditures Experience-earnings profile. See Age-earnings profile Experimental design, 1:234, 350, 2:521, 595. See also Quasi-experimental methods; Randomized control trials Experiments, natural, 1:239–240, 2:852 Expert briefs, in the PJ approach, 1:33–34 Explicit versus implicit costs, 1:244, 245 Expulsion of students, and due process, 1:229 Extended day, 1:29, 126, 127, 320–321, 2:465 Extended school year, 1:126, 127, 2:465 External social benefits and costs, 1:321–326 civic engagement as benefit, 1:72 cost-benefit analysis of, 1:180

Index crime reduction as benefit, 1:262, 264 data on, 1:323 (table), 325 demand for education and, 1:193 economic development and, 1:246 and intergovernmental fiscal relationships, 1:411 Internet/computer skills and, 1:209 measurement/valuation methods for, 1:321–324 in the neighborhood effect, 1:292 theory of markets on, 1:252 total benefits and, 1:323–324, 325 true social costs and, 1:411 See also Benefits of higher education; Benefits of primary and secondary education; Education and civic engagement; Education and crime External validity: of CAGR, 1:157 generalizability and, 2:827 in lottery-based research, 2:449 in randomized experiments, 1:66, 2:612, 613 in regression-discontinuity, 2:620 selection bias and, 1:81 Externalities: allocative efficiency and, 1:55–56 definition of, 2:850 intergovernmental relationships and, 1:411, 412 market failure and, 2:577, 582 negative, 1:107, 321, 2:796 neighborhood SES and, 2:689 positive, 1:68, 249, 252, 2:455, 577, 582 and social benefits/costs, 1:321, 322, 323 spatial, 1:411–412 spillover effects as, 2:698 in theory of markets, 2:455 Extracurricular activities, 1:19, 177, 286, 387, 2:665, 666, 752 Extrinsic motivation, 1:12–13, 67, 2:711 Facebook (online service), 1:213 Facilities: bonds for funding of, 1:83–84, 85, 368 in capacity building, 1:94 capital financing for, 1:101 at community colleges, 1:153 in cost analysis, 1:172 disparities in, 1:403–404, 405, 406 opportunity to learn and, 2:503 student achievement and, 1:403–406 Factor analysis, 2:457, 529, 850 Factor endowments, 1:328 Factor prices, 1:327–329, 2:850 Factors of production, 1:105, 107, 108, 256, 273, 327, 328, 2:564, 565, 681 Faculty in American higher education, 1:329–333 dataset on, 2:476

879

by institution category, 1:331 (tables), 357 part-time, 1:330, 331, 332, 2:809 salaries for, 1:330–333, 331 (table), 333 (table), 382 tenure for, 1:329–331, 332 tuition and, 2:803 types of, 1:330 (table), 330–332, 331 (table) Fade-out of benefits from early childhood education, 1:235 FAFSA (Free Application for Federal Student Aid), 1:136, 2:525, 526, 700 Failing schools programs, 2:814 Failure: government, 2:578, 580 market (see Market failure) school district, 2:639 in schools’ AYP, 1:39–40, 2:488, 662, 719 student risk of (see Risk factors, students) Fair student funding, 2:587, 652, 835 Faith-based education, 2:583, 674, 814. See also Schools, religious Family education level. See Parental education level Family Educational Rights and Privacy Act, 1:333–335 Family income. See Household/family income; Socioeconomic status and education Family involvement. See Parental involvement Farrell, Michael J., 2:779, 781, 782 Farrington v. Tokushige (1927), 2:672 Fasih, T., 1:80, 81 Fast-tracking, by dual enrollment, 1:226 Federal Academic Competitiveness Grants, 2:707 Federal direct loans, 1:278, 385, 2:699, 700, 715 Federal Family Education Loan Program (FFELP), 2:700–701, 715 Federal Perkins Loan Program, 1:335–337 in federal student loan programs, 2:699, 715 terms of, 1:336, 385 Federal range ratio, 1:312, 2:649, 651, 850 Federal role in education, expansion of: and ESEA, 1:277, 303–304, 315, 2:487, 490 in evolution of authority over schools, 1:314–316 and NCLB, 2:439, 487, 490, 823 and Race to the Top, 2:823 and school district bonds, 1:85 in special education funding, 2:695 and state education agencies, 2:701 and unfunded mandates, 2:817 Federal role in education, versus state and local: fiscal year 2010, 2:702 year 2009–2010, 1:314, 314 (table) year 2011, 2:824 years 1919–2010, 1:313, 314 (table) years 2007–2010, 1:317 Federal Supplemental Educational Opportunity Grant, 1:112 Federal Work-Study program, 1:337–340

880

Index

Federalism, 1:78, 344, 410, 2:574, 579 Federal-state-local revenue sources. See Revenues (public elementary/secondary) by federal-state-local source Federations of schools in England, 1:120–121 Fees, 1:1, 2, 3. See also Tuition and fees, higher education; Tuition and fees, K-12 private schools Fellowships, 1:278, 318, 2:477, 532 Females: achievement gaps for, 1:18 as adult postsecondary students, 1:47 (table) college and high school grades of, 2:636 college enrollment by, 1:138 crowding thesis for, 2:741 in the developing world, 1:373 earnings of, 1:74, 2:602–606 earnings of, lifetime, 2:542 earnings-age profiles of, 1:48, 49, 2:540–541 education, fertility, child mortality, and, 1:4 fields of study chosen by, 1:70 jobs restricted to, 1:227 NSF support for, 2:479 rapid movement through school by, 1:180 reduced labor market barriers for, 2:741, 766 SAT scores of, 2:636 self-selection by, in labor force participation, 2:433 teaching as female dominated, 2:742 in unpaid domestic labor, 1:352 See also Gender differences Feminist struggles for pay equity, 2:743. See also Gender differences Ferguson, Ronald, 2:750 Fermanich, Mark, 1:312 FERPA. See Family Educational Rights and Privacy Act Fetler, Mark, 2:747 FFELP (Federal Family Education Loan Program), 2:700–701, 715 Fields of study, of higher education faculty, 1:332–333, 333 (table). See also College major Fifth Amendment to the U.S. Constitution, 1:228 Figlio, David, 2:732, 793, 815 Filipinos, earnings of, 2:601 Filmer, D., 1:81, 118 Finance equity: at community colleges, 1:154 finance litigation and, 2:652–657 in Goals 2000, 2:504 horizontal equity and, 1:390–391 school finance equity statistics, 2:648–652 vertical equity and, 2:828 See also Educational equity; Equalization models; Fiscal disparity; Horizontal equity; Vertical equity Finance equity statistics. See School finance equity statistics Finance litigation. See School finance litigation

Financial Accounting Foundation, 1:375, 376 Financial aid. See Student financial aid Financial equilibrium, 1:121 Financial literacy and cognitive skills, 1:144, 340–344 Finland, 1:353 (figure), 395, 396 (figure), 420 Finn, Chester, Jr., 2:609 Firm, theory of the. See Theory of the firm Firm-specific human capital, 2:830 First Amendment to the U.S. Constitution, 1:228, 294, 2:633, 812, 815 Firstborn children, 2:514 First-generation college goers, 1:112, 133, 225, 339, 2:686 Fiscal capacity, 1:104, 266, 379, 2:574, 647, 648. See also School district wealth Fiscal disparity, 1:344–346 budget shortfall compared to, 1:344–345 emergence of, 1:344–345 equalizing programs to reduce, 1:347 in evolution of states’ role, 1:266 in infrastructure and facilities, 1:403–404, 405 intergovernmental dimensions of, 1:345 Serrano rulings on, 2:679 See also Educational equity; Fiscal environment; School finance litigation Fiscal environment, 1:346–347 challenging, and budgetary decisions in, 1:4 needs-resources disparities in, 1:344 spending and resource factors in, 1:346–347 See also Fiscal disparity Fiscal equity, 1:249–250, 265, 2:653, 788, 819. See also Fiscal disparity Fiscal federalism, 1:410, 2:574. See also Federalism Fiscal neutrality, 1:348–350 adequacy of inputs/outputs and, 1:287–288 in finance litigation, 1:286, 378 measurement of, 1:349 among principles of equity, 1:285 See also Wealth neutrality Fisher, Ronald, 1:413, 2:459 Fiske, E. B., 1:296 Fisman, Raymond, 1:411 Five New Basics, of A Nation at Risk, 2:464 501(c)(3) tax-exempt status, 1:355, 2:531, 549, 550. See also Tax exemptions 529 Plans, 1:144–145 Fixed versus semifixed costs, 1:346 Fixed-effects models, 1:350–352 advantages and disadvantages of, 1:243 definition of, 2:851 with difference-in-differences, 2:596 limitations of, 1:351 longitudinal state databases and, 1:274 Flanagan, Robert, 1:63 Flat grant funding, 1:92, 266, 310

Index Flat taxes, 1:3, 2:565. See also Proportional taxes Flat vouchers, 1:293 Flexibility waivers. See Waivers for NCLB Flipped classrooms, 1:281–282, 290. See also Blended learning Florida: accountability changes in, 1:254 administrative decentralization in, 2:668 charter high schools in, 1:126 community college system in, 1:152 district and county lines in, 1:217 federal-state balance in, 2:703 finance litigation in, 2:654 food programs in, 1:53 for-profit EMO schools in, 1:271 funding model in, 1:156 high school sizes in, 2:664 lottery proceeds in, 2:443 (table) National Board certified teachers in, 2:470 NCLB waiver for, 2:662 notice requirements case in, 1:229 performance evaluation systems in, 2:529 performance pay in, 2:520 PISA participation by, 1:420 pupil weighting in, 2:587, 588 Race to the Top grant for, 2:608 RIF provisions in, 2:616 school districts in, number of, 2:787 State Board of Education in, 2:440 state education code in, 2:706 student financial aid in, 2:707–708 tax credit scholarships in, 2:813–814, 816 voucher plan in, 1:294, 2:675 Florida Virtual Schools, 2:498 Flow variable, 2:854 Flypaper effect, 1:413 fMRI (functional magnetic resonance imaging), 1:66 Food services: for access to education, 1:5, 61 as an auxiliary service, 1:60, 61–62 contracting for, 1:169 in district cash flow, 2:646 federal, state, and local aid for, 1:61–62 local spending for, 1:279 meal manipulation in, 1:53 See also Meal programs; Nutrition Forcing variable, 2:598, 619, 851. See also Running variable Ford, Gerald R., 1:399 Ford Foundation, 2:532 Foregone alternatives, 1:244–245. See also Opportunity costs Foregone earnings, 1:352–354 in attending college, 2:801

881

of child labor, 2:433 empirical studies on, 1:353 (figures), 353–354 impact of, 1:354, 2:714 as the largest education cost, 1:352 measurement of, 1:352 social benefits and, 1:321 teacher supply and, 2:766 See also Cost of education; Opportunity costs Foreign languages, A Nation at Risk on, 2:464. See also Bilingual education Formal and informal sectors, 2:433. See also Dual labor markets Formula budgeting, 1:91–92. See also Funding formulas and methods Formula categorical grants, 1:412 For-profit EMOs, 1:268, 269–271, 272. See also Education management organizations For-profit higher education, 1:354–359 accreditation of, 1:356, 357 adult students in, 1:47, 354, 357 data on, 1:318, 354, 356, 357, 358, 2:500, 557, 809 educational cost reductions at, 2:809 enrollment and admissions for, 1:138, 357–358 expenditures for, 1:318 growth of, 1:354, 357 instructors in, 1:331 (table), 357 online learning in, 1:358, 2:500 primary goal of, 1:356 privatization and, 2:557 revenues for, 1:319, 356 salaries in, 1:332 student characteristics in, 1:354, 356, 357 tenure in, 1:331 (table), 357 tuition and fees at, 1:356, 2:802 (table), 803 types of, 1:355–356 vocational education in, 1:355, 356, 358, 428, 2:831 For-profit organizations for K-12 virtual schools, 2:499 Förster, Manuel, 1:342 Foundation Center, 2:533 Foundation for the Advancement of Teaching, 2:532 Foundation program model, 1:36, 92, 278, 310–311 Foundational or base funding, 1:92, 278 Foundations, LEF. See Local education foundations (LEFs) Foundations, philanthropic. See Philanthropic foundations in education 401(k) benefits, 2:491, 763 403(b) benefits, 2:491 457 plan benefits, 2:491 Fourteenth Amendment to the U.S. Constitution, 1:87–88, 202, 228, 2:485, 632, 672, 679, 846, 847 Four-year colleges and universities: in college choice, 1:129–131 dropout rates in, 1:134

882

Index

enrollment management in, 1:308–309 enrollment trends for, 1:137, 138 (figure), 318 expenditures per student at, 2:806–807, 808 for-profit, 1:355–356 funding for, 1:383 fundraising at, 2:550–551, 552 per-FTE revenue in, 1:319 returns to education at, 1:69 student persistence, faculty, and, 1:332 tuition and fees at, 1:384, 2:802–803, 805, 806, 808 See also Bachelor’s degree institutions Fowler, William J., Jr., 1:155 Fractional assessment, 2:571 Framework for Teaching (rubric), 2:749, 752, 753 Framing effects, 1:66, 67 France: centralization in, 1:107–108, 115, 116, 117 economic growth and education in, 1:247 foregone earnings in, 1:353 (figure) income inequality in, 1:396 (figure) life expectancy and education benefits in, 1:74 productivity declines in (1970s), 2:465 Frazer, Elizabeth, 1:72 Free Application for Federal Student Aid (FAFSA), 1:136, 2:525, 526, 700 Free Disposal Hull model, 2:780 Free market theories, 1:106. See also Capitalist economy Free rider problem, 1:2, 2:786 Free riders, 2:582 Free speech, 1:75, 260, 2:633 Freedom of choice: in homeschooling, 1:386–387 in public-private partnerships, 2:584, 585, 586 in voucher plans, 1:294–295, 296 See also School choice Freedom to exit, 2:556 Freeman, Richard, 2:605 Freeman v. Pitts (1992), 1:203 Friedman, John, 2:659, 660, 749 Friedman, Milton, 1:292, 293, 294, 295, 296, 2:455, 530, 531, 583, 783, 784, 841 Friedman, R. D., 2:841 Friedman, Thomas, 1:370, 2:609 Fringe benefits, 2:491, 725, 742. See also Nonwage benefits Frontier, definition of, 2:851. See also Frontier analysis Frontier, T., 2:752 Frontier analysis, 1:191–192, 2:780 Frow, John, 1:189 Fryer, Roland, 1:21, 2:711 FTE. See Full-time equivalent (FTE) F-test, 1:299 Fuchs, Thomas, 1:118 Full rationality, assumed, 1:64–65. See also Behavioral economics

Full state funding, 1:104, 278, 312 Full-time equivalent (FTE): for calculating district size, 1:217 in dual enrollments, 1:225 in economies of scope, 1:257 for funding formulas, 1:152, 153, 225 of staff, 1:34 in tuition analysis, 2:804, 806–808 Function codes and categories: in cost accounting, 1:171 in fund accounting, 1:360 (table), 361 under GASB, 1:376 (table), 377 Functional magnetic resonance imaging (fMRI), 1:66 Fund accounting, 1:359–362 classifications, 1:359 (table), 360 (table), 361 expenditure codes, 1:359, 360 (table), 361 objects of expenditure, 1:359, 360 (table), 361 segregated systems for, 1:60 Funding formulas and methods: base or foundational, 1:92, 278 catastrophic aid, 2:695, 696, 697 categorical, 1:92, 108–112, 216, 2:587, 818 census-based, 2:589, 694–695, 696 for charter schools, 1:121, 124 cost reimbursement, 1:31, 2:695, 696, 697, 720, 819 district power equalizing, 1:215–216 equalization, 1:92, 215 (see also Equalization models) evidence-based, 1:31 fifty-state analysis of, 1:278 flat grant, 1:92, 266, 310 full, 1:104, 278, 312 guaranteed, 1:92, 311, 378–380, 2:651 percentage equalizing, 1:215, 311, 312, 378 placement neutral, 2:695 reward-for-effort, 1:92 under Title I, 2:788 unit-based, 2:695, 696 weighted student funding, 2:587, 835–837 See also Per-pupil expenditures; Per-pupil funding; Property taxes Fundraising: by groups and organizations, 2:546–547 in higher education, 1:319, 355, 383, 2:548–552 for private schools, 2:811 in school-based management, 1:80–81 Funds of knowledge, 2:686. See also Knowledge Fungibility, 2:445–446, 851 Furloughs, for spending reductions, 2:645. See also Layoffs Furstenberg, Frank, 2:515 Future earnings: as an outcome of schooling, 1:93 borrowing against, 1:46, 2:830 college selectivity and, 1:131

Index forgone present earnings and, 1:354 hours worked while in college and, 1:339 human capital model and, 1:391, 392, 393 incarceration and, 1:75 present value of, 2:539–542 property assessment and, 2:571 student loans and, 2:716 See also Present value Future value (FV), in the present value formula, 1:210– 211. See also Present value of earnings FWS. See Federal Work-Study program Gaduh, A., 1:81 Gainful employment, 1:358, 363–364, 2:851 Gallego, Francisco, 1:118 Gamoran, Adam, 2:792, 795 Gannon v. Kansas (2014), 2:848 GAO (Government Accountability Office), 1:318, 319, 2:717, 820 Gardner, John W., 1:303 Gardner Commission, 1:303 GASB. See Governmental Accounting Standards Board Gates, S., 2:840 Gates Foundation. See Bill and Melinda Gates Foundation GATS (General Agreement on Trade in Services), 1:373, 423 GATT (General Agreement on Tariffs and Trade), 1:423 Gatti, Roberta, 1:411 Gauss Markov theorem, 2:507 Gautreaux Assisted Housing Program, 2:482–483 Gayer, Ted, 1:412 GDP. See Gross domestic product (GDP) Gebhart v. Belton, 1:86 GED Testing Service®, 1:364, 365. See also General Educational Development (GED®) Geiser, Saul, 2:635 Gemin, B., 2:498 Gender differences: in access to technology, 1:280 in achievement, 1:18, 19, 20, 2:594 in age-earnings profiles, 1:48, 49 (table), 49 (figure), 2:540 (figure), 540–541, 541 (figure) in career academy impact, 2:832 in college completion, 1:133 in college enrollment rates, 1:138 in college major, 1:70 in demand for education, 1:195 in earnings, 1:48, 49, 74, 180, 227, 352, 2:540–542, 602 (table), 603, 604, 604 (table) in foregone earnings, 1:352 in high school and college grades, 2:636 in investments in education, 2:710 in job restrictions, 1:227

883

in labor force participation, 1:352 in mathematics, 2:594, 636 in opportunity costs, 1:195 in salary schedules, 2:629, 743 in SAT scores, 2:636 in savings from rapid movement through school, 1:180 in student incentive impact, 2:711 in teaching, 2:629, 743 See also Females; Males Gender discrimination, 1:195 Gender gap, 1:19. See also Gender differences Gender segregation, in the labor market, 1:227, 2:604 Gender spillover effects, 2:711 Gender stereotyping, 1:227 General Agreement on Tariffs and Trade (GATT), 1:423 General Agreement on Trade in Services (GATS), 1:373, 423 General and partial equilibrium. See Partial and general equilibrium General Educational Development (GED®), 1:364–367 as centerpiece of adult secondary education, 1:46 credential recipients, profile of, 1:365 data on, 1:365, 366, 2 542 (table) earnings and, 1:366, 2:542, 542 (table) high school completion and, 1:221, 366, 367 high school dropout and, 1:221, 222, 224, 366–367 labor market success and, 1:187, 366, 367 postsecondary success and, 1:365–366, 367 General equilibrium, definition of, 2:851. See also Partial and general equilibrium General fund, 1:359. See also Fund accounting General (unconditional) grants, 1:412–413 General obligation bonds, 1:367–368 for capital financing, 1:102–103, 367, 368 referenda or elections to approve, 1:103, 368 See also Bonds in school financing; Capital financing for education General Revenue Sharing Program, 1:412–413 Generalizability: effect size and, 1:298–299 external validity and, 1:66, 2:827 of PIRLS, PISA, and TIMSS, 1:417–418 of quasi-experimental methods, 2:614, 615 of randomized control trials, 2:611, 612, 613 Geographic factors: in comparative wage indexes, 1:155–156 cost differences and, 1:34, 36 in difference-in-differences methods, 2:596 in labor pool entry, 2:436 in neighborhood effects, 2:480–481 in operationalizing district size, 1:217 in school tracking, 2:792, 794 Georgia (state): administrative spending analysis for, 1:43

884

Index

Department of Defense schools in, 1:197 (table) finance litigation in, 2:654 HOPE financial aid in, 1:135, 139, 2:443 (table), 708 lottery proceeds in, 2:443 (table), 446 pupil weighting in, 2:588 Race to the Top grant for, 2:608 sales taxes in, 1:103 state board in, 2:440 student financial aid in, 2:712 tax credit scholarships in, 2:814 Germany: apprenticeship in, 1:164, 2:830–831 civic engagement in, 1:260 compulsory schooling laws in, 1:164, 260 Department of Defense schools in, 1:197 (table) economic growth in, 1:247 foregone earnings in, 1:353 (figures) income inequality in, 1:395, 396 (figure) post-WWII economic success of, 1:422 productivity declines in (1970s), 2:465 public-private partnerships in, 2:584 teacher preparation in, 2:768 tracking in, 2:792, 794 Gertler, Paul, 1:81 GI Bill, 1:368–370, 2:707 Gift agreements, endowments as, 2:820–821 Gifts, private. See Private contributions to schools; Private fundraising in postsecondary education Gill, Andrew M., 1:47 Gill, B. P., 2:843 Gilmer-Aikin Laws, 2:632 Gini coefficients, 1:312, 390, 396, 396 (figure), 2:586, 649, 651, 851 Gini index, 1:396, 397 Gintis, Herbert, 1:189, 2:604 Glaser, Robert, 1:341 Glass, Gene, 1:185 Glass’s delta, 1:299 Glazerman, Steven, 2:561, 843 Glickman, Mark, 1:147 Global Education First Initiative, 2:584 Global knowledge economy, 1:105, 373. See also Knowledge Global Partnership for Education Fund, 1:5 Globalization, 1:370–375 local control and, 2:438 localization and, as a hybrid, 1:372 transaction costs and, 2:800 See also International organizations Glocalization, 1:372 GO bonds. See General obligation bonds Goals 2000, 1:7–8, 2:503–504, 639 Gold standard, randomized control trials as, 1:350, 2:611

Goldberger, Arthur S., 2:621 Goldhaber, Dan, 1:59, 253, 2:561, 617, 618, 747, 749, 757, 843 Goldin, Claudia, 1:138 Goldman, C., 2:840 Goldrick-Rab, Sara, 2:708 Golebiewski, Julie, 1:178 Goolsbee, Austan, 1:290 Gorislavky, A., 2:568 Goss v. Lopez (1975), 1:228, 229 Gottfried, Michael, 1:351, 2:689 Gourman Report, college rankings by, 1:142 Gove, A., 1:81 Government Accountability Office (GAO), 1:318, 319, 2:717, 820 Government failure, public choice economics of, 2:578, 580 Government Finance Officers Association, 1:97, 98, 100 Government jurisdictions, number of in the U.S., 1:410 Governmental Accounting Standards Board, 1:375–378 Grace, Kay Sprinkel, 2:551 Grade point average (GPA): in college rankings, 1:142 in college selectivity, 1:147 in enrollment management, 1:308 GED® credentials and, 1:365 hours worked and, 1:339 for identifying dropouts, 2:626 maintenance of, for financial aid, 2:712 peer effects on, 2:482 SAT, college admissions, and, 2:635 Grade retention, 1:180, 223, 234, 2:717 Grades, school, in school report cards, 2:662, 663 Grades, student GPA. See Grade point average (GPA) Graduate education. See Doctoral institutions; Master’s degree institutions Graduation rates. See College graduation rates; High school graduation rates Grants: block (see Block grants) for capital spending, 1:104 categorical (see Categorical grants) for charter management organizations, 1:120, 121 in ESEA Title I (see Title I) in ESEA Title V, 1:303 as flat grant funding, 1:92, 266, 310 general (unconditional), 1:412–413 grants-in-aid, 1:410 i3, 1:120, 160, 424–426 intergovernmental, 1:412–414 lump-sum, 1:385, 404, 413, 414 matching, 1:104, 404, 412, 413, 414, 2:532 from the NSF, 2:478, 479

Index Pell (see Pell grants) performance pay and, 2:520 philanthropic, 2:532, 533–534 political economy of, 1:108, 111, 413 for private schools, 2:671 RTT (see Race to the Top) strategic programs for, 2:707 for students (see Student grants) for traditional higher education institutions, 1:356, 357 See also Student financial aid Grants-in-aid, 1:410 Great Britain, 1:46, 247. See also United Kingdom Great Divergence, in income inequality, 1:396 Great Recession, 1:265, 345, 374, 2:690, 730, 806, 808, 824 Great Society movement, 1:46 Greece, 1:396 (figure) Green, Donald, 1:260 Green, Preston C., III, 1:379 Greenberg, D., 1:181 Greenwald, Rob, 1:250, 2:843 Grievances, under collective bargaining, 2:640, 641, 776, 777 Groot, W., 1:260, 261, 264 Gross, B., 2:537, 538 Gross domestic product (GDP): economic development and, 1:245, 248 globalization, rate of return, and, 1:373 higher education expenditures in, 1:385 income inequality and, 1:397 per-capita, 1:175, 373 transaction costs in, 2:799 Grossman, Pamela, 2:769, 770 Group-based performance pay, 2:631. See also Pay for performance Growth accounting, 1:251, 2:846 Growth rate, compound annual. See Compound annual growth rate GTB. See Guaranteed tax base Guam, 1:196, 197 (table) Guáqueta, Juliana, 2:583 Guaranteed student loans, 1:384–385, 2:699–700, 715. See also Student loans Guaranteed tax base, 1:92, 311, 378–380 key element of, 1:379 taxpayer equity measures and, 2:651 See also Equalization models Guarino, Cassandra, 2:748, 750 Guryan, Jonathan, 1:290 Guthrie, James, 1:175, 2:841 Gwynne, Julia, 2:718 Hack, W. G., 2:841 Haertel, Edward H., 2:623

