Practical Macroeconomics for Non-Economists: A Question-and-Answer Approach 1032488409, 9781032488400

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Practical Macroeconomics for Non-Economists

Practical Macroeconomics for Non-Economists provides the tools, the theory, and the empirical understanding of macroeconomics without the heavy lifting of the mathematical and econometric models. This accessible book introduces the building blocks of macroeconomic thinking and challenges the reader to apply these insights to learn why economists say what they do and what guides economic policymakers. Linking actual data to theoretical concepts, it explores competing economic theories, and uncovers some of the key controversies in macroeconomic theory and how different perspectives lead to alternative and vastly different policy recommendations. Key features include: •

• • • • •

Coverage of all the key macroeconomic topics, such as GDP, inflation, unemployment, output and productivity, business cycles, aggregate demand/supply, and fiscal and monetary policy. Question-and-answer format, covering the foundations of each topic in a logical progression, to provide the reader with a quick reference and more focused discussion. “Advanced questions” to encourage deeper discussion. Start-of-chapter learning objectives, which allow the reader to “see” the road ahead for each section. End-of-chapter “Issues to think about” boxed features, which offer the reader an opportunity to apply critical thinking to the issues covered. Resource manual and PowerPoints for instructors.

Practical Macroeconomics for Non-Economists is the ideal textbook for anyone looking for a practical and non-technical introduction to the subject. Michael P. Niemira is the chief economist of The Retail Economist, LLC. He is the former chief economist and director of research for the International Council of Shopping Centers and has held economist positions for the Bank of Tokyo-Mitsubishi (now MUFG), PaineWebber (now UBS), Chemical Bank (now JPMorgan Chase), and Merrill Lynch. Niemira also has been an adjunct assistant professor of economics at New York University’s Stern School of Business and Graduate School of Arts and Sciences, as well as taught at the New York Institute of Finance. Currently, he is an adjunct instructor for the Maricopa Community Colleges, Arizona, USA.

“If you have questions regarding the workings of the macroeconomy, you can find well-articulated and reasoned answers in Michael P. Niemira’s book. From a thorough analysis of the key economic statistics to a detailed description of the main theories of macroeconomics, it is here.” Mark M. Zandi, Chief Economist, Moody’s Analytics “Consumers and businesses are bombarded with an ongoing list of economic numbers: GDP, inflation, unemployment, retail sales. This book is very hands on and provides a great approach to learn, understand and apply economic thinking. It is clearly written, clearly spells out economic questions and clearly provides cogent and meaningful perspectives.” Jack Kleinhenz, Ph.D., CBE, Chief Economist, National Retail Federation and former President of the National Association for Business Economists “Rising prices and big swings in the job market have put macroeconomics front and center, as questions that were once confined to college seminars and Congressional hearings are now raised in supermarket checkout lines and employee break rooms. In this timely introduction, Michael P. Niemira connects the dots between inflation, employment, productivity, and government policy. The question-and-answer format allows the casual reader to quickly get up to speed, while the more serious student will find ample detail to satisfy one’s curiosity.” Scott Horsley, Chief Economic Correspondent, National Public Radio “Mr. Niemira has managed to create a book that simultaneously provides a great introduction to the field for students, a refresher and reference work for practicing economists, and answers to questions the curious lay reader may pose. All of this comes with well-organized, readily understandable, and pertinent text and illustrations. A valuable addition to any bookshelf.” Ken Simonson, Fellow and Past President, National Association for Business Economics and Chief Economist, Associated General Contractors of America “Economics permeates our lives. Where we work, how we are rewarded for work, stock market cycles, and interest rates levels that determine our ability to look forward to a comfortable retirement are determined by macroeconomic events. Michael P. Niemira lays out the foundations of these macroeconomic events in a question and answer format by topic that leads off with key learning objectives so you can anticipate what you will learn from each chapter and reflect if your interests align with the content provided. Macroeconomics is often considered to be remote and theoretical. Mike makes it practical and accessible. When at some point in the future a topic comes up in the newspaper or in everyday conversation, Practical Macroeconomics for Non-Economists lays out content in a way that it can be referenced years into the future. This book is an important handbook for navigating the economic opportunities and risks that life offers and should be kept handy in everyone’s bookshelf.” Gail D. Fosler, President, The GailFosler Group

Practical Macroeconomics for Non-Economists A Question‐and‐Answer Approach

Michael P. Niemira

Designed cover image: © Getty Images First published 2024 by Routledge 4 Park Square, Milton Park, Abingdon, Oxon OX14 4RN and by Routledge 605 Third Avenue, New York, NY 10158 Routledge is an imprint of the Taylor & Francis Group, an informa business © 2024 Michael P. Niemira The right of Michael P. Niemira to be identified as author of this work has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloguing-in-Publication Data Names: Niemira, Michael P., 1955- author. Title: Practical macroeconomics for non-economists : a question and answer approach / Michael P. Niemira. Description: Abingdon, Oxon ; New York, NY : Routledge, 2023. | Includes bibliographical references and index. | Identifiers: LCCN 2022060422 | ISBN 9781032488394 (hardback) | ISBN 9781032488400 (paperback) | ISBN 9781003391050 (ebook) Subjects: LCSH: Macroeconomics. Classification: LCC HB172.5 .N535 2023 | DDC 339--dc23/eng/20230407 LC record available at https://lccn.loc.gov/2022060422 ISBN: 978-1-032-48839-4 (hbk) ISBN: 978-1-032-48840-0 (pbk) ISBN: 978-1-003-39105-0 (ebk) DOI: 10.4324/9781003391050 Typeset in Bembo by MPS Limited, Dehradun Access the Support Material: www.routledge.com/9781032488400

Dedication

I dedicate this book to my wife, Shirley Lazo, for her encouragement, support, and love. Her distinguished career in financial journalism at Barron’s magazine showed me how to be a better writer and communicator of ideas. I also dedicate this book to my son, Andrew Niemira, and my daughter-in-law, Lindsay Day, with the hope that their curiosity and questions will always unlock the doors of knowledge.

Contents

List of Figures

ix

List of Tables

xiii

Preface

xvi

Reader Notes

PART 1 Macroeconomic Thinking and Tools

xviii

1

1

Macroeconomic Basics

2

The Importance of Circular Flow in the Economy

18

3

Key Macroeconomic Statistics—Jobs and Unemployment

31

4

Key Macroeconomic Statistics—Inflation

48

5

Key Macroeconomic Statistics—Output and Productivity

76

6

Understanding Business Cycles and Trends

99

7

A Macroeconomic Toolbox for Descriptive Analysis

141

8

Understanding the Aggregate Demand (AD) and Aggregate Supply (AS) Model

180

Money and Banking

223

Monetary Policy—Goals, Theories, Rules, Discretion, and Implementation

246

9 10

3

viii

CONTENTS

11

Government Spending, Taxation, and Fiscal Policy

278

12

Policy Implications of Macroeconomic Theories

307

PART 2 Macroeconomic Issues

319

13

The Disappearing Worker

321

14

Implications of an Evolving Economy

328

15

The Great Moderation Followed by the Great Volatility

337

16

The Global Debt Explosion and Worries

346

17

Setting Tax Rates

359

18

Measuring Macroeconomic Uncertainty

365

PART 3 Macroeconomics Reshaped

385

19

387

The Future of Macroeconomics

Epilogue—Developing and Interpreting Macroeconomic Policy Choices

392

Index

395

Figures

2.1 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.10 5.1 5.2 5.3 5.4

The Circular Flow Union Membership as Percentage of Employed Derivation of the Unemployment Statistics U.S. Unemployment Rate, Average by Decade, 1950–2019 Civilian Labor-Force Participation Rate Employment-to-Population Ratio vs. Consumer Price Inflation “Relationship between the Actual Unemployment Rate and the Theoretical (or Natural) Unemployment Rate” Actual and Natural Rate of Unemployment Beveridge Concept of Full Employment Ratio of Unemployment Level of Job Openings Comparison of the Consumer Price Index (CPI-U) and the Personal Consumption Expenditure Price Index Consumer Price Inflation vs. Asset Inflation Tobin’s Q Ratio for the U.S. Stock Market Shiller’s Cyclically Adjusted Price-Earnings Ratio “Buffet Ratio”: Stock-Market Capitalization to GDP Ratio Comparison of “Core” U.S. Inflation Measures Sticky-Price CPI vs. Flexible-Price CPI The Katona Effect: Real Consumer Spending vs. (CPI) Price Level Volatility Long-term Phillips Curve, 1960–2020 Ten-Year Averages Ten-Year Growth Rates in M2 and the CPI For the United States, 1920–2021 Rates of High School Completion and Bachelor’s Degree and Higher Attainment Among Persons Aged 25 and Older, 1910–2021 International Comparison of Total-Factor Productivity, 1900–2019 Average Annual Growth Over Ten-Year Spans Average Annual Percent Change Non-farm Productivity Growth by Decade, 1950–2019 Gross National Income Per Capita Adjusted for Purchasing Power Parity in Constant 2017 International Dollars

19 33 37 38 38 39 43 44 45 56 60 61 62 62 65 66 67 69 70 90 92 93 95

x

FIGURES

5.5 W. W. Rostow’s Stages of Economic Growth 6.1 The Stylized Classical Business Cycle: Tracking the Business Cycle 6.2 Employment and Production Diffusion Indexes (Six-Month Spans, 2000–2022) 6.3 Tracking the Growth Cycle 6.4 Classical Business Cycle Nine-Stage Analysis: U.S. Real GDP 6.5 Classical Business Cycle Nine-Stage Analysis: U.S. Payroll Jobs 6.6 Classical Business Cycle Nine-Stage Analysis: U.S. Payroll Jobs 6.7 U.S. Payroll Employment: Recession Patterns 6.8 U.S. Payroll Employment: Recovery Patterns 6.9 Real U.S. GDP vs. Its Extended 2000–2007 Trend 6.10 Home Demand as a Trigger for the Diderot Effect 7.1 CPI-U Growth Rate Comparison: 12-Month % Change vs. Six-Month Smoothed Annualized Rate 7.2 Example of Economic Variable with Exponential Growth 7.3 Natural Log Transformation of Data Characterized by Exponential Growth into Linear Pattern 7.4 Real GDP: Level and Natural Log Transformation 7.5 8-Quarter Moving Standard Deviation of Real GDP and GDP Price Deflator 7.6 U.S. Retail Sales Excluding Food Services Seasonally Adjusted vs. Unadjusted 7.7 Comparison of 2021 U.S. Retail Sales Adjusted Using “Crude” Seasonal Factor vs. Census Bureau Method 7.8 Time Trend and Real GDP 7.9 Example: U.S. Real GDP with Alternative Historic Trends (Estimated 1947: Q1 to 2020: Q1) 7.10 Example: U.S. Real GDP with Linear Deterministic Trend (1980–2022) 7.11 Example: U.S. Real GDP with Alternative Trends (Estimated 1980–2007) 7.12 U.S. Payroll Employment Changes Month-over-Month Change and 12and 24-Month “Trailing” Moving Averages 7.13 U.S. Payroll Employment Changes Month-over-Month Change and 12and 24-Month “Centered” Moving Averages 7.14 Annualized Growth Rates for the U.S. Consumer Price Index with “Binominal Filtered” Growth Rate 7.15 Examples of Different Types of Mean and Variance Stationary 7.16 Business Cycle Recovery Patterns U.S. Consumer Price Index 8.1 Aggregate Demand Curve 8.2 Short-run Aggregate Supply Curve 8.3 Long-run Aggregate Supply Curve (Potential Output) 8.4 Keynesian SRAS 8.5 Aggregate Demand for Q3 2021

95 101 108 109 120 121 122 123 124 131 132 146 148 148 149 151 152 157 160 161 161 162 164 165 165 167 168 183 183 184 187 189

FIGURES

8.6 Aggregate Demand and Short-Run Aggregate Supply for Q3 2021 8.7 Full Model of Aggregate Demand (AD), Short-Run Aggregate Supply (SRAS) and Long-Run Aggregate Supply (LRAS) for Q3 2021 8.8 Output Gap (Also Known as GDP Gap) 8.9 U.S Output Gap, 1950–2021 8.10 Showing Change in Aggregate Demand (AD), Short-Run Aggregate Supply (SRAS) and Long-Run Aggregate Supply (LRAS) between Q3 2020 and Q3 2021 8.11 Three “Zones” of the Short-Run Aggregate Supply Curve 8.12 Demand-Side Determination of SRAS 8.13 Static Perspective: Constructed with Ex Post (Actual) and Ex Ante (Estimates) for a Specific Period 8.14 Dynamic Perspective: Growth Rate of Actual (Ex Post) Data Only (Points on Chart) Compared with Estimate of Potential Growth and Target Inflation Rate between Two Periods 8.15 Aggregate Demand (AD), Short-Run Aggregate Supply (SRAS) and Long-Run Aggregate Supply (LRAS) Changes between 2007Q4 and 2008Q4 8.16 Complete Aggregate Demand/Aggregate Supply Model for 2020Q2 9.1 Three Functions of Money 9.2 M1 Money Stock Measure 9.3 M2 Money Stock Measure 9.4 U.S. Monetary Base 9.5 Number of Commercial Banks in the United States, 1934–2022 9.6 Shadow Bank Liabilities versus Traditional Bank Liabilities 9.7 Non-bank Financial Intermediation (NBFI) Sector Assets as Percent of Total Global Financial Assets 10.1 NY Federal Reserve Bank Estimates of R-Star (Three Approaches) 10.2 The Effects of Monetary Policy on the Economy 10.3 “Normal” U.S. Treasury Yield Curve (February 2018) 10.4 “Inverted” U.S. Treasury Yield Curve (September 2019) 10.5 The Spread between the Ten-Year U.S. Treasury Yield and the ThreeMonth U.S. Treasury Bill Rate 10.6 Velocity of M2 10.7 Taylor “Original” Monetary-Policy Rule for Setting the Federal Funds Rate 10.8 Taylor “Inertial” Monetary-Policy Rule for Setting the Federal Funds Rate 10.9 The Twelve Federal Reserve Banks 10.10 Bank of Canada: Total Assets 10.11 European Central Bank: Total Assets 10.12 U.S. Federal Reserve System: Total Assets 10.13 Bank of Japan: Total Assets

191 192 193 194

196 196 202 205

206

210 220 225 228 229 230 232 236 237 251 253 254 255 256 258 261 261 264 267 268 268 269

xi

xii

FIGURES

11.1 11.2 11.3 11.4 12.1 13.1 13.2 13.3 13.4A 13.4B 14.1A 14.1B 14.2 14.3 14.4 15.1 15.2 15.3 16.1A 16.1B 16.2 16.3A 16.3B 16.4 16.5 17.1 18.1 18.2 18.3 18.4 18.5

Composition of U.S. Federal Government Spending: Mandatory, Discretionary & Net Interest Shares, FY1962–2021 Shares of U.S. National Debt Publically Held by the Federal Reserve (FRB) and Non-FRB Public Share of U.S. Federal Government Debt Owned by Foreigners Primary Federal Budget Deficit/Surplus The Basic Structure of DSGE Models U.S. Labor Force Participation Rate Labor-Force Participation Rates, 25–64-Year-Olds U.S. Employment-to-Population Ratio Labor Force Participation Rate: 25–54 Years, Men Labor Force Participation Rate: 25–54 Years, Women Net Value Added of Farm Output as a Share of GDP (1910–2022) Share of U.S. GDP (1947–2021) How Much Different Would Real GDP Growth Be If the Economy’s Shares Were Fixed at Its 1950s Shares? Comparing CPI Service Inflation with CPI Commodity Inflation, 1936–2021 A Comparison of Private Goods-Producting Industry Productivity vs. Private Service-Producing Industry Productivity Real GDP Growth (1875–2022) GDP Price Defaltor Growth (1875–2022) The Taylor Curve Trade-Off Total U.S. Debt, 1986–2022 U.S. Debt Shares of Total U.S. Debt Shares of GDP Concordance between Public Sector and Other Institutional Sectors of the Economy U.S. Debt Shares of GDP Global Debt as a Percent of GDP Relationship between Debt and Economic Growth The Laffer Curve University of Michigan’s Index of Consumer Sentiment and Variance Measure of Uncertainty University of Michigan’s Index of Consumer Sentiment and Dispersion Measure of Uncertainty University of Michigan’s Index of Consumer Sentiment and Relative Entropy Measure of Uncertainty Mood: Cumulative Net Mood U.S. Economic Policy Uncertainty Metric

292 294 296 303 311 322 322 323 324 324 329 329 331 333 334 338 339 342 347 348 349 349 350 353 356 360 374 375 376 378 381

Tables

2.1 National Income T-Account Showing Current-Dollar GDP Equals Current-Dollar GDI 2.2 National Income T-Account Showing Real-Dollar GDP Equals RealDollar GDI 2.3 Real Gross Domestic Product, Chained Dollars 2.4 Real Value Added by Industry 2.5 Gross Domestic Income by Type of Income 3.1 2021 Union Membership of Employed Workers Ranked by U.S. States 3.2 Interchangeable Terms and Concepts 3.3 Inflation and the Unemployment Rate Gap 3.4 Inflation and the Beveridge Full-Employment Ratio 4.1 Example Data for a Three-Item Market-Basket 4.2 Using the Formula for the Laspeyres Price Index 4.3 Using the Formula for the Paasche Price Index 4.4 Calculation of a Fisher Price Index 4.5 Calculation of a Chained Index 4.6 Calculation of a Chained Index Backwards in Time 4.7 Calculation of a Cost-of-Living Adjustment for Wages 4.8 Percentage Changes in CPI for All Urban Consumers (CPI-U): U.S. City Average 5.1 Interchangeable Terms Used to Measure Actual GDP 5.2 Largest Economies in 2020 5.3 Trends in Long-Term Total-Factor Productivity by Country 6.1 U.S. Business Cycle Expansions and Contractions 6.2 United Kingdom Classical Turning Points in the Business Cycle 6.3 Turning Points for U.S. Real Gross Domestic Product 6.4 Turning Points for U.S. Real Gross Domestic Income 6.5 Comparison Between Classical and Growth Cycles 6.6 U.S. Business Cycle Duration, 1969–2020 6.7 U.S. Reference Business Cycle Contractions, 1969–2020 6.8 U.S. Reference Business Cycle Contractions, 1969–2020: Non-farm Payroll Employment

20 21 22 23 24 33 42 46 46 50 51 52 52 53 54 55 59 80 84 90 103 105 105 106 110 111 112 112

xiv

TABLES

6.9 U.S. Reference Business Cycle Contractions, 1969–2020: CPI-U % Change 6.10 U.S. Reference Business Cycle Contractions, 1969–2020: Industrial Production Diffusion (Six-Month Spans—Percentage Point Change) 6.11 Post-WWII U.S. Business Cycle History 6.12 Long-Trend Growth Rates and Cyclical Percentage Change in the U.S. Economy 6.13 The “Steady-State” Economy 6.14 What is Trend Growth in the U.S. Economy? 7.1 Example of Calculating Month-over-Month Growth Rates 7.2 Example of Calculating Month-over-Month and Quarter-over-Quarter Annualized Growth Rates 7.3 Example of Calculating Year-over-Year Monthly and Quarterly Growth Rates 7.4 Example of Calculating Six-Month Smoothed Annualized Rate (SMSAR) Growth (With Comparison to Year-over-Year Growth Rate) 7.5 Summary of Common Growth Rate Formulae Used in Economics 7.6 How Long Will It Take for Real GDP to Multiply? 7.7 U.S. Retail Sales (2000–2021) 7.8 How to Adjust a Time Series for Seasonality 7.9 Key U.S. Federal Government and Selected International Sources of Economic Data 8.1 Calculation of the Aggregate Demand Curve Based on Actual Data and the Elasticity of Aggregate Demand 8.2 Calculation of the Aggregate Supply Curve Based on Actual Data and the Elasticity of Aggregate Supply 8.3 The Fourth-Quarter of 2007 and the Fourth-Quarter of 2008, along with 20-Year Historical Averages for Comparison 9.1 The Origins of Central Bank Powers 9.2 Percent Share of Retail Non-Cash Payments, 2020 9.3 How the Banking Sector Creates Money 10.1 Setting Monetary Policy with Yield-Curve Control Strategies 11.1 Fiscal Multipliers as Estimated by the CBO 11.2 Fiscal Year Budgets: Number of Continuing Resolutions, Date of Regular Appropriations Legislation Signed by the President, Days after New Fiscal Year 11.3 CBO Rules-of-Thumb for How Changes in Economic Factors Affect Federal Budget Balance Based on 2022 Baseline 11.4 Federal Receipts, Outlays, and Debt 11.5 U.S. Federal Budget by Category, Fiscal Years 2018–2021 11.6 Total U.S. National Debt Held by the Federal Reserve System 11.7 Major Foreign Holders of U.S. Treasury Securities 12.1 Remedy Based on Theory 14.1 Relationship Between Economic Growth, Volatility, and the Composition of the Economy

113 114 114 134 135 136 143 144 145 146 147 150 154 155 174 188 190 208 226 235 241 270 284

286 288 291 293 295 297 314 331

TABLES

14.2 15.1 15.2 15.3 15.4 18.1

Median Annual U.S. Growth, 1948–2021 Changing Volatility Patterns in the U.S. Economy, 1875–2022 Changing Volatility Patterns in the U.S. Inflation, 1875–2022 Real GDP Volatility—The Great Moderation and Great Volatility U.S. Real GDP Volatility by Expenditures Comparison of Calculated Uncertainty Measures

331 338 340 340 343 379

xv

Preface

In the opening of Prof. Edward Leamer’s 2009 macroeconomics book on patterns and stories,1 he admitted that his subject and focus was inspired by a National Public Radio (NPR) commentator that he was listening to who observed, “human beings are pattern-seeking, story-telling animals.” Indeed, Leamer thought that NPR observation exactly described what economists do and even added in a footnote that “once indoctrinated in graduate school, it takes a long time for new Ph.D’s in Economics to learn that it is only stories and patterns that we do.” Long before I read Leamer’s comment—about 30 years before his book—I worked for an economist whose department policy was that you could use all the econometrics you wanted to understand an issue, but never report those results to the readers—only tell the story what the analysis suggested. Later, an economist—whom I had previously worked with—was reflecting on his experience of routinely making economic presentations to the senior-most echelon of a major New York City bank. He told me that we are all storytellers. I chuckled a bit at that, but he was, of course, correct. I have embraced that thought ever since. Economics is about telling stories. You can have the most insightful analysis and brilliantly prescient forecast about the economy, but if you cannot tell a convincing story to others to believe it, then it is all for naught. Indeed, I witnessed this in the late 1970s when consumer inflation was running at a double-digit pace and the chief economist of a major brokerage was forecasting that inflation would ease dramatically to about four percent in the next three to four years. Few believed that forecast and he ultimately was replaced, but he turned out, in hindsight, to have had a correct view of where inflation was headed. Unfortunately, he did not have a convincing story for that forecast. Stories matter in economics. Stories matter especially in introductions to macroeconomics. No one expects a novice to the field to understand the mathematical formulations of theories or the econometric estimations of issues without first understanding the economic stories and patterns of the data. As I was completing this book, an opinion piece by Gillian Tett appeared in the Financial Times on August 26, 2022, which was entitled: “Economists shouldn’t underestimate the power of a good story.” The subtitle opined that “narrative matters as least as much as models.” Of course, I heartily agree—which is the premise of this book. This non-technical question-and-answer format for this book allows readers more flexibility to read what interests them and skip some discussions (or return later). This Q&A format also makes it a useful textbook or supplemental text for students who want to review or absorb the

PREFACE

macroeconomic concepts without lots of reading to understand the points. But hopefully, it also allows the reader to understand the stories that economists tell, which often are not easily accessible in the economic literature due to their technical nature. Finally, to all the readers, Rudyard Kipling’s words from “The Elephant’s Child” in Just So Stories teach us about learning, which is the cornerstone of this book. Kipling penned, “I keep six honest serving-men: (They taught me all I knew) Their names are What and Where and When and How and Why and Who.” So it is with these ideas that this book was launched.

ACKNOWLEDGMENTS Recently I saw a U.K. poster that said, “Keep Calm, You Have a Project Manager.” With this thought in mind, I offer a special word of thanks to Michelle Gallagher, Senior Editor– Economics, Finance & Accounting at Routledge, and to Chloe Herbert, Senior Editorial Assistant (Economics and Law), for their superb guidance and help in shaping this book. I also acknowledge Yogesh Malhotra from MPS Limited, which managed the copyediting and layout of this book. Michael P. Niemira August 2022

NOTE 1 Edward Leamer, Macroeconomic Patterns and Stories (Springer-Verlag, 2009).

xvii

Reader Notes

•

•

• • • • •

•

This book is about practical macroeconomics. Stanford University Prof. John Taylor set out the core principles of practical macroeconomics in a May 1997 paper entitled “A Core of Practical Macroeconomics,” and those principles still ring true today. Taylor opined that practical macroeconomics was built upon five principles that you will discover in this book: (1) “Over the long term, labor productivity growth depends on the growth of capital per-hour of work and on the growth of technology or, more precisely, on movements along as well as shifts of a production function … If one adds to this labor productivity growth an estimate of labor-force growth, one gets an estimate of the longrun growth rate of real GDP, or what is typically referred to as potential GDP growth.” (2) There is no long-term trade-off between inflation and the unemployment rate implying that a central bank, which expands the money supply too much will simply produce more inflation without affecting the unemployment rate. (3) There is a short-run trade-off between inflation and the unemployment rate, which Taylor opined was best thought of affecting the variability of each. (4) The public’s expectations are “highly responsive” to policy. (5) Taylor’s last principle is that in evaluating monetary and fiscal policy one should not think about a policy change as a “one-time isolated change,” but having a series of impacts. This speaks to the dynamics of the economy. Each chapter begins with “learning objectives,” which allow the reader to “see” the road ahead for each section. In many ways, this is a traditional approach to learning based on a systematic plan. Each chapter also ends with “issues to think about,” which offer the reader an opportunity to apply critical thinking to issues in that section. The questions and answers are structured sequentially to address a logical progression. The questions-and-answers format provides the reader a quick reference, less reading, and a more focused discussion. Historical context is included in various sections to help provide an understanding of a topic’s development. Some questions are indicated to be “advanced,” which allow a reader to skip over those topics without loss of the broader theme of the chapter and return to them later. Those advanced questions provide deeper discussions. Questions are heavily referenced for even further exploration of a topic.

READER NOTES

•

•

•

Answers to the questions rely largely on what is termed “literary exposition” or narrative economics to explain, which provide more accessible discussions of the economic literature that may be highly technical, mathematical, or theoretical. Chapters 3 through 5 discuss the “big three” statistics in economics—employment, inflation, and output (GDP)—and those chapters provide the “what, why, and how” of those statistics. Economic policies are tied to those three statistics, the global financial markets react to them, and economic theories are built around them. Therefore, it is important to understand what they measure and how to evaluate the story the data tell. But if one needs more motivation for the detailed discussions about those statistics, one only needs to see a study entitled Public Understanding of Economics and Economic Statistics published in November 2020 by the U.K. Economic Statistics Centre of Excellence of the National Institute of Economic and Social Research and in collaboration with U.K.’s Office of National Statistics to assess the British public’s understanding of economics and economic statistics. Although the study’s results are only for the United Kingdom, there are global takeaways as well—which probably apply for most industrialized countries, including the United States. Some of the key findings from that study are: (a) The U.K. public have “misperceptions about how economic figures, such as unemployment and inflation rates, are collected and measured. These misperceptions tended to support the commonly held view that actual unemployment and inflation rates are higher than official figures suggest, and therefore may explain some of people’s distrust in unemployment and inflation data.” (b) “The British public generally have a fairly good understanding of what inflation is, especially relating it to price growth and changes in prices over time, [but] generally preferred to speak in broad terms about different levels of inflation, such as ‘low inflation’, ‘steady price growth’ and ‘prices staying the same’, rather than in absolute numbers such as 2%.” Typically, they did not know what the official data was for the average price growth. (c) “While [the study] suggests that the British public have a fairly good understanding of unemployment as a concept … the British public overwhelmingly lack knowledge about how the rate of unemployment is measured.” (d) The study found that “less than half of the British public are able to correctly identify the definition of GDP from a list of options, and that the vast majority … demonstrated little to no understanding … about the size of GDP growth rates and did not typically understand what was meant when economic indicators were reported as a proportion of GDP. In fact, GDP was seen as economic jargon, contributing to the feeling that economics was largely inaccessible to them.” It is for reasons similar to these that these chapters delve into measurement issues of these strategically important economic statistics. In Chapter 8, the discussion of the aggregate demand/aggregate supply model is unique in that it provides the reader with a step-by-step process to apply this framework to real data. Why is this important? Most textbook discussions of this macroeconomic framework muddle the time element for change, blur the static and dynamic aspects of the model, only qualitatively discuss the picture when there are quantitative points to be made for better clarity, and fail to distinguish “ex ante” and “ex post” elements of the model. If the reader can use actual economic data to visualize the state of the economy along with estimates of the theoretical concepts, then it is hoped that the broader macroeconomic ideas of “gap analysis,” recession, inflation, interest rates all come together in a more

xix

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READER NOTES

• • •

•

coherent fashion. To paraphrase a widely held sentiment in economics that was stated in a 1988 article by Princeton University economists Ben Bernanke (who would later become a Federal Reserve Board chair) and Alan Blinder (who later was a Federal Reserve Board vice chair), the aggregate demand/aggregate supply model, which is a foundational paradigm for macroeconomics, is not interpreted literally, nor does anyone think it is directly suitable for econometric estimation, but it is useful for telling simple yet coherent stories about the transmission of monetary, fiscal, and other shocks through the economy. Part 1 offers the building blocks of macroeconomic thinking. Part 2 offers some key issues facing macroeconomics today. Finally, this macroeconomic journey should leave the reader with a knowledge of key economic data (and their measurement), key economic theories and ideas about how the economy works, and an understanding of the economic goals and challenges that shape economic policy today. Part 3 is an optional section for the reader to put on your “armchair economist” hat and go beyond the basics. Here the reader will consider the value and consequence of some modifications being suggested for macroeconomics. It challenges the reader to weigh the pros and cons of some of these ideas to reframe what is meant by macroeconomics.

REFERENCES Ben S. Bernanke and Alan S. Blinder, “Credit, Money, and Aggregate Demand,” American Economic Review, Papers and Proceedings of the One-Hundredth Annual Meeting of the American Economic Association, vol. 78, no. 2 (May 1988), pp. 435–439. Johnny Runge and Nathan Hudson, Public Understanding of Economics and Economic Statistics (Economic Statistics Centre of Excellence, National Institute of Economic and Social Research in Collaboration with the U.K. Office for National Statistics, November 2020) ESCoE Occasional Paper 03. John B. Taylor, “A Core of Practical Macroeconomics,” American Economic Review, AEA Papers and Proceedings, vol. 87, no. 2 (May 1997), pp. 233–235.

Part 1 Macroeconomic Thinking and Tools

CHAPTER

1

Macroeconomic Basics

LEARNING OBJECTIVES This chapter introduces you to what, who, why, and how we can study macroeconomics. You will learn about: • • • • • • • • •

Topics studied in macroeconomics. The role of John Maynard Keynes. Why macroeconomics is important. How economists study macro issues. The basic goals for an economy. The key economic statistics in macroeconomics. How fiscal policy differs from monetary policy. How theories and models help to provide insight. The key macroeconomic theories embraced, over time, to understand the economy.

[1] WHAT IS MACROECONOMICS? Macroeconomics is the study of economic activity, inflation—both goods and services prices and asset prices, interest rates, and exchange rates in the overall economy of a nation.

[2] WHO IS CREDITED WITH LAUNCHING MACROECONOMICS? John Maynard Keynes—a British economist who lived between June 5, 1883 and April 21, 1946—is considered the father of macroeconomics. Keynes re-envisioned how the economy operated in his 1936 book, The General Theory of Employment, Interest, and Money, and, in turn, rejected many of the foundations of the Classical school’s teaching, which dominated the DOI: 10.4324/9781003391050-2

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thinking of the time. Keynes opined in his preface to the 1939 French edition of this book that “I have called my theory a general theory. I mean by this that I am chiefly concerned with the behavior of the economic system as a whole—with aggregate incomes, aggregate profits, aggregate output, aggregate employment, aggregate investment, aggregate saving rather than with the incomes, profits, output, employment, investment and saving of particular industries, firms, or individuals.”

[3] WHY DO WE STUDY MACROECONOMICS? There are lots of reasons to study macroeconomics to provide insight into how the economy operates and how policymakers attempt to improve the functioning of the economy. Arthur F. Burns—who was chair of President Eisenhower’s Council of Economic Advisers (1953 to 1956) and prior to and after that stint for Eisenhower was a Columbia University professor, and would later become chair of the Federal Reserve Board serving between 1970 and early 1978—said in his presidential address to the American Economic Association in 1959 that, “The American people have of late been more conscious of the business cycle, more sensitive to every wrinkle of economic curves, more alert to the possible need for contra-cyclical action on the part of government, than ever before in history. Minor changes of employment or of productivity or of the price level, which in an earlier generation would have gone unnoticed, are nowadays followed closely by laymen as well as experts.”1 If this was true in 1959, it certainly is even truer today as there is more economic information available to track the economy and more sources for reporting the economy’s performance.

[4] HOW DO WE STUDY THE MACRO ECONOMY? The process for studying the macro economy can be summarized as looking, theorizing, modeling, estimating, and evaluating. •

• • • •

Looking: The starting point is to analyze the macroeconomic data for growth patterns, trends, possible relationships with other data, and any other data characteristic that may help to understand how the economy operates. Theorizing: Macroeconomists suggest how the economy may work by hypothesizing an economic theory—a logical framework to explain a particular economic phenomenon. Modeling: Economic theory involves developing an economic model—a simplified representation of the economic phenomenon that takes a mathematical or graphical form. Estimating: Econometric methods—which are statistical estimation tools—can help to quantify the relationship of the economic model by estimation of the model parameters. Evaluating: Finally, not every theory is correct or relevant, so an evaluation is needed. That assessment can be how well does the theory/model explain the behavior or how well does it forecast the behavior.

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[5] WHAT ARE THE KEY CONCEPTS IN MACROECONOMICS? Macroeconomics is built around: (1) employment and unemployment; (2) inflation—mostly goods and services inflation, but increasingly around asset inflation; and (3) output and productivity.

[6] WHY ARE THOSE KEY CONCEPTS SO IMPORTANT IN MACROECONOMICS? Each of those key concepts is associated with a macroeconomic goal. It is widely accepted that it is desirable to achieve strong economic growth and productivity; a low unemployment rate; and a low rate of inflation.

[7] WHY ARE INTEREST RATES NOT CONSIDERED AMONG THE KEY CONCEPTS IN MACROECONOMICS? Clearly, interest rates are extremely important to the performance of the economy and are built into all major macroeconomic theories, but just as Congress gave the Federal Reserve, as part of The Federal Reserve Reform Act of 1977, three mandated goals “to promote effectively the goals of maximum employment, stable prices, and moderate long-term interest rates,” the “goal” associated with long-term interest rates has been dropped by the Federal Reserve. Former Federal Reserve Board Governor Frederic S. Mishkin succinctly described the reason that the Federal Reserve has a “dual” and not a triple mandate. In a 2007 speech, he said, “Because longterm interest rates can remain low only in a stable macroeconomic environment, these goals are often referred to as the dual mandate; that is, the Federal Reserve seeks to promote the coequal objectives of maximum employment and price stability.”2 Similarly, low interest rates (both long-term and short-term) are a result of those other goals being successfully achieved.

[8] WHAT ARE SOME OF THE CHALLENGES TO ACHIEVE THOSE MACROECONOMIC GOALS? Although generically those qualitative goals for employment/unemployment, inflation, and output/productivity, are universally embraced by citizens of all nations and policymakers, there are practical problems in the implementation. To start with, what is meant by “strong” and what is “low”? Strong growth is relative to a country’s trends, demographics, and generally the country’s potential. Low inflation also is relative to a country’s trends. For example, presently monetary policymakers in the United States and Canada have an inflation goal of 2%, but in Mexico that target is 3%. Similarly, a low unemployment rate is highly dependent on demographic trends and at times the labor markets may or may not be able to absorb an influx of new entrants fully and rapidly. Witness the effect of the baby-boom generation entry into the labor markets had on the U.S. unemployment rate in the 1970s and 1980s, which averaged

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6.2% and 7.3%, respectively, versus an average of 4.8% in the 1960s. Another crucial challenge for achieving those macroeconomic goals is to determine which economic theory among competing theories offers the best policy and insights to reach those goals. Finally, value judgments (normative views) also play a role in the selection of any policy path and often clash with views of individuals holding different values.

