Evaluating Public Programs: The Impact of General Revenue Sharing on Municipal Government 9781400869978

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Evaluating Public Programs

Evaluating Public Programs The Impact of General Revenue Sharing on Municipal Government Patrick D. Larkey

Princeton University Press

Princeton, New Jersey

Copyright © 1979 by Princeton University Press Published by Princeton University Press, Princeton, New Jersey In the United Kingdom: Princeton University Press, Guildford, Surrey All Rights Reserved Library of Congress Cataloging in Publication Data will be found on the last printed page of this book Phototypeset in V.I.P. Times by Western Printing Services Ltd, Bristol Clothbound editions of Princeton University Press books are printed on acid-free paper, and binding materials are chosen for strength and durability. Printed in the United States of America by Princeton University Press, Princeton, New Jersey "The Theory That Jack Built," from The Space Child's Mother Goose by Frederick Winsor and Marian Parry, is reprinted by permission of Simon and Schuster, a division of Gulf and Western Corporation. Copyright © 1956, 1957, 1958 by Frederick Winsor and Marian Parry. "The Hardship of Accounting" by Robert Frost, from The Poetry of Robert Frost, edited by Edward Connery Lathem, is reprinted by permission of Holt, Rinehart and Winston, Publishers. Copyright © 1969 by Holt, Rinehart and Winston.

For My Parents, Ike and Carrie Larkey, whose support has always been there.

Contents List of Tables List of Figures

ix xi

Acknowledgments

xv

1 Introduction The Program and Research Objectives Obstacles to the Analysis of GRS Organization of the Book 2 Research Design Problem Formulation Program Evaluation Sample Selection Data Collection and the Unit of Analysis Criteria for Model Development Expenditure Models 3 Theoretical Foundations Models and Theories Alternative Views Municipal Resource Allocation Processes The Impact of Assistance Programs 4 Model Testing FurtherWork 5 Empirical Results Revenue Effects GRS Fiscal Effects 6 "Basic" The Impact of GRS Possible Extensions Summary and Appraisal of Approach Appendix A Appendix B Bibliography Index

3 8 13 18 21 22 28 40 43 51 55 61 61 68 95 115 121 156 162 162 178 216 217 222 225 230 242 247 261

List of Tables Table 1-1 1-2 1-3 2-1 4-1 4-2 4-3 4-4 4-5 4-6 4-7 4-8 4-9 4-10 4-11 4-12 4-13 4-14 4-15 4-16 4-17 5-1 5-2 5-3 5-4 5-5 5-6

Federal Assistance Programs by Function Growth in Federal Transfer Payments Federal Assistance in State and Local Expenditures Characteristics of the Five Cities Albuquerque: Large Functional Accounts Ann Arbor: Large Functional Accounts Cincinnati: Large Functional Accounts Detroit: Large Functional Accounts Worcester: Large Functional Accounts Albuquerque: RSQR—Level of Expenditure (OnePeriod Change) Ann Arbor: RSQR—Level of Expenditure (OnePeriod Change) Cincinnati: RSQR—Level of Expenditure (OnePeriod Change) Detroit: RSQR—Level of Expenditure (One-Period Change) Worcester: RSQR—Level of Expenditure (OnePeriod Change) Summary Statistics: One-Period Change Models Albuquerque: RSQR—Level of Expenditure (Simula­ tion) Ann Arbor: RSQR—Level of Expenditure (Simula­ tion) Cincinnati: RSQR—Level of Expenditure (Simula­ tion) Detroit: RSQR—LevelofExpenditure(Simulation) Worcester: RSQR—Level of Expenditure (Simula­ tion) Summary Statistics: Simulation Models Ann Arbor: General Fund Revenues Detroit: General Fund Revenues Cincinnati: General Fund Revenues Albuquerque: Comparative Elements of General Fund Revenues, 1959-1975 Albuquerque: Revenue Projections Worcester: Property Tax

5 6 7 42 128 129 130 131 132 138 139 140 141 142 142 150 151 152 153 154 155 164 165 167 169 171 174

EVALUATING PUBLIC PROGRAMS

5-7 5-8 5-9 5-10 5-11 5-12 5-13 5-14 5-15 5-16 5-17 5-18

χ

Worcester: Revenue Projections Albuquerque: GRS Fiscal Effects (All Models—All Years) Ann Arbor: GRS Fiscal Effects (All Models—All Years) Cincinnati: GRS Fiscal Effects (All Models—All Years) Detroit: GRS Fiscal Effects (All Models—All Years) Worcester: GRS Fiscal Effects (All Models—All Years) Albuquerque: ORS Comparisons Ann Arbor: ORS Comparisons Cincinnati: ORS Comparisons Detroit: ORS Comparisons Worcester: ORS Comparisons Cumulative Comparisons for All Cities and All Revenue—Sharing Years

177

181 182 183 184 185 188 189 190 191 192 214

List of Figures Figure 1-1 1-2 2-1 2-2

Federal Grants to State and Local Governments Stated Objectives for General Revenue Sharing General Fund Revenues Model Summary: Constant Proportion of Base (CPB) 2-3 Model Summary: Constant Proportion of the Revenue Increment (CPRI) 2-4 Model Summary: Constant Growth-Revenue Incre­ ment (CGRI) 2-5 Model Summary: Dollar Change-Fiscal Pressure (DCFP) 3-1 Municipal Resource Allocation 3-2 Typical "General Fund" Resource Allocation Process 3-3 The Municipal "Fiscal Problem" 4-1 Albuquerque: RSQR—Level of Expenditure (OnePeriod Change) 4-2 Ann Arbor: RSQR—Level of Expenditure (OnePeriod Change) 4-3 Cincinnati: RSQR—Level of Expenditure (OnePeriod Change) 4-4 Detroit: RSQR—Level of Expenditure (One-Period Change) 4-5 Worcester: RSQR—Level of Expenditure (OnePeriod Change) 4-6 Albuquerque: RSQR—Level of Expenditure (Simula­ tion) 4-7 Ann Arbor: RSQR—Level of Expenditure (Simula­ tion) 4-8 Cincinnati: RSQR—Level of Expenditure (Simula­ tion) 4-9 Detroit: RSQR—Level of Expenditure (Simulation) 4-10 Worcester: RSQR—Level of Expenditure (Simula­ tion) 5-1 Albuquerque: Time-Series Plot of Predicted and Actual Revenues 5-2 Worcester: Comparative Computation of Property Tax Rate, 1971-1972

4 15 47 57 58 59

60 96 104 117 133 134 135 136 137 144 145 146 147 148 172 176 xi

ΕνΑΙΛίΑΉΝΟ PUBLIC PROGRAMS 5-3 5-4 5-5 5-6 5-7 5-8 5-9 5-10 5-11 5-12 5-13 5-14 5-15 5-16 5-17 5-18 5-19 5-20 5-21 5-22

Worcester: Time-Series Plot of Predicted and Actual Revenues Albuquerque: Time-Series Plot, Police/Personnel Expenditures Albuquerque: Time-Series Plot, Police/Nonpersonnel Expenditures Albuquerque: Time-Series Plot, Court/Personnel Expenditures Albuquerque: Time-Series Plot, Administra­ tion/Personnel Expenditures Albuquerque: Time-Series Plot, Health/Personnel Expenditures Ann Arbor: Time-Series Plot, Police/Personnel Expenditures Ann Arbor: Time-Series Plot, Police/Nonpersonnel Expenditures Ann Arbor: Time-Series Plot, Fire/Personnel Expendi­ tures Ann Arbor: Time-Series Plot, Fire/Nonpersonnel Expenditures Ann Arbor: Time-Series Plot, Nondepartmental/Total Only Expenditures Cincinnati: Time-Series Plot, Public Safety/Personnel Expenditures Cincinnati: Time-Series Plot, Public Safety/Nonpersonnel Expenditures Cincinnati: Time-Series Plot, City Manager/Nonpersonnel Expenditures Cincinnati: Time-Series Plot, Public Health/Personnel Expenditures Cincinnati: Time-Series Plot, Public Health/Nonpersonnel Expenditures Detroit: Time-Series Plot, Police/Personnel Expendi­ tures Detroit: Time-Series Plot, Police/Nonpersonnel Expenditures Detroit: Time-Series Plot, Fire/Personnel Expendi­ tures Detroit: Time-Series Plot, Fire/Nonpersonnel Expen­ ditures

178 194 194 195 195 196 198 198 199 199 200 201 201

202 203 203 204 205 205 206

LIST OF FIGURES

5-23 5-24 5-25 5-26 5-27 5-28 5-29 5-30 5-31 5-32 5-33 5-34

Detroit: Time-Series Plot, Council/Personnel Expendi­ tures Detroit: Time-Series Plot, Council/Nonpersonnel Expenditures Detroit: Time-Series Plot, Nondepartmental/Total Only Expenditures Worcester: Time-Series Plot, Police/Personnel Expen­ ditures Worcester: Time-Series Plot, Police/Nonpersonnel Expenditures Worcester: Time-Series Plot, Fire/Personnel Expendi­ tures Worcester: Time-Series Plot, Fire/Nonpersonnel Expenditures Worcester: Time-Series Plot, Public Schools/Personnel Expenditures Worcester: Time-Series Plot, Public Schools/Nonpersonnel Expenditures Worcester: Time-Series Plot, Vocational Schools/Per­ sonnel Expenditures Worcester: Time-Series Plot, Vocational Schools/Nonpersonnel Expenditures Worcester: Time-Series Plot, Capital/Total Only Expenditures

206 207 207 209 209 210 210 211 211

212 212

213

Acknowledgments

This book began as a doctoral dissertation at the University of Michigan. The research, essentially completed in 1975 and revised for this book, would not have been possible without generous financial support and the assistance of many people. Although I must take primary responsibility for successes and failures in design, execution, and reporting, others are impli­ cated, if not responsible. Let me take this opportunity to implicate and thank them. The magnitude of my intellectual debt to Dean John P. Crecine will be obvious to anyone reading beyond the table of contents. His work on municipal budgeting was truly the basis of my research; and through him, I became acquainted with the extensive works of Herbert A. Simon, Richard M. Cyert, and James G. March, on which I drew heavily. What may not be apparent to the reader is the importance of his friendship, advice, encouragement, and criticism over the past eight years. In retrospect, I appreciate being subjected to his consis­ tently high and zealously administered standards. Professor Thomas J. Anton was the Principal Investigator for the National Science Foundation project that supported my work. He has been invaluable as advisor, colleague, and friend. His superb effort in meeting our joint commitments to NSF enabled me to finish the larger research rapidly. Profes­ sors Stephen M. Pollock and Daniel R. Rubinfeld made many insightful criticisms and useful suggestions during the research. Their extraordinary technical competence in opera­ tions research and economics is not fully reflected in this work, but not because of any lack of ability or effort on their part. Deans Otto A. Davis and Aaron Wildavsky were kind enough to read the dissertation and make useful comments for converting it to a book. Ms. Toni Linton must be singled out for her herculean efforts on data collection and numerous other support tasks.

EVALUATING PUBLIC PROGRAMS

We discovered together why most academics avoid construct­ ing their own longitudinal data sets from archival records. Without her help, this research would not have been possible. Steve Davidson, Joel Epstein, John Fox, and Bob Trakimas provided valuable assistance on various aspects of data collec­ tion and the computer work. Nancy Townsend and Claudia Zawacki devoted many long hours to preparing the original manuscript. Teresa Salvucci, Harriet Hynes, and Adrienne Meyer devoted similar efforts to the final manuscript. Donna Fillo was helpful as an editorial assistant and in manag­ ing various aspects of final manuscript preparation. Sanford Thatcher, social science editor of Princeton University Press, has been an ideal first editor. He has helped at every turn. The local officials in the cities studied who helped me with this work are too numerous to mention individually. The usual comment after we outlined our research strategy and data requirements was, "Good luck!" However, these officials gave freely of their time and knowledge to further the research. Candor, congeniality, intelligence, dedication, and energy were their typical attributes. Such people made this research both possible and pleasurable. I was unusually fortunate in the quality of my personal and professional associations during this research. Garry Brewer, Ronald Brunner, John Chamberlin, Michael Cohen, Paul Courant, George Downs, Stephen Horner, Ken Warner, and Gail Wilensky all helped me with this work as friends and colleagues. Particular thanks are due to my former employers, Mr. Guy Larcom, former City Administrator of Ann Arbor, and Mr. Robert Harris, former Mayor. They gave me a unique oppor­ tunity to manage a federal program designed to upgrade the city government's management and service delivery capabilities through the application of various analytic techni­ ques. Two years of flailing away, occasionally with success, at "real" municipal problems with newly acquired, hard-won analytic skills from academe is an experience that I value highly. If I display occasional impatience with armchair con­ ceptions of local officials as decision makers, particularly

ACKNOWLEDGMENTS

rational models, it should be attributed to a flaw in a profes­ sor's background—too much time out of the classroom—and excused. I am grateful to the National Science Foundation (Research Applied to National Needs), the Ford Foundation, the Uni­ versity of Michigan (Institute of Public Policy Studies), the University of British Columbia, and Carnegie-Mellon Univer­ sity for supporting my work. The usual institutional absolu­ tions apply. I am also grateful to the National Tax Association-Tax Institute of America (NTA-TIA) for selecting this work as the 1976 Outstanding Dissertation in Public Finance. Given the extent of my criticism of past theoretical and empirical work on intergovernmental assistance programs, this award speaks highly of the public finance profession. This research has taken a great deal of time and effort. Janet, Jana, and Stephanie have routinely shown understand­ ing, support, and patience in wholly unreasonable circum­ stances. Although circumstances may never become wholly reasonable as every project becomes three and there are no "ends," only resting places and outputs, the people I live and work with are truly more important than the work.

Evaluating Public Programs

1 Introduction This is the Cybernetics and Stuff That covered Chaotic Confusion and Bluff That hung on the Turn of Plausible Phrase And thickened the Erudite Verbal Haze Cloaking Constant K That saved the Summary Based on the Mummery Hiding the Flaw That lay in the theory that Jack Built. Winsor and Parry Space Child's Mother Goose

Federal assistance programs to state and local governments are increasingly important instruments of federal domestic policy. Assistance programs have constituted an increasing proportion of gross national product (GNP), of total federal domestic expenditures, and of total state and local expenditure. Figure 1-1 shows federal grants to state and local govern­ ments, by gross types, for ten fiscal years, 1967-1977. There has been substantial growth in both total grants and in those grant programs in which recipient governments have signifi­ cant discretion in executing the programs (i.e., Revenue Shar­ ing and Other Grants). Table I-I1 summarizes expenditures through federal grantin-aid programs by major category for fiscal year 1976. State and local governments received approximately thirty-seven 1 Charles L. Schultze, "Federal Spending," in Setting National Priorities: The Next Ten Years, Henry Owen and Charles L. Schultze (eds.), p. 360.

EVALUATING PUBLIC PROGRAMS

FIGURE 1-1 Federal Grants to State and Local Governments $Billion

$Billion

75—R

T- 75

Revenue Sharing

Total

Unemployment Assistance Other Grants

Other Human Resources

Highways

Estimate Source: Special Analyses, Budget of the United States Government, 1977, Special Analysis 0, p. 256.

billion federally collected tax dollars for a variety of purposes. More than one-third of this amount was expended through revenue sharing and block grants (e.g. Community Develop­ ment Block Grants), program forms that did not even exist in the 1960s. Table 1-22 shows federal expenditures on domestic pro­ grams, by major category, for fiscal years 1955 and 1977. The growth in grants to state and local governments has greatly exceeded the growth in baseline domestic expenditures. Grants to state and local governments have increased dramat­ ically both in terms of nominal dollars and as a percentage of (nonrecession) GNP. 2

Ibid., p. 33.

INTRODUCTION

TABLE 1-1 Federal Assistance Programs by Function Category Payments to individuals AFDC Medicaid Public service employment" Other Revenue sharing and block grants General revenue sharing Comprehensive manpower training6 Community development Law enforcement assistance (part) Social service grant programs Categorical grants Major capital grants Highways Urban mass transit Municipal waste treatment plants Other categorical grants Education Health Social services Manpower training Other Total

Amountc 22.8 5.9 8.2 3.4 5.3 14.0 6.3 2.3 2.6 0.4 2.4 23.0 10.1 6.2 1.5 2.4 12.9 4.0 1.8 1.6 0.8 4.7 59.8

Source: Special Analyses, Budget of the United States Government, Fiscal Year 1977, Special Analysis 0. * Includes $800 million from Title II of the Comprehensive Manpower Act. b Excludes $800 million of grants for public service employment. e Billions of dollars.

Table 1-33 shows grants as a percentage of state and local government expenditures, by purpose, for fiscal years 1969 and 1974. Grants are an increasingly important source revenue for state and local governments, as the growth from 17% of total expenditures in 1969 to 22% in 1974 indicates. 3 Congressional Budget Office, Analysis of FY 1976 Budget, Part II-I: Program Issues, Section C: Grants to other governments, p. 250.

EVALUAnNG PUBLIC PROGRAMS TABLE 1-2 Growth in Federal Transfer Payments Percent of nonrecession Billions of dollars GNP Category 1955 Baseline domestic expenditures Payments to individuals (in cash and in kind)" Grants to state and local governments" Outlays for net interest Other Addendum: Recession-induced expenditures

1977 1955 1977

26.4

292.2

7.0

14.7

12.1 1.7 4.8 7.8

167.4 43.5 33.0 48.3

3.2 0.5 1.3 2.1

8.4 2.2 1.7 2.4

—

14.5

—

0.7

" Some individual payments, principally aid to families with dependent chil­ dren (AFDC) and housing assistance payments, are made through grants to state or local governments. These are shown in the "payments to individu­ als" rather than the "grants to state and local governments" category. The amounts are $1.5 billion in 1955 and $25.0 billion in 1977.

Table 1-3 also shows some of the changes in grant emphasis that have occurred. Grants for highways, health and hospitals, and social insurance administration have declined in relative importance, whereas grants for natural resources, housing and urban renewal, air transportation, and other purposes have gained in importance. Perhaps the most interesting shift is the more than fourfold increase in grants for "other purposes" as a percentage of total state and local expenditures. The Gen­ eral Revenue Sharing (GRS) Program4 and the Comprehen­ sive Employment and Training Act (CETA) account for most of this increase. In providing financial assistance to "subordinate" units of government (e.g., states and cities), the federal government has relied traditionally on programs designed to retain a significant measure of federal control over the expenditure of 4

The State and Local Fiscal Assistance Program of 1972.

$19.4

$4.8 4.4 6.4 0.7 0.3 0.9 0.1 0.6 1.3 0.0

Federal" grants

$117.3

$47.3 15.5 12.3 8.6 2.6 1.9 0.7 0.7 24.0 3.7 17%

10% 28 52 8 12 48 16 92 6 0

Grants as Total" percent of state local state-local expenditure expenditure

$42.9

$7.5 4.6 12.8 1.1 0.8 2.4 0.3 0.8 12.6 0.0

Federal" grants

$198.6

$75.9 20.0 25.0 16.1 3.7 3.5 1.3 1.3 45.1 7.7

22%

10% 23 52 7 20 69 19 63 28 0

Grants as percent of Total" state-local state-local expenditure expenditure

Fiscal Year 1974

U.S. Bureau of the Census, Governmental Finances in 1968-69, GF69, No. 5, Table 6; and Governmental Finances in 1973-74, GF75, No. 5, Table 6.

Billions of dollars.

Source:

Total

Education Highways Public welfare Health and hospitals Natural resources Housing and urban renewal Air transportation Social insurance administration Other Interest

Purpose

Fiscal Year 1969

TABLE 1-3 Federal Assistance in State and Local Expenditures

EVALUAnNG PUBLIC PROGRAMS

"federal funds." These have been programs that: (1) require extensive planning and application efforts on the part of gov­ ernments seeking to participate; (2) require periodic, often annual, reporting and reapplication for continued participa­ tion; (3) limit participation to a subset of all units applying, with the selection of participants nominally based on such factors as need and quality of local plans; (4) require a cash or "in-kind" contribution (match from participating units); (5) specify limited functional areas reflecting national priorities for the expenditure of program funds; and (6) are subject to annual review by Congress and the federal Executive.

The Program and Research Objectives Inside every large problem is a small problem struggling to get out. Hoare's Law of Large Problems5

The General Revenue Sharing (GRS) Program is significantly different from traditional program forms. It provided $30.2 billion in general financial assistance to all units of "generalpurpose" government (e.g., states, counties, municipalities, townships, and Indian Tribal Councils) over a five-year period, 1972-1976. Participation in GRS is universal and automatic for units designated as "general-purpose."6 Degree of participation (viz., the amount of money participants receive) is determined on the basis of fixed formulas. The planning and reporting requirements associated with GRS are minimal. Recipient governments are not required to match the amounts of GRS support they receive with Local Funds. Although the GRS legislation does specify "priority expendi­ ture categories," these are broad and leave substantial, if not total, discretion to state and local officials with regard to disposition of GRS funds. The differences between GRS and traditional forms of 5

David Wallechinsky, Irving Wallace, and Amy Wallace, The Book of Lists, p. 481. 6 A few small governments have refused to participate because of strong antifederalist sentiments or because they calculated that complying with the law would cost them more than they would receive.

INTRODUCTION

assistance point to a fundamental tension in designing federal assistance programs: Loose guidelines or controls may allow state and local gov­ ernments to waste the money or spend it according to their own, rather than national priorities. Tight guidelines, impose high administrative costs, and lower the effective­ ness of services.7 GRS can be viewed as an "experiment" with loose guidelines and controls that shifts much of the discretion with regard to the expenditure of federal funds from federal officials to state and local officials. Empirical evidence as to how state and local officials exer­ cise this discretion is (and should be) important to Congress and the Executive branch in deciding the future composition of federal assistance. Much of the controversy on GRS has centered on whether or not state and local governments8 have "spent the money responsibly." Of course, what constitutes a "responsible" expenditure is largely a matter of taste.9 In order to address the many interesting normative ques­ tions in evaluating General Revenue Sharing (e.g., Are out­ comes (expenditures) resulting from GRS responsible? Desirable? Preferable to alternative uses?), it is first necessary to describe GRS outcomes (fiscal effects) empirically.10 Data on fiscal effects are also prerequisite to any careful analysis of 7

Edward R. Fried et al., Setting National Priorities: The 1974 Budget, p.

35. 8 Everything said to this point applies to all types of recipient units, and much of what follows also applies to all types. However, the empirical work that is the basis of this book is focused exclusively on city governments, and the discussion will relate primarily to that form of recipient government. 9 Some GRS expenditures, particularly those for recreation facilities (e.g., golf courses and tennis courts), have been criticized as "frivolous." There are more extreme examples, including the use of GRS funds by the City Fathers of Heidelberg, Mississippi, to relocate a confederate war memorial more centrally (see Ann Arbor News, May 4, 1975). Considering that more than 38,000 units of government have received and expended GRS funds for three years, however, controversial reported uses have been surprisingly few. 10 "Outcomes" are defined for the purpose of this study as "changes in local patterns of revenue and expenditures attributable to GRS."

EVALUATING PUBLIC PROGRAMS

other GRS effects. For example, the redistributive implica­ tions of GRS are the object of great interest. The usual mode of analysis in this area is to compare the distribution of GRS funds with the tax contributions and socioeconomic charac­ teristics of recipients. If "poor" jurisdictions receive relatively IargerGRS allocations than "rich" jurisdictions, the inference might be that GRS is progressive. The problem with this analysis is that grants to governments are not the same as grants to people, and poor governments may find highly regressive uses for their GRS funds whereas rich governments may find very progressive uses for theirs. Until the change in revenue and expenditure patterns and the redistributive implications of these are specified, research on the redistribu­ tive implications of GRS will be inconclusive. The impact of GRS on the fiscal behavior of recipient units of governments is one of the most important, most widely studied, and least understood aspects of revenue sharing. The central question in this area, "What impact has GRS had on the revenue and expenditure patterns of recipients?", has proven to be difficult to formulate, and when formulated correctly, very difficult to study with conventional methods of data collection and analysis. The primary objective of this research was to develop and demonstrate a method of assessing the "fiscal effects" of GRS superior, both conceptually and methodologically, to the approaches now being used.11 Fiscal effects are best under­ stood as the differences between the revenue and expenditure patterns that are realized with revenue-sharing monies avail­ able and the revenue and expenditure patterns that "would have been realized without the availability of revenue-sharing monies." One reference on program evaluation puts the research design problem in the following way: Ideally, we would like (in evaluation research) to compare 11

The principal sources of data on GRS fiscal effects are (1) official reports on actual use prepared by state and local officials for the Office of Revenue Sharing (ORS); and (2) research projects based largely on survey research techniques (e.g., mail questionnaires, telephone interviews, or personal inter­ views with state and local officials).

INTRODUCTION

what "actually happened" to what "would have happened if the world had been exactly the same as it was except that the program had not been implemented." Since it is impossible to determine exactly what "would have happened, i f . . . , " the problem is to use procedures that approximate this.12 The success of such a formulation obviously hinges on our ability to replicate the behavior of decision-making pro­ cesses—in this case, the city government resource allocation processes that determine local revenue and expenditure pat­ terns. The task requires formal models of ongoing processes that can be used to project hypothetical fiscal behavior (i.e., the revenue and expenditure patterns for revenue-sharing entitlement periods without revenue-sharing monies) and to facilitate the separation of programmatic from nonprogrammatic effects. The models developed and utilized in this research view municipal resource allocation systems as bureaucratic pro­ cesses, asserting that these processes are best characterized as historically dependent, highly routinized decision-making sys­ tems.13 The formal models utilized are in the form of computer-based "simulations." These models take explicit advantage of certain key characteristics of the processes such as the balanced budget requirement (i.e., expenditures must be less than or equal to revenues), stability (i.e., prior year's appropriations are the initial and dominant basis for calculat­ ing this year's appropriations), and "rule-of-thumb" decision 12 Harry P. Hatry, Richard E. Winnie, and Donald M. Risk, Practical Program Evaluation for State and Local Government Officials, p. 39. 13 The most complete and competent articulation of this view for municipal budgetary processes is found in John P. Crecine, Governmental Problem Solving: A Computer Simulation of Municipal Budgeting. The intellectual foundations of the view are found in a number of places, including: H. A. Simon ,Administrative Behavior; C. E. Lindblom, "The Science of Muddling Through"; R. M. Cyert and J. G. March, A Behavioural Theory of the Firm; J. G. March and H. A. Simon, Organizations; and Aaron Wildavsky, The Politics of the Budgetary Process. An excellent discussion of the "Bureau­ cratic (organizational) process view" from the perspective of competing/complementary "views" is found in Graham T. Allison, Essence of Decision: Explaining the Cuban Missile Crisis.

EVALUATING PUBLIC PROGRAMS

making (i.e., projected surpluses and deficits are eliminated through the application of simple decision rules such as across-the-board percentage adjustments). The analysis with formal models is augmented with data from unstructured interviews with local officials. The demonstration of the approach for analyzing the fiscal effects of GRS consists of actually doing the analysis for five cities.14 The central research question was "What changes in revenue and expenditure patterns are attributable to GRS?" The models were used to project "what revenue and ex­ penditure patterns would have been without GRS" for com­ parison with the realized revenue and expenditure patterns with GRS. The differences are "fiscal effects," ceteris paribus. 15 This research was also concerned with the relative merit of alternative approaches to the study of GRS fiscal effects. There are a number of significant differences between the approach taken here and the approaches used by other resear­ chers and federal agencies to study fiscal effects. This research was founded on the perception that there are major concep­ tual and/or methodological problems with much of the research and data on GRS fiscal effects. Appraising the accuracy and reliability of information on fiscal effects from alternative sources (i.e., the Office of Revenue Sharing, the General Accounting Office, and major research projects) is potentially important for policy formulation (i.e., if data are bad, users should know it) and for the evidence it provides on the relative worth of different methodologies for studying an 14 Thecitiesare (1) Albuquerque, New Mexico; (2) Ann Arbor, Michigan; (3) Cincinnati, Ohio; (4) Detroit, Michigan; and (5) Worcester, Mas­ sachusetts. See Chapter 2, "Sample Selection." 15 Of course, models are never perfect representations of "reality," and model error resulting from incomplete or inaccurate characterization of the underlying processes is always a competing explanation for such differences. See Chapters 3 and 4 for discussion of these issues and Chapter 5 for the analytic results. The analysis with the formal models was supplemented by return visits to cities with preliminary model results to search, through infor­ mal, unstructured interviews with local officials, for causal explanations other than GRS for the estimates of GRS effects.

INTRODUCTION

important class of problems—local responses to fiscal assis­ tance programs.

Obstacles to the Analysis of GRS Once you open a can of worms, the only way to recan them is to use a larger can. Zymuyrgy's First Law of Evolving Systems Dynamics16

Although important and timely, GRS is a "less-than-ideal" topic for analytic evaluations intended to influence policy decisions. First, there are political characteristics of the pro­ gram that will make its elimination or substantial modification very difficult, even in a period of pressing domestic problems and rising federal budget deficits. The program's constituency is formidable. It includes virtually all state and local officials.17 To the extent that revenue-sharing funds are being used to meet recurrent expenditure obligations (e.g., permanent per­ sonnel), these officials will have a large stake in the con­ tinuance of the program. With cessation or substantial refor­ mulation of GRS, many of these officials would face the choice, perhaps a Hobson's choice, of raising additional revenue from local sources, reducing levels of service, or incurring operating deficits. Modification of the formulas for the distribution of funds may prove to be particularly difficult. Congress may be re­ luctant to rejoin an issue where the inherent conflict is based on "who gets what" along both geographic and urban/rural lines. Such issues cannot be considered, much less resolved, on a "macro" level without leaving intense dissatisfac­ tions; conflict of this kind is best "managed" by diffusing the issues through committee structures and sequentially 16

Wallechinsky et al., The Book of Lists, p. 480. Officials from very large cities that experienced a decline in categorical assistance concurrent with GRS may be reluctant to support an expansion of GRS that entails further contraction of categorical assistance. Such cities have a greater comparative advantage in the piecemeal processes for funding categorical programs than on a formula basis that provides universal entry, a smaller pie, and relatively stable shares. 17

EVALUATING PUBLIC PROGRAMS

attending to small portions of the larger allocation prob­ lem.18 A second complicating factor in the study of revenue shar­ ing is the extreme ambiguity of the program's intent. Ambigu­ ous intent may be the "price of approval" for programs that are products of political processes. The first rule of the successful political process is, "Don't force a specification of goals or ends." Debate over objec­ tives should be minimized partly because ends and means are inseparable. More important, the necessary agreement on particular policies can often be secured among individu­ als or groups who hold quite divergent ends.19 The proponents of revenue sharing had multiple, and even conflicting, objectives in creating the program. Figure 1-2 is a simple listing of some of the objectives for GRS that the program's proponents articulated in the debate prior to pas­ sage of the original legislation.20 The analyst's problem in discovering a concise and coherent intent for the program from such a list is obvious. Some objectives are inconsistent (e.g., #3 and #11) and others are so ambiguous (e.g., #4 and #6) that they might be operationalized in any number of significantly different ways. Many of these objectives are concerned with processes of government (e.g., dissatisfaction with performance on categorical assistance programs, excessive "red tape," the "fiscal mismatch," etc.) rather than with particular expendi­ ture needs. In fact, one of the most distinctive features of GRS 18

Some theoretical and empirical bases for such a prediction, respectively, are found in Cyert and March, A Behavioral Theory of the Firm; and Richard F. Fenno, The Power of the Purse: Appropriations Politics in Congress. [Author's note: This prediction was made before the GRS renewal in 1976 that left the program (and formulas) essentially unchanged. The prediction stands for subsequent reconsiderations.] 19 Charles L. Schultze, The Politics and Economics of Public Spending, p. 47. 20 This list was culled from a much larger list taken directly from Edward R. Fried et al., Setting National Priorities; and William Willner and John P. Nichols, Revenue Sharing.