885

Hagedorn, Lisa Serra, 2:584 Haig, Robert, 1:249, 311 Haig-Simons criterion, 2:565 Haiku Learning (learning management system), 1:212 Hainmueller, Jens, 2:596 Hamermesh, Daniel, 1:69 Hammurabi’s Babylonian Code, 2:830 Handicapped Children’s Protection Act, 1:400. See also Individuals with Disabilities Education Act Hanisch, Bárbara, 2:584 Hanisch-Cerda, B., 1:185 Hansen, Benjamin, 2:597 Hansen, J. S., 2:841 Hanson, Bradley A., 2:624 Hanushek, Eric, 1:71, 178, 249, 250, 254, 255, 268, 273, 274, 275, 287, 304, 2:524, 688, 718, 747, 759, 779, 794, 843, 847 Hao, Lingxin, 2:593 Happiness, 1:70, 323 (table), 2:434, 458 Hardin, Garrett, 2:796, 797 Harlem-Scarsdale achievement gap, 1:21 Harlow, Caroline, 1:262 Harmon, Colm, 2:531 Harr, Jenifer J., 2:691, 692 (table), 694 (table), 695 Harris, Douglas, 1:184, 2:708, 749, 841 Hart, C., 2:841 Hart, C. M. D., 2:815 Hartman, William T., 1:359, 2:841 Harvard, John, 2:549 Harvard Strategic Data Project, 2:704 Harvard University endowment fund, 2:821, 822, 823 Haveman, Robert, 1:70, 71 Hawaii, 1:104, 278, 312, 2:588, 608, 672 Hawley, Willis, 2:560 Hazard models, 2:496 He, Xuming, 2:593 Head Start program, 1:233, 234, 235, 303, 337, 2:515, 612 Health: accountability pressures linked to, 1:53 achievement gap and, 2:605 benefits of education and, 1:70, 74, 75–76, 180, 254, 2:434 datasets on, 2:474, 476 income inequality and, 1:397 PROGRESA program and, 2:615, 710 social benefits and, 1:323 (table) See also Food services; Nutrition Health Care and Education Reconciliation Act, 2:700 Health care costs in district budgets, 2:645 Health insurance benefits, 1:332, 2:491, 493, 761 Health sciences, job training programs for, 1:429 Hearing impairment, in special education, 2:693 (table)

886

Index

Heckman, James J., 1:68, 234, 264, 324, 366, 2:433, 605 Hedges, Larry, 1:250, 300, 2:843 Hedonic wage models, 1:156, 381–382, 2:851. See also Comparative wage index Held, David, 1:370 Held-back students. See Grade retention Helping Outstanding Pupils Educationally (HOPE) program, 1:135, 139, 2:443 (table), 708 Hemelt, Steven, 2:597 Henig, Jeffrey R., 2:538 Henry, Gary T., 2:524 Herbers, Janette E., 2:717 Herders’ dilemma, 2:796. See also Tragedy of the commons Herfindahl index, 2:586 Herman, Joan L., 2:661 Herrell, K. C., 1:314 (table) Herrmann, Mariesa, 2:717 Herrold, Kathleen, 2:547 Hess, Frederick M., 2:609, 610, 639 Hess, Rick, 2:609 Heterogeneity bias, 2:678. See also Selection bias Heuristics, in decision making, 1:65, 66, 67 Hewes, G. M., 1:160, 2:669 Hewlett Foundation. See William and Flora Hewlett Foundation Hidden acts and hidden information, 2:460, 544 Hierarchical linear modeling, definition of, 2:851 High School and Beyond dataset, 1:253, 2:472, 474–475, 604, 658 High school completion: compulsory schooling laws and, 1:163 in cost-effectiveness analysis, 1:185 crime reduction and, 1:263 GED® credential as substitute for, 1:46 GED® credential versus, 1:366 by traditional college students, 1:137, 138 (figure) vouchers, choice, and, 1:295 See also High school graduation; High school graduation rates High school dropout, 1:220–224 adult education after, 1:45, 46 civic engagement and, 1:260 compulsory schooling laws and, 1:163 early-warning systems for, 1:223–224 earnings and, 1:48, 74, 181, 220, 223, 366 educational inequality and, 1:398 GED® credentials and, 1:365, 366 GI Bill benefits and, 1:369 graduates compared to, 1:74, 75, 220 interventions and prevention for, 1:181, 185, 223–224 on-track indicator and, 2:626

rates of, 1:220–223 state income tax losses from, 1:248 See also High school completion High school graduation: civic engagement and, 1:260 earnings and, 1:48, 49 (table), 49 (figure), 50, 74, 134, 181, 366, 2:466, 542, 602 (table), 714 GED® recipients compared to graduates, 1:365 on-track indicator of, 2:542, 626 See also High school graduation rates High school graduation rates: compulsory schooling laws and, 1:163 crime reduction and, 1:264 dropout rates and, 1:220, 221, 222, 223 dual enrollment and, 1:226 Goals 2000 on, 1:7 in the high school movement, 1:313 investment in education for, 1:248, 425 in NCLB/AYP framework, 1:39, 2:488 public versus charter, 1:126 socioeconomic status and, 1:20 student mobility and, 2:717 See also High school graduation High school longitudinal datasets, 1:221–222, 365, 2:472, 474–475 High school movement, 1:313, 2:831 High school size, 2:664, 665, 666 High school tracking, 2:793–794, 795 High school within-a-school format, 2:664 Higher education: accreditation in, 1:16–17 adult education and, 1:45, 47, 47 (table) categorical grants aimed at, 1:112 categories of institutions in, 1:383–384 datasets in, 2:475–476 datasets in, IPEDS, 1:47 (table), 142, 330 (table), 331 (table), 2:472, 473 distance learning in, 1:212 dropout from, 1:134–136, 357, 358 early childhood education and, 1:234 earnings and (see Earnings; Earnings comparisons, data for) enrollment data for, 1:318 enrollment management in, 1:306–310 faculty in, 1:329–333, 382 financial aid for (see Student financial aid) multinationalization of, 1:373–374 A Nation at Risk on, 2:464 online learning and open education in, 1:282–283 peer effects in, 2:524 philanthropic support for, 2:532, 533 private institutions of, 1:355, 2:554–555 rankings in, 1:129, 142–143, 147, 2:636, 804 SAT and, 2:633–636

Index savings mechanisms for, 1:144–146, 2:707 See also Benefits of higher education; College choice; College completion; College enrollment; College selectivity; For-profit higher education; Higher education finance; Tertiary education; Tuition and fees, higher education; University endowments Higher Education Act, 1:112, 337, 357, 358, 363, 2:525, 700, 715 Higher education finance, 1:383–386 endowments in, 1:383, 384, 385, 2:819–823 enrollment management in, 1:306–309 expenditure per student in, 2:802, 804, 806–807 expenditures in, and GDP, 1:385 expenditures in, current trends, 1:318, 319 financial models for, 2:808–809 funding mechanisms for, 1:384–385 price discrimination in, 2:542, 543 private fundraising in, 1:319, 355, 383, 2:548–552 revenues in, 1:306, 307, 308, 318–319 states’ role in, 1:383–384, 385, 2:802–803, 804 tuition discount rate in, 2:802, 805–806 See also Student financial aid; Tuition and fees, higher education Highest and best use, principle of, 2:571 High-powered incentives, definition of, 2:851 HighScope Perry Preschool project, 1:234, 260, 264. See also Perry Preschool Hilger, Nathaniel, 2:659 Hill, Heather, 2:749 Hill, Paul, 2:537, 538 Hiring discrimination, 1:227 Hispanic institutions, federal public spending to, 1:278 Hispanics: at charter and public schools, 1:127 college completion rates for, 1:134 college enrollment rates of, 1:138 in Department of Defense schools, 1:197, 198 dropout rate calculation for, 1:221 high school graduation rates for, 1:220 household socioeconomic status of, 2:689 increased proportion of, in U.S. schools, 1:205 NAEP scores for, 1:198 in race earnings differentials, 2:480, 601, 603–606 on school boards, 2:638 student mobility of, 2:717 wealth of, 2:690 See also Latinos HIV/AIDS drug prices, 1:423 Hobson, C. J., 2:842 Hodge, Graeme, 1:170, 171 Hoekstra, Mark, 1:148 Hoke County Board of Education v. State of North Carolina (2012), 2:656 Hollands, F., 1:185

887

Hollister, Robinson, 2:568, 598 Home environment. See Parental involvement Home values: school quality linked to, 1:347, 2:479, 787 See also Neighborhood effects: values of housing and schools Home visiting programs, 2:515 Homeschooling, 1:386–389 cost of, 1:387–388 data on, 1:196, 386 evaluation of, 1:386–388 freedom of choice in, 1:386–387 social cohesion and, 1:388 of U.S. military children, 1:196 Homestead exemptions, 2:573, 727 Homework, 1:94, 2:463, 464, 513, 514, 738 Homogeneous goals, 1:93 Homoscedasticity assumption, and OLS estimators, 2:507 Honadle, Beth Walter, 1:93 Honegger, S. D., 1:360 (table) Hong, Guanglei, 2:793 Hong, Y., 2:793 Hoover index, 1:396 HOPE (Helping Outstanding Pupils Educationally) program, 1:135, 139, 2:443 (table), 708 Hopkins, Edward, 2:671 Horizontal equity, 1:389–391 ability-to-pay and benefits principles and, 1:3, 2:564 accountability and, 1:13–14 adequacy, PJ approach, and, 1:33 district power equalizing for, 1:216 elements of, 1:390 for equal treatment of equals, 1:389 equalization and, 1:310 in income taxes, 2:729 limitations of, 1:390 measures of, 2:649–650 property taxes and, 2:574, 729 in tax burden, 2:725, 726 vertical equity and, 1:390, 391 See also Vertical equity Horizontal stratification, for admissions lotteries, 2:448 Horn, S. P., 2:844 Horton v. Meskill (1977), 2:653 Hosek, Adrienne, 1:111 Household wealth, 2:690 Household/family income: achievement and, 1:18, 397–398, 2:487, 636 college completion and, 1:133 college enrollment and, 1:138, 139, 338, 397 college selectivity and, 1:70, 397 district wealth and, 2:647, 648 Internet use and, 1:281

888

Index

parcel tax and, 2:512 SAT scores and, 2:635 tuition and, 2:802, 806, 813 See also Socioeconomic status and education Housing prices, 2:479–480, 523. See also Neighborhood effects: values of housing and schools Howell, Jessica, 1:140 Hoxby, Caroline, 1:21, 140, 146, 147, 241, 304, 338, 409, 2:524, 741, 787 Hsieh, Chang-Tai, 1:296 Hulburt, S., 2:807 (tables) Human capital, 1:391–393 accountability and, 1:12 in adult education, 1:46 in age-earnings profiles, 1:50 asset specificity and, 2:486 in capitalist economic systems, 1:105, 107, 108 central office and, 1:113–114 college enrollment and, 1:138–139 college major and, 1:130 credential effect and, 1:186 definition of, 2:851 development of, 1:391, 2:846 as a direct effects model, 1:72 disincentive effects of, on crime, 1:262–263 economic development and, 1:246, 247 education, labor market productivity, and, 1:73 foregone earnings and, 1:352 globalization, modern shifts, and, 1:373 model of, basic, 1:391–393 professional development and, 2:559, 561 proxies for, 1:108 race earnings differentials and, 2:603, 604 (table), 605 school quality, earnings, and, 2:657, 659 schooling decisions, investment, and, 1:xxv, 391 signaling and, 1:186–187, 252, 2:451, 452 social benefits, education attainment, and, 1:324 socioeconomic status and, 2:688 supply and demand, and, 1:193 teacher effectiveness and, 2:747 underinvestment in, 1:325, 2:700 vocational skills as, 2:829, 830 Human rights, 1:321, 322, 323 (table) Humphries, John, 1:366 Hungary, 1:353 (figure), 396 (figure) Hungerford, Thomas L., 1:246, 247 Hwang, J., 2:504 Hybrid learning, 2:498, 499. See also Blended learning “I Have a Dream” Foundation, 1:260 i3. See Investing in Innovation Fund (i3) Idaho: curriculum collaboration in, 2:440 finance litigation in, 1:404, 2:654

lottery proceeds in, 2:443 (table) per-pupil expenditures in, 1:175 service consolidation in, 2:683 State Board of Education in, 2:440 statuary control in, 2:706 IDEA. See Individuals with Disabilities Education Act Ideas, ownership of, 1:107 Ifenthaler, D., 1:341, 342 Illinois: administrative spending analysis for, 1:43 curriculum collaboration in, 2:440 finance litigation in, 2:654 lottery proceeds in, 2:443 (table) National Board certified teachers in, 2:470 property tax limits in, 2:732 Race to the Top grant for, 2:608 service consolidation in, 2:683 Social Security participation in, 2:491 State Board of Education in, 2:440 tax credit/deduction programs in, 2:815 Imazeki, J., 2:843 Imazeki, Jennifer, 1:23, 2:546, 547 Imbens, Guido, 1:206 Imberman, Scott, 1:241 IMF. See International Monetary Fund (IMF) Immigration: bilingual education and, 1:76, 77, 78 dropout rate calculation and, 1:221 race earnings differentials and, 2:601 voucher plans and, 1:296 Immigration Act of 1965, 1:77 IMPACTplus compensation plan, 2:522 Imperfect competition, 2:455 Implicit versus explicit costs, 1:244, 245 Improving America’s Schools Act, 1:199, 303–304, 2:487, 790 Imputation methods, 2:569 Incarceration, 1:74, 75, 262, 263. See also Education and crime Incentive categorical funding, 1:109–110 Incentive pay, 2:518, 521. See also Pay for performance Incentives: in agency theory, 1:50–53 behavioral economics of, 1:67 in capacity building, 1:94, 95, 96 compensating wage differentials and, 1:382 for economic development, 2:572–573 high-powered, 2:851 low-powered, 1:51, 2:851 moral hazard and, 1:51, 2:460, 461, 544–545 for National Board certification, 2:470 in No Child Left Behind, 2:487, 488 pay for performance as, 2:518–522, 631 See also Student incentives; Teacher incentives

Index Incidence, tax. See Tax incidence Income: as a basis for taxation, 2:735 educational inequality and, 1:248, 395–398 elasticity of, 1:302, 2:446 Haig-Simons criterion for, 2:565 household (see Household/family income) as a measure of SES, 2:687 permanent, 2:530–531 See also Earnings; Earnings comparisons, data for; Teacher compensation Income approach, in property valuation, 2:571 Income effect, 1:104, 413 Income elasticities, 1:301, 302, 2:446, 531, 851 Income inequality and educational inequality, 1:395–398 causes of, 1:395–396, 397 consequences of, 1:397, 398 measurement of, 1:395, 396, 396 (figures), 397–398 See also Educational equity Income tax deductions: for contributions, 1:355, 2:549, 820 for educational expenses, 2:815 for other taxes paid, 1:411 tax incidence and, 2:728–729, 730 for teacher-bought supplies, 1:276–277 See also Income taxes Income taxes: deferred benefits under, 2:491, 492 elasticity of, 2:727 in evolution of states’ role, 1:266 exemptions on, 1:104–105, 2:491 nonwage benefits and, 2:491 progressivity of, 2:565, 729–730, 736 stability of, 2:737 for state general revenues, 1:104 structure for, 2:565 tax burden and, 2:724, 725, 726 tax yield and, 2:735, 736–737 See also Income tax deductions; Tax credits; Tax exemptions Incremental budgeting, 1:91 Independent schools, 2:553, 671, 810, 812. See also Schools, private Independent variable, definition of, 2:851 India, 1:116, 290, 371, 374, 417, 2:558 Indiana: collective bargaining laws in, 2:641 federal-state balance in, 2:703 finance litigation in, 2:654, 656 school vouchers in, 1:293, 2:456 tax credit scholarships in, 2:814 tax credit/deduction programs in, 2:815

889

Indigenous languages, 1:77, 78, 424. See also Bilingual education Indirect and direct costs. See Direct and indirect costs Indirect effects model, 1:72, 73, 73 (figure), 74, 75 Indirect spending, 2:731, 851 Individual Development Accounts, 1:145 Individual retirement accounts (IRAs), 1:144 Individualization in education, and due process, 1:230 Individualized education programs, 1:400, 401, 402 Individuals with disabilities: appropriate education for, 1:399, 400, 401, 402 free public education for, 1:400, 401 higher education grants for, 1:112 longitudinal datasets on, 2:474 not a homogeneous population, 2:697 rights of, and limited resources, 1:399, 403 weighting for, 2:587, 588, 589, 836, 836 (figure) See also Individuals with Disabilities Education Act; Special education; Special education finance Individuals with Disabilities Education Act, 1:399–403 aligned with NCLB requirements, 1:401 data on, 1:399, 403 due process requirements in, 1:230 federal categorical grants under, 1:109 federal funding under, 2:694–695, 824 in the history of educational regulation, 1:199 individualized education programs and, 1:400, 401, 402 infants and toddlers covered by, 1:400, 401 parental involvement and, 1:400, 401, 402 provisions of, 2:691 state agencies affected by, 2:701, 704 See also Individuals with disabilities; Special education; Special education finance Individuals with Disabilities Education Act Amendments of 1997, 1:400–401 Individuals with Disabilities Education Act of 1990, 1:400 Individuals with Disabilities Education Improvement Act of 2004, 1:399, 401–402 Indonesia, 1:81–82 Induction programs for teachers, 2:522, 559, 561 Industrialized countries: benefits of primary/secondary education in, 1:73, 74 gains from GATT agreement in, 1:423 labor market rate of return in, 2:433, 434 productivity declines in, 2:465 See also Developed countries Inefficiency: in administrative spending, 1:43–44 as a motive for decentralization, 1:118 technical, 2:779, 781 See also Efficiency

890

Index

Inelastic demand, 1:301, 302 Inelastic taxes, 2:726, 727, 728 Inequalities, income and educational. See Income inequality and educational inequality Inequities: angle of inequity, 2:649–650 in community college finance, 1:154 deregulation and, 1:200–201 district size and, 1:218 as education finance issues, 1:266–268 infrastructure financing and, 1:403, 404 litigation on, 1:27, 266, 279, 2:652–654, 655, 679–680 market failure and, 2:577 in opportunity to learn, 2:503, 504 policy, poverty, and, 1:398 private giving and, 2:547 in property taxes, 2:574–575, 633 in salary schedules, 2:630 school finance equity statistics, 2:649–650, 651–652 school size and, 2:666 social capital and, 2:684, 686 as a state issue, 2:653 in tax bases and rates, 1:266–267, 378–379, 2:574–576 for unserved disadvantaged children, 2:584 See also Educational equity; Finance equity; Fiscal disparity Inequity aversion, 1:66, 67 Infant mortality rates, 1:75 Infants and Toddlers with Disabilities Act, 1:400 Inferior good, and income elasticity of demand, 1:302 Inflation adjustments, 1:175, 183, 278, 316, 384, 2:803–805. See also Discount rate Inflation rates, 1:63, 210, 211, 384, 2:466, 803 Inflows and outflows, and internal rate of return, 1:414–416 Informal and formal sectors, 2:433. See also Dual labor markets Information and communication technology, 1:289–290, 370, 371, 2:499. See also Education technology; Internet Information asymmetry, 1:96, 2:451, 460, 484, 488, 544, 582 Information literacy, 1:416 Infrastructure financing and student achievement, 1:403–407 auxiliary services and, 1:60 inconsistent evidence on, 1:404, 405, 406 See also Capital financing for education; Facilities Ingersoll, Richard, 2:740 Ingredients method of cost analysis, 1:172, 183–184 In-kind private contributions, 2:545, 546, 547, 548–549

Innovation: educational (see Educational innovation) i3 Fund on, 1:120, 160, 424–426 intellectual property and, 1:107 Input approach. See Adequacy: professional judgment approach Input budgeting, 1:89. See also Line-item budgeting Input distance function, 1:43 Input-output model, definition of, 2:851 Inputs-outputs link, production function study of. See Education production functions and productivity In-service programs, 2:559, 560. See also Professional development Inspector visits, in performance evaluation, 2:528 In-state versus out-of-state college tuition, 2:543, 801, 802 (table), 802–803 Institute for Education Sciences, 1:278 Institute of Medicine, 2:717, 718 Institutional agents, 2:686 Institutional environment, 2:485 Institutions, roles of. See New institutional economics Instrumental variables, 1:240–241, 407–409 in analysis of college selectivity, 1:148 in analysis of compulsory schooling, 1:263 in analysis of small class sizes, 1:255 conditions for consistent estimates, 2:597 in the cost function approach, 1:177–178 definition of, 2:851 fixed effects and, 1:351 methodology and assumptions for, 1:408 noncompliance problem and, 2:612 in peer effects analysis, 2:524 permanent income used with, 2:531 as a quasi-experimental method, 1:255, 2:597, 612 selection bias limited by, 2:678 Instruments, in instrumental variables methods, 1:241 Insurance nonwage benefits, 1:332, 2:491, 492–493, 761 Integrated Postsecondary Education Data System (IPEDS), 1:47 (table), 142, 330 (table), 331 (table), 2:472, 473 Intellectual property: copyright, 1:107, 283, 2:500 in higher education, 1:319 patents, 1:107, 325 WTO agreements on, 1:423 Intelligence quotient (IQ), 2:513, 634, 688 Intent to treat estimates, 2:612, 615, 659 Intent to treat samples, 1:185 Intergenerational benefits of education, 1:70, 75–76, 260. See also Parental involvement Intergenerational transmission of knowledge, 1:329 Intergovernmental fiscal relationships, 1:409–414 in evolution of authority over schools, 1:313–316 in expenditures, 1:102, 104, 412–414

Index fiscal disparity and, 1:345 grants in, 1:412–414 for revenue collection, 1:411–412 in student financial aid, 2:709 in unfunded mandates, 2:817–818 See also Service consolidation Interjurisdictional spillovers, 2:786–787 Internal labor market, 1:227. See also Dual labor markets Internal rate of return, 1:91, 414–416, 2:431, 851 Internal Revenue Service, 1:58, 136, 144, 276, 2:531. See also Income tax deductions; Income taxes; Tax exemptions Internal validity: fixed-effects models and, 1:350, 351 in lottery-based research, 2:449 in quasi-experimental methods, 2:613, 614, 615 in randomized experiments, 1:66, 2:611, 612, 613 in regression-discontinuity design, 2:619 in tracking studies, 2:792 types of, 2:826–827 International Adult Literacy Survey, 1:421, 2:509 International assessments, 1:416–418 cost of participation in, 1:417–418 Early Grade Reading Assessment, 1:418 economics of education and, 1:253 limitations of, 1:417–418 media attention on, 1:417, 420 PASEC, 1:417, 418 PIRLS, 1:416, 417, 418–419, 420 PISA, 1:11, 118, 253, 295, 416, 417, 418, 419, 420, 423–424, 2:508–509 SACMEQ, 1:417, 418 SERCE, 1:417, 418 subnational participation in, 1:420 TIMSS, 1:253, 295, 416, 417, 418, 419, 420 See also Cross-national analyses; International datasets in education International Association for the Evaluation of Educational Achievement, 1:416, 418 International Baccalaureate program, 1:225. See also Dual enrollment International Civic and Citizenship Education Study, 1:416 International datasets in education, 1:418–421 PIRLS, 1:418–419, 420 PISA, 1:418, 419, 420 TIMSS, 1:418, 419, 420 See also Cross-national analyses; International assessments; National datasets in education International Labor Organization, 2:433 International Monetary Fund (IMF): austerity measures of, 1:374 criticisms of, 1:423

891

founding of, 2:845 growing influence of, 1:371, 373 programs of, 1:422, 423 roles of, 1:424 International organizations, 1:421–424 International Monetary Fund, 1:371, 373, 374, 422, 423, 424, 2:845 World Bank, 1:5, 195, 371, 422–423, 2:536, 583 World Trade Organization, 1:371, 373, 422, 423 See also Organisation for Economic Co-operation and Development; United Nations International private contributions, 2:547 International tests. See International assessments; International datasets in education Internet: access to, 1:208–209, 280, 281, 290 digital divide and, 1:208–209, 281 distance learning via, 1:212–213, 214, 2:557 educational innovation and, 1:289–290 globalization and, 1:370 for online education, 1:282, 2:498, 499 service consolidation via, 2:682, 683 stark gaps in usage of, 1:281 tutoring via, 2:720 See also Education technology; Online learning Interorganizational relations, in capacity building, 1:94 Interrater reliability, 2:623, 749 Interstate New Teacher Assessment and Support Consortium, 2:764 Interstate Teacher Assessment and Support Consortium, 2:753 Interval variable, 2:825 Intrinsic motivation, 1:12–13, 66, 2:711 Investing in Innovation Fund (i3), 1:120, 160, 424–426 Investing in Innovation program, 2:536 Investment budgeting. See Capital-investment budgeting Investment in education: in adult education, 1:45–46 age-earnings analysis of, 1:48 as an economic development tool, 1:246–248 capital investment, 1:102 college completion and, 1:133 by the community, 2:603 demand for education and, 1:193, 194 in developing countries, 2:432, 434 in early childhood education, 1:233–234 economics of education and, 1:251, 253, 254 equity in, 2:547 external social benefits/costs and, 1:321, 325 gender differences in, 2:710 in higher education, 1:68, 70, 71, 139 human capital and, 1:xxv, 46, 247, 325, 391, 392, 393, 2:559 in job training programs, 1:248, 428–429

892

Index

for local, state, and national economies, 1:247–248 overinvestment, 2:832 schooling decisions as investments, 1:xxv theories of schooling investments, 1:186 underinvestment, 1:71, 193, 2:715–716 See also Benefits of higher education; Benefits of primary and secondary education Investment management of endowments, 2:821–822 Investments, earnings on, 2:492, 549, 644, 646. See also Return on investment Iowa, 1:103, 2:673, 814, 815 IPEDS. See Integrated Postsecondary Education Data System (IPEDS) IQ (intelligence quotient), 2:513, 634, 688 IRAs (individual retirement accounts), 1:144 Ireland, 1:353 (figures), 2:810, 813 IRR. See Internal rate of return Isenberg, E., 2:561 Ishitani, Terry T., 1:339 Isolation measures of segregation, 1:203 Isoquants, and technical efficiency, 2:781, 782 Israel, 1:290, 353 (figures), 395, 396 (figure), 2:711 Italy, 1:197 (table), 260, 264, 353 (figure), 396 (figure) Item response theory, 2:457, 622, 624, 851 ITT Educational Services, 1:355 iTunes University, 2:499 IV. See Instrumental variables Jackson, C. Kirabo, 2:711 Jackson, Erika, 2:593 Jacob, B., 2:843 Jacob, Brian, 1:141, 230, 240, 255, 409, 2:490, 598, 749 Jacobs, Sandi, 2:761 James Irvine Foundation, 1:226, 2:536 Jamison, Dean, 1:324 Japan: Department of Defense schools in, 1:197 (table) economic growth and education in, 1:247 foregone earnings in, 1:353 (figure) income inequality in, 1:396 (figure) post-WWII economic success of, 1:422 productivity declines in (1970s), 2:465 ratio of highest to lowest earners in, 1:395 Japanese Americans, earnings of, 2:601. See also Asian Americans Jefferson, Thomas, 1:265 Jencks, Christopher, 1:21 Jewish Americans, earnings of, 2:601 Jewish school associations, 2:554 Jewish schools, 2:673, 674 Jeynes, William, 2:675 Jha, S., 1:82 Jian, Huang, 1:260, 261 Jimenez, Emmanuel, 1:257