[9] WHAT IS A MACROECONOMIC THEORY? A macroeconomic theory is a set of ideas and principles that attempt to depict the workings of the economy based on what is perceived to be the primary drivers or factors affecting how the economy operates. Theories are developed to aid in the interpretation of economic activity, to provide explanations and to offer solutions to economic problems.

[10] WHAT IS THE FOCUS OF MACROECONOMIC THEORY? A report by the Congressional Research Service (CRS) clearly described the short-term and long-term focus of macroeconomic theory. The authors said, “Traditional macroeconomic theory addresses two main questions. First, macroeconomic theory and policy seek to mitigate short-term economic fluctuations (or stabilize the economy) that leave productive resources idle for a time. Second, macroeconomists seek to recommend public policies that maximize living standards (economic growth) over the long term, while keeping debt at sustainable levels. The role of monetary policy and the maintenance of a stable price level are embedded in both issues.”3

[11] WHAT ARE THE MAIN SCHOOLS OF MACROECONOMIC THEORY? The Classical school The Classical school of economics generally referred to itself as the study of the “political economy.” It did not distinguish between microeconomics and macroeconomics as we do today, but its theories built upon work of Adam Smith, David Ricardo, Jean-Baptiste Say, Robert Malthus, John Stuart Mill, and others. Some of the concepts promulgated by the Classical school are integrated into our macroeconomic perspective—if for no other reason than as a comparison to later macroeconomic thinking. One of the tenets of this thinking was flexible wages and prices, which is generally rejected today based on empirical evidence and theoretical challenges.

The Keynesian school This is the approach originated by John Maynard Keynes and expanded upon by numerous economists, including Sir John Hicks, Lawrence Klein, Franco Modigliani, Paul Samuelson, Robert Solow, and James Tobin. Princeton University Prof. Alan Blinder observed that there are six basic tenets of Keynesian economics.4 (1) “A Keynesian believes that aggregate demand is influenced by a host of economic decisions—both public and private—and sometimes behaves

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erratically.” (2) “According to Keynesian theory, changes in aggregate demand, whether anticipated or unanticipated, have their greatest short-run effect on real output and employment, not on prices.” (3) “Keynesians believe that prices, and especially wages, respond slowly to changes in supply and demand, resulting in periodic shortages and surpluses, especially of labor.” (4) “Keynesians do not think that the typical level of unemployment is ideal—partly because unemployment is subject to the caprice of aggregate demand, and partly because they believe that prices adjust only gradually. (5) “Many, but not all, Keynesians advocate activist stabilization policy to reduce the amplitude of the business cycle, which they rank among the most important of all economic problems.” (6) “Finally, and even less unanimously, some Keynesians are more concerned about combating unemployment than about conquering inflation.”

Monetarism The monetarist school was initially based on the Quantity Theory of Money, which uses the equation of exchange (denoted as MV = PQ, where M is the money supply, V is the velocity of money, P is the price level, and Q is real output, and PQ also equals nominal GDP). From that equation of exchange, a monetary theory was built based on assumptions of the stability of velocity (which empirically is not the case anymore if it ever was) and the quantity (Q) is also invariant (fixed). For velocity (V), it was assumed that it was fixed (or relatively so) because it was dependent on the population, amount of trade, habits of people (to hold money), and the type of financial system in place—all factors that were assumed to be unrelated to the value of money. Similarly, the quantity (Q or real GDP) also was assumed to be dependent on factors that are unrelated to the value of money, including factors such as the abundance of natural resources in the country, methods of production, skills of labor, and the country’s infrastructure. Therefore, this showed that money and the price level were directly related. From that beginning, the monetarist school evolved to the view that supply of money was the cause of inflation and/or economic activity. It has evolved since its heyday in the 1960s and 1970s into advocating rule-based growth in the money supply or interest rates, which has become part of today’s New Classical and “New Monetary Consensus” approaches. Some people that were instrumental to the monetarist movement in the 1960s were University of Chicago Prof. Milton Friedman, University of Rochester Prof. Karl Brunner, and Carnegie Mellon Prof. Allan Meltzer. One of the lasting views of this perspective is that in the long run, changes in the quantity of money lead to proportional changes in the price level over the long run.

New Classical school/new monetary consensus The New (or Neo) Classical school, also sharing similar ideas with the Real Business Cycle or Rational Expectations approach and the New Keynesian school,5 attempts to integrate microeconomics fundamentals into its macroeconomics theory. This school of thought originated with the work of Robert Lucas, and was expanded by Thomas Sargent, Neil Wallace, and others and has morphed into what some economists refer to as the New Monetary Consensus. New Classical economists believe that individuals opt for the best choices available given prices and wages to move quantities supplied equal to quantities demanded (equilibrium) without the need for discretionary fiscal policies and that monetary policy can control

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inflation. As such, early work on this theory only argued for policy adjustments by monetary authorities, but after the 2008 global financial crisis, some proponents accepted the need for some fiscal policy to complement monetary policy, but these policies largely focus on longerrun aggregate supply-side factors. Despite the recognition of the need for some fiscal policy at times, this economic perspective largely places the stabilization role with monetary policy using some-type of rule for setting interest rates (such as the Taylor rule—which will be discussed in Chapter 10). If aggregate demand is too high and creates elevated inflation expectations, the central bank needs to increase interest rates and vice versa. The main channel through which this theory works is by controlling expectations.

Austrian school The Austrian school of macroeconomics largely focuses on capital and business cycle theory and tends to embrace a free-market solution to an aggregate supply and aggregate demand equilibrium. Absent fiscal and monetary policy attempts to stabilize the business cycle, the Austrian school argues that the economy ultimately will find its equilibrium without any misallocation of resources. This perspective also embraces the monetarist Milton Friedman’s view that “inflation is always and everywhere a monetary phenomenon.” Economists behind this school include Carl Menger, Eugen Böhm-Bawerk, and Ludwig von Mises.

Modern Monetary Theory (MMT) school MMT is based on a view that sovereign nation that issues its own currency cannot be forced into default on debt it issued, which is denominated in its own currency since the country can always create more money to pay its debt. This perspective developed in the 1990s has been described as “descriptive” and “separated from policies advocated by MMT’s proponents.”6 On the descriptive side, Prof. Stephanie Kelton observed that, “to describe the Modern Monetary Theory (MMT) project in a single sentence, I would say it is about replacing an artificial [federal deficit constraint] with an inflation constraint. So there are limits [to government deficit spending]. But the limit is not a predetermined numerical level or a percentage of the budget. There is no strict financial constraint. A currency-issuing government can afford to buy whatever is priced and available for sale in its own currency. The limit [to] spending, [however, is] not the spending itself, and not the deficit, [but] the limit is inflation. That is central to MMT.” L. Randall Wray, Warren Mosler, Bill Mitchell, Stephanie Kelton, and Scott Fullwiler are among the developers of this perspective.

[12] [ADVANCED] WHAT IS THE INSTITUTIONAL ECONOMICS MOVEMENT? The name for the institutional economics movement came from one of its first proponents, Walton Hamilton, who used the term in a presentation at the 1918 American Economic Association meeting. Hamilton observed, “The proper subject-matter of economic theory is institutions … Economic theory is concerned with matters of process … Economic theory

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must be based upon an acceptable theory of human behavior.”7 Reviewing the beginning of the movement, Hodgson’s summarized its key positions: 1 2

3 4

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Although institutional economists are keen to give their theories practical relevance, institutionalism itself is not defined in terms of any policy proposals. Institutionalism makes extensive use of ideas and data from other disciplines such as psychology, sociology, and anthropology in order to develop a richer analysis of institutions and of human behavior. Institutions are the key elements of any economy, and thus a major task for economists is to study institutions and the processes of institutional conservation, innovation, and change. The economy is an open and evolving system, situated in a natural environment, effected by technological changes, and embedded in a broader set of social, cultural, political, and power relationships. The notion of individual agents as utility-maximizing is regarded as inadequate or erroneous. Institutionalism does not take the individual as given. Individuals are affected by their institutional and cultural situations. Hence individuals do not simply (intentionally or unintentionally) create institutions [though] institutions affect individuals in fundamental ways.”8 The movement’s heyday was between World War I and World War II. During that time, this largely American movement in economics made many contributions. Rutherford observed, “This group … became heavily involved in the establishment of such research institutes as the National Bureau of Economic Research, the (Brookings) Institute of Economics, and in educational experiments such as the New school for Social Research and the Robert Brookings Graduate school … The institutionalist movement made a number of positive contributions during this period. First, following from their view of science, institutionalists took the issue of improving economic measurement seriously … Secondly, institutionalists made contributions to a number of key debates in economics on issues such as psychology and economics, business cycles, the pricing behavior of firms, ownership and control of corporations, monopoly and competition, unions and labor markets, various types of market problems and failures, public utilities and regulation, and law and economics.”9 Today, this view is known as the “old” or “original” institutional economics, which has given way to the “new institutional economics” movement.10 The new institutional economics has shifted away from broader macroeconomic issues and focuses on three main directions of research, namely property rights, transaction costs and public choice—largely topics in microeconomics.

[13] [ADVANCED] IS THERE A LOSS FOR THE ECONOMICS PROFESSION BY ABANDONING THE “OLD” INSTITUTIONAL ECONOMICS SCHOOL? This loss of the old institutional economics perspective is largely in the eye of the beholder. •

Malcolm Rutherford’s said of the shift away from the old institutional economics perspective that, “Many different ‘institutionalisms’ have flourished at various times and

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•

places within the social sciences and the discipline of economics. Over time, the interest in institutions has come from different sources and with different, even opposing, motivations. Institutional analysis has been used both to explain the failings of unfettered markets and the need for a greater degree of government intervention, and the failings of government interventions and the need for a greater degree of market freedom. But a common theme is that institutions matter a great deal, and that economists need to think hard about the ways in which institutions shape economic behavior and outcomes, and are themselves shaped by economic, political, and ideological factors. This is not a simple task. As the old institutionalists fully realized, discussion of institutions tends to lead into areas difficult to handle with the standard neoclassical tools.”11 Economist Geoffrey Hodgson also lamented the loss of the old institutionalism for today’s economic perspective. He wrote that the benefits of “the old institutionalism are enormous. Conceptions of social power and learning can be placed at the center of economic analysis. This means that institutionalism is more able to address questions of structural change and economic development. It is more useful, for instance, in dealing with issues such as long-term economic development, the problems of less-developed economies, or the transformation processes in the former Soviet bloc countries. On the other hand, the analysis becomes much more complicated and less open to formal modelling. In normative terms, the individual is no longer taken as the best judge of his or her welfare. This opens up the difficult question of the discernment and evaluation of human needs. This theoretical agenda—including matters of power, learning and welfare—is at the center of institutionalism, and it remains as vital and exciting as it was 100 years ago.”12

[14] WHAT ARE SOME OF PARADIGMS THAT ARE USED TO UNDERSTAND MACROECONOMIC DYNAMICS? The IS-LM model (the model of the equilibrium between the commodity markets and liquidity or money markets) and the aggregate demand/aggregate supply model (derived from the IS-LM model) are two foundational paradigms used to trace out macroeconomic interactions in the economy. Another increasingly popular paradigm to understand the macroeconomic dynamic is the 3-equation model that is based on an IS curve (where aggregate demand equals aggregate supply at given interest rates), the Phillips curve (a tradeoff between wage or price changes, that is inflation, and the unemployment rate), and a monetary policy rule.

[15] WHAT ARE SOME AREAS OF RESEARCH THAT MACROECONOMICS IS CONCERNED ABOUT? There are numerous topics of concern in macroeconomics, including (1) economic growth; (2) business cycles; (3) inflation; (4) unemployment; (5) interest rates; (6) taxes; (7) trade issues; (8) financial markets; and more.

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[16] WHAT ARE THE MAIN POLICY TOOLS USED TO GUIDE THE ECONOMY? The main policy tools are fiscal and monetary. Fiscal policy is a course of action by a central government in support of the nation’s economic goals. Monetary policy, on the other hand, is a course of action by the nation’s central bank in support of the nation’s economic goals. These are segmented because fiscal policy is determined by the legislative and/or executive branches of government—which is the president and Congress for the United States, while monetary policy is determined by the governing board of the central bank—which is the Federal Reserve in the United States.

[17] HOW DOES FISCAL POLICY DIFFER FROM MONETARY POLICY? Although the objectives of fiscal and monetary may be the same or at least shared, what each policy impacts is different. The fiscal approach sets the nation’s tax policy, and in turn, federal government revenue, and determines the amount of federal government spending (and on which priorities). If the amount of federal spending exceeds the federal revenue from taxes, then the federal government will issue debt to cover the expenditure. The monetary approach, however, sets interest rate policy consistent with its objectives of the amount of liquidity needed in the economy and, in recent years and if needed, has provided direct lending facilities to various segments of the economy.

[18] WHAT IS MEANT BY STABILIZATION POLICY? A stabilization policy is any governmental or central bank policy that seeks to influence the level of aggregate demand towards its long-run goals for the economy by minimizing businesscycle fluctuations. Economic approaches to stabilization policy fall into two broad camps—activist and non-activist. Activist policy will often follow from macro theories, such as the Keynesian theory or the Modern Monetary Theory, that argue the economy will not correct on its own without a substantial cost, while non-activist policy—such as from Classical theories or the Austrian school argue the economy will correct on its own because of selfcorrecting mechanisms in the economy.

[19] HOW DOES FISCAL POLICY IMPACT THE ECONOMY? Fiscal policy can affect and effect aggregate demand and/or aggregate supply. Typically, fiscal policy is thought of as a tool to primarily stimulate aggregate demand, however, fiscal policy also has numerous channels through the supply-side affecting production costs and productivity by way of its regulatory role in the economy. Moreover, successful fiscal policy implementation is dependent on the so-called, three T’s. These are the fiscal taxation and spending program’s (1) targets—Who are the fiscal programs designed to help?; (2) totals—How much money must

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be spent to see a meaningful impact?; and (3) timing for implementation—How soon will the fiscal package take effect?

[20] WHAT TOOLS DO FISCAL POLICYMAKERS HAVE AVAILABLE? Generically, economists refer to fiscal policy as federal government taxation and expenditures. However, there are a host of specific fiscal policy instruments that policymakers may use at different times to address different economic issues to guide the economy in the short term and over the long run. These tools include: Fiscal Policy Tools • • • • • • • •

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•

• •

• • •

Tax Rates What Items are Subject to Taxes Government Subsidies Government Spending Stimulus “Checks” (“Economic Impact Payments”) Tax Credits Direct Government Investment on projects where market failure leads to underinvestment Labor-Market Policies Affecting: - Training and Education - Minimum Wage - Foreign Worker Quotas Government Regulations: - Financial Market Regulations - Environmental Protection Regulations - Public Natural/Natural Resource Use - Labor Safety Regulations - Product Safety Regulations - Permits International Trade Policies - International Tariffs - Controls on Trade (Quotas or Restricting Trade in Certain Products) Public Ownership of Factors of Production (e.g., energy reserves and public land holdings) Value of Country’s Currency (Exchange Rate Policies–This is sometimes considered a monetary policy tool; however, in the United States the exchange-rate policy is set by the U.S. Treasury and not the Federal Reserve Board.) Price and Wage Controls Rationing Compelling Private-Sector Production (such as invoked in the United States under the Defense Production Act during wars and national emergencies—it was used in 2020 for personal protective product production)

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[21] HOW DOES MONETARY POLICY IMPACT THE ECONOMY? Monetary policy can affect and effect aggregate demand and/or aggregate supply. Typically, monetary policy is thought of as a tool to primarily impact aggregate demand through higher borrowing costs for the purchase, on credit, of such items as vehicles, houses, plant and equipment, and more. However, monetary policy also may affect aggregate supply since interest payments are a cost of production (which impacts aggregate supply) and the central bank policymakers also use their speeches and announcements to influence business and financial market expectations about interest rates, inflation, and economic growth (which impacts both aggregate demand and aggregate supply).

[22] WHAT TOOLS DO MONETARY POLICYMAKERS HAVE AVAILABLE? Generically, economists think of monetary policy as changes in the central bank’s policy interest rate (which is the federal funds rate in the United States). However, there are a host of specific monetary policy instruments that policymakers may use and different central banks around the world may have slightly different sets at their disposal. The Federal Reserve—which is the U.S. central bank—certainly can control its short-term policy interest rate through what is known as open market operations and has expanded its balance sheet of longer-term assets during the financial crisis in 2008 and the pandemic crisis in 2020. These episodes when the Federal Reserve made large purchases of long-dated assets or “large-scale asset purchases” (LSAPs) are known as periods of “quantitative easing.” The current set of monetary policy tools (listed here, but explained later) that the Federal Reserve has available includes: Monetary Policy Tools •

• • • • • • • •

Open Market Operations (the purchase and sale of securities in the open market by a central bank to guide its policy interest rate to its desired target—this is the “interest-rate policy lever”) Discount Window and Discount Rate Reserve Requirements Interest on Reserve Balances Overnight Reverse Repurchase Agreement Facility Term Deposit Facility Central Bank Liquidity Swaps Foreign and International Monetary Authorities Repo Facility Standing Overnight Repurchase Agreement Facility

Many of these tools are technical lending programs to ensure adequate liquidity in today’s global financial system.

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[23] WHY DO ECONOMISTS USE MODELS? U.K. economist Joan Robinson13 described the use of models in economics as a road map. Following up on that observation, Bank of England Monetary Policy Committee member Silvana Tenreyro said of the analogy, “Maps simplify our complex world to small-scale, flat figures. When used for travelling, a map can show us the route we need to follow, abstracting from a host of information that is not essential to reach the destination. The map might be different (and more or less stylized) depending on the means of transportation (think of your favorite bicycle, foot, train, bus or car maps). In the same way, economic models might be different depending on the structure and characteristics of the economy; policy instruments available; institutional constraints; etc.”14

[24] WHAT IS MEANT BY STORYTELLING IN MACROECONOMICS? Economists use economic data patterns and stories to explain economic situations—such as a tradeoff between inflation and unemployment, a recession, a financial crisis, or just about any economic motivation and more. The early economists relied heavily on stories to explain their ideas. Todd Knoop even noted that “Keynes [in his 1936 General Theory, which has defined macroeconomics ever since,] largely resisted using equations and empirical data in his analysis, believing that economic processes were too complex to be described by simple equations and that appropriate empirical data were often unavailable and unreliable.”15 Nonetheless, today, the economics profession embraces those methods. However, applied introductory approaches to macroeconomics cover the foundational paradigms relying more on basic understanding of concepts using narratives. Indeed, Prof. Edward Leamer observed that economic patterns, economic stories, and economic analysis really are the foundations to understand the economy.16 Finally, Prof. Robert Shiller has taken the economic story idea one step further. He has given more economic importance to stories that go viral as catalysts for economic change.17

[25] [ADVANCED] WHAT ARE THE BENEFITS AND DRAWBACKS OF LITERARY VERSUS MATHEMATICAL EXPOSITION OF ECONOMIC IDEAS? Cambridge University economist Arthur C. Pigou18—who was a brilliant student of the highly influential economist Alfred Marshall—discussed the pros and cons of using literary versus mathematical exposition to explain economic theories. Pigou noted that Alfred Marshall held that it was fine to work with mathematical constructs of economic ideas, but Marshall would not use mathematics to convey those ideas so that they would be accessible to the nonspecialist. Indeed, Pigou further opined in his 1933 book that, “By doing so, [Marshall] made his Principles of Economics, not only a great work of science, but also a great instrument of general education.”19 However, Pigou, himself, wavered on this point. In a 1941 Economic Journal article, he returned to this idea and expanded upon it. He wrote: “The view that

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mathematics ought to be used sparingly in economics is thoroughly respectable. There is behind [this view] no less an authority than Marshall. He was emphatic that an economist’s ultimate concern is the real, not any imaginary, world. But, he argued, the real world in its economic aspect is so complex that to represent it adequately in mathematical terms is impracticable. Any mathematical treatment we can reasonably attempt must, necessarily, deal only with highly simplified artificial models. His fear was that we may develop such an affection for these models as to forget that they are only models, and bad ones at that; that we may be led to neglect important aspects of reality which cannot be worked into them; and so may get our whole picture of actual economic life distorted and wrongly proportioned. This is a real danger. To spend our lives playing with mathematical toys is not the proper business of economists. But there is something to say on the other side. The very complexity of the real economic world makes an attack on it in its concrete actuality extraordinarily difficult. By constructing models in which a comparatively small number of dominant influences only are present we may get to understand the working of these influences, whereas, if we were forbidden to isolate them in thought, this might well prove impossible. Moreover, when we operate with these models we know exactly what it is to which any conclusions reached are applicable. The assumptions made are explicit. But in attempts to investigate the real world directly we are almost bound from time to time to introduce implicit assumptions of whose existence we are only half conscious. We are thus liable to reach conclusions which appear to be of general application, but are in fact restricted much more narrowly than we suppose.” Pigou goes on to write that if we recognize that models are just the prologue to economics, not economics itself, then we may be “safeguarded against the danger Marshall feared.”20 Of course, as the field of economics matured, the use of mathematics took a greater hold on the subject with less literary exposition,21 for better or worse as Pigou aptly discussed the positives and negatives of both approaches.

Issues to think about Much of macroeconomics is taught and developed from the standpoint of “settled questions,” that is, theories and evidence that are generally accepted throughout the profession. However, over time, some of those so-called “settled” issues may be replaced by new thinking. • • • • •

Is that the correct perspective to take or should the controversies be highlighted to advance insights? What do alternative macroeconomic theories offer? Are the macroeconomic goals that are generally accepted the best set of objectives for an economy? if not, what should those goals be today? Should macroeconomics reconnect with the “old” institutional economics school? Should macroeconomics use more “literary” exposition rather than “mathematical” exposition as Alfred Marshall advocated?

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NOTES 1 Arthur F. Burns, “Progress Towards Economic Stability,” reprinted in The Business Cycle in a Changing World (NBER, 1969), pp. 101–128. 2 Frederic S. Mishkin, “Monetary Policy and the Dual Mandate,” address at Bridgewater College, Bridgewater, Va., April 10, 2007. 3 Grant A. Driessen and Jane G. Gravelle, “Deficit Financing, the Debt, and ‘Modern Monetary Theory’,” Congressional Research Service, U.S. Library of Congress, October 21, 2019. 4 Alan S. Blinder, “Keynesian Economics,” in D. Henderson, ed., The Concise Encyclopedia of Economics (The Liberty Fund, 2008), pp. 316–319, https://www.econlib.org/library/Enc1/KeynesianEconomics. html. Also, see: Sarwat Jahan, Ahmed Saber Mahmud, and Chris Papageorgiou, “What is Keynesian Economics?,” Finance & Development, International Monetary Fund, vol. 51, no. 3 (September 2014), https://www.imf.org/external/pubs/ft/fandd/2014/09/basics.htm. 5 A very succinct description of the New Keynesian versus the New Classical schools is “New classical economists base their models on perfectly competitive consumer, producer and labor markets. On the other hand, new Keynesians base their models on the real world imperfectly competitive markets where consumers, producers and labor market participants operate with imperfect information.” See https://www.yourarticlelibrary.com/notes/macroeconomics/main-differences-between-new-classical-and-new-keynesian-macroeconomics/31235. 6 L. Randall Wray, “The ‘Kansas City’ Approach to Modern Money Theory,” Levy Economics Institute Working Paper No. 961, July 2020, p. 31. The ideas are spelled out in greater detail in William Mitchell, L. Randall Wray, and Martin Watts, Macroeconomics (Red Globe Press, Macmillan International, 2019). Despite the recent prominence of MMT, Thomas I. Palley, Money, fiscal policy, and interest rates: A critique of Modern Monetary Theory, Macroeconomic Policy Institute Working Paper 109, January 2013, argued that “The principal conclusion [of his review] is that the macroeconomics of MMT is a restatement of elementary well-understood Keynesian macroeconomics. There is nothing new in MMT’s construction of monetary macroeconomics that warrants the distinct nomenclature of MMT.” 7 See: Geoffrey M. Hodgson, “What Is the Essence of Institutional Economics?,” Journal of Economic Issues, vol. 34, no. 2 (June 2000), p. 317. 8 Ibid, p. 318. 9 Malcolm Rutherford, “Institutional Economics: Then and Now,” Journal of Economic Perspectives, vol. 15, no. 3 (Summer 2001), pp. 179–180. 10 See: Douglass C. North, “The New Institutional Economics,” Journal of Institutional and Theoretical Economics (JITE) / Zeitschrift für die gesamte Staatswissenschaft, vol. 142, no. 1 (March 1986), 3rd Symposium on The New Institutional Economics (March 1986), pp. 230–237. 11 Malcolm Rutherford, “Institutional Economics: Then and Now,” Journal of Economic Perspectives, vol. 15, no. 3 (Summer 2001), p. 190. 12 Geoffrey M. Hodgson, “What Is the Essence of Institutional Economics?,” Journal of Economic Issues, vol. 34, no. 2 (June 2000), p. 329. 13 Joan Robinson, Essays on the Theory of Economic Growth (Palgrave Macmillan, London, 1962), p. 33. 14 Silvana Tenreyro, “Models in Macroeconomics,” Speech, Bank of England, June 4, 2018. 15 Todd A. Knoop, Modern Financial Macroeconomics: Panics, Crashes, and Crises (Blackwell Publishing, 2008), p. 82. 16 See for example, Edward E. Leamer, Macroeconomic Patterns and Stories (Springer-Verlag, Berlin), 2009. 17 Robert J. Shiller, “Narrative Economics,” American Economic Review, vol. 107, no. 4 (2017), pp. 967–1004. 18 Although Pigou may be largely a forgotten name in macroeconomics today, John Maynard Keynes and Arthur C. Pigou were Cambridge University colleagues. Both Keynes and Pigou also were students of Alfred Marshall. After Marshall retired from teaching at Cambridge in 1908, Pigou was elected to hold the academic chair previously held by Marshall. Pigou, who regarded Keynes highly,

MACROECONOMIC BASICS “offered Keynes a lectureship [at Cambridge] paid by Pigou himself.” (See: Gerhard Michael Ambrosi, “Keynes, Pigou, and The General Theory,” unpublished paper, January 25, 2009.) However, Pigou’s writings, especially his Theory of Unemployment (1933), played an important role in Keynes‘ 1936 General Theory as Keynes attempted to show through Pigou’s writings what was wrong with the Classical school theory (which Pigou held) of unemployment. 19 Alfred C. Pigou, Theory of Unemployment (London, Macmillan, 1933). 20 A.C. Pigou, “Newspaper Reviewers, Economics and Mathematics,” The Economic Journal, vol. 51, no. 202/203 (June-September 1941), pp. 277–278. 21 The late MIT Prof. Paul Samuelson in his influential 1947 book, Foundations of Economic Analysis, famously argued in favor of the mathematical approach. Samuelson wrote that he “hoped that the [discussions in his book] could be nontechnical. Very quickly it became apparent that such a procedure, while possible, would involve a manuscript many times the present size. Moreover, [he came] to feel that Marshall’s dictum that ‘it seems doubtful whether anyone spends his time well in reading lengthy translations of economic doctrines into mathematics’ [was incorrect.] The laborious literary working over of essentially simple mathematical concepts such as is characteristic of much of modern economic theory is not only unrewarding from the standpoint of advancing the science, but involves as well mental gymnastics of a peculiarly depraved type.” However, after forcefully arguing in favor of using math, Samuelson—who was the first American to win the Nobel Prize in Economics in 1970 (which was only the second year the memorial prize was presented since it was established)—wrote that he attempted to avoid all but “essential math” because he felt his “interest in mathematics [was] secondary and subsequent to [his] own interest in economics.” See Paul Anthony Samuelson, Foundations of Economic Analysis (Harvard University Press, Cambridge, MA, 1947), p. 6.

17

CHAPTER

2

The Importance of Circular Flow in the Economy

LEARNING OBJECTIVES Macroeconomic measurement is based on a national income and product accounts structure (much like financial accounting) that equates two sides of a ledger. In this chapter, you will learn: • • • • • • •

Unlike financial accounting, those two sides of the national income ledger have a feedback relationship over time, which is portrayed as a circular flow. The national income and product ledger has one side for expenditures and other side for income. The expenditure side represents gross domestic product. The income side represents gross domestic income. After all transactions in the period are completed, gross domestic product will equal gross domestic income. The expenditure multiplier is a feedback loop between the expenditure and income sides of the ledger. Some national income accounting relationships have a significant implication for the economy.

[26] WHAT IS CIRCULAR FLOW IN THE ECONOMY? Most textbooks introduce some form of the circular flow chart early in the discussion of economy, but often that discussion is not integrated with future macroeconomic topics (such as aggregate demand and aggregate supply or the expenditure multiplier). The circular flowchart is portrayed with various levels of embellishment. However, maybe the simplest form of this relationship is used by the U.S. Commerce Department’s Bureau of Economic Analysis (BEA) to relate the expenditure and income sides of the economy. That BEA’s depiction of the circular flow in the economy is shown below. DOI: 10.4324/9781003391050-3

CIRCULAR FLOW IN THE ECONOMY Goods and Services Expenditures

Businesses

Individuals

Income Labor FIGURE 2.1

The Circular Flow

Source: U.S. Bureau of Economic Analysis

[27] WHY IS CIRCULAR FLOW USEFUL? The reason the BEA uses this circular flowchart is to introduce how the national income and product accounts (the “GDP report” or NIPA) are constructed in a similar manner to financial accounting’s use of a “T-account” for double-entry bookkeeping. For the entire economy, that double-entry bookkeeping concept is that the sum of all “expenditures” will equal the sum of all “income.”

[28] HOW ARE EXPENDITURES AND INCOME MEASURED? Expenditures are represented by gross domestic product (GDP). Incomes are represented by gross domestic income (GDI).

[29] WHY ARE EXPENDITURES AND INCOME EQUAL? The BEA describes the GDP and GDI as follows: “Gross domestic product (GDP) and gross domestic income (GDI), are measures of the same concept of total activity in the U.S. economy. GDP measures activity as the sum of all final expenditures in the economy; it is detailed on the product side of the domestic income and product account. GDI measures activity as the sum of all incomes generated in production; it is detailed on the income side of the account. By design, the flows of final expenditures and incomes generated are equal. Thus, GDP and GDI give the same measure of economic activity, but in practice, they differ because each is estimated from largely different source data. The difference between GDP and GDI is allocated to the income side of the domestic income and product account and is

19

20

MACROECONOMIC THINKING AND TOOLS TABLE 2.1

National Income T-Account Showing Current-Dollar GDP Equals Current-

Dollar GDI 2021, Billions of Dollars Expenditures Income Gross domestic product (GDP) =

$23,315.1

$23,444.0 −0.6% −$128.9

= Gross domestic Income (GDI) = Statistical discrepancy as a percentage of GDP = Statistical discrepancy as difference of GDP and GDI

known as the statistical discrepancy.” For the calendar-year 2021, those nominal GDP and GDI data are as follows.

[30] WHAT ARE THE COMPONENTS OF GDP? GDP is the sum of expenditures on consumption, investment, government spending, and net exports—that is, exports minus imports.

[31] WHAT ARE THE COMPONENTS OF GDI? In essence, GDI represents the sum of all payments to factors of production (labor, capital, etc.). These payments encompass the compensation of employees, proprietors’ income, corporate profits, rental income, and net interest.

[32] HOW DO AGGREGATE DEMAND AND AGGREGATE SUPPLY RELATE TO GDP AND GDI? In the aggregate demand and aggregate supply model framework, aggregate demand equals real or inflation-adjusted GDP as measured in constant 2012 dollars for the U.S. economy. Shortrun aggregate supply equals real or inflation-adjusted GDI also as measured in constant 2012 dollars for the U.S. economy. As a side note, to derive real GDI it is the nominal or marketvalue GDI deflated by the implicit price deflator for gross domestic product. These data and the derivation of real income are shown in the table below. For practical reasons, the aggregate demand and aggregate supply model would largely ignore the statistical discrepancy, so that real GDP would equal real GDI.

CIRCULAR FLOW IN THE ECONOMY TABLE 2.2

National Income T-Account Showing Real-Dollar GDP Equals Real-Dollar GDI Calendar-Year 2021, Billions of 2012 Dollars Real Expenditures Real Income

Gross domestic product (GDP) =

$19,609.8

$19,718.3

= Gross domestic income (GDI)

$23,444.0

= Market-value of gross domestic income (GDI) = Statistical residual = Implicit price deflator for gross domestic product = Real income NOT adjusted for statistical residual = Real income with statistical residual = 100*[{Market-value 100*[{Market value of GDI + Statistical Residual}/Price Deflator]

−$128.9 118.895 $19,718.3 $19,609.8

[33] WHY DO WE CARE ABOUT BOTH REAL GDP AND REAL GDI? The distinction between aggregate demand equaling real GDP on the expenditure side and short-run aggregate supply equaling real GDI on the income side is important for discussing the dynamic effecting aggregate demand or aggregate supply. It is not a distinction without difference; it is the causal link through aggregate demand and short-run aggregate supply.

[34] [ADVANCED] WHAT IS MEANT BY AGGREGATE OUTPUT? The term “aggregate output” is the generic terminology for gross domestic product (GDP) because GDP represents current production. It can be measured in three ways, as: (1) gross domestic product (GDP) from the expenditure side; (2) GDP by industry from the valueadded side; and (3) gross domestic income (GDI)—which measures payment to factors of production. These three measures are conceptually all equal in the national income accounts (except for a statistical discrepancy due to measurement error). All three of these terms can be expressed in market prices (nominal or current dollars) or in real (inflation-adjusted) terms. The three concepts are shown, in detail, on a real-dollar basis from the Bureau of Economic Analysis’ National Income and Product Accounts. The inflation-adjusted or real measures are equated with aggregate demand or short-run aggregate supply in mac­ roeconomic theories.

21

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MACROECONOMIC THINKING AND TOOLS

Real GDP as aggregate demand

Gross domestic product (GDP) is the sum of consumption, investment, net exports, and government spending. These data can be thought of as measuring aggregate demand when adjusted for inflation (constant dollars).

TABLE 2.3

Real Gross Domestic Product, Chained Dollars

[Billions of chained (2012) dollars] Bureau of Economic Analysis Last Revised on: September 29, 2022 Line 1 2 3 4 5 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Gross domestic product Personal consumption expenditures Goods Durable goods Nondurable goods Services Gross private domestic investment Fixed investment Nonresidential Structures Equipment Intellectual property products Residential Change in private inventories Net exports of goods and services Exports Goods Services Imports Goods Services Government consumption expenditures and gross investment Federal National defense Nondefense State and local Residual

2019

2020

2021

19036.1 13092.3 4711.6 1740.1 2985.4 8421.0 3492.7 3404.2 2804.6 567.9 1236.5 1003.2 606.2 73.1 −892.6 2572.1 1791.5 783.1 3464.7 2913.5 552.9 3321.7

18509.1 12700.7 4955.7 1914.2 3066.7 7863.0 3306.5 3326.8 2666.0 510.4 1107.3 1051.2 649.8 −54.6 −922.6 2231.7 1609.7 635.8 3154.3 2744.6 431.3 3406.7

19609.8 13754.1 5561.9 2268.8 3336.2 8361.1 3603.0 3574.6 2835.4 477.5 1221.8 1153.0 719.4 −19.4 −1233.4 2366.8 1728.9 656.9 3600.2 3143.0 484.2 3426.3

1279.3 778.5 500.7 2041.1 −26.0

1358.9 801.1 556.6 2048.5 −74.6

1390.5 791.3 597.0 2037.9 −93.5

Note: Chained (2012) dollar series are calculated as the product of the chain-type quantity index and the 2012 current-dollar value of the corresponding series, divided by 100. Because the formula for the chain-type quantity indexes uses weights of more than one period, the corresponding chained-dollar estimates are usually not additive. The residual line is the difference between the first line and the sum of the most detailed lines.

CIRCULAR FLOW IN THE ECONOMY

Real GDP as aggregate industry supply or production •

Gross domestic product (GDP) might also be thought of as aggregate production (value-added only) or supply. In this context, aggregate supply can be thought of as GDP by industry.