INTRODUCTION

is the virtual absence of "national" expenditure objectives; funds are to be disbursed by those officials "closest to the problems" and "most subject to citizen pressures."

FIGURE 1-2 Stated Objectives for General Revenue Sharing 1. To reduce the direct involvement of the federal government on domestic problems. 2. To reduce the amount of "red tape" associated with federal domestic programs. 3. To stimulate the creation and expansion of innovative local programs. 4. To increase the influence of each citizen as to how the money is used, make govenment more responsive to taxpayer pressures, and enhance accountability. 5. To increase the involvement of local citizens in governmental decision-making processes. 6. To stimulate the development of effective and responsive plan­ ning and priority-setting mechanisms at the local level. 7. To help improve the management and administration of state and local governments, including the consolidation of units. 8. To allocate to the states and local governments on a permanent basis a portion of the very productive and highly "growthelastic" receipts of the federal government. 9. To compensate for the federal government's use of the best tax sources. 10. To use more equitable tax systems by substituting federal for state and local taxes as a way of financing state and local services. 11. To provide relief to state and local taxpayers. 12. To stabilize spiraling local tax rates. 13. To moderate the variation that now exists in state and local tax rates and public service levels. 14. To improve the quality and quantity of services offered at the local level and to equalize their distribution. 15. To redistribute resources among states and localities so as to enable the poorer ones to raise the level of public services they provide. 16. To alleviate some of the intense fiscal pressures on local, and particularly urban, governments.

EVALUATING PUBLIC PROGRAMS

Finally, there are conceptual and methodological problems that complicate the study of revenue sharing. There are immense problems in attempting to distinguish programmatic from nonprogrammatic effects in the first few years of the program.21 Although touted as a trial period, the program was not well designed for learning purposes; except for a limited statutory life, there is nothing experimental about the pro­ gram's design.22 Also, GRS is a small proportion of state and local expenditures, however defined. This makes it potentially difficult to attribute effects causally to GRS. There are always more plausible competing explanations for small changes than for large, dramatic changes. In spite of the foregoing problems, the research problem, assessing the impact of GRS on municipal fiscal behavior, represents a somewhat unique opportunity for social scien­ tists. There is good (i.e., accurate in predictions and plausible in narrative) positive theory of municipal resource allocation processes. And there are reliable archives (i.e., budget and audit documents) on the financial variables central to an evaluation of GRS. With positive theory and reliable data, it is possible to evaluate GRS with much greater rigor than is usually possible for "natural experiments" by government. This book is nominally about GRS. It reports research on the impact of GRS on the fiscal behavior of five municipalities 21 The fiscal behavior of local officials may be very different for what they view as a "temporary" revenue source from what it would be with a stable revenue source. For example, many local officials attempt to use funds from temporary sources for nonrecurrent expenditure items (e.g., capital projects) rather than for objects that become recurrent obligations (e.g., permanent personnel). 22 In testimony before the U.S. Senate, Selma Mushkin argued for "evalua­ tion provisions" in the legislation without success. See Willner and Nichols, Revenue Sharing, p. 16. A great deal of effort was expended by the National Science Foundation and others to evaluate GRS thoroughly. The point here is simply that all such efforts were hampered by a poor (from a research standpoint) program design. A specific example is that the data generated routinely in the administration of the program have suspect analytic value and became a topic rather than a tool for evaluation. For a discussion of problems in analyzing "natural experiments," see Alice M. Rivlin, Systematic Thinking for Social Action.

INTRODUCTION

in great detail. And the work provides insights on the program that are somewhat unique. But the book is perhaps best viewed as a treatise on the conceptual and methodological foundations of program evaluation specifically and "policy research" generally. The most important argument in the book is that positive theories, theories of how individuals and organizations actually do behave, have an indispensable role in policy research. Program decisions are always choices among alternative futures; forecasts are the basis for such choices; and models (theories) of behavioral processes are the means of forecasting. The models used may vary greatly in their explicitness; the effects and values they consider; the extent to which they accurately portray the "real" processes; and the accuracy of the predictions they yield. But models are used. This central argument is so straightforward that it verges on being trivial. But the current fashion of distinguishing be­ tween "basic" and "applied" research, the enormous emphasis on "normative" vis-a-vis "positive" theory in the methodologically sophisticated social sciences (e.g., econo­ mics), and the prevalent view that research with possible "applications" is inevitably a second-class intellectual pursuit, indicate that the central argument in this book needs to be made again and again. And further, the argument needs to be made in the context of particular empirical research problems rather than as another wave on the sea of methodological meta-discussions that threatens to drown the emerging policy sciences. The problems GRS poses for systematic evaluation are generic, not idiosyncratic. GRS represents an important class of public programs including most tax, regulatory, and trans­ fer programs. These are programs that are not implemented as controlled experiments and whose effects are determined largely by the discretionary responses of many individuals and organizations beyond the direct, full control of agencies initiating the programs. Such programs have proved recalci­ trant to systematic evaluation using conventional research designs and methodologies (e.g., survey research and

EVALUATING PUBLIC PROGRAMS

econometric modeling). Schultze outlined the policy design problem: . . . even where programs are predominantly confined to the public sector—as is the case with elementary and secon­ dary education—the major instrument of federal policy is not direct action but joint action with state and local gov­ ernments through the grant-in-aid mechanism. To a grow­ ing extent, therefore, public program performance depends upon the behavior of a large number of independent decision-makers. . . . Actions cannot be commanded. There is no hierarchy of officials in a single line of command who can be directed toward a set of predetermined objec­ tives. In such cases the careful specification of plans and objectives by a public agency will not suffice to guarantee effective programs. The program must also be explicitly designed to provide incentives or inducements for the relevant decision-makers outside the public agency to act in directions which are consistent with program objectives.23 This book presents a somewhat new approach to understand­ ing the effects of such programs, an approach to extracting information from a program experience to support the revi­ sion of existing programs and the design of new programs.

Organization of the Book The only practical problem is what to do next. Bloggin's Working Rule No. 20Z4

In this chapter, the topic, objectives, and design for the research have been introduced in general terms.25 Chapter 2, 23 Charles L. Schultze, "The Role of Incentives, Penalties, and Rewards in Attaining Effective Policy," in Public Expenditures and Policy Analysis, edited by R. H. Haveman and J. Margolis, p. 146. 24 I. J. Good, The Scientist Speculates, p. 23. 25 The background on GRS provided here is intentionally brief, and the reader should refer to other sourcesfor a fuller description of the program and its history. Perhaps the most comprehensive sources for descriptive (as opposed to analytic) information are Richard P. Nathan, Allen D. Manvel, and Susannah E. Calkins, Monitoring Revenue Sharing', and Willner and Nichols, Revenue Sharing.

INTRODUCTION

provides a more detailed discussion of the research design. Research design, formulating and operationalizing the research question(s), is viewed as a complex, multifaceted, and highly constrained decision problem. The more important alternatives and problems in designing research for the analysis of GRS fiscal effects are treated explicitly. Chapter 3, "Theoretical Foundations," is an extension of the research design discussion in Chapter 2. In Chapter 2 the argument is made that explicit models are essential to sys­ tematic understanding of local responses to GRS, and criteria are stated for model development. One of these criteria is that the model(s) have some basis in a positive theory of municipal resource allocation processes. Selecting and elaborating a positive theoretical basis for the model(s) is the task of Chapter 3. After specifying four models in Chapter 2 and checking their consistency with a positive theory of municipal resource allocation processes in Chapter 3 and other criteria for model development, we turn in Chapter 4 to the problem of choosing among the models. The chapter begins with a discussion of "goodness-of-fit" criteria. The results from estimating and testing the models in each of the five cities studied are pre­ sented and one model is selected for use in the analysis of GRS fiscal effects for comparison with data from other sources. In Chapter 5, the empirical results from this study on GRS fiscal effects are presented. These results are then compared with data from the Office of Revenue Sharing (ORS) that some have interpreted as "net" fiscal effects. Comparisons between the results from this work and the results from other major sources of data on GRS fiscal effects were impractical for two reasons. First, in some cases the results from other projects were not available. And second, where results were available, they were reported in a form incommensurate with our data and ORS data that are organized by function. Com­ parisons with these projects are therefore limited to a discus­ sion of problems and trade-offs in the research design choices in Chapters 2 and 3.

EVALUATING PUBLIC PROGRAMS

Finally, in Chapter 6, more general conclusions about the impact of GRS are drawn and a few prescriptions are offered, potentially useful extensions of the research are discussed, and the developmental research strategy is summarized and appraised.

2 Research Design Rational choice depends upon the construction of two causal series, only one of which can be made to exist. . . thus all statesmanship, and all rational conduct of life, is based upon the frivolous historical game, in which we discuss what the world would be if Cleopatra's nose had been half an inch longer. Bertrand Russell The Principles of Mathematics1

Imbedded in a "research de­ sign" are decisions on the scope and content of the problem and a number of lesser, albeit important, methodological choices. For work within academic disciplines, these choices are often "sensible by convention." Disciplinary audiences are familar with the conventions; and the choices neither require nor receive extensive, explicit consideration. It is cus­ tomary in reporting research to convey the impression that research design choices are clear-cut, grounded in the theories of the discipline, and occurring prior to and neatly defining the actual work.2 In fact, for most empirical work in the social sciences, the many interrelated choices that constitute a "research design" are rarely, if ever, clear-cut. Research designs tend to evolve over the life of a project. In practice, research designs reflect 1

Quoted in H. A. Simon, Models of Man, p. 4. Abraham Kaplan calls these ex post rationalizations of what researchers do "reconstructed logics." They tend to be poor descriptive theories of what researchers do and lead to much unnecessary frustration and confusion in scientific enterprises. See Abraham Kaplan, The Conduct of Inquiry; Methodology for Behavioral Science, chap. 1. 2

EVALUATING PUBLIC PROGRAMS

resource constraints (i.e., limits on time, intelligence, and dollars), the researcher's predilections and training, data availability, and unforseen difficulties and opportunities. Because this research is interdisciplinary and concerned, in part, with alternative approaches to the study of a particular problem, it is important to consider explicitly the more impor­ tant conceptual and methodological choices underlying the "research design." The context for these choices is not barren. There are extensive academic literatures on methodology, on theoretical and empirical views of municipal resource alloca­ tion processes, and on intergovernmental fiscal relations. There is also a great deal of research, recently completed or in progress, on GRS fiscal effects. The discussion of conceptual and methodological choices must consider this context.

Problem Formulation If a problem has less than three variables it is not a problem. If it has more than eight you cannot solve it. Bloggin's Working Rule No. 23

One source of difficulty for many concerned with evaluating GRS, researchers and policy makers alike, is a fixation on the question, "How did they (viz., local officials) spend the money?" The question conjures up an image of a "special checking account" from which "they" spend money that is separable from preexisting "regular checking accounts" and the past, present, and future expenditures made from those accounts. In reality, revenue-sharing dollars are similar to funds from local sources (e.g., revenues from a general prop­ erty tax) in most important respects; they can be spent for many of the same things by the same people at the same time in the same way—the pie to be sliced is simply larger. The conception of a revenue-sharing "program" is valid from a national perspective: Revenue-sharing legislation represents (nominally) a major departure in federal inter­ governmental fiscal policy. There is a distinct appropriation 3

I. J. Good, The Scientist Speculates, p. 212.

RESEARCH DESIGN

associated with the program; and there is even an agency charged with the administration of the program bearing its name, the Office of Revenue Sharing (ORS). From a local perspective, however, the "program" is essen­ tially "more money." With few apparent exceptions, revenue-sharing money is being received and allocated by existing resource allocation processes. At the local level, the "program" has no staff, particular organizational affiliation, or differentiable objectives. In other words, the program has no separate identity at the local level except as a source of flexible revenues to which a few special accounting and report­ ing requirements are attached. The revenue-sharing legislation does specify a number of "priority" expenditure categories in which units of local gov­ ernment must spend all revenue-sharing funds that they receive. These categories are:4 1. Ordinary and necessary maintenance and operating expenses for: (a) Public safety (including law enforcement, fire pro­ tection, and building code enforcement); (b) Environmental protection (including sewage dis­ posal, sanitation, and pollution abatement); (c) Public transportation (including transit systems and streets and roads); (d) Health; (e) Recreation; (f) Libraries; (g) Social services for the poor and aged; and (h) Financial administration. 2. Ordinary and necessary capital expenditures authorized by law. 3. Debt retirement (principle only on debts incurred for a priority expenditure purpose after January 1, 1972). The legislation specifically prohibits the use of the funds for the local matching portion on other federal grants, requires 4 51.31, "Permissible Expenditures," in Federal Register, Part II (Washington, D.C.: Department of the Treasury, April 10, 1972), p. 9138.

EVALUATING PUBLIC PROGRAMS

the establishment of a separate "trust fund" for holding the money and all interest it earns, and prohibits expenditures on projects that are discriminatory or fail to meet minimum wage rates and labor standards. Cities are also required to publish in local newspapers and submit reports to ORS on both the "planned" and "actual" use of revenue-sharing money. The funds may be used to reduce local taxes or deficits with the stipulation that any increase in local matching on federal programs, in any enti­ tlement period, is offset by an increase in local revenues at least as great in the same entitlement period. In addition, the chief executive officer in each local government is required to "certify compliance" with all of these restrictions and direc­ tives annually. The substantive importance of these restrictions in affecting local expenditure behavior is questionable. With the possible exception of the prohibition against the use of funds for matching other federal programs, the primary impact of the restrictions appears to be on formal accounting and reporting. To avoid the expenditure category restrictions, or in under­ world (White House?) parlance, "launder the money," local officials need only report the money as spent for a "priority" activity normally supported by flexible dollars from a local source. GRS funds thus displace existing expenditure requirements and release money that can be used within limi­ tations, if any, on the sources of released funds.5 For example, in fiscal year 1975, the City of Ann Arbor, Michigan, according to its ORS reports, "spent" all of its revenue-sharing allocation, approximately $1.2 million, on 5 The General Accounting Office (GAO) concluded that "The priority expenditure requirements of the act are illusory. In a number of cases local governments were using or intended to use the [GRS] funds for operations and maintenance expenses that were not within one of the eight priority categories. Such uses technically violate the act. However, had the local officials possessed a better understanding of the technical details of the act and regulations, these problems could have been avoided through displace­ ment." Report to the Congress, Revenue Sharing: Its Use by and Impact on Local Governments, by the Comptroller General of the United States (B146285) (Washington, D.C.: General Accounting Office, April 25, 1974).

RESEARCH DESIGN

fire protection. This is a priority use under the "public safety" heading in the legislation and is a function that is normally funded from the City's General Fund in the annual operating budget. This allocation to a single functional area greatly simplified the City's task in complying with the program's formal accounting and reporting requirements. The city's pub­ lished plans for the use of revenue-sharing money and its reports on "actual use" indicate fire protection, although there was an absolute decline in the initial budget for that function from the previous fiscal year and a decrease in the function's relative share of available local revenues (which increased from the previous year) and revenue-sharing monies combined. The implication is clear: The official reports prepared by state and local officials on "planned" and "actual" use of revenue-sharing funds cannot be used to assess the "net" fiscal effects of the General Revenue Sharing Program. The empirical results reported here, conversations with officials in other cities, and review of other research projects indicate that this behavior is not unique; it may be the rule rather than the exception. The interpretation of this example and counterparts in other cities should not be simply that local officials are purposely attempting to mislead the public, ORS, or the Congress on the "actual" uses of the funds. Although this may be the case in a few cities, a more reasonable interpre­ tation is that it would be very difficult for officials, particularly those with relatively large, complex budgets, to understand and report fiscal effects accurately even if they were highly motivated to do so. The reporting requirements as conceived and administered are not sensible. Unfortunately, data from these ORS reports were used in official Treasury Department reports to Congress and reported publicly as if they accurately represented the pro­ gram's fiscal effects.6 Also, a number of evaluation projects 6 For example, see Joan C. Szabo, "Federalism Report/New Data Shows States, Localities Use Revenue-Sharing Funds to Hold Down Taxes." Perhaps the most blatant academic use of related data, a mail questionnaire to chief administrative officers of every city over 50,000 in 1973, is found in the work of David A. Caputo and Richard L. Cole. In spite of a 51.8 % response

EVALUATING PUBLIC PROGRAMS

have relied to some extent on the ORS data. There is, how­ ever, great variation among these projects with respect to (1) the extent to which problems with ORS data are explicitly recognized; (2) the extent to which the problems dissuade the investigators from using the data to measure fiscal effects (ORS had made the data easily accessible and relatively cheap and thus attractive); and (3) the extent to which research designs that are being used or proposed adequately augment or supplant the ORS data. The extent to which ORS data are misleading on the fiscal effects of the program in any particular city is an empirical question. Because the ORS data have been a visible and confusing factor in both analytic work and policy debates, it is extremely important to have analysis that points up interpre­ tive problems with those data, questions its accuracy, and provides alternative estimates of fiscal effects. From a problem formulation point of view, "How did they spend the money?" is the wrong question because it directs attention to nominal expenditures of GRS funds. The formu­ lation abets superficiality and confusion in the study of GRS fiscal effects. The interpretive problems with ORS data are considerable.7 A slightly more sophisticated version of the original ques­ tion is "How did they really spend the money?" This version recognizes some difficulty in interpreting the official reports, primarily because they are "public pronouncements" of politicians and administrators that may or may not reflect "private beliefs." It assumes that most public officials are introspective about and have a high degree of control over their expenditure behavior. Public officials could tell us "how rate and enormous interpretative difficulties with their methods, they have widely reported their data as if it reflects "the actual impact of revenue sharing on spending patterns of local governments." The latest version of this work is "The Initial Impact of Revenue Sharing on the Spending Patterns of Ameri­ can Cities" in Kenneth M. Dolbeare (ed.) Public Policy Evaluation, pp. 119-150. 7 See Chapter 5 for comparisons of the results from this research with GRS results and analysis of the differences.

RESEARCH DESIGN

they are spending the money" if they were only willing. A number of projects are using questionnaire and/or interview techniques to acquire data from local officials on fiscal effects that are not contaminated by "publicness." There are at least three problems with attempts to use survey techniques to measure the program's fiscal effects. First, which officials are to be surveyed? It is very likely, if not certain, that different local officials in the same city will have very different perceptions of GRS' impact on fiscal behavior.8 If several officials are surveyed, there is the difficult problem of arriving at a "correct" set of fiscal effects for the city. Second, assurance of anonymity may not lead to descriptions of fiscal effects that are very different from the official reports.9 Many officials will believe that the reports honestly reflect "how they spent the money." And third, local officials, particularly those with large, complex budgets, will probably be unable to respond meaningfully to the questions relevant to an analysis of the program's fiscal effects. These officials can articulate obvious differences in outcomes where they exist and they can describe differences in the processes used to arrive at outcomes. But to describe fiscal impacts analytically would require insight into their own behavior and decision processes and an implausible degree of sophistication about the outcomes of difficult choices that were never faced (viz., budgetary outcomes that would have resulted had revenue sharing not been available). From a problem formulation point of view, "How did they really spend the money?" improves the original formulation but is still not adequate. It recognizes interpretive problems with published, nominal expenditures; but it implies that there 8 Major perceptual differences between mayors and finance officers on the impact of GRS are reported in Thomas J. Anton and Richard Hofferbert, "Assessing the Political Impact of General Revenue Sharing." Also, see Robin Barlow, F. Thomas Juster, and Gail Wilensky, "Economic Aspects of Revenue Sharing in Municipalities." 9 It is very difficult to know how different the results are because they are not comparable. ORS uses functional categories (e.g., "public safety"), whereas the surveys use other categories (e.g., "new expenditures," "main­ tain existing expenditures," etc.) in describing fiscal effects.

ΕνΑΙΧΤΑΉΝΟ PUBLIC PROGRAMS

is a set of fiscal effects known to proximate actors (i.e., state and local officials). The formulation suggests a research strategy of identifying the relevant actors and extracting the "correct" information on fiscal effects from them by some means. The problem formulation preferred here on grounds of conceptual clarity is "What changes in the revenue and expenditure patterns of recipient units of government are attributable to GRS?" This formulation recognizes that GRS affects ongoing resource allocation processes and that the research problem is to understand behavior changes resulting from the program. Clarity in problem formulation is extremely important in that, to the extent possible, methods should be adapted (matched) to problems and not problems to methods. Donald T. Campbell's distinction between "trapped" and "experi­ mental" administrators is a useful analogy here.10 The "ex­ perimental administrator (researcher)" is committed to solv­ ing a problem, whereas the "trapped administrator (researcher)" is committed to a particular solution (method). Strong commitments to particular methods (survey research, regression analysis, etc.) often result in inappropriate and ineffective research applications. Research designs should be problem-directed.

Program Evaluation If a research project is not worth doing at all, it is not worth doing well. Gordon's First Law"

Evaluation can be usefully viewed as ex post rational choice activity with two related, but separable, analytic tasks. The first task is to describe programmatic effects by comparing two alternative states—a hypothetical status quo set of outcomes (e.g., "What would have happened") and an observed set of outcomes (e.g., "What did happen")—and then sorting pro10 11

Donald T. Campbell, "Reforms as Experiments." Wallechinksy et al., The Book of Lists, p. 481.

RESEARCH DESIGN

grammatic effects from nonprogrammatic effects. The second task is normative. The programmatic effects that emerge from the descriptive effort are related to normative criteria, and judgments are formed on the desirability of program effects. A frequent analytic simplification of this second task is to ask the effectiveness question, "To what extent did the program do what it set out to do?" rather than the broader normative question, "Are the programmatic effects, intended and unin­ tended, desirable?" For several reasons, the focus of this research is on the descriptive rather than the normative task. First, the descrip­ tion of programmatic effects must precede any careful norma­ tive judgments made on other than a philosophic basis. Sec­ ond, the descriptive task is more amenable to analytic treat­ ment than the normative task. And third, because of the chaotic nature of objectives for GRS (see Chapter 1), using the effectiveness formulation—comparing program accom­ plishments with program intentions—to make a full evaluation analytically tractable would require an extremely arbitrary specification of program intent. The literature of public finance and welfare economics was another possible source of value attributes for evaluating GRS. A standard list of attributes from this source would include economic efficiency, equity, price stability, and em­ ployment. This approach was rejected for several reasons. First, policy makers probably will not share an analyst's belief in the importance of all of these attributes in appraising GRS. At minimum, there will be other important attributes. Second, these are attributes—dimensions of value—not norms. The analyst must still describe GRS effects in terms of these attri­ butes and compare them, attribute by attribute, with norma­ tive standards derived abstractly, from political consensus, or analysis of the probable effects of some alternative inter­ governmental fiscal arrangement(s). Abstract derivation is arbitrary. The political consensus does not exist. And there are an infinite number of alternative intergovernmental fiscal arrangements. And third, the attributes pose individually dif­ ficult analytic problems. The analytic problems in attempting

EVALUATING PUBLIC PROGRAMS

to understand the distributive-equity consequences of GRS were discussed in Chapter 1. Economic efficiency is a theoretical concept, intractably nonoperational for empirical work. The normative standard for discussions of economic efficiency is a contrived world of rational actors used for analysis of static equilibrium positions in markets, a world in which people and organizations know precisely what they are doing and why. In this world of cardi­ nally measurable objective functions and perfect knowledge of (or rational expectations about) the impact of all possible actions on welfare, all governments need do (sic) to achieve allocative efficiency is set benefits from the marginal public expenditure equal to benefits from the marginal private expenditure and ensure that the marginal benefit of public expenditures is equal across all programs, activities, and ele­ ments. Until it is reasonably possible to map from the world as we know it (where we are) to a contrived world (where economists want us to be), it is difficult to see how analysis within a contrived world can usefully inform policy choices. Price stability and employment are perhaps more suitable attributes for evaluating GRS. These are prominent, enduring concerns of policy makers. It is not clear, however, how to analyze the impact of GRS on these attributes; the analysis would appear to require much richer and predictively accurate models of our economic system than we now have. These models, capturing the importance of institutional arrange­ ments (e.g., intergovernmental fiscal structure), would be used to address "what if" questions about alternative inter­ governmental fiscal arrangements. Such models do not pres­ ently exist, and they are not apt to be developed in the near future. James G. March has argued more generally that effective­ ness formulations are, perhaps, counterproductive in our attempts to learn from experience: . . . we need to reconsider evaluation . . . there is nothing in a formal theory of evaluation that requires that the criterion function for evaluation be specified in advance. In particu-

RESEARCH DESIGN

lar, the evaluation of social experiments need not be in terms of the degree to which they have fulfilled our α priori expectations. Rather we can examine what they did in terms of what we now believe to be important. The prior specifica­ tion of criteria and the prior specification of evaluational procedures that depend on such criteria are common pre­ sumption in contemporary social policy making. They are presumptions that inhibit the serendipitous discovery of new criteria. Experience should be used explicitly as an occasion for evaluating our values as well as our actions.12 Conclusions from this research will take the form of condi­ tional statements (e.g., if the program's intent was to . . ., then . . .), but the research focus is decidedly descriptive. Although the evaluative task of describing effects can never be wholly value free because analysts must always make implicit value judgments (e.g., in circumscribing the domain for analysis), its normative content is (or should be) small relative to its analy­ tic content. The paucity of descriptive theories of behavioral processes, theories of how individuals and organizations do behave that are both plausible in narrative and accurate in prediction, is an important source of difficulty for social scientists qua policy analysts. The effects of an increasing number of government programs (e.g., tax, regulatory, and transfer programs) are determined largely by the responses of individuals and organ­ izations beyond the direct control of agencies initiating and operating the programs. For the analysis of such programs, models based on descriptive (as opposed to normative) theories of behavioral processes have an indispensable role. Descriptive or "process" models are essential to forecast the consequences of alternative program designs ex ante. And where programs are not implemented as controlled experi­ ments, symbolic models of processes are essential to generate the counterfactual hypotheses on "what would have hap­ pened without the program" that are essential to understand program effects ex post. 12

James G. March, "Model Bias in Social Action."

EVALUAnNG PUBLIC PROGRAMS

In the last two decades, there have been some important trends in "policy analysis tradition." These trends consist of periodic changes in the activities (and methods) that analysts of government programs and policies have perceived as feas­ ible and useful. These changes can be interpreted, albeit nar­ rowly, as adaptations to the experience of confronting a dif­ ficult task environment with inadequate tools, particularly descriptive theories. Richard R. Nelson13 has observed that "the scriptures of the policy analysis tradition have been marked by a shifting of emphasis from before the fact analysis, to evaluation of pro­ grams ex post, to deliberate experimental development of policy." Each shift has been taken in response to experimental feedback, often negative, and reflects (1) a heightened appre­ ciation of the formidable complexity of "real" policy prob­ lems vis-a-vis existing tools for policy analysis; (2) reduced aspirations for impact of policy analysis on policy formulation processes where timing and magnitude of impact are key attributes of aspirations; and (3) a new focus of analytic atten­ tion (or "analytic strategy") in a continuing quest by policy analysts for a substantive role in policy formulation. These shifts in "scripture" have taken us from the euphoric pursuit of planning, programming, and budgeting systems (PPBS) in the 1960s to the carefully qualified program "experiments" of the 1970s. It is important to note that each shift in emphasis has resulted in an analytic strategy that relies less than its pre­ decessor on the existence of "usable" theories of behavioral processes—theories on how individuals and organizations do behave that can be used systematically to forecast responses to alternative program designs. The extreme difficulty of providing accurate forecasts, par­ ticularly when the variables of interest are the outputs of 13 Richard R. Nelson, "Intellectualizing About the Moon-Ghetto Metaphor: A study of the Current Malaise of Rational Analysis of Social Problems." Much of this section has been adapted from P. D. Larkey, "Pro­ cess Models of Governmental Resource Allocation and Program Evalua­ tion."