Job training, 1:427–429 in adult education, 1:46 earnings affected by, 2:568 in the history of education, 1:162, 246–247 human capital and, 1:391 incentives, agency theory, and, 1:52 investment in, 1:248, 428–429 on-the-job training, 1:162, 164, 248, 2:830 vocational education and, 1:427, 428, 429, 2:830– 831, 832 See also Continuing education; Federal work-study program Job Training Partnership Act, 1:52 Jobs. See Earnings; Earnings comparisons, data for; Job training; Labor market outcomes; Working conditions Johnes, Jill, 1:192 Johns, Roe L., 2:587 Johnson, A., 2:561 Johnson, F., 1:360 (table) Johnson, Lyndon B.: ESEA and, 1:302, 303, 315, 399 Great Society and, 1:46 War on Poverty and, 1:46, 302, 315, 337, 2:487, 789 Joint enrollment, 1:225. See also Dual enrollment Jordan, T., 2:695 (table) Journal of Education Finance, 1:59, 2:839 Judgment approach. See Adequacy: professional judgment approach Junk food sales revenue, 1:53 Jurisdictions (governmental), number of in the U.S., 1:410 Kahneman, Daniel, 1:65 Kain, John F., 1:275, 2:524, 718, 747, 759, 843 Kaldor efficiency, 1:54, 249 Kane, Thomas, 1:137, 253, 2:707, 749 Kang, J., 1:21 Kansas: Brown v. Board of Education and, 1:86–87 finance litigation in, 2:654, 848 Gannon v. Kansas in, 2:848 private schools in, 2:673 65 percent solution used in, 1:42 spending gap and racial disparities in, 1:379 successful school district approach in, 1:177 Karam, R., 1:82 Katz, Lawrence, 1:138, 409 Keller, Katrina, 1:324 Kelly, Andrew, 2:610 Kentucky: cap and tiers system in, 1:216 Department of Defense schools in, 1:197 (table) finance litigation in, 1:23, 27, 315, 2:654, 655, 847

Index Race to the Top grant for, 2:608 revenues for schools in, 1:314, 314 (table) Rose v. Council for Better Education in, 1:23, 27, 315, 2:847 Kenya, 1:81, 2:711, 794, 795 Keppel, Francis, 1:303 Keyes v. School District No. 1, Denver (1973), 1:87 Keys, Benjamin, 2:712 Kezar, Adriana, 1:332 Khan, Salman, 2:499 Khan Academy, 1:290, 2:499 Khattri, Nidhi, 1:82 Kidron, Yael, 2:696 Kimko, Dennis, 1:71 King, E., 2:711 Kingdon, G., 2:432 (figure) Kinsler, Josh, 2:749 KIPP (Knowledge Is Power Program), 1:125, 126, 160–161, 2:533 Kirshstein, R. J., 2:807 (tables) Klenow, Peter J., 1:324 Kling, Jeffrey, 1:409 Knoeppel, Robert C., 2:695 Knowledge: in capitalist economic systems, 1:106, 107, 108 cognitive skills and, 1:340–343 cultural capital and, 1:188, 189 funds of, 2:686 intellectual property and, 1:107 intergenerational transmission of, 1:329 mental models of, 1:341–342 in models of education and benefits, 1:72, 73, 74, 75 Knowledge economies, 1:105, 108, 371, 373 Knowledge Is Power Program (KIPP), 1:125, 126, 160–161, 2:533 Knowledge maps, 1:341 Knowledge structure, 1:341, 342 Knowledge-based pay, 2:631 Knuth, E., 2:768, 770 Kocherginsky, Maria, 2:593 Koedel, Cory, 2:763 Koenker, Roger, 2:593 Kohn, Melvin, 2:514 Kolen, Michael J., 2:624 Korea, 1:353 (figure). See also South Korea Korean Conflict GI Bill, 1:369 Korthagen, Fred, 2:769, 770 Koshal, Manjulika, 1:257 Koshal, Rajindar K., 1:257 Kraaykamp, Gerbert, 1:189 Kremer, E., 2:711 Kremer, Michael, 2:711, 794, 795 Kresge Foundation, 2:532 Krop, Cathy, 2:545, 546

893

Krueger, Alan, 1:69, 129, 148, 163, 290, 409, 2:496, 596, 597, 658, 659, 842, 843 Kuziemko, Ilyana, 1:138 Labor, land, and capital in capitalism, 1:105. See also Capitalist economy Labor and personnel costs. See Expenditures, on personnel Labor market equilibrium, 1:381, 382 Labor market outcomes: as benefits of higher education, 1:68–70 as benefits of primary/secondary education, 1:72, 73–74 of college choice, 1:128, 129, 130, 131 cost-benefit analysis for, 1:181 desegregation policy impact on, 1:204 earnings as (see Earnings; Earnings comparisons, data for) economics of education, ROR, and, 1:252, 254 for GED® recipients, 1:187, 366, 367 globalization and, 1:373 Internet/computer skills and, 1:209 of job training programs, 1:429 market signaling and, 2:452 strong influence of education on, 1:252 See also Benefits of higher education; Benefits of primary and secondary education Labor market productivity, 1:72, 73–74, 251. See also Labor market outcomes; Productivity Labor market rate of return, definition of, 2:851 Labor market rate of return to education in developing countries, 2:431–434 by education level, 1:74, 2:432 (figure), 432–433 and many nonmonetary benefits, 2:434 Labor market segmentation, 1:226–227, 427, 428, 429 Labor markets, dual. See Dual labor markets Labor organizations. See School boards, school districts, and collective bargaining; Teachers’ unions and collective bargaining Laboratorio Latinoamericano de Evaluación, 1:416–417 Labor-intensive services, and the cost disease, 1:63–64 Ladd, Helen, 1:296, 2:757, 841 Laine, Richard, 1:250, 2:843 Lakdawalla, Darius, 2:743 Land, labor, and capital in capitalism, 1:105. See also Capitalist economy Land trusts, for school district capital spending, 1:104 Landes, William, 1:162 Land-grant colleges, 1:355, 2:820, 845 Landis, Rebecca, 1:163 Lang, Kevin, 2:524 Lange, Fabian, 1:324 Lange-Topel spatial equilibrium model, 1:324 Language impairment, in special education, 2:693 (table) Languages: computer-based instruction in, 1:290 globalization and, 1:374

894

Index

indigenous, 1:77, 78, 424 international assessments of, 1:417 A Nation at Risk on, 2:464 standards for, in CCSS, 1:150 See also Bilingual education; English language arts (ELA); English language learners (ELLs) Lankford, Hamilton, 1:382, 2:617, 757, 769, 770 Lareau, Annette, 1:19, 2:514 LaRocque, Norman, 2:583 Latin America: decentralization in, 1:117 International Monetary Fund and, 1:423 labor market rate of return in, 2:432 policy analysis impact in, 2:536 regional international assessments in, 1:416–417 school vouchers in, 1:293, 294, 295, 2:556–557 Latinos: college selectivity and, 1:397 in community colleges, 1:152, 154 educational inequality among, 1:397 NAEP scores for, 1:17, 18 in race earnings differentials, 2:602 (table), 604 (table) resegregation of, 1:20 on school boards, 2:638 See also Hispanics Lau v. Nichols (1974), 1:78 Lavy, Victor, 1:255, 2:597, 711 Law of large numbers, 2:456, 457, 851 Layard, Richard, 1:187 Layoffs, 2:616–618, 645, 831 Lazear, Edward, 2:460, 461 Learning disability, specific, 2:693, 693 (table), 697 Learning management systems, 1:212–213, 215, 2:498 Learning Point Associates, 2:609 LEAs. See Local education agencies (LEAs) Lease-purchase bonds, 1:103 Least restrictive environment, 1:400, 401, 2:691, 695 Least squares. See Ordinary least squares; Two-stage least squares Leaves, as nonwage benefits, 2:491, 492 Leaving age, and compulsory schooling laws, 1:163 Lee, John, 1:319 Lee, Kyung Hee, 2:712 Lefgren, Jacob, 1:409 Lefgren, Lars, 1:255, 409, 2:598, 749 LEFs (local education foundations), 2:546–547 Leigh, Andrew, 2:741 Leigh, Duane E., 1:47 Lemieux, Thomas, 1:69, 2:594 Lenz, Gabriel, 1:260 Levin, Henry M., 1:56, 73–74, 75, 183, 184, 185, 293, 296, 386, 387, 2:555, 584, 585, 586, 681, 841 Levin, Jesse, 1:34, 2:695 (table) Levitt, Steven, 1:240, 2:712

Levitt, Theodore, 1:370 Levy, Frank, 2:604 Liberal arts colleges, 1:47, 143, 257, 383, 384, 2:767, 804 Libraries, 1:209, 280, 303, 2:532, 808 Licensure and certification, 2:434–438 alternative routes to, 1:201, 2:436–437, 768, 769 in high-need fields, 1:428 National Board for, 2:437, 469–471, 631, 747, 754, 764 in new institutional economics, 2:486 Praxis exams for, 2:435, 759 in private schools, 2:673 for screening out the unqualified, 2:758–759 student achievement and, 1:253, 2:434, 437, 747 teacher supply improvements and, 2:767 traditional route to, 2:435–436 in voucher plans, 1:294 See also Certificate programs; Continuing education; Credentialing programs Lichtenberg, Frank, 1:324 Lieberman, Myron, 1:171 Liebman, Jeffrey, 1:409 Life course experiences, datasets on, 2:476 Life cycle investments model, 1:45–46 Life expectancy, 1:74, 220, 2:433–434, 657 Life insurance benefits, 2:491, 492–493 Lifeskills survey, 1:421 Light, Audrey, 1:204 Lilly Endowment, 2:533 Linden, Leigh, 1:290 Lindo, Jason, 2:598 Line of best fit, 1:237, 237 (figure). See also Regression line Linear programming techniques, 1:192, 2:779–780 Linear regression, 1:237, 2:495–497, 620, 851. See also Regression analysis Line-item budgeting, 1:89, 90, 2:563 Ling, C., 1:82 Linguistic dimension of globalization, 1:372, 374 Linguistic diversity, 1:401 Linked Learning approach to high school reform, 2:536 Linquanti, R., 1:111 Liquidity, 2:646, 690, 715, 821. See also Cash flow List, John, 2:712 Literacy: adult, 1:75, 278, 421, 2:509 computer, 1:416, 2:672 financial, 1:144, 340–344 See also Progress in International Reading Literacy Study (PIRLS) Litigation, finance. See School finance litigation Living standards, 1:245, 246, 2:465, 467 Livingston, D., 2:752 Lleras-Muney, Adriana, 1:162

Index Lloyd, William Foster, 2:796 LMRRE. See Labor market rate of return to education in developing countries Loan guarantees, for student loans, 1:384–385, 2:699–700 Loans, student. See Student loans Lobato v. State of Colorado (2011), 1:36 Local control, 2:438–441 charter schools and, 2:440–441 in evolution of authority over schools, 1:313–314, 314 (tables), 315, 316, 2:438 historical development of, 2:438–439 variations in the extent of, 2:439–440 See also Charter schools; Local financial issues; School-based management Local education agencies (LEAs): categorical grants for, 1:108–111 public spending to, 1:277, 279 service consolidation between, 2:681–683 Local education foundations (LEFs), 2:546–547 Local financial issues: block grants, 1:79, 81 bonds, 1:83–85, 367–368 capital financing, 1:101–105 district budgets, 2:629, 644–645, 646 district cash flow, 2:645–647 district power equalizing, 1:92, 215–217, 378–379 district size, 1:44, 217–220, 2:706, 745–746 enrollment counts, 1:305, 306 equalization models, 1:310–312 fiscal neutrality, 1:348 guaranteed tax base, 1:378–379 infrastructure financing, 1:403–406 parcel tax, 2:511–512 property taxes (see Property taxes) revenues in, compared to state and federal, 1:313, 314, 314 (tables), 317, 2:702 shifts in federal/state/local control, 1:278, 313–316 weighted student funding, 2:835–837 See also Local control; School boards; School district wealth Local public goods, 1:410, 411, 2:786, 787 Local-state-federal revenue sources. See Revenues (public elementary/secondary) by federal-state-local source Lochner, Lance, 1:68, 71, 263, 264 Lockwood, Benjamin, 2:718 Loeb, Susanna, 1:81, 110, 178, 382, 2:617, 757, 769, 770, 843 Lofstrom, Magnus, 1:366 Logarithm, natural, definition of, 2:852 Logistic regression, 2:568, 851 Logit models, 2:496 Long, Bridget Terry, 1:139, 2:708 Longevity, 1:323 (table). See also Life expectancy

Longitudinal risk factors, 2:626 Long-term disability insurance benefits, 2:491, 493 Long-term versus short-term bonds, 1:102 Lorenz curve, 1:396, 396 (figure), 2:649 Loss aversion, 1:66, 67 Lotteries for school funding, 2:441–447 data on, 2:441–442, 446, 569 earmaking and fungibility in, 2:445–446 equity of, 2:446–447 as a tax, 2:446 uses of proceeds, 1:104, 2:442–444 Lotteries in school admissions, 2:447–450 for charter schools, 1:21, 125, 2:448, 449 econometric analysis of, 1:240 school choice and, 2:447–448, 449 in some voucher programs, 1:294, 296 Lottery for rental assistance, 2:483 Lottery for the Vietnam War draft, 2:658 Lottery-based randomization, 2:614 Louisiana: college graduation rate in, 1:131 finance litigation in, 1:404, 2:654 private schools in, 2:673 Race to the Top grant for, 2:608, 609 school vouchers in, 1:293, 2:557, 675 student financial aid in, 2:709 successful school district approach in, 1:177 tax credit programs in, 2:814, 815 Lovenheim, Michael, 1:133 Low-powered incentives, 1:51, 2:851 Lucas, Samuel, 1:20 Ludwig, Jens, 2:483 Lugo-Gil, J., 2:561 Lumina Foundation, 2:500, 532 Lump-sum grants, 1:385, 404, 413, 414. See also Categorical grants Lunch programs. See Meal programs Luppescu, S., 1:94 Luxembourg, 1:396 (figure) Lyceum movement, 1:46 Ma, Jennifer, 1:68, 70 Machlin, Stephen, 1:264 Madden, Nancy, 1:160 Madden, Trisha M., 2:761 Mader, Nicholas, 1:366 Magna Carta, 1:228 Magnet schools: capacity building and, 1:95 difference-in-differences study of, 2:614 Race to the Top grants for, 1:277–278 as a school choice option, 2:447 theory of the firm and, 2:783 for voluntary desegregation, 1:204

895

896

Index

Magnetic resonance imaging, functional (fMRI), 1:66 Maguire, Jack, 1:307 Maimonides’ Rule, 2:597 Maine, 1:175, 216 Maintenance of effort (Title I requirement), 2:789 Maintenance services, 1:42, 60, 99, 276 Major field of study. See College major; Fields of study, of higher education faculty Majority voting process, 2:458–459, 578–579. See also Median voter model Malamud, Ofer, 2:832 Males: college and high school grades of, 2:636 college enrollment by, 1:138 earnings of, 1:74, 2:602–606 earnings of, foregone, 1:353 (figures), 354 earnings of, lifetime, 2:542 (table) earnings-age profiles of, 1:48, 49, 2:540–541 jobs restricted to, 1:227 male-centric nature of crime, 1:262, 264 NAEP achievement-gap data for, 1:18 rapid movement through school by, 1:180 returns to education estimated for, 2:659 SAT scores of, 2:636 student jobs and grades of, 1:18 See also Gender differences Malkus, Nathaniel, 1:365 Mandated schooling. See Compulsory schooling laws Mandates, unfunded. See Unfunded mandates Mandatory nonwage benefits, 2:491 Mandatory retirement for tenured faculty, end of, 1:329–330 Mann, Horace, 1:199, 265, 2:674 Manpower Development and Training Act, 1:428 Manski, Charles, 2:480 Manski, Charles F., 2:523, 524 Manufacturing, job training programs for, 1:429 Maquiladoras (Mexican-U.S. border factories), 1:371 Marcotte, Dave, 2:597 Marginal benefit, 1:52, 54, 55, 2:459, 784 Marginal cost: of additional consumption, 2:581, 582 in agency theory, 1:52 in allocative efficiency, 1:54, 55 of an intervention, 1:173 in economies of scale estimates, 1:257–258 in theory of markets, 2:455 and tragedy of the commons, 2:796 Marginal tax rate, 2:565 Marie, Oliver, 1:264 Market clearing, 2:745, 799, 852 Market data approach, in property valuation, 2:571 Market distortion, 1:156, 183, 2:852 Market equilibrium, 1:381, 382, 2:453 (figure), 453–454, 517

Market failure: allocative efficiency and, 1:55 in education, 2:580, 582 in factor markets, 1:327 in highly regulated markets, 2:785 public choice economics on, 2:577–578, 579, 580 theory of markets on, 1:252, 2:455 Market signaling, 2:451–453 benefits of, 1:70, 2:451–452 costs of, 2:452 employees, employers, and, 1:72, 2:451, 452, 658 human capital and, 1:186–187, 252, 2:451, 452 Marketing in higher education, 1:142, 307, 309, 358 Marketization, 2:555. See also Privatization and marketization Markets, self-regulating, 1:106 Markets, theory of, 2:453–456 definition of, 2:852 economics of education and, 1:251–252 equilibrium in, 1:54, 2:454 supply-demand model in, 1:55, 2:453–454 Markman, J. M., 2:524 Markman, Lisa, 1:290 Marshall, Alfred, 1:251, 2:453, 454, 681, 845 Marshall, J., 1:81 Marshall, J. H., 1:82 Marshall, Thurgood, 1:86, 2:633 Marshall Plan, 2:508, 846 Martin, Ruby, 1:304 Martin, S., 2:769, 770 Marx, Karl, 1:188 Marxist and non-Marxist analyses of centralization, 1:117 Maryland, 2:440, 608, 648, 654, 683 Marzano, Robert, 2:752, 753 Masarik, K., 2:768, 770 Mason, Patrick L., 1:227 Massachusetts: college graduation rate in, 1:131 common schools in, 1:265 early history of, 2:845 finance litigation in, 2:654, 655 first public spending on student transportation in, 1:60 first state with compulsory education, 1:162 PISA participation by, 1:420 Proposition 2½ in, 2:480, 732 pupil weighting in, 2:588 Race to the Top grant for, 2:608 school committee system in, 2:638 state agencies in, 2:704 Massive open online courses (MOOCs), 1:282–283, 284, 358, 374, 2:499, 809 Master’s degree institutions: adult students at, 1:47, 47 (table) expenditures per student at, 2:806, 807 (tables)

Index faculty at, 1:330, 331 (table) tenure at, 1:331 (table) tuition and fees at, 2: 802 (table), 803 Matching grants, 1:104, 404, 412, 413, 414, 2:532. See also Categorical grants Matching strategies in statistical analyses, 1:148 Mathematica Policy Research, 1:271 Mathematical Quality of Instruction (rubric), 2:749 Mathematics: CCSS for, 1:149, 150, 151 in charter schools, 1:21, 125 in CMO schools, 1:272 constructivist versus traditional approaches to, 1:420 earnings and achievement in, 2:604–605 gender and, 2:594, 636 high school longitudinal surveys on, 2:475 i3 grants in, 1:425 Internet access for, 1:290 longitudinal scores as a risk indicator in, 2:626 NAEP assessment of, 1:17–18, 198, 2:467, 468, 469 A Nation at Risk on, 2:464 NCLB focus on, 1:9, 40, 2:489, 490, 790 in NCLB/AYP framework, 1:38, 39, 40, 41, 2:488, 489, 662 NSF support for, 2:477, 478, 479 online learning in, 2:500 opportunity to learn in, 2:503 PASEC assessment of, 1:417 PISA assessment of, 1:253, 416, 418, 419, 420, 423, 2:508 Praxis I assessment of, 2:435 SACMEQ assessment of, 1:417 SAT and, 2:634, 636 SERCE assessment of, 1:417 socioeconomic status and, 2:689 student grants in, 2:707 student incentives in, 2:711 tax limit impact on, 2:732 teacher evaluation systems and, 2:755, 756 teacher market incentives in, 2:521, 522 teacher preparation in, 2:768–769 teacher professional development in, 2:561 teacher retention in, 2:740 teacher shortage in, 1:255, 2:436, 630 test-score gaps in, 1:17–18, 2:605 TIMSS assessment of, 1:253, 295, 416–420, 2:468–469, 472, 847 Title I, and standards in, 2:790 tracking in, 2:795 Matsudaira, Jordan, 1:111 May, Henry, 2:740 McCall, Brian, 1:141 McCall, Brian P., 1:338 McCarty, Therese, 2:531

McClure, Phyllis, 1:304 McDonnell, Lorraine, 2:504 McEwan, Patrick, 1:183, 2:681, 688, 840, 841 McGaw, B., 2:602 (table), 604 (table) McGee, Joshua, 2:763 McGrath, Michael, 1:338 McGrew, Anthony, 1:370 McGuinn, Patrick, 2:610, 750 McGuire, Therese, 2:732 McInnis v. Shapiro, 2:846 McLoone index, 1:285, 312, 390, 2:649, 650 McMahon, Walter W., 1:322, 324 McMaken, J., 2:504 McPartland, J., 2:842 McPherson, M., 2:841 McUsic, M., 2:843 Meal programs, 1:60, 62, 197, 2:689, 720. See also Food services; Nutrition Mean imputation, 2:569, 852 Mean preserving, 2:649, 852 Measurement error, 2:456–457 definition of, 2:852 with value-added models, 2:748 ways to address, 1:409, 2:457 Measures of Effective Teaching project (MET), 1:20, 2:749, 750, 753, 755, 756, 774 Mechanics’ Institutes, 1:46 Median voter model, 1:13, 2:457–459, 579 Median voter theorem, 2:459, 579 Medicaid, 2:491, 819 Medicare, 2:491, 761 Meet-and-confer agreements, 2:641 Meier, Kenneth, 1:170 Meister, Gail, 1:185 Melguizo, Tatiana, 2:584 Men. See Gender differences; Males Menefee-Libey, D., 2:537, 538 Mental accounting, 1:65 Mental models of knowledge, 1:341 Mental retardation category, in special education, 2:693 (table) Mentoring, 2:436, 470, 521, 522, 559, 560, 561, 685, 699 Mercantilism, 1:106 Merickel, A., 1:111 Merit good, 1:2–3, 2:723, 852 Merit pay, 1:13, 67, 2:520, 631. See also Pay for performance Messick, Samuel, 2:827 MET (Measures of Effective Teaching) project, 1:20, 2:749, 750, 753, 755, 756, 774 Metrick, Andrew, 1:147 Mettler, Suzanne, 1:369 Mexican-origin subgroup, 2:601, 603, 632

897

898

Index

Mexican-U.S. border factories, 1:371 Mexico: decentralization in, 1:117 income inequality in, 1:396 (figure) PROGRESA program in, 2:615, 710 school-based management in, 1:80–81, 82, 2:669 teachers’ unions and decentralization in, 1:116 Meyer, Rob, 2:754 Meyer, Stephen, 1:111 Meyer v. Nebraska (1923), 2:672 Michael and Susan Dell Foundation, 1:120, 2:532 Michigan: charter schools in, 1:43 college savings plans in, 1:144 federal-state balance in, 2:703 finance litigation in, 2:654 for-profit EMO schools in, 1:271 lottery proceeds in, 2:443 (table) private school teacher certification in, 2:673 property tax plus state funding in, 1:83–84 service consolidation in, 2:683 state agencies in, 2:704 teacher dismissal hearings in, 1:229 Middle East, 1:5, 420, 2:432 Miguel, Edward, 2:711 Military personnel, children of. See Department of Defense schools Military recruiters, 1:334–335 Mill, John Stuart, 2:564 Millennium Development Goals, 1:4–5 Miller, Robert J., 2:747 Milligan, Kevin, 1:260, 409 Milliken v. Bradley (1974), 1:87 Mills, in property tax rate percentage, 1:378 Mills v. Board of Education (1972), 1:230 Mincer, J., 2:841 Mincer, Jacob, 1:50, 68, 186, 251, 2:432, 846 Mincer regression, 1:68 Mincerian equation, definition of, 2:852 Mincerian method, 2:431–432, 433–434 Minimum competency exams, 2:463 Minimum foundation programs, 1:266, 267 Minnesota: first charter school opened in, 1:119, 125, 2:847 performance pay in, 2:520, 522 private school teacher certification in, 2:673 pupil weighting in, 2:588 service consolidation in, 2:683 statuary control in, 2:706 tax credit/deduction programs in, 2:815 tuition tax credits in, 2:812 Minority students: access to mobile devices by, 1:281 achievement gap data on, 1:17–18

in Department of Defense schools, 1:197 fields of study chosen by, 1:70 in for-profit institutions, 1:357 SAT scores of, 2:636 summer learning loss and, 1:19 in value-added measurement, 2:773 See also Racial/ethnic groups Mississippi, 1:162, 177, 2:706 Missouri, 1:177, 2:443 (table), 654, 673, 761, 762 Missouri v. Jenkins (1995), 1:203 Mobile devices, 1:281, 290, 2:500 Mobility: social, 1:188, 194, 397, 2:482, 687, 688, 690 student, 1:151, 197, 342, 2:716–719, 750 teacher, 2:492, 763 as voting with their feet, 2:579, 786 Modified quadriform method, 1:43 Moe, T. M., 2:840 Moffitt, Robert A., 2:523, 524 Moments, method of, 2:597, 852 Money-follows-the-child funding system, 1:216 Monk, David H., 2:747, 841 Monks, James, 2:804 Monopolies, 1:55, 2:455, 555, 670, 783, 784, 852 Monopsonies, 1:55, 327, 2:852 Montana, 1:155, 2:654 Monte Carlo simulation, 1:182, 2:852 Montessori schools and methods, 1:125, 2:672 MOOCs (massive open online courses), 1:282–283, 284, 358, 374, 2:499, 809 Mood, A. M., 2:842 Moodle (learning management system), 1:212, 2:498 Moon, S., 1:264 Mora, Marie, 1:261 Moral hazard, 2:459–461 definition of, 2:852 employment contracts and, 2:460–461 hidden actions in, 2:460, 544 incentive structures and, 1:51, 2:460, 461, 544–545 Moretti, Enrico, 1:71, 263, 264, 324, 409 Morrill Land-Grant Acts, 1:355, 2:820, 845 Morris, Pamela, 2:515 Morrison, Henry, 1:312 Morrissey v. Brewer (1972), 1:229 Morse v. Frederick (2007), 1:228 Mort, Paul, 1:249, 311, 2:843 Mortensen, Dale, 2:799 Motivation, extrinsic and intrinsic, 1:12–13, 66, 67, 2:711. See also Student incentives; Teacher incentives Motivational theory, 2:518, 519–520 Moving to Opportunity studies, 2:483 Mueser, Peter, 2:568 Muller, Chandra, 1:296