TABLE 2.4

Real Value Added by Industry

[Billions of 2012 chain dollars] Bureau of Economic Analysis Last Revised on: September 29, 2022 Line 1 Gross domestic product 2 Private industries 3 Agriculture, forestry, fishing, and hunting 8 Mining 12 Utilities 15 Construction 24 Manufacturing 25 Durable goods 56 Nondurable goods 72 Wholesale trade 84 Retail trade 95 Transportation and warehousing 106 Information 119 Finance, insurance, real estate, rental, and leasing 120 Finance and insurance 128 Real estate and rental and leasing 135 Professional and business services 136 Professional, scientific, and technical services 146 Management of companies and enterprises 147 Administrative and waste management services 153 Educational services, health care, and social assistance 154 Educational services 155 Health care and social assistance 165 Arts, entertainment, recreation, accommodation, and food services 166 Arts, entertainment, and recreation 169 Accommodation and food services 172 Other services, except government 177 Government

2019 2020 2021 19036.1 18509.1 19609.8 16758.3 16231.1 17315.9 222.3 228.6 209.2 493.8 283.8 674.3 2230.9 1236.6 993.6 1102.3 1116.8 562.5 1241.9 3576.0

488.3 294.2 648.4 2129.5 1180.5 948.4 1102.1 1084.8 495.0 1290.9 3573.6

408.9 282.2 664.3 2271.8 1295.0 976.6 1163.3 1113.2 532.0 1470.7 3744.0

1242.5 2334.6 2535.2 1527.6 433.0 578.1

1276.1 2292.9 2504.7 1526.0 440.2 545.5

1368.0 2366.3 2796.8 1695.5 488.1 619.6

1666.7

1618.8

1692.7

228.3 1439.4 733.4

205.8 1415.2 530.9

212.6 1482.7 680.9

201.6 531.8 367.7 2215.7

129.1 401.1 331.3 2207.9

174.5 506.0 349.2 2219.8

(Continued )

23

24

MACROECONOMIC THINKING AND TOOLS

TABLE 2.4

(Continued)

178 Federal 183 State and local 189 Not allocated by industry Addenda: 190 Private goods-producing industries (1) 191 Private services-producing industries (2) 192 Information-communications-technology-producing industries (3)

714.1 736.4 743.6 1501.1 1472.8 1477.7 −200.4 −218.4 −190.9 3627.1 3488.2 3596.3 13127.9 12736.9 13699.6 1710.4 1805.1 2033.4

Notes 1 Chained (2012) dollar series are calculated as the product of the chain-type quantity index and the 2012 current-dollar value of the corresponding series, divided by 100. Because the formula for the chain-type quantity indexes uses weights of more than one period, the corresponding chained-dollar estimates are usually not additive. The value of the Not allocated by industry line reflects the difference between the first line and the sum of the most detailed lines, as well as the differences in source data used to estimate GDP by industry and the expenditures measure of real GDP. 2 Consists of agriculture, forestry, fishing, and hunting; mining; construction; and manufacturing. 3 Consists of utilities; wholesale trade; retail trade; transportation and warehousing; information; finance, insurance, real estate, rental, and leasing; professional and business services; educational services, health care, and social assistance; arts, entertainment, recreation, accommodation, and food services; and other services, except government. Note: The Bureau of Economic Analysis does not include these detailed estimates in the published tables because their quality is significantly less than that of the higher-level aggregates in which they are included.

Real GDI as aggregate income •

Gross domestic income (GDI) represents the payment to the factors of production and might also be thought of as aggregate supply since its income is generated by the production of all industries. In this context, aggregate supply can be thought of as payments by industry in the production of GDP. Due to mismeasurement, the GDI will equal GDP only after the fact when the Commerce Department statisticians add the statistical discrepancy to force the identity. These data are calculated at a high level in real or inflationadjusted terms, which means starting with current-dollar figures and adjusting for real GDI in the final step (as shown in the table below).

TABLE 2.5

Gross Domestic Income by Type of Income

[Billions of dollars] Bureau of Economic Analysis Last Revised on: September 29, 2022 Line 1 2

Gross domestic income Compensation of employees, paid

2019

2020

2021

21486.5 11460.0

21275.4 11600.6

23444.0 12549.1 (Continued )

CIRCULAR FLOW IN THE ECONOMY TABLE 2.5

3 4 5 6 7 8 9 10 11 12 13 14 15

16 17 18 19

20 21 22 23

24 25 26 26

(Continued)

Wages and salaries To persons To the rest of the world Supplements to wages and salaries Taxes on production and imports Less: Subsidies (1) Net operating surplus Private enterprises Net interest and miscellaneous payments, domestic industries Business current transfer payments (net) Proprietors’ income with inventory valuation and capital consumption adjustments Rental income of persons with capital consumption adjustment Corporate profits with inventory valuation and capital consumption adjustments, domestic industries Taxes on corporate income Profits after tax with inventory valuation and capital consumption adjustments Net dividends Undistributed corporate profits with inventory valuation and capital consumption adjustments Current surplus of government enterprises (1) Consumption of fixed capital Private Government Addendum: Statistical discrepancy Gross domestic income with statistical discrepancy Real Gross domestic income (from NIPA Table 1.17.6) Real Gross domestic income with real statistical discrepancy

9336.5 9317.5 18.9 2123.5 1530.0 73.0 5132.8 5147.1 810.0

9465.2 9450.8 14.4 2135.4 1526.3 657.3 5228.0 5228.2 890.7

10300.8 10283.2 17.6 2248.4 1663.4 481.9 5881.8 5879.8 872.7

164.0 1601.4

144.1 1643.1

171.0 1753.6

698.2

719.8

723.8

1873.4

1830.5

2358.7

297.4 1576.0

288.9 1541.6

388.2 1970.5

1050.5 525.5

1221.6 320.0

1401.6 568.9

−14.2 3436.6 2850.1 586.5

−0.1 3577.8 2971.8 605.9

2.1 3831.6 3184.5 647.1

−105.5 21,381.0

−214.9 21,060.5

−128.9 23,315.1

19,130.0

18,698.0

19,718.3

19,036.1

18,509.2

19,609.8

Note 1 Prior to 1959, subsidies (line 8) and the current surplus of government enterprises (line 20) are not shown separately; subsidies are presented net of the current surplus of government enterprises.

25

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MACROECONOMIC THINKING AND TOOLS

Real GDP = Aggregate Demand (AD) = Short-Run Aggregate Supply (SRAS) = Aggregate Income (AI) = Real GDI This is what is known as a national income identity ex post, or after all the adjustments have been made in the economy in that period. From a practical standpoint, since GDP by industry is produced with a slight lag relative to GDP or GDI, it is sufficient to assume that real GDP equals aggregate demand (AD) and real GDI equals short-run aggregate supply (SRAS) for discussion purposes in the aggregate demand/aggregate supply model.

2019

AD SRAS AI

Aggregate Demand = Real GDP by Expenditure Short-Run Aggregate Supply = Real GDP by Industry Aggregate Income = Real GDI Therefore, AD = SRAS = AI

2020

2021

Billions of 2012 Dollars $19,036 $18,509 $19,610 $19,036

$18,509

$19,610

$19,036

$18,509

$19,610

[35] HOW DOES THE CIRCULAR FLOW RELATE TO THE EXPENDITURE MULTIPLIER? The circular flowchart also is the conceptual foundation for the expenditure multiplier. The expenditure multiplier concept was first developed by F. A. Kahn in the early 1930s, and later refined by J. M. Keynes, which asserts that a change in an “autonomous expenditure” (either investment, government expenditure and/or exports) will lead to a larger proportionate change in real GDP. It measures some “multiple” of the total new spending injection over time. To understand this concept, for every increase (or decrease) in expenditures in the economy, there is an increase (or decrease) in income in the economy (as the circular-flow chart demonstrates). This represents the direct or first-round effect. However, that increase (or decrease) in the incremental income today will create an incremental increase (or decrease) in expenditures tomorrow (the second-round effect). This initial incremental increase (or decrease) will continue to ripple because of the circular flow in the economy between expenditures and income. However, for each new round, the amount of the change (positive or negative) becomes increasingly smaller and the ripple effect will eventually fade out (much as tossing a stone into a lake and watching the water ripple get increasingly less pronounced in the outer rings). Thus, an increase of $10 billion in government spending, for example, will likely (depending on how it is deployed) result in an economic impact that would be greater than that $10 billion over the following year or two. Therefore, one way of thinking about the expenditure multiplier is as a “feedback loop,” which is enabled because of the circular flow between the economy’s expenditures and income flows.

CIRCULAR FLOW IN THE ECONOMY

[36] WHY ARE WE INTERESTED IN NIPA COMPONENTS THAT ADD TO OR SUBTRACT FROM GDP? The circular flowchart also gives rise to the expenditure “injections and leakages” concept for the economy. This concept is sometimes referred to as the “magic equation” and is based on national income and product accounting. In essence, the basic expenditure-income circular flow can be disaggregated such that aggregate expenditure components can be designated as an injection into GDP or a leakage from GDP. Injections add to the GDP tally and its feedback loop effect, leakages subtract from the GDP tally and, in turn, its feedback loop impact.

[37] WHICH EXPENDITURE COMPONENTS ADD TO GDP? Within the circular flow model, those expenditure components that are additive to GDP are: (a) investment, which includes expenditures on nonresidential structures (buildings—such as offices and warehouses), capital goods (such as machinery and computers), business software, and residential structures (houses); (b) government spending, which includes expenditures on public goods and services; and (c) exports, which include goods and services sold to foreigners (and money brought into the country).

[38] WHICH EXPENDITURE COMPONENTS SUBTRACT FROM GDP? Within the circular flow model, those expenditure components that reduce GDP are: (a) saving, which is money not spent today, but invested for future use; (b) taxes, which is money paid by businesses and individuals to the government and therefore not available to businesses and individuals to spend today; (c) imports, which are goods and services that are domestically purchased from foreigners.

[39] WHAT ARE THE IMPLICATIONS FROM BALANCING INJECTIONS AND LEAKAGES IN GDP? If the sum of savings, taxes, and imports [leakage from GDP] is greater than the sum of investment, government spending, and exports [injection into GDP], then there is downward pressure on real GDP. However, if the sum of savings, taxes, and imports [leakage from GDP] is less than the sum of investment, government spending, and exports [injection into GDP], then there is upward pressure on real GDP. In the national income and product account, an equilibrium between injections and leakages would exist if (a) saving equals investment; (b) taxes equal government spending; and (c) imports equal exports, so that there are no shortfalls or balances (that is, these sector net balances equal zero).

27

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MACROECONOMIC THINKING AND TOOLS

[40] [ADVANCED] HOW IS THE INJECTIONS-LEAKAGES IDENTITY DERIVED FROM THE NATIONAL INCOME AND PRODUCT ACCOUNTS? The derivation of the injections-leakages identity is based on NIPA economy-wide accounting. It can be shown that: (a) (b) (c) (d) (e)

GDP = Consumption (C) + Investment (I) + Government (G) + Net Export (NX) Investment (I) = Savings (S) - Net Lending/BorrowingROW Savings (S) = SavingsPrivate + SavingsGovernment SavingsGovernment = Taxes (T) - Government (G) Net Lending/BorrowingROW = Net Export (NX)

The GDP equation (a) is the basic GDP expenditure identity. The investment equation (b) is an identity that relates investment to domestic savings (S) minus net lending/borrowing from foreigners or the rest-of-world (ROW), such that an inflow of foreign capital is net borrowing, but an outflow is net lending. Domestic savings—equation (c)—simply divides domestic savings into private and government savings. Government savings—another concept from the national income and product accounts and shown in equation (d)—is the difference between the government revenue (taxes raised—T) and its spending (G). Finally, the net lend/bor­ rowing from foreigners, which is equation (e), relates the physical flow of net exports (NX) to the payment flow (Net Lending/BorrowingROW). This relationship in the identity (e) comes from sector balances and the circular flow concept that the value of net exports (the ex­ penditure side of the ledger) will equal the value of net foreign borrowing (the income side of the ledger).

•

•

•

• •

Start with the basic expenditure identity (a): GDP = Consumption (C) + Investment (I) + Government (G) + Net Export (NX) Replace Investment with relationship (b), Savings minus Net Lending/BorrowingROW, in the Identity: GDP = Consumption (C) + Savings (S) - Net Lending/BorrowingROW + Government (G) + Net Export (NX) Since Net Lending/Borrowing from the rest of world equals Net Exports, Net Export cancels Net Lending/Borrowing from the rest of the world, which yields: GDP = Consumption (C) + Savings (S) + Government (G) Now, set the first and the last GDP identity equal: C + I + G + NX = S + C + G Rearrange the terms, such that we have: S = C + I + G + NX – C – G Then, S – I = NX

CIRCULAR FLOW IN THE ECONOMY

•

•

This implies that the net capital flow equals the trade balance based on national income and product accounting. This last identity leads to the relationship of the so-called “twin deficits”: the federal budget and trade deficits. However, to show that relationship, clearly we need to take a few additional steps. Since government savings (SavingsGovernment)—which is equation (d) and is taxes minus government spending (SG = T – G), then we can also write the net capital flow equals the trade balance relationship as: S – I = NX SPrivate + SGovernment – I = NX, or SPrivate + (T – G) – I = NX Therefore, we can rearrange the terms: SPrivate + T = I + G + NX = I + G + (X – M) Now, move imports (M) to the left-hand side. SPrivate + T + M = I + G + X, which is the leakages-injections identity where the left-hand side terms represent leakages and the right-hand side terms represent the injections. For simplicity, the savings subscript could be removed, and savings simply defined as private domestic savings in the economy.

[41] HOW IS THE INJECTIONS-LEAKAGES IDENTITY RELATED TO THE SUPPLY AND DEMAND FOR FINANCIAL CAPITAL? We also can think of the injections-leakages relationship as suggesting the supply of financial capital will equal the demand for financial capital. • •

Supply of financial capital = Demand for financial capital S + (M – X) = I + (G – T) S = private saving by individuals and firms (M – X) = imports (M) - exports (X) = trade deficit I = private sector investment G = government spending T = taxes collected Regular letters = leakages Italic letters = injections

Then, If G greater than T, then the government would be a demander of financial capital. If T greater than G, then the government would contribute as a supplier of financial capital.

29

30

MACROECONOMIC THINKING AND TOOLS

[42] HOW DOES THE INJECTIONS-LEAKAGES IDENTITY INFORM US ABOUT THE “TWIN DEFICITS”—THE FEDERAL BUDGET DEFICIT AND THE INTERNATIONAL TRADE DEFICIT? If investment (I) equals private savings (S), then the injections-leakages identity, S + (M – X) = I + (G – T), can be rearranged to show that there must be an equilibrating offset between the U.S. government budget balance (deficits or surplus) and the international trade balance (deficits or surplus), such that this condition is met: (T – G) = (X – M) + (I – S). Hence, if the U.S. budget deficit grows, then the U.S. international trade deficit will as well, and vice versa. However, if investment is not equal to saving, then this gap will have to be accounted for in the identity.

Issues to Think About The empirical and theoretical foundations of macroeconomics are intertwined with the circular flow concept for national income and product accounting. •

• •

If aggregate demand equals real GDP, and aggregate supply equals real GDI, and real GDP equals real GDI, and aggregate demand equals aggregate supply, then is this just a lot of distinctions without difference or helpful paradigms for thinking about how the economy works? Is the concept of an expenditure multiplier more simply cast as a “feedback loop”? Why should we care about GDP components that add to it—such as investment, government spending, and exports—versus those that subtract from it—such as imports or taxes or savings?

CHAPTER

3

Key Macroeconomic Statistics—Jobs and Unemployment LEARNING OBJECTIVES Employment is a key macroeconomic statistic that is intertwined with economic growth and built into macroeconomic theories. In this chapter, you will learn: •

• • • •

•

The U.S. Bureau of Labor Statistics compiles two measures of employment— one based on a household survey and the other based on administrative records (the establishment survey). From the household survey, the unemployment rate and a host of demo­ graphic data about the labor market are derived. From the establishment survey, the number of non-farm jobs and the average wage are estimated. Why the labor-force participation rate and the employment-to-population ratio are important supplemental labor market indicators. A theoretical counterpart of the actual unemployment rate is the “natural rate of unemployment,” which is estimated at potential output. This concept has additional names—the full-employment unemployment rate, the non-cyclical unemployment rate, and the non-accelerating inflation rate of unemployment. What factors are used to estimate the natural rate of unemployment.

[43] HOW IS U.S. EMPLOYMENT MEASURED? The U.S. Bureau of Labor Statistics (BLS) compiles two measures of employment on a monthly basis. One measure is based on the Current Population Survey (CPS, or “household” survey) and measures the number of civilian, non-institutionalized people aged 16 years and

DOI: 10.4324/9781003391050-4

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MACROECONOMIC THINKING AND TOOLS

over that are working. The other measure is based on the Current Employment Statistics survey (CES, or establishment or “payroll” survey) and measures the number of nonfarm wage and salary jobs.

[44] WHY ARE THERE TWO MEASURES OF EMPLOYMENT? The BLS says, “The household survey and establishment survey both produce samplebased estimates of employment, and both have strengths and limitations. The establishment survey employment series has a smaller margin of error on the measurement of month-tomonth change than the household survey because of its much larger sample size.” Generally, the payroll measure (CES) is viewed as more stable and more informative of the labor market trends. However, the household measure (CPS) is more expansive in its coverage “because it includes self-employed workers whose businesses are unincorporated, unpaid family workers, agricultural workers, and private household workers, who are excluded by the establishment survey.” The household survey is used to derive the unemployment rate, as well as a wealth of demographic information about the labor markets.

[45] IS IT IMPORTANT TO MEASURE AGRICULTURAL WORKERS TODAY? In 2021, agricultural workers accounted for 1.5% of U.S. employment, but agricultural workers accounted for 11.2% of workers in 1951. These data are important to show the transformation of the economy and workforce over time. Similarly, using the establishment data, it is also possible to observe the transformation of the economy and workforce through the changing share of manufacturing jobs. In 1943, manufacturing jobs accounted for 37.9% of all payroll jobs (a high point), but by 2021 that share was 8.5% with a relatively trend-like decline since 1943.

[46] WHAT PERCENTAGE OF THE U.S. WORKFORCE IS UNIONIZED? The BLS reported that in 2021, the number of wage and salary workers belonging to a union continued to decline to 14.0 million, which represented 10.3% of the workforce. The publicsector dominated union membership with 33.9% of its workforce unionized—especially in education. By state, Hawaii and New York were tied for the highest percentage of unionized workers (24.1%) in 2021, while South Carolina had the lowest share (2.0%). In 1945, 35.5% of the workforce was unionized—its record high—but that percentage has been trending lower with shifts in the composition of the economy away from manufacturing jobs towards service jobs, as shown in Figure 3.1.

JOBS AND UNEMPLOYMENT

Percent of Wage & Salary Workers

Union Membership as Percent of Employed 40%

40%

35%

35%

30%

30%

25%

25%

20%

20%

15%

15%

10% 1930 FIGURE 3.1

10% 1940

1950

1960

1970

1980

1990

2000

2010

Union Membership as Percentage of Employed

Source: U.S. Bureau of Labor Statistics

2021 Union Membership of Employed Workers Ranked by U.S. States

TABLE 3.1

Percent of Employed Represented by Unions Hawaii New York Washington Oregon New Jersey California Rhode Island Alaska Minnesota Connecticut Michigan Illinois Maine Vermont

24.1 24.1 20.0 18.8 17.9 17.8 17.4 17.2 17.1 16.3 15.3 15.2 14.7 14.2 (Continued )

2020

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MACROECONOMIC THINKING AND TOOLS TABLE 3.1

(Continued) Percent of Employed Represented by Unions

Nevada Massachusetts Pennsylvania Ohio Montana Maryland Kansas New Hampshire West Virginia United States Delaware Indiana Missouri District of Columbia Kentucky Wisconsin New Mexico Iowa Nebraska Colorado Alabama Mississippi North Dakota Wyoming Oklahoma Arizona Utah Virginia Florida Tennessee Georgia Louisiana Idaho South Dakota Texas Arkansas North Carolina South Carolina

14.1 13.6 13.6 13.0 12.9 12.3 11.4 11.3 10.5 10.3 10.2 10.2 10.2 9.9 9.8 9.3 9.1 8.3 8.0 7.5 6.9 6.9 6.9 6.9 6.8 6.7 6.5 6.5 6.1 5.9 5.8 5.7 5.5 5.0 4.7 4.4 3.4 2.0

Source: U.S. Bureau of Labor Statistics

JOBS AND UNEMPLOYMENT

[47] WHAT IS TELEWORK OR REMOTE WORK? The U.S. government’s Telework Enhancement Act of 2010 says, “the term ‘telework’ or ‘teleworking’ refers to a work flexibility arrangement under which an employee performs the duties and responsibilities of such employee’s position, and other authorized activities, from an approved worksite other than the location from which the employee would oth­ erwise work.” This 2010 law also directed the Office of Personnel Management to “(1) research the utilization of telework by public and private sector entities that identify best practices and recommendations for the Federal Government; (2) review the outcomes associated with an increase in telework, including the effects of telework on energy con­ sumption, job creation and availability, urban transportation patterns, and the ability to anticipate the dispersal of work during periods of emergency; and (3) make any studies or reviews performed under this subsection available to the public.” Expanding on this legal definition, the U.S. government (https://www.telework.gov/) describes telework and its benefit to the labor market as “a work arrangement that allows an employee to perform work, during any part of regular, paid hours, at an approved alternative worksite (e.g., home, telework center). It is an important tool for achieving a resilient and results-oriented workforce. At its core, telework is people doing their work at locations different from where they would normally be doing it. This makes sense when you consider that ‘tele’ comes from the Greek word meaning ‘from a distance’—when combined with work it means ‘work from a distance.’” The terms “telework,” “remote work,” and “work-fromhome” are largely interchangeable.

[48] WHAT PERCENTAGE OF THE U.S. WORKFORCE WORKS REMOTELY? The COVID-19 pandemic in 2020 spurred the telework trend. According to estimates from WFH Research,1 the percentage of paid full-days worked from home pre-COVID was about 5%, which sky-rocketed in May 2020 to over 60%, but has stabilized to around 30% by July 2022. Estimates through 2021 from the U.S. Bureau of Labor Statistics suggest that about 35% of U.S. private-sector establishments increased telework for some employees, which represented about 60 million jobs have some component of “work from home.” Through 2021, the BLS reported that the U.S. industries with the highest percentage of telework in response to the coronavirus pandemic were: (1) educational services (85%); (2) information (84.8%); (3) financial activities (76.5%); (4) professional and business services (70.6%); and (5) health care and social assistance (61.3%). The industries with the lowest percentage of telework were: (1) accommodation and food services (7.5%); (2) retail trade (23.0%); (3) natural resources and mining (26.9%); (4) construction (37.1%); and (5) trans­ portation and warehousing (40.1%). With the receding COVID threat, employees in many industries have return to their workplace. This is especially true for education. Nonetheless, various nationwide surveys suggest work-from-home arrangements among workers are quite popular. A Gallup poll, for example, taken in May/June 2021 found that 91% of U.S. workers that had been working at least some of their hours remotely hope that it persisted after the pandemic.2

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[49] HOW IS TELEWORK CHANGING THE LABOR MARKET? A Los Angeles Times article chronicled how businesses are beginning to think about work-fromhome. “Many workers enjoyed a better quality of life plus savings on commuting, office wardrobe and other expenses. Companies boosted productivity and lowered costs.”3 The popularity of telework for those employees able to work in that manner is now being viewed by employers as an employee benefit. Although it is not clear how far this employer trend will go, the Los Angeles Times article notes how one law firm in the United Kingdom has told employees that they could continue to telework, but on the condition that they take a 20% pay cut. In a June 2022 research study from the Becker Friedman Institute of the University of Chicago opined the implications of this rise in telework are: (1) Moderating near-term inflation pressures through the wage channel; (2) contributing to a smaller labor share (employee compensation) of national income and, consequently, a higher employer profit; and (3) forcing wages lower with a pool of workers nationwide or even globally, rather than just regionally. This, in turn, allows employers to tap into lower cost-of-living regions for workers and it provides greater competition for the work-fromhome jobs without the need for the would-be employee to relocate. Additionally, a smaller inoffice workforce reduces the employer’s need for office space, which lowers the employer cost associated with leased floor space. Obviously, telework is a nascent trend that will continue to shape the global labor markets and with it, surely will spur new economic research about how the work-from-home employee-employer arrangement will impact the macroeconomy.

[50] HOW IS THE CIVILIAN LABOR FORCE SIZE DETERMINED FOR THE HOUSEHOLD SURVEY OF EMPLOYMENT? The BLS starts with the size of the total U.S. population and subtracts out persons under the age of 16, persons in the armed forces, and persons institutionalized (people confined to, or living in, institutions or facilities such as prisons, jails, and other correctional institutions and detention centers residential care facilities such as skilled nursing homes). Keep in mind that the definition of the civilian labor force includes citizens of foreign countries who reside in the United States but do not live on the premises of an embassy.

[51] HOW IS THE UNEMPLOYMENT RATE DETERMINED FROM THE CIVILIAN LABOR FORCE SIZE? From its monthly current population survey (CPS), people are classified based on their responses to some questions for a “reference week” in the month as either (1) employed, (2) unemployed, or (2) not in the workforce. The reference week usually is the seven-day calendar week (Sunday–Saturday) that includes the 12th of the month, with occasional exceptions in November and December due to holidays and the amount of time needed for processing the results. An employed person for this survey is one who worked at least one hour as a paid employee, or who worked at least one hour in their own business, profession, trade, or farm, or were temporarily

JOBS AND UNEMPLOYMENT

FIGURE 3.2

Derivation of the Unemployment Statistics

absent (whether paid or not) or worked without pay for a minimum of 15 hours in a business or farm owned by a family member. This does not include people who do volunteer work, are unpaid interns, are in unpaid training programs, on military reserve duty, on jury duty, or work around one’s home. If a person was not working during the reference period, but was on vacation or sick, that person is considered employed. However, if the person was not working because of temporary layoff or was actively looking for a job that person is classified as unemployed. All others are considered not to be in the labor force. The BLS has produced a graphic (Figure 3.2) to illustrate the relationship of these measurement concepts. Once these three classifications—employed, unemployed, and not in the workforce—are known, then the unemployment rate can be calculated. The formula for the unemployment rate calculation is: 100 ×

Number of People Unemployed Number of People in the Labor Force

[52] WHY IS THE UNEMPLOYMENT RATE AN IMPORTANT STATISTIC IN MACROECONOMICS? The unemployment rate fluctuates with the business cycle and with long-term demographic trends. Therefore, this metric has short-term implications for the economy and longer-run implications for labor-market policies. An example of where demographics affected the trend unemployment rate was when the Baby Boom generation caused a bulge in new entrants into the labor market and contributed to an elevated level of the unemployment rate during the 1970s and 1980s, as shown in Figure 3.3.

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MACROECONOMIC THINKING AND TOOLS

FIGURE 3.3

U.S. Unemployment Rate, Average by Decade, 1950–2019

Source: U.S. Bureau of Labor Statistics

[53] WHAT IS THE LABOR-FORCE PARTICIPATION RATE? The labor-force participation rate tells us the percentage of the population in the labor force. It is measured as: Participation Rate = 100 × (Labor Force/ Population)

This metric has been important for policymakers as the labor-force participation rate has been trending lower since the year 2000—as shown in the chart below. The participation rate (see Figure 3.4) also was temporarily depressed even further during the COVID-19 shutdowns and its aftermath. For a more detailed discussion of this trend, see Chapter 13: The Disappearing Worker.

FIGURE 3.4

Civilian Labor-Force Participation Rate

Source: U.S. Bureau of Labor Statistics

JOBS AND UNEMPLOYMENT

FIGURE 3.5

Employment-to-Population Ratio vs. Consumer Price Inflation

Source: U.S. Bureau of Labor Statistics

[54] WHAT IS THE EMPLOYMENT-TO-POPULATION RATIO? The employment-to-population ratio measures the percentage of the population employed. It is measured as: Employment to Population Ratio = 100 × (Household Employment/Population).

This metric has two episodes in history. In the post-WWII period up until about 1980, this measure was often considered a leading cyclical indicator of the inflation, as shown in Figure 3.5 However, that cyclical relationship largely weakened or vanished in the period after about 1980. Nonetheless, it still remains one of the important labor market indicators of the full-employment potential in the economy.

[55] WHAT ARE THE MEASUREMENT REASONS FOR UNEMPLOYMENT? The BLS tracks four reasons why a person, who is looking for a job, may be unemployed. Those reasons a person may be unemployed are: (1) Job loser: The person was employed and was fired or laid off; (2) Job leaver: The person was employed and quit; (3) Reentrant: This person was previously employed, but has not worked for some time, and is currently re­ entering the labor force; and (4) New entrant: This person has never held a full-time job for two

39

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MACROECONOMIC THINKING AND TOOLS

weeks or longer and is now in the civilian labor force looking for a job. These household survey data are report by the number of workers in each category, percentage distribution, and percent of the civilian labor force. The job-loser percentage share of the civilian labor force typically moves inversely with the business cycle. That is, it is high when the economy is in recession and low when the economy is booming. The job-leaver percentage share of the civilian labor force typically is pro-cyclical. That is, it is high when the economy is booming and low when in recession. Workers are more likely to leave a job without having one immediately to go to and then begin a job search if the labor markets are strong (that is, when workers perceive a lot of available jobs for their skill set). The new-entrants per­ centage share of the civilian labor force typically is associated with demographic trends. The reentrants percentage share of the civilian labor force may be affected by various factors. If wage rates are rising, then that could encourage people to return to the labor markets. If inflation is high, then that could encourage retirees to return to the labor markets to sup­ plement their income.

[56] WHAT IS THE THEORETICAL REASON FOR UNEMPLOYMENT? Although numerous theoretical reasons and variations on John Maynard Keynes’ theory of wages and unemployment exist, the core idea was that wages were not flexible or changed slowly relative to changes in labor demand resulting in unemployment. Keynes put it this way: He first defined unemployment as the sum of three categories—frictional unemployment, voluntary unemployment, and involuntary unemployment. The concept of involuntary unemployment was thought not possible under the Classical school of thought (since wages were assumed flexible and a labor market wage adjustment would occur to equate the supply of and demand for labor). In Keynes’ General Theory, he opined that, “Clearly we do not mean by ‘involuntary’ unemployment the mere existence of an unexhausted capacity to work. An eight-hour day does not constitute unemployment because it is not beyond human capacity to work ten hours. Nor should we regard as ‘involuntary’ unemployment the withdrawal of their labor by a body of workers because they do not choose to work for less than a certain real reward. Furthermore, it will be convenient to exclude ‘frictional’ unemployment from our definition of ‘involuntary’ unemployment. My definition is, therefore, as follows: Men are involuntarily unemployed if, in the event of a small rise in the price of wage-goods [inflation] relatively to the moneywage, both the aggregate supply of labor willing to work for the current money-wage and the aggregate demand for it at that wage would be greater than the existing volume of employment.” Although this may not be the clearest description, Keynes essentially is writing about the difference between the real wage (wages adjusted for inflation or using his term “wage-goods”—that is, the amount of goods that the wage could afford) and the money wage. Elsewhere in the General Theory, Keynes clarified this relationship: “When money-wages are rising, that is to say, it will be found that real wages are falling; and when money-wages are falling, real wages are rising. This is because, in the short period, falling money-wages and rising real wages are each, for independent reasons, likely to

JOBS AND UNEMPLOYMENT

accompany decreasing employment.” Remember that Keynes was trying to understand the Depression and he observed, “It is not very plausible to assert that unemployment in the United States in 1932 was due either to labor obstinately refusing to accept a reduction of money-wages or to its obstinately demanding a real wage beyond what the productivity of the economic machine was capable of furnishing.” This led Keynes to reconcile the real wage and money wage impacts on labor demand and labor supply. His answer was rejecting the Classical view of flexible wages and opining that money wages are rigid or inflexible in the downward direction, but flexible in the upward direction. The result of that moneywage pattern created unemployment.4

[57] WHAT ARE “DISCOURAGED WORKERS” FOR MEASUREMENT PURPOSES? An unemployed person, by definition, must be actively looking for work. However, the BLS also tracks persons that are not looking for work because they get discouraged and stop looking. These people are classified as discouraged workers.

[58] ARE THERE ADDITIONAL CONCEPTS OF LABOR MARKET UTILIZATION? The BLS reports a range of alternative measures of labor underutilization, which they designate as U-1 through U-6. These concepts range from a narrow definition of under­ utilization (U-1) through a broad definition (U-6). The metrics were devised by a former BLS Commission and NBER scholar Julius Shiskin to conceptually mirror the logic used to define money supply measures based on narrow and broad definitions. The official civilian unemployment rate is U-3 in this scheme. The broadest of these measures, U-6, defines underutilization as the total unemployed plus all persons marginally attached to the labor force, plus total employed part time for economic reasons. The measures U-4 through U-6 include “discouraged workers” in their tallies.

[59] WHAT IS THE THEORETICAL COUNTERPART OF THE UNEMPLOYMENT RATE? The natural rate of unemployment is the theoretical counterpart of the actual unemployment. It is the sum of frictional and structural unemployment rates, but excludes the unemployment due to the business cycle, which is termed “cyclical unemployment.” The natural rate and its components are unobserved but have been estimated statistically. The concept of the natural rate of unemployment is largely interchangeable with the terms: full-employment unemployment rate, the Non-Accelerating Inflation Rate of Unemployment (NAIRU), and the noncyclical rate of unemployment.

41

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MACROECONOMIC THINKING AND TOOLS TABLE 3.2

Interchangeable Terms and Concepts

Interchangeable Terms

Definitional

Tied to Inflation

Tied to Economic Growth

Natural Rate of Unemployment Full-Employment Unemployment Rate Non-Accelerating Inflation Rate of Unemployment (NAIRU) Noncyclical Rate of Unemployment

The sum of frictional and structural unemployment. Does not include cyclical unemployment. Might be thought as the “trend” unemployment rate

The difference between the actual unemployment rate and the natural rate implies a certain pressure on inflation. If the actual unemployment rate is higher than the natural rate, this tends to put downward pressure on inflation, and vice versa. If the actual unemployment rate equals the natural rate, then this implies steady inflation and is the NAIRU

When the natural rate equals the actual unemployment rate, then this implies that real GDP equals real potential GDP. This implies that the economy is at full-employment and by inference the natural rate is the same as the full-employment unemployment rate.

[60] HOW DO THE THEORETICAL COMPONENTS OF UNEMPLOYMENT DIFFER BETWEEN KEYNES’ VIEW AND CURRENT THINKING? Although Keynes defined the theoretical components of unemployment as the sum of three categories, frictional unemployment, voluntary unemployment, and involuntary unemployment, the terminology and component distinctions have evolved. Today, the theoretical components of unemployment are still based on three components, but those components are frictional, struc­ tural, and cyclical unemployment.

[61] HOW ARE THE THEORETICAL CONCEPTS OF FRICTIONAL AND STRUCTURAL UNEMPLOYMENT DEFINED? The natural unemployment rate is the sum of frictional and structural unemployment. Frictional unemployment is due to the natural turnover in the economy that is caused by changing market conditions and represented by qualified individuals with transferable skills who change jobs. This type of unemployment tends to be of short duration and may be thought of as people “between jobs”—as Keynes put it. Structural unemployment, on the other hand, tends to be of long duration and results from structural changes in the economy that eliminate some jobs and create others for which those job losers are unqualified (that is, do not possess the needed skills or education).

JOBS AND UNEMPLOYMENT

[62] WHAT IS THE RELATIONSHIP BETWEEN THE ACTUAL UNEMPLOYMENT RATE AND THE THEORETICAL (OR NATURAL) UNEMPLOYMENT RATE? The difference between the actual and theoretical unemployment rate at full employment is referred to as the unemployment gap, which is the measure of cyclical unemployment rate. This gap between the actual unemployment rate minus the natural unemployment rate is the cyclical unemployment rate. Alternatively, the actual unemployment rate equals the cyclical unemployment rate plus the natural unemployment rate, or the actual unemployment rate equals the cyclical unemployment rate plus the frictional unemployment rate plus the structural unemployment rate Figure 3.6.

Relationship between the Actual Unemployment Rate and the Theoretical (or Natural) Unemployment Rate

FIGURE 3.6

[63] WHAT FACTORS DOES THE CONGRESSIONAL BUDGET OFFICE USE TO ESTIMATE THE NATURAL RATE OF UNEMPLOYMENT (WHICH THE CBO REFERS TO AS THE NON-CYCLICAL RATE OF UNEMPLOYMENT)? The Congressional Budget Office uses a statistical model to estimate the natural rate of unemployment. The key factors that determine the natural rate are: (1) the age distribution of the population (which accounts for frictional unemployment associated with long-trend de­ mographics); (2) the scale of structural change (which accounts for structural unemployment); (3) the real wage rate; and (4) the amount of unemployment benefits (which affects the opportunity cost of job search). The natural rate of unemployment, known as the non-cyclical rate of unemployment by the CBO, is largely a trend rate that is adjusted for demographics and

43

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MACROECONOMIC THINKING AND TOOLS

FIGURE 3.7

Actual and Natural Rate of Unemployment

Sources: Congressional Budget Office; U.S. Bureau of Labor Statistics

other economic factors. Figure 3.7 shows the relationship between the actual unemployment rate that is very cyclical and the non-cyclical component of the unemployment rate as esti­ mated by the CBO.