RESEARCH DESIGN

behavioral systems, is a persuasive explanation for the shift from "before-the-fact-analysis" to "evaluation of programs ex post." PPBS is an ambitious and conceptually seductive scheme for institutionalized, comprehensive policy analysis. It is voracious in its forecasting and, hence, descriptive theoreti­ cal demands. Our ability to satisfy these demands today is only slightly better than it was in 1960 at the advent of PPBS. The analytic task of predicting observables can have unpleasant characteristics: Predictions that are sufficiently specific to be of interest to policy makers are often demon­ strably wrong. Even nonexperts, not fully appreciative of forecasting difficulties, can often see prediction errors.14 Ex post explanation is a much safer haven for analysts than ex ante prediction, and ex post explanation is the object of more recent strategies for policy analysts. "Evaluation of programs ex post" and "deliberate experi­ mental development of policy" are closely related. The dis­ tinction between the two is not always sharp, but rests on the degree to which the program experience is preplanned to facilitate understanding of the experience ex post. Experi­ mentation, which Kaplan15 describes as "only experience carefully planned in advance," is an approach to program evaluation. It is an approach that, through careful planning of the program experience, permits some substitution of the logic of classical experimentation for theory of behavioral processes in analyzing experience. To the extent that two 14 When forecasts are complex and include many years, such as the fore­ casts often made by cost-benefit analysts, the accuracy of the forecasts is usually never appraised. The appraisal can be technically difficult; and the persons best qualified to do the appraisal, those who made the forecasts, have no incentive to do it. Also, the benefits from appraising forecasts ex post are largely improved forecasting capabilities. There are no "direct policy payoffs" from appraising stale forecasts and, therefore, no funding for the work. One excellent appraisal of cost-benefit forecasts in the area of water resource projects indicates very significant, biased forecasting errors. These errors in working with physical systems with known technologies do not augur accuracy for forecasts working with "black box" behavioral systems. See Robert H. Haveman, The Economic Performance of Public Investment. 15 A. Kaplan, The Conduct of Inquiry, p. 147.

EVALUAWNG PUBLIC PROGRAMS

groups can be made equivalent in all respects, except program participation, program effects can be determined without understanding the behavior of either group. The ethical, political, and technical difficulties in pursuing a social experimentation strategy have received extensive comment,16 but there is an even more fundamental limitation to the strategy, a limitation on the extent to which the logic of experimentation can substitute for theories of behavioral pro­ cesses in policy analysis. All evaluation, experimental or nonexperimental, is directed at understanding a program experience ex post. It is important to recognize that such understanding, even if it is absolutely conclusive about the past, is a necessary, but not sufficient, basis for policy choices because such choices require forecasts. Prediction based on existing descriptive theory is a burden that policy analysts, advising on program choices with future consequences, cannot evade by altering strategic philosophy or research methods. Social experimentation, an evaluative strategy and the lat­ est "scripture" in the policy analysis tradition, should be viewed as an opportunity to garner the knowledge of behavioral processes and data required to develop and esti­ mate models for forecasting. Experiments should not be viewed as necessary and sufficient means for testing programs to support "go/no-go" decisions on large-scale versions of the experimental program forms. It is absurd to extrapolate implicitly and naively the results from a rigorous program experiment into an uncontrolled, dimly understood future. Such extrapolations are particularly absurd if time and the variables that move with it were critical controls in arguing for the validity of the experimental trial. Success in program evaluation depends on our ability to construct a causal series that is counterfactual. We must com­ pare "what would have happened" with "what did happen" to 16 Alice M. Rivlin, Systematic Thinking for Social Action; Alice M. Rivlin and P. Michael Timpane (eds.), Ethical and Legal Issues of Social Experi­ mentation ; Ronald N. Taylor and Ilan Vertinsky, "Experimentation in Organ­ izational Behavior and Strategy," in Handbook of Organization Design, W. Starbuck (ed.).

RESEARCH DESIGN

determine what the effects of a program have been. Since we can never know a counterfactual state with certainty, we must argue through analogies to attribute effects causally to pro­ grams. One form of analogy for evaluation is the "controlgroup analogy" of classical experimental design. In a perfect experiment, the "control group" is identical to the "treatment group" in every respect save one, program participation (treatment). The control group is a physical analogue of the treatment group. By observing and comparing the behavior of both groups over the same time period in the same context, it is inferred that behavioral differences are attributable to the program (treatment). Ceteris paribus conditions are ensured through controls in the perfect experimental design (i.e., the analogy is perfect); and the evaluative inferences on pro­ grammatic effects are incontrovertible because no plausible alternative causal explanations exist. Because perfect experimental design is unattainable in any applied setting, even when working with the best-understood physical systems, approximate methods are used such as the random selection of participants to control and treatment groups. These methods are intended to strengthen the anal­ ogy, and hence, the inferences from comparisons of the groups. For evaluation research, the behavior of the control group is the counterfactual causal series: "how the treatment group would have behaved without the program." The control-group analogy is an extremely useful, if not essential, analytic tool for determining program effects ex post when the "process physics" of the program participants' behavior are poorly understood. This is, of course, the usual circumstance in the social sciences. The control-group anal­ ogy, when feasible, enables the researcher to focus on behavioral outcomes and to ignore underlying structure and parameters of behavior; the participants can be treated as "black boxes." The principal advantage of the control-group analogy in evaluation research is that one can determine program effects in spite of ignorance about behavioral processes. This is an advantage only as long as experiments are viewed as a means

EVALUAnNG PUBLIC PROGRAMS

to further understanding of what is inside the "black boxes."17 Information on program effects of an absolute sort (e.g., the program achieved X, Y, and Z) is inherently less useful in improving future programs than information on why pro­ grams produce particular effects or information on program performance relative to other program designs.18 The principal disadvantage of the control-group analogy for analyzing governmental programs is the infrequency with which it can be used effectively. The analogy is totally infeasible for evaluting many governmental programs because the programs (e.g., GRS) are implemented in ways (e.g., univer­ sally or nonrandomly) that make it impossible to identify a plausible control group. The counterfactual arguments for evaluation, when the control-group analogy is not feasible, impose the same theoretical demands as ex ante prediction with the added twist that the events being described never occur. Ex ante forecasts of factual events can be checked through observation, but counterfactual hypotheses are intractably hypothetical and untestable. The alternative analogy for making the counterfactual arguments required to understand program effects is a model or symbolic analogue of the behavior of program participants. The past behavior of participants is crucial in developing and estimating such models. An ideal form of the symbolic analogy is conceivable just as it was for the control-group analogy. In this form we would possess a "perfect" model of a behavioral process developed from past behavior. This model would be capable of replicat­ ing exactly the behavior of the real system under all condi­ tions, including the conditions (e.g., no treatment) required for counterfactual arguments in evaluation research. The analogy would be perfect because the model would incorpo­ rate all factors, internal and external to the process, that might affect behavior, thus precluding plausible alternative causal explanations for observed behavioral differences (program 17 D. T. Campbell, "Considering the Case Against Experimental Evalua­ tions of Social Innovations"; E. S. Quade, Analysis for Public Decisions. 18 Quade, Analysis for Public Decisions, pp. 225-226.

RESEARCH DESIGN

effects). Like the perfect form of the control-group analogy, the perfect behavioral model (symbolic analogy) is unattain­ able, and we must use approximate methods. These are the methods for iteratively building and testing symbolic models of behavioral processes. To the extent that a model captures important elements of the processes it purports to represent and is able to replicate observed outcomes of the "real" process in the period prior to the treatment (program) under a variety of conditions, including conditions that approximate those the treatment (program) brings about, a model can be used confidently as an analogy for making counterfactual arguments. The most common application of the symbolic analogy has been a static comparison, using a single group, of behavior at one point in time prior to a program with behavior at a point in time after the program is in effect. These are pretest-posttest or before-after research designs that are the social sciences' equivalent of the Charles Atlas body-building advertisements. The literature on quasi-experimental research designs19 is generally (and correctly) critical of the weak research designs (e.g., pretest-posttest, one-shot case studies) that have been the dominant instruments for program evaluation in the past. This literature describes several more sophisticated forms of the historical analogy (e.g., the "cross lag panel design" and the "interrupted time-series design") and research designs that attempt to merge the historical and control-group analogies (e.g., the "control-series design"). The literature's prescription for the malady of weak research designs in pro­ gram evaluation is stronger research design, particularly designs that use the control-group analogy. There are two problems with this literature and its prescrip­ tion. First, the literature is heavily and unnecessarily biased in favor of the control-group analogy. Applications of the sym­ bolic analogy are characterized as inherently inferior, and their use is recommended only when use of the control-group 19

D. T. Campbell and J. C. Stanley, Experimental and Quasi-Experimental Designs for Research; J. A. Caporaso and L. L. Roos, Jr. (eds.), QuasiExperimental Approaches: Testing Theory and Evaluating Policy.

EVALUAnNG PUBLIC PROGRAMS

analogy is impossible. Yet there is no inherent inferiority. Choosing the form of analogy for the evaluation of any par­ ticular program must rest on which analogy can be operationalized in the strongest fashion and on coarser con­ siderations such as cooperation of policy makers, data availa­ bility, costs, and time. There are numerous threats to the internal and external validity of experimental trials, just as there are numerous threats to the validity of models as approx­ imations of behavioral processes. The validity of an analogy is, in either case, a matter for judgment and a matter of degree. Second, the literature's treatment of research design over­ emphasizes methodological mechanics, particularly the use of classical statistics to approximate the logic of experimenta­ tion. It underemphasizes problem formulation (i.e., the pur­ pose of the research design). Specifically, there is little or no explicit recognition that the statistical models employed in the various time-series designs are being used to construct the counterfactual argument, that is, "what the individuals or organizations would have done without the program." The statistical models that are the basis of quasiexperimental designs are being utilized as analogues of the processes generating pretreatment, posttreatment data. But the models are not evaluated as analogues of process because they are not recognized as such. The only test of a model's correspondence with process in procedures such as the "double-mood test" or the "Walker-Lev test"20 is implicit in the standard procedures (e.g., ί-test and/-test) used for testing hypotheses on differences in the pretreatment and posttreat­ ment periods. To the extent that goodness-of-fit is poor [i.e., the linear regression line(s) in each period is a poor ana­ logue of process and the standard error(s) is large], the tests are less able to show statistically significant treatment ef­ fects (i.e., differences in the level and slope of the lines in each period). When symbolic analogies are used to make counterfactual arguments for evaluation, they are theories of process regard20

Caporaso and Roos, Quasi-Experimental Approaches, pp. 28-29.

RESEARCH DESIGN

less of the form they take (e.g., static, pretest-posttest com­ parisons, simple extrapolations of trends, or detailed models of behavior). They should be appraised as theories of process. There are real benefits that can accrue from explicit recogni­ tion of the theoretical requirements in making counterfactual arguments for evaluation. One important benefit is more and better theory. And further, theory of processes, developed in an evaluation context, can be used predictively for the ex ante exploration of the consequences of alternative program forms.21 At the present time, more effort is devoted in evalua­ tion research to methods that evade theoretical requirements than to using existing theory and to improving the theoretical base. This book reports research on the effects of General Revenue Sharing on municipal fiscal behavior. The research exemplifies most of the important, albeit abstract, points made in the foregoing methodological discussion. GRS is an excellent illustrative program because most of the more dif­ ficult conceptual and methodological problems that it poses for systematic analysis are not idiosyncratic to the program, but generic. The same problems are posed by a large number of government programs, particularly programs whose effects depend critically on the discretionary responses of individuals or organizations. The problem formulation used here, and the recognition that a "symbolic analogy" must be used, provide a broad outline of an appropriate research strategy. The systematic analysis of GRS fiscal effects requires a systematic means of generating a complex hypothesis on what behavioral patterns of recipient units would have been without GRS. The task clearly requires a longitudinal perspective and a model of recipient behavior. A number of highly interrelated research design decisions must be made to operationalize the general 21 Rivlin's advocacy of "systematic experimentation" stems, in part, from her perception, in the context of designing poverty programs, of the need for "a behavioral model of the population—at least of the low-income popula­ tion—that would make it possible to simulate the effects of alternative policies." See Rivlin, Systematic Thinking for Social Action, p. 32.

EVALUATING PUBLIC PROGRAMS

approach. For discussion, these are grouped under the over­ lapping headings of (1) sample selection; (2) data collection and the unit of Analysis; and (3) criteria for model develop­ ment.

Sample Selection The five city governments included in this empirical research were those of Albuquerque, New Mexico; Ann Arbor, Michigan; Cincinnati, Ohio; Detroit, Michigan; and Worces­ ter, Massachusetts. A "sample" of only five of more than 38,000 units of government receiving GRS funds obviously limits the general­ ity of any conclusions. Also, the analysis considered only one type of recipient unit—city governments in cities with over 100,000 population—and aside from suggesting that a research strategy analogous to the one used for cities would be appropriate for understanding the impact of GRS on other types of recipients (states, counties, etc.), the conclusions are neccessarily restricted to city governments. And further, although the study includes cities that are diverse in population and areal size, institutional arrange­ ments, fiscal condition, geographic location, and demographic characteristics, the group is simply not large enough to be "representative" in the statistical sense. Given limited research resources, the trade-off, in simplistic terms, is often between broad coverage with superficial and imprecise methods or limited coverage with more precise methods. Obviously, this study has exchanged breadth in coverage for depth of understanding. There were two primary reasons for choosing city govern­ ments for analysis rather than other types of recipient units. First, a large part of the "fiscal problems" that prompted national support for GRS are problems of cities, particularly large urban centers. Many cities are fiscally hard-pressed, and understanding the impact of GRS on these fiscal problems is particularly important for policy formulation. And second, the existing positive theory of municipal resource allocation

RESEARCH DESIGN

processes is more fully developed than that for other types of recipients. Two criteria guided the selection of the particular cities. First, each city was required to be part of one or more other research projects intended to measure the fiscal effects of GRS. The original intent was to devote considerable effort to comparing this research's results with ORS data and with the results from other research projects using more extensive methods to describe fiscal effects. With the exception of ORS data, these comparisons on a city-by-city basis were not pos­ sible. And second, diversity was sought among the city govern­ ments in terms of (1) budget size; (2) size and physical charac­ teristics of service area; (3) size and demographic characteris­ tics of service population; (4) climate; (5) political organiza­ tion (form of government); (6) statutory constraints on fund usage and revenue sources; (7) intergovernmental relation­ ships; (8) the ratio of the city's population to the population in its larber urban area; (9) financial condition; (10) history of growth (e.g., physical area and population) and development (e.g., employment base) of the city served; and (11) the values and capabilities of local officials. The "sample" of five cities includes considerable diversity on all of the characteristics mentioned above, particularly over a 17- to 23-year period for each of the cities.22 Table 2-1 provides some evidence in support of this assertion. One objective of this research is to test the feasibility of adopting the research strategy on a much larger scale. Diversity is important in satisfying this objective. Also, the diversity is important in ascribing some generalizability to the results. This set of five cities contains many, albeit not all, of the differences among U.S. cities on a variety of dimensions. The sample is not adequate for comprehensive analyses (e.g., estimating aggregate GRS fiscal effects), but the analysis is considerably more than five "case studies." 22 Some further information of this type is given in the discussion of empirical results in Chapter 5. Also, see Chapter 3 for a discussion of differ­ ences in the structure of decision making.

-18%

-13%

1,500,000

176,000

$10,038

$10,045

$8,894

$12,812

$9,641

1970 median family income

Strong mayorcouncil Modified city manager

Commissionmanager (changing to strong mayor) Mayor-council (with councilappointed manager) City manager

Form of government

Nonpartisan

Nonpartisan

Partisan (with three political parties) Nonpartisan

Nonpartisan

Type of election

$112,000,000

$473,000,000

$79,000,000

$11,000,000

$37,000,000

1974 general fund expenditure*

Income and property tax Property tax

Income tax

Sales tax (plus miscellaneous smaller taxes) Property tax

Major revenue source(s)

Variation in general fund expenditures is due to differences in the scope of organizational responsibility for services (e.g., Worcester is the only city with general fund responsibility for public schools) as well as differences in levels of spending.

8

-10%

450,000

Cincinnati, Ohio Detroit, Michigan Worcester, Massachusetts

+ 107%

100,000

Ann Arbor, Michigan

+152%

244,000

Albuquerque, New Mexico

City/state

1950-1970 1970 population population change

TABLE 2-1 Characteristics of the Five Cities

RESEARCH DESIGN

Data Collection and the Unit of Analysis Public agencies are very keen on amassing statistics—they collect them, add them, raise them to the nth power, take the cube roots and prepare wonderful diagrams. But what you must never forget is that every one of those figures comes in the first instance from the village watchman, who just puts down what he damn pleases. Sir Josiah Stamp23

The units of analysis for this research are municipal resource allocation processes. These are the behavioral units with proximate responsibility for the use of GRS. In each of the five cities studied, GRS funds were received and appropriated by existing processes.24 Data were gathered from individual city governments, and cities were analyzed as autonomous decision-making units.25 For aggregate results, relevant results from the analysis of individual units were mapped onto more aggregate categories. This is a "building-block" approach to aggregation that should be distinguished from an alternative approach that constructs and estimates models on aggregate data—data that 23 Quoted in Thomas H. Wonnacott and Ronald J. Wonnacott, Introduc­ tory Statistics for Business and Economics, p. 397. 24 With the exception of Ann Arbor, this was not known at the time the cities were selected. There is evidence that a few cities (e.g., Raleigh, North Carolina) have established somewhat separate resource allocation mechan­ isms for GRS. Since such cases were not encountered, it is difficult to specu­ late on what changes in the research design would have been required. The extent to which such arrangements are truly separate from existing decision processes and the substantive difference they make in outcomes are, of course, unanswered empirical questions. 25 The extent to which the units are autonomous is also important because the domain required of models is a function of that autonomy. All processes are embedded in larger processes. The linkages, in the direction and strength of interactions between a process and its larger environment, are important in determining what can be excluded from consideration in modeling it. The models used in this research assert that municipal resource allocation pro­ cesses can be viewed as a "nearly decomposable" subsystem of a larger socioeconomic environment. The linkages—with the exception of the "revenue constraint" (see Chapter 3)—can be ignored for "short-run" analy­ tic purposes. See J. P. Crecine, Governmental Problem Solving: A Computer Simulation of Municipal Budgeting, pp. 176-179, for discussion and further references.

EVALUATING PUBLIC PROGRAMS

summarize the behavior of two or more behavioral units. To the extent that the units are autonomous decision-making units, the analysis of aggregate data obscures the underlying behavioral responses of the units. It is very difficult to work back from such aggregate models to understand the underly­ ing causal structures leading to particular responses to the program. The assumptions about motivation and the structure of behavior that are required to construct a model of distinct decision-making units, as Chapter 3 indicates, are "heroic." The assumptions required to model two or more distinct units simultaneously are, however, far more "heroic." Such model­ ing may become nothing more than a mindless exercise in "curve fitting" that, at best, provides us with aggregate indices of behavior. Given the need for a longitudinal perspective, the principal reason for collecting data from primary sources (i.e., budget and audit documents in municipalities) was the lack of alterna­ tive sources. In spite of the enormous amount of work that a data-collection effort of this type entails, there are tremen­ dous advantages. "Uncertainty absorption" is a major problem in the use of published data: Uncertainty absorption takes place when inferences are drawn from a body of evidence and the inferences, instead of the evidence itself, are then communicated. The succes­ sive editing steps that transform data obtained from a set of questionnaires into printed statistical tables provide a simple example. Through the process of uncertainty absorption, the recipient of a communication is severely limited in his ability to judge its correctness. . . . To the extent that he can interpret it, his interpretation must be based primarily on his confidence in the source and his knowledge of the biases to which the source is subject, rather than on direct examination of the evidence.26 Constructing data sets from primary sources in municipalities 26

J. G. March and H. A. Simon, Organizations, p. 165.

RESEARCH DESIGN

is an extremely difficult judgmental task when those data sets are intended to be consistent either longitudinally or in cross section. Longitudinally, the problems are (1) the expiration of functions or the transfer of a function from one fund to another; (2) the creation of new functions; (3) the combina­ tion or separation of existing functions; and (4) changes in accounting (report) formats.27 In cross sections, the general problem is a fundamental incompatibility of accounting struc­ tures between cities. For consistency, detailed account categories must be mapped onto more aggregate categories. Regardless of how the data are collected, there are a host of decisions that must be made to arrive at a usable data set. Proximity to these decisions is invaluable in subsequent analysis. In this research, data problems were encountered at two points: when the data sets were constructed for each city, and in mapping model results onto the ORS categories for com­ parison with ORS data. In constructing data sets, inconsisten­ cies within cities over time were resolved and inconsistencies between cities were largely ignored. The detailed "objects of expenditure" were consolidated into "personnel" and "nonpersonnel" categories wherever possible. When such separa­ tion would have been wholly arbitrary (deficit accounts, func­ tions supported by two or more funds, etc.), "total only" functions were included. Also, some combinations of related functions were required, particularly those that were merged in some years and separate in others. In other cases such functions were disaggregated, particularly when one of the 27 In collecting data longitudinally, other problems were (1) missing docu­ ments; (2) data errors such as imbalances and incorrect decimal placements; and (3) several versions of nominally the same documents but with different numbers. These problems and the problems mentioned in the text above are detectable (and correctable) when data are being collected and used in a longitudinal fashion. Numbers only make sense in context, and a longitudinal context is far better suited for human perception of error than a crosssectional context when collecting and using data that are stable across years but not between functions in one city or between cities in a single year. This raises some interesting and potentially disturbing questions about the quality of census information on municipal revenues and expenditures.

EVALUATING PUBLIC PROGRAMS

other did not. Finally, some functions that were part of "gen­ eral fund" budgets in early years and dropped prior to GRS were excluded from the data sets. In mapping model results onto ORS for comparison, an attempt was made to discover and use the rules that city officials used to map their budget detail onto the more aggre­ gate ORS categories (see Chapter 5). Since the ORS categories are not a mutually exclusive set describing the full range of municipal services, some of this "mapping" is arbi­ trary; and therefore, following the cities' procedures, to the greatest extent possible, avoided differences between the ORS data and fiscal effects identified from analysis due to arbitrary categorization decisions. From the initial data collection efforts, including informal interviews with city officials, an important simplification of the task was discovered that could be made with some impun­ ity: It was possible to restrict attention in both data collection and modeling in all cities to revenues flowing into the "general fund" and appropriations flowing from the "general fund." The discovery leading to this simplification was that in all five cities, all of the GRS money appropriated from the GRS trust funds was appropriated to "general fund" accounts.28 To appreciate why the simplification is possible, one must have some understanding of fund accounting as practiced by municipal governments. Figure 2-1 is a hypothetical city with three funds—fund A, the GRS trust fund, and a general fund. Revenue source A is "earmarked" and must be accounted for separately in a corresponding fund, fund A, which then sup­ ports activities (e.g., A-I and A-2) legally consistent with the earmarking. The earmarked fund may be assessed a "munici­ pal service charge" that is paid to the general fund for adminis­ trative services. And the earmarked fund may receive a direct contribution (subsidy) from the general fund. Local receipts not earmarked are placed in the general 28 This was not known at the time the cities were selected, with the exception of Ann Arbor. Direct appropriations from GRS trust funds to funds other than the "general fund" were expected. The task would have been somewhat more complicated if this had been the case.

Function A-I

Fund

A-2

Function

Revenue Source

FIGURE 2-1

Function G-I

'Subsidy'

Function G-2

General Fund

Function G-3

GENERAL FUND REVENUES 1. Tax Receipts 2. Other

General Fund Revenues

Function G-4

Function G-5

' GRS ,Trust Fund

General Revenue Sharing

EVALUATING PUBLIC PROGRAMS

fund, where they can be held as reserve or allocated to a variety of functional accounts.29 GRS monies are placed in a separate trust fund as the law requires and either appropriated directly from there to legally permissible accounts normally supported from the general fund or appropriated to the gen­ eral fund and then accounted for, ex post, as "spent" for legally permissible activities. There is no substantive differ­ ence in these two accounting procedures. The important point is that with either procedure, GRS money must pass through the general fund and the only effects that GRS can have on other funds is through the various connections between the general fund and these other funds. Specifically, the connections are the "municipal service charge" (accounted for as a general fund receipt), general fund contributions to other funds (a general fund appropria­ tion), or functional accounts (e.g., A-2 on Figure 2-1) that are supported jointly by the general fund and another fund (a general fund appropriation). If the analysis with the model shows these connections to other funds with GRS were "as expected without GRS," then GRS effects on other funds can be ignored. This was almost always the case, with the major exception being a reduction in a "municipal service charge" in Albuquerque (see Chapter 5). Deciding what data to collect (and use) is also deciding what behavioral outcomes are relevant and what knowledge about a process is needed to model its behavior. In this research, revenue and expenditure patterns are taken as the relevant outcomes. "Revenue" is defined as the sum of general fund expenditures from all but federal sources.30 This definition 29 "Functional accounts" is used broadly. It may refer to functionally specific accounts such as "police/personnel" or functionally more ambiguous accounts such as "deficit reduction" or centralized "capital outlay." 30 In most cases, federal funding was distinguished, at some level, in accounting records from other types of funding, and it was possible to exclude it. There were a few instances, however, where federal funding was present but not identifiable as an amount from any source. These were always small amounts and did not threaten the validity of the inferences on GRS fiscal effects. The reasons for factoring out federal funding, other than GRS, was to avoid

RESEARCH DESIGN

includes as "revenue" for each year in each city (1) all receipts from local sources (e.g., taxes) in the current year except those held as unappropriated surplus; (2) all realized deficits; (3) revenues from state and other local governments;31 and (4) surpluses carried forward from prior years and appropriated in the current year. The practical reason for this definition, complicated conceptually and simple operationally, is that municipal finance is best characterized as a "floating crap game."32 The game is dynamic and continuous, constantly shifting in response to opportunities (e.g., unanticipated revenues) and problems (e.g., unanticipated expenses). For legal reasons, there are distinct annual budgetary cycles and there are cash management systems; but it is difficult, if not impossible, for local officials to know precisely where they stand in either "cash" or "appropriation" terms at any point in time. The accounting systems are always running behind the actual expenditure processes, and the information they pro­ vide is always out of date. Audit reports, the only serious attempt to evaluate the balance between revenues and expenserious conceptual problems. GRS and categorical program funding are linked in the federal resource allocation process. Conceptually, it can be argued, with merit, that changes in categorical funding, particularly reduc­ tions, can be attributed to GRS. However, careful analysis would require consideration of national resource allocation processes to attribute these changes to GRS. This was clearly impractical. With the exception of Model Cities, no major reductions in the level of categorical funding were found. Worcester was the only city in which the presence of GRS apparently contri­ buted to continued local support of a federal program (i.e., Model Cities). 31 Analysis of these sources indicated enormous stability in recent years. State contributions were another source of potential inferential confusion. GRS might affect state behavior toward local governments in two ways: (1) States might increase their intergovernmental transfers because of their GRS funding; or (2) states might decrease their funding because of the presence of greater federal funding, GRS, at the local level. For the cities studied here, there do not appear to be any such changes important to the analysis. 32 There are actually two loosely coupled games running concurrently: cash-flow management and appropriations. This description should not be construed to imply that the revenue constraint is not real, but that it is changing marginally through time and that there is no single number or set of numbers that can be taken from primary sources that precisely reflects the revenue constraint.

EVALUATING PUBLIC PROGRAMS

ditures at a point in time (i.e., the end of a fiscal year), are never available until a municipality is several months into the next fiscal year. The procedures used by auditors in evaluating the balance are complicated and, although legally sound, the accounting conventions used are often arbitrary in analytic terms. In short, there is no conceptually clean definition of "revenue" (e.g., a single number from a single source) that corresponds to the set of constraints that the larger environ­ ment of the municipal government imposes on its expenditure behavior. A surrogate—the sum of general fund expendi­ tures—for that set of constraints was used. On the expenditure side, dollars expended on particular functional accounts (e.g., "police/personnel") were taken as the relevant behavioral outcomes. Where data on "actual expenditures" were not available, the closest data were used.33 The advantage of taking expenditures in this form as outcomes is that it is the form in which decisions are made and the data are found. The disadvantage is that the meas­ ure—dollars expended for function χ—is ambiguous for further analytic purposes. Specifically, these dollar outcomes represent service inputs, at some level, that are translated through production functions, unknown and unspecified, into services that have value, unknown and unspecified, to citizens. Measuring the quantity and quality of public services is a very difficult methodological and "data availability" problem that was recognized but not addressed. The amount of detail with our definition of "outcomes" is considerable. The amount of detail (and work) that is involved in taking any one of the several possible "next steps" in improving the measure 33 There are a series of numbers for any one account in any year that one might take. The usual list is (1) departmental requests; (2) mayor's (man­ ager's) recommended budget; (3) council (commission) approved budget; (4) several revised budgets over the year; (5) two versions of "estimated expendi­ tures"; and (6) actual expenditures. Actual expenditures were used wherever possible, and where these were not available, we worked back on the list. There was only one instance—Worcester in fiscal 1975—where it was neces­ sary to go as far down the list as manager's recommendations.

RESEARCH DESIGN

of expenditure outcomes is forbidding.34 Perhaps these dif­ ficulties are a partial explanation for the tendency of financial decision makers in cities to budget to service inputs rather than to service outputs and for the problems that have been encountered in implementing PPBS in cities. "Nominal" dollars were used rather than "constant" dol­ lars. One important reason for this choice is that decision makers work with the data in "nominal" dollar terms. Also, the use of a deflator introduces another source of potential error into the analysis. Inflation has differential effects. The effects vary with location, time, and the types of personnel or commodities that are purchased. The mechanisms through which inflation operates (union negotiations, supplier prices, etc.) are not well understood. The length of time before infla­ tion is reflected in allocation levels is apt to vary between cities and within cities by what is being purchased. The simple application of deflators of the type available, whether national or regional and whether product-specific or not, was apt to lead to as much error as explanation. The work required for more complex and conceptually satisfactory applications is substantial and is discussed as a potentially important exten­ sion of this research (see Chapter 6). Criteria for Model Development Do not ask questions on which people have no real opinions, or which they will not answer truthfully. Socratic dialogue is more potent than any arithme­ tic. Bloggin's Working Rule No. 1535

The models used by various researchers to estimate the fiscal impact of GRS vary in the degree to which they are explicit. Surveys are the best example of the use of implicit models. The model of "what would have happened without GRS" is presumed to exist in the mind of respondents. By providing stimuli in the form of questions [e.g., If GRS funds had not 34 See J. Burkhead and J. Miner, Public Expenditures, pp. 300-304, for an excellent discussion of the problem. 35 I. J. Good, The Scientist Speculates, p. 213.