Index Multicost functions, 1:257–258 Multinational corporations, 1:371, 372 Multinationalization of higher education, 1:373–374 Multiperiod contracts, 2:460–461 Multiple imputation, 2:569 Multiple regression analysis, 2:828. See also Regression analysis Multiple threshold circuit breakers (tax relief), 2:573 Multiplier effects in economic development, 1:246, 247, 248 Multiproduct cost functions, 1:257–258 Multitasking, 1:51, 52 Multivariate analysis, 1:350, 390, 2:495–496 Mundy, Karen, 2:583 Municipal bonds, 1:103. See also Bonds in school financing; General obligation bonds Municipal jurisdictions, number of in the U.S., 1:410 Murarka, S., 1:21 Murin, A., 2:498 Murnane, Richard, 1:187, 366, 2:520, 604 Murphy, Joseph F., 1:386, 388 Murray, Sheila, 2:651, 843 Musgrave, P., 2:841 Musgrave, R., 2:841 Muslim Americans, 1:387 Muslim religious schools, 2:674 Mustard, David, 1:139, 2:712 NAACP (National Association for the Advancement of Colored People), 1:86, 304 NAEP. See National Assessment of Educational Progress NAFTA (North American Free Trade Agreement), 1:372 Naiman, Daniel Q., 2:593 Narrative program design, 1:33 Nash, Roderick, 2:549 Nation at Risk, A, 2:463–467 “at risk” terminology and, 2:624 criticisms of, 2:464–465 on curriculum weakness, 2:739 economic analysis and, 2:465–466 on economic efficiency in education, 1:249 economic growth, school quality, and, 2:466 as education policy, 2:464–465 education reform catalyzed by, 1:149 finance litigation and, 2:655 historical impact of, 2:463–464, 466, 639, 847 and increased scrutiny of public education, 1:246 NCLB rooted in, 2:487 performance pay and, 2:520 recommendations of, 2:464 research following, amount of, 1:xxvi standards movement in response to, 1:6, 7, 2:464 state education agencies affected by, 2:702 on teacher salaries, 2:630

899

Nation Prepared, A (report), 2:469 National Advisory Council on State and Local Budgeting, 1:98, 100 National Alliance for Public Charter Schools, 1:120, 2:440 National Assessment of Educational Progress, 2:467–469 applications of, 1:17–18, 149, 253, 420 criticisms of, 2:467, 469 as a dataset, 2:473 Department of Defense schools in, 1:197–198 NCES and, 2:471, 472 scope of, 2:467–468, 473 technical detail on, 2:468 TIMSS scores and, 1:420, 2:468–469 National Association for the Advancement of Colored People (NAACP), 1:86, 304 National Association of College and University Business Officers, 2:548, 805, 806, 820, 821 National Association of Independent Colleges and Universities, 2:554 National Association of Independent Schools, 2:553, 811 National Association of Private Catholic and Independent Schools, 2:553 National Association of State Budget Officers, 1:100–101 National Board certification for teachers, 2:469–471 financial incentives for, 2:521, 631 teacher effectiveness and, 2:470, 747 teacher evaluation, portfolios, and, 2:754 National Board for Professional Teaching Standards, 2:437, 469–471, 521, 631, 747, 754, 764 National Catholic Education Association, 2:553–554, 555, 675 National Center for Academic Transformation, 2:809 National Center for Education Evaluation and Regional Assistance, 2:824 National Center for Education Statistics, 2:471–472 on college enrollment, 1:138, 141 comparative wage index made, 1:155, 156 on dollars spent on schools, 1:167 on dual enrollment, 1:225 on elementary/secondary enrollment, 1:317 on elementary/secondary funding sources, 2:702 expenditure categories used by, 1:42 on for-profit enrollments, 1:357 fund classifications identified by, 1:359, 359 (table) on gifts to postsecondary education, 2:548, 549 on higher education enrollment, 1:318 on Internet access, 1:280 on number and size of U.S. schools, 2:664 on per-FTE expenditures and revenues, 1:318–319 on per-pupil expenditures, 1:173, 174, 317 on private school attendees, 2:674 private school categorization by, 2:810 on professional development, 2:559

900

Index

on public schools of choice, 2:447–448 reports and data collections of, 1:253, 2:471–472, 474–475 on SAT participation rates, 2:635 on special education identification rate, 2:693 on student to computer ratios, 1:280 on teacher experience, 2:757 on teacher turnover, 2:766 TIMSS, NAEP, and, 2:468–469, 472 on vocational education, 1:428 National Center for Special Education Research, 1:402, 2:475 National Christian Schools Association, 2:554 National Commission on Civic Investment in Public Education, 2:547 National Commission on Excellence in Education, 1:249, 2:463, 520, 654–655, 702, 739, 847. See also Nation at Risk, A National Commission on Fiscal Responsibility and Reform, 1:104 National Conference of State Legislatures, 1:100 National Council for Accreditation of Teacher Education, 1:16 National Crime Victimization Survey, 2:475 National cultural identity, 1:372 National datasets in education, 2:472–477 on college education, 2:475–476 on early childhood education, 2:473–474 on educators’ experiences, 2:476 individual-level, 2:473–476 institution-level, 2:473 on K-12 education, 2:474–475 on life course experiences, 2:476 See also International datasets in education; National Center for Education Statistics National Defense Education Act, 1:335–336 National Education Association (NEA), 1:303, 2:609, 775 National Education Goals Panel, 1:198 National Education Longitudinal Studies program, 1:253, 2:472, 847 National Education Longitudinal Study of 1988, 1:222, 253, 2:474, 475 National Education Policy Center, 1:271, 2:537 National Environmental Policy Act, 1:179 National Gardening Association, 2:557 National Governors Association, 1:149, 2:503, 609 National Household Education Surveys, 1:386, 388, 2:476, 547 National Longitudinal Study of Adolescent Health, 2:474 National Longitudinal Study of the Class of 1972, 1:221–222 National Longitudinal Study of the High School Class of 1972, 1:253, 2:472, 474, 475

National Longitudinal Survey of Young Men, 2:658 National Longitudinal Survey of Young Women, 2:658 National Longitudinal Survey of Youth, 1:263, 2:474, 476, 605, 658 National Longitudinal Transition Study-2 (of special education services), 2:475 National Postsecondary Student Aid Study, 1:338, 2:472, 475 National Research Council, 1:142, 2:717, 718 National School Boards Association, 2:637, 638, 640 National Science Foundation, 2:477–479 National Student Clearinghouse, 2:475 National Study of Charter Management Organization Effectiveness, 1:121–122 National Survey of Postsecondary Faculty, 2:476 Nation’s Report Card, 1:197. See also National Assessment of Educational Progress Native Americans, 1:77, 197, 303, 397, 2:601, 603. See also American Indian communities Native languages, 1:77, 78, 424 Nativism, 2:674 Natural experiments, 1:239–240, 2:852 Natural logarithm, definition of, 2:852 NBPTS. See National Board for Professional Teaching Standards NCES. See National Center for Education Statistics NCES-Barron’s Admissions Competitiveness Index Data Files, 2:473 NCLB. See No Child Left Behind Act NEA (National Education Association), 1:303, 2:609, 775 Neal, Derek, 2:593 Nearest neighbor matching, 2:568. See also Propensity score matching Nebraska, 2:443 (table), 588, 654, 672, 673, 706 Neckermann, Susanne, 2:712 Need equalization, 1:310 Neelakantan, Pattabiraman, 1:246 Negotiating, in collective bargaining, 2:640–643 Neighborhood attendance zones, 1:204 Neighborhood effect and public purpose, 1:292 Neighborhood effects: values of housing and schools, 2:479–484 defining of neighborhood for, 2:480–481, 483 educational outcomes and, 2:480–483 random shock technique and, 2:482–483 school quality and, 2:479, 481–482, 483 socioeconomic status and, 2:481–482, 689 sorting and, 2:479–480, 786, 787 Neighborhood schools, 1:204, 251, 293, 402, 2:448, 479, 538, 659, 784. See also Neighborhood effects: values of housing and schools Neighborhood sorting, 2:479–480, 483, 786–787 Neoclassical economic theory, 2:484–485 Neoliberal, as a pejorative term, 1:421–422

Index Neoliberal theories, 1:421–422, 423 Neoliberal trade policies, 1:371, 372 Neoliberalism, 1:371 Net present value (NPV), 1:91, 181, 210, 391, 392, 415–416. See also Discount rate; Present value of earnings Netherlands: Department of Defense schools in, 1:197 (table) education and crime in, 1:264 foregone earnings in, 1:353 (figure) income inequality in, 1:396 (figure) information technology access in, 1:290 public-private partnerships in, 2:584 school vouchers in, 1:293, 294, 296, 2:812, 813 school-based management in, 1:80 teacher preparation in, 2:768, 769–770 Netzer, D., 2:841 Nevada, 1:131, 314, 2:641, 673 New American Schools Development Corporation, 1:158–160 New Basics, and A Nation at Risk, 2:464 New Deal legislation, 1:61 New Hampshire: community colleges in, 1:153 early history of, 2:845 finance litigation in, 2:654 lottery proceeds in, 2:443 (table) private schools in, 2:673 pupil weighting in, 2:588 revenues for schools in, 1:314, 314 (table) tax credit program in, 2:814, 815 New institutional economics, 2:484–487 definition of, 2:852 uncertainty and, 2:484–485, 486 New Jersey: Abbott v. Burke in, 2:656, 847 alternative teacher certification in, 2:436, 768 finance litigation in, 1:404, 2:653, 654, 655, 656, 847 lottery proceeds in, 2:443 (table) per-pupil expenditures in, 1:175 pupil weighting in, 2:588 Race to the Top grant for, 2:608 Robinson v. Cahill in, 2:653, 847 school districts in, number of, 2:787 state board of education in, 2:706 New Leaders for New Schools (organization), 1:120 New Mexico, 1:131, 404, 2:443 (table), 654, 662 New Schools Venture Fund, 1:120 New Teacher Project, 2:436, 533 New York (state): accountability changes in, 1:253 charter schools in, 2:440 community college system in, 1:152 Department of Defense schools in, 1:197 (table)

901

finance litigation in, 2:654 lottery proceeds in, 2:443 (table) online courses in, 1:358 per-pupil expenditures in, 1:173, 175 production function studies for, 1:43 Race to the Top grant for, 2:608 revenues for schools in, 1:314 (table) state board in, 2:440 successful school district approach in, 1:177 teacher experience profile in, 2:757 New York City Pathways study, 2:769 New Zealand: college completion rate in, 1:134 decentralization in, 1:118 foregone earnings in, 1:353 (figures) income inequality in, 1:395, 396 (figure) PISA versus TIMSS in, 1:420 political engagement in, 1:75 school choice in, 2:448 Next Generation Science Standards, 1:151, 2:765 Neyman, Jerzy, 2:595 Nguyen-Hoang, Phuong, 1:351 Ni, Shawn, 2:763 Nicaragua, 1:81, 2:669 NIE. See New institutional economics No Child Left Behind Act, 2:487–491 accountability and, 1:6, 8–10, 315, 2:487–489, 490 achievement gap closing and, 1:234 achievement under, 2:487, 489–490, 593 adequacy and, 1:26 adequate yearly progress under, 1:38–41, 2:488, 489, 490, 662 as an unfunded mandate, 1:118 bilingual education under, 1:109, 199 capacity building and, 1:95 categorical grants under, 1:109, 110, 111 centralization tendencies related to, 1:116, 117 cheating and manipulation reported under, 2:490 comprehensive school reform models and, 1:160 control of teachers’ work by, 2:739 criticisms of, 2:487, 489, 607 design issues in, 1:9–10, 229–230 EMO expansion and, 1:270 English-only approaches under, 1:77, 78 finance litigation and, 2:655 graduation rate requirements under, 1:223 history of, 1:199–200, 2:487, 848 IDEA legislation aligned with, 1:401 mandates in, 1:304, 315, 2:468, 487, 488, 489 opportunity to learn and, 2:504 performance evaluation as a key to, 2:527 provisions of, 2:488–489 Race to the Top and, 1:9, 2:607 on the release of student information, 1:334

902

Index

sanctions and incentives in, 2:488, 489, 490 school choice provisions in, 2:489 school report cards under, 2:488–489, 660, 661, 662–663 school-based management and, 2:670 standardized tests under, 1:200, 304, 2:439, 487–490, 662, 775, 783, 790 state data tracking encouraged by, 1:253–254 state education agencies affected by, 2:702 supplemental educational services of, 2:719–721 on teacher qualification criteria, 2:435 Title I and, 1:315, 2:487, 489, 490, 789, 790, 824 unintended consequences of, 2:490 virtual schools and, 2:499 waivers for (see Waivers for NCLB) Nobel Prize winners, 1:65, 70, 2:431, 433, 513, 583, 799 Noise, definition of, 2:852 Non-ad valorem taxes, 2:511. See also Property taxes Noncognitive and cognitive skills. See Cognitive and noncognitive skills Noncompliance, in randomized control trials, 2:612, 613 Nonexcludable, definition of, 2:852 Nonexcludable and nonrival goods, 1:2, 2:577, 581–582. See also Public good Nonexcludable and rival goods, 2:796 Nonlinear models, 2:495, 496, 517 Nonmatching grants, 1:104, 412, 413. See also Matching grants Nonparametric methods, definition of, 2:852 Nonprofit organizations, tax-exempt status of, 1:355, 2:531, 546, 549, 550 Nonprofit properties, exempted from the tax base, 2:571–572 Nonrival and nonexcludable goods, 1:2, 2:577, 581–582. See also Rival and excludable goods Nonsectarian private schools, 2:671, 810. See also Schools, private Nonwage benefits, 1:332, 2:491–493. See also Teacher pensions Nordhaus, William, 1:63 Normal curve. See Standard normal curve Normal distribution, 1:184, 185. See also Standard normal distribution Normal good, and income elasticity of demand, 1:302 North, Douglass, 2:799 North Africa, 2:432 North American Free Trade Agreement (NAFTA), 1:372 North Carolina: charter schools in, 2:440 Department of Defense schools in, 1:197 (table) finance litigation in, 2:654

lottery proceeds in, 2:443 (table) National Board certified teachers in, 2:470 private schools in, 2:673 Race to the Top grant for, 2:608 returns to education for men born in, 2:659 state board in, 2:440 state education code in, 2:706 teacher experience profile in, 2:757 North Dakota, 2:588, 664, 673 Norway, 1:353 (figure), 395, 396 (figure) Notice requirements, under due process, 1:229–230 NPV. See Net present value (NPV) NSF. See National Science Foundation Nurse-family partnership programs, 2:515 Nursery programs, 2:671. See also Early childhood education Nursing, distance learning in, 1:212 Nutrition, 1:61, 2:615, 710. See also Food services; Meal programs Oakes, Jeannie, 2:791 Oates, Wallace E., 2:480 Obama, Barack, 1:65, 337, 2:607, 609 Object codes and categories: in cost accounting, 1:171–172 in fund accounting, 1:359, 360 (table), 361 under GASB, 1:377, 377 (table) Object-oriented budget systems, 1:359, 360 (table), 361 Observable and unobservable variables, 2:825–826, 827 Observation of teacher practices. See Teacher observation Occupation choice, and benefits of education, 1:70, 72 Occupational Employment Statistics survey, 1:155 Occupational prestige, in measurement of SES, 2:687 Occupy Movement, 1:374 O’Day, Jennifer, 1:6 Odden, Allan, 1:24, 28, 110, 177, 312, 379, 2:650, 841, 842 Odden-Picus Adequacy Index (OPAI), 2:650 Odds ratio, 1:297 O’Donnell, Kevin, 2:547 OECD. See Organisation for Economic Co-operation and Development Ogbu, John, 2:482 Ohio: charter schools in, 2:440 Cleveland voucher plan in, 1:293, 295 collective bargaining laws in, 2:641 college savings plans in, 1:144 federal-state balance in, 2:703 finance litigation in, 2:654, 656 for-profit EMO schools in, 1:271 lottery proceeds in, 2:443 (table) private school teacher certification in, 2:673

Index Race to the Top grant for, 2:608 school vouchers in, 1:293 student financial aid in, 2:708 student incentives in, 2:711 successful school district approach in, 1:36, 177 Oketch, Moses, 1:324 Oklahoma, 1:84, 175, 2:444 (table), 654, 706, 814 Old Deluder Satan Act, 2:845 Oligopolies, 1:55, 2:455, 852 Oliva, Maria-Angels, 1:324 OLS. See Ordinary least squares Omitted variable bias, 2:495–498 definition of, 2:852 IV methods and, 1:240 in matching of crime and education, 1:263 in ordinary least squares, 1:238, 2:495–497 in returns to education studies, 2:495, 832 in tracking studies, 2:792, 793 See also Selection bias O’Neil, H. F., 1:343 Online communities, 1:282. See also Social media Online GED® exam, 1:365. See also General Educational Development (GED®) Online learning, 2:498–501 academic outcomes in, 2:499 for continuing education, 1:165, 166 for cost reductions, 1:291, 384 data on, 2:498 distance learning and, 1:212 education technology for, 1:281–283 in for-profit institutions, 1:358 globalization and, 1:374 for instructional cost reductions, 2:809 online schools and, 1:38, 282, 283 privatization and, 2:557–558 for tutoring, 2:720 types of, 2:498–499 See also Digital divide; Virtual classroom; Virtual schools Online schools, 1:38, 282, 283, 2:500. See also Online learning Onofa, Mercedes, 1:290 On-the-job training, 1:162, 164, 248, 2:830. See also Job training On-time graduation rates, 1:221, 222. See also College graduation rates; Dropout rates; High school graduation rates On-track indicator of risk, 2:626 OPAI (Odden-Picus Adequacy Index), 2:650 OPEC (Organization of Petroleum Exporting Countries), 2:465–466 Open Courseware Initiative, 1:282 Open (online) education, 1:282–283, 284, 358, 2:809. See also Online learning

Open enrollment and open access: admission lotteries and, 1:240 at charter schools, 2:440 at community colleges, 1:154, 309 in continuing education, 1:166 as a desegregation policy, 1:204 districtwide, 2:447, 448–449 in for-profit higher education, 1:357 privatization and, 2:556 in vocational education, 2:831 Open-ended categorical matching grants, 1:412, 413, 414. See also Categorical grants Operating budget, 1:97–98, 99 Opportunity costs, 2:501–503 accounting costs compared to, 1:244 of adult education, 1:45–46 of attending college, 2:801 of child labor, 1:194 of committing a crime, 1:71, 72, 75, 263 cost-effectiveness analysis and, 1:183 decision making and, 1:65 definition of, 2:853 demand for education and, 1:194, 195 in developing countries, 1:254 developmental curricula and, 1:154 economics of education on, 1:254, 255 ingredients method of analysis and, 1:172 in market signaling, 2:452 of parent participation, 1:173 of remaining in school, 1:161 of repeating a grade, 1:180 teacher compensation and, 1:255, 2:631 teacher supply and, 2:766 whenever budget decisions are made, 2:501–502 See also Foregone earnings Opportunity to learn, 2:503–505 adequacy and, 1:22, 2:504 distance learning for, 1:212 See also Access to education; Equal educational opportunities Ordinal variable, 2:825–826 Ordinary least squares, 2:505–507 on achievement and class size, 1:237–238, 2:507 in admission lotteries research, 1:240 on college selectivity, 1:147 corrected OLS, 2:780 in the cost function approach, 1:177 definition of, 2:853 difference-in-differences and, 1:206, 207 in instrumental variable analysis, 1:408 interpretation of estimates from, 2:505–506 multivariate, 1:350 omitted variable bias and, 1:238, 2:495–497 propensity score matching compared to, 1:243

903

904

Index

for randomized control trials, 1:239 See also Regression analysis Oregon: charter schools in, 2:440 collective bargaining laws in, 2:641 finance litigation in, 2:654 lottery proceeds in, 2:444 (table) private schools in, 2:672, 673 professional judgment approach in, 1:175 school district size in, 2:706 Washington (state) sales tax and, 2:730 Oreopoulos, Philip, 1:139, 163, 164, 324, 409, 2:598, 712 Organisation for Economic Co-operation and Development, 2:508–509 on definitions of higher education, 1:355 on financial literacy, 1:340 on forgone earnings, 1:353–354 income inequality for members of, 1:395, 396 (figure) role of, 1:423–424, 2:508 surveys conducted by, 2:508–509 See also Programme for International Student Assessment (PISA) Organization capacity building. See Capacity building of organizations Organization of Petroleum Exporting Countries (OPEC), 2:465–466 Organization of schools, unique nature of, 2:737–738 Organizational change, 1:15, 315, 2:486 Organizational culture, 1:13, 198, 2:666 Organizational effectiveness, 1:424, 2:665 Orthopedic impairment, in special education, 2:693 (table) Osborne-Lampkin, La’Tara, 2:616 Osorio, Felipe Barrera, 2:583 Ostrom, Elinor, 2:797, 799 O’Sullivan, Vincent, 2:531 OTL. See Opportunity to learn O’Toole, Laurence, 1:170 Ouchi, William, 1:110 Outcomes, accountability for. See Accountability, standards-based; Accountability, types of Outcomes, adequacy of. See Adequacy Outcomes maximization, 1:43, 53, 255, 295 Outcomes-focused budgeting, 1:90 Outflows and inflows, and internal rate of return, 1:414–416 Out-of-state versus in-state college tuition, 2:543, 801, 802 (table), 802–803 Output maximization, 1:50, 51, 167 Outputs-inputs link, production function study of. See Education production functions and productivity Outsourcing: contracting for services as, 1:167 globalization and, 1:371, 372

service consolidation compared to, 2:681, 682 by the traditional central office, 1:114 of transportation services, 1:60 See also Contracting for services OVB. See Omitted variable bias Overinvestment in education, 2:832 Overman, L. T., 1:160, 2:669 Overmatching, in college enrollment, 1:140 Oversubscribed schools, 1:21, 125, 240, 2:447–448, 449–450 p value (probability value), 1:298 Pacific Islander students, 1:197 Page, Marianne, 2:593, 793 Paid leave benefits, 2:491, 492 Pakistan, 2:584 Palma ratio, 1:396 Panel data: definition of, 2:853 fixed effects and, 1:207, 350, 351 frontier methods and, 2:780 for permanent income calculations, 2:531 in resource impact studies, 1:274 Panel deliberations, in the PJ approach, 1:33, 34 Panel Study of Income Dynamics, 2:476 Papke, Leslie, 1:413 Paradox, voting, 2:458 Paradox of choice, 1:66, 67 Parallel forms method, in reliability, 2:623 Parallel trends assumption, in the DID method, 1:206, 207 Parametric method, definition of, 2:853 Parcel tax, 2:511–512 Parent and Family Involvement in Education Survey, 1:386, 2:547 Parent Loans for Undergraduates Program, 2:715 Parental education level: civic engagement and, 1:260 college choice and, 1:129 college completion and, 1:133 health and, 1:74 homeschooling and, 1:386, 387, 388 intergenerational benefits and, 1:75 parental involvement and, 2:514, 515 school choice programs and, 1:96 in SES measures, 2:687, 688, 689 social capital and, 2:684, 685 societal implications of, 1:70 student achievement and, 1:18, 397, 407 student education level and, 1:18, 397 student GED® recipients and, 1:365 student job training programs and, 1:428 Parental income. See Household/family income Parental involvement, 2:512–516

Index in capacity building, 1:94 child outcomes and, 2:513–514, 515 cost analysis of, 1:173 decentralization and, 1:118 in demand for education, 1:193, 194 in homeschooling, 1:386–389 in homework, 2:513 i3 grants for, 1:425 under NCLB, 2:488, 489 parent-teacher relationships, 1:80–81, 2:513, 514 in portfolio districts, 2:538 in privacy of student records, 1:333–335 in private contributions to schools, 2:546 school performance, accountability, and, 1:14 school report cards and, 2:660–661 school size and, 2:665 in school-based management, 1:80–81, 82, 2:667, 668, 669, 670 social capital and, 2:685 by socioeconomic status, 2:514, 515 student achievement and, 1:19, 2:513 for students with disabilities, 1:400, 401, 402 time invested in, 2:513–514 Parenting styles: achievement gaps and, 1:19 child outcomes and, 2:513–514, 515 dataset on, 2:476 family SES and, 2:688 and rate of return in education, 1:254 Parents as stakeholders, 1:51, 84, 96, 118, 219, 251, 2:667, 776 Parents Involved in Community Schools v. Seattle School District No. 1 (2007), 1:203 Parents’ right to choose, 2:672. See also School choice Parent-Teacher Associations (PTAs), 1:334, 2:546 Parent-Teacher Organizations (PTOs), 2:546 Pareto efficiency, 1:54, 249 Parochial schools, 1:303, 2:674, 675, 676. See also Schools, religious Parrish, Thomas B., 1:24, 111, 2:691, 692 (table), 694 (table), 695 Parteka, Aleksandra, 1:192 Partial and general equilibrium, 2:454, 516–518, 851, 853 Partial effects, 2:490, 853 Partnership for Assessment of Readiness for College and Careers, 1:150, 2:703, 756 Part-time faculty in higher education, 1:330, 330 (table), 331 (table), 332, 2:809 Part-time students, 1:45, 47, 47 (table). See also Adult education PASEC (international assessment program), 1:417, 418 Pastorek, Paul, 2:609 Patents, 1:107, 325. See also Intellectual property Pathak, Parag, 1:241

Pathways study, 2:769 Patrinos, Harry, 1:74, 80, 81, 118, 252, 2:432, 432 (figure), 583 Patterson, Margaret Becker, 1:365, 366 Paulsen, Michael B., 1:339 Pay for performance, 2:518–522 in applications of agency theory, 1:52 data on, 2:520 effects of, 2:521–522 incentives in, 2:518–522, 631 in salary schedule reform, 2:631 student achievement and, 2:518, 519, 521–522 See also Merit pay; Student incentives; Teacher performance assessment Pay-as-you-go financing, 1:102 Pay-as-you-use financing, 1:102, 103 Payback method, in capital budgeting, 1:91 Payea, Kathleen, 1:68, 70 Payroll taxes, 1:317, 2:491, 569 Pearson correlation coefficients, 1:297, 349 Peer effects, 2:522–525 college performance, SAT, and, 2:482, 635 debate on existence of, 2:522–523 definition of, 2:853 identification of, 2:523 neighborhoods and, 2:482, 523 socioeconomic status in, 2:524, 688–689 spillover effects and, 2:522–523, 699 in student mobility, 2:718 among teachers, 2:758 tragedy of the commons and, 2:797 Peer evaluation mechanisms, 2:528 Peer networks, 2:685 Pell, Claiborne, 1:153 Pell grants, 2:525–527 as categorical grants, 1:112 college choice and, 1:129–130, 131 college dropouts and, 1:135, 136 at community colleges, 1:153 data on, 2:525, 707 eligibility for, 2:525–526 expenditures per student and, 2:808 impact of, 2:707, 708 institutional aid and, 2:709 tuition and, 2:805, 806 Pelletier, J., 2:793 Penaloza, Roberto, 1:20 Pender, Matea, 1:140 Penner, Andrew, 2:594 Pennsylvania: charter schools in, 2:440 college graduation rate in, 1:131 competency exams (in 1834) in, 2:435 district consolidation in, 1:218