[64] HOW DO WE MEASURE FULL EMPLOYMENT? Full employment does not mean no unemployment. There are two common ways that economists have used to determine when the economy is at full employment. ∘

∘

The natural rate of unemployment: An economy that is at its natural rate of unemployment is thought of as at full employment. It is also known as the noncyclical unemployment rate. Beveridge definition: Another definition of full employment was offered by British social economist Sir William Beveridge (1879–1963), who in 1944 published his book entitled, Full Employment in a Free Society, in which he wrote: “Full employment … means having always more vacant jobs than unemployed.”5

[65] IS IT POSSIBLE TO MEASURE THE BEVERIDGE CONCEPT OF FULL EMPLOYMENT? Yes, the Beveridge concept of U.S. full employment has become possible to measure since the U.S. BLS began its “Job Openings and Labor Turnover Survey” (JOLTS) program in 1998.

JOBS AND UNEMPLOYMENT

FIGURE 3.8

Beveridge Concept of Full Employment Ratio of Unemployment Level of Job

Openings Source: U.S. Bureau of Labor Statistics

One way to look at labor supply is as the number of people looking for work and labor demand as the number of job openings. The JOLTS survey allows for the comparison of unemployment (labor supply) to job openings (labor demand). The BLS notes that, “The number of unemployed persons per job opening factors in both the supply of unemployed persons and the demand of employers. The number of unemployed persons per job opening is a ratio of the level of unemployed persons, as published by the Current Population Survey [from the monthly employment report], and the level of job openings [from JOLTS]. A ratio of 1.0 means there is a job available for every unemployed person. Lower ratios signal tighter labor markets where firms have more job openings than there are people available to work. Higher ratios indicate there are more unemployed persons competing for each job opening.” This ratio is shown in Figure 3.8.6 A ratio of 1.0 means there is a job available for every unemployed person. Lower ratios signal tighter labor markets where firms have more job openings than there are people available to work. Higher ratios indicate there are more unemployed persons competing for each job opening.

[66] WHAT IS THE THEORETICAL RELATIONSHIP BETWEEN THE UNEMPLOYMENT RATE GAP AND INFLATION? Theory suggests that when the economy is at the natural rate of unemployment, there is no tendency for inflation to rise or fall. That is, when the actual unemployment rate equals the natural rate of unemployment then inflation is steady. However, if the actual rate of unemployment is greater than the natural rate, then there is a tendency for inflation to fall. The

45

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MACROECONOMIC THINKING AND TOOLS

implication is that when there is slack in the economy then the unemployment rate will be higher than the natural or full-employment unemployment rate, which in turn dampens inflation. Similarly, when the actual unemployment rate is below the natural rate, then there is a tendency for inflation to rise. Here the implication is that when there is a boom in the economy then the actual unemployment rate is likely to be lower than the full-employment unemployment rate, which in turn puts upward pressure on inflation. These theoretical re­ lationships are summarized in the table below.

TABLE 3.3

Inflation and the Unemployment Rate Gap

Inflation and the Unemployment Rate Gap U = Actual Unemployment Rate, U* = Natural Unemployment Rate U – U* = Unemployment Rate Gap If U > U*, then U – U* > 0 → inflation falls If U < U*, then U – U*< 0 → inflation rises If U = U*, then U – U* = 0 → inflation steady

Alternatively, the inflation rate can be related to the Beveridge Full-Employment Ratio and the relationship would be summarized in the subsequent table below.

TABLE 3.4

Inflation and the Beveridge Full-Employment Ratio

Inflation and the Beveridge Full-Employment Ratio LS = Unemployment Level (Labor Supply), LD = Job Openings (Labor Demand), LS/LD = Beveridge Ratio If (LS/LD) > 1.0 → inflation falls If (LS/LD) < 1.0 → inflation rises If (LS/LD) = 1.0 → inflation steady

[67] HOW CAN THE ACTUAL UNEMPLOYMENT RATE FALL BELOW THE NATURAL OR FULL-EMPLOYMENT UNEMPLOYMENT RATE? The natural or full-employment unemployment rate is estimated based on “normal” or typical output (production levels) in the economy. However, there are times when pro­ duction can be above normal output, such as during wars, production facility reopening in the aftermath of the COVID-19 pandemic-induced shutdowns, or even periods when the economy is booming.

JOBS AND UNEMPLOYMENT

Issues to Think About Employment is an extremely important indicator that is integral to macroeconomic theories, as well as fiscal and monetary policymaking. As a consequence, employ­ ment is the single most important statistic for the financial markets. • • • •

• •

•

Why is there so much emphasis on employment or the absence of employment—the unemployment rate? Is the unemployment rate a better metric than the beveridge full-employment ratio? Is the labor-force participation rate or the employment-to-population rate a better metric to set a policy goal for rather than the unemployment rate? Why has the economics profession created so many interchangeable theoretical terms for the same unemployment rate concept—the natural rate of unemployment, full-employment unemployment rate, the non-accelerating inflation rate of unemployment, and the noncyclical rate of unemployment? Should the BLS create actual measures of the theoretical concepts of frictional and structural unemployment? Does the basic idea that underlies the theoretical concept of potential output at full-employment—that is, the natural rate of unemployment—need more critical questioning? Is the relationship between the unemployment rate gap and the inflation rate—which is a cornerstone of macroeconomics—still valid?

NOTES 1 Survey of Working Arrangements and Attitudes (SWAA), see www.wfhresearch.com. This is based on research methodology by Jose Maria Barrero, Nicholas Bloom, and Steven J. Davis, “Why Working from Home Will Stick,” National Bureau of Economic Research Working Paper No. 28731, 2021. 2 https://news.gallup.com/poll/355907/remote-work-persisting-trending-permanent.aspx 3 Don Lee, “You May Soon Be Asked to Take a Pay Cut to Keep Working from Home, Los Angeles Times, August 23, 2022, https://www.latimes.com/politics/story/2022-08-23/will-employerscharge-employees-for-working-from-home-from-home. 4 John Maynard Keynes, The General Theory of Employment, Interest, and Money, 1936, reformatted online by Eidgenössische Technische Hochschule (ETH) Zürich, https://www.files.ethz.ch/isn/125515/ 1366_KeynesTheoryofEmployment.pdf. 5 Full Employment in a Free Society, A Report by Lord Beveridge (George Allen & Unwin, Ltd., London, 1944), p. 18. 6 This data are from the U.S. Bureau of Labor Statistics and also can be found in the St. Louis Federal Reserve Bank’s FRED database. The unemployment level mnemonic in FRED is UNEMPLOY and the job openings statistic is JTSJOL.

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CHAPTER

4

Key Macroeconomic Statistics—Inflation

LEARNING OBJECTIVES This chapter introduces you to inflation, its measurement, and related concepts. You will learn: • • • • • • • • •

What inflation is. Some ways inflation is measured. What “sticky prices” are. What the Katona effect is. What the interest-rate gap theory of inflation is. What the Phillips curve is. How money and inflation are related. What the fiscal theory of inflation implies. How inflation expectations are measured.

[68] WHAT IS INFLATION? Inflation represents a general rise in the price level.

[69] WHAT IS THE PRICE LEVEL? The price level, which is generally measured as an index, is a snapshot of the cost of a market basket of goods and services, purchased in a specific period, relative to some base period expenditure for the same or near-similar goods and services. The price level, therefore, is an average price for that market basket.

[70] HOW IS INFLATION MEASURED? An inflation rate measures the change in that price level or cost of the market basket over time. An inflation rate, therefore, is a growth rate of the price level or index. DOI: 10.4324/9781003391050-5

INFLATION

[71] SO HOW DOES THE PRICE LEVEL CONCEPTUALLY DIFFER FROM THE INFLATION RATE? These concepts are clearly related. A price level is the cost of the market basket relative to the base comparison period’s cost of that market basket, while inflation is calculated as a growth rate of that price-level estimate, which is generally represented as a price index.

[72] WHAT IS AN INDEX NUMBER? One of the clearest and earliest comprehensive discussions of an index number comes from Irving Fisher, who wrote: “An index number of prices … shows the average percentage change of prices from one point of time to another. The percentage change in the price of a single commodity from one time to another is, of course, found by dividing its price at the second time by its price at the first time. The ratio between these two prices is called the price relative of that one particular commodity in relation to those two particular times. An index number of the prices of a number of commodities is an average of their price relatives. This definition has, for concreteness, been expressed in terms of prices. But in like manner, an index number can be calculated for wages, for quantities of goods imported or exported, and, in fact, for any subject matter involving divergent changes of a group of magnitudes. Again, this definition has been expressed in terms of time. But an index number can be applied with equal propriety to comparisons between two places or, in fact, to comparisons between the mag­ nitudes of a group of elements under any one set of circumstances and their magnitudes under another set of circumstances. But in the great majority of cases, index numbers are actually used to indicate price movements in time.”1

[73] [ADVANCED] WHAT TYPES OF PRICE-INDEX FORMULAE ARE USED? There are numerous ways that price indexes could be formulated.2 Some common formulae are: (1) Laspeyres price index, (2) Paasche price index, (3) Fisher price index, and (4) Törnqvist index. •

•

“The Laspeyres price index is an index formula used in price statistics for measuring the price development of the basket of goods and services consumed in the base period. The question it answers is how much a basket that consumers bought in the base period would cost in the current period. It is defined as a fixed-weight, or fixed-basket, index that uses the basket of goods and services and their weights from the base period. It is also known as a ‘base-weighted index’.”3 The U.S. Bureau of Labor Statistics (BLS) uses a “modified Laspeyres index number formula” in the compilation of its Consumer Price Index (CPI-U and CPI-W). “The Paasche price index is an index formula used in price statistics for measuring the price development of the basket of goods and services that is consumed in the current period. The question it answers is how much a basket that consumers buy in the current period would have cost in the base period. So, it is also defined as a fixed-weight, or fixed-basket, index,

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MACROECONOMIC THINKING AND TOOLS

•

•

because it uses the basket of goods and services and their weights from the current period. It is therefore also known as a ‘current weighted index.’”4 “The Fisher price index is an index formula used in price statistics for measuring the price development of goods and services, on the basis of the baskets from both the base and the current period. It is defined as the geometric average of the Laspeyres price index (which only uses the base period basket) and the Paasche price index (which only uses the current period basket). For this reason, the Fisher price index (named after American economist Irving Fisher) is also known as the “ideal” price index.”5 In the national income and product accounts (the GDP report), percent changes in quantities and prices are computed from chain-type indexes that are calculated using a Fisher formula. “A Törnqvist price index is a weighted geometric average of the price relatives using arithmetic averages of the value shares in the two periods as weights.”6 The U.S. Bureau of Labor Statistics uses this formula for upper-level aggregation in a monthly chained construct in its chain CPI-U (C-CPI-U).

[74] [ADVANCED] HOW IS A LASPEYRES PRICE INDEX CALCULATED? The Laspeyres price index formula—also thought of as a base-period quantity-weighted measure—uses this formula: •

•

(P Current × Q Base )

i Laspeyres Price Index = (Pi Base × Q Base × 100, where the ∑ is the summation of all ) i i of the price (P) terms (designated as i) times all of the quantity (Q) terms, also for some i number of terms. The base-period expenditure (designated as Base) is the relative comparison in the denominator, and the numerator is determined as the sum of base quantities of each item (which is fixed) multiplied by the changing current prices in each period (designated as Current). Consider this example for three items in the market basket (that is, i = 3) for six periods. The base period is Period 1 (the selection of the base period is arbitrary, but a recent period is likely to better represent the market-basket item expenditures). The base market basket expenditure is ($5 × 100) + ($10 × 200) + ($20 × 250) = $7,500, which is used as the price relative for all the periods in the subsequent price calculations.

TABLE 4.1

Period Period Period Period Period Period

1 2 3 4 5 6

Example Data for a Three-Item Market-Basket Price 1

Quantity 1

Price 2

Quantity 2

Price 3

Quantity 3

$5 $10 $7 $7 $8 $8

100 125 150 60 63 67

$10 $12 $13 $14 $15 $17

200 225 250 200 190 180

$20 $25 $24 $25 $26 $27

250 300 325 325 350 375

INFLATION

Using the designation of item number dash period—for example, for price 3 in period 5, this is shown as Price 3–5 and is $26, in this case—then the Laspeyres price indexes can be calculated as: TABLE 4.2

Using the Formula for the Laspeyres Price Index

Period 1 =

(Price 1 (Price 1

1 × Quantity 1 1 × Quantity 1

1) + (Price 2 1) + (Price 2

1 × Quantity 2 1 × Quantity 2

1) + (Price 3 1) + (Price 3

1 × Quantity 3 1 × Quantity 3

1) 1)

× 100 =

$ 7, 500 $ 7, 500

1) + (Price 2 1) + (Price 2

2 × Quantity 2 1 × Quantity 2

1) + (Price 3 1) + (Price 3

2 × Quantity 3 1 × Quantity 3

1) 1)

× 100 =

$ 9, 650 $ 7, 500

1) + (Price 2 1) + (Price 2

3 × Quantity 2 1 × Quantity 2

1) + (Price 3 1) + (Price 3

3 × Quantity 3 1 × Quantity 3

1) 1)

× 100 =

$ 9, 300 $ 7, 500

1) + (Price 2 1) + (Price 2

4 × Quantity 2 1 × Quantity 2

1) + (Price 3 1) + (Price 3

4 × Quantity 3 1 × Quantity 3

1) 1)

× 100 =

$ 9, 750 $ 7, 500

1) + (Price 2 1) + (Price 2

5 × Quantity 2 1 × Quantity 2

1) + (Price 3 1) + (Price 3

5 × Quantity 3 1 × Quantity 3

1) 1)

× 100 =

$ 10, 300 $ 7, 500

1) + (Price 2 1) + (Price 2

6 × Quantity 2 1 × Quantity 2

1) + (Price 3 1) + (Price 3

6 × Quantity 3 1 × Quantity 3

1) 1)

× 100 =

$ 10, 950 $ 7, 500

× 100 = 100.00 Period 2 =

(Price 1 (Price 1

2 × Quantity 1 1 × Quantity 1

× 100 = 128.67 Period 3 =

(Price 1 (Price 1

3 × Quantity 1 1 × Quantity 1

× 100 = 124.00 Period 4 =

(Price 1 (Price 1

4 × Quantity 1 1 × Quantity 1

× 100 = 130.00 Period 5 =

(Price 1 (Price 1

5 × Quantity 1 1 × Quantity 1

× 100 = 137.33 Period 6 =

(Price 1 (Price 1

6 × Quantity 1 1 × Quantity 1

× 100 = 146.00

[75] [ADVANCED] HOW IS A PAASCHE PRICE INDEX CALCULATED? The Paasche price index formula—which is a current-period quantity weighted index—uses this formula: •

•

(P Current × Q Current )

i Paasche Price Index = (Pi Base × Q Current × 100, where the ∑ is the summation of all of the ) i i price (P) terms (designated as i) times all of the quantity (Q) terms. The base-period price multiplied by the current period’s quantity is the relative comparison in the denominator, and the numerator is determined as the sum of current price of each item multiplied by the current quantity in each period. Consider this example for three items in the market basket (that is, i = 3) for six periods. The base-period prices will be used in all of the denominator calculations multiplied by the current period’s quantity of each item. Applying that formula for period 6, then the numerator is the sum of the current price multiplied by the current quantity—which is ($8 × 67) + ($17 × 180) + ($27 × 375) = $13,721. The denominator for period 6’s Paasche price index is ($5 × 67) + ($10 × 180) + ($20 × 375) = $9,635.

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MACROECONOMIC THINKING AND TOOLS

Period Period Period Period Period Period

•

1 2 3 4 5 6

Price 1

Quantity 1

Price 2

Quantity 2

Price 3

Quantity 3

$5 $10 $7 $7 $8 $8

100 125 150 60 63 67

$10 $12 $13 $14 $15 $17

200 225 250 200 190 180

$20 $25 $24 $25 $26 $27

250 300 325 325 350 375

The full calculations of the price indexes in this example are shown here: TABLE 4.3

Using the Formula for the Paasche Price Index

Period 1 =

(Price 1 (Price 1

1 × Quantity 1 1 × Quantity 1

1) + (Price 2 1) + (Price 2

1 × Quantity 2 1 × Quantity 2

1) + (Price 3 1) + (Price 3

1 × Quantity 3 1 × Quantity 3

1) 1)

× 100 =

$ 7, 500 $ 7, 500

2) + (Price 2 2) + (Price 2

2 × Quantity 2 1 × Quantity 2

2) + (Price 3 2) + (Price 3

2 × Quantity 3 1 × Quantity 3

2) 2)

× 100 =

$ 11, 450 $ 8, 875

3) + (Price 2 3) + (Price 2

3 × Quantity 2 1 × Quantity 2

3) + (Price 3 3) + (Price 3

3 × Quantity 3 1 × Quantity 3

3) 3)

× 100 =

$ 12, 100 $ 9, 750

4) + (Price 2 4) + (Price 2

4 × Quantity 2 1 × Quantity 2

4) + (Price 3 4) + (Price 3

4 × Quantity 3 1 × Quantity 3

4) 4)

× 100 =

$ 11, 345 $ 8, 800

5) + (Price 2 5) + (Price 2

5 × Quantity 2 1 × Quantity 2

5) + (Price 3 5) + (Price 3

5 × Quantity 3 1 × Quantity 3

5) 5)

× 100 =

$ 12, 454 $ 9, 215

6) + (Price 2 6) + (Price 2

6 × Quantity 2 1 × Quantity 2

6) + (Price 3 6) + (Price 3

6 × Quantity 3 1 × Quantity 3

6) 6)

× 100 =

$ 13, 721 $ 9, 635

× 100 = 100.00 Period 2 =

(Price 1 (Price 1

2 × Quantity 1 1 × Quantity 1

× 100 = 129.01 Period 3 =

(Price 1 (Price 1

3 × Quantity 1 1 × Quantity 1

× 100 = 124.10 Period 4 =

(Price 1 (Price 1

4 × Quantity 1 1 × Quantity 1

× 100 = 128.92 Period 5 =

(Price 1 (Price 1

5 × Quantity 1 1 × Quantity 1

× 100 = 135.15 Period 6 =

(Price 1 (Price 1

6 × Quantity 1 1 × Quantity 1

× 100 = 142.41

[76] [ADVANCED] HOW IS A FISHER PRICE INDEX CALCULATED? The Fisher price index formula is the square root of the Laspeyres price index times the Paasche price index. TABLE 4.4

Calculation of a Fisher Price Index

Price 1 Quantity 1 Price 2 Quantity 2 Price 3 Quantity 3 Laspeyres Paasche Fisher Price Price Price Index Index Index Period 1

$5

100

$10

200

$20

250

100.00

100.00

100.00

Period 2 Period 3

$10 $7

125 150

$12 $13

225 250

$25 $24

300 325

128.67 124.00

129.01 124.10

128.84 124.05

Period 4

$7

60

$14

200

$25

325

130.00

128.92

129.46

Period 5 Period 6

$8 $8

63 67

$15 $17

190 180

$26 $27

350 375

137.33 146.00

135.15 142.41

136.24 144.19

INFLATION

•

Assuming that the Laspeyres and Paasche price indexes are first calculated, then multiply the two indexes and take the square root, as shown for the example.

[77] [ADVANCED] HOW IS A CHAIN-WEIGHTED PRICE INDEX CALCULATED? A chain-weighted price index (or simply a chained or chain-linked price index) calculates growth rates and then increments the past price index by those growth rates. This approach also is used for quantity metrics and real GDP dollar data in the national income and product Accounts. •

If the sample (or market basket) is changing modestly or dramatically from month to month, it is possible to calculate a weighted growth rate based on a matched sample in the current and base periods (which may be a month apart, or a year apart, or whatever span chosen). Then the formula for calculation of the chained index is: Chained Data Price Indext = Chained Data Price Indext

1

× (1 + Growth Ratet / 100)

Subscript “t” means Today’s Period; “t−1” means Today Minus One-Period (the chosen span for the growth rate). •

Consider this example where the growth rates per year are derived, as shown in the table.

TABLE 4.5

Period Period Period Period Period Period

1 2 3 4 5 6

Calculation of a Chained Index Growth Rate per Period

Chain Price Index

--2.0 4.5 −1.0 3.0 5.0

100.00 102.00 106.59 105.52 108.69 114.12

Then, to use those growth rates per period in a chained index, set the initial period to some arbitrary number, such as 100. Then use the growth rate for each period to increment the prior period’s index, as follows:

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MACROECONOMIC THINKING AND TOOLS

Period Period Period Period Period

2: 3: 4: 5: 6:

(1 (1 (1 (1 (1

+ + + + +

(2.0/100)) × 100 = 102.00 (4.5/100)) × 102.00 = 106.59 (−1.0/100)) × 106.59 = 105.52 (3.0/100)) × 105.52 = 108.69 (5.0/100)) × 108.69 = 114.12

If one is linking to historical data prior to period 1 (the base), then it is possible to use historical growth rates to extrapolate back in time with this version of the chain formula: Chained Data Price Indext

1

= Chained Data Price Indext ÷ (1 + Growth Rate t / 100)

•

For example, consider extrapolating back in time from period T, when you know the growth rates for period T and for period T−1. TABLE 4.6

Period T-2 Period T-1 Period T

Calculation of a Chained Index Backwards in Time Growth Rate per Period

Chain Price Index

4.1 3.7

92.63 96.43 100.00

Then the chained index for period T−1 is calculated as: 100.00 ÷ (1 + (3.7/100)) = 96.43.

Knowing the growth rate in period T−1, the chained index for period T−2 is calculated as: 96.43 ÷ (1 + (4.1/100)) = 92.63.

[78] HOW ARE PRICE INDEXES USED FOR COST-OF-LIVING ADJUSTMENT? Once the appropriate price index is selected and the reference period for the adjustment, then a cost-of-living adjustment (COLA) can be applied in a straight-forward manner. As inflation was relatively subdued from about 1990 through 2020, interest in COLA clauses in labor contracts dwindled from a high of 10.8% of all workers covered by a labor contract in 1970 to 5.4% by 1995 and likely lower since that BLS estimate. Nonetheless, with the spike in inflation in 2021–2022, interest may increase in the COLA. To apply the adjustment, let’s assume that we use the CPI-W from the BLS and adjust an assumed wage beginning in 2013 based on the starting wage of

INFLATION

$7.50 per hour that prevailed in 2012. Using the growth rates from the CPI-W, this adjustment for 2013 would multiply the 2012 hourly wage by the ratio of the 2013 CPI-W divided by the 2012 CPI-W. That is, (229.324/226.229) × $7.50 = $7.60 per hour for the subsequent year. Then for 2014, the wage rate indexed by the CPI-W would be: (232.771/229.324) × $7.60 = $7.72. This adjustment continues in a similar manner for all subsequent periods. TABLE 4.7

Calculation of a Cost-of-Living Adjustment for

Wages Year

CPI-W (1982–84 = 100)

Wage (per Hour)

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

226.229 229.324 232.771 231.810 234.076 239.051 245.146 249.222 252.248 265.510

$7.50 $7.60 $7.72 $7.69 $7.76 $7.93 $8.13 $8.26 $8.36 $8.80

[79] WHAT SHOULD INFLATION MEASURE? There is almost no end to the variations of price measurement. •

• • •

Prices could be measured for the overall economy, for consumer expenditures, for producer purchases, based on prices charged for export or import, by the breadth of change, or volatility. Prices could be measured based on well-being or based on outlay. The CPI-U (or CPIW), for example, is a mix. Prices could be measured in total or excluding key parts such as food and energy or the most volatile components. Prices could be measured in terms of those aggregates that are “sticky” versus those that are “flexible.”

[80] WHY IS IT IMPORTANT TO HAVE GOOD PRICE MEASUREMENT? In the editor’s preface of Irving Fisher’s 1922 book on price indexes, the editor wrote a very convincing argument for good price measurement: “All sciences are characterized by a close approach to exact measurement. How many of them could have made much progress without units of measurement, generally understood and accepted, it is difficult to imagine. In order to

55

56

MACROECONOMIC THINKING AND TOOLS

determine the pressure of steam, we do not take a popular vote: we consult a gauge. Concerning a patient’s temperature, we do not ask for anybody’s opinion: we read a ther­ mometer. In economics, however, as in education, though the need for quantitative mea­ surement is as great as in physics or in medicine, we have been guided in the past largely by opinions and guesses. In the future, we must substitute measurement for guesswork. Toward this end, we must first agree upon instruments of measurement.”7 Over the subsequent years since Fisher’s book, that objective has been met.

[81] WHAT ARE THE MAIN MEASURES OF CONSUMER INFLATION? The U.S. Bureau of Labor Statistics’ Consumer Price Index (three variants) and the U.S. Bureau of Economic Analysis’ Personal Consumption Expenditure (PCE) price index are the two main consumer inflation measures for the U.S. economy.

[82] HOW DOES THE CPI DIFFER FROM THE PCE PRICE INDEX? The CPI-U and the CPI-W are fixed-weight (modified) Laspeyres price indexes, while the PCE price index is based on a Fisher price formula, which is not affected by the choice of a reference period for the price comparison and eliminates the substitution bias that fixed-weighted price indexes encounter. The coverage, weights, and data sources also differ between the CPI and PCE price index methodologies. With regard to the coverage, the PCE price index by definition includes the non-profit sector, for example, while the CPI-U represents all urban consumers. Historically, in low or stable inflation environments, there is not much difference in the growth rates between the two measures, but in high-inflation periods the substitution bias is greater (consumers shifting to lower priced goods and services), which typically amplifies the difference between the two measures. Figure 4.1 shows the growth rates of the two price indexes.

Comparison of the Consumer Price Index (CPI-U) and the Personal Consumption Expenditure Price Index

FIGURE 4.1

Sources: U.S. Bureau of Labor Statistics; U.S. Bureau of Economic Analysis

INFLATION

[83] WHAT ARE THE MEASUREMENT PROBLEMS WITH FIXEDWEIGHT PRICE INDEXES, SUCH AS THE CPI-U? One of the main fixed-weight price indexes in the United States is the consumer price index, which is compiled by the U.S. Bureau of Labor Statistics. The Federal Price Statistics Review Committee, chaired by George Stigler, provided an expansive review of U.S. price indexes in the early 1960s. The study explored the general methodology problems with the numerous price indexes—consumer, wholesale (later producer), prices paid and received by farmers, and export and import—produced by the federal government. Moreover, this committee identi­ fied the conceptual measurement problems in the consumer price index as follows: “It is often stated that the Consumer Price Index measures the price changes of a fixed standard of living based on a fixed market basket of goods and services. In a society where there are no new products, no changes in the quality of existing products, no changes in consumer tastes, and no changes in relative prices of goods and services, it is indeed true that the price of a fixed market basket of goods and services will reflect the cost of maintaining (for an individual household or an average family) a constant level of utility. But in the presence of the introduction of new products, and changes in product quality, consumer tastes, and relative prices, it is no longer true that the rigidly fixed market basket approach yields a realistic measure of how consumers are affected by prices. If consumers rearrange their budgets to avoid the purchase of those products whose prices have risen and simultaneously obtain access to equally desirable new, low-priced products, it is quite possible that the cost of maintaining a fixed standard of living has fallen despite the fact that the price of a fixed market basket has risen.”8 Therefore, the Stigler committee opined, “The periodic revision of price indexes, and the almost continuous alterations in details of their calculation, are essential if the indexes are to serve their primary function of measuring the average movements of prices.”9 As so it still is. In 1995, the U.S. Senate Finance Committee appointed a fact-finding advisory group to again investigate the problems with the consumer price index (CPI). Boskin, who was a former chair of the President’s Council of Economic Advisers (1989–1993), was asked to chair the group. These members of the “Advisory Commission to Study the Consumer Price Index” identified biases in the methodology used to compile the CPI, which were largely the same concerns of the Stigler committee. The Boskin Commission noted that, “estimating a cost-of-living index requires assumptions, methodology, data gathering and index number construction. Biases can come from any of these areas. The strength of the CPI is the underlying simplicity of its concept: a fixed (but representative) market basket of goods and services over time as consumers respond to price changes and new choices.” The Boskin Commission found that “there are several categories or types of potential bias in using changes in the CPI as a measure of the change in the cost-of-living. (1) Substitution bias occurs because a fixed market basket fails to reflect the fact that consumer substitute relatively less for more expensive goods when relative prices change. (2) Outletsubstitution bias occurs when shifts to lower price outlets are not properly handled. (3) Qualitychange bias occurs when improvements in the quality of products, such as greater energy efficiency or less need for repair, are measure inaccurately or not at all. (4) New-product bias occurs when new products are not introduced in the market basket or included only with a long lag.”10 Since the Boskin report was issued, the U.S. Bureau of Labor Statistics has largely addressed all these concerns with its evolving methodology.

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MACROECONOMIC THINKING AND TOOLS

[84] HOW DOES THE BUREAU OF LABOR STATISTICS COMPILE THE CPI? First, the BLS develops its market basket of goods and services purchased by consumer based on the consumer expenditures that are collected for the Bureau of Labor Statistics by the Census Bureau. There are two parts of the consumer expenditure collection program: (1) the “Interview Survey” for major and/or recurring items, and (2) the “Diary Survey” for more minor or frequently purchased items. From these regular survey data, the Bureau of Labor Statistics develops the market basket of expenditures (quantity weights) used in the consumer price index. Historically, the BLS would update the expenditure weights every ten years, but beginning in January 2002, the BLS began to update those expenditure weights every two years based on those Consumer Expenditure Surveys. Then, on a monthly basis, the “BLS data collectors visit (in person or on the web) or call thousands of retail stores, service establishments, rental units, and doctors’ offices, all over the United States to obtain infor­ mation on the prices of the thousands of items [about 80,000 items] used to track and measure price changes in the CPI.”11 Sales taxes are included in the calculations. If a product is no longer available, then a new item is selected with similar characteristics. If the quantity or quality of that product has changed, then those characteristics are recorded and adjusted for when the price indexes are compiled. Finally, the BLS combines those prices with the ex­ penditure weights to produce the CPI-U, the CPI-W, and the chain CPI-U.

[85] WHAT DOES THE CPI MEASURE? The BLS writes that, “the CPI represents all goods and services purchased for consumption by the reference population (U or W). BLS has classified all expenditure items into more than 200 categories, arranged into eight major groups (food and beverages, housing, apparel, trans­ portation, medical care, recreation, education and communication, and other goods and services). Included within these major groups are various government-charged user fees, such as water and sewerage charges, auto registration fees, and vehicle tolls.”12 The typical summary output is presented in the BLS’ monthly press release in its “Table A”, which is shown as Table 4.8.

[86] SHOULD ASSET-PRICE INFLATION ALSO BE MEASURED ALONG WITH GOODS AND SERVICES? Former Fed Chair Paul Volcker observed that there are two kinds of inflation: (1) asset inflation and (2) goods and services price inflation, such as the consumer price index. He called them “cousins.” Early work on the inclusion of asset prices in measures of inflation can be traced to Irving Fisher in 1911. Fisher’s intent appeared to be a desire to find a broad transactions price metric to guide the monetary authority in establishing the price of gold. However, the idea that asset prices should receive some consideration in the construction of aggregate price movements remained a largely dormant issue until Armen Alchian and Benjamin Klein published their paper, “On a Correct Measurement of Inflation,” in 1973. More recently, it was argued that tracking some aggregate of asset prices is important for monetary policy and it is important to have some

Note (1) Not seasonally adjusted.

1.0 1.2 1.4 0.7 3.9 4.5 4.1 16.9 3.0 1.3 8.0 0.6 0.7 1.0 1.8 0.7 0.3 0.6 0.6 1.3 0.4

1.1 −0.4 −0.8 0.1 0.7 0.5 3.1 0.5

May 2022

0.3 0.9 1.0 0.6 −2.7 −5.4 −6.1 2.7 1.3 0.7 3.1 0.6 0.2

Apr. 2022

0.7 1.6 0.8 0.4 0.7 0.6 2.1 0.7

1.3 1.0 1.0 0.9 7.5 10.4 11.2 −1.2 3.5 1.7 8.2 0.7 0.8

Jun. 2022

0.6 −0.4 −0.1 0.6 0.4 0.5 −0.5 0.4

0.0 1.1 1.3 0.7 −4.6 −7.6 −7.7 −11.0 0.1 1.6 −3.6 0.3 0.2

Jul. 2022

0.8 −0.1 0.2 0.2 0.6 0.7 0.5 0.8

0.1 0.8 0.7 0.9 −5.0 −10.1 −10.6 −5.9 2.1 1.5 3.5 0.6 0.5

Aug. 2022

0.7 −1.1 −0.3 −0.1 0.8 0.7 1.9 1.0

0.4 0.8 0.7 0.9 −2.1 −4.7 −4.9 −2.7 1.1 0.4 2.9 0.6 0.0

Sep. 2022

Seasonally adjusted changes from preceding month

Percentage Changes in CPI for All Urban Consumers (CPI-U): U.S. City Average

All items Food Food at home Food away from home (1) Energy Energy commodities Gasoline (all types) Fuel oil (1) Energy services Electricity Utility (piped) gas service All items less food and energy Commodities less food and energy commodities New vehicles Used cars and trucks Apparel Medical care commodities (1) Services less energy services Shelter Transportation services Medical care services

TABLE 4.8

0.4 −2.4 −0.7 0.0 0.5 0.8 0.8 −0.6

0.4 0.6 0.4 0.9 1.8 4.4 4.0 19.8 −1.2 0.1 −4.6 0.3 −0.4

Oct. 2022

8.4 2.0 4.1 3.1 6.7 6.9 15.2 5.4

7.7 10.9 12.4 8.6 17.6 19.3 17.5 68.5 15.6 14.1 20.0 6.3 5.1

Unadjusted 12-mos. ended Oct. 2022

INFLATION

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MACROECONOMIC THINKING AND TOOLS

metric to target. A 2002 paper by Michael F. Bryan, Stephen G. Cecchetti, and Róisín O’Sullivan (“Asset Prices in the Measurement of Inflation”) notes that “Virtually all of this (and earlier) work on incorporating asset prices into an aggregate price statistic has been motivated by a presumed, but unidentified transmission mechanism through which asset prices are leading in­ dicators of inflation at the retail level.”

[87] [ADVANCED] HOW SHOULD ASSET-PRICE INFLATION BE MEASURED? The measurement of asset-price inflation has posed a tougher theoretical challenge to construct an asset-price index than to measure inflation based on current expenditures. Asset-price measures are sometimes referred to as wealth price measures and represent future spending. Typically, at least three asset classes should be included in such an asset-price index: (1) equities, (2) bonds, and (3) real estate. Asset price inflation also has been far more volatile than goods and services inflation, which has added another difficulty in determining asset bubbles in real time. One example of a fixed-weighted asset-price index based on the Federal Reserve’s Flow-of-Funds (FOF) data is shown in Figure 4.2.13

FIGURE 4.2

Consumer Price Inflation vs. Asset Inflation

Sources: U.S. Bureau of Labor Statistics; Federal Reserve Board of Governors

[88] [ADVANCED] ARE THERE OTHER METRICS OF ASSET-PRICE INFLATION? There are several popular measures of asset-price inflation or “overvaluation,” including those mea­ sures mainly or exclusively focused on the stock market. A sampling of some of the popular ones are:

INFLATION

•

•

Price-to-Earnings Ratio (P-E ratio): This measure, which is the inverse of the earnings yield, is the average share price divided by the 12-month trailing earnings (sometimes expected earnings). A low P-E ratio may indicate an overvalued stock market, while a high value P-E ratio may indicate an undervalued stock. This measure, however, also is evaluated in terms of fixed-income yields or other investment assets. In a lowinterest rate environment, there may be P-E expansion meaning that the multiple of ratio of price to earnings is rising. Similarly, in a high or rising-interest rate environment, there may be P-E compression meaning that the multiple of price to earnings is falling. There is no absolute number for this ratio, only historical averages for comparison. Tobin’s Q: Yale University Prof. James Tobin, a Nobel Prize winner, proposed an investment theory based on the ratio of the stock market valuation of the firms’ assets relative to the replacement cost of these assets. The Q metric used in that theory is based on data that are readily available from the Federal Reserve’s Flow-of-Funds.14 The Q ratio, which is bounded by 0 and 1, implies that the replacement cost of those asset is higher than the stock market valuation of the firms, which implies that the stock market is undervaluing the firms. When the Q ratio is above 1, then the stock market valuation of the firms is greater than the replacement value, which suggests the stock market is overvalued Figure 4.3.