EVALUAHNG PUBLIC PROGRAMS

been available in fiscal year 1974, would expenditures for function χ have been more, the same, or less? If less, how much less (in dollars)?], survey methods evoke responses from "models" that respondents hold implicit. The burden of analysis is shifted almost entirely onto the respondents. It is very difficult (impossible?) in a survey format, whether personal interview, mailed questionnaire, or some combina­ tion, to evaluate the quality (meaning?) of responses because there is no comparative benchmark independent of the tech­ nique. Questions are sometimes included to gather data on "phenotypical" characteristics of respondents (e.g., the respondent is a 32-year-old, male, Jewish (orthodox) finance director who has been on the job for five years, holds a masters degree in accounting, belongs to the Municipal Finance Offi­ cers Association, votes Democratic most of the time, and believes that GRS is a good—eight on a scale of ten—pro­ gram), that may be correlated with the types of models the respondent holds and the way he or she applies them in an interview or questionnaire environment and may "bias" responses. There are, however, no "unobtrusive" bench­ marks against which responses can be checked and no methods, particularly in extensive survey efforts, for sensibly exploring the model(s) the respondent holds and the way in which they are applied.36 A great deal of effort is devoted, in the application of survey methods, to ensuring a "representative sample." This is a concern for the "sample as an analogue of the population." The logical and statistical apparatus for these efforts is im­ pressive, if not conclusive, and discussions of "validity" tend to be dominated by sampling considerations—the extent to which "randomization" holds in sample selection and response—from which our ability to "generalize" on the survey results purportedly flows. Users of survey results, par­ ticularly those not privy to the mystiques and totems of the profession, should be occasionally reminded that there is little 36 See Eugene J. Webb et al., Unobtrusive Measures: Non-Reactive Meas­ urement in the Social Sciences, for a fascinating and enlightening discussion of these issues.

RESEARCH DESIGN

comfort in the ability to generalize on uninterpretable results. There are numerous threats beyond sampling considerations to the "validity" and "interpretability" of survey results. Where the opinions or attitudes of human subjects are the object of research, surveys, in some form, at some level of intensity, are the appropriate, perhaps the only, research approach. Where the "behavior" of individuals or collec­ tivities is the object of research, there should be some funda­ mental concerns about the use of survey techniques. The correspondence between opinions and behavior, whether it is the behavior (past, present, or future) of the respondent or the behavior of some "chunk" of the world that we are asking the respondent to describe, is an empirical question. Human per­ ceptions are notoriously affective in orientation and con­ strained by individual cognitive abilities. The "fiscal effects of GRS" in most cities of any size and organizational complexity is a rather complex puzzle that requires a longitudinal per­ spective and a model of "what would have been." In a survey for ascertaining GRS fiscal effects, local officials, who vary greatly in their affective attachments and cognitive skills and who tend to be immersed in the day-to-day details of an often complex environment, are asked on the spur of the moment to speculate on a complex and hypothetical problem. Relying on the models that are apt to be evoked and applied in such a setting for the "measurement" of GRS fiscal effects is, at best, a risky research strategy. The strength of survey research for a problem such as GRS fiscal effects is that when a question is asked, generally some answer is given. The weakness of the approach is in the reliance on implicit models that are apt to be uneven in quality and not accessible to the researcher for appraisal. For any rigor in understanding the fiscal effects of GRS, it is essential to use explicit models that, regardless of their inadequacies deriving from conceptual and methodological limits, are accessible to the researcher and permit direct appraisal. The first criterion for model development is explicitness. For testing (comparison with observed outcomes), the hypothesis on "what revenue and expenditure patterns would

EVALUAWNG PUBLIC PROGRAMS

have been" must be in the form of numerical (dollar) time series. Computer-based "simulations" are ideally suited to the task. The programming language used, FORTRAN IV, is explicit and numerical. Models can easily be constructed to iteratively generate time series. The language is more flexible in the functional forms of models that it accommodates than the alternatives—analytic and statistical models. Also, with­ out machine information-processing capabilities, it would be very difficult, if not impossible, to cope with the computa­ tional demands of generating hypotheses on fiscal patterns in the required detail. A second criterion for model development is plausibility. For a model to be plausible in the context of this research, it must be consistent with a positive theory37 of municipal resource allocation processes and it must have predictive force. Explicit models are (should be) explicit theoretical statements. It is not the primary intent of this research to posit and empirically test a rich, positive theory of municipal resource allocation processes, but rather to make use of exist­ ing positive theory in assessing the impact of GRS. If the models, however, do not reflect, at least superficially, the underlying allocative mechanisms, the models are not apt to inspire much confidence in their use as "symbolic analogies." Conversely, models that have intuitive (theoretical) appeal but yield poor predictions (in a statistical sense) are inappro­ priate. For a model with some basis in theory to be plausible, it must also be able to replicate observed outcomes in the period prior to GRS—the "estimation period"—with some preci­ sion. Testing the models is the topic of Chapter 4. A third and final criterion for model development is feasibi­ lity. For a model to be feasible in the context of this project, it must be possible to specify and estimate it with limited research resources for a number of city governments. These governments are diverse on many dimensions. In asserting that such modeling activity is even possible, the contention is that much of this diversity can either be modeled or safely 37 "Positive theory" means a formal description of how resource allocation decisions are actually being made.

RESEARCH DESIGN

ignored in the determination of short-run changes in revenue and expenditure patterns. This contention stems directly from the conceptualization of municipal resource allocation pro­ cesses elaborated in Chapter 3. The diversity among the five city governments, particularly diversity in the structure and content of their financial deci­ sion processes, did force some compromises in model con­ struction. Specifically, it was necessary to develop and employ models that are much more abstract than if the task were to model a single city or a set of cities with striking similarities in their financial decision processes. City governments face many common problems in financial decision making, and there are a great many similarities in the methods they employ to solve these problems and in the solutions they reach; but there is also a great deal of diversity in the specifics of financial decision making among the cities studied here (see Chapter 3).

Expenditure Models Theexplicitness and feasibility criteria for model development are satisfied with the explication of the models as computer programs and their estimation and application for each of the cities studied. The plausibility criterion is the subject of both Chapters 3 and 4. In Chapter 4 the statistical performance of the models is examined under a variety of testing conditions, and some general issues of statistically "validating" models are considered. The primary purpose of Chapter 3 is to relate the models as specified to a positive theory of municipal resource allocation processes. Two secondary purposes for Chapter 3 are (1) to argue that predictive perfor­ mance—statistical "goodness-of-fit"—is a necessary, but not sufficient, criterion for appraising the models as analytic tools for the estimation of GRS fiscal effects; and (2) to examine some prevalent alternative theoretical and empirical views of municipal resource allocation processes, particularly those views (models) that have been used to study the impact of federal or state assistance programs on local fiscal behavior.

EVALUATING PUBLIC PROGRAMS

Before turning to a discussion of the plausibility criterion, however, it will be useful to introduce the four models that were developed, tested, and used in this research to analyze the fiscal effects of GRS, rather than continuing to refer to them abstractly. The models are introduced here without extensive comment since their plausibility is the subject of the next two chapters.38 The models, their primary predictive assertions and the form for parameter estimation, are shown in Figures 2-2 through 2-5. Each model consists of a set of linear equations embodying a change rule—a "basic predictive asser­ tion"—and a "balancing routine." The basic predictive asser­ tion is used to make an initial prediction of what the change in expenditures will be for any particular functional account (e.g., "police/personnel") from one year to the next. The balance routine compares the sum of initial predictions for all functional accounts in each year with the constrained total—"available revenue"—for that year and revises the predicted changes to achieve a balance. The principal difference between models is in the primary predictive assertion. The models are particularized for cities by estimating parameters for each functional account in each city;39 with one exception,40 the models have been used for all cities in the form presented here.41 Each model consists of a 38

Appendix A contains annotated flow charts for each of the models. All parameters are estimated using ordinary least-squares techniques with the line (plane) constrained to pass through the origin. These procedures are described in K. A Brownlee, Statistical Theory in Science and Engineering, pp. 298-302. 40 The presence of an 18-month-long "budget year" in Worcester necessi­ tated an alteration in model structure for application to Worcester. A city official indicated that a factor of 1.5 was applied to the prior year's expendi­ tures before the usual marginal adjustments were made. The models as applied to Worcester incorporated this adjustment and the equivalent defla­ tion, a factor of 0.677, for the 12-month "year" following the 18-month "year." 41 The decision not to particularize models for cities (e.g., specify different functional forms for different cities and for particular functional accounts within cities) except through estimation of "free parameters" was an impor­ tant one. In retaining the simple, general models, we ignored a great deal of 39

RESEARCH DESIGN

FIGURE 2-2 Model Summary Constant Proportion of Base (CPB) This model predicts that this year's expenditure for any functional account will be equal to a constant times last year's expenditure for the same functional account. For example, if the police department of a city spent $1,000,000 last year on personnel and the constant growth increment, empirically estimated, is 5.321%, CPB's initial prediction of this year's expenditure level would be $1,053,210. The balance routine revises this prediction in accordance with the com­ parison between the sum of initial predictions for all functional accounts and "available revenue."* The basic predictive equation is E X P l l k = β „ EXP,^ 1 where E X V l j k = predicted expenditure for function i , account /, in year k ΕΧΡ, λ _! = actual expenditure in year(k — 1) β 1 } = empirically estimated parameter for function i , account j (estimated in basic predictive equation) * Each model uses the same balance routine (see Appendix A) in revising initial predictions. This will not be noted in the description of the next three models.

set of linear equations42 with one equation for each functional account43 in a city. Within models, the equations are identical in variables and mathematical form for all functional what was known about the processes in particular cities. Given the task, however, the judgment was that the gains in model performance would be marginal and would not justify the added model complexity. 42 One model, DCFP, is nonlinear in its independent variable, but linear in estimating form. 43 A "functional account" is an object of expenditure (e.g., "personnel" or "nonpersonnel") for a particular function (e.g., police, fire, etc.). The objects of expenditure used for all cities are "personnel" and "nonpersonnel." The number and type of functions, and consequently the number of equations and parameters, vary by city.

EVALUAnNG PUBLIC PROGRAMS

FIGURE 2-3 Model Summary Constant proportion of the Revenue Increment (CPRI) This model predicts that this year's expenditure for any functional account will be equal to last year's expenditure plus a constant proportion of the revenue increment (decrement). For example, if a police department spent $1,000,000 for personnel last year, total available revenue for the city government increased by $1,000,000 from last year, and the empirically estimated proportion of the revenue increment for "police/personnel" is 17.321%, the initial predicted expenditure would be $1,173,210. The basic predictive equation is EXP,# = EXP1 Jfc-! + β „ (REVt - REV,.,) where EXP,;t = predicted expenditure for function i , account j , in year k EXP

1J,-!

=

actual expenditure in year (k — 1)

REVt = total revenue available in year k β,! = empirically estimated parameter for function i , account j [estimated in EXPilt — EXPllt-, = /?,/REVt - REVt-O ]

accounts, differing only in parameter values. All models use an identical routine for achieving balance between the sum of predicted expenditures and the total available revenue in each year.44 44 Each model uses information about revenue level in the balancing routine. Two of the models, CPRI and CGRI, use "change in revenue" as an independent variable. For non-GRS years in all cities and for GRS years in Ann Arbor, Detroit, and Cincinnati, observed revenue data is used. For GRS years in Albuquerque and Worcester, revenue forecasts are used because analysis indicates that revenue displacements occurred. See Chapter 5 for the analysis of revenue effects.

RESEARCH DESIGN

FIGURE 2-4 Model Summary Constant Growth—Revenue Increment (CGRI) This model predicts that this year's expenditure for any functional account will be equal to a constant times last year's expenditure plus a constant proportion of the revenue increment (decrement). For example, if last year's expenditure for "police/personnel" was $1,000,000 and the parameters for growth on base—last year's expenditure—and proportion of the revenue increment are 5 and 15%, respectively, the initial predicted expenditure for "police/per­ sonnel" would be $1,200,000. The basic predictive equation is EXP llk =fi llk

EXPl/k-i +0,, (REVk-REVw)

where ΕΧΡ,;λ, EXPljj^1, and REV11 are defined as above and S11 are empirically estimated parameters, estimated in the basic predictive equation β,,

The basic predictive equations in each model take the gen­ eral form y = bX

or

y = btXt + b2X2

where y, the dependent variable, is either the level of expendi­ ture or the change in expenditure from year (f-1) to yeart for a particular functional account.45 There is obviously not one-to-one correspondence between the decision mechanisms in these simple models and decision mechanisms in municipal resource allocation processes. The models are more "reflective" than "simulative" of the under­ lying processes. However, they are consistent with more detailed models of process and do capture key characteristics of the decision processes. This assertion is defended at length in the next chapter. 45 The intercepts (that is, a in y = a + b X ) are all constrained to be zero because there is no natural, sensible interpretation for them. And from experiments with a = 0 and α Φ 0, it was determined that there was no significant loss of predictive power with this form.

EVALUATING PUBLIC

PROGRAMS FIGURE 2 - 5

Model Summary Dollar Change—Fiscal Pressure (DCFP) This model predicts that this year's expenditure for any functional account will be equal to last year's expenditure plus a constant times the product of the proportional change in revenue (i.e., the difference between this year's revenue level and last year's level divided by last year's level) and last year's expenditure for the same functional account. For example, if the city's revenue increased by 10%, and the empirically estimated parameter is 1.5, the initial predicted expenditure would be $1,150,000. The basic predictive equation is

where are defined as above empirically estimated parameter, estimated in

3 Theoretical Foundations It is always better to say right out what you think without trying to prove any­ thing much: for all our proofs are only variations of our opinions, and the contrary-minded listen neither to one nor the other. Goethe

Models and Theories One criterion for model development introduced in the last chapter stated that for a model to be plausible in the context of this research it must be (1) consistent with a positive theory of municipal resource allocation processes; and (2) able to replicate observed out­ comes in the period prior to GRS with some statistical relia­ bility. The primary purpose of this chapter is to argue that in spite of their simplicity and lack of one-to-one correspondence with "observables" in the decision processes they represent, the models introduced at the conclusion of Chapter 2 are consis­ tent with a positive theory of municipal resource allocation processes—a theory of how municipal resource allocation decisions are actually being made—and appropriate for the purpose of analyzing GRS. Models that purport to explain and aspire to predict the behavior of decision-making units must make assumptions (assertions), explicitly or implicitly, about the motivations and structure of the behavioral unit. For appraising such models as statements of positive (descriptive) theory, these assumptions are crucial. It is well known—at least among mathematical statisti­ cians—that the theory of statistical tests gives us no real

EVALUAnNG PUBLIC PROGRAMS

help in choosing between an approximate generalization and an invalid one. By imbedding our generalization in a probability model, we can ask: If this model describes the real "facts" what is the probability that data would have occurred at least as deviant from the generalization as those actually observed? If this probability is very low—below the magic one percent level—we are still left with two alterna­ tives: the generalization has been disconfirmed, and is invalid; or the generalization represents only a first approx­ imation to the true, or "exact" state of affairs. . . . Just as statistically significant deviations of data from a generaliza­ tion should not always, or usually, lead us to abandon the generalization, so we should not be unduly impressed by excellent fits of data to theory. More important than whether the data fit is why they fit—i.e., what components in the theory are critical to the goodness of fit. To answer this question, we must analyse the internal structure of the theory.1 The upshot of this view is that the second part of the plausibi­ lity criterion—statistical goodness-of-fit—is a necessary, but not sufficient, step in appraising the strength (validity) of our models.2 It is also important to examine the models' assump­ tions about the motivations and structure of the behavioral units. To the extent that we understand why models work in 1 H. A. Simon, "On Judging the Plausibility of Theories," in Logic, Methodology and Philosophy of Science III, J. van Rootselaar and H. Staal (eds.), pp. 439-459. A less demanding (and less productive) position for social science modeling is that "predictive power is the only thing which counts." See Milton Friedman, "The Methodology of Positive Economics," chap. 1 in Essays in Positive Economics. Also, see R. M. Cyert and E. Grunberg, "Assumption, Prediction, and Explanation in Economics," app. A inyl Behavioral Theory of the Firm, R. M. Cyert and J. G. March (eds.) pp. 298-311; and Sidney G. Winter, "Concepts of Rationality in Behavioral Theory," for intelligent critiques of Friedman's position. 2 See Chapter 4 for further discussion of problems with statistical testing and for the results from testing the expenditure models. The emphasis here on nonstatistical considerations in model appraisal is not motivated by weak statistical results. On the contrary, the statistical results are very strong. This chapter considers "why the models fit."

THEORETICAL FOUNDATIONS

explanation, we understand conditions that are important to appraising the accuracy of model forecasts and counterfactual arguments. Explanation and prediction are complementary, recipro­ cally informing intellectual activities in the context of con­ structing and applying formal models for a particular research purpose (e.g., assessing the impact of GRS or developing a positive theory of municipal resource allocation). Research purpose determines the appropriate degree of relative emphasis on explanation or prediction in model specification and in analyzing and reporting results, but the two activities are inextricably linked. The means of predicting process out­ comes—models—are explanations of process at some level of detail and some degree of correspondence to the "real" pro­ cesses determining outcomes. The level of detail and "cor­ rectness" of the process explanation in a model directly influ­ ences the types of possible predictions—their level of detail, specificity, and accuracy. The accuracy of predictions com­ ments, in turn, on the quality of the process explanation that generated them. The models developed and employed in this research are more "reflective" than "simulative" of the underlying pro­ cesses in that they are structurally simple, summarizing exten­ sive, complex processes in a very parsimonious fashion. There is no attempt to follow Crecine's lead in establishing detailed correspondence between models and decision processes,3 because the primary objective of this research is not to expound and test a positive theory of municipal resource allocation processes but to use existing theory to analyze the fiscal effects of GRS. The specifics of the interactions among subunits within our unit of analysis are important for theoreti­ cal statement but not important here except as they alter collective outcomes (i.e., for the analysis of GRS, it is not 3 Crecine decomposed the budgetary decision process into (1) depart­ ments; (2) the mayor's office; and (3) the council. See I. P. Crecine, Govern­ mental Problem Solving: A Computer Simulation of Municipal Budgeting. See below for further comment on the importance of disaggregation.

EVALUATONG PUBLIC PROGRAMS

important how departmental requests changed perse, except as a step in determining expenditure outcomes). The models are consistent with several underlying causal structures. For precise theoretical statement, this is not desir­ able.4 In order to satisfy the feasibility criterion, however, it was essential. There are differences in the structural detail (e.g., organization and procedures) for financial decision mak­ ing among the five cities. Although it would be possible to construct detailed models, this would require substantially more data (e.g., data from different levels in the decision pro­ cesses) and more particularized models for individual cities. The models developed and utilized in this research view municipal resource allocation systems as bureaucratic pro­ cesses, systems producing outcomes that are adjustments of prior outcomes.5 The central assumptions about "behavioral structure" are found in the aggregation choices made in specifying the models. All models, with the possible exception of those systematically aggregating from the behavior of sub­ atomic particles, are subject to the criticism that the level of aggregation obscures underlying causal relations (struc­ tures).6 The models used in this research are at the level of a city government and, therefore, are aggregate in that they sum­ marize in events (i.e., annual revenue and expenditure pat­ terns) the complex behavior of organizational subunits and human actors without describing that underlying behavior in detail. The correctness or appropriateness of such aggregation 4 See Lee W. Gregg and Herbert A. Simon, "Process Models and Stochas­ tic Theories of Simple Concept Formation," for a superb discussion and illustration of this fundamental point. 5 This view is based on the work of a number of organization theorists, but most directly on the work of John P. Crecine on a positive theory of municipal budgeting. Crecine's work is reported in several places. The sources used here are Governmental Problem Solving', "A Simulation of Municipal Budgeting: The Impact of the Problem Environment," in Simulation in the Study of Politics, W. D. Coplin (ed.), pp. 115-146; and "A Computer Simulation Model of Municipal Budgeting." 6 See Chapter 2 for discussion of the unit of analysis and criticism of models that aggregate units of state and local governments.

THEORETICAL FOUNDATIONS

choices is a function of the problem context (e.g., types of predictions desired and characteristics of the behavioral units); attempts at absolute determination put us on the infi­ nite regress bounded by subatomic particles. In this research, the emphasis is on prediction rather than description and the important behaviors are the responses of city governments, not their constituent components, to GRS. The models make two important and strong aggregation assertions (structural assumptions). First, they assert that municipal resource allocation processes, when expenditure levels are taken as the relevant behavioral outcomes,7 can be viewed as "nearly decomposable" subsystems imbedded in larger socioeconomic environments. The models hold that the decision-making systems are relatively autonomous and that their primary link with the larger environment is a constraint that the environment places on aggregate levels of spending. Total expenditures must approximately equal total revenues in each year and the level of potential revenue is constrained (i.e., it is not wholly under the control of decision makers for reasons that are either "real"—tax increases require voter approval of a referendum—or perceptual—"the taxpayers will revolt if we raise taxes"). A further implication of the models along this line is that environmental forces (fac­ tors)—i.e., changes in social, political, and economic charac­ teristics of the environment—are not often important in directly adjusting expenditure levels from year to year; but rather, these forces are "filtered through the revenue con­ straint."8 7

The importance of this qualification is discussed below. See Crecine, "A Simulation of Municipal Budgeting." The models use "free" parameters estimated from data that reflect direct environmental influences on expenditure levels; to the extent that such influences exist, it cannot be argued that the models are totally inconsistent withsuch influences. However, in using parameters that are not estimated with direct reference to environmental forces other than change in aggregate level of spending, one would expect that if the structural assumption is "wrong"—environmental changes are a direct, routine basis for making year-to-year changes in expen­ diture levels for functional accounts—serious predictive errors would result. This issue will be discussed further in the next section of this chapter, "Alter­ native Views," and in subsequent chapters. 8

EVALUATING PUBLIC PROGRAMS

The second important structural assumption is that the constituent components—subunits and humans—of the behavioral units are not behaviorally autonomous with respect to the outcomes, but are behaving in a somewhat coordinated fashion toward resolving a common prob­ lem—the "budget problem." And further, there are char­ acteristics of individual and collective problem-solving behavior that result in enormous stability in solutions (out­ comes). Allocation (expenditure-level) decisions are made by human beings with finite cognitive and informationprocessing skills. The press of time and the type of information available to support decisions that "have to be made" con­ strain and channel problem-solving behavior. In spite of the diversity in cognitive skills, value preferences, and informa­ tion resources of local officials, there are important similarities among cities and through time in how the repeti­ tive, potentially complex problem of "balancing the budget" is simplified and "solved." For example, in determining this year's expenditure levels, the expenditures required to main­ tain last year's level of service inputs (e.g., personnel) are treated as givens. Attention is focused on marginal changes to achieve and maintain a balance between expenditures and revenues.9 Also, there are characteristics of the "budget prob­ lem" that encourage (necessitate) the use of extremely simple decision rules for "solutions" (e.g., cut 5 % across-the-board). The use of such rules reinforces the stability of outcomes, particularly the relative proportions of the amounts expended for particular functions over time. The key motivational assumption in the models, already introduced in the foregoing discussion of structural assump­ tions, is that local officials are engaged in solving a large, 9 When "balance" is referred to here and throughout, it means an "appropriations balance" rather than a "cash flow balance" unless otherwise specified. The processes through which cities meet their payment obliga­ tions—maintain a cash flow balance—are of negligible interest here except for cases where expenditure levels are adjusted to solve cash flow problems. This is an important distinction that is rarely, if ever, made.

THEORETICAL FOUNDATIONS

repetitive, "potentially complex"10 problem that is highly con­ strained and operational. Although specific conditions (the relative scarcity of dollar resources, legally permitted revenue sources, functional responsibilities, etc.) vary by city and over time, there are important similarities (the "balanced budget requirement," accounting structures, potential complexity, etc.) in the budget problem that municipal officials must solve each year. The dominant operational goal in setting allocation levels in any particular year is to solve the budget problem in that particular year.11 The operational goal—resolution of this year's budget problem—is an organizational goal stemming from a man­ dated decision situation (i.e., city governments are legally required to prepare, consider, and approve a balanced budget in each year at a certain time). Organizations are, of course, collections of individuals, and these individuals vary greatly in their orientation (and the intensity with which they hold it) to the operational goal of solving the budget problem. Some individuals (e.g., chief financial officers) will have a precise fix on the problem and an enormous commitment to "solving" it. Other subunits or individuals (e.g., heads of operating agen­ cies seeking more money) may be working at cross-purposes with those attempting to solve the problem (e.g., when the problem is a projected deficit). In order to ensure the resolution of such an important and potentially complex problem in a specified time frame, the organization must simplify the problem drastically and either "manage" or avoid most of the potential subunit conflict on 10

The use of "complexity" is taken fromCrecine: The presence of "a large number of interdependent real variables, a high degree of uncertainty attached to key variables, and non-linear relationships between real vari­ ables." See Crecine, "A Simulation of Municipal Budgeting," p. 145. 11 The budget problem is discussed more fully below. On the "operationality" of goals, March and Simon say that "The goals that are included in the definition of the situation influence choice only if there are some means, valid or illusory, for determining the connection between alternative actions and goal satisfaction—only if it can somehow be determined whether and to what extent these goals will be realized if particular courses of action are chosen." J. G. March and H. A. Simon, Organizations, p. 155.

EVALUAnNG PUBLIC PROGRAMS

the decision.12 This is accomplished in several ways. The deci­ sion processes are highly structured and routinized. Formal, largely hierarchial, roles are defined for participants. Final responsibility for preparing a balanced budget often rests with an individual (e.g., the mayor or administrator). Statutory provisions to ensure a decision or, at least, continuity often exist (e.g., if the legislative body does not approve a budget by a certain date, the chief executive's budget as proposed is law). And, most of the substantive issues implicit in resource alloca­ tion decisions (e.g., the relative merit of the various functions) are never considered or considered in a nonoperational public dialogue that results in few, if any, changes in allocation levels. The four models introduced at the conclusion of Chapter 2 are explicit, albeit simple, statements of positive theory. To a great extent these models are operational variations on the same theoretical theme; they do not represent sharply differ­ ent, competing views. In a later section, "Municipal Resource Allocation Processes," their correspondence with "real" deci­ sion processes will be described in more detail. Before turning to this description, however, it will be useful to consider briefly an approach that has been widely used to study local responses to intergovernmental transfers—an approach that is based on a different conceptualization of municipal resource allocation processes than the theory embodied in the models. Alternative Views The tools of the mind become burdens when the environment which made them necessary no longer exists. Henri Bergson

Although the General Revenue Sharing Program is a recent 12 Although not developed here, the "four major relational concepts"—"quasi-resolution of conflict," "uncertainty avoidance," "problemistic search," and "organizational learning"—described by Cyert and March are easily adaptable and highly relevant to understanding municipal resource allocation processes. See Cyert and March, A Behavioral Theory of the Firm, pp. 114-127. Also, see Crecine, Governmental Problem Solving.

THEORETICAL FOUNDATIONS

phenomenon and represents a different type of assistance program, theoretical and empirical research on local responses to various types of grant programs is not new. The "theory of public expenditure" found in the literature of public finance and welfare economics considers, theoretically, how recipient responses might vary according to the type of grant (e.g., unconditional transfers and closed-end categorical programs). Also, since Census data on state and local expendi­ tures and computerized statistical packages have been avail­ able and statistically significant correlations between grant and expenditure variables were discovered, there have been many analyses of the effects of grants on state and/or local expenditures. With few exceptions, this has been the work of public finance economists and econometricians.13 In many important respects, determining the effects of GRS is the same research problem as that of determining the effects of categorical programs. The broad outlines of the research question are fundamentally the same regardless of program type. The question requires counterfactual argu­ ments—"what outcomes would have been without the pro­ gram"—to separate programmatic from nonprogrammatic effects. And for empirical work, constructing the counterfac­ tual arguments obviously requires a model of process—a model of mechanisms (or "forces") determining year-to-year changes. The research reported here has profited enormously from the economic approach to the study of the effects of categori­ cal programs, but retains virtually nothing from the traditional approach. There are fundamental differences between the 13 The focus in this section is on views of municipal resource allocation processes, implicit or explicit, in empirical or theoretical work that has sought to comment directly on local responses to grant programs. The discussion of "process" conceptualizations, theoretical or empirical, is therefore limited. For discussion of some alternative views of resource allocation not considered here, see Crecine, Governmental Problem Solving, pp. 8-21,186-216. Afew political scientists, notably Ira Sharkansky and Thomas Dye, have used an empirical strategy very close to that of public finance economists. For example, see Ira Sharkansky , "Some More Thoughts About the Determin­ ants of Government Expenditures."

EVALUAnNG PUBLIC PROGRAMS

approach here and the orthodox theoretical and empirical approaches, particularly in the conceptualization of municipal resource allocation processes. The view of municipal resource allocation processes, intro­ duced in the last section and described more fully in the next section, stresses the importance of internal bureaucratic deci­ sion processes, constrained in their aggregate level of spend­ ing by their external environment, as active determinants of change in expenditure patterns. The "economic approach,"14 both theoretical and empirical, stresses the importance of environmental factors (e.g., socioeconomic and demographic "forces") as determinants. In the theory, municipal decision processes are viewed as mechanisms of (perfect) adapta­ tion—mechanisms that "maximize community welfare" through resource allocation decisions. In the empirical work, the decision processes are also viewed as adaptive mechan­ isms—mechanisms that adjust expenditures to environmen­ tal changes with the environment most often characterized through some set of socioeconomic, demographic, and politi­ cal variables, often corresponding to Census categories (or transformations of those categories). Crecine commented on the differences in views: The assumption that participants in the budget making process are passive instruments who will come up with a predetermined solution to the problem of municipal resource allocation either by following economic dictates and service demands or by following the dictates of com­ munity power figures versus the assumption that budget makers are organizational decision makers and problem solvers who structure complex problems, generate alterna14 Although the reference in this section is to "the" approach, there are numerous versions of the theory and great diversity in models employed for empirical work. However, most of this diversity consists of methodological variations on the same theoretical theme much like this research's four models on a different theme. The concern here is with propositions on what factors are important in determining municipal expenditure out­ comes—propositions that contribute to (or detract from) the ability to under­ stand "what expenditure outcomes would have been without GRS."