905

906

Index

finance litigation in, 2:654 private school teacher certification in, 2:673 Race to the Top grant for, 2:608 tax credit scholarships in, 2:814 Pennsylvania Association for Retarded Children v. Commonwealth of Pennsylvania (1971), 1:230 Pensions, teacher. See Teacher pensions Percentage equalizing model, 1:215, 311, 312, 378. See also Guaranteed tax base Peressini, Dominic, 2:768, 770 Perez, M., 1:111, 2:693 (table) Perfect competition, 2:454–455 Performance, pay for. See Pay for performance Performance budgeting, 1:89 Performance contracts, in agency theory, 1:50–51, 53 Performance evaluation systems, 2:527–530. See also Teacher evaluation; Teacher performance assessment Performance-based accountability, 1:52, 2:538 Performance-based budgeting, 1:89, 92 Performance-based compensation, 1:59, 255, 2:464, 521, 522, 630, 631, 741. See also Teacher compensation Performance-based funding, 1:23, 152, 154 Performance-based teacher evaluation. See Teacher evaluation; Teacher performance assessment Per-FTE expenditures in higher education, 1:318. See also Per-pupil expenditures Per-FTE revenues in higher education, 1:318–319. See also Per-pupil revenues Perkins, Carl D., 1:336 Perkins Career and Technical Education Act, 1:428 Perkins loans. See Federal Perkins Loan Program Permanent income, 2:530–531. See also Present value of earnings Per-pupil assessed property valuation, 2:647 Per-pupil expenditures: court-ordered finance reform and, 2:481 current trends in, 1:317 in Department of Defense schools, 1:197 in education costs across states, 1:175, 176 (figure) in education costs over time, 1:174, 174 (figure) in evolution of authority over schools, 1:314 in evolution of states’ role, 1:266 fiscal environment of, 1:346 in higher education, 2:802, 804, 806–807 housing prices and, 2:479 main drivers of, 1:275 per-pupil property wealth and, 1:379 Rodriguez case, district wealth, and, 1:378, 2:632 Serrano case, property taxes, and, 2:679–680 in special education, 2:691–693, 692 (table), 695–696 in Title I formulas, 2:788, 791 See also Per-FTE expenditures in higher education

Per-pupil foundation amount, in the PJ approach, 1:34 Per-pupil funding: adequacy of, 1:30, 34 for charter school facility expenses, 1:103, 125 at DoD schools, 1:197 fiscal neutrality and, 1:348–349 service consolidation and, 2:680 for special education, 2:695 (table), 695–696 See also Funding formulas and methods Per-pupil monetary contributions, 2:547 Per-pupil revenues: in evolution of authority, 1:313, 314, 314 (table), 316 flat grant model and, 1:310 guaranteed tax base and, 1:378 per-pupil wealth and, 1:348 (figures), 348–349 in vertical equity measurement, 2:828–829 See also Per-FTE revenues in higher education Per-pupil wealth, 1:348 (figures), 348–349 Perry Preschool, 1:179–180, 234, 235, 260, 264, 2:846 Persistence, student. See Student persistence in higher education Personal enrichment programs, 1:166, 194, 2:711. See also Continuing education Personal property, and property taxes, 2:570 Personnel ratios, 1:36 Personnel/labor costs. See Expenditures, on personnel PES. See Performance evaluation systems Pescatrice, Donn, 1:338 Peterson, P., 2:602 (table), 604 (table) Pew Internet & American Life Project, 1:281 Phay, Robert, 2:618 PhD-granting institutions. See Doctoral institutions Philadelphia, educational management organizations in, 1:269–270 Philanthropic foundations in education, 2:531–534 debate over the role of, 2:533–534 endowments from, 2:820 Philanthropy: college savings plans and, 1:145 for community college revenue, 1:154 foundations for, 2:531–534, 820 in postsecondary fundraising, 2:548, 549, 550, 551 See also Private contributions to schools; Private fundraising in postsecondary education Philippines, 1:81, 82, 2:556 Physics, international achievement in, 1:419. See also Science Picus, Lawrence O., 1:24, 28, 177, 312, 379, 2:650, 842 Pieper, Paul, 1:329 Pierce, Kim, 1:320 Pierce v. Society of Sisters of the Holy Names of Jesus and Mary (1925), 2:672, 674 Piggyback tax rate, 1:411–412 Pioneer Drama, funding by, 2:557

Index Piore, Michael J., 1:226 PIRLS. See Progress in International Reading Literacy Study (PIRLS) PISA. See Programme for International Student Assessment (PISA) Pischke, Jörn-Steffen, 1:164, 2:593 Pissarides, Christopher, 2:799 PJ approach. See Adequacy: professional judgment approach Place, S., 2:769, 770 Placement neutral funding, 2:695 Planning programming budgeting system, 1:90 Plausible value test scores, in NAEP assessments, 2:468 Plessy, Homer, 1:86 Plessy v. Ferguson (1896), 1:86, 202 PLUS loans, 2:699 Podgursky, Michael, 2:520, 742 (figure), 761, 762, 763 Poland, 1:353 (figure) Policy analysis in education, 2:534–537 Policymakers as stakeholders, 2:439, 585–586, 760 Political economy: on the benefits of education, 1:75 of grants, 1:108, 111, 413 of international organizations, 1:421 tragedy of the commons and, 2:797 Political interactions, 2:484. See also New institutional economics Political stability, estimated value of, 1:323 (table) Politics, engagement in, 1:72, 75, 260. See also Education and civic engagement Politics, public choice economics on, 2:579–580, 581 Ponce, Juan, 1:290 Pooled standard deviation, 1:298 Pooled time-series cross-sectional data, 1:351, 2:853 Pop-Eleches, Christian, 2:832 Porter, Andrew, 2:504 Portfolio districts, 2:537–539 components of, 2:537–538 as a market-based reform, 2:455, 456 weighted student funding in, 2:836 Portfolio management, 2:456, 537, 848 Portfolios: for National Board certification, 2:469–470, 471, 754 for performance evaluation, 2:528, 765 in teacher preparation, 2:770 Portugal, 1:197 (table), 353 (figures) Positive psychology, 1:363, 364 Post 9/11 GI Bill, 1:369 Postsecondary education. See Adult education; Benefits of higher education; College choice; College completion; College dropout; College enrollment; College rankings; College savings plan mechanisms; College selectivity; Higher education; Tuition and fees, higher education Post-Vietnam War Veterans Education Assistance Program, 1:369

907

Potential outcomes model, 2:591 Poverty: and access to board-certified teachers, 2:470 at-risk status linked to, 2:624 as a common and pervasive obstacle, 1:5–6, 195 great disadvantages posed by, 1:398 high cost for education of students in, 1:178 Internet access and, 1:209 less experienced teachers for students in, 2:745 neighborhood effects and, 2:481, 482, 483 in neighborhood SES, 2:689 special education services and, 2:694–695 Title I funds and, 2:787–791 value of reductions in, 1:323 (table) in value-added measures, 2:773 War on, 1:46, 199, 302, 315, 337, 2:487, 789 weighted student funding and, 2:835–836 Powell, Lewis, 2:633 Power analysis of a study, 1:300 Power equalizing systems, 1:215, 311. See also District power equalizing; Guaranteed tax base PPPs. See Public-private partnerships in education Practice measures, 2:753–754, 755. See also Teacher evaluation Pradhan, Menno, 1:81 Praxis examinations, 2:435, 759 Predatory lending practice accusations, 2:500 Predictive validity, 2:826 Pre-Elementary Education Longitudinal Study, 2:474, 475 Preintervention/postintervention analysis, DID for. See Difference-in-differences Prekindergarten programs, 1:59, 233, 377, 2:446, 671, 674, 787. See also Early childhood education Prepaid tuition plans, 1:144. See also College savings plan mechanisms Present value: of an additional year of schooling, 2:657 discount rate for, 1:210–211 of expected benefits in attending college, 2:714 of expected lifetime utility, 1:130 net (NPV), 1:91, 181, 210, 391, 392, 415–416 See also Future earnings; Present value of earnings Present value of earnings, 2:539–542 age profile of earnings and, 2:540, 540 (figure), 541 (figure) college enrollment and, 1:139 definition of, 2:853 discounting and, 2:541, 542 (table) formula for, 2:539 growth in earnings and, 2:540–541, 541 (figure) in human capital theory, 1:391, 392 of property, 2:571 See also Discount rate; Permanent income; Present value

908

Index

Preservice teacher assessment, 2:764–765 Presidential Teaching Fellows, 1:278 President’s Commission on Higher Education, 2:846 Pressey, Sidney, 1:289 Price, in theory of markets, 1:252, 2:453 (figure), 453–455 Price discrimination, 2:542–544, 853 Price elasticity, 1:301–302, 413 Pricing out ingredients, 1:172, 183 Primary and secondary labor markets, 1:226–227 Primary/secondary education benefits. See Benefits of primary and secondary education Principal-agent problem, 2:544–545 in agency theory, 1:50, 51, 53 definition of, 2:853 in deregulation, 1:200 with hidden acts or information, 2:460 moral hazard and, 2:459–461, 544–545 No Child Left Behind and, 2:488 in standards-based accountability, 1:8 in teacher autonomy, 2:739, 740 Principals (school administrators): accountability of, 1:11, 12 in comprehensive school reform, 1:159–160 labor market analysis for, 1:96 in pay for performance programs, 2:518, 521 in performance evaluation systems, 2:527, 528 in Race to the Top, 2:607, 608 recruitment of, 1:95, 96, 2:607 reports from, for datasets, 1:417, 419, 2:474, 475, 476 in school-based management, 1:80, 81, 82, 2:667, 668–669, 670 as stakeholders, 1:82, 2:439, 667, 669, 670 standards for, 2:528 teacher evaluation by, 2:738, 749, 759 Principle of highest and best use, 2:571 Prison population, 1:262. See also Education and crime Pritchett, Lant, 1:324 Privacy, 1:151, 212, 333–335, 388 Private benefits of education, 1:72, 73–74, 76 Private contributions to schools, 2:545–548 data on, 2:546, 547 as endowments, 2:820 international aspects of, 2:547–548 monetary versus in-kind, 2:545–547 to private schools, 2:671, 811, 812 to religious schools, 2:675 See also Philanthropic foundations in education; Private fundraising in postsecondary education Private decision rights, 2:556, 557, 558. See also School choice Private fundraising in postsecondary education, 2:548–552

alumni and, 1:154, 2:549, 550–551, 552 data on, 2:548, 549 federal tax benefits of, 2:549 increasing use of, 1:319, 383, 2:548, 550 institutionalization of, 2:549–550 process of, 2:551–552 tax deductions and, 1:355 See also Philanthropic foundations in education; Private contributions to schools; University endowments Private goods, 1:2, 2:577, 582. See also Public good Private provision, in privatization typology, 1:2, 124, 2:486, 555–556, 557, 583, 585 Private school associations, 2:553–555. See also Schools, private Private School Survey, 1:222 Private School Universe Survey, 2:473 Private schools. See Schools, private Privatization and marketization, 2:555–559 data on, 2:556 demand for, 2:783 service consolidation compared to, 2:681 tragedy of the commons and, 2:798 typology of, 2:555–556 See also Capitalist economy Probability distribution, definition of, 2:853 Probit models, 2:496, 853 Procedural due process, 1:228–229, 2:618 Procedural knowledge, 1:341 ProComp compensation plan, 2:521, 522 Producer and consumer surplus, 1:54–55, 55 (figure) Production and consumption, 1:55, 105, 327, 381, 2:577, 581, 698 Production factors (land, labor, and capital) in capitalism, 1:105. See also Capitalist economy Production function, definition of, 2:853 Production function analysis: of administrative spending, 1:43 capacity building and, 1:93, 96 college selectivity and, 1:146 cost function approach and, 1:27 in data development analysis, 1:191–192 district size as input for, 1:219 human capital model and, 1:393 See also Education production functions and productivity Production possibility frontier, 1:191, 2:853 Production theory, 2:681–682. See also Education production functions and productivity Productive efficiency: allocative compared to, 1:54 definition of, 2:853 of educational vouchers, 1:295 of homeschooling, 1:387–388 in public-private partnerships, 2:584, 585, 586

Index Productivity: as an education finance issue, 1:268 and benefits of education, 1:68, 69, 70, 71, 72, 73–74 and cost disease, 1:63–64 declines in, 2:465–466 and education production functions, 1:273–276 of educational vouchers, 1:295 improved by better education, 1:251 labor market, 1:72, 73–74, 251 A Nation at Risk and, 2:465–466 observation of, and moral hazard, 2:460 performance evaluation rooted in, 2:527 shift in focus to, 1:265 See also Labor market outcomes Product-specific economies of scale, 1:257–258 Professional credentials and licenses. See Licensure and certification Professional development, 2:559–562 in capacity building, 1:94, 95 contracting for, 1:170 cost of, 2:559–560 effectiveness of, 2:560–561 financial incentives for, 2:521, 522 financial support for, 1:7, 201, 2:532 mandates for, 1:114, 401 National Board certification and, 2:469–471 OECD international survey on, 2:509 in school-based management, 2:669 standards on, 1:16 teacher evaluation as a driver of, 2:752, 753, 754 technology and, 1:167, 281 Professional judgment approach. See Adequacy: professional judgment approach Profiles of American Colleges, Barron’s, 1:129, 146, 147 Profit maximization, 1:55, 245, 252, 2:454–455, 542, 543 Program and planning budgeting, 1:89. See also Program budgeting Program budgeting, 1:89–90, 2:562–564 components of, 2:562–563 program classifications for, 1:361 Program coding and categories. See Coding and categories Program planning budgeting and evaluation system, 1:90 Programme for International Student Assessment (PISA): criticism of, 1:424 decentralization and, 1:118 establishment of, 2:847 low-income countries in, 1:417 NCES participation in, 2:472 overviews of, 1:253, 416, 418, 419, 423–424, 2:508 PIRLS and TIMSS compared to, 1:419–420 and students in voucher programs, 1:295 U.S. results on, 1:11, 253

909

Programme for the International Assessment of Adult Competencies, 2:509 PROGRESA education, health, and nutrition program, 2:615, 710 Progress in International Reading Literacy Study (PIRLS): establishment of, 2:847 low-income countries in, 1:417 NCES participation in, 2:472 overviews of, 1:416, 418–419 PISA and TIMSS compared to, 1:419–420 Progressive Era reforms, 2:638–639 Progressive tax and regressive tax, 2:564–566 equity, ethics, and, 2:564, 566, 575 income taxes and, 2:565, 729–730 progressive, 1:3, 396, 2:575, 729 progressivity, 2:565–566, 727, 736 property taxes and, 2:575 regressive, 1:216, 2:446, 512, 575 Project categorical grants, 1:412. See also Categorical grants Project STAR (Student/Teacher Achievement Ratio): for adequacy determination, 1:24 on civic engagement, 1:260 propensity score matching on, 2:598 quantile regression analysis of, 2:593 randomized control trial in, 1:239, 274, 2:496, 612–613 on the school quality/earnings link, 2:659–660 Propensity score matching, 2:566–569 challenges in, 2:568–569, 598 definition of, 2:853 in international assessment data, 1:417 ordinary least squares compared to, 1:243 quasi-experimental methods and, 1:255, 417, 2:594, 598 randomized control trials and, 2:613–614 in tracking studies, 2:792 unbiased estimates from, 2:567–568 Property rights, 1:106, 107, 228, 2:466. See also Intellectual property Property system, in capitalistic economic systems, 1:106, 107 Property tax revolt, 2:574 Property taxes, 2:569–576 ad valorem and non-ad valorem, 2:511 administration of, 2:570–574 bond measures and, 1:83–84, 85 cash flow and, 2:646 changing role of, in state revenues, 1:266, 314, 2:570 circuit breaker relief from, 1:286, 2:573, 575, 727, 728, 729 data on, 2:511, 569, 570 disproportionate reliance on, 2:680 in district power equalizing, 1:215, 216, 266

910

Index

economic development and, 1:248, 2:572–573 efficacy of, 2:574–575 in finance litigation, 2:653, 655, 679–680 in the fiscal environment, 1:347 formulas for, 2:570, 573, 731 higher education support from, 1:385 history of, 1:266, 314, 2:569–570 housing prices and, 2:480 incidence of, 2:575, 729 inequities in, 2:574–575 limits on, 2:573–574, 731, 732 in local financing of capital spending, 1:103 as most common local source of school revenue, 2:647 opportunity gaps created by, 1:88 property tax revolt on, 2:574 Proposition 2½ and, 2:480, 732 Proposition 13 and, 2:511, 574, 651, 653, 679, 680, 731 Rodriguez case and, 2:633 Serrano rulings and, 2:679 stability of, 2:574, 736, 737 tax burden and, 2:723, 724, 725, 726 tax yield and, 2:736, 737 valuation of property for, 2:480, 570–572, 573, 725 vulnerability of, in district budgets, 2:644 Property value: for defining wealthy districts, 1:22, 267, 349 education expenditures and, 1:248 fiscal disparity and, 1:344 in the fiscal environment, 1:347 per pupil, 1:22, 267, 349, 378 property taxes and, 2:480, 570–573, 725 tax base and, 1:378, 379 Proportional taxes, 1:3, 2:564, 565, 575. See also Flat taxes Proposition 2½ (Massachusetts), 2:480, 732 Proposition 13 (California), 2:511, 574, 651, 653, 679, 680, 731 Prospect theory, 1:65 Protestant school associations, 2:554 Protestant schools, 2:673. See also Schools, religious Protocol design, in cost analysis, 1:172 Provision privatization, 1:2, 124, 2:486, 555–556, 557, 583, 585 Prussia, 1:117 Psacharopoulos, George, 1:74, 187, 195, 252, 2:432 PSM. See Propensity score matching Psychological costs, 1:74, 140 Psychostimulant prescriptions, 1:53 PTAs (Parent-Teacher Associations), 1:334, 2:546 PTOs (Parent-Teacher Organizations), 2:546 Public assistance benefits, 1:181, 2:491, 737 Public Charter Schools Program, federal, 2:448 Public choice economics, 2:576–581

collective choice and, 2:577–578 definition of, 2:853 key insights of, 2:578–580 on the provision of education, 2:580–581 on voting, 2:578–579, 581 Public engagement approach, to adequacy, 1:32, 33 Public good, 2:581–582 definition of, 2:853 education as, 1:115, 249, 2:577, 581, 582, 783 in intergovernmental fiscal relationships, 1:409, 410–411, 413 knowledge as, 1:107 local, 1:410, 411 market failure and, 2:577–578, 582 nonrival and nonexcludable, 1:2, 2:577, 581–582 pure, 1:2, 2:581, 582 Tiebout sorting and, 2:786–787 See also Private goods Public sector economics, 2:574 Public-private partnerships in education, 2:582–587 capital budgeting for, 1:99, 100 for capital financing, 1:103 for curriculum enrichment, 2:558 in different contexts and countries, 2:583–584, 585 Puerto Rican origin subgroup, earnings of, 2:601 Puerto Rico, 1:197 (table), 2:672, 673 Pull and push factors, in teacher compensation, 2:741, 762 Pupil weights, 2:587–589 determination of, 2:587, 588–589 in full state funding, 1:312, 2:589 in successful school district approach, 1:37 See also Weighted student funding Pupil-teacher ratios. See Student-teacher ratios Push and pull factors, in teacher compensation, 2:741, 762 PVE. See Present value of earnings Q Comp compensation plan, 2:522 Qualified School Construction Bonds, 1:105 Qualified Tuition Plans (529 Plans), 1:144–145 Qualified Zone Academy Bonds program, 1:85, 105 Quality, school. See School quality; School quality and earnings Quality, teacher. See Teacher quality Quantile regression, 2:591–594 definition of, 2:853 in educational settings, 1:69, 2:593–594 interpretation of, 2:593 Quantile scores, 1:150 Quasi-endowment, 2:821 Quasi-experimental methods, 2:594–599 for causal estimates, 1:255, 2:594–595 in credential effect studies, 1:187 definition of, 2:853

Index difference-in-differences as, 2:595–596 for eliminating omitted variable bias, 2:496 instrumental variables for, 2:597 propensity score matching as, 2:598 randomized control trials and, 2:595, 613–615 regression discontinuity as, 2:597–598 selection bias and, 1:350 treatment effects and, 2:595–596, 597, 598 See also Difference-in-differences; Fixed-effects models; Instrumental variables; Propensity score matching; Regression-discontinuity design Question construction, and test reliability, 2:623–624 Quota-based reward systems, 1:52. See also Incentives R software package, 2:569 Race earnings differentials, 2:601–607 ability, educational quality, and, 2:604–605 Black schools, White schools, and, 2:659 changes over time in, 2:605–606 data on, 2:601–603, 602 (table), 604 (table) explanations for, 2:603–604, 605–606 See also Earnings; Earnings comparisons, data for Race to the Top, 2:607–611 CCSS and, 1:9, 150, 316, 2:439, 609, 848 centralization and, 1:117 charter schools encouraged by, 1:201 core reforms and purpose of, 1:277–278, 2:607 grant scoring rubric, 1:315–316, 2:608 grant winners, 2:608 guiding principles of, 1:8 No Child Left Behind and, 1:9, 2:607 performance evaluation under, 2:527, 529 policy agenda acceptance and, 2:536 program design of, 2:607–608 state education agencies and, 2:702 teacher salary structures and, 2:630 value-added measures and, 2:775 Racial discrimination, 2:601, 604–605. See also Race earnings differentials Racial matching of students to teachers, 2:747–748 Racial segregation and desegregation. See Brown v. Board of Education; Desegregation; Segregation (racial) Racial stereotyping, 1:19, 2:605 Racial/ethnic groups: achievement gaps and, 1:12, 17–18, 20, 21, 285, 397, 2:531, 605, 606, 771 in adequate yearly progress calculations, 1:39, 40 college completion and, 1:133 college enrollment rates and, 1:138 community colleges and, 1:154 earnings differentials for, 2:601–607 educational inequality and, 1:397, 2:689 higher education, labor market outcomes, and, 1:69–70 income correlation with, 2:480

911

job training, vocational programs, and, 1:428 jobs restrictions and, 1:227 in NCLB testing subgroups, 2:488, 489 in neighborhood sorting, 2:480 parcel taxes and, 2:512 peer effects and, 2:523–524, 688 SAT and, 2:636 school admissions decisions and, 2:447 school board representation of, 2:638, 639 socioeconomic status and, 2:688, 689 spending gaps, de facto segregation, and, 1:379 student mobility and, 2:717 and students with disabilities, 1:401 in value-added measurement, 2:773, 774 wealth and, 2:690 See also Ethnic groups Ragtag, Andrew, 1:80, 82 RAND Corporation, 1:159–160, 272, 2:562 Random error, definition of, 2:853. See also Measurement error Random measurement error, 2:456–457 Random selection in admissions lotteries, 2:448 Random shock technique, 2:482–483 Randomized control trials, 1:238–239, 2:611–616 definition of, 2:853 in developing countries, 1:274 as a gold standard, 1:350, 2:611 omitted variable bias and, 2:496 problems that arise in, 2:611–612 quasi-experimental approaches and, 2:595, 613–615 STAR as an extended example of, 2:612–613 Range (dispersion), 1:285, 2:853 Rankings of colleges. See College rankings Rapp, C., 2:498 Rask, Griffith, 2:635 Rask, Kevin, 2:635 Rate of return (ROR): on an additional year of schooling, 1:252, 2:657 calculations for, 1:414–416, 2:431 compound annual growth rate and, 1:157 data on, 1:74, 252, 2:432–434 definitions of, 2:851 in developing countries, 1:74, 2:431–434 economics of education on, 1:251, 252, 254 globalization and, 1:373 internal, 1:91, 414–416, 2:431, 851 labor market ROR, 2:431–434, 851 1960s–1970s compared to 1980s, 2:466 present value of earnings and, 2:541 private versus social, 1:252, 254 social, 1:252, 254, 324, 325 See also Benefits of higher education; Benefits of primary and secondary education; Labor market outcomes

912

Index

Ratio analysis, 2:829 Rational ignorance, 2:579 Rationality, economic, 1:64–65, 66, 67, 2:484 Ratios: aid ratio, in percentage equalization, 1:311 cost-effectiveness, 1:183, 184, 185, 2:586 in data envelopment analysis, 1:191, 192 of debt to capital outlays, 1:102 of debt to revenue raising ability, 1:99 federal range ratio, 1:312, 2:649, 651, 850 of highest to lowest earners in a country, 1:395 odds ratio, 1:297 Palma ratio, 1:396 personnel ratios, 1:36 risk ratio, 1:297 spending ratio, 2:691–693 staffing ratio, 1:34, 2:742–743 STAR, 1:24, 239, 260, 274, 2:496, 593, 598, 612–613, 659–660 of students to computers, 1:280, 289 student-teacher (see Student-teacher ratios) tutor-student ratios, 2:721 20:20 ratio, 1:396 Raudenbush, Stephen W., 2:483, 843 Ravitch, Diane, 2:449, 609 RAVSAK Jewish school association, 2:554 Ray, J. R., 2:841 Ray economies of scale, 1:257–258 RCTs. See Randomized control trials RDD. See Regression-discontinuity design Reading: achievement gap in, and race, 1:397 CCSS for, 1:150 in charter schools, 1:125 in CMO schools, 1:272 cost analysis in programs for, 1:184 Early Grade Reading Assessment of, 1:418 education technology and, 1:283 Internet access for, 1:290 NAEP assessment of, 1:17–18, 198, 2:467, 468, 469 NCLB and remediation in, 2:719 NCLB focus on, 2:489, 490, 790 in NCLB/AYP framework, 2:488, 489 parenting styles and, 2:513 PIRLS assessment of, 1:416–420, 2:472, 847 PISA assessment of, 1:416, 418, 419, 423, 2:508 Praxis I assessment of, 2:435 precursor knowledge for, 2:513 SAT and, 2:634 SERCE assessment of, 1:417 socioeconomic status and, 2:689 student incentives in, 2:711 tax limit and, impact of, 2:732 test-score gaps in, 1:17–18, 2:605