FIGURE 4.3

•

Tobin’s Q Ratio for the U.S. Stock Market

Shiller’s Cyclically Adjusted Price-to-Earnings Ratio: Yale Prof. Robert J. Shiller has suggested a variation on the standard P-E ratio that attempts to reduce the volatility by using a ten-year average of the EPS adjusted for inflation in the denominator. (Note that is not simply taking the EPS and a price index, such as the CPI, and dividing the ratio for each year to get a “real EPS” rate while adjusting the past EPS figures for what they would be in today’s dollars and then averaging the ten-year period.)15 This measure does not have an absolute number of what is “overvalued,” but only historical comparisons Figure 4.4.

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FIGURE 4.4

•

Warren Buffett Ratio: The “Buffett Ratio” is the ratio of total U.S. stock market valuation to GDP. There is no absolute value for assessment, but the historic average between 1971 and 2021 was 0.822, as shown on the graphic below in Figure 4.5.16

FIGURE 4.5

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Shiller’s Cyclically Adjusted Price-Earnings Ratio

“Buffet Ratio”: Stock-Market Capitalization to GDP Ratio

These and other measures and models exist to judge the fair value of the stock market, such as the capital asset pricing model, or housing-market price bubbles, but none of these metrics or models is explicitly used to measure asset inflation, per se.

[89] [ADVANCED] IS ASSET-PRICE INFLATION GOOD OR BAD FOR THE ECONOMY? Generally, economic theories suggest that increasing housing and financial wealth is a supporting factor for economic growth through higher consumption. In an influential 1963 article by Ando

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and Modigliani on the life-cycle model of consumption,17 they argue that wealth is an important variable to determine consumption. But the question is how important empirically is wealth versus income in the determination of consumer spending? In a 2016 article, Lim and Zeng observed that “The effect of wealth on consumption is an enduring topic for research, and in recent years it has been further motivated by different performances in the stock and housing markets. While there is some evidence to support the existence of a wealth effect on con­ sumption, there is less information on the life-cycle pattern of the relationship between income, wealth, and consumption. Furthermore, empirical results on relationships across ages and across time have been mixed, with respect to the significance of the relationship, the magnitude of the effects, and even the possible explanations for the results.”18 Additionally, a 2014 study by Bansal et al. also found that asset-price volatility tends to be associated with a decrease in consumption.19

[90] WHAT ARE SOME USES OF PRICE INDEXES IN THE FEDERAL GOVERNMENT? Two key examples of the use of price indexes in the federal government are in the cost-ofliving adjustments in Social Security payments and with federal income tax brackets. •

•

Social Security Benefits: In 1973, Congress amended the Social Security Act of 1935 with Public Law 92–3363. Part of that amendment called for automatic annual cost of living increases to be made to Social Security payments based on the CPI. Today, the CPI-W is used by the Social Security Administration to calculate its cost of living adjustments (COLAs) for Social Security recipients. The COLA is defined as the percent increase between the third quarter average of the CPI-W for a given year and the previous peak third-quarter average of the CPI-W. Federal Tax Brackets: In December 2017, Congress passed Public Law 115–97, The Tax Cuts and Jobs Act, which directed the IRS to change how the federal tax brackets were adjusted for inflation. In previous legislation, the brackets were adjusted by the CPI-U, however, this new law substituted the Chained Consumer Price Index for All Urban Consumers (C-CPI-U) for the CPI-U. The C-CPI-U tends to grow more slowly than the CPI-U, because this price index formulation adjusts for the price substitution bias (people changing preferences due to a change in relative prices). The adjustment for any calendar year is based on the average of the C-CPIU as of the close of the 12-month period ending on August 31 of such calendar year.

[91] WHICH MEASURES OF INFLATION ARE USED FOR SETTING U.S. MONETARY POLICY? Although monetary policymakers are aware of all of the major inflation metrics, the Federal Reserve Board of Governors sets its inflation target and measures its success towards meeting stable inflation against the personal consumption expenditure (PCE) price index, which is

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compiled by the U.S. Bureau of Economic Analysis. The Federal Reserve targets both the overall PCE price index and the PCE price index less food and energy prices.

[92] WHY DOES THE FEDERAL RESERVE FOCUS ON THE PCE PRICE INDEX AND NOT THE CPI? The Federal Reserve’s shift towards the PCE price index as a policy metric rather than the CPI largely came about due to the concerns that were raised by the Boskin Commission that the CPI overstated inflation because of its construction. Of course, the Federal Reserve is not locked into that PCE measure and may shift to a measure, such as the chain CPI-U, which has similar characteristics to the PCE price index.

[93] WHAT IS THE PROBLEM WITH INFLATION? If inflation is accurately foreseen or correctly anticipated, it can be accounted for in pricing decisions, wage demands, investment decisions, and a host of other economic activities. However, unforeseen or unexpected inflation is primarily the problem for an economy, which would create distortions from prior economic decisions. Keynes noted that unforeseen inflation violates the principles of distributive justice and increases business risk. Typically, unforeseen inflation will hurt creditors/savers, but will benefit debtors/borrowers that pay back loans in a currency that has a lower purchasing power. Unforeseen inflation tends to hurt lower income households more than higher income households, and may create “social discontent”—as Keynes himself opined. Governments that do not index or adjust their tax rates (if progressive) for rising inflation also tend to benefit from higher revenue. Finally, there is a risk that surprises in inflation will cause future inflation expectations to be higher (or “unanchored” from the recent past) and there could be second-round effects from higher inflation expectations, such as higher interest rates and slower economic growth.

[94] WHY DO POLICYMAKERS AND ECONOMISTS EXCLUDE FOOD AND ENERGY FROM PRICE MEASURES AND REFER TO THOSE MEASURES AS “CORE” INFLATION? The concept of “core” inflation is about measuring some underlying pace of inflation. One early idea of how to measure core inflation was to define it as wages growth minus trend productivity. Another definition was offered by the founder of the econometric forecasting firm Data Resources and Harvard Prof. Otto Eckstein, who defined core inflation in his 1981 book as “the trend in the aggregate supply price,” which was a weighted average of trend growth rates of a rental price of capital and unit labor costs.20 However, increasingly it has become common practice to exclude volatile components, such as food and energy, from a measure of prices and to think of that as an underlying pace of inflation. Of course, what is considered volatile in one

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CPI Less Food and Energy

Median CPI

16% Trimmed Mean CPI

Trimmed Median PCE Price Index

FIGURE 4.6

Comparison of “Core” U.S. Inflation Measures

Sources: Bureau of Labour Statistics; Dallas Federal Reserve Bank; and Cleveland Federal Reserve Bank

country may not be the same in another country. For example, Japan excludes fresh food from its CPI as its core measure, but the U.S. excludes food and energy. An alternative to excluding a fixed set of components, such as food and energy, from a price index is to use a distribution of component growth rates for each period and exclude a set of the lowest and a set of the highest growth rates and average the remaining components as a measure of underlying inflation. The Federal Reserve Bank of Dallas compiles a core measure of the monthly personal consumption expenditure price index using this type of methodology, which is its “trimmed mean PCE inflation rate.” The trimmed mean PCE inflation rate, which is based on about 180 components, eliminates 24% of the observations from the lower tail (largest declines) and 31% of the weight in the upper tail (largest increases). The reason for those percentages is based on statistical research in which it found that, “those proportions have been chosen, based on historical data, to give the best fit between the trimmed mean inflation rate and proxies for the true core PCE inflation rate.”21 Another example of this is a monthly release from the Federal Reserve Bank of Cleveland, which produces a “median CPI” and a “16 percent trimmed-mean CPI.” “The Cleveland Fed ranks the inflation rates of the components of the CPI and picks the one in the middle of the distribution—that is, the item whose expenditure weight is in the 50th per­ centile of the price change distribution. The Cleveland Fed also calculates the 16 percent trimmed-mean CPI by taking a weighted average across the component inflation rates after excluding, or trimming, items whose expenditure weights fall in the top 8 percent and bottom 8 percent of the price change distribution.”22 The Cleveland Fed opined that, “According to research from the Cleveland Fed, the median CPI provides a better signal of the underlying inflation trend than either the all-items CPI or the CPI excluding food and energy. The median CPI is even better at forecasting PCE inflation in the near and longer term than the core PCE price index.” A graphical comparison of each of these measures is above in Figure 4.6.23

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[95] WHAT ARE “STICKY” PRICES? Economists (including Keynes) embrace the idea that both prices and wages can be sticky, which means that prices and wages are slow to adjust to changing economic conditions.

[96] IS THERE EMPIRICAL SUPPORT FOR STICKY PRICES? Yes. There are a number of studies supporting this observation of sticky prices. Research by Bryan and Meyer24 examined how frequent components of the CPI changes and segmented those components into two groups--the sticky or slow-changing group (which ranged from over five months to almost 26 months and represented about 70% of the CPI components) and the flexible or relatively quick-changing group (which adjusted within less than five months). The researchers also found that sticky prices appear to incorporate expectations about future inflation to a greater degree than prices that change on a frequent basis. While flexible prices respond more powerfully to economic conditions—economic slack or boom. Importantly, the sticky-price measure may be useful, Bryan and Meyer’s statistical work demonstrated, when trying to gauge where inflation is heading. Generally, the study found that prices of goods and services which are more labor-intensive tend to be sticky; prices of goods that are raw-material intensive tend to be flexible. The Federal Reserve Bank of Atlanta produces a monthly update of this work in its Sticky-Price CPI release. These data25 are portrayed above in Figure 4.7.

FIGURE 4.7

Sticky-Price CPI vs. Flexible-Price CPI

Source: Federal Reserve Bank of Atlanta

[97] IS THERE EMPIRICAL SUPPORT FOR STICKY WAGES? In a study by Barattieri, Basu, and Gottschalk,26 they found that wages in manufacturing sector appear to be stickier than wages in the services sector. For salaried workers, the study also

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concluded that earnings change by only 5% per quarter, which implied 20 quarters of sticky wages on average. For hourly workers, a wage change is about 18% per quarter, thus implying an expected duration of wage contracts of 5.6 quarters.

[98] WHAT IS THE KATONA EFFECT? The University of Michigan’s Survey Research Center’s Director and Prof. George Katona observed that when there was a jump in unanticipated inflation (and probably all large changes in inflation are unexpected by the consumer), consumers cut back on their spending and increase their savings. It was Arthur Okun, in his 1981 book Prices and Quantities, who dubbed this idea the “Katona effect” which was the view that one motivation for consumer spending was price-level volatility—that is, spending is inversely related to price-level volatility.27 Although academics largely have shunned the concept of Katona effect, empirical support appears relatively strong for the United States and other major international economies, as shown in the graphic below, where U.S. real consumer spending growth rates (on a six-month smoothed annualized basis) are shown inversely related with the 12-month standard deviation of the CPI Figure 4.8.

FIGURE 4.8

The Katona Effect: Real Consumer Spending vs. (CPI) Price Level Volatility

[99] HOW MUCH OF INFLATION IS AGGREGATE DEMAND-DRIVEN AND HOW MUCH IS AGGREGATE SUPPLY-DRIVEN? There is no hard and fast answer to this question, but a “simple” evaluation of U.S. inflation by Adam Shapiro, who is an economist with the San Francisco Federal Reserve Bank, found that demand factors between December 1989 and April 2022 accounted for about one-third (32%—an average of 0.63 percentage points of 1.96%) of the total year-over-year percentage change in the PCE price index, while 43% of the overall percentage change was due to supply factors (an average of 0.845 percentage points of 1.96%).28 The remaining share of inflation was ambiguous and could not be definitively classified as supply or demand driven. The Shapiro

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model to make this assessment was based on “surprises” or unexpected changes in price and quantity. If the changes in price and quantity both moved in the same direction it was deemed demand-driven; if the changes in prices and quantity each moved in opposite directions than that was classified as a supply-driven change. (The logic for this model is derived from micro­ economics: “Shifts in demand move both prices and quantities in the same direction along the upward-sloping supply curve, meaning prices rise as demand increases. Shifts in supply move prices and quantities in opposite directions along the downward-sloping demand curve, meaning prices rise when supplies decline.”29)

[100] WHAT ARE SOME KEY THEORIES OF INFLATION? Inflation theories are often part and parcel of broader theories of economic growth and fluctuation. Some of the key inflation perspectives are: (1) Wicksell’s interest-rate gap model of inflation, (2) Keynesian theories of inflation, (3) the Phillips curve trade-off between wage inflation and the unemployment rate, (4) the Quantity Theory of Money, and (5) the Fiscal Theory of Inflation.

[101] [ADVANCED] WHAT IS WICKSELL’S “INTEREST-RATE GAP MODEL”? In 1898, Knut Wicksell developed a theory of inflation that was based on a theoretical gap between the market interest rate and what he described as the “natural rate of interest.”30 Wicksell described the natural rate as the rate of return on capital that brings savings supply into equilibrium with planned investment expenditure. Although Wicksell thought the market interest rate would ultimately adjust to the natural interest rate, it was not instantaneous; before those two interest rates fully converged, there would be either excess demand putting upward pressure on prices or excess supply putting downward pressure on prices. Although Wicksell saw this as an explanation for inflation, the idea was the foundation for later theories offered by John Maynard Keynes and Sir John Hicks. Today, this concept of the natural rate of interest or r-star is alive and well; it is integrated with the concept of an economy at long-run potential. R-star, which is an unobserved, but measurable, concept is often discussed by Federal Reserve policymakers.31

[102] HOW DO KEYNESIANS VIEW THE INFLATION DYNAMIC? Today, the aggregate demand-side focus of John Maynard Keynes’ writings often is thought to be the economic mechanism (a “demand-pull” type process) through which inflation is generated in an economy when aggregate demand is greater than potential output. However, in a 1981 article, Thomas M. Humphrey of the Richmond Federal Reserve closely explored Keynes views on inflation. Humphrey opined that Keynes’ own theory of inflation was “essentially the same as the modern monetarist version and embodies the following monetarist elements: (1) a money supply and demand theory of price level determination, (2) the notion

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of money stock erogeneity, implying money-to-price causality, (3) the concept of the demand for money as a stable function of a few key variables, and (4) a focus on the special role of price expectations in the money demand function.”32

[103] WHAT IS THE PHILLIPS CURVE? The original Phillips curve shows a trade-off between wage inflation and the unemployment rate.33 Although that trade-off was attributed to Phillips, editors of the Journal of Political Economy discovered an early work by Irving Fisher, whom they claim discovered this rela­ tionship first.34 Today, the original Phillips curve has morphed into the so-called New Keynesian Phillips curve. This New Keynesian Phillips curve posits that inflation is a function of inflation expectations and the degree of slack in the economy (which can be measured by the output gap).

[104] IS THE PHILLIPS CURVE EMPIRICALLY VALID? Economists generally think of the Phillips curve as either a short-run relationship or a long-run relationship. The short-run relationship has largely been weakened dramatically over time and Federal Reserve policymakers often refer to that short-run empirical relationship between wage or price inflation and the unemployment rate as having “flattened.” Longer term, this concept is still embedded in macroeconomic theory and empirically still somewhat ques­ tionable and only slightly more valid than the short-term relationship Figure 4.9.

FIGURE 4.9

Long-Term Phillips Curve, 1960–2020 Ten-Year Averages

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[105] WHAT IS THE QUANTITY THEORY OF MONEY? HOW IS IT TIED TO INFLATION? The quantity theory of money is the hypothesis of the classical economists, who assumed that wages and prices were completely flexible. American economist Irving Fisher gave a clear exposition of this theory in his influential book, The Purchasing Power of Money, published in 1911. This view argued that prices of goods and service and factor prices would fully adjust to the level that equates the supply and demand for a particular good or service in the long run. The quantity theory of money provided a long-run theory of inflation where the relationship between inflation and money growth on a decade basis has a positive or direct relationship, as the tenets of its theory would expect. That observation, which led to Milton Friedman’s statement that “inflation is always and everywhere a monetary phe­ nomenon,” remains relatively accurate in the long run but is not supported by the data for the short run. The graph below shows the long-run pattern of M2 and CPI growth over ten-year periods Figure 4.10.

FIGURE 4.10

Ten-Year Growth Rates in M2 and the CPI For the United States, 1920–2021

[106] [ADVANCED] WHAT IS THE FISCAL THEORY OF INFLATION? This fringe and controversial theory of inflation was developed in various research papers by Sims, Cochrane, and Leeper, in particular.35 Christopher Sims, for example, explained the logic of this view as “The fiscal theory of the price level is based on a simple notion. The price level is not only the rate at which currency trades for goods in the economy, it is also the rate at which dollar-denominated interest-bearing government liabilities trade for goods. Just as inflation reduces the value of a 20-dollar bill, it reduces the value of a ten-thousand-dollar mature treasury bill.”36 Therefore, in this view, the price level is sometimes determined by fiscal policy rather than monetary policy. Bond-financed fiscal deficits not fully backed by

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future taxes lead to increased federal government debt, which causes the price level to adjust and restore fiscal equilibrium. Proponents of this theory hold that the price level is defined as the inverse of the value of government debt.

[107] WHAT IS MEANT BY INFLATION EXPECTATIONS? Inflation expectations represent the anticipated rate of inflation for some future period. Three typical ways of measuring inflation expectations are based on (a) surveys, (b) market-inferred inflation expectations, and (c) statistical formulations. •

•

There is a broad range of survey-based measures of future inflation, including those of consumers, professional economists, and business. Examples of consumer surveys are the University of Michigan Survey Research Center’s consumer confidence survey, which asks consumers about their expectations for inflation over the “next year” and for the “next 5 years,” and The Conference Board’s consumer survey that includes a question on the expected inflation rate “twelve months hence.” Some examples of professional forecasts of inflation are compiled by the Blue Chip consensus forecast (a publication that originated by an economist at RCA, who later turned the project into a business) or the Philadelphia Federal Reserve Bank’s Survey of Professional Forecasters (which originated as an NBER survey). Finally, an example of inflation expectations from surveys is the Federal Reserve Bank of Atlanta’s Business Inflation Expectation survey, which asks its business survey group about the firm’s year-ahead inflation expectations. A second type of inflation expectation metric is known as the “breakeven inflation rate,” which is inferred from interest rates traded in the financial markets. A market-inferred inflation expectation is derived as the difference between a U.S. Treasury constant maturity security yield and the U.S. Treasury inflation-indexed constant maturity security (TIPS) yield of the same duration. The most popular breakeven inflation rate is based on the ten-year maturity, but other maturities exist (5, 7, 20, and 30 years). Some economic theories today argue that surveys of professional forecasters provide the most appropriate concept for measuring inflation expectations theoretically, rather than relying on consumers who often are not truly aware of actual inflation rate patterns, but only perceptions of inflation, while business managers may have only a limited perspective of inflation. As such, these expectation metrics can be quite different empirically. To demonstrate the differences between these types of measures, the Survey of Professional Forecasters consensus expected CPI inflation rate in May 2022 was 3.0% for the second quarter of 2023. This compares with the Atlanta Fed’s business survey for the same period which showed an anticipated inflation rate of 3.7%. Meanwhile, the University of Michigan’s survey found consumers expected inflation to be 4.6% in one year. Although the breakeven inflation rate is for a longer period, the five-year breakeven inflation rate was 2.9% in May 2022. Meanwhile the actual U.S. CPI reported in May 2022 (for April) rose by 8.2% from 12 months earlier or rose 4.1% in April 2022 compared with March 2022 at a compound annualized rate.

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The third type of inflation expectation is based on statistical research and began in the 1940s with the “adaptive expectations” model, which assumed that past (“backward looking”) information determined today’s expectation for the near future. However, this concept gave way to new thinking in the 1970s. In the 1970s, the “rational expectations” model took hold of the theory as a reaction to adaptive expectations arguing that it was unrealistic to assume as adaptive expectations did, that expectations did not react to changing policies and economic environments. Therefore, rational expectations posited a “forward-looking” expectation. The idea for this perspective was, for example, “If a central bank tries to systematically inflate the economy to boost employment, the information that this action is being taken, regardless of past price changes, will promote inflationary expectations. This mechanism was central to accounting for ‘stagflation’ patterns of high unemployment and inflation rates in the 1970s. Likewise, if an economy is stuck in an inflationary spiral but a central bank credibly announces its commitment to end inflation, this information itself, regardless of past price changes, will moderate expected inflation. The backward-looking nature of adaptive expectations and their fixed coefficients do not allow for an immediate response of beliefs to news.”37 Although the rational expectations perspective continues to dominate macroeconomic theory, some newer ideas are developing such as what is dubbed “diagnostic expectations.”38 Diagnostic expectations are one formulation of expectations that tries to account for a forward-looking overreaction because of “selective memory.” This type of expectations model incorporates forward-looking expectations about an economic variable, such as inflation, based on two components: one component anchored to a rational-expectations forecast, and a second component that weights recent news more heavily. In this way, the diagnostic expectations concept is a blend of forward and backward-looking information.

[108] WHAT IS MEANT BY THE POLICY GOAL OF “RATIONAL INATTENTION” TO INFLATION? The foundation for “rational inattention” to inflation was put forth by economist Christopher Sims39 but stated simply by former Federal Reserve Board Chairman Alan Greenspan in 1989 that “For all practical purposes, price stability means that expected changes in the average price level are small enough and gradual enough that they do not materially enter business and household financial decisions.”40 In that speech, Greenspan went on discuss the importance of price stability and how policymakers achieve that inattention to inflation. He said, “Price stability contributes to economic efficiency in part by reducing the uncertainties that tend to inhibit investment. Also, it directs resources to productive economic activity that otherwise would tend to be diverted to mitigating the financial effects of inflation. Price stability—indeed, even pre­ venting inflation from accelerating—requires that aggregate demand be in line with potential aggregate supply. In the long run, that balance depends crucially on monetary policy. Inflation cannot persist without a supporting expansion in money and credit; conversely, price stability requires moderate growth in money.”

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Issues to Think About There are lots of different measures of inflation and various methods of compiling price statistics. As such, different price measures can tell very different stories about inflation. • • • • • • •

How can policymakers set an inflation goal with so many options to use for a target inflation rate? Why is asset-price inflation not included in policymakers’ goals for the macroeconomy? Should the chain CPI-U (C-CPI-U) be the featured metric for consumer inflation? What is the best way to measure “core” or underlying inflation in the economy? What are the short-run determinants of inflation? Is the long-run determinant of inflation driven by growth in the money supply or growth in fiscal debt or both? How important are inflation expectations?

NOTES 1 Irving Fisher, The Making of Index Numbers (Houghton Mifflin Co., New York, 1922), p. 3. 2 Ibid. Fisher’s book review hundreds of variations of index formulation. 3 Eurostat Statistics Explained: Laspeyres price index, https://ec.europa.eu/eurostat/statisticsexplained/index.php?title=Glossary:Laspeyres_price_index. 4 Eurostat Statistics Explained: Paasche price index, https://ec.europa.eu/eurostat/statistics-explained/ index.php?title=Glossary:Paasche_price_index. 5 Eurostat Statistics Explained: Fisher price index, https://ec.europa.eu/eurostat/statistics-explained/ index.php?title=Glossary:Fisher_price_index. 6 OECD Glossary of Statistical Terms, https://stats.oecd.org/glossary/detail.asp?ID=2711. 7 Irving Fisher, The Making of Index Numbers (Houghton Mifflin Co., New York, 1922). 8 George J. Stigler (chair), Dorothy S. Brady, Edward F. Denison, Irving B. Kravis, Philip J. McCarthy, Albert Rees, Richard Ruggles, and Boris C. Swerling, The Price Statistics of the Federal Government: Review, Appraisal, and Recommendations, A Report to the Office of Statistical Standards Bureau of the Budget Prepared by the Price Statistics Review Committee of the National Bureau of Economic Research, National Bureau of Economic Research, Inc., 1961, p. 51. 9 Ibid., p. 25. 10 Final Report of the Advisory Commission to Study the Consumer Price Index, Committee on Finance, U.S. Senate, December 1996, p. 1. The committee members were Michael J. Boskin, Ellen R. Dulberger, Robert J. Gordon, Zvi Griliches, and Dale Jorgenson. 11 The BLS’s Handbook of Methods provides details on the construction of the CPI, see https://www. bls.gov/opub/hom/cpi/pdf/cpi.pdf 12 See U.S. Bureau of Labor Statistics: https://www.bls.gov/cpi/questions-and-answers.htm 13 This asset-price inflation metric is based on FOF data with weights determined between 2015Q1 and 2018Q4 for All Sectors; Corporate Equities; Asset, Market Value Levels (BOGZ1LM893064105Q), All Sectors; Total Mortgages; Asset, Level (ASTMA); and All Sectors; Total Debt Securities; Liability, Level (ASTDSL). The mnemonics shown are from the St. Louis Federal Reserve Bank’s FRED database.

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MACROECONOMIC THINKING AND TOOLS 14 This is calculated as the Nonfinancial Corporate Business; Corporate Equities; Liability, Level (NCBEILQ027S) divided by Nonfinancial Corporate Business; Net Worth, Level (TNWMVBS­ NNCB), and adjusted for a comparable unit of measure. The mnemonics shown are from the St. Louis Federal Reserve Bank’s FRED database. 15 For more information on Shiller’s calculations and historical data, see http://www.econ.yale.edu/ ~shiller/data.htm 16 This measure is based on the Wilshire 500 stock price index, which is based on approximately 3,000 capitalization-weighted security returns. The stock index is based on December 31, 1980 capitalization of $1,404.596 billion. This price index was used to extrapolate the market capitalization at the end of 1980 and divided by nominal or current dollar GDP. The Wilshire 500 stock price index was accessed from FRED with the mnemonic WILL5000PR. The current-dollar GDP also was accessed from FRED with the mnemonic GDP. The World Bank also has compiled annual estimates of this stock market capitalization to GDP ratio for various countries. The mnemonic in FRED for this World Bank estimate of the stock market capitalization to GDP for United States is DDDM01USA156NWDB. 17 A. Ando and F. Modigliani, “The ‘Life Cycle’ Hypothesis of Savings: Aggregate Implications and Tests,” American Economic Review, vol. LIII (March 1963), pp. 55–84. 18 G.C. Lim and Q. Zeng, “Consumption, Income, and Wealth: Evidence from Age, Cohort, and Period Elasticities,” Review of Income and Wealth, Series 62, no. 3 (September 2016), p. 489. 19 Ravi Bansal, Dana Kiku, Ivan Shaliastovich, and Amir Yaron, “Volatility, the Macroeconomy, and Asset Prices,” The Journal of Finance, vol. 69, no. 6 December 2014, p. 2471. 20 Otto Eckstein, Core Inflation (Prentice Hall, Englewood Cliffs, NJ, 1981), p. 13. 21 See: Jim Dolmas, “Trimmed Mean PCE Inflation,” Working Paper 0506, Federal Reserve Bank of Dallas, July 25, 2005, https://www.dallasfed.org/~/media/documents/research/papers/2005/wp0506.pdf. 22 This methodology is described at: https://www.clevelandfed.org/en/our-research/indicators-anddata/median-cpi/background-and-resources.aspx. 23 The data for this graph is available from FRED. The mnemonics are: MEDCPIM158SFRBCLE is the Median Consumer Price Index, Percent Change at Annual Rate, Monthly, Seasonally Adjusted; TRMMEANCPIM158SFRBCLE is the 16% Trimmed-Mean Consumer Price Index, Percent Change at Annual Rate, Monthly, Seasonally Adjusted; PCETRIM12M159SFRBDAL is the Trimmed Mean PCE Inflation Rate, Percent Change from Year Ago, Monthly, Seasonally Adjusted; and CPILFESL is the Consumer Price Index for All Urban Consumers: All Items Less Food and Energy in U.S. City Average, Index 1982–1984 = 100, Monthly, Seasonally Adjusted. The CPIL­ EFSL is graphed on a year-over-year percentage change basis. 24 Michael F. Bryan and Brent Meyer, “Are Some Prices in the CPI More Forward Looking than Others? We Think So,” Economic Commentary, Federal Reserve Bank of Cleveland, Number 2010–2, May 19, 2010. 25 These data are available in FRED: Sticky Price Consumer Price Index (STICKCPIM159SFRBATL) and Flexible Price Consumer Price Index (FLEXCPIM159SFRBATL). 26 Alessandro Barattieri, Susanto Basu, and Peter Gottschalk, “Some Evidence on the Importance of Sticky Wages,” NBER Working Paper No. 16130, June 2010. 27 Arthur M. Okun, Prices and Quantities: A Macroeconomic Analysis (Brookings Institution, Washington, DC, 1981). 28 Adam Hale Shapiro, “How Much Do Supply and Demand Drive Inflation?”, FRBSF Economic Letter, Federal Reserve Bank of San Francisco, No. 2022–15, June 21, 2022. Also see, Adam Hale Shapiro, “A Simple Framework to Monitor Inflation,” Federal Reserve Bank of San Francisco Working Paper 2020–29, Federal Reserve Bank of San Francisco, June 2022. https://doi.org/10.24148/wp2020-29. 29 Adam Hale Shapiro, FRBSF Economic Letter, p. 2. 30 See: Knut Wicksell, Interest and Prices: A Study of the Causes Regulating the Value of Money, 1898, English translation, London: Macmillan and Company, 1936. 31 See, for example, Thomas A. Lubik and Christian Matthes, “Calculating the Natural Rate of Interest: A Comparison of Two Alternative Approaches,” Economic Brief (EB15–10), Federal Reserve Bank of

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32 33 34 35

36 37 38 39

40

Richmond, October 2015. Also, Jeffery D Amato, “The role of the natural rate of interest in monetary policy,” BIS Working Papers, No. 171, Bank for International Settlement, March 2005. Thomas M. Humphrey, “Keynes on Inflation,” Economic Review, Federal Reserve Bank of Richmond, January/February 1981, p. 6. A.W. Phillips, “The Relationship Between Unemployment and the Rate of Change of Money Wage Rates in the United Kingdom, 1861–1957,” Economica, vol. 25 (1958), pp. 283–299. Irving Fisher, “I Discovered the Phillips Curve: ‘A Statistical Relation between Unemployment and Price Changes,’” Journal of Political Economy, vol. 81, no. 2, Part 1 (March-April 1973), pp. 496–502. See, for example, E.M. Leeper and X. Zhou, “Inflation’s Role in Optimal Monetary-Fiscal Policy,” Discussion Paper Working Paper 19686, National Bureau of Economic Research, 2013, http:// www.nber.org/papers/w19686. Thomas J. Sargent, “The Ends of Four Big Inflations,” in R. E. Hall, ed., Inflation: Causes and Effects, (University of Chicago Press, 1982), chap. 2, pp. 41–98. http:// www.nber.org/chapters/c11452.pdf. Christopher A. Sims, “A Simple Model for Study of the Determination of the Price Level and the Interaction of Monetary and Fiscal Policy,” Economic Theory, vol. 4 (1994), pp. 381–99. Eric M. Leeper, “Real Theory of the Price Level,” Briefing, September 21, 2015. John H. Cochrane, “The Fiscal Theory of the Price Level and its Implications for Current Policy in the United States and Europe”, paper presented at the “Next Steps for the Fiscal Theory of the Price Level” conference at the Becker Friedman Institute for Research on Economics at the University of Chicago, April 1, 2016. Also, John H. Cochrane, “Next Steps for the FTPL,” The Grumpy Economist, John Cochrane’s Blog, 2016, http://johnhcochrane.blogspot.com/2016/04/ next-steps-for-ftpl.html. Christopher A. Sims, “Fiscal policy, monetary policy and central bank independence,” paper pre­ sented at the Kansas City Federal Reserve Bank’s Jackson Hole Conference, August 26, 2016. Pedro Bordalo, Nicola Gennaioli, and Andrei Shleifer, “Overreaction and Diagnostic Expectations in Macroeconomics,” Journal of Economic Perspectives, vol. 36, no. 3 (Summer 2022), p. 225. Ibid., pp. 223–244. Christopher A. Sims, “Rational Inattention and Monetary Economics,” in Benjamin M. Friedman and Michael Woodford, eds., Handbook of Monetary Economics, vol. 3 (North-Holland, Amsterdam, 2010), pp. 155–181. Alan Greenspan, “Statement before the Committee on Banking, Housing, and Urban Affairs, U.S. Senate, February 21, 1989,” Federal Reserve Bulletin, vol. 75 (April 1989), pp. 274–275.

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5

Key Macroeconomic Statistics—Output and Productivity LEARNING OBJECTIVES This chapter introduces you to measures of economic output and related concepts. You will learn: • • • • • • • • •

What gross domestic product (GDP) is. How gross domestic product is measured. What inflation-adjusted GDP represents. How gross domestic product is analyzed. The theoretical concept of GDP, known as “natural” or “potential” GDP. The limitations of GDP. How GDP is related to productivity. The drivers of economic growth. How international productivity compares.

[109] HOW IS THE ECONOMY’S PERFORMANCE ASSESSED? Economists and policymakers use the National Income and Product Accounts (NIPA) to address questions about the strength, weakness, or compositional shifts in the U.S. economy. These economic accounts1 are compiled by the U.S. Commerce Department’s Bureau of Economic Analysis (BEA) and measure the value and composition of output produced in the United States during a given period from a final-demand expenditure perspective, which is gross domestic product, and from the perspective of the income-generated from that production—which is gross domestic income.

DOI: 10.4324/9781003391050-6

OUTPUT AND PRODUCTIVITY

[110] WHY IS THE GROSS DOMESTIC PRODUCT SUCH AN IMPORTANT STATISTIC? The gross domestic product (GDP) is one of the broadest economic statistics that measures the value of goods, services, and structures produced in the U.S. economy. As such, it provides one of the most comprehensive pictures of the economic health of the economy. Colloquially, this measure is considered the nation’s output in economic theory.

[111] WHAT IS MEANT THAT GDP “ONLY” REPRESENTS FINAL DEMAND? GDP as a concept eliminates double-counting and only measures the final value-add of production. For example, the value of an automobile in GDP is measured as the cost of the vehicle to the final-demand user (which may be a consumer, a business, or even government), but it does not double count the intermediate production of items that go into the making of a vehicle, such as steel, aluminum, copper, lithium, plastics, rubber, glass, etc.

[112] [ADVANCED] WHY SHOULD GDP EXCLUDE THE VALUE OF THE SUPPLY CHAIN SINCE IT TOO REPRESENTS CURRENT PRODUCTION? Purchasing managers have long argued in practice and in theory that a broader aggregate of total production is a more representative metric of what purchasing managers (or supply-chain managers) see happening in the economy.2 And that observation is true. BEA produces a supplemental measure to GDP, which is known as gross output, that does sum the value of final and intermediate production. About 57% of gross output is GDP (final demand) and about 43% is the value of intermediate output. However, for economic analysis purposes gross output overstates the size of production. The BEA describes this relationship between gross output and GDP as follows: “gross output is principally a measure of sales or revenue from production for most industries, although it is measured as sales or revenue less cost of goods sold for margin industries like retail and wholesale trade. Intermediate inputs are the foreign and domestically-produced goods and services used up by an industry in the process of pro­ ducing its gross output. Value added is the difference between gross output and intermediate inputs and represents the value of labor and capital used in producing gross output. The sum of value added across all industries is equal to gross domestic product for the economy. Value added is also measured as the sum of an industry’s compensation of employees, taxes on production and imports, less subsidies, and gross operating surplus.”3

[113] [ADVANCED] WHY AND WHEN DID GDP REPLACE GNP AS THE PRIMARY MEASURE OF NATIONAL OUTPUT? In November 1991, the U.S. Bureau of Economic Analysis began reporting gross domestic product (GDP) as “its featured measure of U.S. production, rather than gross national product

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(GNP), the measure in use since 1934.”4 The two measures are empirically very close with nominal GNP slightly larger than nominal GDP by an average annual difference of 0.67% between 1929 and 2021. The largest annual gap between the two measures was in 2011 when GNP was 1.5% larger than GDP; the smallest difference was in 1945 with GNP only 0.1% higher than GDP. The BEA further explained: “Although both GDP and GNP conceptually represent the total market value of all goods and services produced over a defined period, there are differences between how each defines the scope of the economy. GDP measures the goods and services produced within the country’s geographical borders, by both U.S. residents and residents of the rest of the world. GNP measures the goods and services produced by only U.S. residents, both domestically and abroad. The change from GNP to GDP reflected a more appropriate measure for U.S. aggregate production, particularly in short-term monitoring and analysis of the economy. Moving towards this as the primary measure of production was advantageous for several reasons. GDP was the primary measure of production in the System of National Accounts, the set of international guidelines for economic accounting. Many other countries had adopted GDP as their featured measure, allowing for reliability in comparisons of economic activity across countries. It was also consistent in coverage with other economic indicators such as employment and productivity.”5 GNP continues to be compiled and re­ ported by the U.S. Commerce Department’s Bureau of Economic Analysis (BEA), but GDP is the primary summary measure of the economy’s performance that is highlighted.