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tives, and make choices on the basis of some criteria, is a real difference [in assumptions].15 The real debate, however, is not between two competing views (positive theories) of process but on the question of whether or not "useful" predictions of behavioral outcomes can be made with little or no information about the internal structure of the process generating those outcomes. AsSidney G. Winter observed, the approach that we find economists taking to this research problem—the impact of grants—is not unusual: In almost all orthodox theoretical and applied economics as it exists today, the implicit or explicit premise is that the characteristics of decision processes are a matter of negli­ gible importance relative to the motivational and environ­ mental forces in the situation. Perfect information and cost­ less computation assumptions are typical, for cogent reasons of analytical tractability and empirical content.16 The "theory" of municipal resource allocation processes used by public finance economists to speculate on the way in which local responses might vary according to the type of grant is a direct extension of the model of rational individual choice from price theory.17 In this view, resource allocation is an "optimizing process" in which a "community welfare func­ tion" is posited and the "governing body" attempts to maxi­ mize "community welfare" for reasons of altruism, efficiency, or reelection. In all of its various forms,18 the theory is an 15

Crecine, Governmental Problem Solving, p. 20. Sidney G. Winter, "Satisficing, Selection, and the Innovating Remnant." 17 For examples of the theory applied to grants, see Selma J. Mushkin and John F. Cotton, Sharing Federal Funds for State and Local Needs; M. McGuire, "Notes on Grants-in-Aid and Economic Interaction Among Gov­ ernments"; Wallace Oates, Fiscal Federalism; Charles Waldauer, "Grant Structures and Their Effects on Aided Government Expenditures: An Indif­ ference Curve Analysis"; Lester Thurow, "The Theory of Grants-in-Aid"; J. A. Wild, "The Expenditure Effects of Grants-in-Aid Programs"; J. A. Wilde, "Grants-in-Aid: The Analytics of Design and Response." 18 There is a lot of attention in this literature to changing assumptions 16

ΕΥΑΙΛΜΉΝΘ PUBLIC PROGRAMS

adaptation of essentially the same "general-purpose" model that has been used extensively to speculate on the behavior of individual consumers and business firms. One informal version of the "theory of grants" is summar­ ized as follows: The (model) we present . . . is based on the premise that governments act to some extent in their own interest, but not necessarily to the degree that decisions are . . . made on the basis of hard quantitative analysis of costs and benefits. . . . The model draws upon price theory and treats the . . . city as analogous to the rational consumer. The [city]—as represented by its decisionmakers—is considered to be rational in the following senses: (1) it has a consistent concept of the utility it would derive from the program it might undertake; (2) it moves to increase this utility, sub­ ject to the budget constraint under which it must oper­ ate. . . . In this framework, the immediate effect of the offering of a particular grant-in-aid can be described in terms of an alteration to the budget constraint within which the [city] operates. For a matching grant, this comes about through a reduction in the apparent price. The lowering of the price of one program relative to all others has two effects: (1) a substitution effect in which a larger portion of that program is purchased relative to the others, and (2) an income effect leading to an increase in all programs arising from the increase in real income due to the lower average price level. For a general unrestricted grant, the effect appears as a general increase in available revenue, that is, the grant has only an income effect.19 The model is employed to describe static shifts between equilibrium positions in response to changes (e.g., changes in relative prices or income levels). There is no time dimension; the adjustment takes place instantaneously, in the millennium marginally and tracing the implications of such changes for the conventional (pure) analysis. The essential points can be made here, however, without considering the elaborations one by one. 19 Mushkin and Cotton, Sharing Federal Funds.

THEORETICAL FOUNDATIONS

(i.e., the "long run"), or somewhere in between. There is no description of the mechanisms of adjustment (adaptation) because the organization is perfectly adaptive. The equilibrium behavior of a perfectly adapting organism depends only on its goals and its environment; it is other­ wise completely independent of the internal properties of the organism. . . . To predict the short-run behavior of an adaptive organism, or its behavior in a complex and rapidly changing environment, [however,] it is not enough to know its goals. We must also know a great deal about its internal structure and particularly its mechanisms of adaptation. . . . Similarly, in an organism having a multiplicity of goals or afflicted with some kind of internal goal conflict, behavior could be predicted only with information about the relative strengths of the several goals and the way in which the adaptive processes responded to them.20 Truly impressive assumptions such as unitary rational action, perfect (and costless) information on technological pos­ sibilities, and the existence of some objective (welfare) func­ tion that is stable and maximized over time are required to support the view of municipal government as a "perfectly adapting organism"—the view that would enable us to make behavioral predictions with no knowledge of internal struc­ ture. But more important, since no one would defend the pure theory as description, is the extent to which the "economic shortcut" to behavioral prediction is reasonable and useful; this is a function of the extent to which the behavioral unit approximates the "perfectly adapting organism." This research holds that the approximation is very bad, particularly if that adaptation to the external environment is held to take place through annual adjustments in expenditure levels. The quality of the approximation is, of course, a matter for empiri­ cal investigation. The simple models used in this research are also a "short­ cut," but one that is based in large part on the imperfections of 20 Herbert A. Simon, "Theories of Decision-Making in Economics and Behavioral Science," p. 255.

EVALUATING PUBLIC PROGRAMS

municipal governments in adapting to their external environ­ ment. Some of the important assumptions in the approach are (1) "satisficing" rather than maximizing behavior (e.g., multi­ ple and often conflicting goals that enter the decision calculus as independent aspiration level constraints); (2) very imper­ fect information on both technological possibilities (produc­ tion functions) and the external environment (demand); and (3) expenditure outcomes (as among functions) that reflect adaptation to the "internal environment" much more than adaptation to the external environment. The "as if' defenses of using a version of the price-theory analogy as a surrogate for "positive theory" in analysis of business firm behavior21 are much weaker in the application of the analogy to public expenditures. There are simply no satis­ factory equivalents for "profit maximization" or "natural selection through competition" that would support the defenses.22 Problems with the "theory of grants" have been noted by economists: There exists at the present time a serious deficiency in the theory of intergovernmental grants. One either treats the effects of intergovernmental matching and lump-sum grants in terms of the standard theory of individual choice, in which the grant recipient is viewed as an individual 21 The criticism of the orthodox theory of the firm is extensive, complex, and continuing. Much of this criticism has direct relevance for applications of the price-theory analogy to public expenditures where applications are even more strained. Although some of these criticisms will be used in our discus­ sion, it is far beyond the scope of this section to develop the arguments fully. For some incisive criticism of the orthodox theory of the firm as "positive theory" and of the approach in general, see: Cyert and March, A Behavioral Theory of the Firm ·, Winter, "Satisficing, Selection, and the Innovating Remnant"; Winter, "Concepts of Rationality in Behavioral Theory"; Simon, "Theories of Decision Making; and Winter, "Cost Reduction and Input Proportions." 22 Political competition is the most seductive candidate in this area for replacing market competition. It is, however, an extremely poor substitute in a theory of determinants of municipal expenditures for reasons given below (e.g., the role of the legislative body in resource allocation is limited).

THEORETICAL FOUNDATIONS

decision-maker with preferences defined over public and private goods, or one must fall back on the use of commun­ ity indifference curves, whose validity and usefulness are open to serious question. The central point is that inter­ governmental grants are not grants to individuals. They are grants to (administrative) groups of people, and as such, their allocative and distributive effects depend on the politi­ cal process by which the group makes its collective fiscal decisions; it is clear that we need a theoretical framework in which we can treat explicitly the political (administrative) process (that is, the collective decision-making procedures) by which the community makes its public budgetary choices.23 The theory is clearly deficient, and the empirical researcher is left with a choice of either ignoring the theory, leaving the decision-making mechanism(s) unspecified (e.g., the option exercised by all of the single-equation—usually additiveform—regression analyses of expenditure determinants), or attempting to specify an operational allocative mechanism(s) (e.g., studies specifying objective functions, utility functions, or incorporating voting rules).24 However this choice has been 23

Oates, Fiscal Federalism, p. 105. There are numerous examples of the single-equation analysis—Bahl, for instance, examined more than sixty of them. R. W. Bahl, Jr., "Studies on Determinants of Public Expenditures: A Review," app. in Sharing Federal Funds for State and Local Needs, S. J. Mushkin and J. F. Cotton (eds.). Also, Edward Gramlich and Harvey Galper, "State and Local Fiscal Behavior and Federal Grant Policy," in Brookings Papers on Economic Activity, vol. I Arthur M. Okun and George L. Perry (eds.), pp. 15-65, is a recent example of the use of a utility function with an "optimization" procedure. There is more theoretical work than empirical work using voting rules. An example of a theoretical piece is David F. Bradford and Wallace E. Oates, "The Analysis of Revenue Sharing in a New Approach to Collective Fiscal Decisions." An example of a partially empirical piece is Otto A. Davis and George H. Haines, Jr., "A Political Approach to a Theory of Public Expenditure: The Case of Municipalities." Also, when a "public choice" mechanism is appended, the analysis becomes complicated and perhaps inconclusive. See Charles J. Goetz and Charles McKnew, Jr., "Paradoxical Results in a Public Choice Model of Alternative Government Grant Forms," in The Theory of Public Choice: Essays in Application, James M. Buchanan and Robert D. Tollison (eds.). 24

EVALUATING PUBLIC PROGRAMS

resolved, the theoretical mechanisms bear little resemblance to the mechanisms operating to determine year-to-year changes in municipal allocation patterns; the specifications of the mechanism(s) are more the result of deductive than empirical efforts.25 Simon commented a number of years ago on some general reasons to expect the heavy deductive emphasis in the "eco­ nomic approach" to understanding local responses to grants: Economists have been relatively uninterested in descriptive microeconomics—understanding the behavior of individual economic agents—except as this is necessary to provide a foundation for macroeconomics. The normative microeconomist "obviously" does not need a theory of human behavior: he wants to know how people ought to behave, not how they do behave. On the other hand, the macroeconomists' lack of concern with individual behavior stems from different considerations. First, he assumes that the economic actor is rational, and hence he makes strong predictions about human behavior without performing the hard work of observing people. Second, he often assumes competition, which carries with it the implication that only the rational survive. Thus, the classic economic theory of markets with perfect competition and rational agents is deductive theory that requires almost no contact with empirical data once its assumptions are accepted.26 25 Funk and Wagnalls define "empirical" as "relating to or based upon direct experience or observations alone." A common perversion of the term in social science is its use to describe any work using "real" data even when "observation" consists of attempting to "fit" models derived deductively to data of dubious quality using statistically sophisticated "fitting" techniques. For example, it is hard to understand how an argument about whether a utility function used in a model of state and local fiscal behavior should be quadratic or logarithmic in form could arise from any direct observation of state and local fiscal behavior (see Martin McGuire's comments on Gramlich and Galper's model in Gramlich and Galper, "State and Local Fiscal Behavior," pp. 61-62). See A. Kaplan, The Conduct of Inquiry: Methodology for Behavioral Science, for a discussion of the inevitable fusion of inductive and deductive reasoning in problem-directed research. 26 Simon, "Theories of Decision Making, p. 254.

THEORETICAL FOUNDATIONS

As a basis for empirical research on local responses to grants, the assumptions are unacceptable. They are not a useful basis for empirical work and not accurate positive theory. One inconsistency (or, perhaps, "inferential leap") in the economic approach to the study of grant effects should be noted in moving from the theory to the empirical work. The theoretical model takes individual units of government as its unit of analysis, whereas much of the empirical work is either cross-section or time-series analysis on aggregates. Although always implicit, the rationale for aggregation is in the sweep­ ing deductive assumptions that Simon described above. The implication for interpreting the aggregate empirical work is that either the structural dissimilarities among units are unim­ portant or that they can be adequately summarized in the description of differences in their external environments. New York City and Truth or Consequences, New Mexico, are not in any sense interchangeable, and interpreting parameters from models that aggregate such dissimilar units, particularly in cross section, is very hard work. The empirical work on determinants largely upholds the long theoretical tradition in economics of treating the decision-making unit as a "black box."27 The simplest and most common form of model used in determinant studies is the single-equation additive regression model: EXP; -

Ct0 +

Σ βJi l

+

u,

J=I

EXPy = expenditure in ;'th functional category (e.g., police expenditures) X 1 = i,h independent variable (including one or more variables for grants) a = empirically determined constant β, = empirically determined regression coeffi­ cient for rth variable u, = error term 0

27 The exceptions recognize that budgets must balance or that prior-year expenditures are important determinants of this year's expenditures. See

EVALUATING PUBLIC PROGRAMS

The dependent variable, EXP;, has been expressed in per capita (e.g., police expenditures per capita), constant dollar, and nominal dollar terms. A host of independent variables have been tried and reported in the literature. Usually the variables tried include "environmental factors" such as median per capita income, population size, population age distribution, population density, crime rates, total assessed valuation, racial composition of population, form of govern­ ment, percentage of democratic voters, and so forth. Some "theory" (or at least disciplinary preferences for sub­ ject matter) is found in the selection of explanatory variables. Political scientists using such models attempt with varying degrees of success to explain variation in expenditure levels with "political" variables such as governmental form, party competition, partisanship, and apportionment. Economists look for surrogates for the demand (or need) for public ser­ vices. Variables such as per capita income, degree of urbaniza­ tion, crime rates, population density, per capita property valuation, and federal and state aid have been used in the statistical models and found "significant" for particular categories of municipal expenditure for particular sets of municipalities in particular years. There have been a number of attempts to study the response of local governments to state and federal assistance programs by including levels of assistance as explanatory vari­ ables.28 Single-equation formulations are much more common than simultaneous equation formulations.29 Additive relaI. Sharkansky, "Economic and Political Correlates of State Government Expenditures: General Tendencies and Deviant Cases"; I. Sharkansky, Spending in the American States; Gramlich and Galper, "State and Local Fiscal Behavior"; and Robert P. Strauss, "The Impact of Block Grants on Local Expenditures and Property Tax Rates." 28 See E. M. Gramlich, "The Effect of Federal Grants on State-Local Expenditure: A Review of the Econometric Literature," for reference to a number of such studies. 29 For examples of simultaneous equation models, see Ann Horowitz, "A Simultaneous-Equation Approach to the Problem of Explaining Interstate Differences in State and Local Government Expenditures"; and Gramlich and Galper, "State and Local Fiscal Behavior."

THEORETICAL FOUNDATIONS

tionships are most prevalent. The use of cross-sectional data for estimating model parameters is much more common than the use of time-series data.30 Given the extreme stability that characterizes municipal allocations patterns, there is an infinite variety of statistical and simulation models that will perform "well" on conven­ tional statistical criteria (e.g., result in a high R2) by simply taking advantage of the stable, linear trend components in time-series data on municipal expenditures and some environmental factors.31 Economists and political scientists concerned with specifying determinant models have dis­ covered in recent years that incorporating expenditures from the prior year or components of the dependent variables increases the statistical performance of the models dramati­ cally. The reason why these factors and others such as mean per capita income and per capita property valuation work (in a statistical sense) is that they do capture elements of process. "Prior-year expenditures" work well because budgeting is an adaptive process and last year's appropriation is the normal point of departure for calculating this year's appropriation. The budget and accounting forms used at all levels [e.g., department, mayor (manager), and council (commission)] force historical comparisons by incorporating columns for prior-year appropriations and actual (audited) expenditures in the latest year for which such data are available. "Categori­ cal assistance by function" works because it is a portion of the dependent variable, "expenditure by function."32 Ability-topay variables such as "mean per capita income" and "per 30 For examples of the use of aggregate time-series data, see Gramlich and Galper, "State and Local Fiscal Behavior"; for the use of time series, see William E. Whitelaw, An Econometric Analysis of a Municipal Budgetary Process Based on Time-Series Data, and Claudia Scott, Forecasting Local Government Spending. 31 Two time-series with linear trend components that are wholly unrelated causally will, of course, show a statistical relationship. See Karl A. Fox, Intermediate Economic Statistics, pp. 81-83. 32 Alternatively, if the amount of assistance is subtracted from the depen­ dent variable, expenditures by function, before the regression is calculated, there is a circular relationship in aggregate cross-section studies. Levels of

EVALUATING PUBLIC PROGRAMS

capita property valuation" work statistically because they par­ tially describe the "revenue constraint." The experience with "the economic approach" to the empirical study of the effects of federal grants has not been wholly satisfactory, as Edward M. Gramlich noted after reviewing the economic literature on the topic: Either because of peculiar statistical difficulties surround­ ing this problem or because of the vagaries of statistics in general, there is little consensus even on a basic estimate of the partial derivative of expenditures with respect to grants, let alone some of the finer points. Of the more reliable studies, over half of these studies indicate a response in the complementary range, a quarter in the limited substitution range, and the remainder in the substitution range. Given the known existence of simultaneous equations bias, which none of the studies have done anything about, possible spurious correlation between grants and expenditures on different functions, and the theoretical improbability of complementary responses it seems fair to say that many of the responses in the complementary range are biased . . . just as policy-oriented work on federal grants has been weak in not surveying the theoretical and econometric liter­ ature, and theoretical work has been weak in not devel­ oping testable hypotheses, econometric work has been weak in not answering to theoretical and policy-oriented questions.33 In his review, Gramlich provides an excellent discussion of the methodological problems and, to a lesser extent, the con­ ceptual (theoretical) issues that have plagued this literature.34 local expenditure are almost always key determinants of the amount of federal grant monies various cities receive. The causal arrow runs both ways. See Bahl, "Studies on Determinants of Public Expenditures." 33 Gramlich, "The Effect of Federal Grants on State-Local Expenditure," pp. 589-590. 34 Three other good reviews of this empirical literature on federal grants as part of the larger "determinants literature," and with more emphasis on theoretical issues than Gramlich, are Bahl, "Studies on Determinants of Public Expenditures"; Jesse Burkhead and Jerry Miner, Public Expenditure,

THEORETICAL FOUNDATIONS

He offers a number of specific and general prescriptions, including: (1) improved understanding of the relative merits of cross-section and time-series analysis; (2) the use of pooled cross-section observations, including terms to measure crosssection price variability, investigating for possible internal feedbacks and firm effects, and checking cross-section regres­ sions for heteroskedasticity; (3) the use of some form of simultaneous estimation or instrumental variable approach to deal with the "simultaneous bias problem" or "at least to check the validity of the estimates we have now"; (4) disag­ gregation between current expenditures and capital construc­ tion expenditures and between types of federal grants; and (5) "a much stronger effort to make econometric studies of the effects of federal grants relevant to questions of theory and policy."35 Gramlich's last prescription is the key, although we would almost certainly disagree with him on the specific steps needed to make the econometric work relevant to theory and policy. Although the "theory of grants" is primarily deductive and not rich in empirically testable hypotheses, the theoretical framework has influenced the empirical work, primarily as a source of conceptual problems. The framework was developed for the study of "market phenomena" and the determinant studies are, in part, attempts to impose this framework on a "nonmarket" process—municipal resource allocation—where the framework's conceptual value (viz., utility as a body of concepts for simplifying, organizing, underchap. 9; and Gail Wilensky, "Determinants of Local Government Expendi­ tures," in Financing the Metropolis, John P. Crecine (ed.), vol. XI of Urban Affairs Annual Review, pp. 197-218. The serious methodological and con­ ceptual problems (e.g., multicollinearity, simultaneous equation bias, dynamic inferences from cross-section estimates, heteroskedasticity, and sen­ sitivity of parameters to model specification and choice of estimation pro­ cedure) with specific studies will not be considered in detail. These problems are well catalogued in the four literature reviews cited, and recantation of the criticism, except as it touches the arguments here, would serve no real pur­ pose. 35 Gramlich, "The Effects of Federal Grants on State-Local Expendi­ tures," pp. 590-591.

EVALUATING PUBLIC PROGRAMS

standing, and, perhaps, changing a complex "chunk" of the world) is extremely questionable. Burkhead and Miner commented at length on the similarities and differences of a public budgetary process and a market allocation process: The budget process is in some ways similar to private mar­ ket processes. Like market processes, the budget reflects decisions concerning the allocation of resources. The opti­ mal allocation of scarce resources is [should be? might be?] the common objective of both. The procedures for budget­ ary examination and review [should] impose a kind of disci­ pline which is ordinarily associated with the discipline of expenditure decisions by households or business firms in a reasonably competitive market. But in other ways the anal­ ogy is faulty. . . . A market is usually described as an imper­ sonal mechanism generating and responding to price signals for factors and for products. The prices, in turn, interact with preferences and technology to constitute the basis for decisions by households and business firms. The budget, however, does not generate comparable price signals for budget-makers to respond to. Rather, the budget reflects the representations of the executive and of the citizenry with respect to the appropriate level and composition of government output and the revenues that should be associ­ ated therewith. The representations of the citizenry are often made to both administrators and legislators. Or, administrators and legislators may react to indirect evi­ dences of the demands and preferences of the citizenry for public goods and services. Or, administrators and legis­ lators may acquire a kind of independence of the citizenry and shape and influence the demand for an acceptability of their programs. But in any case there are no quantitative signals to be interpreted in the way that an increase in copper prices, for example suggest to copper producers that output should be expanded, or when an increase in unsold inventory is the signal to cut back production. . . . The budget process in government thus differs from the mar-

THEORETICAL FOUNDATIONS

ket process in ways that are significant for economic analysis. The market is a social, man-made institution that serves up information on which consumers and producers make decisions about the purchase and sale of factors and products. The market does not "make" the decision; the market does not allocate resources. The market pro­ vides information. Even as producers make decisions, so do those government officials who propose and adopt budgets.36 It is striking that the only descriptive (nonnormative) simi­ larity between budget processes and the idealized market processes given in this comparison is that there are decisions on resource allocation made on the basis of some information; otherwise, there are only differences, particularly as the description becomes more specific (viz., we move to operationalize the concepts in an empirical setting). Although these differences have been frequently recognized, there have been many persistent attempts to operationalize the concepts drawn from an idealized description of market processes in a nonmarket context. The persistence of such fundamental notions as the separation of supply and demand, decision makers and voters as price takers, the price and income elas­ ticities of demand for goods and services, and the existence of stable, describable equilibrium positions poses problems when the "empirical reality" of the context for applying the concepts includes: 1. A single, complex "actor"—municipal government —that has a primary role in determining both supply and demand. As Burkhead and Miner commented: In terms of the theory of consumer demand, it is most difficult to conceptualize a demand function for a final product that embraces both the demands by factor suppliers [local officials] based on anticipated profits (personal and intra-organizational values not determined by the external environment) and the demands by final consumers [the 36

Burkhead and Miner, Public Expenditure, pp. 12-13.

EVALUATING PUBLIC PROGRAMS

municipal service population] based on the utilities gener­ ated by the consumption of these products.37 Researchers often suggest that data inadequacies are the prin­ cipal obstacle to the separation of supply and demand. The problem may be more fundamental in a context where coer­ cion exists, the "exclusion principle" does not often apply, the ultimate "consumers" of the goods and services do not have decision-making autonomy in a continuous set of "armslength transactions," and where both supply and demand are manifested in a single decision—a budget allocation. 2. Prices are associated with factors but not outputs and the distinction between outputs and valuation is difficult, if not impossible, to maintain because many of the outputs are not produced in physical units. Measurement of inputs and outputs is one of the econo­ mist's stocks in trade. Nonetheless, the conceptualization and estimation of service outputs, whether public or pri­ vate, has proved virtually intractable. Inability to measure units of service outputs is a serious if not fatal barrier to the estimation of production functions, costs and supply curves, and productivity changes. In addition, it imposes extreme difficulties in determination of identified demand relation­ ships. The lynchpin of empirical studies of economic behavior, relatively unambiguous measures of output, is lacking for the public sector.38 Decision makers, whether producers or consumers, cannot be "price takers" in the normal sense, and the "price (or income) elasticity for public goods" is a nonoperational, and perhaps misleading, concept. No conceptually satisfactory empirical estimate of the price elasticity of demand for "public goods" can be devised. Studies of the association between, say, growth in income and growth in a "chunk" of the public sector 37 Ibid., p. 14. The examples in brackets are ours, not those of the authors from whom the quotation was drawn. 38 Ibid., p. 296. All is not well in empirical analyses of the private sector. P. W. S. Andrews, in On Competition in Economic Theory, levels similar criticisms at analyses of retail markets.

THEORETICAL FOUNDATIONS

are not apt to reveal citizen preferences for private versus public goods but rather other dynamics of the situation (e.g., outcomes from imperfectly adaptive decision processes) because citizens have only occasional and imperfect choice opportunities (e.g., voting) and poor relative price informa­ tion for influencing such outcomes. The preoccupation with equilibrium positions in contrived market worlds, elasticity measurements, and providing macro-policy inputs is reflected in the narrow focus of economists researching grant effects. The research problem has been viewed almost exclusively as one of estimating behavioral responses in the form of partial derivatives of various expenditures with respect to various types of grants (e.g.,SEJSG1, where E, is the expenditure on theith functional category or the /th level of aggregate public expenditure and Gj is the amount of theyth type of assistance). For matching grants, the research question becomes simply whether the partial derivative (SEJSGj) is between zero and one (i.e., the grant substitutes for local expenditures because a dollar of assistance results in less than a dollar of additional expendi­ ture) or between one and one plus the required matching proportion (i.e., the dollar of assistance stimulates more than a dollar of additional expenditure but less than the match requirement implies) or between one plus the required match­ ing proportion and infinity (i.e., a dollar of assistance stimu­ lates more expenditure than the dollar plus the matching fraction).39 Gramlich summarized the approach for unconditional grants: For unconditional grants this partial derivative is anal­ ogous to the effect of transfer payments on consumption expenditures—that is, the partial derivative is the states' [municipalities'] marginal propensity to spend uncondi­ tional grants. But whereas the only alternative to persons' consuming transfer payments is personal saving, govern39 Gramlich, "The Effect of Federal Grants on State-Local Expenditure," pp. 572-573.

EVALUATING PUBLIC PROGRAMS

ments have much more attractive alternatives than spend­ ing unconditional grants, thus making for lower propen­ sities to spend these grants. Governments can save uncondi­ tional grants and move farther away from the point of fiscal bankruptcy, or, more importantly, they can reduce taxes and allow their citizens to consume more private goods.40 There are essentially two problems with this formulation. First, the response question is too narrowly framed. The prob­ lem formulation emphasizes the macroeconomic concerns about grants as instruments of fiscal policy for stimulating expenditures in a sector of the economy. For unconditional programs, the "theory of grants" holds that there will be an "income effect" and not a "price effect" and, therefore, there will be no interesting effects on the allocations within the recipients among functions; and the sole object of research interest is in the effect on the split between public and private uses. Although the "revenue displacement" and "savings effects" of unconditional programs are important, so are the expenditure effects. Preferences change with income levels in more complicated ways than a family of isoquants indicates. In terms of understanding local responses, the problem formulation in economics can provide some indirect evidence on the efficacy of various "matching" and "maintenance-ofeffort" provisions in grant legislation, although it would require finer disaggregation among types of grants than sim­ ply "unconditional" and "categorical,"41 time-series analysis, and much less aggregation of behavioral units. It is too dif­ ficult to work back from extreme aggregation to understand the processes resulting in differential responses. In a related example, it would have been difficult to predict (or explain) New York City's fiscal crisis using aggregate models. The 40 Ibid., p. 572. It is interesting that Gramlich includes individual philan­ thropy as consumption and governmental philanthropy—a tax reduction—as a separate option in his analogy. The notion that spending is less attractive for governments than saving or tax reduction is contestable, if not wrong, in most cases. The recipients' "fiscal condition" partially determines relative attrac­ tiveness. 41 Gramlich suggests this. See ibid., p. 590.

THEORETICAL FOUNDATIONS

City's fiscal crisis was most severe during a period of record state and local government surpluses in the National Income Accounts. The second and most important problem with this formula­ tion is its emphasis on disovering "true" parameters (e.g., marginal propensities to spend) when there is no a priori reason to believe that such parameters exist in a stable form relevant to policy. The parameters are sensitive to a host of factors that make their interpretation, much less their use for policy formulation, virtually impossible. The parameters are sensitive to choice of estimation procedures,42 choice of time period and level of aggregation in cross-section analysis, level of aggregation in time-series analysis, and, most important, choice of functional form and model content. In the regression models widely used for this research, particularly with high levels of aggregation, there are simply too many confounding factors and sources of bias ever to get interpretable, usable results. The confusion surrounding the estimates of 6 E J S G , that Gramlich describes in his literature review is symptomatic of fundamental problems with the approach. These are problems that are not apt to be "solved" through the use of pooled cross-section data, the use of simultaneous estimation techni­ ques, entering variables multiplicatively, evading multicollinearity and heteroskedasticity, or the like. A useful first step towards a "solution" would be the recognition that the model, regardless of form, in which the parameters of interest are imbedded is being used to generate the counterfactual argu­ ment on "what would have happened without the program." These models must be satisfactory approximations of process for reliable results. Data are a persistent and important problem for those using the determinants approach. Data availability has, in many respects, been more responsible for determining the character of the empirical research than "theoretical" or "research problem" considerations; it would be hard to explain the number of cross-section studies and the selection of census 42

See Thomas O'Brien, "Grants-in-Aid: Some Further Answers."

EVALUATING PUBLIC PROGRAMS

categories as explanatory variables otherwise. In Chapter 2, some of the problems in acquiring accurate and interpretable data on municipal expenditures were discussed and some important reasons were given for mistrusting expenditure data from published sources. The problems in getting data on environmental factors that is reliable, systematic, periodic, and timely are much greater. The researcher's problem is shared by local officials who have the added problem of an overload of unsystematic, undigested information on their immediate environment. Time-series data on expenditures and environmental fac­ tors are particularly scarce. The National Income Accounts have been used by several researchers43 as a source of quar­ terly data on expenditures for the entire "state and local sector." There are problems with the use of these data for the study of state and local responses to federal grants. Sam Rosen commented on the reliability problems: The problems relating to [data on] expenditures of state and local governments are especially serious because the data come from thousands of units and because there is no standardized record keeping or central collection agency. A continuing difficulty has been the irregularity of Censuses of Governments, the last two having been taken in 1942 and 1957. Currently, the statutes require such a census every five years which, if effectuated, would help considerably. Quarterly estimates of state and local government spend­ ing, for which the NID depends upon the annual summary of government finances compiled by the Bureau of the Census, are especially deficient. By August, 1957, when the summary for the fiscal year ending in 1956 was issued, the NID had already made six quarterly estimates without the benefit of the summary.44 43 Gramlich and Galper, "State and Local Fiscal Behavior"; Ε. M. Gramlich, "State and Local Governments and Their Budget Constraint"; and Roger E. Bolton, "Predictive Models for State and Local Government Pur­ chases," in The Brookings Model: Some Further Results, chap. 8, J. S. Duesenberry, G. Fromm, L. Klein, and E. Kuh (eds.). 44 SamRosen, National Income and Other Social Accounts, pp. 158-159.