Title I, and standards in, 2:790 tracking in, 2:792, 793 See also English language arts (ELA) Reagan, Ronald, 1:303, 2:463, 520 Real property, 2:570, 572. See also Property taxes Reardon, Sean, 1:18, 397, 2:605 Reback, Randall, 2:593 Rebell, M. A., 2:842 Recapture clause, in guaranteed tax base programs, 1:379 Recapturing, in state funding systems, 1:215 Recession, Great, 1:265, 345, 374, 2:690, 730, 806, 808, 824 Reciprocal immunity doctrine, 1:104 Reckase, Mark, 2:748, 750 Reduction in force, 2:616–618. See also Layoffs Rees, Daniel, 2:793 Referenda. See Elections and referenda Reflection problem, 2:523 Regional adjustments, with a wage index, 1:155–156 Regression, quantile. See Quantile regression Regression analysis: for college selectivity, 1:146–147 for credential effects, 1:187 definition of, 2:853 for difference-in-differences, 1:206 of education and crime, 1:263 horizontal equity and, 1:390 of labor market outcomes, 1:68–69 omitted variable bias in, 2:495–497 overview of, 1:237–238 quantile regression, 1:69, 2:591–594, 853 for randomized control trials, 1:239 in sensitivity analysis, 1:181 for taxpayer equity measurement, 2:651 of technical efficiency, 2:780 for tracking studies, 2:793 for value-added measurement, 2:773 vertical equity and, 2:650, 828 See also Ordinary least squares; Regressiondiscontinuity design Regression line, 2:620, 620 (figure). See also Line of best fit Regression-discontinuity design, 2:597–598, 618–622 for college selectivity, 1:148 critical requirement of, 2:620 definition of, 2:854 for eliminating omitted variable bias, 2:496 examples of, 1:241–243, 242 (figures) fuzzy and sharp, 2:598, 621 for international assessment data, 1:417 key to the rigor of, 2:619 in production function analyses, 1:274 randomized control trials and, 2:614–615 for remedial education and achievement, 1:255 selection bias limited by, 2:678

Index Regressive taxes, 1:216, 2:446, 512, 575. See also Progressive tax and regressive tax Regulatory capture, theory of, 1:200 Reilly, Gilbert J., 1:379 Reimbursement, cost. See Cost reimbursement Reinforcement, in behavioral philosophy, 1:46 Reliability, 2:622–624 definition of, 2:854 how to increase, 2:623–624 interrater reliability, 2:623, 749 item response theory and, 2:624 measurement error and, 2:456, 457 measurement of, 2:622–623 of school report card measures, 2:663 split-half reliability, 2:623 standard error and, 1:238 of teacher effectiveness measures, 2:631, 749 of teacher evaluation measures, 2:752, 753, 754, 755, 756 test measure variability and, 2:622 test reliability, 2:622–624 validity compared to, 2:623, 825 of value-added measures, 2:773–774, 775 Reliability coefficient, 2:622, 623, 624 Religious organizations, constitutionality of public money received by, 2:815. See also Separation of church and state Religious schools. See Schools, religious Remedial education, 1:47, 154, 203, 255, 309, 2:598 Remediation programs, 2:496, 533, 719–720 Rent seeking, 2:579–580, 733 Repayment: discount rate calculations and, 1:210, 211 of school district debt, 1:83, 102, 103, 368 of student loans, 1:112, 277, 336–337, 340, 358, 364, 2:460, 713, 714–715 unfunded mandates and, 2:819 Repeating a grade, 1:180, 223, 234, 2:717 Replacement cost approach, in property valuation, 2:571 Replication of research, 2:827. See also External validity Report cards, school. See School report cards Representations, mental, 1:341 Reschly, Amy, 1:163 Reschovsky, Andrew, 1:23, 379, 2:843 Research expenditures, in higher education, 1:318 Research universities, 1:129, 131, 258, 383, 385, 2:809, 820. See also Doctoral institutions Research-practitioner partnerships, 1:159 Resegregation (racial), 1:20, 203. See also Desegregation; Segregation (racial) Residential mobility, 2:480, 716. See also Student mobility Residential property tax relief, 2:573 Residential sorting, 2:786. See also Tiebout sorting Residual regression analysis, 1:390. See also Regression analysis

913

Resource method of cost analysis, 1:172 Results neutrality, 1:287–288 Retail sales as consumption, 2:735–736 Retention (repetition), grade, 1:180, 223, 234, 2:717 Retention (staying in school) of college students, 1:147, 307, 308, 309, 339, 2:708. See also College completion Retention of teachers, 1:253, 255, 2:464, 519, 521, 561, 740, 762, 769, 770, 777 Retirement benefits. See Nonwage benefits; Teacher pensions Return, internal. See Internal rate of return Return on investment: capital budgeting and, 1:97 from childhood education, 1:234–235 in college rankings, 1:143 from endowments, 2:820 GDP, globalization, and, 1:373 See also Investment in education; Investments, earnings on; Rate of return (ROR) Returns to education. See Benefits of higher education; Benefits of primary and secondary education; Rate of return (ROR) Revenue Act of 1954, 1:355 Revenue and expenditure trends. See Expenditures and revenues, current trends of Revenue bonds, 1:103, 367. See also Bonds in school financing; General obligation bonds Revenues (public elementary/secondary) by federal-statelocal source: fiscal year 2010, 2:702 year 2009–2010, 1:314, 314 (table) year 2011, 2:824 years 1919–2010, 1:313, 314 (table) years 2007–2010, 1:317 Revenues per pupil. See Per-pupil revenues Reward-for-effort funding, 1:92 Rewards. See Incentives; Student incentives; Teacher incentives Reynolds, Arthur J., 2:717 Rhine, Sherrie L. W., 1:257 Rho, Eunju, 1:170 Rhoads, Robert, 1:372 Rhode Island: charter schools in, 2:440 college graduation rate in, 1:131 curriculum standards in, 2:440 district power equalizing in, 1:216, 378 finance litigation in, 2:654 money-follows-the-child system in, 1:216 Race to the Top grant for, 2:608 special education in, 1:403 tax credit scholarships in, 2:814 Rhodes, Edward, 2:779 Richardson, Peter, 1:176 Rickman, Dana K., 2:524

914

Index

RIF. See Reduction in force Right to choose, parental, 2:672. See also School choice Risk aversion, 2:582 Risk difference, 1:297 Risk factors, students, 2:624–627 “at risk” terminology and, 2:624–625 categorical grants and, 1:108 dropping out and, 1:223–224, 2:624–627 focus on students versus factors, 2:626–627 GED® credentials and, 1:366 hits versus false alarms in identifying, 2:625 job training and, 2:831 pupil weighting and, 2:588 in special education, 2:696–697 teacher-assigned grades and, 2:627 Risk management programs, 2:696 Risk ratio, 1:297 Rival, definition of, 2:854 Rival and excludable goods, 2:581, 582 Rival and nonexcludable goods, 2:796. See also Nonrival and nonexcludable goods Rivera-Batiz, Luis A., 1:324 Rivkin, Steven G., 1:275, 2:524, 718, 747, 759, 843 Rivlin, Alice, 1:58 Robert Wood Johnson Foundation, 2:533 Robertson, Roland, 1:372 Robertson, Susan L., 2:583 Robinson, Joan, 1:63 Robinson v. Cahill (1973), 2:653, 847 Rockefeller Foundation, 2:532 Rockoff, Jonah, 2:660, 718, 749 Rodrigo, P., 1:264 Rodriguez, Demetrio, 2:632 Rodriguez, Gloria, 1:287 Rodriguez case. See San Antonio Independent School District v. Rodriguez Rodriquez-Oreggia, E., 1:81 Romagnano, L., 2:768, 770 Romania, 2:832 Ronfeldt, M., 2:769, 770 Roosevelt, Franklin D., 1:208, 2:477 ROR. See Rate of return (ROR) Rose, Heather, 1:176 Rose v. Council for Better Education (1989), 1:23, 27, 315, 2:847 Rosen, Harvey, 1:412 Rosenbaum, James E., 1:47 Rosenbaum, Janet, 1:47 Rosenwald Fund, 2:532 Ross, K. E., 2:843 Rothstein, Jesse, 2:531, 634, 749 Rothstein, Richard, 2:605 Rouse, Cecilia, 1:137, 290, 295 Rousseau, Jean-Jacques, 2:564

Rowan, Brian, 2:747 RTT. See Race to the Top Rubenstein, Ross, 2:446 Rubin, Donald, 2:595 Ruggiero, John, 2:780 Ruijs, N., 1:296 Rumberger, Russell W., 2:718 Running variable, 2:496, 598, 614–615. See also Forcing variable Russia, 1:117. See also Soviet Union, former Sacerdote, Bruce, 2:482, 524 Sacrifices and foregone alternatives, 1:244. See also Opportunity costs Sadoff, Sally, 2:712 Saez, Emmanuel, 2:659 Sala-i-Martin, Xavier, 1:324 Salary. See Earnings; Earnings comparisons, data for; Salary schedule; Teacher compensation Salary schedule, 2:629–632 in charter and private schools, 2:745 moral hazard and, 2:461 in new institutional economics, 2:486 in performance pay systems, 2:520, 521 in the principal-agent model, 2:545 rigid, consequences of, 2:630, 745 for setting target revenue levels, 1:36 single, 2:629, 630, 743, 745, 752 for standardized pay, 2:519, 520 structure of, 2:629–630, 743–746, 744 (table) See also Teacher compensation Sales tax: for district capital spending, 1:103 for district general revenues, 1:104 in district wealth measurement, 2:648 elasticity of, 2:727–728 in evolution of states’ role, 1:266 exemptions for, 2:735–736 incidence of, 2:730 instability of, 2:737 local piggybacked to state, 1:412 lotteries and, 2:447 tax shifted, 2:725 tax yield and, 2:735–736, 737 Sallie Mae (Student Loan Marketing Association), 2:700 Salsbury, Stephen, 1:162 Salvanes, Kjell G., 1:324 Sample selection bias, 2:677. See also Selection bias Sample size, 1:298, 300, 417, 2:529, 621, 748–749 Sampling bias, 2:677. See also Selection bias Sampson, Robert J., 2:483 Samuelson, Paul, 1:328 San Antonio Independent School District v. Rodriguez, 2:632–633

Index background for, 2:632–633, 653, 847 per-pupil spending and, 1:378, 2:632 the ruling on, 2:633, 653 Serrano v. Priest and, 2:632, 680 substantive due process and, 1:228 Sanbonmatsu, Lisa, 1:139, 2:483 Sanders, Nicholas, 2:598 Sanders, William, 2:520, 748, 749, 754, 844 Santibañez, Lucrecia, 1:81 Santos, Maria C., 1:257 SAS software package, 2:569 SASS (Schools and Staffing Survey), 1:253, 2:472, 476, 529, 559, 738, 740 Sass, Tim R., 2:524, 749 SAT, 2:633–637 ACT competition with, 2:634–635 economics of education and, 2:635–636 for measuring college selectivity, 1:147, 2:636 for placement in English remediation, 2:615 preparation classes for, 1:165 race, gender, and, 2:636 reliability and validity of, 2:624, 826 selection bias and, 2:636 Satisfaction maximized, 1:252, 2:454. See also Utility Savelyev, P. A., 1:264 Savings plans for college. See College savings plan mechanisms Sawada, Yasuyuki, 1:80, 82 Sax, Linda, 1:261 Say, Jean-Baptiste, 2:564 SBM. See School-based management Scafidi, Benjamin, 2:446 Scale economies, 1:411, 2:681. See also Economies of scale; Economies of scope Scarcity, 1:53, 54, 244, 345, 2:454, 484 Scenario approach, in sensitivity analysis, 1:181–182 Schanzenbach, Diane Whitmore, 2:593, 634, 659 Schapiro, Dennis, 1:287 Schapiro, M., 2:841 Schelling, Thomas, 2:480 Schick, Allan, 2:563 Schmidt, Stephen, 2:531 Scholarship aid. See Student financial aid Scholarship tax credit programs, 2:813, 814–815. See also Tuition tax credits Scholastic Aptitude Test. See SAT School Administrator (magazine), 1:57 School block grants, 1:79–82. See also Block grants School boards, 2:637–640 accountability, achievement, and, 1:13 collective bargaining and, 2:640, 642 data on, 2:637, 638 due process and, 1:228 in the principal-agent model, 2:544, 545

915

as stakeholders in local control, 2:439 state boards, 2:439, 440, 637, 701, 705–706 state governments and, 1:266, 2:637 teachers’ union influence over, 2:776–777 School boards, school districts, and collective bargaining, 2:640–644 bargaining unit recognition and, 2:641 impact of, 2:777–778 negotiating the contract, 2:641–643, 776, 777 reduction in force and, 2:616, 617, 618 service consolidation and, 2:683 state law and, 2:641, 642, 706, 776 union role in, 2:640–643, 777 work rules in, 2:640–641, 642, 776 See also Teachers’ unions and collective bargaining School buses, 1:60, 61, 346, 2:682, 694. See also Transportation services School choice: and admission lotteries, 2:447–448, 449 capacity building, market mechanisms, and, 1:95, 96 charter schools in (see Charter schools) data on, 2:447–448 EMOs in, 1:270, 272 equity in, 1:292, 293, 294, 295–296, 387 forms of, 2:447–448 freedom of choice in, 1:294–295, 296, 386–387 homeschooling as, 1:386 (see also Homeschooling) under NCLB, 2:489 opportunity to learn and, 2:504 parent choice, behavioral economics, and, 1:67 and parents’ right to choose, 2:672 policy analysis of, 2:535 for private and religious schools, 2:672, 674, 676 privatization and, 2:556, 557 school monopolies threatened by, 2:783 social cohesion and, 1:296, 388 student mobility and, 2:718 theory of the firm on, 2:783, 784, 785 See also Educational vouchers School closures, 2:538, 539, 718, 783. See also School consolidations; School takeovers School committees, 2:637, 638. See also School boards School consolidations, 1:60, 2:664. See also School closures; School takeovers School councils, 1:79, 80, 81–82, 82, 2:668 School Crime Supplement (dataset), 2:475 School day, extended, 1:29, 126, 127, 320–321, 2:465 School design teams, 1:158–159, 160 School district budgets, 2:644–645 personnel as the largest expense for, 2:646 teacher compensation impact on, 2:629 See also Budgeting approaches; Expenditures and revenues, current trends of

916

Index

School district cash flow, 2:645–647 School district competition, 2:733, 786, 787 School district consolidations, 1:60, 217, 218–219, 2:616, 618, 664, 681, 682 School district jurisdictions, number of in the U.S., 1:410 School district size. See District size School district takeovers, 1:269–270, 2:639 School district uniformity, state codes on, 2:706 School district wealth, 2:647–648 in evolution of states’ role, 1:266 fiscal neutrality and, 1:348–349 guaranteed tax base and, 1:378, 379 neighborhood effects and, 2:481 parcel taxes and, 2:512 property taxes and, 2:576, 647, 648 Rodriguez case and, 1:378, 2:632–633 in traditional fiscal disparity, 1:267 in urban versus rural districts, 1:265 School finance equity. See District power equalizing; Educational equity; Equalization models; Fiscal disparity; School district wealth; School finance equity statistics; School finance litigation; Weighted student funding School finance equity statistics, 2:648–652 Berne-Stiefel framework for, 2:648–649 horizontal equity measures, 2:649–650 taxpayer equity measures, 2:650–651 vertical equity measures, 2:650 See also Finance equity School finance litigation, 2:652–657 adequacy as an issue in, 1:23, 27, 267, 2:654–655 Brown ruling and, 1:87–88 as a continuing strategy, 1:279 cost function approach in, 1:28 data on, 2:653, 654, 655 equity as an issue in, 2:652–654, 655 in evolution of authority over schools, 1:313–314 in evolution of states’ role, 1:266 fiscal neutrality in, 1:378 Rodriguez ruling and, 2:633, 653 on school facility financing, 1:404, 405 Serrano rulings and, 2:653, 679–680 on services to students with disabilities, 1:403 successful school district approach in, 1:36 vertical equity and, 2:828 See also Education finance School Improvement Grants, 1:95, 2:790, 824 School monopolies, 2:783, 784 School quality: competition and, 1:270 earnings and, 2:601, 603, 604, 657–660 economic growth and, 2:466 home values linked to, 1:347, 2:479, 787 IMF impact on, 1:423

neighborhood and, 2:479, 481–482, 483 principal’s impact on, 1:82 SAT as a measure of, 2:635–636 student mobility and, 2:716, 717, 718 School quality and earnings, 2:657–660 correlation, causation, and, 2:658 estimates of returns to education, 2:657–660 race earnings differentials and, 2:601, 603, 604 School reform, comprehensive. See Comprehensive school reform School report cards, 2:660–664 accountability indicators in, 2:662–663 under NCLB, 2:488–489, 660, 661, 662–663 School safety, 1:6, 94, 199, 317, 386, 2:475, 683. See also Education and crime School size, 2:664–667, 672 School Superintendents Association, 1:56. See also American Association of School Administrators School Survey on Crime and Safety, 2:475 School takeovers, 1:269, 270, 272. See also School closures; School consolidations School vouchers. See Educational vouchers School year, extended, 1:126, 127, 2:465 School-based management, 2:667–671 block grants and, 1:79–82 charter schools and, 2:670 decentralization in, 1:79, 114, 2:667 implementation challenges in, 2:668–669 See also Local control; Site-based budgeting; Site-based management School-based performance pay, 2:631. See also Pay for performance Schooling investments, theories of, 1:186 Schools, charter. See Charter schools Schools, number of in the U.S.: higher education (2011), 1:383 private elementary/secondary (2011–2012), 2:672 public K-12 (1930), 2:664 public K-12 (2010), 1:228, 2:664 public primary/secondary (2012–2013), 1:xxv Schools, private, 2:671–673 as an option in school choice, 2:447 associations of, 2:553–555 court cases and laws on, 2:671–672 data on, 2:671, 672, 674, 810, 811 funding of, private, 2:671, 811, 812 funding of, public, 2:671, 673, 674, 811, 812–813 parental rights and, 2:671–672 Private School Universe Survey of, 2:473 privatization and, 2:555–556, 558 religious (see Schools, religious) salary schedules in, 2:745 state regulation of, 2:672–673 teacher experience in, 2:757

Index teacher turnover in, 2:766 tuition at, 2:671, 674, 675, 810–813 types of, 2:671, 810 Schools, religious, 2:673–677 accreditation of, 2:553, 554 achievement gaps and, 1:20–21 associations of, 2:553–554, 555 Catholic (see Catholic schools) converting to charter school status, 2:675 court cases on, 2:674–675 data on, 2:671, 674, 675 effectiveness of, 2:675–676 funding of, 2:675 funding of, public, 1:294, 2:674 price discrimination in, 2:543 privatization and, 2:555, 556 tuition at, 2:674, 675, 812 vouchers and, 1:294, 2:674, 676 See also Schools, private Schools and Staffing Survey (SASS), 1:253, 2:472, 476, 529, 559, 738, 740 Schools as stakeholders, 2:439, 585–586 School-site budgeting, 1:90. See also Site-based budgeting Schools-within-a school format for high schools, 2:664 Schultz, T. Paul, 2:710 Schultz, T. W., 2:844 Schultz, Theodore, 1:xxv, 73, 251, 252, 352, 391, 2:431, 846 Schwab, Robert, 2:651, 766, 843 Science: CCSS and, 1:151 in charter schools, 1:125 high school longitudinal surveys on, 2:475 i3 grants in, 1:425 international assessments on, 1:253 Internet access for, 1:290 NAEP assessment of, 2:467, 468, 469 A Nation at Risk on, 2:464 Next Generation Science Standards, 1:151, 2:765 No Child Left Behind and, 1:39 NSF support for, 2:477–478, 478–479 online learning in, 2:500 PISA assessment of, 1:253, 416, 418, 419, 420, 423, 2:508 SERCE assessment of, 1:417 student grants in, 2:707 student incentives in, 2:711 tax limit impact on, 2:732 teacher bonuses in, 2:521 teacher professional development in, 2:561 teacher retention in, 2:740 teacher shortage in, 1:255, 2:436, 630 TIMSS assessment of, 1:253, 295, 416–420, 2:468–469, 472, 847

917

Science and Mathematics Access to Retain Talent grants, 2:707 Scott-Clayton, Judith, 1:139 Screening models as correlated effects models, 1:72 Seaman, Jeff, 2:498 SEAs. See State education agencies Sebring, P. B., 1:94 Second language learning, 1:77. See also Bilingual education Secondary and primary labor markets, 1:226–227 Secondary education, ESEA for. See Elementary and Secondary Education Act Secondary/primary education benefits. See Benefits of primary and secondary education Sectarian private schools, 2:810, 811, 812, 813. See also Schools, private; Schools, religious Security services, 1:169 Sedgley, Norman H., 2:636 Segmentation of the labor market, 1:227–228, 427, 428, 429 Segregated fund accounting systems, 1:60 Segregation (in choice of a private school), 2:676 Segregation (racial): de facto, 1:87, 379, 397 de jure, 1:86, 87, 202 deregulation and, 1:201 in Friedman’s voucher plan, 1:296 measurement of, 1:202–203 neighborhood ethnic composition and, 2:480 prior to the Brown ruling, 1:86, 202 resegregation, 1:20, 203 residential, 1:202, 203, 397, 2:605 See also Brown v. Board of Education; Desegregation Segregation (residential), 1:202, 203, 397, 2:605 Segundo Estudio Regional Comparativo y Explicativo (international assessment), 1:417, 418 Selection bias, 2:677–678 assignment bias as, 2:678 attrition bias as, 2:677–678 in credential effect studies, 1:187 definition of, 2:854 in dual enrollments, 1:226 fixed-effects models and, 1:350, 351 in labor market outcome studies, 1:68 limiting of, methods for, 2:678 limiting of, propensity score matching for, 2:567, 613, 792 limiting of, value-added measures for, 2:771, 773–774 in lottery analysis, 2:449 measurement error and, 2:457 in neighborhood effects studies, 2:482 in regression discontinuity, 2:619 sampling bias and, 2:677

918

Index

SAT and, 2:636 in tracking studies, 2:792, 793, 795 validity and, 2:677 See also Omitted variable bias Selection problem, 1:206. See also Difference-indifferences Selectivity, college. See College selectivity Self-regulating markets, 1:106 Self-selection bias, 1:388–389 Self-study for accreditation, 1:15, 16 Semifixed versus fixed costs, 1:346 Sen, Anindita, 1:365 Sengupta, Piyali, 2:710 Sensitivity analysis, 1:181–182, 2:569 Separate but equal concept, 1:86, 202 Separation of church and state, 2:812, 813. See also Establishment Clause of the U.S. Constitution Separation of powers, 2:654, 656, 848 Serrano v. Priest, 2:678–680, 847 in evolution of authority over schools, 1:313–314 finance equity statistics for study of, 2:651 fiscal neutrality and, 1:378 Proposition 13 as a result of, 2:653, 679, 680 Rodriguez case and, 2:632, 680 the rulings on, 2:653, 679–680 school district wealth and, 2:647–648 Serrano I, 2:678, 679, 680 Serrano II, 2:679, 680 substantive due process and, 1:228 on traditional fiscal disparity, 1:267 Service consolidation, 2:680–683. See also Contracting for services; Intergovernmental fiscal relationships Service or commodity, education as a, 1:373 Servicemen’s Readjustment Act, 1:368, 2:845. See also GI Bill SES. See Socioeconomic status and education Severance pay benefits, 2:491 Shand, R., 1:185 Shanghai Jiaotong University, college rankings by, 1:142, 143 Sharkey, Patrick, 2:483 Shavelson, Richard J., 2:623 Sheep herders’ dilemma, 2:796. See also Tragedy of the commons Sheepskin effect, 1:186, 2:658. See also Credential effect Sherman, Daniel R., 1:339 Shi, Shishan, 2:763 Shkolnik, J., 2:693 (table) Shleifer, Andrei, 2:583 Shortage, in the supply-demand model, 2:453 (figure), 454 Short-term disability insurance benefits, 2:493 Short-term versus long-term bonds, 1:102 Sibling groups, in datasets, 2:476. See also Twins studies

Sick leave benefits, 2:491, 492 Siedler, Thomas, 1:260 Signaling, market. See Market signaling Significance, statistical, 1:238, 298, 300, 2:754 Simar, Leopold, 1:192, 2:780 Simon, Herbert, 1:65 Simpson, Alan, 1:104 Simpson-Bowles Commission, 1:104 Sims, David, 1:178 Simulations, 1:181, 182, 342, 343, 2:852 Simultaneity problem, 2:523 Sindh Education Foundation, 2:584–585 Singapore, 1:108 Single parents, 1:197, 2:513–514 Single salary schedules, 2:629, 630, 743, 745, 752. See also Salary schedule Single threshold circuit breakers (tax relief), 2:573 Site-based budgeting, 1:90–91, 92, 2:438, 441 Site-based management, 2:545–546, 667. See also School-based management Sixteenth Amendment to the U.S. Constitution, 1:104 65 percent solution, and administrative spending, 1:42–43, 44 Size: of classes (see Class size) of districts, 1:44, 217–220 of schools, 2:664–667, 672 Skill-based pay, 2:631 Slater Fund, 2:532 Slavin, Robert, 1:160 Slemrod, Joel, 2:566 Sliding scales for payments, 1:3, 17, 216, 286 Sliding-scale circuit breakers (tax relief), 2:573 Slovak Republic, 1:353 (figures) Slovenia, 1:353 (figure) Smarter Balanced Assessment Consortium, 1:150, 2:703, 756 Smartphones, 1:281. See also Mobile devices Smith, Adam, 1:106, 251, 256, 2:564, 845 Smith, Jonathan, 1:140 Smith, Marshall, 1:6 Smith-Hughes Act, 1:427, 428 Snyder, T. D., 1:314 (table) Social achievement gaps, 1:234. See also Achievement gap Social benefits and costs, external. See External social benefits and costs Social capital, 2:683–687 benefits of education and, 1:72, 2:685–686 cultural capital and, 2:685, 686 definition of, 2:854 in new institutional economics, 2:486 social reproduction and, 2:684–685, 686 socioeconomic status and, 2:689