[114] [ADVANCED] WHAT IS THE INTERNATIONAL SYSTEM OF NATIONAL ACCOUNTS? The national income accounts provide key “building blocks of macroeconomic statistics” for all nations, which are used for economic analysis and policy formulation. The United Nations Statistics Division (UNSD), which “contributes to the international coordinated development and updating of the System of National Accounts (SNA)” along with the European Commission (EC), Organisation for Economic Co-operation and Development (OECD), International Monetary Fund (IMF), and the World Bank Group, explains that, “The System of National Accounts (SNA) is the internationally agreed standard set of recommendations on how to compile measures of economic activity. The SNA describes a coherent, consistent and integrated set of macroeconomic accounts in the context of a set of internationally agreed concepts, definitions, classifications and accounting rules … The SNA is intended for use by all countries, having been designed to accommodate the needs of countries at different stages of economic development.” The goal of consistency in the measurement of national income is to allow for international comparison of economic performance and needs.6

[115] WHAT GOES INTO THE CALCULATION OF GDP? In Chapter 2, the circular-flow accounting concept was introduced with a discussion of GDP and GDI. This discussion, however, focuses on the measurement aspects of the concepts, which are valued both at market prices (in nominal dollars) and at real prices (in inflation-

OUTPUT AND PRODUCTIVITY

adjusted dollars). Gross domestic product (the “expenditure side”) or gross domestic income (the “payments to factors of production” side) measure the nation’s output from a productionflow standpoint. Therefore, only the value of newly-produced completed output is counted in GDP. This means, for example, the cost of a used vehicle is not included in today’s production—but was included in an earlier count of production. With that said, any currently incurred expenditures or income associated with that sale used merchandise would be cap­ tured. Following that example of a used vehicle sale, there would be some marketing and sales costs involved with the used vehicle transaction that would be included in the current count of GDP. A second key point is related to the first. Since GDP is a flow concept, the measurement of inventory stock must conform to this concept. To do that, the value of the inventory stock enters GDP as a change from the end of the prior quarter. This, in turn, converts a stock concept of inventories to a flow concept measuring only the value of the change in inven­ tories. This is the rationale why GDP is the sum of consumption, investment, government spending, and net exports, where investment is the sum of fixed investment (such as buildings and factories) and the change in inventories. Additionally, GDP subtracts out imports.

[116] WHAT IS THE DIFFERENCE BETWEEN U.S. GDP MEASURED IN MARKET PRICES VERSUS IN 2012 DOLLARS? The conceptual difference between market-valued GDP and 2012 dollar, or inflation-adjusted, GDP is: •

•

Market-valued or nominal GDP is the nation’s value (aggregate price multiplied by aggregate quantity) of all currently produced goods and services (final demand) sold on the market during a particular time interval. Real GDP, measured in an arbitrary base period of 2012 dollars, is nominal GDP adjusted to remove the impact of inflation (conceptually, just the aggregate quantity).

[117] IS THERE A THEORETICAL COUNTERPART FOR ACTUAL GDP? Yes. The theoretical counterpart of actual GDP is “natural GDP” or more commonly referred to as “potential GDP.” Potential GDP is considered an economic goal for policymakers to achieve.

[118] IS POTENTIAL GDP CALCULATED IN MARKET-VALUE AND IN INFLATION-ADJUSTED TERMS? Yes, the two theoretical GDP concepts at full employment are defined as: •

Natural Nominal GDP (also known as potential nominal GDP) is the level of current dollar GDP in an analytical measure where the economy is at full-resource use and there is no tendency for inflation to rise or fall.

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•

Natural Real GDP (also known as potential real GDP) is the level of real GDP in an analytical measure (estimated by the Congressional Budget Office) where the economy is at full-resource use and there is no tendency for inflation to rise or fall.

[119] WHAT IS THE “TREND” OF REAL GDP GROWTH? From an empirical standpoint, a specific growth rate could be calculated over a long period or some reference period as the “trend” growth rate in the economy. However, the trend real GDP is also thought of as the growth rate in real potential (or natural) GDP, because that estimate of potential is based on estimates of current trend growth in the labor force, pro­ ductivity, and so forth.

[120] WHAT ARE ALL OF THE INTERCHANGEABLE TERMS USED TO MEASURE ACTUAL GDP? Economists use several terms interchangeably to describe both GDP at market prices and GDP adjusted for inflation. Those terms are: • • •

Market-valued GDP equals nominal GDP equals GDP at market prices equals GDP in current dollars. Real GDP equals GDP adjusted for inflation equals GDP in constant dollars (which also is 2012 constant dollars for U.S. real GDP). These interchangeable terms are listed in the table. TABLE 5.1

Interchangeable Terms Used to Measure Actual GDP

Concept

Interchangeable Terms

Gross domestic product (GDP)

1. 2. 3. 1. 2. 3.

GDP adjusted to remove the effects of inflation

Nominal GDP; GDP at market prices; Current-value GDP. Real GDP; Inflation-adjusted GDP; Constant-value GDP.

[121] HOW IS INFLATION-ADJUSTED OR REAL GDP MEASURED? The measurement of real GDP and its components begins with estimates of market-valued GDP and its components. Separately, the GDP price indexes are computed. Then the esti­ mates of real GDP and its components are determined by dividing the market-valued GDP and its components by the GDP price indexes for each component. For aggregate real GDP, then the computation is:

OUTPUT AND PRODUCTIVITY

Real GDP =

Nominal GDP Price Level

Of course, conceptually, Nominal GDP = Price Level × Real GDP—but this not how it is calculated. Therefore, generically, real GDP is the “quantity” and the price level is the “price”, then the price multiplied by the quantity equals nominal GDP.

[122] WHY ARE SHARES OF GDP ALWAYS SHOWN IN CURRENTDOLLAR PRICES? When you analyze shares of the economy, you should always look at market-value or nominal shares, because this measures expenditure shares, not quantity shares. •

• •

The expenditure share of the economy is by definition the price multiplied by the quantity of that “slice” of the economy divided by the total expenditure (price multiplied by quantity). If you remove the price component, then you no longer have an expenditure share of the total. Real shares and nominal shares will be the same only in the base year for the price index, which currently is 2012 for U.S. GDP. Real shares will overstate the share of the economy in periods prior to the price index base (2012) and will understate the share of the economy in periods after the price index base.

[123] DOES THE GDP MEASURE OF INVESTMENT INCLUDE THE PURCHASE OF STOCKS AND BONDS? No. Although non-economists may think of investment as including the purchase of common stocks or bonds, economists define investment spending as the purchase of physical assets, such as new machines or new houses—purchases that add to GDP.

[124] [ADVANCED] DEFINITIONALLY, WHAT IS THE DIFFERENCE BETWEEN GROSS DOMESTIC PRODUCT, GROSS DOMESTIC PURCHASES, COMMAND-BASIS GROSS DOMESTIC PRODUCT, GROSS NATIONAL PRODUCT, AND COMMANDBASIS GROSS NATIONAL PRODUCT? There clearly is a maze of concepts for thinking about the nation’s output—most of which are not integrated with any economic theory, but each of these measures is routinely reported by the U.S. Bureau of Economic Analysis or other international statistical offices and provide a

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slightly different empirical perspective on the economy. Here are four related concepts re­ ported by the BEA: • • •

•

Gross Domestic Product: This measures production in the United States. It is the sum of consumption, investment, government spending, and net exports. Gross Domestic Purchases: This measure captures final expenditures by U.S. residents. It is the sum of consumption, investment, and government spending. Gross National Product: This measure is the market value of goods and services produced by labor and property supplied by U.S. residents, regardless of where they are located. It equals Gross Domestic Product plus Income receipts from the rest of the world minus Income payments to the rest of the world. Command-Basis Gross Domestic Product: This measure of real GDP is described by the BEA as providing “information on the real purchasing power of the income generated by the production of goods and services. It reflects the impact of changes in the prices of traded goods and services as well as changes in production. Command-basis GDP is calculated by deflating both exports and imports by the price index for gross domestic purchases. Thus, it reflects the prices of purchased goods and services, while real GDP reflects the prices of produced goods and services. In the SNA, the term for this measure is ‘real gross domestic income’.”7

[125] [ADVANCED] WHY WOULD COMMAND-BASIS REAL GDP BE USEFUL? From time-to-time this measure is important since the traditional measure of real GDP tends to underestimate the increase in real domestic income and welfare when the terms of trade improve. The logic is clearly described by the BEA, which writes, “Foreign trade enables a nation to consume a different mix of goods and services than it produces, so to measure real gross domestic income (GDI) for an open economy, we must deflate by an index of the prices of the things that this income is used to buy, not the price index for GDP. The differences between these two indexes come from the export and import components of the GDP, and are measured by the trading gains index.” The BEA calculated in 2009 that the median absolute effect on U.S. real GDP was a modest 0.15% per year on average. Although the United States may have had a modest impact from changing terms of trade, one international study found in some countries that gap was more than 10% of GDP.8

[126] [ADVANCED] WHAT IS MEANT BY “TERMS OF TRADE” IN THE GDP REPORT? Terms of trade is a measure calculated by the BEA that shows the relationship between the prices that are received by U.S. producers for exports of goods and services and the prices that are paid by U.S. purchasers for imports of goods and services.

OUTPUT AND PRODUCTIVITY

•

•

Terms of trade index = Ratio (multiplied by 100) of the price index for exports of goods and services to the price index for imports of goods and services. Additionally, foreign trade (which is the essence of an open economy) allows a nation to consume a different mix of goods and services than that nation produces, which is captured by the “trading gains index.” Trading gains index = Ratio (multiplied by 100) of price index for gross domestic product to the price index for gross domestic purchases.

[127] [ADVANCED] ARE THERE OTHER IMPORTANT STATISTICS FOUND IN THE GDP REPORT? Absolutely. Economists often look at various measures included in the quarterly statistics for a richer understanding of the stories driving or that will potentially drive the economy in the nearterm. These important indicators include the personal saving rate, inventories, and corporate profits. For example, a back-up in the saving rate would imply potentially more spending in the future (and the opposite situation is true as well). A hefty drawdown in inventories would likely imply more inventory rebuilding in the upcoming quarter (and vice versa). Corporate profits are tied to broader theories of the economy, as well. Profitability has been classified as a leading indicator of the economy, so these data also provide useful guideposts for the economy’s future.

[128] DOES GDP MEASURE THE STANDARD OF LIVING IN A NATION? Yes, real GDP is one measure of a nation’s standard of living. But comparisons between nations must be adjusted for a common currency and a common set of prices—which is known as purchasing power parity.

[129] [ADVANCED] HOW IS REAL GDP COMPARED ACROSS NATIONS? It is common to compare economic (real GDP) growth rates of different nations but com­ paring the level of real GDP across different nations poses a problem. Which economy is the largest, for example, requires a common currency for that comparison. So, one way to answer that question of relative size would be to convert every country’s real GDP (which is consistently measured based on international standards for national income) by an exchange rate between pairs of countries, where one of those country pairs will be the common country. For example, if you wanted to compare the relative size of all other countries in U.S. dollars compared with U.S. real GDP, then the bilateral exchange rates for each country relative to the U.S. dollar would be calculated and each non-U.S. country’s real GDP would be divided by an exchange rate measured as the number of the foreign currency per U.S. dollar (not all foreign exchange rates are expressed that way, some are the inverse relationship—so care should be taken so that the calculation is correct). But even if you did that calculation, there is another fundamental problem—an exchange rate does not represent the purchasing power in that country, but only

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the value of tradable goods and services. However, a consumer price index or similar price measures would represent the local economy’s purchasing power. Thus, each exchange rate could be adjusted by the relative price level between countries. This adjustment is known as a real exchange rate. The real exchange rate equation is e’ = e (Pd/Pf), where e’ denotes the real exchange rate, e is the nominal exchange rate quoted as foreign currency per U.S. dollar, and (Pd/Pf) is the ratio of domestic (Pd) to foreign price (Pf) levels. There still remains one more measurement problem using local inflation measures since each country’s market basket of goods and services varies from other countries, which does not standardize the purchasing power of that exchange rate across multiple countries. This final adjustment is done using a standardized global market basket of goods and services, instead of local market baskets to determine comparative price levels. Using those standardized price indexes to adjust exchange rates yields what is known as purchasing power parity (PPP)—which is a real exchange rate using a common global market basket for relative price comparison.

[130] WHAT IS THE LARGEST ECONOMY IN THE WORLD, MEASURED IN TERMS OF REAL GDP USING PURCHASING POWER PARITY? The World Bank estimates world real GDP in 2020 (the latest estimate) at 125,653.5 trillion international dollars. Real gross domestic product is converted by The World Bank into international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. In 2020, China accounted for 18.3% of the world’s real GDP followed by the United States with 15.8%. TABLE 5.2

Rank

Largest Economies in 2020 Country

GDP, PPP (constant 2017 international $, in Millions)

Share of World

World 125,653,507 100.0 1 China 23,020,463 18.3 2 United States 19,863,485 15.8 3 India 8,508,758 6.8 4 Japan 5,062,661 4.0 5 Germany 4,276,401 3.4 -----------------------------------------------------------------------------------------------------------------9 United Kingdom 2,868,465 2.3 10 France 2,851,553 2.3 12 Italy 2,322,896 1.8 13 Mexico 2,301,754 1.8 15 Canada 1,752,155 1.4 -----------------------------------------------------------------------------------------------------------------(Continued )

OUTPUT AND PRODUCTIVITY TABLE 5.2

Rank

(Continued) Country

GDP, PPP (constant 2017 international $, in Millions)

Share of World

Euro area 15,049,528 12.0 European Union 18,666,107 14.9 North America 21,620,537 17.2 OECD members 58,242,319 46.4 -----------------------------------------------------------------------------------------------------------------High income 58,350,252 46.4 Middle income 65,697,790 52.3 Low income 1,322,601 1.1 Source: The World Bank (2022)

[131] WHAT ARE THE LIMITATIONS OF GDP? GDP does not cover every aspect of the economy and life unless there is some measured transaction associated with production. Therefore, GDP does not capture the “underground economy” where transactions are not recorded. It does not cover what is called “household production,” that is unpaid family members providing childcare and other services. It does not include the value to people of leisure time and it does not capture environmental quality. There is no adjustment in GDP for the quality of life—including health and life expectancy, political freedom, or social justice.

[132] WHAT TYPES OF ECONOMIC STATISTICS CAN ADDRESS SOME OF THE LIMITATIONS OF GDP? There are vast amounts of economic, social, and demographic statistics that can fill in addi­ tional aspects of an economy’s broader development. One group of indicators are “develop­ ment indicators” that are compiled by international organizations, such as the World Bank. Among those types of indicators9 are the following. •

Gini index (World Bank estimate): Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus, a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.

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•

•

•

Human capital index (HCI) (scale 0–1): The HCI calculates the contributions of health and education to worker productivity. The final index score ranges from zero to one and measures the productivity as a future worker of child born today relative to the benchmark of full health and complete education. Statistical capacity score (overall average): The statistical capacity indicator is a composite score assessing the capacity of a country’s statistical system. It is based on a diagnostic framework assessing the following areas: methodology, data sources, and periodicity and timeliness. Countries are scored against 25 criteria in these areas, using publicly available information and/or country input. The overall statistical capacity score is then calculated as a simple average of all three area scores on a scale of 0–100. Statistical performance indicators (SPI): Overall score (scale 0–100). The SPI overall score is a composite score measuring country performance across five pillars: data use, data services, data products, data sources, and data infrastructure. The new statistical performance indicators (SPI) will replace the statistical capacity index (SCI), which the World Bank has regularly published since 2004. Although the goals are the same, to offer a better tool to measure the statistical systems of countries, the new SPI framework has expanded into new areas including in the areas of data use, administrative data, geospatial data, data services, and data infrastructure. The SPI provides a framework that can help countries measure where they stand in several dimensions and offers an ambitious measurement agenda for the international community.

These four examples are just the tip of a statistical iceberg of additional statistics—which can include environmental statistics (including work on environmental-economic accounting), crime statistics, health statistics, and more—to supplement and address the limitations of GDP. Moreover, it is widely agreed that a broad range of statistics compiled by international organi­ zations helps to “support evidence-based decision-making,” according to the United Nations.

[133] WHAT IS THE THEORETICAL RELATIONSHIP BETWEEN REAL GDP AND REAL POTENTIAL (OR NATURAL) GDP? This is a foundational (“core”) economic relationship that connects the gap or difference between the actual real GDP and the estimate of real potential GDP (either as a simple difference or as that difference divided by potential—which is known as the GDP or output gap) and the unemployment rate, the inflation rate, and interest rates. If the gap is negative (that is, actual real GDP is less than real potential GDP) then that implies slack in the economy, which in turn implies downward pressure on the inflation rate, downward pressure on interest rates, and upward pressure on the unemployment rate. The reverse is true when the difference between actual real GDP and real potential GDP is positive—which means that the economy is booming. If actual real GDP equals real potential GDP, then the economy is at “full employment” output where there is no slack in the economy, inflation is steady, the actual unemployment rate equals its full-employment unemployment rate, and interest rates are “well-balanced” neither putting upward nor downward pressure on economic growth.

OUTPUT AND PRODUCTIVITY

[134] [ADVANCED] WHAT IS “BIG DATA” AND HOW IS IT USED? Big data is different from traditional data sources compiled by governmental statistical offices. It is usually available faster, may have greater coverage and scope, and includes new types of observations and measurements that previously were not available. Modern data sets also can be less structured or have a more complex structure than the traditional cross-sectional, timeseries, or panel data used in economics. Big data draws upon new or more limited data sources—often company-based or Internet-based—and requires new methodologies for compiling these timely (though often incomplete) data. These “high volume, high velocity and wide variety of data [require data-science tools—such as artificial intelligence (AI) and machine learning—] and methods for capturing, managing, and processing them efficiently.”10 Big data sources of information occur due to innovations in technology that allow digital information to be continuously generated from lots of different sources, including automated teller machines (ATMs), scanning devices, mobile phones, social media, online purchasing, online banking, Internet searches, and more. In time, big data will likely affect conventional macroeconomic theories by providing new windows on how the economy works.

[135] [ADVANCED] ARE ECONOMIC DATA NEEDS CHANGING AND WHAT WILL IT MEAN? Official government statistical data provide a consistent picture of economic, social, and demographic behavior over time and across nations. However, today new private and publicprivate data sources are popping up to supplement official statistics. But are they helpful for economic analysis? As aptly put by the United Nations, “Big data sources pose new challenges across a range of expert areas, including methodology, quality assurance, technology, security, privacy, legal matters, and skills. The breadth of challenge adds to the complexity of incor­ porating big data into regular research or organizational operations and ensures that the transition to their use is difficult, or hindered, for many national statistical organizations.” Therefore, the United Nations Statistical Commission created a Global Working Group on Big Data that has been meeting since 2014. To be sure, use of these big data sources with guidance from data experts will help to provide more granule understanding of the how the economy operates and potentially provide that insight at a faster pace. However, it is too soon to answer the questions: (a) How will big data change official statistics? And (b) How will big data change economic theory?

[136] WHAT IS PRODUCTIVITY AND HOW IS IT RELATED TO REAL GDP? Productivity has been measured by different formulae, but essentially is output or real GDP (or some slice of real GDP—such as non-farm output) divided by some factor that “nor­ malizes” the relationship. That output measure could be divided by the number of people or the number of hours worked. Productivity is based on an aggregate production function for the nation.

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[137] WHAT IS MEANT BY AN AGGREGATE PRODUCTION FUNCTION? The aggregate production function measures a country’s production or output. It is the process whereby an economy, as a whole, turns economic inputs such as human capital, physical capital, and technology into output, and is typically measured as GDP per capita or per hour.

[138] WHEN IS REAL GDP USED AND WHEN IS PRODUCTIVITY USED IN ECONOMIC THEORY AND ANALYSIS? Typically, macroeconomic theories that focus on short-term movement in the economy are linked to output (real GDP). However, macroeconomic theories that tend to explain longterm movement are tied to productivity.

[139] WHAT IS LABOR PRODUCTIVITY? The BLS compiles a measure of labor productivity that measures the growth in output relative to the growth in hours worked. These quarterly data are updated monthly following the monthly updates to the quarterly real GDP statistic—which serves as the numerator of the labor productivity statistic.

[140] WHAT IS “TOTAL-FACTOR PRODUCTIVITY”? Total-factor productivity (TFP), also known as multifactor productivity, is a measure of efficiency that accounts for capital and labor inputs. The BLS produces a measure of mul­ tifactor productivity only on an annual basis, which shifts the focus to the more-timely labor productivity statistic. However, the Federal Reserve Bank of San Francisco’s Economic Research now produces a “real-time” quarterly estimate of TFP, which decomposed TFP into its growth components from capital input, labor productivity, and utilization. Economists consider factor utilization, which is estimated in the San Francisco TFP measure, important over the business cycle because it depicts the efficiency of the combined inputs. Factor utilization is calculated as a residual11 that “captures anything that affects the rela­ tionship between measured inputs and output. Over long periods of time, TFP is driven primarily by innovation, knowledge, and efficiency—factors that raise the economy’s pro­ ductive capacity.”12

[141] [ADVANCED] WHAT IS ECONOMIC GROWTH ACCOUNTING? According to Crafts and Woltjer, economic growth accounting came into prominence in the 1950s and early 1960s due to research at the National Bureau of Economic Research.13

OUTPUT AND PRODUCTIVITY

Growth accounting uses statistical methods to decompose (breakdown) the factors that con­ tribute to economic growth—mainly labor, capital, and technology.

[142] [ADVANCED] WHAT ARE SOME OF THE KEY FINDINGS FROM GROWTH ACCOUNTING STUDIES? The key findings of growth accounting, according to a survey by Crafts and Woltjer,14 have “produced some eye-catching results which probably are reasonably robust and provide food for thought both for economic historians and for growth economists.” Those findings show: •

•

•

• •

The economic impact of technology was quite modest during the 19th-century industrial revolution in Great Britain—despite what Crafts and Woltjer observe was a period of a lot of famous inventions. Robert Solow’s 1957 study that, over a 40-year period, found that almost 90% of totalfactor productivity in America was accounted by labor productivity. However, the survey by Crafts and Woltjer found that period to be atypical of other times in the United States and other places through the globe. It typically takes a long time for new technology to impact labor productivity. The authors cited the long lag on labor productivity from the widespread introduction of electricity and steam and later computers. Another takeaway from this study was that (physical) capital-deepening was a bigger factor for TFP growth for East Asia nations than for Western European countries. Finally, other growth accounting studies in the post-WWII period in the United States tend to conclude: (1) Technology is typically the most important contributor to U.S. economic growth. (2) Growth in human capital and physical capital explain only about half or less of economic growth. And all three factors of human capital, physical capital, and technology must all be present for a nation to prosper.

[143] WHAT IS MEANT BY CAPITAL DEEPENING? Capital deepening means that the amount of capital per person increases. This concept can apply to both physical-capital deepening—which is the more common application—and human-capital deepening. Physical-capital deepening is generally measured as an increase in the capital-to-labor ratio. Human-capital deepening (or sometimes referred as improvement in labor quality) is increased when the skill level of the workforce is elevated through training and education.15 For the United States, this trend is shown in the chart. The rising levels of education per person aged 25 years and older show the deepening in human capital for the U.S. economy since 1910. Today, about a quarter of U.S. adults (23.5% in 2021), 25 years and older, has completed a bachelor’s degree as a terminal degree, which suggests that there is additional room for increasing human-capital deepening as shown in Figure 5.1.

89

90

MACROECONOMIC THINKING AND TOOLS

Rates of High School Completion and Bachelor’s Degree and Higher Attainment Among Persons Aged 25 and Older, 1910–2021

FIGURE 5.1

[144] [ADVANCED] WHAT ARE THE LONG-TERM INTERNATIONAL TRENDS IN TFP? The historical trends in total-factor productivity for some of the major economies are shown in the table and chart below, using data compiled in the Long-Term Productivity Database—a product of research originally done at the Bank of France.16 These data suggest that over the very long term—130 years—TFP for these nations has ranged from 0.6% per year for Mexico to 2.0% per year for Ireland. The median of those nations was 1.4% per year. From the 1950s forward through 2019, these data have a similar pattern with the lowest for Mexico (+0.4% per year) and the highest for Ireland (+2.7% per year) and a median of 1.7% for all the nations shown Figure 5.2. TABLE 5.3

Trends in Long-Term Total-Factor Productivity By Country

Countries

1890–2019 Average Annual Growth in Total-Factor Productivity (%)

1950–2019 Average Annual Growth in Total-Factor Productivity (%)

2000–2019 Average Annual Growth in Total-Factor Productivity (%)

AUS

Australia

0.78

1.23

0.52

AUT

Austria

1.26

1.90

0.81

BEL

Belgium

1.59

1.72

0.39

CAN

Canada

1.28

1.01

0.46

CHE

Switzerland

1.34

1.30

0.96 (Continued )

OUTPUT AND PRODUCTIVITY TABLE 5.3

(Continued)

Countries

1890–2019 Average Annual Growth in Total-Factor Productivity (%)

1950–2019 Average Annual Growth in Total-Factor Productivity (%)

2000–2019 Average Annual Growth in Total-Factor Productivity (%)

CHL

Chile

1.46

1.32

0.81

DEU

Germany

1.41

2.23

0.59

DNK

Denmark

1.41

1.43

0.77

ESP

Spain

1.17

1.97

0.07

FIN

Finland

1.79

1.99

0.87

FRA

France

1.86

2.29

0.57

GBR

United Kingdom

1.16

1.35

0.57

GRC

Greece

1.01

1.97

0.29

IRL

Ireland

1.98

2.67

2.18

ITA

Italy

1.60

1.92

-0.16

JPN

Japan

1.61

2.37

0.78

MEX

Mexico

0.62

0.39

-0.06

NLD

Netherlands

1.34

1.31

0.45

NOR

Norway

1.73

1.89

0.38

NZL

New Zealand

0.99

0.83

0.72

PRT

Portugal

1.49

1.88

0.44

SWE

Sweden

1.71

1.63

1.05

USA

United States

1.63

1.42

0.94

[145] [ADVANCED] WHAT IS THE J-CURVE IN PRODUCTIVITY? In a paper by Brynjolfsson, Rock, and Syverson,17 the authors develop an economic model based on the thesis that new general-purpose technologies are engines for growth; however, they are not initially embodied in production, which leads to estimates of total factor pro­ ductivity growth that initially will underestimate productivity growth in the early years of a new [widespread technology, and] later when the benefits of that technology are “harvested” then productivity growth will be overstated (thus following an error pattern in productivity measurement that is J-shaped). This thesis is offered to make “appropriate adjustments” in the estimates of total factor productivity.

91

92

MACROECONOMIC THINKING AND TOOLS

International Comparison of Total-Factor Productivity, 1900–2019 Average Annual Growth Over Ten-Year Spans

FIGURE 5.2

Source: Long-Term Productivity Database, A. Bergeud, G. Cette, and R. Lecat, August 2020 version, http:// www.longtermproductivity.com.

[146] WHAT ARE SOME TRENDS THAT HAVE CONCERNED ECONOMISTS ABOUT U.S. PRODUCTIVITY? U.S. non-farm labor productivity has grown by an average of about 2% per year in the postWWII period. However, there have been noticeable and extended deviations from that trend over time. Such was the case in the 1980s and again in the 2010s. Each episode raised questions about its cause and fueled worry about the consequences of the reduced pace of productivity. Typically, lower productivity is associated with higher inflation, which is one of the key consequences that economist worry about.18 The decade changes in U.S. non-farm produc­ tivity growth are portrayed in Figure 5.3.

[147] WHAT ARE THE PRIMARY CAUSES OF CHANGE IN PRODUCTIVITY? The key causes of productivity change are: • •

Factor price change—especially energy prices; Workforce demographic change: For example, as the post-WWII Baby Boom generation entered the workforce in early 1970s that caused an influx of low-productivity workers—since they were relatively inexperienced—and by the early 2000s that

OUTPUT AND PRODUCTIVITY

FIGURE 5.3

Average Annual Percent Change Non-farm Productivity Growth by Decade,

1950–2019 Source: U.S. Bureau of Labor Statistics.

• •

demographic group began retiring, which caused these now high-productivity workers to be lost from the workforce; Shifts in the use and intensity of capital—This is “capital deepening”—that is, an increase in the ratio of capital investment to labor; and Changes in technology.

[148] WHAT ACCOUNTS FOR DIFFERENCES IN ECONOMIC GROWTH IN NATIONS? This question typically is addressed with the aggregate production function in mind. The pro­ duction function is assumed to be dependent on physical capital, human capital, and technology as the long-term determinants of economic growth. Physical capital means the economy’s infrastructure—plant, equipment, transportation networks, and so forth. Human capital means the skill level of the labor force. Technology is all the ways the factors of production can be combined to produce the output. Associated with capital is a concept of “capital deepening.”

[149] HOW DOES LONG-RUN GROWTH TAKE PLACE? There are numerous theories and studies about long-run economic growth; however, in a book by the former Federal Reserve Board Chairman Alan Greenspan and Adiran

93

94

MACROECONOMIC THINKING AND TOOLS

Wooldridge, entitled Capitalism in America: An Economic History of the United States, they suggest three factors explain the process—productivity, creative destruction, and politics. They observe, “Productivity describes society’s ability to get more output from a given input. Creative destruction defines the process that drives productivity growth. Politics deals with the fallout of creative destruction.”19 The term “creative destruction,” which was embraced by Greenspan and Wooldridge, was coined by Austrian economist Joseph Schumpeter in his 1942 book, Capitalism, Socialism, and Democracy. Schumpeter—who believed that innovations were the engine of the economy—wrote about how capitalism regenerates through creative destruction (which also can be thought of as “disruptive innovations”) where innovations lead to a constant replacing of existing processes and/or products, which become obsolete.20 Examples abound. For example, how the horse and buggy was replaced by the motor vehicle, or how the internet ushered in new ways of selling goods and services, transacting banking and business, and more. Schumpeter also argued that these innovations came in waves, or they occurred in bunches or clusters, and they were subsequently diffused. The stages of that process began with the invention fol­ lowed by the commercialization of that invention (which is the innovation in the economy) and then followed by imitation.

[150] WHAT IS INTERNATIONAL ECONOMIC CONVERGENCE? Economic convergence is defined as the pattern where economies with low per capita incomes (real GDP per capita) grow faster than economies with high per capita income so that percapita incomes converge to nearly identical levels across countries.

[151] WITH GREATER COMMUNICATION AND TECHNOLOGY SHARING AMONG NATIONS, IS IT LIKELY THAT NATIONS OF THE WORLD WILL SEE THEIR ECONOMIC GROWTH RATES CONVERGE? No. The convergence idea is that if the middle- and low-income countries21 are experiencing faster growth over the long haul than the high-income countries, which they are, could lowand middle-income national income collectively converge with the level of national income in high-income countries? Between 2000 and 2020, high-income countries saw real gross national income grow by 0.9% per year, middle-income countries saw its income grow by 4.0% per year, while low-income nations posted a 1.6% per year growth rate. Although the middle- and low-income countries are growing faster than high-income countries, the income gap is almost insurmountable with high-income countries national income better than 25 times as large as the low-income countries in 2020, while high-income countries national income was greater than four times as large as the middle-income countries. So, practically, conver­ gence is unlikely anytime soon as shown in Figure 5.4.

OUTPUT AND PRODUCTIVITY

Gross National Income per Capita Adjusted for Purchasing Power Parity in Constant 2017 International Dollars

FIGURE 5.4

[152] [ADVANCED] ARE THERE OTHER ECONOMIC PARADIGMS TO UNDERSTAND LONG-TERM ECONOMIC DEVELOPMENT?

National Income (Real GDP)

Indeed. One such theory was W. W. Rostow’s stages of economic growth22 in which he offered a stylistic “way of generalizing the sweep of modern economic history” through a set of five stages of economic development, as portrayed in Figure 5.5.

Stage V Stage IV Drive to Maturity

Stage III Stage II Take-off Stage I

PreTraditional conditions for Society Take-off

Time (Years) FIGURE 5.5

W. W. Rostow’s Stages of Economic Growth

Age of High Mass Consumption

95

96

MACROECONOMIC THINKING AND TOOLS

•

Traditional Society: This stage of development is characterized by limited production functions and is heavily oriented towards agricultural-subsistent output with low skill levels. Preconditions to Take-off: This stage of development become more national or international and there is an increase in capital investment (at least 5–10% of national income), which largely goes into transportation infrastructure. Take-off: This stage, which is a short period, sees more capital investment (10% or more), which increases income. Rostow opines during this stage there is “development of one or more substantial manufacturing sectors, with a high rate of growth.” Drive to Maturity: This stage in Rostow’s model is when the economy is extending modern technology over a wide range of economic activities. Investment accounts for 10–20% of national income. This takes place over a long period and is associated with rising national standard of living. Age of High Mass Consumption: This stage is a “we made” phase where there is mass production and a high degree of consumption.

•

•

•

•

Issues to Think About Output and productivity are core metrics of macroeconomics. Theories are built around those concepts. • • • •

• • •

Why is GDP the primary statistic to measure a nation’s output? Should economic goals embrace a more diverse measure of social welfare? Is the emphasis on output or on productivity the better objective for policymakers to target? Does the gap model used in macroeconomics between actual real GDP and an estimated real potential GDP the most effective way to conceptualize a policy goal for output? if not, how can that goal be better framed? Are measures of productivity that are based on an aggregate production function with only two inputs—labor and capital—too simplistic? Is labor productivity a misleading measure of a nation’s efficiency or can it be used in conjunction with total-factor productivity? How can a country become a high-income country if it is among the middleincome countries? is it possible for a high-income country to slip down to a middle-income country with the wrong policies?

NOTES 1 The NIPA accounting framework includes seven accounts for “presenting the value of production, distribution, consumption, and saving for the U.S. economy.” Those seven accounts are: Account 1: Domestic Income and Product Account; Account 2: Private Enterprise Income Account; Account 3: Personal Income and Outlay Account; Account 4: Government Receipts and Expenditures Account;

OUTPUT AND PRODUCTIVITY

2

3

4 5 6

7 8 9

10 11

12 13 14 15

16

Account 5: Foreign Transactions Current Account; Account 6: Domestic Capital Account; Account 7: Foreign Transactions Capital Account. These transaction accounts represent: Domestic Production = Account 1; Domestic Income and Outlays = Account 2 + Account 3 + Account 4; Saving and Investment = Account 6 + Account 7; and Rest of World = Account 5 + Account 7. Michael E. Heberling, Joseph R. Carter, and John H. Hoagland. “An Investigation of Purchases by American Businesses and Governments,” International Journal of Purchasing and Materials Management, vol. 28, no. 4 (Fall 1992), pp. 39–45. “What is gross output by industry and how does it differ from gross domestic product (or value added) by industry?,” U.S. Bureau of Economic Analysis, February 12, 2018, https://www.bea.gov/ help/faq/1197. Kelly Ramey, “The Changeover from GNP to GDP: A Milestone in BEA History,” Chronicling 100 Years of the U.S. Economy, U.S. Bureau of Economic Analysis, vol. 101, no. 3 (March 2021), p. 1. Ibid. System of National Accounts 2008, jointly produced by the United Nations, European Commission, Organisation for Economic Co-operation and Development, International Monetary Fund and the World Bank Group, New York, 2009. Available online, https://unstats.un.org/unsd/nationalaccount/ docs/SNA2008.pdf. https://www.bea.gov/help/glossary/command-basis-gross-domestic-product. Ulrich Kohli, “Real GDP, Real Domestic Income, Terms-of-Trade Changes,” Journal of International Economics (January 2004), pp. 83–106. The following four examples and their definitions are among the hundreds of statistics and definitions from The World Bank’s World Development Indicators Database, available online at: https:// databank.worldbank.org/source/world-development-indicators. Description from the United Nations Committee of Experts on Big Data and Data Science for Official Statistics, see: https://unstats.un.org/bigdata/about/index.cshtml. This productivity “residual” has taken on economic significance due to Robert Solow work in which he attributed that residual—the amount of productivity that cannot be accounted for by capital and labor inputs—to technological innovation. See: Robert M. Solow, “Technical Change and the Aggregate Production Function,” Review of Economics and Statistics, vol. 39 (1957), pp. 312–320. John Fernald and Kyle Matoba, “Growth Accounting, Potential Output, and the Current Recession,” FRBSF Economic Letter, Federal Reserve Bank of San Francisco, August 17, 2009 (Issue 2009–26), p. 2. Nicholas Crafts and Pieter Woltjer, “Growth Accounting in Economic History: Findings, Lessons and New Directions,” Journal of Economic Surveys, vol. 35, no. 3 (2021), pp. 670–696. Ibid. Human capital is considered an important factor in theories of economic growth. In Abraham and Mallatt’s survey of measurement of human capital, the researchers describe the three approaches how human capital has been measured. Those approaches are: (1) The Indicator Approach: This approach measures a country’s investment in or stock of human capital and if there are multiple measures as a weighted average index. The World Bank’s Human Capital Index (HCI) is one example of this approach. (2) The Cost Approach: This approach defines current gross investment as direct spending plus estimated value of unpaid time devoted to human capital development. (3) The Income Approach: This third approach defines current investment as the year-over-year change in present value of future labor income. See: Katharine G. Abraham and Justine Mallatt, “Measuring Human Capital,” NBER Working Paper 30136, National Bureau of Economic Research, June 2022. For global annual historical trends in total-factor productivity since 1890, see the Long-Term Productivity database ( http://longtermproductivity.com/download.html) created as a project of the Bank of France in 2013 by Antonin Bergeaud, Gilbert Cette and Remy Lecat. See: A. Bergeaud, G. Cette, and R. Lecat, “Productivity Trends in Advanced Countries between 1890 and 2012,” Review of Income and Wealth, vol. 62, no. 3 (2016), pp. 420–444.