THEORETICAL FOUNDATIONS

Even assuming that the data are in fact collected and not simply a collection of interpolations and extrapolations, the collection problems must be enormous. Municipalities oper­ ate on annual fiscal cycles. Financial decisions are not nor­ mally made on a quarterly basis (i.e., the bulk of appropria­ tions decisions are made annually and cash flow decisions are made continually). Financial reports (e.g., balance sheets, budgets, audits, and status of appropriations reports) are not normally prepared on a quarterly basis. And, further, municipalities have different accounting conventions; some are "cash," some "accrual," and still others use an admixture, "modified accrual." Different accounting systems will result in numbers that mean different things, particularly when the numbers are extracted on a quarterly basis. And cities using one type of system will not usually be able to provide meaning­ ful numbers on another basis. It is difficult to see how quar­ terly data on municipal expenditures can be meaningful, but since there is no systematic measurement of error in the data,45 the magnitude of the problems is unknown. Even assuming that the data are perfect measurements of cash outlays by quarter, there are still serious conceptual problems in using them to estimate behavioral responses to federal grants. Quarterly data on expenditures reflect timing of cash outlays during a fiscal year.46 This timing and the size of the cash flow surplus are, at any point in time, a function of many factors that have nothing to do with behavioral 45

Ibid., pp. 166-167. If the data do not reflect this actual timing but rather are smoothed interpolations between annual data points, what is the sense, other than a "larger N," in using quarterly observations? Interpreting "behavioral responses" when data have been invented is hard work. Gramlich and Galper's use of interpolations (quarterly) of three demographic variables from an already "smoothed" annual time series in their modeling is a ques­ tionable "empirical" procedure. See Gramlich and Galper, "State and Local Fiscal Behavior, p. 30, n. 13. Also, there is a problem with aggregate models based on time series in that if the behavioral elements are dissimilar, the regression coefficient(s) obtained for the aggregate "will depend in part on the relative variability of the different elements of the aggregate during the period for which the equation is fitted." Karl Fox, Intermediate Economic Statistics. 46

EVALUAnNG PUBLIC PROGRAMS

responses to past, present, or anticipated federal grant pro­ grams. For example, timing may reflect factors including (1) "lumpiness" and "pace" of receipts; (2) liquidity of "sur­ plus"; and (3) seasonal payment requirements. It is hard to imagine "cash flow surplus" on a quarterly basis as a "dis­ cretionary budgetary variable."47 The timing of outlays during a fiscal year do reflect resource allocation policies (e.g., deci­ sions determining the composition of payrolls), but there are many confounding factors. In short, there are serious problems with the use of quar­ terly data to gauge local responses to federal programs. The "natural" fiscal decision cycles are annual, and discretionary local responses to federal programs are primarily on that basis even when "program years" do not coincide with "fiscal years." The data, perhaps annualized, may have some use in giving us a "chartist's feel" for the gross effects of federal grants on state and local expenditures. And if there are policy makers in Washington who still believe in "fine tuning," this information may have policy consequences. However, attempting to construct complex "behavioral" models at such a high level of aggregation on such data will not lead to useful and operational insights on "how federal programs should be designed to elicit desired state and local responses." Statistical testing poses a further set of problems with empirical work in this area. With few exceptions, the tests and statistical criteria used to appraise the goodness-of-fit of determinant models and to report empirical results have been extremely weak, albeit conventional.48 In most cases, the statistical results presented are from parameter estimation—a test of a model's ability to forecast levels of the dependent 47

See Gramlich and Galper, "State and Local Fiscal Behavior," p. 29. Gramlich and Galper's work in this area is something of an exception to the general criticism that follows. Although they do not subject their models to "simulation" tests, they do report "fit," R2, on first differences from one-period change tests. See Gramlich and Galper, "State and Local Fiscal Behavior." Also, Gramlich suggests and uses in his literature review a hypothesis other than the "extreme null hypothesis" for testing the "coeffi­ cient of grants." See Gramlich, "The Effect of Federal Grants on State-Local Expenditure," p. 582 48

THEORETICAL FOUNDATIONS

variable(s) one period ahead over the estimation period.49 There are no tests of the model's ability to predict change or "simulation" tests of internal consistency and sensitivity to cumulative forecasting errors. The "significance" of the regression coefficients, often the sole objects of interest in the analysis, is determined from tests against "extreme null hypotheses" (e.g., a one- or two-tailed test of difference from zero).50 Much of this weakness in testing procedures arises from the static nature of the models that have been most commonly used. It is difficult to test the "dynamic properties" of models consisting of a single-equation, "state" explanatory variables, and estimated on cross-sectional data. The problem of under­ standing local responses to grant programs is, however, one of understanding behavioral dynamics. There is an increasing use of dynamic models and recognition of process characteris­ tics such as the balance requirement.51 Extensions of this literature, however, would benefit from (1) less reliance on deductive theory and more attention to positive theory; (2) less aggregation; (3) more use of time-series data and dynamic models;52 (4) less reliance on published data; (5) more rigor­ ous model testing, including "simulation" tests; and (6) expanding the research focus beyond the search for "true" SEJdG i .

Theoretical comparison was not the primary objective of 49

The difference between "simulation" and "one-period-change" tests is explained in Chapter 4. 50 See Simon, "On Judging the Plausibility of Theories," for comment on the usefulness of such tests. 51 See Gramlich and Galper, "State and Local Fiscal Behavior"; and Strauss, "The Impact of Block Grants." 52 With the exception of Gramlich and Galper, "State and Local Fiscal Behavior," there appears to be an extreme reluctance to experiment with model forms that do not permit classical statistical testing of model perfor­ mance and parameters, even though such forms are obviously required to characterize processes adequately. Since statistical tests, whether it is possible to make conventional probability statements about performance or not, are useful only in comparing models (hypotheses) and not in forming absolute, conclusive judgments about a single model's predictive power, this reluctance is unnecessary.

EVALUATING PUBLIC PROGRAMS

this research, and a rigorous comparison of the two alternative theoretical views has not been done. Such a comparison would require operationalizing competing models and comparing their predictive ability on the same data. Two recent attempts to make such theoretical comparisons have been neither suffi­ ciently rigorous nor conclusive,53 and useful work in this area remains to be done. This study provides, however, some evi­ dence in the continuing debate about what "determines" expenditures. One criticism leveled at Crecine was that he "constructed his model and selected his data" such that the "external factors" (i.e., the economic, demographic, and poli­ tical variables of the environment) are held constant. And he used "rather small samples, ranging from one to ten years in the late 1950s and early 1960s, a period during which practi­ cally all the variables were moving together."54 This study of five cities, including the one that was studied by Whitelaw —Worcester, Massachusetts—over 17 to 23 years in a period when all of the external variables were not "moving together" provides further empirical evidence on the general questions and the validity of Whitelaw's criticism. For the purposes of our research, the "determinant model" of municipal resource allocation processes is implausible. Local officials set allocation levels. The information on environmental forces that is available to local officials (and researchers) is unsystematic and ambiguous. Available infor­ mation is most often out of date, undigested, and never quite appropriate to decisions at hand. 53

See J. Danziger, "Budget Making and Expenditure of Variations in English County Boroughs,"; and John E. Jackson, "Politics and the Budget­ ary Process." Danziger does not test an operational bureaucratic process model, leaving the crucial comparisons at a verbal level. Jackson's operational form of the bureaucratic process model is unsatisfactory. It is a determinant model with "internal factors" used as explanatory variables and his statistical testing procedures are weak (i.e., the one-period-change tests implicit in parameter estimation). 54 Whitelaw, An Econometric Analysis of a Municipal Budgetary Process, p. 11. This criticism is somewhat unjust, since Crecine's data covered the recession of the late 1950s and the rapid economic expansion of the early 1960s. Whitelaw does not specify the set of variables that were "moving together."

THEORETICAL FOUNDATIONS

The decision-maker's information about his environment is much less than an approximation to the real environment. The term "approximation" implies that the subjective world of the decision-maker resembles the external envi­ ronment closely, but lacks, perhaps, some fineness of detail. In actual fact the perceived world is fantastically different from the "real" world. The differences involve both omis­ sions and distortion, and arise in both perception and infer­ ence. The sins of omission in perception are more important than the sins of commission. The decision-maker's model of the world encompasses only a minute fraction of all the relevant characteristics of the real environment, and his inferences extract only a minute fraction of all the informa­ tion that is present even in his model.55 And further, the mechanisms for translating environmental forces into expenditure levels are unspecified except as implicit weighting schemes in the statistical models. Even casual observation of the processes reveals that local officials are not systematically scanning their larger environment and simply not passive in formulating appropriations. This section began with a quote from Henri Bergson that may seem a bit harsh given the relative brevity of the discus­ sion of such important and complex topics. Bergson's point has, however, been made within the economics profession. Sometimes it is relatively easy to discern the real world conditions that give rise to particular approaches and to discern the problems to which theorists address themselves. It is frequently more difficult to perceive why a given body of theory or a particular approach, in this case to the public sector, persists over time, often lasting beyond the condi­ tions that give rise to its existence. A given pattern of thinking may perhaps have an internal dynamic of its own to encourage elaboration and extension beyond the original conditions of relevance. Or it may continue its existence as prescription, not as theory that explains reality. The 55

H. A. Simon, "Theories of Decision Making, p. 272.

EVALUATING PUBLIC PROGRAMS

development of classical and neoclassical theory about the economics of government illustrates the kind of degenera­ tion that can occur.56 The criticism of this theoretical framework as descriptive (positive) theory is extensive and justified. The theory's assumptions about the motivations and structure of the behavioral unit—maximizing community welfare and unitary omniscience—are empirically vacuous (i.e., they have nothing to do with actual behavior and lead to no empirically testable propositions). The theory consists almost entirely of unobservable parameters of behavior. The assumptions are a con­ venient way to ignore what is inside the "black box"—munici­ pal government—and to fall back on a deductively derived framework from which an enormous number of untestable hypotheses (stories) can be derived (told). Continued attempts to use the framework to talk about or research "real-world" phenomena reflect, among other things, an enormous, persistent confusion about the appropriate relative roles for positive (descriptive) theory and normative (pre­ scriptive) theory. Some have argued further that the deductive framework may have no prescriptive value: The distinction between operational and non-operational goals . . . serves to explain why a theory of public expendi­ tures has never developed a richness comparable to that of the theory of public revenues. The economic approach to a theory of public expenditures would postulate some kind of "utility" or "welfare" function. A rational expenditure pat­ tern would be one in which the marginal dollar of expendi­ ture, in each direction, would make an equal marginal con­ tribution to welfare. Although statements of this kind are encountered often enough in the literature of public finance, they are infrequently developed. The reason is that in the absence of any basis for making the welfare maximi­ zation goal operational (because of the absence of an opera­ tional common denominator among the sub-goals of gov56

Burkhead and Miner, Public Expenditure, pp. 16-17.

THEORETICAL FOUNDATIONS

ernment services), the general statement leads neither to description nor prescription of behavior.57 This critical review may be viewed by some as "jousting with a strawman" (i.e., defenders of the theoretical assump­ tions as useful description are hard to find), but the primacy of the theory in educating a new generation of empirical resear­ chers and the manifestations of the theory in more than a decade of empirical research on an important topic make it an "important strawman." The alternative offered here in the context of a particular research problem is neither complete nor wholly satisfactory in its execution. The models are, how­ ever, useful positive theoretical tools, and their use in this empirical problem context is, at least, "a step in the right direction." In the first section of this chapter, "Models and Theories," it was argued that "process description" is an important part of the research problem and that the models used here are reasonable approximations, albeit simple and abstract, of the complex processes determining municipal expenditure out­ comes. This section has considered briefly another approxi­ mation of "process" that has been used extensively to study local responses to grant programs; and it has been argued that the "bureaucratic process" approximation is more consistent with "real" processes, and therefore more useful in this research effort, than the "determinants" approximation used historically. In the next section of this chapter, municipal resource allocation processes are described more fully before turning in subsequent chapters to the results from testing and using the models. Municipal Resource Allocation Processes58 Those who cannot remember the past are condemned to repeat it. George Santayana

Figure 3-1 is a schematic representation of the municipal 57 58

See March and Simon, Organizations, p. 157. The discussion of the processes here will be somewhat abbreviated. For a

EVALUATING PUBLIC PROGRAMS

resource allocation decision process.59 This view of the pro­ cess holds that the "revenue constraint" is the most important exogenous (environmental) influence on resource allocation decisions when those decisional outcomes are measured as appropriation or expenditure levels for particular functional accounts. Economic, political, social, and demographic influ­ ences from the environment are filtered through this con­ straint. Although the models summarize this constraint as a single number—the sum of general fund expenditures from all but federal sources—the revenue constraint is complex and dynamic. Figure 3-1 also indicates an interaction between the exterFIGURE 3-1

Municipal Resource Allocation

Capital Budget Decisions

Revenue or "Level" Decisions

Operating Budget Decisions

Budget Ceilings

Actual Department Operations and Activities (Expenditures)

External Environment, Problems, etc.

much richer description of the processes and more complete discussion of theoretical issues, see Crecine, Governmental Problem Solving. 59 This figure is adapted from Crecine, "A Simulation of Municipal Budgeting," p. 121.

THEORETICAL FOUNDATIONS

nal environment and "actual department operations." In choosing to study expenditure levels (budget ceilings) rather than levels of service output (programmatic outcomes), much of the richness of the resource allocation system's adaptation to its external environment is lost. A great deal of the "pres­ sure/accommodation" behavior (i.e., the organization's nego­ tiations with and adaptation to its external environment) takes place through alterations in programmatic content (e.g., which roads get fixed and how they are fixed or where the tennis courts are built) without affecting allocation levels (i.e., account category-functional-proportions). Most munici­ palities are characterized by a lack of rigid program planning. Flexibility enables them to be extremely responsive to the host of small matters (filling potholes, removing trees, etc.) that are often the most important governmental matters to the indi­ viduals comprising "the environment." Arnold J. Meltsner and Aaron Wildavsky have argued that: The operating budget is insulated from the environ­ ment. . . . If social scientists want to ascertain how, if at all, environmental demands are met, they will have to examine detailed allocations within city departments; whose street is repaired and what neighborhood gets the traffic signal are appropriate questions.60 This is an important point. It partially explains why the com­ plexity of municipal resource allocation processes that economists and political scientists observe or intuit to exist with their particular "conceptual lenses" (e.g., pluralist or articulated demand models) is not generally present in deci­ sions on allocation levels. Decisions on allocation levels are something of a paradox. "Success" in the sense of actually arriving at an approved, balanced annual budget in a specific time frame is too substan­ tively important (i.e., the organization must have it to con­ tinue to function legally) for participants (decision makers) to 60 Arnold J. Meltsner and Aaron Wildavsky, "Leave City Budgeting Alone!: A Survey, Case Study, and Recommendations for Reform," in Financing the Metropolis, John P. Crecine (ed.).

EVALUATING PUBLIC PROGRAMS

permit the decision-making processes to become cluttered with ambiguous, disputatious, substantive policy issues [e.g., what the optimal "guns (police) butter (social services) split" would be]. Requiring the explicit and considered resolution of all of the substantive policy issues implicit in setting allocation levels would seriously impair, if not destroy, the organiza­ tion's ability to make the requisite set of decisions (i.e., a balanced budget) on time (i.e., prior to the start of the fiscal year). The latent policy implications of the decisions embodied in an annual budget are enormous. The document is de facto resolution of such important issues as (1) the aggregate split between public and private consumption of community resources; and (2) relative community preferences for all conceivable activities of municipal government. Choice opportunities such as budget preparation need not, however, coincide with explicit consideration and resolution of the problems implicit in the opportunity as viewed from a "rational actor" perspective.61 There is a significant difference between the abstract "problem space" derived deductively by academic observers and the "task environment" perceived by local officials. The economist's maxim—"Expenditures should be distri­ buted among alternative public purposes such that the benefit of the last dollar spent in any one program should be equal to the dollar of cost"—is impossible for local officials to operationalize in the context of preparing a municipal budget. The information available to decision makers on the demand and production functions for public service ranges from ambiguous to nonexistent. Crecine has argued that: The existence of potential complexity delimits the range of appropriate behavior. There are not many [decision] pro61 An extreme alternative to "rational actor" models characterizes the "choice opportunity [resource allocation decision] as a garbage can [an ambiguous stimulus] into which various kinds of problems and solutions are dumped by participants as they are generated." See Michael D. Cohen, James G. March, and Johan P. Olsen, "A Garbage Can Model of Organizational Choice."

THEORETICAL FOUNDATIONS

cedures that are both feasible in terms of individual cogni­ tive and informational limitations and likely to produce a problem solution (a balanced budget).62 In fact, most municipal decision makers never recognize the potential complexity of their task because they do not use the rational model to understand their world. Participants join an organization that has learned procedures for coping with the potential complexity of the budget problem and that con­ tinues to adapt these procedures. Municipal resource allocation processes are extremely stable systems. Crecine observed that: Stability guarantees the ability to reach a decision—to come up with a balanced budget, while upsetting a minimum number of people and programs. The balanced budget requirement forces a simultaneous consideration of all city activities. . . . This potentially difficult problem is solved by holding most of the budget constant.63 This stability is also a characteristic of the revenue (or level) decisions. Revenue yields change annually, but rate changes and the utilization of new revenue sources are infrequent occurrences, particularly when municipal decision makers need voter approval to make such changes. Precedent is an important legitimizing device that operates at two levels in resource allocation. First, established agencies that have been previously funded have an enormous advan­ tage over new agencies in the appropriation process. Their demands for resources at existing levels are automatically viewed as legitimate, and further, their demands for resources are solicited overtly each year. It is very difficult for new agencies to gain legitimacy; the services that they promise to deliver (or have been delivering with other sources of funds) must be very different from those being provided by any existing agency or the program is liable to be absorbed into the operations of an existing agency. Also, if the service is very 62 63

Crecine, "A Simulation of Municipal Budgeting," p. 119. Crecine, Governmental Problem Solving, p. 235.

ΕνΑΙΧΓΑΉΝΟ PUBLIC PROGRAMS

different, it must have unusually strong support, both internal and external to the process, to be funded. Second, funding that maintains current levels of inputs has an enormous advantage over the purchase of new inputs. This is particularly true for personnel. There are usually large internal costs associated with cutting personnel, whether the personnel are unionized or not. Crecine commented on another rationale for the impor­ tance of precedent as a decision premise: A basic property of decision making in the public sector (vs. the private) is the realization that both decisions and deci­ sion procedures are always subject (at least potentially) to public scrutiny . . . the openness of public decisions rein­ forces the use of rather straightforward methods of parti­ tioning the budget problem, the use of precedent as a defensible decision strategy, and encourages the use of simpler, easier-to-understand decision procedures than might otherwise be found.64 The use of precedent is reinforced by several factors in addi­ tion to the openness of decisions and decision processes to public review. Decisions must also be defensible internally. Internal defensibility is perhaps more important in constrain­ ing allocation decisions than the need for external defensibi­ lity. The internal audience (i.e., municipal employees) is much more cognizant of the decisions and has a much greater, often personal, stake in the outcomes. In the virtual absence of reliable, systematic information about the demand for public services or the production functions for the same services, precedent is the most plausible (defensible) rationale, inter­ nally and externally. The importance of "internal defensibility" explains the sta­ bility of the relative budget shares of functions drawn from a single fund. Except under conditions of surplus, arguments for more from one agency are simultaneously arguments for less for one or more of the other agencies drawing from the same 64 Crecine, "A Computer Simulation Model of Municipal Budgeting," p. 788.

THEORETICAL FOUNDATIONS

source (e.g., general fund). It is a zero-sum game with main­ tenance of the status quo a "prominent solution," if not a "saddle point." Values internal to the organization (e.g., stable expectations for employment) often dominate external values (e.g., citizen preferences for emphasis on one service versus another) in resource allocation decision making because internal values are (1) more strongly held; (2) more clearly articulated; (3) operational (means-ends connections are clear); and (4) held by internal organizational participants who attend to these decisions and have much greater access to decisional information and decision makers than the citizenat-large. Arguments about relative service priorities are inherently arbitrary. In a tight budget year the implications of moving $25,000 from the "health/personnel" account to the "control­ ler/personnel" account may not be clear in terms of change in the value of services to citizens. But one implication will be very clear: if Joe and Martha, who have been with the city for many years and who support families, cannot be moved from the health department to the controller's office with the re­ allocation of dollars, they will be unemployed.65 Resource allocation is a fund-by-fund activity. Capital pro­ jects and other activities that are funded outside the general fund are considered separately.66 Decisions on allocation levels are most often decisions on levels of service inputs, not service outputs. The information does not exist to base deci­ sions on the relative merits of service outputs. Decision mak65

Such moves can also negatively affect the expectations of other em­ ployees (e.g., if it can happen to Joe and Martha . . .). Also, city administra­ tions may find in making such moves that they cannot enforce them. For example, Detroit attempted in 1975 to lay off police in accordance with seniority rules in union contracts, but the city was ordered by the courts to reinstate the employees and find some basis other than seniority for making the layoffs because the act was judged to be discriminatory. Police with the least seniority tended to be minority group members. 66 See Figure 2-1 for the interconnections of funds. General fund accounts usually include capital outlays that cover such items as the purchase of office equipment. These should be distinguished from capital projects, which are largely funded through bonding and treated as a separate problem in budget­ ing.

EVALUATING PUBLIC PROGRAMS

ers focus on proposed changes in the level of inputs (e.g., new positions). Except where projected deficits and a firm revenue constraint compel otherwise, participants accept the current level of inputs with mandated changes (e.g., union contracts, merit and longevity increases, increases in the cost of supplies due to inflation) as "givens." Discretion in municipal resource allocation decision making is extremely limited. Resource allocation tends to be an executive dominated process. The chief executive (i.e., mayor or city manager) is usually charged with presenting a balanced budget to the legislative body. The executive has control of the bulk of the staff whose primary responsibilities are financial matters and control of the critical financial information. The reasons for the limited role of legislative bodies will be discussed below. The importance of the role of managers of fundamental units (e.g., department heads) varies from city to city, and from department to department and year to year within cities. With "fiscal pressure," their role tends to be very limited;67 when there is no "fiscal pressure" (i.e., there is discretionary money at the margin), the quality of plans (arguments) for more money to particular services are important in determin­ ing allocations at the margin. Department heads and their immediate employees are an important source of such argu­ ments. Unions are another important class of participants in muni­ cipal financial decisions. They influence resource allocation decisions primarily through negotiated contracts that force subsequent adjustments in existing and future budgets. The nonmonetary provisions can be as important as the monetary provisions in constraining financial behavior. For example, in Ann Arbor the contract with the police union representing patrolpersons specifies the maximum mileage that patrol cars can be used, thus constraining administrative behavior on one of the city government's largest nonpersonnel expenditure accounts. Also, union contracts in many cities include 67 In such circumstances, submitting "departmental budget requests" often becomes a charade, with all requests for additional funding ignored by a central budget authority, regardless of the substantive merit of requests.

THEORETICAL FOUNDATIONS

administrative rules for position reclassifications, hiring, fir­ ing, and layoffs that further limit the discretion of city admini­ strations on financial decisions. To varying degrees (by city and by service), the production functions for municipal ser­ vices are frozen. Figure 3-2 illustrates one full cycle for a "typical" municipal (general fund) resource allocation process. Details such as sequence, forecasting procedures, and the relative importance of the steps vary from government to government. However, this illustration is roughly consistent (i.e., captures the key elements) with the allocation processes in each of the five cities studied here. The specific "budget problem" faced by any municipality in any particular year is perhaps best understood as the differ­ ence between estimated revenue receipts (at constant rates) and existing expenditure levels adjusted for "uncontrollable" increases in factor prices and the balance, positive or negative, carried forward from prior years.68 This difference may take the form of a surplus (i.e., estimated receipts exceed estimated "fixed" expenditure requirements) or a deficit (i.e., estimated 68 Crecine's model used the difference between total departmental requests and "estimated revenues" as the problem for the mayor's office. The problem then evokes either "surplus or deficit elimination routines." See Crecine, Governmental Problem Solving, pp. 22-75. One important reason for the difference in definitions is that with the increasing importance of unions and inflationary cost pressures on commodities, local officials no longer have discretion to the extent they had in the period Crecine studied. All of the cities studied here make use of a "basic budget" concept in some form. The "basic budget" is essentially last year's level of service inputs with permitted/unavoidable increases, Cincinnati has institutionalized this con­ cept, distinguishing between "basic" and "service betterment" portionsof the budget from departmental requests through budget approval. In the four other cities, the concept exists in a variety of forms, including: (1) cutting rules that automatically disallow new positions and nonpersonnel expenditure increases not supported by firm evidence of inflationary effects; (2) the use of "departmental requests" that are actually filled out by the central budget authority or that departments fill out under extremely stringent guidelines; and (3) the use of legislation resolutions at the turn of the fiscal year that extend spending authority at last year's level into the new year. Changes occur in a piecemeal fashion, and at some point a budget document is produced.

FIGURE 3-2 Typical "General Fund" Resource Allocation Process

Revenue Side

Expenditure Side

Assumptions (Forecasts) 1 Local revenues (Δ base, constant rates) 2 State and federal funding 3. Projected surplus/deficit from current year

Assumptions (Forecasts) 1 Fixed levels of service inputs 2. Mandated increases (union contracts, etc )

Estimated revenues First trial balance

Test revenue constraints

Revised revenue estimate

Estimated expenditure requirements

Instructions to subunits for budget preparation (forms and "tone")

Second trial balance

2 subunit "basic" requests

^Administrative hearings^

Given direction and magnitude of problem, apply problem-solving routines to achieve balance

Chief executive's recommended budget

C 0). These residu­ als indicate that certain functional accounts receive less money with GRS than models predict they would have received without GRS. There is little in the positive theory of budgeting to aid us in interpreting such residuals. Two pos­ sibilities are that: (1) organizational shifts (e.g., moving pro­ gram responsibility from one organizational subunit to another) may have occurred that were not noticed and accommodated in constructing the data sets; and (2) shifts may have occurred in service priorities that are reflected in allocations. Although rare, such shifts do occur. Perhaps added money at the margin presents program opportunities that, when undertaken, require more resources than GRS amounts available and result in shifts. In any event, there were few such residuals and in almost every case they were small. These positive residuals were interpreted as resulting from model error and as indicating no GRS impact on the func­ tional account rather than negative impact. The procedures for aggregating the results for comparisons were consistent with this interpretation. The estimates of GRS fiscal effects, from taking each of the above steps, with all models for each city, are given in Tables 5-8 through 5-12. In Chapter 4, "Model Estimation and Test­ ing," CPRI was the preferred model, on the basis of predictive performance under various model testing conditions, for analyzing GRS. Tables 5-8 through 5-12 indicate that the results are not very sensitive to the choice of models. Although there are some differences, particularly for "public safety" and "general government" in the smaller cities (Tables 5-8 and 5-9), the patterns in percentage terms are

%

2,128,000 5,302,700

—

48,500

—

$2,508,200 103,700 260,900 201,200 632,800

CPRI

100.0 11,186,000

—

25.4 47.4

—

2,840,600 5,302,700

11,185,900

—

0.4

—

49,200

14.5 2.9 1.5 1.7 6.2

CPB

$1,619,900 327,400 166,800 189,500 689,800

* Differences due to rounding errors.

Total"

Public safety Public works Recreation Health Planning Social services Schools and libraries Financial administration General government Revenue displacement

Function

CGRI

19.0 47.4

—

0.4

—

11,185,900

2,731,100 5,302,700

—

38,700

—

22.5 $1,894,900 211,400 0.9 261,000 2.3 154,300 1.8 591,800 5.7

%

100.0

TABLE 5-8 Albuquerque GRS Fiscal Effects (All Models—All Years) DCFP '

2,444,300 5,302,700

—

100.0

21.9 47.4

—

—

0.6

—

19.1 2.2 1.2 1.9 5.7

%

72,000

100.0 11,185,900

24.4 47.4

—

0.4

—

16.9 $2,136,800 243,100 1.9 135,200 2.3 209,100 1.4 642,600 5.3

%

100.0

—

—

—

3,456,500

—

—

—

—

3,456,500

40,100 1,825,100

—

3.2 51.3

$982,400 393,000 12,000 36,600 164,1004 3,200

CPRI

—

23.6 15.6 0.3 1.2 4.5 0.2

%

112,200 1,773,200

$816,900 540,000 12,000" 40,500 155,200' 6,500

CPB

100.0

—

—

1.2 52.8

—

28.4 11.4 0.3 1.1 4.7 0.1

%

* Includes a special, "one-time" allocation of $147,000 to the housing commission. b Includes a special, "one-time" allocation of $120,000 to the municipal airport. c Differences due to rounding errors.

Totalc

Public safety Public works Recreation and culture Health Planning and housing Social services Schools and libraries Financial administration General government Capital contribution Revenue displacement

Function

TABLE 5-9 Ann Arbor GRS Fiscal Effects (All Models—All Years)

3,456,400

—

—

72,000 1,740,100

—

$1,067,100 379,600 12,000 23,900 154,300" 7,400

CGRI

100.0

—

—

100.0

—

3,456,400

— —

2.1 54.2

—

25.5 11.7 0.3 1.2 4.9 0.1

%

—

—

71,000 1,873,200

—

$882,600 403,900 12,000 40,500 168,5006 4,700

DCFP

2.1 50.3

30.9 11.0 0.3 0.7 4.5 0.2

%

CPB

CPRI

36,175,500

100.0 36,175,600

—

—

—

—

342,500 4,409,100 4,459,400

—

1.3 9.3 13.5

—

41.3 $14,617,100 18.1 5,472,000 7.8 2,575,100 3,078,900 5.6 2.3 873,200 0.7 351,300

%

45,400 3,352,800 4,881,900

$14,954,900 6,550,900 2,828,600 2,042,200 839,000 270,700

" Differences due to rounding errors.