Index Social class, compared to SES, 2:687–688 Social cohesion: in homeschooling, 1:388 in public-private partnerships, 2:584, 585, 586 in voucher plans, 1:296 See also Education and civic engagement Social control, 2:684, 685 Social efficiency, 1:55–56, 321, 325, 2:854 Social interactions, 2:484. See also New institutional economics Social justice, 1:202, 285, 307, 309, 2:584, 686 Social media, 1:213, 281, 282, 371, 372 Social mobility, 1:188, 194, 397, 2:482, 687, 688, 690 Social networking (electronic), 1:209, 281 Social rate of return, 1:252, 254, 324, 325 Social reproduction, 1:189, 2:684–685, 686 Social return on investment, 1:234–235 Social Security Act, 1:363–364 Social Security benefits, 1:239, 2:491, 707, 708, 761 Social Security payments in district budgets, 2:645 Social studies, 1:125, 151, 292, 296, 365, 2:464, 732 Socialization, civic, 1:124, 126, 388–389. See also Education and civic engagement Socialization, teacher, 2:768 Socias, M., 1:81, 111 Socioeconomic status and education, 2:687–691 access to technology and, 1:280, 281 achievement gaps and, 1:18, 19, 20, 21, 2:605, 606, 687, 689 benefits of education and, 1:72, 74 classroom SES, 2:688–689 at community colleges, 1:154 cultural capital and, 1:188–189 demand for education and, 1:194 digital divide and, 1:208–209 educational inequality and, 1:397, 398 family SES, 1:14, 2:688, 690 higher education and, 1:70, 133, 139, 140 human capital and, 2:688 income as the common measure of, 1:395, 2:530–531, 687 inequality and, 1:395, 2:689 in international assessments, 1:417 in job training programs, 1:428 A Nation at Risk and, 2:464 in NCLB testing subgroups, 2:488 neighborhood effects and, 2:481–482 neighborhood SES, 2:689 parental involvement and, 2:514, 515 peer effects and, 2:524, 688–689 as powerful predictor of test performance, 2:688 race earnings differentials and, 2:601, 606 social class compared to, 2:687–688 student mobility and, 2:717

919

voucher plan stratification and, 1:295–296 wealth and, 2:689–690 See also Household/family income Software packages, 1:209, 408, 2:432, 505, 569 Sokoloff, Kenneth, 1:328 Solmon, Lewis, 1:162 Sondheimer, Rachel, 1:260 Song, Wei, 1:365, 366 Sonstelie, Jon, 1:176 Sorting: across schools, grades, classes, 2:523 in choice of private and religious schools, 2:676 neighborhood, 2:479–480, 483, 786–787 Tiebout, 2:651, 716, 786–787 tracking as, 2:791 South Carolina, 1:197 (table), 2:444 (table), 470, 654, 659 South Carolina v. Baker (1988), 1:104 South Dakota, 2:440, 654, 656, 757 South Korea, 1:197 (table). See also Korea Southern African Consortium for Monitoring Educational Quality, 1:417, 418 Soviet Union, former, 1:247, 2:556. See also Russia SpaceX, funding by, 2:557 Spain, 1:116, 117, 197 (table), 353 (figures), 2:584 Spain, A., 1:111 Spain, Angeline K., 2:696 Spatial equilibrium model, 1:324 Spatial externality, 1:410–411 Speaking and listening standards, in CCSS, 1:150. See also English language arts (ELA) Special education: in charter schools, 1:127 datasets for, 2:474, 475 due process in, 1:230 in early childhood education, 1:233, 234 enrollment rates in, 1:403 IDEA legislation and, 1:399, 401, 402, 403 identification rate in, 2:693 under NCLB, 2:488, 489 transportation services in, 2:694 (table) See also Individuals with Disabilities Education Act; Special education finance Special Education Elementary Longitudinal Study, 2:475 Special education finance, 2:691–697 categorical grants in, 1:109 data on, 2:691–697 expenditure constraints in, 2:645 fair student funding for, 2:652 funding for, 1:278, 403, 2:694–696, 697 pupil weights in, 2:588, 589, 695–696 requirements for fund uses, 2:696–697 teacher compensation in, 1:382, 2:521, 522 See also Special education

920

Index

Special school district jurisdictions, number of in the U.S., 1:410 Specialty-service contracts, 1:169–170 Specific learning disability, in special education, 2:693, 693 (table), 697 Speech impairment, in special education, 2:693 (table) Spence, A. Michael, 1:70, 2:451, 452 Spence, M., 2:844 Spence, Michael, 1:186 Spiegel, Mark M., 1:324 Spillover effects, 2:698–699 definition of, 2:854 peer effects and, 2:522–523, 699 of student incentives, 2:710, 711 and supply and demand, 2:698 (figure), 698–699 See also Spillovers Spillovers: of benefits, 1:2, 325, 2:699 cost, 1:410–411 of family SES, 2:688 interjurisdictional, 2:786–787 of market failure, 2:579 See also Spillover effects Split-half reliability, 2:623 Sports facility maintenance, 1:169 Springer, Matthew, 1:175 SPSS software package, 2:432, 569 Sputnik satellite, 1:247, 315 Sridhar, Deepa, 1:139 St. John, Edward P., 1:339 Stability, tax, 2:574, 727, 736–737 Stable unit treatment value assumption, 2:567, 568 Staffing ratios, 1:34, 2:742–743. See also Student-teacher ratios Staffing-based allocation, 2:835, 836, 837 Stafford, Robert T., 2:699 Stafford loans, 2:699–701, 715 loan guarantees and, 1:384–385, 2:699–700 Perkins loans compared to, 1:336, 337 Stagnation of wages, 1:325, 396, 2:541 Staiger, D., 2:843 Staiger, Douglas, 2:749 Standard deviation, 1:185, 298–299, 2:456, 457, 854 Standard error, 1:207, 238, 241, 2:506, 624 Standard normal curve, 1:299 Standard normal distribution, 2:592, 592 (figures). See also Normal distribution Standard of living, 1:245, 246, 2:465, 467 Standardized mean difference, 1:297, 298–299 Standardized tests: control of teachers’ work by, 2:739 in the essence of accountability, 1:95 homeschooling and, 1:386, 387 increasing focus on, 1:246, 2:783

in the military, 2:634 A Nation at Risk and, 2:464, 639 under NCLB, 1:200, 304, 2:439, 487–490, 662, 775, 783, 790 as the norm in K-12 education, 2:503 pay for performance and, 2:518, 520, 521 reliability of, 2:624 teacher autonomy and, 2:739 teacher effectiveness and, 2:747, 748, 750 technical efficiency and, 2:779 Title I and, 1:303, 315 See also International assessments; International datasets in education; National datasets in education Standards: accountability and, 1:6–11, 2:487, 488, 490, 503, 528, 654–655 accounting, 1:375–378 accreditation, 1:15–17 adequacy and, 1:22–23, 24, 25, 26 in capacity building, 1:94 college and career ready standards, 1:225, 2:703, 790 cultural capital, dominant group, and, 1:188 curriculum and instruction aligned to, 1:7, 8, 2:490 for education of students with disabilities, 1:401 for fiscal neutrality, 1:349 of high-quality professional development, 2:561 A Nation at Risk on, 2:463, 464, 465 No Child Left Behind and, 2:487–490 opportunity to learn and, 2:503–504 in performance evaluation systems, 2:528 under Race to the Top, 2:607, 608, 609 for school facilities, 1:405 for teacher board certification, 2:437, 469–471, 747 for teacher edTPA assessment, 2:764–765 teacher performance assessment and, 2:765 under Title I, 2:788, 790, 791 of What Works Clearinghouse, 1:425–426 See also Common Core State Standards; Standardized tests Standards movement, 1:23, 149, 2:464, 465, 654–655 Standards-based accountability. See Accountability, standards-based Stange, Kevin, 1:141 Stanton-Salazar, Ricardo, 2:686 STAR. See Project STAR (Student/Teacher Achievement Ratio) Startz, Richard, 1:49 (table), 49 (figure) Stata software package, 2:432, 505, 569 State action, local actions as, 1:228 State assessment tests. See Standardized tests State boards of education: appointment or election of, 2:637, 705 local control and, 2:439, 440

Index roles of, 2:637 state codes on, 2:705–706 state education agencies and, 2:701 See also School boards State Children’s Health Insurance Program, 1:351 State college savings (529) plans, 1:144–145 State constitutions, 1:xxvi on an adequate education, 1:31, 32, 2:654 due process clauses in, 1:228 early history of, 2:845 education clauses in, 1:26, 27, 228, 378 equal protection clauses in, 1:267, 378, 2:679, 680, 847 finance litigation and, 1:265, 266, 267, 2:653–657, 679–680 on religious schools, 1:294, 2:673 state education codes and, 2:705 states’ rights and, 1:313 State education agencies, 2:701–705 CCSS and, 2:703 ESEA and, 2:701–702 federal-state balance and, 2:702–703 IDEA funds and, 2:701, 704 increasing power of, 2:701 No Child Left Behind and, 2:702, 703 Race to the Top and, 2:702 as stakeholders in local control, 2:439 Title I and, 2:701, 703, 704 State education codes, 2:705–707 State entities as stakeholders, 2:439, 663 State expenditure differences (2008–2009), 1:175, 176 (figure) State 529 plans, 1:144–145 State funding for education: for bilingual education, 1:78 categorical grants as, 1:109, 110 for district capital spending, 1:104–105 district power equalizing for, 1:215–216 for early childhood education, 1:233, 235 enrollment counts for determination of, 1:305, 306 equalization models for, 1:310–313 fiscal neutrality and, 1:348, 349 formulas for (see Funding formulas and methods) guaranteed tax base system in, 1:378–379 for higher education, 1:383–384, 385, 2:802–803, 804 lotteries for, 2:441–447 for private school education, 2:673, 811, 812 property taxes and (see Property taxes) pupil weights and, 2:587–589 revenues for, versus local and federal, 1:313, 314, 317, 2:702 and shifts in federal/state/local control, 1:278, 313–316 for special education, 1:403, 2:695–696, 695–697 vertical equity in, 2:829 weighted student funding and, 2:835, 837

921

State land trusts, 1:104 State tax credit scholarship programs, 2:813, 814–815 State-local-federal revenue sources. See Revenues (public elementary/secondary) by federal-state-local source States’ rights, 1:303, 304, 313 Statistical packages. See Software packages Statistical significance, 1:238, 298, 300, 2:754 Statuary incidence, 2:565, 575. See also Tax incidence Status quo bias, 1:66, 67 Status rates, 1:220. See also Dropout rates Stay-put provision, for student infractions, 1:402 Steele, Claude, 1:19, 2:605 Stereotype threat, 1:19, 2:605 Stereotyping: of dropouts, 2:626 gender, 1:227 homeschooling and, 1:387 racial, 1:19, 2:605 Stiefel, Leanna, 1:109, 178, 286, 288, 390, 2:648, 828, 840 Stigler, George, 1:329 Stiglitz, Joseph, 1:199, 372 Stinebrickner, Ralph, 1:339 Stinebrickner, Todd R., 1:339 Stock and flow, definition of, 2:854 Stock variable, 2:854 Strang, David, 1:217 Strategic default behavior, 2:715 Strategic enrollment management, 1:307. See also Enrollment management in higher education Stratification: in admissions lotteries, 2:448 neighborhood effects and, 2:480 in propensity score matching, 2:568 voucher plans and, 1:295–296 Strayer, George, 1:249, 311 Strembitsky, Mike, 2:835 Structural adjustment programs, 1:423 Student achievement. See Achievement (student) and; Achievement gap Student debt, size of, 1:134, 385, 2:714. See also Student financial aid; Student loans Student disciplinary matters, and due process, 1:228, 229 Student financial aid, 2:707–710 ability-to-pay and benefit principles of, 1:3 behavioral economics of, 1:67, 2:712 college attendance and, 2:707–708, 711–712 college choice and, 1:129–130 college dropouts and, 1:135, 136 college enrollment and, 1:139 college retention/completion and, 2:708–709 college success and, 2:708, 709, 712 at community colleges, 1:153 complexity in the process of, 2:709 datasets on, 1:338, 2:472, 475

922

Index

enrollment management and, 1:308 fellowships, 1:278, 318, 2:477, 532 GI Bill for, 1:368–369, 2:707 grants (see Student grants) higher education finance and, 1:384–385 loans (see Student loans) net tuition after, 2:802 in private K-12 schools, 2:811–812 student debt and, 1:134, 385, 2:714 tuition growth and, 2:803–804, 805, 806 work-study, 1:278, 337–340 See also College savings plan mechanisms; Tuition and fees, higher education Student grants: for community college students, 1:153 Federal Academic Competitiveness Grants, 2:707 impact of, on college attendance, 2:707–708 impact of, on college success, 2:708, 709, 712 Pell (see Pell grants) SMART grants, 2:707 from states, 2:707–709 for students in for-profit institutions, 1:356, 357 Supplemental Educational Opportunity Grants, 1:278 See also Student financial aid Student incentives, 2:710–713 Student Loan Marketing Association (Sallie Mae), 2:700 Student Loan Reform Act, 2:700 Student loans, 2:713–716 behavioral economics of, 2:712 data on, 2:714, 715 defaults on, 1:129, 336, 358, 364, 429, 2:500, 700, 714–715 educational credit market and, 2:715–716 federal direct loans, 1:278, 385, 2:699, 700, 715 FFEL Program, 1:278, 2:700–701, 715 guaranteed, 1:384–385, 2:699–700, 715 Parent Loans for Undergraduates Program, 2:715 Perkins loans, 1:278, 335–337, 385, 2:699, 715 PLUS loans, 2:699 predatory lending practice accusations and, 2:500 private bank loans, 1:356 repayment of, 1:112, 277, 336–337, 340, 358, 364, 2:460, 713, 714–715 Stafford loans, 1:336, 337, 385, 2:699–701, 715 for students in for-profit institutions, 1:356, 357 See also Student financial aid Student mobility, 2:716–719 academic outcomes and, 2:717–718 CCSS and, 1:151 between countries, 1:342 frequency of, 2:717 in military families, 1:197 teacher effectiveness measures and, 2:750 See also Transfer students

Student need equalization, 1:310 Student perceptions of teacher effectiveness, 2:750, 755 Student persistence and GED® credentials, 1:366, 367 Student persistence in higher education: college choice and, 1:131 dual enrollment and, 1:226 expenditures per student and, 2:808 faculty employment conditions and, 1:332 Federal Work-Study program and, 1:338–339 student financial aid and, 2:708 See also College completion Student population density, and district size, 1:217 Student suspensions, 1:53, 228, 229, 402 Student teacher ratios. See Student-teacher ratios Student teaching, 2:765, 767, 768, 769–770. See also Teacher training and preparation Student weighting systems. See Pupil weights; Weighted student funding Student-based budgeting, 2:587, 835 Student-faculty ratios in higher education, 1:332. See also Student-teacher ratios Students, number of in the U.S.: higher education (2010), 1:318 higher education (2010–2021), 1:141 higher education (2011), 1:383 private elementary/secondary (2011–2012), 2:672 public K-12 (2010), 1:228, 317 public primary/secondary (2012–2013), 1:xxv Students as stakeholders, 1:84, 219, 251, 2:667 Students of color, 2:470, 512, 686. See also Children of color; Racial/ethnic groups Students with disabilities. See Individuals with disabilities; Individuals with Disabilities Education Act Student/Teacher Achievement Ratio. See Project STAR (Student/Teacher Achievement Ratio) Student-teacher ratios: in CMO charter schools, 1:122 collective bargaining agreements and, 2:778 in extended day programs, 1:29, 320 in higher education, 1:332 in private schools, 2:672 student outcomes and, 1:275 teacher compensation and, 2:742–743 over time, 1:175 See also Class size; Project STAR (Student/Teacher Achievement Ratio) StudentTracker system, 2:475 Studley, Roger, 2:635 Sub-Saharan Africa: access to education in, 1:5 centralization/decentralization in, 1:115, 116–117 cost of primary schooling in, 1:195 labor market rate of return in, 2:432, 434

Index life expectancy in, 2:434 structural adjustment programs in, 1:423 Subsidiarity principle, 1:411 Substantive due process, 1:228 Substitution effect, and intergovernmental grants, 1:413 Success for All (comprehensive reform design), 1:160 Successful district approach. See Adequacy: successful school district approach Sugarman, Stephen D., 1:285, 378, 2:840, 847 Sullivan, Alice, 1:189 Summer learning loss, 1:19 Summer leave benefits, 2:491 Summer remedial education, 2:598. See also Remedial education Sunstein, Cass, 1:65 Superintendents: AASA for, 1:56–57 in collective bargaining, 2:640, 642, 643, 777 compensation for, 2:609 school boards and, 2:637, 638, 639 in the traditional central office, 1:113–114 urban, average tenure of, 1:13 Supplement not supplant (Title I requirement), 1:109, 2:445, 704, 789 Supplemental Educational Opportunity Grants, 1:112, 278 Supplemental educational services, 2:719–721 guidelines for, 2:719–720 implementation of, 2:488, 504, 720 Supplemental Security Income benefits, 1:411, 2:491 Supplemental tax rate, 1:411–412 Supply: law of, 2:454 price elasticity of, 1:301, 302 See also Supply and demand Supply and demand: in capitalism, 1:105 and demand for education, 1:193, 194 dual labor markets and, 1:226 elasticity and, 1:301–302 equilibrium concept in, 2:516–517 in factor markets, 1:327 model for, 1:55, 55 (figure), 2:453 (figure), 453–454 rigid salary schedules and, 2:745 spillover effects and, 2:698 (figure), 698–699 See also Demand for education Supply chains, global, 1:371 Supply curves: in allocative efficiency, 1:54, 55 (figure) elasticity and, 1:301 partial equilibrium and, 2:517 spillover effects and, 2:698 (figure), 699 in theory of markets, 2:453 (figure), 454

923

Supreme Court of the United States: Board of Education of Oklahoma City v. Dowell (1991), 1:203 Brown II (1955), 1:87 Brown v. Board of Education (1954), 1:60, 86–88 Farrington v. Tokushige (1927), 2:672 Freeman v. Pitts (1992), 1:203 Goss v. Lopez (1975), 1:228, 229 Keyes v. School District No. 1, Denver (1973), 1:87 Lau v. Nichols (1974), 1:78 Meyer v. Nebraska (1923), 2:672 Milliken v. Bradley (1974), 1:87 Missouri v. Jenkins (1995), 1:203 Morrissey v. Brewer (1972), 1:229 Morse v. Frederick (2007), 1:228 Parents Involved in Community Schools v. Seattle School District No. 1 (2007), 1:203 Pierce v. Society of Sisters of the Holy Names of Jesus and Mary (1925), 2:672, 674 Plessy v. Ferguson (1896), 1:86 San Antonio Independent School District v. Rodriguez (1973), 1:228, 378, 2:632–633, 653 South Carolina v. Baker (1988), 1:104 Swann v. Charlotte-Mecklenburg Board of Education (1971), 1:60 Wisconsin v. Yoder (1972), 2:674 Zelman v. Simmons-Harris (2002), 2:674–675 Surplus, in the supply-demand model, 1:54–55, 55 (figure), 2:453 (figure), 454 Survey datasets. See International datasets in education; National datasets in education Survival of the fittest, 1:252, 2:454 Suryadarma, D., 1:81 Suspensions. See Student suspensions; Teacher suspensions Swaim, Nancy, 2:672 Swann v. Charlotte-Mecklenburg Board of Education (1971), 1:60 Sweden: foregone earnings in, 1:353 (figure) income inequality in, 1:395, 396 (figure) ratio of highest to lowest earners in, 1:395 tracking policy in, 2:794 voucher plan in, 1:293, 294, 295 Switzerland, 2:831 Synchronous settings for learning, 1:213, 214, 2:498 Synthetic control design, 2:596 Systematic error, 2:457, 854 Tablets (mobile devices), 1:281, 2:500 Takeovers: of school districts, 1:269–270, 2:639 of schools, 1:269, 270, 272 See also School closures

924

Index

Talent Search program, 1:112, 185 Talent-seeking strategy, in portfolio districts, 2:538 Tastes: in demand for education, 1:193–194 in models of education and benefits, 1:72, 74, 75 Tiebout sorting and, 2:786 Tax and expenditure limits, 2:573–574, 730–734 Tax base: tax yield and, 2:734–736 in the taxation process, 2:724 (table), 724–726 Tax base, guaranteed. See Guaranteed tax base Tax burden, 2:723–726 allocative efficiency for analysis of, 1:56 district size and, 1:218, 219 equity and, 1:3 estimation of, 2:724 (table), 725 excess burden, 2:566 incidence analysis of, 2:564–565 sources of, 2:723–724 taxpayer mobility and, 2:574 Tax capitalization, 2:480 Tax credits: bond program, 1:85, 105 earned income, 1:411 federal expenditures on, 2:707 between governmental levels, 1:411 school choice and, 2:785, 813 tax burden and, 2:724 (table), 725 tax yield and, 2:735 tuition (see Tuition tax credits) See also Income tax deductions Tax deductions: for contributions, 1:355, 2:549, 820 for educational expenses, 2:815 for other taxes paid, 1:411 tax incidence and, 2:728–729, 730 for teacher-bought supplies, 1:276–277 See also Tax credits Tax deferred benefits, 2:491, 492 Tax effort equalization, 1:310 Tax elasticity, 2:726–728 of income taxes, 2:727 progressivity and, 2:565–566, 727 of property taxes, 2:727 of sales taxes, 2:727–728 tax yield and, 2:726–727, 736 Tax equity, 2:564, 575, 649, 650–651, 828 Tax exemptions: for college savings plans, 1:144, 145 in the debt market, 1:102, 104–105 for employee benefits, 2:491 for nonprofit organizations, 1:355, 2:531, 546, 549, 550 on property taxes, 2:573, 727 on sales taxes, 2:735–736

Tax exportation, 2:725 Tax incidence, 2:728–730 in burden determination, 2:564–565 of income taxes, 2:729–730 of property taxes, 2:575, 729 of sales taxes, 2:730 tax shifting and, 2:728–729, 730 See also Tax burden Tax increment financing, 2:572 Tax limits, 2:730–734 on property taxes, 2:573–574, 731, 732 student performance and, 2:732–734 Tax revenue anticipation notes (TRANs), 2:646 Tax revolts, 2:545, 574, 730 Tax sheltered benefits, 2:491 Tax shifting, 1:411, 2:724 (table), 725, 728–729, 730 Tax stability, 2:574, 727, 736–737 Tax yield, 2:726–727, 734–737 Taxes, types of: excise, 2:491 flat, 1:3, 2:565 income (see Income taxes) parcel, 2:511–512 payroll, 1:317, 2:491, 569 progressive (see Progressive tax and regressive tax) property (see Property taxes) proportional, 1:3, 2:564, 565, 575 regressive, 1:216, 2:446, 512, 575 sales (see Sales tax) Tax-exempt status, 1:355, 2:531, 546, 549. See also Tax exemptions Taxpayer equity: district power equalization and, 1:379 fiscal neutrality compared to, 1:286 measures of, 2:648, 650–651 property taxes and, 2:574, 575 Taxpayers as stakeholders, 2:585–586 Taxpayers’ Bills of Rights, 2:730–731 Taylor, L., 2:842 Taylor, Lori L., 1:155 Teach for America program, 1:120, 123, 2:436, 533, 747, 767, 769 Teacher autonomy, 2:737–741 control versus, 2:739–740 professional capacity and, 1:94 in school-based management, 2:667 teacher effectiveness and, 2:737, 738, 739, 740 teacher performance measurement and, 2:771 Teacher certification. See Licensure and certification Teacher compensation, 2:741–746 behavioral economics of, 1:67 capacity building and, 1:94, 95, 96 at charter and private schools, 1:122, 2:745 collective bargaining on, 2:641, 642, 775, 776, 777, 778

Index compensating wage differentials and, 1:381–382, 2:745 components of, 2:519 data on, 2:629, 741, 742–744 degrees earned and, 2:630, 743, 744 (table) in Department of Defense schools, 1:197 differentiated pay in, 2:630–631 economics of education on, 1:255 experience and, 2:629–630, 631, 743, 744 (table), 757 funding windfalls and, 1:178 in higher education, 1:330–333, 382 moral hazard and, 2:461, 545 A Nation at Risk on, 2:464 nonwage benefits in, 1:332, 2:491–493 opportunity cost and, 1:255 pay for performance in, 1:52, 2:518–522, 631 quantity versus quality trade-offs in, 2:742 (figure), 742–743 rent seeking and, 2:733 salary schedules and, 2:629–631, 743–746, 744 (table) school productivity and, 1:275 structure of, 2:629–631, 743–746 teacher effectiveness and, 2:745 teacher supply and, 2:766–767 theory of the firm on, 2:784 over time, inflation-adjusted, 1:175 transaction costs and, 2:799 within-district versus between-districts, 1:382 See also Expenditures, on personnel; Teacher pensions Teacher Council Movement, 2:667 Teacher dismissals, 1:228, 229, 2:616–618, 800 Teacher education. See Licensure and certification; Teacher training and preparation Teacher effectiveness, 2:746–752 autonomy and, 2:737, 738, 739, 740 economics of education on, 1:254–255 as the key schooling input, 2:746 MET project on, 1:20, 2:749, 750, 753, 755, 756, 774 National Board certification and, 2:470 reduction in force and, 2:617–618 reliability and validity in measuring, 2:623, 631, 749, 755 student perceptions of, 2:750 teacher compensation and, 2:745 transaction costs and, 2:799 value-added approach to, 2:748–749, 775 years of experience and, 2:630, 757 See also Teacher evaluation; Teacher value-added measures Teacher evaluation, 2:752–756 accountability and, 1:13 in capacity building, 1:94 due process in, 1:228, 229

925

edTPA for, 2:765 measures for, 2:752, 753–755, 772 moral hazard and, 2:460 performance evaluation systems for, 2:527, 528, 529 public release of measures of, 2:750 Race to the Top and, 2:607, 609 school productivity and, 1:275 state requirements for, 2:750 student achievement and, 2:607, 609, 623, 754–755 teacher observation for, 2:749–750, 752, 753–754 for tenure decisions, 2:752, 753, 755 transaction costs and, 2:800 validity and reliability in, 2:753, 755, 756 value-added measures for, 1:275, 288, 2:750, 753– 755, 772 See also Teacher performance assessment Teacher experience, 2:756–758 compensation and, 2:629–630, 631, 743, 744 (table), 757 pensions plans and, 2:761 poorer schools and, 2:745, 757 student achievement and, 1:275, 2:743, 747, 757 teacher effectiveness and, 2:630, 757 Teacher Incentive Fund, 2:520, 535, 536, 630, 741, 775 Teacher incentives: autonomy and, 2:739 for board certification, 2:521, 631 in the economics of education, 1:254, 255 freedom in design of, 1:96 in pay for performance, 2:518, 519–522, 745 pensions as, 2:746, 761, 762, 763 teacher evaluation linked to, 2:753 Teacher Incentive Fund, 2:520, 535, 536, 630, 741, 775 See also Salary schedule; Teacher compensation Teacher intelligence, 2:758–760 licensure/certification exams and, 2:759 student achievement and, 2:758, 759 teacher verbal ability, 1:250, 2:747, 758, 766 Teacher mobility, 2:492, 763 Teacher observation, 2:749–750, 753–755 reliability and validity of, 2:774 rubrics for, 2:749, 752, 753 Teacher pensions, 2:760–764 benefits as a percent of earnings, 2:746, 761 contributions to, 2:645, 760–761, 763 data on, 2:761 nonwage benefits and, 2:491, 492 reforms to, 1:318, 2:763 See also Nonwage benefits Teacher performance assessment, 2:764–765. See also Teacher evaluation; Teacher training and preparation Teacher Performance Assessment Consortium, 2:764 Teacher preparation programs. See Teacher training and preparation