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MACROECONOMIC THINKING AND TOOLS 17 Erik Brynjolfsson, Daniel Rock, and Chad Syverson. “The Productivity J-Curve: How Intangibles Complement General Purpose Technologies,” NBER Working Paper 25148, National Bureau of Economic Research, Cambridge, MA, January 2020. 18 The statistical correlation between the year-over-year growth in U.S. nonfarm labor productivity and the U.S. consumer price index between 1950Q1 and 2022Q1 was −0.35, which suggests that the inverse relationship is empirically supported directionally, though its degree of association is not high. 19 Alan Greenspan and Adrian Wooldridge, Capitalism in America: An Economic History of the United States (Penguin Books, 2018), p. 12. 20 For additional thoughts on these disruptive technologies affecting economic growth, see: Beth-Anne Schuelke-Leech, “A Model for Understanding the Orders of Magnitude of Disruptive Technologies,” Technological Forecasting & Social Change, vol. 129 (April 2018), pp. 261–274. 21 For a discussion of The World Bank’s classification, see: Nada Hamadeh, Catherine Van Rompaey, and Eric Metreau, “New World Bank country classifications by income level: 2021–2022,” The World Bank, July 1, 2021, https://blogs.worldbank.org/opendata/new-world-bank-country-classificationsincome-level-2021-2022. 22 Walt W. Rostow, The Stages of Economic Growth: A Non-Communist Manifesto, Cambridge University Press, 1960. Also, W. W. Rostow, “The Stages of Economic Growth,” The Economic History Review, vol. 12, no. 1 (1959), pp. 1–16.

CHAPTER

6

Understanding Business Cycles and Trends

LEARNING OBJECTIVES This chapter discusses the patterns of economic growth through cycles and trends. You will learn: • • • • • • • • • •

Why understanding business cycles is important. The competing views of the business cycle—Jevons vs. Mitchell. How a business cycle is determined. Techniques to analyze the business cycle. The difference between “classical” and “growth” cycles. The various theoretical causes of the business cycle. How risk and instability play a role in cycles. Why economists worry about incomplete recovery from recession, known as weakness “persistence” or “hysteresis.” Empirical findings that explain aspects of the cycle. How capital-deepening and economic mobility are related to long-term economic growth prospects.

[153] WHY DOES MACROECONOMICS FOCUS ON BUSINESS CYCLES AND TRENDS IN THE ECONOMY? Macroeconomics is the study of economic dynamics, which are characterized by cycles and trends. As such, theories and empirical studies explore all aspects of how the economy adjusts to changing rates of acceleration or deceleration in aggregate demand and aggregate supply, to changing short-run and long-run production, productivity, technology, and population, and to questions about the pattern, shape, and timing of those adjustments. One of the earliest questions addressed about the economic dynamic in macroeconomics was posed by John Maynard Keynes as to whether the economy was self-adjusting or not and its implication. DOI: 10.4324/9781003391050-7

100 MACROECONOMIC THINKING AND TOOLS

[154] WHAT IS THE BUSINESS CYCLE? Victor Zarnowitz, who was a University of Chicago professor and NBER business cycle scholar, described the business cycle as “an empirical phenomenon founded upon historical experience.”1 The cause of the business cycle (or “classical” business cycle) has been either an external shock or change to the growth path (the “crisis” model) or by an internal deteri­ oration (the “endogenous” model). William Stanley Jevons (1835–1882), who was a highly respected and influential economist and statistician of his time, argued in his book, Investigations in Currency and Finance, that the economy underwent a series of “commercial crises,” which he traced back to the 18th century. Jevons’ view of the trade or business cycle as a sequence of crises was embraced broadly throughout the economics profession until the 1920s. Then as more economic and financial data were compiled and newer statistical techniques were crafted to analyze them, Wesley Mitchell’s “statistical cycles,” which fo­ cused on the internal dynamic of the change, replaced the event-driven concept of the business cycle. Statistical time-series cycles continue to underlie modern business cycle research. However, the literature again rediscovered the traditional Jevons view of the cycle, where a turning point is triggered by some economic and/or political event to explain financial crises. “Financial crises are sudden events that may and often do occur after a growth cycle slowdown begins or classical business cycle recession ensues. Crises are predicated on some development, such as a collapse of a financial or nonfinancial institution or the recognition of a major imbalance in the financial sector, such as heavy debt holdings or too much dependence on foreign capital. In modern crisis theory of the business cycle, there are three typical sources of crisis: fiscal, banking, and currency.2 A fiscal crisis occurs when a government cannot roll over foreign debt and/or attract new loans. A currency crisis occurs when investors shift demand to foreign denominated assets and away from domestic assets. A banking crisis occurs when a bank cannot attract enough new deposits to meet sudden withdrawal of reserves. Each of these crises can exist independently or in conjunction with one or more other crisis.”3

[155] WHAT IS THE TRADE CYCLE? The trade cycle is the same concept as the business cycle. The use of the term “trade cycle” is widely used by British economists (such as Keynes or Hicks), but the term “business cycle” is widely used by American economists (such as Mitchell and Burns).

[156] WHY DO ECONOMISTS NEED TO UNDERSTAND THE CYCLICAL TURNING POINTS IN THE BUSINESS CYCLE? In John Maynard Keynes’ 1936 book, The General Theory of Employment, Interest and Money, he wrote about the importance of understanding the turning points in the business cycle. He wrote, “By a cyclical movement we mean that as the system progresses in, e.g., the upward

BUSINESS CYCLES AND TRENDS

direction, the forces propelling it upwards at first gather force and have a cumulative effect on one another but gradually lose their strength until at a certain point they tend to be replaced by forces operating in the opposite direction; which in turn gather force for a time and accentuate one another, until they too, having reached their maximum development, wane and give place to their opposite.” Keynes opined that any theory of the trade or business cycle had to explain that phenomenon.

[157] HOW ARE “STATISTICAL CYCLES” OR “ENDOGENOUS CYCLES” DEFINED? The classic definition of the statistical business cycle was formulated in the 1920s by the first research director of the National Bureau of Economic Research, Wesley Mitchell, and later affirmed in 1946 by Mitchell and Arthur F. Burns as a working definition for measuring the business cycle. That definition stated, “Business cycles are a type of fluctuation found in the aggregate economic activity of nations that organize their work mainly in business enterprises: a cycle consists of expansions occurring at about the same time in many economic activities, followed by similarly general recessions, contractions, and revivals which merge into the ex­ pansion phase of the next cycle; this sequence of changes is recurrent but not periodic; in duration business cycles vary from more than one year to ten or twelve years; they are not divisible into shorter cycles of similar character with amplitudes approximating their own.”4 The stylized view of what is known as the “classical” business cycle with its segments is portrayed in Figure 6.1.

Recovery

Prosperity

Economic Activity

Peak

Return to Previous Peak

Peak Trough

Recession FIGURE 6.1

Expansion

Time

The Stylized Classical Business Cycle: Tracking the Business Cycle

101

102 MACROECONOMIC THINKING AND TOOLS

[158] [ADVANCED] WHAT IS THE DIFFERENCE BETWEEN REFERENCE AND SPECIFIC CYCLE TURNING POINTS? Much as the words imply, reference cycle turning point dates represent a comparison with a broader concept, such as the nation’s aggregate business cycle or, sometimes other aggregates—such as inflation. A specific cycle is the indicator’s own cycle that may or may not have regular fluctuations with the reference cycle; the specific cycle fluctuations may lead, lag, or be coincident with the reference cycle or have no relationship with the ref­ erence cycle.

[159] WHY DO WE CARE THAT THERE IS A REFERENCE BUSINESS CYCLE CHRONOLOGY OF PEAKS AND TROUGHS IN THE CYCLE? The acceptance of the classical cycle turning point dates (whether it is from the NBER for the United States or other official organizations for other countries) provides researchers with a common benchmark for analyzing the state of the economy.

[160] WHAT ARE TURNING POINT DATES IN THE U.S. BUSINESS CYCLE? In 1978, the NBER created its U.S. Business Cycle Dating Committee to determine, by consensus of its academic committee, the dates for the peaks and troughs in the U.S. business cycle. Prior to 1978, turning point dates were based on individual researcher studies—including its founding research director Wesley Mitchell. The NBER chronology begins in 1854, as shown in the table, and is determined based on a wide range of economic indicators that generally turn up or down about the same time, such as employment, pro­ duction, and income.

[161] IS A “TWO-QUARTER CONSECUTIVE DECLINE IN REAL GDP” RULE HOW A RECESSION IS DETERMINED? No. A two-quarter decline in real GDP is part of broader set of rules-of-thumb for spotting recession that business cycle scholar and former BLS Commissioner Julius Shiskin offered to the public in his New York Times article in 1974.5 Even then it was not a new idea, but Shiskin

BUSINESS CYCLES AND TRENDS TABLE 6.1

U.S. Business Cycle Expansions and Contractions Contraction

Expansion

Duration, peak to trough (months)

Duration, trough to peak (months)

Cycle

Peak month (Peak quarter)

Trough month (Trough quarter)

Duration, trough to trough (months)

Duration, peak to peak (months)

June 1857 (1857Q2)

Dec. 1858 (1858Q4)

18

30

48

Oct. 1860 (1860Q3)

June 1861 (1861Q3)

8

22

30

40

April 1865 (1865Q1)

Dec. 1867 (1868Q1)

32

46

78

54

June 1869 (1869Q2)

Dec. 1870 (1870Q4)

18

18

36

50

Oct. 1873 (1873Q3)

March 1879 (1879Q1)

65

34

99

52

March 1882 (1882Q1)

May 1885 (1885Q2)

38

36

74

101

March 1887 (1887Q2)

April 1888 (1888Q1)

13

22

35

60

July 1890 (1890Q3)

May 1891 (1891Q2)

10

27

37

40

Jan. 1893 (1893Q1)

June 1894 (1894Q2)

17

20

37

30

Dec. 1895 (1895Q4)

June 1897 (1897Q2)

18

18

36

35

June 1899 (1899Q3)

Dec. 1900 (1900Q4)

18

24

42

42

Sept. 1902 (1902Q4)

Aug. 1904 (1904Q3)

23

21

44

39

May 1907 (1907Q2)

June 1908 (1908Q2)

13

33

46

56

Jan. 1910 (1910Q1)

Jan. 1912 (1911Q4)

24

19

43

32

Jan. 1913 (1913Q1)

Dec. 1914 (1914Q4)

23

12

35

36

Aug. 1918 (1918Q3)

March 1919 (1919Q1)

7

44

51

67

Jan. 1920 (1920Q1)

July 1921 (1921Q3)

18

10

28

17

May 1923 (1923Q2)

July 1924 (1924Q3)

14

22

36

40

Oct. 1926 (1926Q3)

Nov. 1927 (1927Q4)

13

27

40

41

Aug. 1929 (1929Q3)

March 1933 (1933Q1)

43

21

64

34

May 1937 (1937Q2)

June 1938 (1938Q2)

13

50

63

93

Feb. 1945 (1945Q1)

Oct. 1945 (1945Q4)

8

80

88

93

Nov. 1948 (1948Q4)

Oct. 1949 (1949Q4)

11

37

48

45

July 1953 (1953Q2)

May 1954 (1954Q2)

10

45

55

56

Aug. 1957 (1957Q3)

April 1958 (1958Q2)

8

39

47

49

April 1960 (1960Q2)

Feb. 1961 (1961Q1)

10

24

34

32

Dec. 1854 (1854Q4)

Dec. 1969 (1969Q4)

Nov. 1970 (1970Q4)

11

106

117

116

Nov. 1973 (1973Q4)

March 1975 (1975Q1)

16

36

52

47

Jan. 1980 (1980Q1)

July 1980 (1980Q3)

6

58

64

74

July 1981 (1981Q3)

Nov. 1982 (1982Q4)

16

12

28

18

July 1990 (1990Q3)

March 1991 (1991Q1)

8

92

100

108 128

March 2001 (2001Q1)

Nov. 2001 (2001Q4)

8

120

128

Dec. 2007 (2007Q4)

June 2009 (2009Q2)

18

73

91

81

Feb. 2020 (2019Q4)

April 2020 (2020Q2)

2

128

130

146

Source: National Bureau of Economic Research. (Continued ) Note: The “bold” highlighted dates indicate when the peak or trough month has been outside the peak or trough quarter.

103

104 MACROECONOMIC THINKING AND TOOLS TABLE 6.1

(Continued)

Peak month (Peak quarter)

Trough month (Trough quarter)

Contraction

Expansion

Duration, peak to trough (months)

Duration, trough to peak (months)

Cycle Duration, trough to trough (months)

Duration, peak to peak (months)

Averages 1854−2020

17.0

41.4

58.4

59.2

1854−1919

21.6

26.6

48.2

48.9

1919−1945

18.2

35.0

53.2

53.0

1945−2020

10.3

64.2

74.5

75.0

Source: National Bureau of Economic Research.

sketched out some simple rules of thumb. He wrote: (1) real GDP should decline for two successive quarters and industrial production should decline for a six-month period; (2) real GDP should decline by at least 1.5%, payroll employment also should decline by at least 1.5%, and the unemployment rate should increase by 2 percentage points; and (3) for six months or longer employment diffusion should be less than 25% (that is no more than 25% of industries are expanding its workforce).

[162] CAN THE TURNING-POINT DATES IN A NATION’S ECONOMIC ACTIVITY BE MEASURED BY THE CYCLICAL TURNING POINTS IN REAL GDP? Generally, no. However, as a short-hand method, some researchers rely just on real GDP to mark off the business cycle as was done, for example, in a U.K. research study and that U.K. chronology is shown below. However, using a single indicator only serves as a simple shorthand, but incomplete, way to determine a country’s business cycle chronology. To bring home this point, compare the cyclical turning points in the U.S. real GDP cycle (which is known as a specific cycle in real GDP and presented below) and those determined by the NBER research group—as presented in the table for the question above. Notice there are clear differences—especially in the determination of the 2001 business cycle, which was not detected in real GDP that had more of a flattening pattern in 2001, but other indicators such as employment had a pronounced cyclical peak in February 2001. Therefore, a single indicator does not capture the full dimensions of a recession—even if it is a broad measure, such as real GDP.

BUSINESS CYCLES AND TRENDS

United Kingdom Classical Turning Points in the Business Cycle

TABLE 6.2

Peak Date (Quarter)

Trough (Quarter)

1955-Q4 1961-Q2 1973-Q2 1974-Q3 1979-Q2 1990-Q2 2008-Q1 2019-Q4

1956-Q3 1961-Q4 1974-Q1 1975-Q3 1981-Q1 1991-Q3 2009-Q2 2020-Q2

Based on Ana Beatriz Galvão and Amit Kara, “The impact of GDP data revisions on identifying and predicting UK Recessions,” ESCoE Discussion Paper 2020−12, Economic Statistics Centre of Excellence, National Institute of Economic and Social Research, London, July 2020.

TABLE 6.3

Turning Points for U.S. Real Gross Domestic Product

Peak Date

Peak Value (Billions $)

Trough Date

Trough Value (Billions $)

1948Q4 1953Q2 1957Q3 1960Q1 1969Q3 1973Q4 1980Q1 1981Q3 1990Q3 2008Q2 2019Q4

2136.4 2720.6 3017.0 3277.8 4971.3 5731.6 6842.0 6982.6 9404.5 15792.8 19215.7

1949Q2 1954Q1 1958Q1 1960Q4 1970Q4 1975Q1 1980Q3 1982Q1 1991Q1 2009Q2 2020Q2

2099.8 2651.6 2908.3 3234.1 4938.9 5551.7 6693.1 6799.2 9275.3 15161.8 17378.7 Mean Standard Deviation Median

Recession PeakSpan (in Trough % Quarters) Change

% Change per Quarter

2 3 2 3 5 5 2 2 2 4 2 3 1

−1.7 −2.5 −3.6 −1.3 −0.7 −3.1 −2.2 −2.6 −1.4 −4.0 −9.6 −3.0 2.4

−0.86 −0.85 −1.80 −0.45 −0.13 −0.63 −1.09 −1.31 −0.69 −1.00 −4.78 −1.23 1.26

2

−2.5

−0.86

105

106 MACROECONOMIC THINKING AND TOOLS

[163] [ADVANCED] SINCE REAL GDP AND REAL GDI ARE TWO MEASURES OF THE SAME CONCEPT, HOW DO THE TWO MEASURES COMPARE IN THEIR CLASSICAL BUSINESS CYCLE QUARTERLY TURNING-POINT DATES? Although it might be expected that the cyclical turning points in real GDP and real GDI would be identical, that is not the case. Real GDI did have a more pronounced decline in 2001 consistent with the NBER’s 2001 recession delineation, however, real GDP experienced only mild weakness and not sufficient enough to be marked off as a cyclical downturn in 2001. Even for matched cycles, real GDP and real GDI quarterly turning points may differ by a quarter or two. This is a good reason to use both real GDI and real GDP to determine turning points in the economy and/or to use the average of the two measures (as is reported by the U.S. Bureau of Economic Analysis) as a better turning point indicator than either alone. The value of using both measures was echoed in August 2020 when real GDP declined in Q1 and Q2 2022, but real GDI did not decline in either Q1 and Q2 2022. However, the average of the two measures did decline (but only after the September 2022 benchmark revision) and was close to unchanged in both Q1 and Q2 2022. Future historical national income benchmark revisions may change turning point dates in both real GDI and real GDP6 for recent data (note that real GDP and real GDI had a historically large gap in the pre-2022 benchmark revision of almost 4% in Q2 2022 suggesting that the benchmark revision was likely to narrow that gap—which it did to just over 1%), which provides another reason to use both measures for real-time tracking. TABLE 6.4

Turning Points for U.S. Real Gross Domestic Income

Peak Date

Peak Value (Billions $)

Trough Date

Trough Value (Billions $)

Recession Span (in Quarters)

Peak-Trough % Change % Change per Quarter

1948Q4 1953Q2 1957Q3 1960Q1 1969Q3 1973Q4 1979Q4 1981Q3 1990Q2 2001Q1 2007Q2 2019Q4

2143.0 2698.0 3012.9 3274.7 4964.1 5727.5 6680.7 6919.6 9248.1 13489.6 15686.2 19308.6

1949Q4 1953Q4 1958Q1 1960Q4 1970Q4 1975Q1 1980Q2 1982Q1 1991Q1 2001Q4 2008Q1 2020Q2

2077.4 2626.7 2912.1 3256.5 4915.4 5498.9 6567.0 6794.5 9179.2 13282.3 15555.2 17605.1 Mean Standard Deviation Median

4 2 2 3 5 5 2 2 3 3 3 2 3 1

−3.1 −2.6 −3.3 −0.6 −1.0 −4.0 −1.7 −1.8 −0.7 −1.5 −0.8 −8.8 −2.5 2.3

−0.77 −1.32 −1.67 −0.18 −0.20 −0.80 −0.85 −0.90 −0.25 −0.51 −0.28 −4.41 −1.01 1.17

3

−1.8

−0.78

BUSINESS CYCLES AND TRENDS

[164] HOW DOES THE NBER DETERMINE A RECESSION IN THE BUSINESS CYCLE? The measurement of turning points in the classical business cycle have been based on the socalled, three Ds—depth, duration, and diffusion. These criteria are: • • •

Depth: How deep the decline is cumulatively. Duration: How long the contraction lasts. Diffusion: How widespread the impact is.

With a long record for comparison, these criteria can be assessed against that historical record for a broad range of coincident indicators of the business cycle—which include employment, real disposable income, industrial production, sales in constant dollars, the unemployment rate, real GDP, and other metrics.

[165] HOW IS DIFFUSION MEASURED? The concept of diffusion is measured by an index that shows the percentage of components of an economic aggregate (such as employment or industrial production) expanding. The BLS compiles employment diffusion indexes for the United States based on the percentage of industries (of 258) rising and another employment diffusion based on the percentage of 387 metropolitan areas (MSAs) that have employment gains. Additionally, the Federal Reserve Board reports a diffusion index for industrial production. Obviously, these measures implicitly are based on two periods of comparison: the current period compared with a prior period to determine if the change was positive, negative, or unchanged. For the employment and production diffusion indexes, those diffusion indexes are calculated for different periods—the current period compared with one month earlier, the current period compared with three months earlier, and the current period compared with six months earlier. (The employment diffusion index also is derived for 12-month comparisons.) The longer the span of coverage the less volatile the series is. It is common to calculate a diffusion index as the percentage of series that increased over the indicated span (1, 3, 6, or 12 months) plus one-half the percentage that was unchanged. Survey-based indicators (such as the PMI—the purchasing managers index) also widely use this formulation as a summary measure. The comparison of the employment and production diffusion indexes over six-month spans is shown in Figure 6.2. Beyond the metrics themselves, the concept of diffusion has been built into some economists’ thinking about how the economy operates. For example, Bert Hickman argued that Paul Samuelson’s widely accepted investment acceleration principle was "unnecessary" to explain a downturn in aggregate investment in the economy. Instead, Hickman observed that there was a positive correlation between diffusion and the change in aggregate activity sug­ gesting that the broadening and narrowing of the positive breadth of change will generate an investment cycle akin to that proposed by Samuelson.7 Similar ideas of the importance of diffusion to the economy were put forth by William Fellner in his theory of diminishing offsets and by Arthur Burns in his loss of industrial balance view.8 These theoretical ideas of diffusion

107

108 MACROECONOMIC THINKING AND TOOLS

FIGURE 6.2

Employment and Production Diffusion Indexes (Six-Month Spans, 2000−2022)

Sources: U.S. Bureau of Labor Statistics; Federal Reserve Board of Governors

never became mainstream teachings and faded from the scene. However, this empirical concept of diffusion is entrenched in the determination criteria for business cycle turning points dates.

[166] ARE THERE OTHER CONCEPTUAL WAYS TO MEASURE ECONOMIC CYCLES? Yes. The National Bureau of Economic Research, which is the arbitrator of turning points in the classical U.S. business cycle, also has produced considerable research on what is termed the “growth cycle” or “deviation cycle.”

[167] WHAT IS A “GROWTH CYCLE”? The growth cycle is a National Bureau of Economic Research (NBER) concept of the business cycle but differs from the classical business cycle. Growth cycles are recurrent fluc­ tuations in the economy but measured as deviations from trend—which is also the same concept of the “output gap.” The history of the concept originated with Burns and Mitchell, who wrote that “If secular trends were eliminated at the outset as fully as seasonal variations, they would show that business cycles are a more pervasive and a more portent factor in economic life.”9 If the trend is removed, then growth cycle contractions would include both slowdowns and absolute declines, but the classical business cycle would only mark off absolute declines. This concept became popular in the 1960s, largely due to work by Ilse Mintz in her NBER research on German business cycles. Mintz also allowed for the growth cycle to be

BUSINESS CYCLES AND TRENDS

Economic Activity

High

Above Average or High Growth

Trend

Below Average or Low Growth Low

0% High-Growth Phase

Low-Growth Phase

Time FIGURE 6.3

Classical Recession

Tracking the Growth Cycle

measured in terms of growth rates (rather than removing a trend from the data), but noted the additional problems with this measurement approach. Economic growth rates tend to be volatile; they also tend to have differences in cyclical timing than trend-adjusted economic growth—largely since growth rates tend to be more rapid from a low base and vice versa. An alternative approach that Mintz used in her analysis was to apply “step cycles” to the growthrate patterns, which was a technique used earlier by Milton Friedman and Anna Schwartz to study money supply growth. This step-cycle approach then was to define “the downturn … as the end of a period of relatively high growth and the upturn as the end of a period of relatively low growth. In terms of growth rates, business cycles thus are defined as alternations of high and low rates, rather than as alternations of rising and falling rates.”10 This growth-cycle concept has been used by the OECD and various international research groups for several reasons, including that a growth cycle peak always will precede an absolute peak in the economy, but not every growth cycle peak will be followed by an absolute peak. Studies have also shown that growth cycles tend to be more highly correlated with stock market cycles than classical cycles.

[168] HOW DO THE NBER’S TWO TYPES OF ECONOMIC CYCLES COMPARE? Classical business cycles are determined based on the level of economic activity, but growth cycles are determined by periods of acceleration and deceleration in economic growth. Growth cycles can encompass classical business cycles and can occur within classical cycles, as well. Growth cycle peaks generally occur prior to (but sometimes concurrent with) classical business cycle peaks largely because a slowdown tends to occur prior to an absolute decline in economic activity. A comparison table is shown below.

109

110 MACROECONOMIC THINKING AND TOOLS TABLE 6.5

Comparison Between Classical and Growth Cycles

Cycle Type

Turning Points

Phases

Classical Business Cycle Growth Cycle

Peaks in Level; Troughs in Level Peaks in Growth; Troughs in Growth

Peak-to-Trough Contraction; Trough-to-Peak Expansion High-to-Low Growth Deceleration; Low-to-High Growth Acceleration

[169] DOES A U.S. GROWTH CYCLE CHRONOLOGY EXIST? Although the original work on growth cycles began at the NBER, the organization does not maintain a growth cycle chronology. However, the Economic Cycle Research Institute,11 which was founded by the NBER business cycle scholar Dr. Geoffrey H. Moore, continues to update the growth cycle chronology for the United States as well as most major economies of the world.

[170] WHAT IS MEANT BY THE TERM “SOFT LANDING”? The common usage of the term “soft landing” has been to describe a policy-induced or an internally driven moderation in economic growth without a contradiction in the economy. Although this terminology is colloquial usage, a formal economic counterpart concept is the NBER “growth recession,” which is the low-growth phase of the growth cycle.

[171] WHAT ARE THE “STYLIZED FACTS” ABOUT CLASSICAL BUSINESS CYCLES, WHICH THEORIES TRY TO EXPLAIN? The stylized facts about business cycles are consistently in flux. The “typical” stylized facts about the business cycle pattern and sequence depends on the period covered. For example, older cycles represent a larger manufacturing share of the economy and inventory invest­ ment may have a bigger role in the cyclical process than for more recent business cycles that are more dependent on the service economy. This compositional shift in the U.S. economy also has affected trend growth since services tend to have a slower growth path than the goods sector.12 The employment cycle also has changed over time, which affects its cyclical timing relative to the reference cycle. Therefore, for our purpose, we will not reach too far back in economic history and will paint the typical sequences for more recent business cycles. However, we will need to have a large enough sample to generalize. For our pur­ pose, this caricature of the U.S. business cycle will be based on eight cycles from 1969 to 2020. Those cycle sequences will be summarized in three ways: (1) For all eight of those

BUSINESS CYCLES AND TRENDS

business cycles; (2) based on cycles that are primarily generated from demand shocks; and (3) based on cycles that are primarily generated from supply shocks. The second and third cycle sequence discussions provide a link with the concepts of aggregate demand and aggregate supply as encountered earlier and this review will address how the primary type of shock to the U.S. economy empirically affects the business cycle. •

Business Cycle Duration—Recessions Last about 11 Months, Expansions about 78 Months: The first observation about the classical U.S. business cycles between 1969 and 2020 is that the average duration of a recession (contraction) was 10.6 months. Supply-shock-induced recessions13 were slightly shorter than demand-shock-induced downturns—11.0 months versus 14.0 months, respectively. The two cycles with relatively equal magnitudes of impact from supply-side and demand-side shocks were short contractions—averaging five months. The average duration of an expansion after a trough in the business cycle since 1969 was 78.1 months with either the expansion following a supply-shock or demand-shock roughly lasting about the same duration of 67−68 months. TABLE 6.6

U.S. Business Cycle Duration, 1969−2020 U.S. Business Cycle Duration, 1969−2020

•

Type of Cycle Impact

Peak-to-Trough Trough-toSubsequent Peak Averages (in Averages (in Months) Months)

Supply-Shock Dominated Cycles: 1969, 1973, 1980 Demand-Shock Dominated Cycles: 1981, 2001, 2007 Both Supply & Demand Impacted Cycles: 1990, 2020 Business Cycles: 1969−2020 Recessions

11.0

66.7

14.0

68.3

5.0

110.0

10.6

78.1

Business Cycle Depth (Amplitude)—Real GDP Tends to Contract by 2–3%, on Average, during Recessions: Some of the high-level observations about the depth of the “typical” recession are that real GDP tends to contract by about 2−3% cumulatively, without a lot of difference between supply-induced shocks versus demand-induced shocks. Of course, you will notice that the COVID-19 pandemic-induced recession is quite atypical—in duration, depth, and diffusion. However, our intent here is to sketch out the typical business cycle pattern. Payroll employment tends to decline cumulatively by about 4%, but here the demand-induced shocks tends to show a larger average decline than the supply shocks. Inflation, however, shows a very different story; supply shocks boost inflation considerably higher (8.7% on average) versus demand shocks (3.2%). A few tables below show these findings.

111

112 MACROECONOMIC THINKING AND TOOLS TABLE 6.7

U.S. Reference Business Cycle Contractions, 1969−2020 U.S. Reference Business Cycle Contractions, 1969−2020 Real GDP Growth

Cycles

Cumulative Peak-to-Trough % Change

Average Peakto-Trough % Change per Quarter

Supply-Shock Dominated Cycles: 1969, 1973, 1980 1969Q4 to 1970Q4 1973Q4 to 1975Q1 1980Q1 to 1980Q3 Demand-Shock Dominated Cycles: 1981, 2001, 2007 1981Q3 to 1982Q4 2001Q1 to 2001Q4 2007Q4 to 2009Q2 Both Supply & Demand Impacted Cycles: 1990, 2020 1990Q3 to 1991Q1 2019Q4 to 2020Q2 Business Cycles: 1969−2020 Recessions (8 Recessions)

−1.8 −0.2 −3.1 −2.2 −2.0 −2.5 0.5 −3.8 −5.5 −1.4 −9.6 −2.8

−0.50 −0.04 −0.63 −1.09 −0.42 −0.50 0.17 −0.64 −3.28 −0.69 −4.78 −0.79

U.S. Reference Business Cycle Contractions, 1969−2020: Non-farm Payroll Employment

TABLE 6.8

U.S. Reference Business Cycle Contractions, 1969−2020 Nonfarm Payroll Employment Cycles

Cumulative Peak-to-Trough % Change

Average Peak-toTrough % Change per Month

Supply-Shock Dominated Cycles: 1969, 1973, 1980 December 1969 to November 1970 November 1973 to March 1975 January 1980 to July 1980 Demand-Shock Dominated Cycles: 1981, 2001, 2007 July 1981 to November 1982 March 2001 to November 2001 December 2007 to June 2009 Both Supply & Demand Impacted Cycles: 1990, 2020 July 1990 to March 1991 February 2020 to April 2020 Business Cycles: 1969−2020 Recessions (8 Recessions)

−1.3 −1.2 −1.6 −1.1 −3.2 −3.1 −1.2 −5.3 −7.8 −1.1 −14.4 −3.6

−0.12 −0.11 −0.10 −0.18 −0.23 −0.19 −0.15 −0.30 −1.56 −0.14 −7.21 −0.34

BUSINESS CYCLES AND TRENDS TABLE 6.9

U.S. Reference Business Cycle Contractions, 1969−2020: CPI-U % Change U.S. Reference Business Cycle Contractions, 1969−2020 CPI-U % Change

Cycles

Cumulative Peak-to-Trough % Change

Average Peak-toTrough % Change per Month

Supply-Shock Dominated Cycles: 1969, 1973, 1980 December 1969 to November 1970 November 1973 to March 1975 January 1980 to July 1980 Demand-Shock Dominated Cycles: 1981, 2001, 2007 July 1981 to November 1982 March 2001 to November 2001 December 2007 to June 2009 Both Supply & Demand Impacted Cycles: 1990, 2020 July 1990 to March 1991 February 2020 to April 2020 Business Cycles: 1969−2020 Recessions (8 Recessions)

8.7 5.0 15.0 5.9 3.2 7.1 0.8 1.6 1.1 3.3 −1.1 4.7

0.79 0.46 0.94 0.98 0.23 0.44 0.10 0.09 0.22 0.41 −0.56 0.44

•

•

Another important stylized fact about U.S. business cycle recessions is that on average non-farm unit labor costs rise by 4.7% with a greater increase of 8.0% for recessions that are supply-induced versus only a 2.2% increase for demand-shocked recessions. Additionally, productivity is considerably weaker for supply-induced recessions (+0.7%) versus demand-led recessions (+2.1%). This may explain why demand-led recessions tend to have less inflation than during supply-induced recessions. Moreover, the pattern of higher inflation and costs for supply-led recessions than demandled recessions also is likely the reason interest rates fall more, on average, during demand-led recessions (–1.45 percentage points of yield in the ten-year constant maturity U.S. Government Note) than for supply-led recessions (–0.12 percentage point change in the ten-year Government Note yield during the reference cycle downturn). Diffusion (Breadth of Change)—On Average, about 20% of Industries Pull Back with a Higher Percentage for Supply-Led Recessions. The change in the industrial production diffusion index (measured over six-month spans) tends to shrink by a little over 20 percentage points during the reference business cycle period with a tendency for demand-led recessions to have less pull-back by industry. Interestingly, the recessions that have both supply and demand shocks (without one more dominant) tend to have an even larger reduction in industry breadth by about 35 percentage points.