Total"

Public safety Public works Recreation and culture Health Planning and housing Social services Schools and libraries Financial administration General government Capital contribution Revenue displacement

Function

CGRI

—

0.9 12.2 12.3

—

36,175,700

—

456,600 5,575,900 3,989,900

—

40.4 $14,418,900 15.1 5,633,900 2,832,400 7.1 2,018,100 8.5 1.0 866,300 383,900 1.0

%

100.0

TABLE 5-10 Cincinnati GRS Fiscal Effects (All Models—All years) DCFP

100.0

•—

1.3 15.4 11.0

—

100.0

— —

36,175,800

1.3 11.5 13.3

—

41.0 16.4 6.7 6.7 2.4 0.8

%

455,200 4,175,200 4,805,400

—

39.9 $14,822,700 15.6 5,919,000 2,428,400 7.8 2,409,600 5.6 873,200 2.4 1.1 287,100

%

CPB CPRI

141,967,500 100.0 141,966,800

100.0 141,966,800

1.1 56.3

—

1,556,700 79,945,500

$31,163,700 19,628;700 2,362,300 9,762,700 700,100 1,652,100

CGRI

1,239,100 75,458,100

2.1 47.9

22,2 11.9 1.7 6.0 0.6 0.2

%

—

21.4 $31,471,700 16.7 16,871,000 3.0 2,396,500 8.1 8,538,300 0.6 871,300 0.2 315,800

%

2,943,000 67,981,100

$30,394,600 23,761,100 4,272,500 11,530,000 844,700 240,500

° Differences due to rounding errors.

Total"

Public safety Public works Recreation Health Planning Social services Schools and libraries Financial administration General government

Function

TABLE 5-11 Detroit GRS Fiscal Effects (All Models—All Years) DCFP

1,792,100 78,511,200 100.0 141,966,700

0.9 53.1

22.0 $31,072,600 13.7 17,769,800 1.7 3,066,400 6.9 8,850,800 0.5 646,300 1.2 257,500

%

100.0

1.3 55.2

21.9 12.5 2.2 6.2 0.5 0.2

%

12,568,400

100.0

—

0.3 3.6 2.1 7.5 2.0 73.5

—

31,000 455,100 253,600 951,700 252,900 9,241,900

% 0.4 3.5 1.5 5.6

CPB

$464,000 439,400 190,500 705,900

" Differences due to rounding errors.

Total"

Public safety Public works Recreation Health Planning Social services Schools and libraries Financial administration General government Capital contribution Revenue displacement

Function

12,568,100

100.0

—

73.5

—

9,241,900

—

12,568,400

9,241,900

—

33,200 629,100 267,700 864,400

—

0.2 4.2 2.2 7.2

$51,400 410,900 183,900 885,900

CGRI

0.4 3.4 1.5 7.4

%

19,700 528,200 274,700 915,900

—

$43,900 428,800 189,800 925,200

CPRI

TABLE 5-12 Worcester GRS Fiscal Effects (All Models—All Years)

100.0

73.5

—

0.3 5.0 2.1 6.8

—

0.4 3.3 1.5 7.1

%

12,568,300

9,241,900

—

21,300 506,100 285,900 881,700

—

$38,500 446,400 197,900 948,600

DCFP

100.0

73.5

—

0.2 4.0 2.3 7.0

—

0.3 3.6 1.6 7.5

%

EVALUATING PUBLIC PROGRAMS

remarkably similar.11 CPRI results were used for the compari­ sons, but that choice was not particularly important to the analytic results.12 Because of the small size of the sample, it is not important to present the numerical results by themselves. We are simply not in a position to draw general conclusions about the aggre­ gate effects of GRS in numerical terms.13 The comparisons between our estimates of GRS fiscal effects and comparable data from other sources are the important results for presenta­ tion here.14 The most visible source of data on GRS fiscal effects is the Office of Revenue Sharing. The data from these reports are essentially accounting data; they indicate how city officials have accounted for GRS funds. The potential problems with attempting to interpret these data on "nominal uses" of GRS funds as "net effects" have been widely recognized,15 but the data have been and continue to be widely reported without adequate explanation and qualification. Also, there has been no careful analysis of the magnitude or direction of the prob­ lems with ORS data as net effects. Tables 5-13 through 5-17 show city-by-city comparisons of the results, from analysis with CPRI, with patterns of expendi­ ture indicated by ORS report data.16 The patterns are given 11 Deriving very similar patterns from each of the models should increase confidence in the results. To the extent that the models are dissimilar, there is a "triangulation effect." Because the models are such "close theoretical kin," however, this argument cannot be pushed very far. 12 The functional categories used are those suggested by the data (i.e., the categories that result in the fewest possible arbitrary aggregation decisions). 13 Overgeneralizations from the work are largely restricted to qualitative results. See Chapter 6, "Conclusions." 14 The small sample size also limits, in a statistical sense, the generality of the conclusions about the reliability of data from other sources. 15 For example, see R. P. Nathan A. D. Manvel, and S. E. Calkins, Monitoring Revenue Sharing, p. 234; and Revenue Sharing: Its Use by and Impact on Local Governments, A Report to the Congress by the Comptroller General of the United States(Washington, D.C.: General Accounting Office, 1974), pp. ii and iii. 16 ORS data taken from "actual use" reports where available, and "plan­ ned use" reports otherwise.

EMPIRICAL RESULTS

for functional categories17 in percentage terms (i.e., the per­ centage of GRS funds expended) by year18 and in total (across years) for each city. The pattern differences between "nomi­ nal effects," as given on the ORS reports, and "net effects," as estimated here are striking. For Albuquerque (Table 5-13), the single most important pattern difference is in the "revenue displacement" category. The ORS reports ask city officials to indicate by checking a box (or boxes) if revenue displacement has occurred, but there is no space on the forms for estimating the dollar mag­ nitudes of revenue effects. Even if officials indicate revenue effects, they are expected to indicate the full amount of GRS funds appropriated from the GRS trust fund as expended in "priority expenditure categories." Wherever GRS has a revenue displacement effect, nominal effects will diverge from net effects. The extent of the divergence is partially a function of the amount of revenue displacement that occurs.19 17 The categories used here are very close to ORS report categories. "Revenue displacement" was added, "financial administration" was included in a larger "general government" category, "environmental protection" and "public transportation" were combined into a "public works" category, and "social services" and "social development" were combined. Also, the ORS report distinction between "operating" and "capital" expenditures was ignored because it is meaningless in many cases (see below). City expenditure accounts and the model outputs are much more detailed than the ORS report categories. The way that detail is mapped onto the aggregate categories is obviously important to comparisons. The aggregate categories are somewhat ambiguous and overlapping. Several functional accounts in each city logically belong to two or more of the aggregate category or apportioning a single account to more than one functional category are often arbitrary choices. The ORS guidelines for report preparation are sketchy, and cities have different account structures. We attempted to map our detailed results onto ORS categories in exactly the same way that indi­ vidual cities mapped their accounts. Our information on what cities did was good, and categorization was not a significant source of error on these comparisons. 18 A potential source of error in year-by-year comparisons is a lack of correspondence between GRS entitlement periods and fiscal years. The summaries for all years avoid most of this problem. 19 See Figure 5-1 for the plot of predicted and actual revenue in Albuquer­ que. The displacement effect, the gap between predicted and actual revenue to the right of the vertical dashed line, is clear.

55.2 24.1

Public safety Public works' Health Recreation Library Social services and development General government Education Housing and community development Economic development Revenue displacement Total —

100%

—

—

—

—

0.7 9.0

—

3.3

—

69.3 17.7

ORS

41.7 100%

—

100%

—

—

47.5 100%

—

—

5.7

—

0.06

19.0

—

22.4 0.9 1.8 2.3 0.4

CPRI

—

0.44 6.9

—

8.6

—

63.8 20.2

ORS

Total

5.9

16.1

—

31.0 0.9 1.6 2.6 0.2

CPRI

1973--1974

* All entries in tables are percentages. b Officially reported capital and operating expenditures combined by functional category. c Combination of environmental protection and public transportation.

100%

56.2 100%

— —

—

5.1

—

0.2

23.6

—

9.2 1.0 2.2 1.9 0.8

CPRI

0.1 3.6

—

16.8

—

ORS*

Function

1972--1973

TABLE 5-13 Albuquerque ORS Comparisons"

48.2 9.9 0.1 1.1

Public safety Public works Health Recreation Library Social services and development Financial administration General government Education Housing and community development Economic development Total —

—

100%

100%

—

—

100%

—

100%

* All entries in tables are percentages. b Officially reported capital and operating expenditures by functional category. c Combination of environmental protection and public transportation.

100%

—

0.5

—

15.5

—

—

—

—

—

—

—

—

100

ORS

—

18.7 0.6 35.3

—

33.2 9.2 1.7 0.8

CPRI

100%

—

0.01

—

1.5 2.6 41.4

—

—

36.0 17.99 0.5

CPRI

1974--75

—

—

—

—

3.5

—

88.4 7.0 0.3 0.8

ORS

—

16.8 0.6 44.2

—

—

14.1 8.1 0.7

CPRI

1973--1974

—

—

—

40.7

—

ORS"

Function

1972--1973

TABLE 5-14 Ann Arbor ORS Comparisons"

—

100%

—

100%

4.7

—

13.3 1.2 39.6

—

28.4 11.4 1.1 0.3

CPRI

—

—

—

—

16.2

—

77.3 5.8 0.1 0.6

ORS

Total

—

— —

100%

—

—

—

— —

3.2 —

10.1 100%

100%

—

—

—

—

—

10.8 100%

—

2.5

—

2.0 1.5 4.9

50.9 13.1 6.8 7.5

CPRI

—

70.4 13.6 9.4 4.7

ORS

1975

0.9 1.0

1.8 1.7 12.0

—

39.6 15.7 8.9 7.0

CPRI

1974

* All entries in tables are percentages. b Officially reported capital and operating expenditures combined by functional account. ' Combination of environmental protection and public transportation.

100%

18.3 100%

—

0.9

—

—

—

—

—

—

1.0 2.5

—

1.2 1.8 16.9

—

56.0 21.1 8.5 10.9

ORS

1.2 2.9

28.1 16.8 10.2 5.8

42.1 33.1 7.5 13.2

Public safety Public works' Health Recreation Library Social services and development Financial administration General government Education Housing and community development Economic development Capital outlay Total

CPRI

ORSi

Function

1973

TABLE 5-15 Cincinnati ORS Comparisons"

100%

—

—

—

—

—

1.0 2.1

—

56.5 22.4 8.5 9.5

ORS

_

—

2.4

—

1.7 1.7 10.8

40.6 15.1 8.5 6.9

CPRI

12.3 100%

Total

84.1 11.9 4.0

Public safety Public works' Health Recreation Library Social services and development Financial administration General government Education Housing and community development Economic development Capital outlay Total 100%

100%

All entries in tables are percentages. Officially reported capital and operating expenditures combined by functional category. Combination of environmental protection and public transportation.

—

100%

—

0.1

0.1

12.7 9.1 7.9 1.3

CPRI

0.1 1.0 67.8

—

—

86.6 12.4 1.0

ORS

1973-1974

0.3 0.8 45.0

37.5 9.4 5.5 1.6

CPRI

100%

—

—

—

—

—

—

—

ORS6

Function

1/72-6/73

TABLE 5-16 Detroit ORS Comparisons"

100%

_

85.5 14.5

ORS

100%

_

1.9

0.3 1.6 60.1

11.2 17.9 4.8 2.2

CPRI

1974-1975

100%

85.3 12.9 1.8

ORS

100%

0.6

0.2 1.1 56.3

11.9 6.0 1.7

22.2

CPRI

Total

—

—

100%

—

—

—

—

—

—

—

—

—

11.7

—

88.3

ORS

53.5 100%

—

—

0.5 0.9 17.5 3.3 1.3 0.1 3.7 16.3 2.9

CPRI

1974--1975

• All entries in tables are percentages. b Officially reported capital and operating expenditures combined by functional account. e Combination of environmental protection and public transportation.

100%

—

84.8 100%

—

0.3

—

—

3.0 4.1

—

74.6 1.4 16.2 0.4

Public safety Public works" Health Recreation Library Social services and development Financial administration General government Education Housing and community development Economic development Revenue displacement Total

CPRI 0.3 4.8 1.7 0.5 0.9 0.2 1.3 2.2 3.3

ORSfc

Function

1973--1974

TABLE 5-17 Worcester ORS Comparisons"

100%

—

—

0.2

—

1.9 2.6

—

79.6 0.9 14.6 0.2

ORS

0.4 3.4 7.4 1.5 1.0 0.2 2.2 7.3 3.2

CPRI

73.4 100%

Total

EMPIRICAL RESULTS

The analysis for Albuquerque (Table 5-13) also indicated that ORS data overreport net effects in public safety, public works, and recreation and underreport net effects in health, housing and community development, and general govern­ ment. Figures 5 -4 through 5 -8 are time-series plots for some of the more important functional accounts comprising the func­ tional categories that show major differences between nomi­ nal and net effects.20 The vertical dashed line is drawn through the last year prior to GRS. Thus, data to the left of the line are in the "estimation period" (i.e., the period of years without GRS from which parameters are estimated) and corres­ pondence between the lines is visual evidence on one-periodchange "goodness-of-fit." Data to the right of the dashed line are in the "forecast period" (i.e., GRS years) where divergences between the predicted and actual expenditure lines are GRS fiscal effects, ceteris paribus. Figures 5-4 and 5-5 are for police/personnel and police/nonpersonnel, respectively.21 These two accounts comprise over 20% of Albuquerque's general fund budget (Table 4-1) and about one-half of the public safety function. The court/personnel account (Figure 5-6) is another compo­ nent of the public safety category (Table 5-13). The impact of GRS on these accounts is reasonably clear, even with the large amount of revenue displacement, which tends to make the analysis of expenditure effects more difficult. The ORS data indicate much larger effects on these and other public safety accounts than our analysis indicates. Revenue displacement makes it more difficult to attribute effects causally to GRS because levels of expenditure directly influence magnitudes for individual functional accounts. When analysis indicates that revenue displacement occurred, 20 These are plots from one-period-change model runs using revenue fore­ casts. For Albuquerque, year 17 (1974/75) was not used in the formal analysis because of data problems. 21 The symbol following function name (e.g., "police") is the code for the type of account. "1" is for personnel accounts; "2" is for nonpersonnel accounts; and "T" is for "total only" accounts (i.e., accounts where separa­ tion of personnel and nonpersonnel was impossible).

EVALUATING PUBLIC

PROGRAMS FIGURE

5-4

Albuquerque Time-Series Plot, Police/Personnel Expenditures

FIGURE

5-5

Albuquerque Time-Series Plot, Police/ Nonpersonnel Expenditures

194

EMPIRICAL

FIGURE

RESULTS

5-6

Albuquerque Time-Series Plot, Court/Personnel Expenditures

FIGURE 5 - 7

Albuquerque Time-Series Plot, Administration/Personnel Expenditures

195

EVALUATING PUBLIC PROGRAMS

FIGURE 5-8 Albuquerque Time-Series Plot, Health/Personnel Expenditures Predicted 800-- Actual

fi

600--

400--

200--

2

5

8

11

14

17

T->

the estimate of "what the revenue (expenditure) level would have been" was used. This estimate was always higher than observed levels and, therefore, the difference between the sum of observed expenditures with GRS and the sum of prediction is smaller than when observed levels less GRS were used (e.g., the procedure for Ann Arbor, Cincinnati, and Detroit). And the smaller the increment, the more difficult it was to distinguish GRS expenditure effects from model errors and alternative causal explanations for differences. The simplest explanation for the pattern differences is that cities have merely reported revenue displacement in the categories where analysis indicates that "nominal" expendi­ ture effects are greater than "net" effects. Of course, this is partially true, but when the models are run with actual revenue (i.e., GRS impact in expenditure effects), the same pattern differences persist although they are less pronounced

EMPIRICAL RESULTS

(e.g., public safety is overreported and general government is underreported.) Although analysis of Ann Arbor indicates that no revenue displacement occurred (Table 5-14), there are substantial differences between "nominal" and "net" fiscal effects. The analysis indicates that public safety is overreported (Table 5-14). Figures 5-9 through 5-12 are time-series plots for the police and fire functions, the bulk of Ann Arbor's public safety category. Year 18 is particularly interesting in that Ann Arbor officially reported the use of all its GRS funds, over $1 million, on fire protection. In contradiction, Figures 5-11 and 5-12 show a very small net effect, approximately $40,000, in fire protection. This is a dramatic example of substitution; funds were reported as used in one area, displacing local funds for use in other areas. Figure 5-13 is the time-series plot for nondepartmental, general fund expenditures. The net effect of GRS on this account is large, accounting for a significant proportion of the net effect indicated in general government (Table 5-14). This effect reflects the reduction of accumulated debt from years prior to GRS. Ann Arbor city government was under extreme fiscal pressure for the last six years of the study period. GRS eased this pressure somewhat, but the only layoffs in twenty years occurred in a GRS year (FY 1973/74). Table 5-15 gives the patterns of "nominal" and "net" effects for Cincinnati. Although the differences are not as dramatic as for Albuquerque and Ann Arbor, there are dif­ ferences of similar direction for public safety and general government. Also, there is a significant difference in capital outlay (Table 5-15).22 22 This difference is somewhat complicated. Cincinnati did report a capital outlay from GRS in the area of environmental protection in an amount slightly less ($816,000) than the estimated effect ($842,000). This report was based on an appropriation. The expenditure was never made. Our effect reflects expenditures for equipment (e.g., rolling stock) made through a general fund capital outlay account not attached to any particular function. In times of light or no fiscal pressure, Cincinnati funds such purchases from the general fund. In times of greater fiscal pressure (e.g., two and three years prior to GRS), these purchases are charged to funds other than the general fund.

EVALUATING PUBLIC

PROGRAMS FIGURE

5-9

Ann Arbor Time-Series Plot, Police/Personnel Expenditures

FIGURE

5-10

Ann Arbor Time-Series Plot, Police/Nonpersonnel Expenditures

198

EMPIRICAL

RESULTS

FIGURE 5 - 1 1

Ann Arbor Time-Series Plot, Fire/Personnel Expenditures

FIGURE

5-12

Ann Arbor Time-Series Plot, Fire/Nonpersonnel Expenditures

199

EVALUATING PUBLIC PROGRAMS FIGURE 5-13

Ann Arbor Time-Series Plot, Nondepartmental/Total Only Expenditures

1.5-

Predicted Actual —a—,

§ 0.5-

2

6

10

14

18 T->

Figures 5-14 and 5-15 are time-series plots for public safety, a combined function in Cincinnati. The net effect estimated is significant, particularly for nonpersonnel ex­ penditures (Figure 5-15), but somewhat less than reported effects. Figure 5-16 is a plot for city manager/nonpersonnel expenditures. The dramatic expenditure increase, coinci­ dent with the advent of GRS, represents a number of service improvement projects, primarily analytic, that the city under­ took. There is no substantial difference between nominal and net effects for health expenditures in Cincinnati (Table 5-15). Figures 5-17 and 5-18 are plots for the health accounts. These are included here because health was an area on which the city placed a high priority even in the "difficult" (in a fiscal sense) years prior to GRS. Also, the health/nonpersonnel account (Figure 5-18) was the single most difficult account in Cincin-

EMPIRICAL FIGURE

RESULTS

5-14

Cincinnati Time-Series Plot, Public Safety/Personnel Expenditures

FIGURE

5-15

Cincinnati Time-Series Plot, Public Safety/Nonpersonnel Expenditures

201

EVALUATING PUBLIC PROGRAMS FIGURE 5-16

Cincinnati Time-Series Plot, City Manager/Nonpersonnel Expenditures GRS -> Predicted — Actual —a—a—

200--

100--

.ifi.

A FIGURE 5-27

Worcester Time-Series Plot, Police/Nonpersonnel Expenditures GRS -* Predicted Actual 800--

600--

400--

200--

2

7

12

22

17

T^

EVALUATING PUBLIC

PROGRAMS FIGURE 5-32

Worcester Time-Series Plot,VocationalSchools/PersonnelExpenditures

FIGURE

5-29

Worcester Time-Series Plot, Fire/Nonpersonnel Expenditures

210

EMPIRICAL FIGURE

RESULTS

5-30

Worcester Time-Series Plot, Public Schools/Personnel Expenditures

FIGURE

5-31

Worcester Time-Series Plot, Public Schools/Nonpersonnel Expenditures

211

EVALUATING PUBLIC

PROGRAMS FIGURE

5-32

Worcester Time-Series Plot, Vocational Schools/Personnel Expenditures

FIGURE 5 - 3 3

Worcester Time-Series Plot, Vocational Schools/Nonpersonnel Expenditures

212

EMPIRICAL RESULTS

FIGURE 5-34 Worcester Time-Series Plot, Capital/Total Only Expenditures

GRS -» Predicted Actual —a—A-

:/' \'l J

V

§ 10" Ό Q

5--

12

17

22

There are at least two reasons why ORS data tend to overreport public safety effects.27 First, local officials perceive that public safety accounts (primarily police, fire, courts, and building inspection) are "politically acceptable" uses for GRS funds in the sense that designating these "nominal" uses of the money will avoid a lot of Congressional and public criticism that might follow designating use for more controversial (less "basic") programs. In Ann Arbor this reasoning goes even further in that police is controversial among important ele­ ments within the community (i.e., opposed by liberals and students and favored by conservatives), whereas fire protec­ tion is politically neutral. Consequently, the designated use is fire protection. 27 A third reason has been suggested: Public safety is the first category on the ORS reports and some officials believe it is therefore the federal govern­ ment's top priority. We did not encounter this, but other researchers have. See Nathan et al., Monitoring Revenue Sharing.

Total

(Unexpended balance) Revenue displacement Capital

Public safety Public works Health Recreation Library Social services and social development Financial administration General government Education Housing and community development

Category

—

(194,676,800) 188,254,500

(6,422,300)

100.00

—

0.02

—

27,200

—

0.9

—

0.63

—

79.05 13.9 3.5 2.0

%

1,713,000

1,091,400

—

$148,800,700 26,224,500 6,603,100 3,794,600

Cumulative ORS data

205,353,000

14,544,600 4,459,400

$49,705,100 23,268,500 12,777,200 5,352,500 176,500 1,407,200 2,468,500 88,251,900 400,200 2,541,400

Cumulative CPRI estimates

TABLE 5-18 Cumulative Comparisons for All Cities and All Revenue-Sharing Years

100.0

7.1 2.2

24.2 11.3 6.2 2.6 0.1 0.7 1.2 43.0 0.2 1.2

%

EMPIRICAL RESULTS

And second, the accounts comprising public safety are always among the largest in a city. Since reported uses corres­ pond to a set of more detailed appropriations, the accounts that receive the GRS appropriations must be large relative to the size of the appropriation unless the city actually intends to expand a function with GRS funds. GRS appropriations, like other appropriations, are authorizations to spend money. In Ann Arbor, there are only four of thirty-seven accounts that are large enough in FY 1974/75 to absorb a $1,013,400 appropriation without that appropriation resulting in an extraordinary "real" expansion of a functional area. Three of these four accounts are in the public safety category. General government activities are systematically underreported because they are among the least popular expendi­ tures in any community. They tend to be viewed as wasteful overhead. Also, only financial administration, of all the myriad "general government" activities, is an "ORS priority expenditure category." To comply with the law, cities must indicate all GRS uses only in priority expending categories. And perhaps more important, the ORS reporting forms only have space for reporting priority expenditure category uses. The time-series plots for individual accounts are offered as evidence that the differences between patterns of "nominal" effects and patterns of "net" effects are real. Although the group of cities studied here is not large, the cities included are quite diverse in terms of size, demographic composition, fiscal condition, and functional responsibility. Because consistent pattern differences were discovered across such a group of cities, the ORS "nominal effects" data are of questionable value as anything more than an indication of how cities choose to account for GRS funds. Their accounting choices are not constrained by the "real" impact of GRS.

6 "Basic" Like error hunting, the love of ease in intellectual matters goes with the Lil­ liputian outlook, whose typical handi­ work is the digest. Whether of an arti­ cle, a book, an opera, or a philosophy, the digest anticipates collective judg­ ment by eliminating what is unex­ pected and difficult. Take out every­ thing that anybody might object to or stumble at and you have reached your goal: the old bare bone, the one simple point, the upshot—all that is worth passing on. The word we apply, with a mystic's confidence, to this residue is "basic." Jacques Barzun The House of Intellect

For research of the type reported here, there is no obviously correct point to stop the empirical work and report results. The data and models can always be refined further. Each step taken in the research reveals several further steps that can and should be taken to strengthen and expand understanding of the topic. Like theories, results are always approximations. This chapter con­ sists of three parts. First, the findings on the impact of GRS on municipal fiscal behavior are given. Second, some potentially worthwhile extensions of this work are described, and third, the research approach is summarized and appraised.

"BASIC"

The Impact of GRS The art of being correct lies in making the weakest possible statements. Bloggin's Working Rule No. 211

The discussion of empirical conclusions must begin with a "blanket qualification." Five city governments in a limited size range is an extremely narrow empirical basis for drawing any general conclusions about the impact of General Revenue Sharing, a program with some 38,000 recipients. However, "micro"-process-oriented view permits insights into behavior of local governments in response to GRS that are not always possible for research attaining more extensive coverage with less intensive methods. Also, the findings of other research projects2 are largely consistent with those of this research. Other researchers encountered conditions (separate decision-making processes for GRS, extensive capital project uses, etc.) that were not found here, but most of the findings are corroborative. Although the "sample" is not "representa­ tive," some conclusions about GRS are worth stating without further qualification. The specific conclusions about the fiscal effects of GRS in particular cities and about the value of ORS data as a reflec­ tion of "net" fiscal effects were given in Chapter 5.3 This research identified important differences between "nominal effects," effects as indicated by municipalities in official reports to the Office of Revenue Sharing, and "net effects," effects determined by comparing what would have happened without GRS with what did happen. Public safety uses are substantially overreported, whereas revenue displacement and general government uses are substantially underreported. In this section we shall explore some more general findings on GRS. The degree of "fiscal pressure" on recipient units of gov1

I. J. Good, The Scientist Speculates, p. 213. See Catherine Lovell, John Korey, and Charlotte Weber, "Effects of General Revenue Sharing on City Policy Choices"; and Richard P. Nathan, Allen D. Manvel, and Susannah E. Calkins, Monitoring Revenue Sharing. 3 Also see T. Anton et al., Understanding the Fiscal impact of General Revenue-Sharing, for further policy commentary resulting from this research. 2

EVALUAnNG PUBLIC PROGRAMS

eminent is the single most important determinant of how GRS funds affect recipients' fiscal behavior. Fiscal pressure exists when the costs of providing a constant level of services are increasing more rapidly than revenues at constant rates. Where fiscal pressure exists, GRS funds tend to be merged with other general operating funds and used to support recur­ rent expenditure obligations (e.g., existing personnel com­ mitments). When "fiscal pressure" does not exist, GRS funds will be used for: (1) revenue displacement; (2) the accumula­ tion of surplus; (3) increased funding of basic services; and/or (4) capital projects and other nonrecurrent expenditure obligations. Another important determinant of GRS' impact is the structure of local decision making. Specifically, if local officials are able to adjust the tax rate to balance their budget (e.g., Worcester), revenue displacement will be the dominant use. GRS effects in the first year are very different from what they have been in succeeding years. Consequently, data on GRS effects in the first year are a poor basis for deciding the program's future because these data do not describe what the program's "steady-state" impact is—its impact after local officials develop a stable set of expectations about the pro­ gram and treat it routinely. Unfortunately, because of data and methodological limitations, much of the research infor­ mation on GRS fiscal effects of national scope pertains only to first-year effects. In the first year, there was a great deal of uncertainty on the part of local officials as to the amount of money they would receive. Also, the start of GRS was out of phase with local budget cycles; GRS checks arrived in the middle of local fiscal years. These factors led to very different outcomes than in subsequent years, when local officials had a firmer notion of the amount of GRS they would receive and could incorporate an estimate of GRS into their annual budgetary processes. The extent to which local officials view GRS as a stable source of revenue can affect the way that funds are used. Local officials attempt to use unstable revenues for nonrecurrent expenditure items—because if they buy personnel and new

"BASIC"

programs and the source of revenue disappears, they face painful adjustments. For cities such as Detroit, cities in dire financial straits, this logic does not hold because they will use all resources at their disposal to deal with their proximate operating problems. As GRS is institutionalized and local officials develop faith in the program's permanence, GRS will become an indistinguishable part of municipal operating budgets except where truly separate allocative processes fund­ ing unique programs were established and maintained for allocation of GRS. To the extent that GRS funds are used to support recurrent expenditure obligations, withdrawal, reduction, or effective earmarking of GRS support would necessitate a set of difficult fiscal adjustments—raising additional revenue from state or local sources, reductions in service levels including layoffs, and/or deficit spending. Where reducing service levels (rate of expenditure) is the only viable option (viz., the case in many large urban centers), there is a high likelihood of layoffs following seniority rules falling disproportionately on minor­ ity groups, the "last hired." If GRS is continued and the new legislation intends to encourage the development of specific services, then some "maintenance-of-effort" provision must be included and the reporting from recipient units of government must include expenditures from all sources for the specified services to monitor compliance effectively. Furthermore, those designing the program should realize that "maintenance-of-effort" pro­ visions placing a flat dollar figure on local effort (e.g., local expenditures for the service in program years must be equal to or greater than the level of local expenditure in the last fiscal year prior to the program) will still permit substitution of GRS funds for local funds. Cities can accomplish this substitution by simply funding the cost increases for a fixed level of service inputs over time from GRS funds. Legislative provisions that work with units of service inputs rather than dollars or that apply an annual inflation factor to determine the "level of local effort" required would be much more effective. If the intent of new legislation is to place full discretion on

EVALUAnNG PUBLIC PROGRAMS

use of GRS funds with local officials but still retain some federal oversight of the program for periodic renewal, we suggest provisions for funding analytic studies that are designed to provide information for the Congress and the Executive Branch to support a renewal decision. These studies should deemphasize detailed effects as studied here because identifying such effects is increasingly problematic as the program ages; rather, the research should focus on gross impacts (e.g., to what extent is GRS merged with the operat­ ing budgets) and examine alternative fiscal arrangements. The Trust Fund and publication requirements should also be reconsidered. Where the potential for substitution exists, the Trust Fund provision does not effectively constrain local behavior; GRS funds are simply appropriated to a legal cate­ gory displacing local effort and the "net effect" of GRS can appear anywhere in the budget. Ifeffective strings" are built into GRS, the Trust Fund provision is not necessary to ensure compliance. If the intent of the Trust Fund provision is to bolster local audit controls or to retain some vestigial, sep­ arate identity for federal funds at the local level for promo­ tional reasons, the provision should be recognized as such. Requiring the publication of "actual use" reports in local newspapers is not an effective means of communicating the program's impact to citizens. The reports are not particularly newsworthy and are most often published in a "public notices" section. Also, the data on the reports do not reflect the net impact of GRS. A more meaningful publication requirement would be to require the publication in local newspapers and reporting to ORS of a summary of the full budget—a summary that would identify all sources of local revenue and functional expenditures by aggregate categories. Many cities are already required to do this by state or local law, but some degree of uniformity and comprehensiveness would be helpful to both citizens and researchers. State gov­ ernments might be required to administer such a provision in conjunction with the Census Bureau. There is not currently a source of reliable annual data on local revenues and expendi­ tures.