926

Index

Teacher professional development. See Professional development Teacher quality: achievement gaps and, 1:20 class size and, 2:613 as a complex construct, 2:746–747 economics of education on, 1:254–255 educational level and, 2:519 experience and, 1:275, 2:743 A Nation at Risk on, 2:464 neighborhood effects and, 2:481 No Child Left Behind and, 2:489, 490 opportunity to learn and, 2:503 physical facilities and, 1:406 policy analysis of, 2:535 quantity versus quality trade-offs, 2:742–743 reduction in force and, 2:617 school productivity and, 1:275 school quality, earnings, and, 2:660 standards for, 2:528 student achievement and, 1:12, 274–275, 328, 2:520, 746 student outcomes dependent on, 2:559 supplemental credential for signaling of, 2:437 teacher intelligence and, 2:758, 759 teacher supply and, 2:766 See also Teacher effectiveness; Teacher value-added measures Teacher recruitment: capacity building and, 1:94, 95 to challenging schools, 2:481, 518, 766 for distance education, 1:212 market-based incentives for, 2:518 Race to the Top and, 2:607 teacher compensation and, 2:630, 742, 745 Teacher retention, 1:253, 255, 2:464, 519, 521, 561, 740, 762, 769, 770, 777. See also Teacher supply Teacher shortages, 1:255, 2:436, 437, 630, 743. See also Teacher supply Teacher socialization, 2:768 Teacher supply, 2:765–767 data on, 2:766 early retirement and, 2:763 factors affecting, 2:766 policies to improve, 2:766–767 reduction in force and, 2:616–618 salary schedule and, 2:630 teacher retention and, 2:561, 740, 769, 770 turnover and, 1:406, 2:436, 616–617, 661, 766, 769 See also Teacher retention; Teacher shortages Teacher suspensions, 2:616, 617. See also Teacher dismissals Teacher tenure. See Tenure Teacher training and preparation, 2:767–771

accreditation of programs in, 1:16–17, 2:435 alternative routes to, 1:201, 2:436–437, 768, 769 in centralized systems, 1:117 data on, 2:767, 768 A Nation at Risk on, 2:464 National Board certification and, 2:469–471 in new institutional economics, 2:486 OECD international survey on, 2:509 for online education, 2:500 performance assessment in, 2:764–765 philanthropic support for, 2:532 policy analysis of, 2:535 student achievement and, 1:253 traditional route for, 2:435–436, 437 voucher plan requirements and, 1:294 See also Licensure and certification; Professional development Teacher turnover, 1:406, 2:436, 616–617, 661, 766, 769. See also Teacher shortages; Teacher supply Teacher value-added measures, 2:771–775 challenges in the use of, 2:750 equity implications in, 1:288 first public dissemination of, 2:848 practice measures and, 2:753–754 selection bias addressed by, 2:771, 773–774 student growth and, 2:754, 755, 772 teacher effectiveness and, 2:748–749, 775 teacher evaluation and, 1:275, 288, 2:750, 753–755, 772 for teacher quality/outcomes link, 2:660 See also Value-added models Teacher-parent relationships. See Parental involvement Teachers, number of in the U.S., 1:228 Teachers as stakeholders, 1:82, 84, 96, 142, 219, 2:439, 667 Teachers’ experiences, datasets on, 2:476 Teachers in higher education. See Faculty in American Higher Education Teachers strikes, 2:668 Teachers’ unions and collective bargaining, 2:640–643, 775–778 decentralization trends and, 1:116 due process and, 1:229 in portfolio districts, 2:538 principal-agent problem and, 2:545 Race to the Top and, 2:609 reduction in force and, 2:616, 617, 618 rent seeking and, 2:733 salary schedule and, 2:629 schools stressed by unions, 2:784 See also School boards, school districts, and collective bargaining Teaching and Learning International Survey, 2:509, 529 Teaching Fellows program, 2:436, 747, 769

Index Teaching to the test: accountability and, 1:95, 2:663, 775 capacity building and, 1:95 Race to the Top and, 2:609 rewards, sanctions, and, 1:67, 2:490, 750 standardized testing and, 2:739, 750 teacher evaluation and, 2:631, 750, 800 Technical and career education programs, 1:278, 428, 2:471, 588, 831. See also Vocational education Technical efficiency, 2:778–782 data envelopment analysis of, 1:191–192, 2:779–780 definitions for, 2:779, 854 Technology: in capitalist economic systems, 1:105, 107 cultural capital and, 1:189 digital divide and, 1:208–210, 280, 281, 282, 2:499 globalization facilitated by, 1:370–371 See also Blended learning; Education technology; Internet; Online learning Temple, Judy, 2:717 Tennessee: district wealth measurement in, 2:648 finance litigation in, 2:654 lottery proceeds in, 2:444 (table) online courses in, 1:358 per-pupil expenditures in, 1:175 Project STAR in, 1:24, 239, 274, 2:496, 593, 612– 613, 659–660 Race to the Top grant for, 2:608, 609 state agencies in, 2:704 Tenth Amendment to the U.S. Constitution, 1:313, 2:485 Tenure: in higher education, 1:329–330, 330 (table), 331 (table), 332 in K-12 schools, 2:616, 745 in new institutional economics, 2:486 reduction in force and, 2:616, 617 in school-based management, 2:668 teacher evaluation for, 2:752, 753, 755 Term endowment, 2:821, 822 Terra Nova standardized achievement tests, 1:197 Tertiary education, 1:5, 134, 354, 373, 2:432–433, 557, 831. See also Benefits of higher education; Higher education Test preparation industry, 2:555–556, 635 Test score gap, 1:17–18, 20, 21, 2:531, 605, 687, 689, 771. See also Achievement gap Test-retest method, and reliability, 2:622 Texas: accountability changes in, 1:254 administrative spending analysis for, 1:43 alternative teacher certification in, 2:436, 768 bond elections in, 1:84

927

charter schools in, 1:201 community college system in, 1:152 contracting for services in, 1:170 cost function approach in, 1:178 education clause in, 1:378 funding disparities in, 1:88, 267 funding for ELLs by, 1:78 funding formula in, 1:155 lottery proceeds in, 2:444 (table) NCLB proficiency levels in, 2:489 nonprofit EMO schools in, 1:271 performance pay in, 2:520, 521 pupil weighting in, 2:588 Rodriguez case and, 1:228, 378, 2:632–633, 653, 847 service consolidation in, 2:683 Social Security participation in, 2:491 State Board of Education in, 2:440 state education code in, 2:706 student incentives in, 2:711 teacher preparation in, 2:435, 436 Textbook publishing, 2:557–558 Thaler, Richard, 1:65 Theater programs, 2:557 Theil indices, 2:649, 651 Theobald, Roddy, 2:617, 618 Theory of the firm, 2:782–786, 854 Third variables, in the correlated effects model, 1:72, 72 (figure) Thistlethwaite, Donald, 2:597, 619 Thompson, C., 2:769, 770 Thornton, Rachel, 2:711 Thornton, S., 1:331 (table) Three tier funding programs, 1:216, 311–312, 379. See also Equalization models 3M, funding by, 2:557 Thresholds, in regression discontinuity, 1:241–243, 2:598, 619, 620, 621 Tiebout, Charles, 2:479, 574, 785, 786, 787, 844 Tiebout competition, 2:787 Tiebout effect, 2:575, 787 Tiebout sorting, 2:786–787 school finance equity and, 2:651 student mobility and, 2:716 tests of, 2:787 See also Neighborhood sorting Tightly coupled systems, 2:737–738 Timar, Thomas, 1:110, 111 Time diaries, in cost analysis, 1:172, 173 Time value of money, 1:210, 2:541. See also Discount rate; Present value of earnings Time-invariant variables, 1:207, 350, 351, 2:854 Times Higher Education Supplement, college rankings by, 1:143 Timpane, P. M., 2:843

928

Index

TIMSS. See Trends in International Mathematics and Science Study (TIMSS) Tinto, Vincent, 1:134 Title I, 1:xxv–xxvi, 2:787–791 for access to education technologies, 1:209 adequate yearly progress and, 1:39–40, 2:790 categorical grants under, 1:109, 110–111 data on, 1:303, 2:487, 787, 788, 791 equal educational opportunities and, 1:398, 2:789, 790 funding formulas for, 2:788–789, 791 in the history of educational regulation, 1:199, 200 as the largest ESEA expenditure, 1:277 misuse of funds from, 1:304 nationalizing tendencies related to, 1:117 NCLB and, 1:315, 2:487, 489, 490, 789, 790, 824 negative funding consequences in, 2:818 as primary source of federal education funding, 2:824 states’ roles in, 2:701, 703, 788–791 student achievement and, 1:304, 2:790 supplemental educational services in, 2:719–721, 787 See also No Child Left Behind Act Title II of ESEA, 1:303 Title III of ESEA, 1:303 Title III of NCLB, 1:78, 109, 199 Title IV of ESEA, 1:303 Title IV of the Higher Education Act, 1:356, 357, 2:525, 715 Title V of ESEA, 1:303, 315, 2:701 Title VI of ESEA, 1:199, 303 Title VII of ESEA, 1:78 Title IX of the Education Amendments of 1972, 2:818 Todd, Petra, 1:68, 2:710 Toddlers and infants. See Infants and Toddlers with Disabilities Act Tomblin v. Gainer (1995), 2:656 Topeka, Kansas, and Brown v. Board of Education, 1:86–87 Topel, Robert, 1:324 Tracking in education, 2:791–795 academic and vocational, 2:831, 832 achievement and, 1:20, 2:792, 793, 794–795 between-classroom, 2:792, 793–794 between-school, 2:792, 794 inequality and, 2:793, 794–795 within-classroom, 2:792–793 within-school, 2:792 Trade agreements, 1:372, 373, 423 Trade policies, and globalization, 1:371, 372, 373 Traditional budgeting, 1:89. See also Line-item budgeting Tragedy of the commons, 2:795–799, 854 Training. See Continuing education; Job training; Teacher training and preparation; Vocational education

TRANs (tax revenue anticipation notes), 2:646 Transaction cost economics, 2:799–801 definition of, 2:854 new institutional economics and, 2:485 theory of the firm on, 2:784 Transfer payments, 1:180, 398 Transfer students, 1:16, 221. See also Student mobility Transformational donors, 2:550 Transformational infinity loop, 2:551 Transitional equity, 1:286 Transnational interactions. See Globalization Transparency: of adequate yearly progress, 1:40 in capital budgets, 1:98–99 lack of, in federal financial aid system, 1:139 lack of, in philanthropic foundations, 2:533–534 in property tax administration, 2:571, 573 in school admissions lotteries, 2:448, 449 in weighted student funding, 2:836 See also Accountability Transportation allowance benefits, 2:491 Transportation services: for access to education, 1:60, 61 as an auxiliary service, 1:60–61, 276 contracting for, 1:169 for desegregation, 1:60, 203 in globalization, 1:371 NCES expenditure category for, 1:42 public, for private school students, 2:673 school district consolidation and, 1:60 service consolidation for, 2:682 in special education, 2:694 (table) voucher plans and, 1:294 Traumatic brain injury, 1:400, 2:693 (table) Treatment-on-the-treated samples, 1:185 Treatment-on-treated effects, 2:612, 615 Trends in International Mathematics and Science Study (TIMSS): economics of education and, 1:253 establishment of, 2:847 low-income countries in, 1:417 NAEP scores and, 1:420, 2:468–469 NCES participation in, 1:420, 2:468–469, 472 overviews of, 1:416, 418, 419 PIRLS and PISA compared to, 1:419–420 voucher systems and, 1:295 Triangulation, to address systematic error, 2:457 Tribal colleges, 1:152 TRIO grant programs, 1:112 Triple difference model, 1:207–208. See also Differencein-differences Troske, K. R., 2:568 Trostel, P., 2:708 True endowment, 2:821

Index True experiments, 1:350. See also Randomized control trials Truman, Harry S., 2:477 Truman Report, 2:846 Trust, civic, 1:259, 260. See also Education and civic engagement Truth-in-taxation laws, 2:573 t-test, 1:299, 2:854 Tuition and fees, higher education, 2:801–810 ability to pay and, 1:3, 383 at all levels of education, 1:277 Baumol’s cost disease and, 1:63, 64 college choice and, 1:129–130 college completion and, 1:133 college dropouts and, 1:135 college enrollment and, 1:139–140 college savings plans for, 1:144–145 data on, 2:802–807 definitions for, 2:801–802 for dual enrollment, 1:225 enrollment management and, 1:308 equilibrium concept applied to, 2:516 expenditures per student and, 2:802, 804, 806–807, 807 (tables) at for-profit institutions, 1:356, 2:802 (table), 803, 809 GI Bill for coverage of, 1:368, 369 growth of, 1:384, 2:707, 801, 802 (table), 802–804, 803 (table) in-state versus out-of-state, 2:543, 801, 802–803 by institution type, 1:153–154, 384, 2:802–803 for job training, 1:428–429 net tuition in, 2:802, 804, 805, 805 (table), 806 for online learning, 2:500 price discrimination in, 2:543 pricing and, 1:307–308 privatization and, 2:556 revenue generated by, 1:319 state setting of, 1:385 See also Student financial aid Tuition and fees, K-12 private schools, 2:810–813 data on, 2:811 expenses covered by, 2:810–811 public funding for, 2:811, 812–813 at religious schools, 2:674, 675, 812 student financial aid for, 2:811–812 for types of private schools, 2:671, 810 Tuition discount rate, 2:802, 805–806 Tuition discounts, 1:308, 309 Tuition tax credits, 2:813–816 data on, 2:814, 815, 816 in higher education, 2:801–802, 805 in private/religious schools, 2:674, 675, 676, 812, 814–815

929

tax credit scholarships, 2:813, 814–815 vouchers compared to, 2:676, 813–814 See also Tax credits Turkey, 1:197 (table), 247, 353 (figure), 396 (figure), 2:831 Turnaround schools, 1:269, 270, 277, 2:790 Turner, Sarah, 1:133, 140, 369 Turnover of teachers, 1:406, 2:436, 616–617, 661, 766, 769. See also Teacher shortages; Teacher supply Tutoring, 1:29, 184, 185, 338, 2:431, 433, 488, 620, 719–721 Tversky, Amos, 1:65 20:20 ratio, 1:396 21st Century Community Learning Centers Program, 1:320 Twins studies, 1:69, 75, 148, 2:658. See also Sibling groups, in datasets Twitter (online service), 1:213 Two tier funding programs, 1:311–312. See also Equalization models 2SLS. See Two-stage least squares Two-stage least squares, 1:177, 241, 243, 408, 2:597, 854. See also Ordinary least squares Two-year colleges: adult students at, 1:47, 47 (table) in college choice, 1:128–129, 131 enrollment trends for, 1:137, 138 (figure), 318 expenditures per student at, 2:807, 807 (tables), 808 faculty at, 1:330, 332 for-profit, 1:355 funding of, 1:383–384 fundraising at, 2:551, 552 retention of students at, 1:309 returns to education at, 1:69 revenue per-FTE at, 1:319 student persistence, faculty, and, 1:332 transfers of students from, 2:809 tuition and fees at, 1:384, 2:802–803, 805, 808 “2-year” as a label, 1:152 See also Associate’s degree institutions; Community colleges; Community colleges finance Tyler, John, 1:187, 366 Udacity (for-profit venture), 1:283, 358, 2:499 Ultimatum game (experiment), 1:66 Unanimous voting rule, 2:578 Unbiased estimates, 1:350, 2:506, 507, 567–568, 598, 614, 851. See also Selection bias Unbounded rationality, 1:65, 2:484 Unconditional grants, 1:412–413 Undergraduate education. See Associate’s degree institutions; Bachelor’s degree institutions; Community colleges; Four-year colleges and universities; Higher education; Two-year colleges

930

Index

Underinvestment: in education, 1:71, 193, 2:715–716 in human capital, 1:325, 2:700 Undermatching, in college enrollment, 1:140 Underperforming schools, 1:270, 412, 2:487, 527, 662, 785 Unemployment compensation, 1:368, 369 Unemployment insurance benefits, 2:491 Unemployment rates: crime rates and, 1:75 by education level, 1:68, 128, 252, 2:714 foregone earnings and, 1:352–353 higher education enrollment and, 1:357 political context of, 1:112 for students in for-profit colleges, 1:47 at the time of A Nation at Risk, 2:465, 466 Unequal treatment of unequals, 2:828, 829. See also Vertical equity UNESCO, 2:547 Unfunded mandates, 2:817–819 as expenditure constraints, 2:645 in intergovernmental relationships, 2:817–818 NCLB as, 1:118, 315 negative funding compared to, 2:818 opportunity-to-learn standards as, 2:504 Unfunded Mandates Reform Act, 2:819 UNICEF, 1:194, 195 Uniform Prudent Management of Institutional Funds Act, 2:822 Uninterrupted Scholars Act, 1:335 Unions, labor. See School boards, school districts, and collective bargaining; Teachers’ unions and collective bargaining Unit fixed effects, 1:350, 351 Unit-based funding, 2:695, 695 (table), 696 United Kingdom: compulsory schooling laws in, 1:164, 260 education and civic engagement in, 1:260 education and crime in, 1:264 foregone earnings in, 1:353 (figure) income inequality in, 1:396 (figure) information technology access in, 1:290 private fundraising in, 2:549 private schools in, 2:810 tracking policy in, 2:794 university endowments in, 2:820 See also England United Nations: Global Education First Initiative, 2:584 impact of globalization on, 1:373 MDGs adopted by, 1:4–5 UNESCO, 2:547 UNICEF, 1:194, 195

United States, compared to other countries: in adults with postsecondary credentials, 1:134 in college completion rates, 1:134 in declining relative achievement, 2:580 in foregone earnings, 1:353 (figures) in Gini index, 1:396, 396 (figure) in income inequality, 1:395–396, 396 (figure) on international tests, 1:11 in post-WWII secondary education growth, 2:832 in ratio of highest to lowest earners, 1:395 in school-based management, 2:667 in student incentives linked to outcomes, 2:711 in teacher socialization, 2:768 in unequal distribution of wealth, 2:690 in voucher utility, 2:813 and the world’s highest incarceration rate, 1:262 and the world’s most competitive economy, 2:466 See also Cross-national analyses; International assessments United States, competitiveness of, 1:315, 2:465, 466, 520, 639, 823 Univariate dispersion measures, 1:390, 2:828, 854. See also Coefficient of variation; Gini coefficients; McLoone index; Theil indices Universal attendance, 1:117 Universal choice, 2:676 Universities and colleges. See Benefits of higher education; College choice; College completion; College dropout; College enrollment; College rankings; College savings plan mechanisms; Higher education; Higher education finance University endowments, 1:385, 2:802, 805, 806, 819– 823. See also Endowments Unmeasured variable bias, 2:496, 792. See also Omitted variable bias Unobservable and observable variables, 2:825–826, 827 Unobservables, definition of, 2:854 Upward bias, for OLS estimators, 2:506, 507 Upward Bound program, 1:112, 2:685 Urquiola, Miguel, 1:296 U.S. Agency for International Development, 1:195 U.S. Census Bureau. See Census Bureau data U.S. Constitution: due process clauses of, 1:228, 230 education not specifically mentioned in, 2:487, 632, 637, 823 Equal Protection Clause of, 1:87–88, 202, 267, 378, 2:485, 632–633, 679, 680, 846, 847 Establishment Clause of, 2:674, 815 Fifth Amendment to, 1:228 First Amendment to, 1:228, 294, 2:633, 812, 815 Fourteenth Amendment to, 1:87–88, 202, 228, 2:485, 632, 672, 679, 846, 847

Index liberties guaranteed by, 1:265 Sixteenth Amendment to, 1:104 Tenth Amendment to, 1:313, 2:485 U.S. Department of Agriculture, 1:61 U.S. Department of Defense, 1:196–198, 2:562 U.S. Department of Education, 2:823–824 CCSS-aligned assessments and, 1:150 on dropouts, 1:134, 221–222 establishment of, 1:315 focus change for, from inputs to outcomes, 2:824 on for-profit institutions, 1:355 functions of, expanded, 1:315, 2:823, 824 i3 grant program of, 1:120, 160, 424–426 IDEA and, 1:402, 2:824 mission of, 2:823 NCLB and, 1:8, 9, 2:490, 703, 721, 823, 824 Race to the Top and, 2:607–608, 823 roles and responsibilities of, 2:823–824 as a stakeholder in local control, 2:439 Title I and, 2:790, 791, 824 wavier role of, 2:824 (see also Waivers for NCLB) See also Individuals with Disabilities Education Act; National Center for Education Statistics; No Child Left Behind Act; Race to the Top; Title I U.S. Department of Housing and Urban Development, 2:483 U.S. Department of Labor, 1:128, 134 U.S. Education Delivery Institute, 2:703 U.S. News & World Report, college rankings by, 1:129, 142, 143, 146, 147, 2:636, 804 U.S. Supreme Court. See Supreme Court of the United States USA PATRIOT Act, 1:335 User charges, 1:1, 2–3, 4 Utah, 1:173, 175, 216, 317, 2:440, 703 Utility: discounted, 1:393, 2:452, 850 in the human capital model, 1:392–393 maximized, 1:65, 169, 252, 386, 2:452, 454, 479, 516, 544, 784 Utility-based decision making, 1:65. See also Behavioral economics Vacation benefits, 2:491, 492 Valencia, S. W., 2:769, 770 Validity, 2:825–828 construct validity, 2:826–827 content validity, 2:826 criterion-related validity, 2:826 definition of, 2:854 in the DID method, 1:206, 207 external (see External validity) internal (see Internal validity)

931

measurement error and, 2:456, 457 reliability compared to, 2:623, 825 of school report card measures, 2:663 selection bias and, 2:677 of teacher effectiveness measures, 2:631, 749, 755 of teacher evaluation measures, 2:752, 753, 754, 755, 756 of value-added measures, 2:773–774, 775 Valli, Linda, 2:560 Value-added models: advantages and challenges in, 2:748–749 assumptions for, 2:774 statistical modeling required for, 2:754–755 student achievement measured by, 2:528 See also Teacher value-added measures Value-Added Research Center, 2:754 van den Brink, H., 1:260, 261, 264 Vandell, Deborah Lowe, 1:320 Variable and base pay, 2:519, 521. See also Teacher compensation Vashaw, L., 2:498 Vedlitz, A., 2:842 Venture capital, 1:159, 2:532 Verbal ability of teachers, 1:250, 2:747, 758, 766. See also Teacher intelligence Verger, Antoni, 2:583 Vermont, 1:175, 216, 378–379, 2:444 (table), 654, 811 Vernez, Georges, 1:82 Verstegen, Deborah, 1:216, 2:588, 649, 695, 695 (table) Verstegen Index, 1:312 Vertical equity, 2:828–829 ability-to-pay and benefits principles and, 1:3, 2:564 accountability and, 1:14 adequacy, PJ approach, and, 1:33 categorical aid for, 1:109, 216 equalization and, 1:310 for fairness, 1:286 horizontal equity and, 1:390, 391 in income taxes, 2:729 measures of, 2:650 property taxes and, 2:574, 729 pupil weighting for, 2:589 in tax burden, 2:725, 726 See also Horizontal equity Vertical stratification, for admissions lotteries, 2:448 Vesting in pension plans, 2:761, 762 Veterans, GI Bill for, 1:368–369, 2:707 Veterans Readjustment Assistance Act, 1:369 Victim costs, 1:75, 180, 181, 264. See also Education and crime Video conferencing software, 1:214 Video games, 1:282, 283 Vietnam War, 1:260, 369, 2:658. See also GI Bill

932

Index

Vigdor, Jacob, 2:757 Vining, A., 1:181 Vinson, Fred, 1:87 Virgin Islands, 2:672–673 Virginia, 1:197 (table), 2:444 (table), 648, 814 Virtual classroom, 1:214–215, 374 Virtual schools, 1:126, 271, 272, 282, 389, 2:498, 499. See also Online learning Vision insurance benefits, 2:491, 493 Visual impairment, in special education, 2:693 (table) Vocabulary, child and family, 1:14, 2:513, 514 Vocational education, 2:829–833 data on, 1:428, 429, 2:831 at for-profit institutions, 1:355, 356, 358, 428, 2:831 gainful employment regulations on, 1:358, 363, 364 human capital and, 2:829, 830 job training and, 1:427, 428, 429, 2:830–831, 832 progressive education philosophy on, 1:46 See also Adult education; Continuing education; Job training Vocational Education Act, 1:428 Vocational tracking, 2:830–831, 832 Voice over Internet Protocol, 1:214 Voluntary nonwage benefits, 2:491 Volunteering (as civic engagement) and education, 1:75, 126, 260, 296 von Hippel, Paul, 1:19 von Wachter, Till, 1:164 Vos, Richard, 1:143 Voter turnout, 1:13, 85, 2:440 Voting: on bond funding, 1:84–85, 103, 153, 368 for education officials, 2:440 election cycle and, 2:706 participation in, as a benefit of education, 1:71, 75, 260, 261, 296 public choice economics on, 2:578–579, 581 for school board members, 2:637–638, 639, 640, 642, 706, 776 See also Median voter model; Voter turnout Voting paradox, 2:458 Voting rules, unanimous and majority, 2:578–579 Voting with their feet, 2:579, 786 Vouchers. See Educational vouchers Vroom, Victor, 2:519 Vujic, Suncica, 1:264 W. K. Kellogg Foundation, 2:533 Wage differentials, compensating, 1:381–382, 2:745 Wage regressions, 1:68–69, 187. See also Regression analysis Wage stagnation, 1:325, 396, 2:541

Wages. See Comparative wage index; Earnings; Earnings comparisons, data for; Hedonic wage models; Race earnings differentials; Teacher compensation Wage-setting units, size of, 2:745–746 Waivers for NCLB: accountability and, 1:9, 10, 41, 2:790 new grading systems and, 2:662 number of, 1:41 relative measures and, 2:490 and school-based management, 2:670 and state education agencies, 2:702, 703, 704, 824 testing under, 1:200, 2:790 Waldford, Geoffrey, 2:584 Wales, 1:74 Walker, Ian, 2:531 Wall Street Journal, college rankings by, 1:143 Wallis, John, 2:799 Walsh, Elias, 1:111 Walton Family Foundation, 2:532 Wang, A., 2:695 (table) War on Poverty, 1:46, 199, 302, 315, 337, 2:487, 789 Warren, Earl, 1:87 Washington (state): adequacy level in, 1:24 excellent schools identified in, 2:662–663 finance litigation in,