"BASIC"

As presently constituted, GRS is a cosmetic, imperma­ nent "solution" to the "fiscal problems" to many large cities. Two of the cities studied here, Detroit and Albuquer­ que, experienced more severe fiscal problems during GRS years, particularly FY 1974/75, than they experienced prior to GRS. With GRS, a constant (no-growth) component of recipients' revenues, the federal government has actually aggravated the fiscal situation for some recipients. GRS is used to buy service inputs (e.g., personnel) that become more costly over time. And yet the funding for these inputs, GRS, covers a smaller proportion of costs in each succeeding year. If GRS is continued, serious consideration should be given to the various proposals that have been made to make the amount of GRS available for distribution grow with inflation and real income (e.g., make the total amount of GRS available a fixed percentage of the federal income tax yield). There is no evidence from this study that GRS is changing the structure of municipal resource allocation decision pro­ cesses. The primary impact of GRS is on budgetary outcomes rather than processes. GRS alters the budget problem by making more money available. And because the budget prob­ lem—the difference between estimated revenues at constant rates and the cost of maintaining the existing level of ser­ vices—directly affects the options open to financial decision makers and channels their budgetary behavior, GRS changes budgetary outcomes. The required preparation of "planned use" reports is not an effective stimulus for substantive plan­ ning. And there are no other provisions in the legislation that would force cities of the type studied here to move away from "business as usual." State enabling legislation is important to the structure and problems of city governments; deficit or "fiscal pressure" conditions exist, in part, because of state limitations on revenue sources and the extent to which those sources can be utilized. This suggests that if consideration is given, in design­ ing new GRS legislation, to including provisions that require "modernization" or "upgraded institutional capacity," seri-

EVALUATING PUBLIC PROGRAMS

ous attention should be directed at states and their relations with local governments. At least several hundred thousand dollars are being expended for ORS data when we consider the effort expended by local officials in preparing, publishing, and submitting ORS reports and the effort expended by ORS in compiling and reporting these data. We question the value of this expendi­ ture. Our research shows that the reporting requirements do not ensure the use of GRS funds in "priority categories" and that the data have little or no analytic value. The current reporting procedures should be revised regardless of the form that GRS program takes in the future.

Possible Extensions4 When Columbus left on his famous voyage, he did not know where he was going, when he landed he did not know where he was, and when he returned he did not know where he had been. But he did it all on government money. Anonymous

There are a number of other policy-related questions that can be effectively addressed with modest extensions of this work. One appropriate extension of this research is to use the research strategy for analyzing the impact of categorical pro­ grams. We have most of the prerequisite data (e.g., amounts of federal aid in particular fiscal years) for five cities. We also have models that can be used to develop the necessary counterfactual arguments. One potential problem in such an exten­ sion is that categorical assistance involves smaller amounts of money than GRS. Although it may be possible to estimate "displacement" and "new" expenditure effects, it may be extremely difficult to understand the "net" effects of dis­ placements; the dollar amounts displaced may be too small to show effects clearly when diffused across the myriad accounts in a city. Also, there are some problems with timing. Categori­ cal programs, particularly those for capital projects, extend over more than one local fiscal year; although the municipality 4 Some possible methodological and theoretical extensions were discussed at the conclusion of Chapter 4.

"BASIC"

has authorization to spend the entire program amount in the first year, planning and implementation often extend programs over several years. Neither of the problems ap­ pears to be insurmountable, but each will require further work. Another step that can and should be taken is to ask and respond to the question, "Was the impact of GRS funds on expenditure patterns different from what the impact of an equivalent increment from local sources would have been?" To answer this question, the models can be operated to pro­ ject the counterfactual portion of the analysis—what expendi­ tures would have been with an amount equivalent to GRS from local sources. The first part of this analysis would com­ pare model projections with observed outcomes in GRS years and attempt to determine if GRS resulted in pattern irregularities. If model goodness of fit in GRS is at least equivalent to fit in pre-GRS years, we can infer that there is no difference in the treatment of GRS funds. If goodness of fit in GRS years is much poorer than in pre-GRS years, we can analyze the differences, function by function or account by account, to understand why there are differences. Because of the way in which GRS has been merged with the general funds, we do not expect to find significant differences; good­ ness of fit in GRS years should be the same as the fit in pre-GRS years of like proportional growth in revenues. The approach of using models of municipal resource alloca­ tion processes to generate counterfactual patterns of revenues and expenditures is, in principle, appropriate and promising for studying the effects of programs other than grant programs on municipal fiscal behavior. For example, the approach can be easily adapted to study the impact of managerial innova­ tions such as planning, programming, budgeting systems (PPBS), zero-base budgeting systems (ZBBS), and management-by-objectives (MBO). Revenue and expendi­ ture patterns are an obvious place to look for changes from such innovations. These normative decision systems are intended to lead to more "rational decision making" than traditional "line-item," history-dominated decision pro-

EVALUAnNG PUBLIC PROGRAMS

cesses, and if the systems are "successful," we would expect allocation patterns to change.5 The goodness-of-fit testing of models in this research (see Chapter 4) provide some evidence on what an evaluation of PPBS would reveal. Every city except Worcester has made some effort at rationalizing their financial decision making during the estimation period for this research. The models did not adjust explicitly for these budgetary system innovations. Any changes in allocation patterns due to these systems were either explained in model parameters or showed up as residu­ als (model errors). The residuals are not large, and the impact of the innovations is not obviously large. Until the models and parameters are reworked to address directly the question of what impact the innovation has had, the "real effects" will remain unclear. For positive theoretical comment on municipal resource allocation processes, we have only begun to exploit the cur­ rent results, the potential of the data sets, and our qualitative understanding of the processes. For example, the analysis to this point has not included a rigorous comparison of para­ meters among cities and detailed analysis of model residuals. This work is important not only for theoretical insights it would provide, but for improving the models as descriptions of process and as practical positive theoretical tools for under­ standing the effects of a variety of public programs. In principle, the research strategy employed for the analysis of GRS is relevant in broad outline to evaluations in areas other than municipal financial systems. The research described in this book was developmental. It was an attempt to develop an approach to the evaluation of GRS not fraught with the conceptual and methodological problems of conven­ tional approaches, notably survey research. The research was also an explicit attempt to use symbolic analogies for program evaluation in a more rigorous fashion than has happened with 5 We would also expect changes in other areas (e.g., improved program planning within appropriation ceilings). Intensive interviews with local offi­ cials would play an even more important role in this possible research than it has in the GRS research.

"BASIC"

the better known quasi-experimental designs. The rigor attainable in any particular application of a symbolic analogy is strictly proportional to the quality of existing descriptive theory. Existing theory of municipal resource allocation pro­ cesses is excellent. The paucity of usable theory in other research areas is an obvious practical obstacle to immediate, widespread application of rigorous symbolic analogies. The potential for using models of behavioral processes in program evaluation, however, has been underexploited. Such an approach is not inherently inferior to experimentation. The preferred approach can be determined only in the context of a particular evaluation research problem. Research designs must be problem-directed.

Summary and Appraisal of Approach This book has presented a somewhat new approach to under­ standing the effects of public programs that are not implemented as controlled experiments and whose effects depend on the discretionary responses of many individuals and organizations beyond the direct, full control of agencies initiating programs. Such programs (e.g., most tax, transfer, and regulatory programs) have proved to be recalcitrant to systematic evaluation using conventional research designs and methodologies (e.g., econometric modeling and survey research). The principal evaluative difficulty is knowing a counterfactual state, "What would have happened without the pro­ gram?" We must compare "what would have happened" with "what did happen" to gauge programmatic effects. The approach advocated generally and used specifically in this book to analyze the impact of the General Revenue Sharing (GRS) program on the fiscal behavior of five municipalities was to use models based on positive theories of program recipients' decision-making behavior to make the requisite counterfactual arguments for evaluation. For the analysis of GRS, simulation models of municipal resource allocation processes were used to generate complex,

EVALUAnNG PUBLIC PROGRAMS

counterfactual hypotheses: "What would the revenue and expenditure patterns of particular municipalities have been without revenue sharing?" These hypotheses were then com­ pared with realized revenue and expenditure pattern out­ comes to attribute changes—fiscal effects—to GRS. Four competing "bureaucratic process" models were esti­ mated and tested for each of the cities using seventeen to twenty-three years of data on revenues and expenditures, collected from primary source documents. Analysis with for­ mal models was augmented by interviews with local officials. Empirical results for each city were presented in detail and results were compared, city by city, with data on the program from the Office of Revenue Sharing (ORS). The conclusions from this research on the impact of GRS on municipal revenue and expenditure patterns are different from the conclusions reached using ORS data. The research strategy employed here represents a some­ what new approach to an old problem in public finance economics—estimating the impact of external funding on recipients' financial behavior. The approach is also different from other research designs developed for the analysis of the same research questions. And the strategy places a degree of faith in existing positive theory for the analysis of an "applied" problem that is very unusual in the social sciences. The research strategy was successful on several dimensions, and elaborations of this basic strategy are extremely promis­ ing for the study of particular policy design and evaluation problems. Although abstract as theoretical statements, the models were consistent with existing positive theory and appropriate to the research problem. Although some plaus­ ible alternative causal explanations certainly exist for some of the findings on fiscal effects, inferences were reasonably direct. Unlike more aggregate approaches, we were able to know in many instances when plausible alternative explana­ tions existed. Also, the processes' experience in the pre-GRS period was relevant to the GRS period because all of the cities had increments in level of expenditures prior to GRS that were at least as great as the increments with GRS.

"BASIC"

The performance of the models, particularly under the somewhat demanding "simulation" tests over an extended period of time, was very satisfactory (see Chapter 4). The empirical work that supported the development of the models and the models' performance provide further support for the validity of a "bureaucratic process" positive theoretical view of municipal resource allocation processes. The use of that theory in this research has demonstrated the potential utility of accurate, operational positive theory for policy analysis in general, and program design in particular. Design problems are no novelty in federal assistance pro­ grams. Most of the "design problems" that lead to "unin­ tended effects" can be traced to an incomplete or inaccurate understanding of the processes determining revenue and expenditure levels. Without understanding the problems and incentives (probable behavior) of the recipient units, design­ ing effective programs is impossible: There is no basis for choosing systematically from an infinite set of possible legisla­ tive provisions, a set of provisions with a high likelihood of achieving intended programmatic effects (e.g., maximize additivity in particular functional areas). One curious finding from this research is that the informa­ tion we need to design effective grant programs (i.e., pro­ grams that achieve their intentions) is exactly the same infor­ mation we need to understand the effects of programs ex post. We need to understand how recipients make financial deci­ sions.6 The tasks of program design, program evalution, and prescription for improving decision-making processes are better grounded on positive theories than on Utopian theories of decision making. Models that are essentially normative have been and con­ tinue to be used extensively for research purposes that require positive-theoretical models. The best examples are uses of models derived from neoclassical microeconomic theory for such tasks as predicting business firms' responses to tax and regulatory policies or predicting the responses of municipal 6 And also "program decisions," although we are a long way from being able to model the full processes effectively.

EVALUATING PUBLIC PROGRAMS

governments to grant-in-aid programs. If these versions of homo economicus are reasonable approximations of the indi­ viduals and organizations they purport to describe and yield accurate predictions of observable behavior, they are appro­ priate policy research tools. Unfortunately, these theories only infrequently make operational predictions of observables (i.e., empirically testable assertions about the world). The relationship between theoretical verbiage and empirically specified models is often unclear (e.g., the determinant mod­ els criticized in Chapter 3 are consistent with processes imper­ fectly adapting as well as those perfectly adapting). And the "applications" of the theory often take the form of casual verbal and graphical arguments (e.g., the theory of grants" with at best numerical illustrations). Although such applications are common and widely accepted among academics as "policy analysis," we suspect that they are rarely appropriate and frequently misleading.7 The practical salvation from such analysis is in the fact that the prescriptions are apt to be as ambiguous as the predictions. The latter are untestable and the former are nonoperational. Most policy makers ignore them most of the time. The conclusions in Chapter 3 about the value of the tradi­ tional (economic) approaches to the research problem of understanding local responses to grant programs in ways that contribute to improved future policy formulations were un­ equivocal. One primary problem with the traditional approach is preoccupation with normative issues and wide­ spread inattention to the enormous amount of positivetheoretical work that is required to address the normative issues effectively. Deduction is too often a substitute for observation in the process of "attempting to describe." Prescriptions for future evaluation research arising from this work are easier to give than to follow, but recognizing the difficulty of evaluation tasks explicitly is an important part of the prescription. Evaluation researchers should accept and act upon the point of view that methods must be adapted to 7 We can never know until the theories are used to generate testable hypotheses about individual and organizational behavior in policy settings.

"BASIC"

problems and not problems to methods. Clarity in problem formulation, including recognition of the counterfactual ques­ tions that need to be answered and of the theoretical require­ ments they impose, is extremely important. The distinction between "trapped" and "experimental" researchers is suffi­ ciently important to bear repetition here. The experimental researcher is committed to solving problems, whereas the trapped researcher is committed to particular methods. Strong commitments to particular methods (e.g., classic ex­ perimental design, survey research, linear regression analysis, or "process models") that are nothing more than the "tools of analysis" often results in inappropriate and ineffective research applications. Research designs for research that intends to inform policy must be problem-directed.

Appendix A

Variable Dictionary total general fund revenue from nonfederal year k actual expenditures for function i, account/, in year k initial prediction of expenditures for function i, a c c o u n t i n year k and are successive revisions of this prediction) empirically estimated parameter for function i, account; (for models that use two parameters per function and account, and are used) N = number of years in sample NN = number of non-GRS years

Constant Proportion of Base Estimation Period (K = 2 to NN) 1. The initial predicted expenditure for function i, account j, in year k is a constant proportion of the expenditure for the same function and account in the prior year.

2. Check the balance between the sum of all predictions and the available revenue. If balanced, proceed; if unbalanced, go to balancing routine. 230

APPENDIX A

3. Compute imbalance.

4. Spread the imbalance proportionately.

Forecast Period

to N

5. Set initial prediction for expenditures in function i, account;', in year A: equal to a constant times the predicted expenditures for the same function and account in the prior year.

6. Check the balance between the sum of all predictions and revenue available in year k. 231

EVALUATING PUBLIC PROGRAMS

7. Calculate the imbalance.

8. Spread the imbalance proportionately.

9. Generate output for run. a. Goodness-of-fit statistics b. Predicted actual, residual, and proportional error by functional account and year c. Plots: actual on predicted, and residuals Estimation: estimated in

232

APPENDIX

B

Constant Proportion of Revenue Increment Estimation Period (K = 1 to NN) 1. The initial predicted expenditure for function i, account j, in year k is the prior year's expenditure for the same function and account.

2. The prediction is revised to include a constant proportion of the revenue increment (decrement).

3. Check the balance in year k.

4. Calculate imbalance.

5. Spread the imbalance proportionately. 233

EVALUATING PUBLIC PROGRAMS

Forecast Period (K = NN + 1 to N) 6. Set initial prediction for expenditures in function i, account j, in year k equal to the predicted expenditures for the same function and account in the prior year.

7. Revise the prediction to include a constant proportion of the revenue (decrement).

8. Check the balance between the sum of all predictions and revenue available in years k.

9. Calculate the imbalance. 234

APPENDIX A

10. Spread the imbalance proportionately.

11. Produce model output. 1. Goodness-of-fit statistics 2. Predictions, actuals, and residuals by function, account, and year 3. Plots of predicted on actual, and residuals

Estimations is estimated in:

Constant Growth and Revenue Increment Estimation Period (K = to NN) 1. The initial predicted expenditure for the same function i account j in year k is the prior year's expenditure for the same function and account. 235

EVALUATING PUBLIC PROGRAMS

2. The prediction is revised to include a constant proportion of the revenue increment (decrement).

3. Check the balance in year k

4. Calculate imbalance.

5. Spread the imbalance proportionately.

236

APPENDIX

A

Forecast Period (K - NN + 1 to N) 6. Set initial prediction for expenditures in function i, account j, in year k equal to the predicted expenditures for the same function and account in the prior year.

7. Revise the prediction to include a constant proportion of the revenue increment (decrement).

8. Check the balance between the sum of all predictions and revenue available in year k.

9. Calculate the imbalance.

10. Spread the imbalance proportionately.

237

EVALUATING PUBLIC

PROGRAMS

11. Produce model output. 1. Goodness-of-fit statistics 2. Predictions, actuals, and residuals by function, account, and year 3. Plots of predicted on actual, and residuals

Estimation and

for: K =2

estimated in:

-NN

Dollar Change—Fiscal Pressure Estimation Period (K = 2 to NN) 1. Set initial prediction for expenditures in function i, account ;',in year A:equal to the prior year's expenditures for the same function and account.

2. Revise the initial prediction in light of the percentage change in revenue and the level of the prior year's expenditures.

238

APPENDIX

B

3. Check the balance between the sum of all predictions and the available revenue. If balanced, proceed; if unbalanced, go to balancing routine.

4. Compute balance.

5. Spread the imbalance proportionately.

Forecast Period (K = NN + 1 to N) 6. Set initial prediction for expenditures in function i, account j, in year k equal to the predicted expenditures for the same function and account in the prior year.

7. Revise the initial prediction in light of the percentage change in revenue and the level of the prior year's expenditures. 239

EVALUATING PUBLIC PROGRAMS

8. Check the balance between the sum of all predictions and revenue available in year k.

9. Calculate the imbalance.

10. Spread the imbalance proportionately.

11. Generate output for run. a. Goodness-of-fit statistics b. Predicted actual, residual, and proportional error by functional account, and year c. Plots: actual on predicted, and residuals

240

APPENDIX

Estimation estimated in:

tor: K = 2 - NN

A

Appendix B

Calculation of Goodness-of-Fit Statistics 1.

R-Square

A. For functional account across years, the statistic is calculated as

where predicted expenditure for function i, account j, in year k actual expenditure mean expenditure for function i, account j,

N = number of years in estimation period B. For each year across functions and accounts, the calculation is

242

APPENDIX B

where

number of functions number of accounts

C. Across all years and functional accounts, the calculation is

where II. Root Mean Squared Error (RMNSQR) A. For functional account across years, the statistic is calculated as

B. For each year across functional accounts, the calculation is

C. Across all years and functional accounts, the calculation is

243

EVALUATING PUBLIC PROGRAMS

III. Percentage Root Mean Squared Error (%RMNSO) This statistic is simply the root mean error divided by the arithmetic mean of the actual expenditures used in the calculation. For example,

where

IV. Thiel's U-Statistic A. For functional account across years, the statistic is calculated as

244 B. For each year across functional accounts, the calculation is

APPENDIX B

C. Across all years and functional accounts, the calculation is

245

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Index

Albequerque, Ν. M., 12, 40, 42, 48, 58, 109, 113, 119, 125, 143, 149, 151,157,163,194-195,197,221; GRS impact on allocations, 181, 187-188, 193-196; GRS impact on revenues, 168-173, 178; large functional accounts of, 128; model fits in one-period-change tests, 133,138; in simulation tests, 144, 150 Andrews, P. W. S., 84 Ann Arbor, Mich., 12, 40, 42, 43, 46, 58, 102, 110, 114, 119, 142, 150, 158, 163; GRS impact on allocations, 182, 189, 196-200, 213, 215; GRS impact on revenues, 163-164, 166, 168; large functional accounts of, 129; model fits in one-period-change tests, 134,139; in simulation tests, 145, 151, 155 Anton, Thoms J., 27, 217 Atlas, Charles, 37 Ayres, Robert U., 122 Bahl, R. W., Jr., 75, 80 Barlow, Robin, 27 Barzun, Jacques, 216 Bergson, Henri, 68, 93 Bloggin's working rules, 18, 22, 51, 217 Bolton, Roger E., 88 Bradford, David F., 75 Brownlee, K.A., 56 Buchanan, James M., 75 budget problem: definitions of, 67, 103-105; GRS impact on prob­ lem, 115-120, 221; as source of fiscal pressure, 156-157,217-218, 221

Burkhead, J., 51, 80, 82, 83, 94 Calkins, Susannah E., 18, 186, 217 Campbell, Donald T., 28, 36, 37 Caporaso, J.A., 37, 38 Caputo, David A., 25 Chou test, 160 Cincinnati, Ohio, 12, 40, 42, 58, 103,109,113,114,119,143,149, 150, 163; GRS impact on alloca­ tions, 183, 190, 196-197, 200-203; GRS impact on rev­ enues, 166-168; large functional accounts of, 130; model fits in oneperiod-change tests, 135, 140; in simulation tests, 146, 152, 155 city governments: Boston, 177; characteristics of sample, 42; Cleveland, Ohio, 112; compari­ sons of sample cities' budgetary processes, 111-114; fund account­ ing, 46-48; GRS administration by, 23-25; Heidelberg, Miss., 9; New York City, 77, 86,114; Pitts­ burgh, 112; Raleigh, N.C., 43; reliance on federal assistance, 7; research sample, 12, 40; Truth or Consequences, N.M., 77. See also Albuquerque, N.M.; Ann Arbor, Mich.; Cincinnati, Ohio; Detroit, Mich.; and Worcester, Mass. Cohen, Kalman J., 123, 124 Cohen, Michael D., 398 Cole, Richard L., 25 Congressional Budget Office, 5 Coplin, W.D., 64 Cotton, John F., 71, 72, 75 counterfactual arguments, 69, 87, 222-225; role in evaluation, 34-39

INDEX Crecine, John P., 11, 43, 63-65, 67-71, 81, 92, 96-100, 103, 105, 107,111,113,115,121,123,124, 154, 157, 161 cumulative comparisons with ORS data, 214 Cyert, Richard M., 11, 14, 62, 68, 74, 123, 124, 156 Danziger, James, 92, 113 data: collection of, 43-51; national income accounts, 87, 88; ORS, 24-26, 186-215; uncertainty absorption, 44, 45 Davis, Otto, 75 Detroit, Michigan, 12, 40, 42, 58, 64, 101, 109, 112-114, 119, 142, 149, 150, 163, 196, 219, 221; GRS impact on allocations, 184, 191, 202, 204-208; GRS impact on revenues, 164-166, 168; large functional accountsof,131; model fits in one-period-change tests, 136, 141; in simulation tests, 147, 153, 155 Dolbeare, Kenneth M., 26 Duesenberry, J.S., 88, 154 Dye, Thomas, 69 expenditure determinants models, 77, 79, 81, 90, 92, 159, 223 expenditure models: constant growth-revenue increment (CGRI), 58-59, 130, 132, 142, 149, 150, 155, 156, 181-185, 235-238; constant proportion of base (CPB), 57-60,124,132,139, 143,149,150,155,181-185,202, 230-232; constant proportion of revenue increment (CPRI), 58, 115,130,132,139,142,150,151, 156,162,180-186,188-192,202, 214, 232-235; criteria for development, 51-59; dollar change-fiscal pressure (DCFP),

57-60, 132, 139, 143, 150, 181-185, 238-241; flow charts, 230-241; goodness-of-fit, 121156; GRS effects, comparative estimates of, 180-186; narrative summaries, 57-60; possible extensions, 156-161; in public finance, 68-95; summary appraisal of application, 127-128; theoretical bases, 61-68, 95-115; use in analysis, 11-12, 178-180 experimental design (analogical reasoning), 33-39; GRS as natural experiment, 9-11, 16, 224-225 federal assistance programs: Aid to Families with Dependent Chil­ dren (AFDC), 6; Comprehensive Employment and Training Act, 6; by function, 5; growth in, 6; model cities, 49; in state and local expen­ ditures, 7 Federal Register, 23 Fenno, Richard, 14 Fried, Edward R., 9, 14 Friedman, Milton, 62 Fromm, Gary, 88, 154 Frost, Robert, 162 Funk and Wagnalls Dictionary, 76 Galper, Harvey, 75, 76, 78, 79, 88-91 General Accounting Office, 24,186 General Revenue Sharing (GRS) Program, 8-10, 22-25; objectives of, 15; observed impact of, 162-215; obstacles to analysis of, 13-18; theoretical impact of, 115-120 Goethe, Johann Wolfgang von, 61 Goetz, Charles J., 75 Good, I.J., 18, 22, 51, 217 goodness-of-fit, 51-59, 170, 193, 223, 224, 232, 235, 238, 240;

INDEX

expenditure models, 121-156; revenue models, 162-178; statis­ tics, calculation of, 242-245 Gramlich, Edward, 75, 76, 78, 79, 80, 81, 85, 86, 87, 88, 90, 91 Gregg, Lee W., 64, 140, 141, 158 Haines, George H., Jr., 75 Hatry, Harry P., 11 Haveman, R.H., 18, 33 Hofferbert, Richard, 27 Holt, Charles C., 151, 154 Horowitz, Ann, 78 inflation, 157 Jackson, John E., 92 Johnston, J., 160 Juster, F. Thomas, 27 Kaplan, Abraham, 21, 33, 76 Klein, L., 88, 154 Korey, John, 217 Kuh, E., 88 La Rochefoucauld, Frangois de, 156 Lave, Charles A., 122 legislatures, role in budgeting, 105-106 Lindbloom, C.E., 11 Lovell, Catherine, 217 McGuire, Martin, 71, 76 McKnew, Charles, Jr., 75 management by objectives (MBO), 223 Manvel, D. Allen, 18,186, 213, 217 March, J.G., 11, 14, 30, 31, 44, 62, 67, 68, 74, 95, 98, 122, 123, 124, 156 Margolis, J., 18 Massachusetts, Commonwealth of, 177 Meltsner, Arnold J., 97 Michigan, state of, 166

Miner, J., 51, 80, 83, 94 Municipal Finance Officers Admini­ stration (MFOA), 52 Mushkin, Selmsa, 16, 71, 72, 75 Nathan, Richard D., 18, 186, 213, 217 National Science Foundation, 16 Nelson, Richard, 32 Nichols, John P., 14, 16, 18 normative budgetary systems: plan­ ning, programming, and budget­ ing systems (PPBS), 4, 22, 32, 51, 225; zero-base budgeting systems (ZBBS), 223 Oates, Wallace, 71, 75 objectives: for GRS, 15; for munici­ pal officials in budgeting, 65-68, 70-75, 97, 98, 100, 101; for this research, 10-12, 63 O'Brien, Thomas, 87 Occam's razor, 122 Office of Revenue Sharing (ORS), 10, 19, 23, 45, 46, 82, 89, 90, 91, 162,163,187,188,214,215,222, 226

Okun, M. Arthur, 75 Olsen, Johan P., 98 Owen, Henry, 3 permissible expenditures (State and Local Fiscal Assistance Act of 1972), 23 Perry, George L., 75 Pindyck, R.S., 124 Pollock, Stephen M., 160 Popper, Karl, 123 public finance and welfare econom­ ics: criteria, 29-30; review of theoretical and empirical work on grants, 68-95 Quade, E.S., 36

INDEX revenue models, 162-178 Risk, Donald M., 11 Rivlin, Alice M., 16, 34, 39 Rootselaar, J. van, 62, 123 Rosen, Sara, 88 Ross, L., Jr., 37, 38 Rubinfeld, Dr., 124 Russell, Bertrand, 21 sample. See city governments Santayana, George, 95 Schultze, Charles L., 3, 14, 18 Scott, Claudia, 79 Sharkansky, Ira, 69, 78, 115 Simon, Herbert A., 11, 21, 44, 62, 64, 67, 73,76, 77, 91,93,95,123, 137, 140, 141, 158 Staal, H., 62, 123 Stamp, Sir Josiah, 43 Stanley, J.C., 37 Starbuck, W., 34 State and Local Fiscal Assistance Actof 1972.See GeneralRevenue Sharing Program Stephenson, James B., 127 Strauss, Robert P., 78 survey research, 51-53 Szabo, Joan C., 25

Taylor, Ronald, 34 theories of resource allocation: descriptive (positive) theories, 17, 31, 34, 40, 54, 61-68, 71, 74, 77, 94, 180, 226, 227; in models, 61-120; normative theories, 17, 68-95; role in evaluation, 10-12, 17, 31-39 Thiel, Henri, 121, 125 Thurow, Lester, 71

Timpane, Michael, 34 Tollison, Robert D., 75 U.S. Bureau of the Census, 7, 220 U.S. Comptroller General, 24 U.S. Congress, 25 U.S. Department of Commerce, 170, 175 U.S. Department of the Treasury, 23, 25 Vertinsky, Ilan, 34 Waldauer, Charles, 71 Walker-Lev test, 38 Wallace, Amy, 8 Wallace, Irving, 8 Wallechinsky, David, 8, 13, 28 Wall Street Journal, 117 Webb, T. Eugene, 52 Weber, Charlotte, 217 Whitelaw, William E., 79, 92, 113 Wildavsky, Aaron, 11, 97 Wilde, J.A., 71 Willner, William, 14, 16, 18 Winnie, Richard E., 11 Winter, Sidney G., 62, 71, 74 Wonnacott, Ronald J., 43 Wonnacott, Thomas H., 43 Worcester, Massachusetts, 12, 40, 42, 49, 50, 56, 58, 92, 111, 112, 119,125,142,143,149,150,151, 157, 163, 218, 224; GRS impact on allocations, 185, 192, 208-213; GRS impact on revenues, 168, 173-178; large functional accountsof, 132; model fits in one-period-change tests, 137, 142; in simulation tests, 148, 154, 155

Library of Congress Cataloging in Publication Data Larkey, Patrick D., 1943Evaluating public programs. A revision of the author's thesis, University of Michigan, 1975. Bibliography: p. Includes index. 1. Municipal finance—United States—Case studies. 2, Revenue sharing—United States— Case studies. I. Title. HJ9157.L37 1978 352.073 78-51176 ISBN 0-691-07601-4