113

114 MACROECONOMIC THINKING AND TOOLS U.S. Reference Business Cycle Contractions, 1969−2020: Industrial Production Diffusion (Six-Month Spans−Percentage Point Change)

TABLE 6.10

U.S. Reference Business Cycle Contractions, 1969−2020 Industrial Production Diffusion (6-Month Spans–Percentage Point Change) Cycles

Average Peak-toCumulative Peak-to-Trough Trough % Point % Point Change Change per Month

Supply-Shock Dominated Cycles: 1969, 1973, 1980 December 1969 to November 1970 November 1973 to March 1975 January 1980 to July 1980 Demand-Shock Dominated Cycles: 1981, 2001, 2007 July 1981 to November 1982 March 2001 to November 2001 December 2007 to June 2009 Both Supply & Demand Impacted Cycles: 1990, 2020 July 1990 to March 1991 February 2020 to April 2020 Business Cycles: 1969−2020 Recessions (8 Recessions)

−23.6 1.3 −43.3 −28.7 −9.2 −9.7 4.2 −22.1 −34.7 −31.2 −38.2 −21.0

−2.14 0.12 −2.70 −4.79 −0.66 −0.61 0.53 −1.23 −6.93 −3.90 −19.09 −1.97

[172] HISTORICALLY, WHAT ARE THE PROXIMATE CAUSES FOR RECESSIONS DURING THE POST-WWII PERIOD IN THE UNITED STATES? Each cyclical episode is different—but certain patterns may be observed. For example, aggregate supply impacts that cause inflation ultimately can lead to recession. Reduction in government spending after wars have slowed economic growth and some led to recession. At times, certain big strikes have been associated with recession. Often different economic the­ ories explain one or more of the recessions better than others. TABLE 6.11

Post-WWII U.S. Business Cycle History Post-WWII U.S. Business Cycle History

U.S. Business Cycle Peak (Year/Month)

Proximate Causes of Recession

November 1948

Slower federal government spending (after WWII); a boom-bust cycle in exports; and an inventory correction Unwinding of defense expenditures following the Korean War Higher interest rates causing lower investment

July 1953 August 1957

(Continued )

BUSINESS CYCLES AND TRENDS TABLE 6.11

(Continued) Post-WWII U.S. Business Cycle History

U.S. Business Cycle Peak (Year/Month)

Proximate Causes of Recession

April 1960

Steel strike involving 519,000 workers and federal government’s attempt to balance the budget (increase in Social Security taxes, higher gasoline taxes, increase in excise taxes, and unemployment insurance taxes). Unchecked government spending associated with the Vietnam War and Great Society programs triggered higher inflation and growing foreign trade deficits. The federal government passed the Revenue and Expenditure Control Act in 1968 to control the federal budget deficits. Monetary policy was raising interest rates to tamp down inflation. Two months auto strike against General Motors added to the recession while Penn Central Railroad (the largest railroad at the time) declared bankruptcy and defaulted on its commercial paper. The United States abandoned fixed exchange rates and the U.S. dollar depreciated. In October 1973, the Organization of Petroleum Exporting Countries (OPEC), which controlled more than 80% of the world’s oil exports, raised the price of a barrel of oil from $6 to $23 over a two-month period. The surge in oil prices caused energy prices in the United States to soar followed by sharply higher food and other raw material prices. Shortages developed and businesses were “buying in advance” of higher prices. A previously imposed wage and price control program was terminated on April 30, 1974, causing a spike in all prices that were held down. The Franklin National Bank failed (it was the 20th largest bank at the time). The real estate investment trusts (REITs) market collapsed. Crude oil prices surged as the overthrow of the Shah of Iran triggered a curtailment of Iranian oil exports to the United States and the price of a barrel of oil rose from $13 to $34, and once again caused a surge in consumer energy prices. An earlier worldwide crop shortage in 1978 caused food prices to rise sharply between 1979 and 1981. The Carter Administration considered imposing credit controls to reign in rampant growth in credit and inflation. President Carter authorized by Executive Order the Federal Reserve to implement credit controls; the Federal Reserve issued its Credit Restraint Program to reign in credit and, ultimately inflation. In late 1979, the Fed implemented a new operating procedure in which the monetary policy focus shifted away from controlling the federal funds rate to controlling bank reserves. The U.S. economy did not see any benefit to restore the balance in the price-cost-profit relationship following the recession that had occurred only months earlier. The Fed pushed its short-term policy interest rate, the federal funds rate, to a record 22.0% in late December 1980 to slay inflation. Throughout the recession, the daily effective federal funds rate averaged a hefty 13.63%. The extremely high interest rates did the trick to curb inflation and inflation expectations by 1982 at the expense of a recession. The Federal Reserve abandoned its bank reserves operating policy implemented in 1979. Once again, oil prices were playing havoc with inflation as worldwide political uncertainty was triggered by Iraq’s invasion of Kuwait on August 2, 1990. The price of crude oil, which was less than $19 per

December 1969

November 1973

January 1980

July 1981

July 1990

(Continued )

115

116 MACROECONOMIC THINKING AND TOOLS TABLE 6.11

(Continued) Post-WWII U.S. Business Cycle History

U.S. Business Cycle Peak (Year/Month)

March 2001

December 2007

Proximate Causes of Recession barrel in July 1990 soared to $40 by early October. The federal government passed major deficit reduction legislation, which included higher gasoline taxes, tobacco and alcoholic beverage excise taxes, and capped annual government spending. The motor vehicle industry was hard hit by this recession and accounted for 38% of the total real GDP decline. The Y2K computer software problem (that is, computer software was not able to handle a four-digit year and numerous programs had to be updated or else potential failure of systems) spurred a lot of investment spending ahead of the year 2000. Additionally, new internet businesses were forming rapidly in the late 1990s and into 2000 fueling excessive stock-market speculation as well as this internet-company boom unfolded. Consumer inflation was heating up too, ranging 5.5−6.0% ahead of the 2001 recession and the Fed moved the fed funds rate largely in lockstep with inflation. The so-called “dot.com” business bubble bust in 2000 as many of these businesses, which were overvalued, failed and ultimately slowed the economy a bit. Then by September, the 9/11 terrorist attack occurred, which added uncertainty and further weakened the economy. The global financial crisis that unfolded in 2007 was triggered by a housing boom-bust cycle that was built on “easy” mortgage borrowing, especially for people having low credit ratings by using subprime mortgages--which were later determined to be mispriced. That easy credit fueled a booming housing market and rapidly pushed up housing prices. Banks and other mortgage brokers “packaged” these mortgages (slices or tranches of the mortgage market) and sold them off to investors. These packaged securities, bundled increasingly with less due diligence about the underlying loans (underwriting standards deteriorated), often had a credit rating higher than the bulk of the individual loans within the packaged security. This lending practice extended beyond the residential real estate market to the commercial real estate market using Commercial Mortgage-Backed Securities (CMBS). Deposit-taking institutions, such as banks and thrifts, which dealt mostly in lower-priced mortgages, sold their mortgages primarily to government sponsored enterprises (GSEs), such as Fannie Mae and Freddie Mac. Independent mortgage brokers, however, typically sold their packaged mortgages through the financial markets. This overall market started to unravel as delinquency rates on those loans started to rise. (The delinquency rate on all-types of single-family mortgages at commercial banks ultimately peaked at 11.36% in the first-quarter 2010.) The trouble spread and began to unfold by May 2007 when the investment bank Bear Stearns saw the value of the assets of two of its hedge funds that were backed by mortgage-backed securities fall and traders began aggressively redeeming their investments. Bear Stearns took various steps from freezing redemptions to getting infusions of liquidity. But the damage was done, and the firm lost $859 million in its fourth quarter of 2007 and wrote off $2 billion in subprime mortgage (Continued )

BUSINESS CYCLES AND TRENDS TABLE 6.11

(Continued) Post-WWII U.S. Business Cycle History

U.S. Business Cycle Peak (Year/Month)

February 2020

Proximate Causes of Recession holdings. By March 2008, many of Bear’s trading partners stopped trading with it, which ultimately signaled its bankruptcy. But the impact on the entire financial market was massive and the Federal Reserve stepped in to prevent contagion. The U.S. Treasury and the Federal Reserve launched various initiative to buy those distressed loans and provide liquidity for the financial system. The COVID-19 pandemic and subsequent business shut-down was the immediate catalyst for this recession, however, the economy was slowing ahead of that as the Trump administration imposed tariffs on imported Chinese products, this caused a ripple effect on business with operations or supply-chain distributors in China. The uncertainty showed up as reduced nonresidential fixed investment, which peaked in Q3 2019. The IMF reported that U.S. import duties on international trade (as a percent of tax revenue) rose to 3.6% in 2019—which was the highest on record back to 1972—as U.S. import duties surged from $53.3 billion in 2018 to $77.8 billion in 2019. The Federal Reserve also was beginning to pare its balance sheet holdings (what some call a “quantitative tightening”) but to offset some of that weakness in the economy, the Fed had cut its funds rate from 2.4% in July 2019 to 1.6% by January 2020.

[173] WHAT IS THE IMPORTANCE OF CLASSIFYING ECONOMIC INDICATORS BY THEIR CYCLICAL TENDENCIES FOR LEADING, BEING COINCIDENT WITH, AND LAGGING THE TURNING POINTS IN THE BUSINESS CYCLE? This classification scheme helped early business cycle researchers understand the sequences of economic activity.

[174] HOW IS THAT CLASSIFICATION OF CYCLICAL INDICATORS ACCOMPLISHED? The NBER published its first formal list of cyclical indicators in 1938, and its research led to a four-part system of classification of economic indicators based on an indicator’s timing relationship—leading, coincident, lagging, or unclassified—with the reference business cycle turning point dates for peaks and troughs. A specific cycle in an economic indicator (such as, payroll employment, industrial production, average workweek, etc.) would be assessed at reference cycle peaks and at reference cycle troughs and as an overall barometer of both peaks and troughs. If the turning point date occurs within three months of the reference turn, then that metric would be considered a coincident indicator of the reference business cycle. If the

117

118 MACROECONOMIC THINKING AND TOOLS indicators turning points preceded the reference cycle by more than three months, but not as far back as the prior cycle, then the economic indicator would be considered leading (that is, changing direction before) cyclical turning point dates in the reference cycle. Similarly, if the indicator consistently lagged turning points by more than three months then that would classify the measure as a lagging indicator of the reference cycle. Classification would be determined for peaks from troughs separately over an extended historical record. An indicator could be a leading indicator of peaks, but a lagging indicator of lower turning points in the reference cycle—or any other combination. An example would be the unemployment rate, which has been classified (inverted—so a high cyclical unemployment rate corresponds to the bottom of the recession and vice versa) by the U.S. Department of Commerce as a leading indicator of cyclical peaks, but a lagging indicator of cyclical troughs, and unclassified overall. Subsequent to that pre-2000 Commerce Department classification, however, the unemployment rate seemingly has not been a leading indicator of business cycle peaks after 2000. Specific cycles may have extra turns relative to the reference business cycle and may have missed turns relative to the reference business cycle.

[175] WHAT IS A COMPOSITE CYCLICAL INDICATOR? A composite cyclical index is a summary measure of economic indicators that share common cyclical timing classification relative to the reference business cycle and is compiled using statistical indicators over a broad range of economic activities for diversification. The most consistent set of leading indicators, coincident indicators, and lagging indicators get compiled into a summary measure, adjusting for each individual indicator’s volatility such that no single measure dominates the change simply due it is volatility. It is also possible to add a weight for each component, though academic research on the U.S. composite index of leading indicators (when it was compiled by the U.S. Commerce Department) found that component weights added little to the predictive value of the index and subsequently those individual component weights were eliminated.14

[176] WHAT IS THE HISTORY OF COMPOSITE CYCLICAL INDICATORS? Early research by the National Bureau of Economic Research began to identify and classify types of consistent cyclical patterns in economic indicators based on whether a statistic led, was roughly coincident with (around three months), or lagged the reference business cycle. Later, the most consistent leading, lagging, and coincident indicators were compiled into summary indexes (initially diffusion indexes, then composite indexes) for leading, lagging, and coinci­ dent economic activities. The NBER’s cyclical indicators research project found an interested partner in the early 1960s with the U.S. Commerce Department. The U.S. Commerce Department’s Bureau of Economic Analysis (Statistical Indicators Division) further developed this business cycle monitoring system and updated, monthly, the core leading, lagging, and coincident composite indexes of the U.S. business cycle. However, in 1995, this project was transferred to The Conference Board, which today continues to compile the U.S. cyclical indicators and has expanded its program globally.

BUSINESS CYCLES AND TRENDS

[177] [ADVANCED] WHAT ARE “BUSINESS CYCLE STAGES,” AS DISCUSSED BY BURNS AND MITCHELL? Arthur Burns and Wesley Mitchell used a business cycle construct that collapsed time into stages of the business cycle change. In a nutshell, the complete cycle was measured from the initial trough, to the peak, and then to the ending trough (or alternatively, from the initial peak to the trough to the ending peak). The stage data for the turning points were based on one month prior to and one month after the turn. The stages between the turning points were divided into thirds. Finally, the nine stages of the cycle were divided by the entire cycle (Initial Trough-Peak-Ending Trough, or T-P-T) to normalize the data. This was done for all complete cycles and then averages were taken for each of the nine stages to assess the average pattern of change. Two examples of this technique are shown for U.S. real GDP and for U.S. payroll employment in Figure 6.4. Stages 1, 5, and 9 represent the T-P-T turning points in economic activity. A related appli­ cation by Burns and Mitchell was to look at the change from one stage to the next, which is demonstrated in the third graphic. This nine-stage technique has been largely forgotten, but may help to conceptualize the stylized dynamic at play during the business cycle as shown in Figure 6.5. Then the percentage change in those cycle relatives is calculated, as shown in Figure 6.6. Looking at the changes in business cycle stages, therefore, highlight the economic dynamic as the business cycle unfolds. For example, this change in the business cycle stages for payroll employment tends to begin to turn around going from stage 1 (recession) to stage 2 (the recovery) but accelerates more rapidly as the cyclical upturn broadens (going from stage 2 to stage 3) and then settles down somewhat going from stage 3 into stage 4. Although each cycle may have its own recovery path, there are a lot of similarities among the various cycles—which is why Burns and Mitchell used this technique.

[178] [ADVANCED] WHAT IS “RECESSION/RECOVERY” ANALYSIS? Recession/recovery or “rec/rec analysis” is a Burns and Mitchell technique to track the unfolding of a business cycle where turning point dates are lined up (arrayed) to show the progress of the cycle compared with prior cycles and tracked by month or quarter prior to and after the turning point date—based on either a reference or specific cycle chronology. Three examples are shown of this method for the U.S. payroll employment cycle based on the NBER’s reference cycle turning point dates with a comparison of all U.S. business cycle peaks from 1948. The second example shows the average cycle pattern for all cycles from 1948 to 2007 compared with the 2020 cycle. Obviously, this technique has been used to track troughs as well with various cycle comparisons—all past post-WWII cycles or the average of the severe versus mild cycles, for example. The third example shows payroll employment unfolding from the troughs of the post-WWII business cycles. Note the convention to indicate the peak or trough on the graphic and dates earlier than the turning point are indicated with a minus sign and dates after the turning point are shown with a plus sign. For example, three months after the lower turning point date would be indicated by T+3 in Figures 6.7 and 6.8.

119

120 MACROECONOMIC THINKING AND TOOLS 120.0

115.0

110.0

Cycle Relatives, Average = 100

105.0

100.0

95.0

90.0

85.0

80.0

75.0

Stage 1 Stage 2 Stage 3 Stage 4 Stage 5 Stage 6 Stage 7 Stage 8 Stage 9

Oct-49-Jul-53-May-54

84.5

91.1

99.9

105.6

108.5

107.8

106.5

106.2

106.3

May-54-Aug-57-Apr-58

91.9

95.7

100.8

103.0

104.4

103.9

102.5

100.7

101.1

Apr-58-Apr-60-Feb-61

92.4

95.1

100.1

102.2

103.3

103.3

103.2

102.6

103.0

Feb-61-Dec-69-Nov-70

76.8

83.9

98.8

112.4

116.6

116.5

116.9

117.3

116.5

Nov-70-Nov-73-Mar-75

90.7

93.7

98.2

103.9

105.3

104.6

104.1

102.8

102.3

Mar-75-Jan-80-Jul-80

88.2

92.2

99.2

106.9

108.3

108.5

106.2

106.2

106.1

Jul-80-Jul-81-Nov-82

97.8

98.7

100.9

101.0

101.6

101.4

99.5

99.5

99.5

Nov-82-Jul-90-Mar-91

81.4

89.0

99.2

108.9

112.4

112.4

111.4

110.9

111.5

Mar-91-Mar-01-Nov-01

82.6

87.1

97.4

111.9

117.7

118.2

117.9

117.9

118.0

Nov-01-Dec-07-Jun-09

90.2

92.6

99.2

104.8

106.9

106.9

105.7

103.2

103.0

Jun-09-Feb-20-Apr-20

88.6

92.5

99.6

107.9

110.6

110.6

110.6

110.6

104.0

Median

88.6

92.5

99.2

105.6

108.3

107.8

106.2

106.2

104.0

FIGURE 6.4

Classical Business Cycle Nine-Stage Analysis: U.S. Real GDP

BUSINESS CYCLES AND TRENDS 120.0

115.0

110.0

Cycle Relatives, Average = 100

105.0

100.0

95.0

90.0

85.0

80.0

75.0

Stage 1 Stage 2 Stage 3 Stage 4 Stage 5 Stage 6 Stage 7 Stage 8 Stage 9

Oct-49-Jul-53-May-54

90.5

94.3

100.4

103.6

105.5

105.2

103.8

102.8

May-54-Aug-57-Apr-58

95.3

96.2

100.8

102.8

103.2

102.7

101.8

100.0

99.3

Apr-58-Apr-60-Feb-61

96.1

96.7

100.1

101.4

102.7

102.3

102.0

101.2

100.9

Feb-61-Dec-69-Nov-70

85.8

89.0

97.9

108.9

113.8

114.0

113.8

113.2

112.9

Nov-70-Nov-73-Mar-75

94.1

95.0

97.9

101.9

103.8

104.3

104.8

103.7

102.3

Mar-75-Jan-80-Jul-80

91.1

93.0

98.8

105.9

107.9

108.1

108.0

107.3

107.0

Jul-80-Jul-81-Nov-82

99.5

99.9

100.6

101.0

101.2

100.9

99.9

98.8

98.2

Nov-82-Jul-90-Mar-91

88.0

92.2

99.3

106.3

108.8

108.6

108.3

107.8

107.4

Mar-91-Mar-01-Nov-01

90.3

91.6

99.1

107.3

110.3

110.1

109.9

109.4

109.1

Nov-01-Dec-07-Jun-09

98.0

97.5

99.2

102.4

103.4

103.2

102.1

99.4

97.9

Jun-09-Feb-20-Apr-20

93.6

94.2

99.9

106.1

108.5

107.9

107.9

107.9

98.8

Median

93.6

94.3

99.3

103.6

105.5

105.2

104.8

103.7

102.3

FIGURE 6.5

Classical Business Cycle Nine-Stage Analysis: U.S. Payroll Jobs

102.3

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122 MACROECONOMIC THINKING AND TOOLS

11.0

Cycle Stage Percent Change

6.0

1.0

-4.0

-9.0

Stage 2/ Stage 3/ Stage 4/ Stage 5/ Stage 6/ Stage 7/ Stage 8/ Stage 9/ Stage 1 Stage 2 Stage 3 Stage 4 Stage 5 Stage 6 Stage 7 Stage 8

Oct-49-Jul-53-May-54

4.3

6.5

3.1

1.9

-0.3

-1.3

-0.9

-0.5

May-54-Aug-57-Apr-58

1.0

4.7

2.0

0.3

-0.4

-1.0

-1.7

-0.7

Apr-58-Apr-60-Feb-61

0.6

3.6

1.3

1.3

-0.4

-0.3

-0.8

-0.3

Feb-61-Dec-69-Nov-70

3.8

9.9

11.2

4.5

0.2

-0.2

-0.5

-0.3

Nov-70-Nov-73-Mar-75

0.9

3.0

4.1

1.9

0.5

0.5

-1.1

-1.4

Mar-75-Jan-80-Jul-80

2.1

6.2

7.2

1.9

0.2

-0.1

-0.6

-0.3

Jul-80-Jul-81-Nov-82

0.4

0.7

0.3

0.3

-0.3

-1.0

-1.1

-0.6

Nov-82-Jul-90-Mar-91

4.7

7.7

7.1

2.3

-0.2

-0.3

-0.4

-0.4

Mar-91-Mar-01-Nov-01

1.5

8.2

8.2

2.8

-0.2

-0.2

-0.4

-0.3

Nov-01-Dec-07-Jun-09

-0.5

1.7

3.3

0.9

-0.2

-1.0

-2.7

-1.5

Jun-09-Feb-20-Apr-20

0.6

6.0

6.2

2.3

-0.6

0.0

0.0

-8.5

Median

1.0

6.0

4.1

1.9

-0.2

-0.3

-0.8

-0.5

FIGURE 6.6

Classical Business Cycle Nine-Stage Analysis: U.S. Payroll Jobs

BUSINESS CYCLES AND TRENDS

FIGURE 6.7

U.S. Payroll Employment: Recession Patterns

[179] HOW DO ECONOMIC THEORIES EXPLAIN BUSINESS CYCLES? Each macroeconomic theory emphasizes a primary dynamic and channel that causes the fluc­ tuation in economic growth. The process of change may be considered the same in many theories, but the catalyst for change may be different. Some theories focus on the monetary channel (money growth, such as the Austrian school, and interest rates), some theories highlight the demand channel (Keynes’ theory and others), and some theories view an “imbalance” as the cause of the business cycle that develops in the economy (lots of different versions of this variant,

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124 MACROECONOMIC THINKING AND TOOLS

FIGURE 6.8

U.S. Payroll Employment: Recovery Patterns

including the Wesley Mitchell view of a price-cost-profit imbalance—which is largely forgotten, but may offer a lot of insight on the cyclical process without emphasis on any particular theory).

[180] WHAT IS THE PRICE-COST-PROFIT IMBALANCE CAUSE OF BUSINESS CYCLES? This price-cost-profit relationship (sometimes considered a “business economy theory”) and its impact on the business cycle is largely due to Wesley Mitchell’s research in which he opined business cycle theories can only be developed once the empirical record is carefully docu­ mented. To this end, Mitchell carefully documented how changes in prices, costs, profits occur over the business cycle and relative to each other. His finding emphasized the important role and market incentive of profits as a catalyst for business cycle fluctuations. This empirical finding also underscored the importance of profits and profit margins as leading cyclical in­ dicators (by timing) of the business cycle.

[181] HOW MIGHT AN “ORIGINAL” INSTITUTIONALIST VIEW TODAY’S BUSINESS CYCLE DYNAMIC? Clearly, the economy has been impacted by a number of changes over the last few decades, including new non-bank sources of liquidity (lending); new payment methods (such as, cryptocurrency); different and faster ways of communicating, purchasing, and selling (the internet); and

BUSINESS CYCLES AND TRENDS

more opportunities for “remote” work. There have been some major shifts in the age of the workforce in some economies (Japan or the United States, for example). There is seemingly a faster response to weakening economic conditions by fiscal and monetary policymakers with increasingly reduced policy implications of the size of the fiscal deficit, and many other “outside” impacts on the macroeconomy. In many respects the original institutionalist will question how each of those and other social and political changes might affect the price-costs-profit balance that Wesley Mitchell (who was considered an original institutionalist) highlighted as a key dynamic of the business cycle. A collective assessment of all these factors, therefore, will shape the expected impact on the business cycle.

[182] HOW CAN EXCESS DEMAND CAUSE BUSINESS CYCLES? The standard Keynesian perspective adequately captures how excess demand can generate cycles. If aggregate demand is over-stimulated (there are various channels how this could occur) then real GDP could continue to increase (potentially with increasing demand-pull inflation) until real GDP reaches its “ceiling” output, which is its level of real potential or its full-employment output. Sir John Hicks’ dynamic model (in the Keynesian tradition) showed that cyclical activity can be generated within periods of economic growth when expansions hit that full-employment level and cannot grow any further or faster, which dampens future growth through the expenditure multiplier and the investment accelerator effects. In turn, at some point the economy bounces off that output ceiling and the expenditure multiplier and the investment accelerator begin to work in the opposite direction to slow the economy– which is the essence of the Hicks’ dynamic growth/cycle model (which focused on the investment and not so much on the consumptions side). At the lower turning point, there is some autonomous investment (for normal replacing inventories and equipment, for example) and autonomous consumption (spending on food and housing, for example) that provides a floor to the cycle, which ultimately turns the cycle upward again, being reinforced by the expenditure multiplier and investment accelerator.

[183] HOW DO CHANGING EXPECTATIONS CAUSE BUSINESS CYCLES? Swings in mass psychology can affect the economic motivation for investment and con­ sumption. During periods of economic optimism, businesses invest for the future and con­ sumers are more willing to increase their spending. Businesses also are more willing to borrow money to expand their businesses with the expectation that their profits will generate sufficient cashflow to cover payments on those loans. Consumers too are more willing to purchase “bigticket” items using credit with the expectation that they will be able to cover their installment debt and loans. Any incremental expenditures by business and consumers get reinforced in subsequent periods through the expenditure multiplier or feedback effect through the circularflow process between expenditures and income. During periods of economic pessimism, the tides turn the other way. Businesses and consumers begin to scale back spending and the expenditure multiplier also works to reduce growth in subsequent periods. This ebb and flow

125

126 MACROECONOMIC THINKING AND TOOLS in economic psychology has been an integral part of numerous economic theories, but it was also an early standalone reason for economic cycles.

[184] HOW DO “SHOCKS” EXPLAIN THE BUSINESS CYCLES? Early writings of real business cycle theorists viewed exogenous shocks to productivity as the main source of cyclical fluctuation in the economy, but later this idea was extended to other potential shocks. Rebelo writes that, “One of the most difficult questions [macroeconomics asks is:] what are the shocks that cause business fluctuations? Longstanding suspects are monetary, fiscal, and oil price shocks.”15 To this list of economic shocks, Rebelo notes, Edward Prescott adds technology shocks. Moreover, Prescott16 argued that technology shocks account for more than half of the fluctuations in the post-WWII period. Other researchers, however, found that technology shocks may be an important source of cyclical fluctuation in the economy, but not as large a factor as Prescott’s early research suggested. In the end, the answer provided by this approach to the question about the role of external shocks is largely a statistical one17 and offers less of an intuitive understanding of specific business cycle episodes and the full channels of influence.18

[185] HOW DOES “EASY” MONEY CAUSE BUSINESS CYCLES? The Austrian theory of the business cycle argues that an artificial “credit-induced” expan­ sion,19 driven by unwarranted money supply growth, reduces the market rate of interest below its natural rate, which also is the concept of r*, which stimulates excessive credit-financed investment. Then when the market rate of interest increases due to the boom, these creditfinanced investments cease, and the economy falls into recession. Foldvary argues, “Not all recessions fit the Austrian theory, but the major ones have done so. The Austrian school theory of the business cycle concludes that it not ‘business’ itself that causes the boom and bust, but rather the governmental monetary and fiscal interventions that skew investment, and relative prices. In the Austrian perspective, it should accurately be called an ‘interventionist cycle.’”20

[186] HOW DOES “RISK” CREATE FLUCTUATION IN THE BUSINESS CYCLE? Business cycle theories based on waves of optimism and pessimism in the economy were widely popular among early macroeconomic theorists and discussed in the writings of Pigou, Taussig, and others. Pigou, in particular, emphasized “errors of judgment” playing a key role in economic instability. A more developed idea that is closely related to those ideas of psychological waves in the economy is risk. Risk is an important element in the cyclical dynamic. Cowen opines, for example, that a business cycle theory based on risk is a “logical outcome of a focus on en­ trepreneurial errors,” which is a traditional assumption of the Austrian school. This risk idea is developed as follows: “After a real interest rate decline, or some other positive inducement to

BUSINESS CYCLES AND TRENDS

invest, investors accept a wider spread of possible outcomes and the economy becomes riskier … The increase in risk implies greater economic cyclicality and a greater likelihood of either a sustained boom or a subsequent economic downturn.”21

[187] WHAT IS THE CONCEPT OF THE “CYCLE OF CYCLES”? In 1946, Arthur Burns and Wesley Mitchell discussed a hypothesis that they believed there exists a “cycle of cycles.” The dynamic, they argued, had two parts—an industrial phase and a speculative phase. These business cycle scholars wrote: “After a severe depression industrial activity rebounds sharply, but speculation does not. The following contraction in business is mild, which leads people to be less cautious. So, in the next two or three cycles, while the cyclical advances become progressively smaller in industrial activity, they become pro­ gressively larger in speculative activity. Finally, the speculative boom collapses and a drastic liquidation follows, which ends this cycle of cycles and brings us back to the starting point.”22 Burns held this view when he was chairman of the Federal Reserve Board in the 1970s and applied the idea to interpret the period from 1961 to 1974, where he felt the industrial phase of the business cycle occurred between 1961 and 1965 and the speculative phase dominated between 1966 and 1974. This idea has been used to explain one economic dynamic that produces mild business cycles versus severe cycles.

[188] WHAT IS MEANT BY THE TERM “CREDIT CRUNCH”? The term “credit crunch” was first applied in 1966 to what has been described as the first financial crisis of the post-WWII period in the United States. In 1965 and 1966, the United States ex­ perienced an “overheated economy.” Real GDP rose by 6.5% in 1965 and 6.6% in 1966, while the pace of CPI inflation accelerated from 1.6% in 1965 to 3.0% in 1966 as the unemployment rate fell to 3.8% in 1966 from 4.5% in the prior year—both year’s actual unemployment rates were below the CBO’s estimate of the full-employment unemployment rate at that time, which was calculated to be 5.7% for both years. As a result, interest rates started to climb and corporate profits peaked in the second quarter of 1966—which was a key source of internal financing for investment. Bank lending also was curtailed as lending risk in the economy grew. As a result, corporate debt repayments also came under pressure as borrowing options were more limited. With rising interest rates promoted by the Federal Reserve to dampen inflationary pressures, the savings and loan industry saw an outflow of its deposits to be invested in higher interest rate money market assets (the so-called disintermediation). To prevent a financial crisis, the Fed loosened its reserve stance somewhat and sent a letter to banks to suggest that member banks should use the Federal Reserve’s discount window rather than liquidating their portfolio of municipal securities (which created havoc in the muni-bond market) to generate short-term funds. It worked and the Fed headed off a full-fledged financial crisis. Nonetheless, the Fed would be tested by other financial crises many more times since 1966—each with different causes. This 1966 credit crunch period was associated with a growth recession in the economy, but not with a classical business cycle recession.

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128 MACROECONOMIC THINKING AND TOOLS

[189] [ADVANCED] WHAT IS HYMAN MINSKY’S “FINANCIAL INSTABILITY HYPOTHESIS”? Minsky’s theory is a business-cycle hypothesis23 that is based on Keynesian theory but differs from the mainstream interpretation, which he believed did not properly account for uncertainty, the business cycle, and finance. As such, Minsky developed his hypothesis around those three concepts tied to investment and opined that internal (endogenous) forces in the economy result in waves of credit expansion and asset price inflation, which are followed by waves of credit contraction and asset price deflation and the sequence is repeated. Minsky observed that “whenever something that approaches stability [in each phase of the business cycle] is achieved, destabilizing processes are set off,”24 which Minsky called his theory of financial fragility. Financial fragility phases are characterized by hedge (secure financing), speculative, and Ponzi finance. The amount of cashflow from receipts of normal operations, in Minsky’s theory, determines the phase of financing in the economy. When cashflow is sufficient to cover debt liabilities then that is the hedge phase in the economy. When cashflow is not sufficient to fully cover debt liabilities then that is the speculative phase of the economy. When cashflow is not sufficient even to cover interest payments on debt, then this is said to be the Ponzi phase of the investment cycle and economy. In the Ponzi phase, companies must continually roll over and increase their debt to stay afloat. However, as interest rates in the economy rise during an expansion or boom phase of the business cycle, this makes the economy more susceptible to a financial crisis. Minsky and Vaughan observed that with the onset of the 1966 credit crunch that event ushered in a new era of financial instability and crisis, which has become commonplace ever since that time.25 Minsky, in a separate article, further expounded on this observation: “The first post-World War II threat of a financial crisis that required Federal Reserve special intervention was the so-called ‘credit crunch’ of 1966. This episode centered around a ‘run’ on bank-negotiable certificates of deposit. The second occurred in 1970, and the immediate focus of the difficulties was a ‘run’ on the commercial paper market following the failure of the Penn-Central Railroad. The third threat of a crisis in the decade occurred in 1974−75 and involved a large number of over-extended financial positions, but perhaps can be best identified as centering around the speculative activities of the giant banks. In this third episode the Franklin National Bank of New York, with assets of $5 billion as of December 1973, failed after a ‘run’ on its overseas branch.”26 Of course, since that article, there have been a number of additional examples of financial crisis, most notably in 2008.

[190] IN APRIL 2018, THE ECONOMIST MAGAZINE PUBLISHED AN ARTICLE ENTITLED, “ECONOMISTS STILL LACK A PROPER UNDERSTANDING OF BUSINESS CYCLES.” WHY IS THIS SO, IF IT IS? That article27 criticized the factions (“schools of thought”) within the profession that cannot find common ground on how the economy works or the policy implications that might follow

BUSINESS CYCLES AND TRENDS

from those theories and empirical studies. To a large extent, this is not new and was a key reason why Wesley Mitchell, many years ago, argued that economic theories cannot be for­ mulated until we understand the empirical trends, cycles, the stable or changing patterns in economic statistics, and the evolving empirical relationships between economic processes. Mitchell’s price-cost-profit framework (though considered a loose theory) may be that em­ pirical, but eclectic, window on—or the common ground—to really understand business cycles without dependence on outdated or controversial theories.

[191] WHAT IS MEANT BY THE TERM “STAGFLATION” IN ECONOMICS? Iain Macleod, a British Conservative Party member of the U.K. House of Commons of Parliament (who in 1970 became Chancellor of the Exchequer) is credited with coining the term. Macleod, in a House debate on November 17, 1965, said of the U.K. economic situ­ ation, “We now have the worst of both worlds—not just inflation on the one side or stag­ nation on the other, but both of them together. We have a sort of ‘stagflation’ situation and history in modern terms is indeed being made.”28 From an economist standpoint, Bronfenbrenner defined stagflation as a “condition in which the price level is rising despite the existence of substantial unemployment.” He also calls it “unemployment inflation.”29

[192] HOW HAVE ECONOMISTS LOOKED AT STAGFLATION? Barsky and Kilian30 observed that the traditional macroeconomic explanation for stagflation, especially because of the 1970s OPEC oil embargo and surging oil prices, was “an adverse shift in the aggregate supply curve that lowers output and raises prices on impact.”31 Although this perspective remains the mainstream thinking,32 the Barsky and Kilian study disagreed with that interpretation. Instead, those researchers concluded that it was not the jump in oil prices that was the cause of the 1970s stagflation, but the major monetary expansion in the early 1970s (which is a view also held by the Austrian school). In 2022, this same concern about another bout of stagflation returned with the same varied views about the potential cause (oil prices or monetary expansion) and the same policy remedies.

[193] IS THERE A QUANTITATIVE RULE FOR DETERMINING STAGFLATION PERIODS? No. There is no hard-and-fast quantitative rule when stagflation periods exist; generally periods in the 1970s (1973−74 and 1978−80) are considered examples of when U.S. stagflation existed. During that time in the 1970s (11 quarters) when economic growth (real GDP) was below 2% (slow growth or recession), consumer inflation rose at median pace of 12.4% with the unemployment rate averaging 5.9%. More commonly, these periods are discussed qualitatively. For example, in Kohler’s “general theory of stagflation,” he simply and qualitatively describes stagflation as a combination of slow economic growth, high inflation, and high unemployment.33

129

130 MACROECONOMIC THINKING AND TOOLS

[194] [ADVANCED] HOW IS THE PHILLIPS CURVE INTERTWINED WITH THE CONCEPT OF STAGFLATION? In the late 1950s into the 1960s, economists felt the Phillips curve would largely preclude stagnation and high inflation occurring together since they believed that there was a robust trade-off between economic growth and inflation. By the 1970s, however, many economists began to believe that the Phillips curve had shifted such that a higher inflation rate was more consistent with a higher unemployment rate (or, slower economic growth). By the 2000s, economists believed that the Phillips curve relationship had largely “flattened” such that the any trade-off between inflation and unemployment (or economic growth) was meager.

[195] [ADVANCED] HOW SHOULD POLICYMAKERS ADDRESS STAGFLATION? If the cause of stagflation is excess stimulation triggering higher input prices, then policy­ makers need to remove the excess stimulation (by higher interest rates, higher taxes, reduced government spending, etc.). This likely will result in an even higher unemployment rate and slower economic growth before inflation recedes. If the cause of stagflation is a supply shock—resulting in higher energy or food prices, for example, then policymakers need to assess whether the shock is temporary (transitory) or permanent. Transitory effects will likely impact aggregate supply and then gradually fade away. However, permanent effects take longer to address as the economic system adjusts to the new cost structure and policymakers may reach for supply-side policies (promoting different energy sources, maybe, or more domestic sources of production, and so forth) to offer longer-term solutions.

[196] [ADVANCED] WHAT IS MEANT BY THE TERM “HYSTERESIS” IN ECONOMICS? “Hysteresis,” which is a term borrowed from physics, means a persistence of a previous state. It has been suggested, for example, that even following circumstances when factors that produced a recession are gone, there still can be a lingering persistence of high unemployment and slow recovery that may create a permanent or long-term (nonpermanent) change in the path or trend of the economy.34 Hence, under this hypothesis, a recession can have permanent effects on the economy with a non-fully reversible adjustment or correction path back to the trend that existed prior to the recession. Other macro­ economic examples of this persistence have been offered for exchange rates and exports, interest rates and investment, the recovery path of the labor force participation rate, and the recovery path of the employment-to-population rate. This idea was prevalent after the 2007−2009 U.S. recession and Global Financial Crisis and is captured visually in the chart

BUSINESS CYCLES AND TRENDS

FIGURE 6.9

Real U.S. GDP vs. Its Extended 2000−2007 Trend

by the dotted line as the trend of real GDP, but the actual path after that recession was subs­ tantially lower. It has been argued that some reasons for this change in the trend are: (1) recessions can alter spending on research and technology that can have long-term impact on future eco­ nomic growth; and (2) recessions can create reduced spending on physical and human capital (“capital deepening”), which also will affect future productivity. This idea also is a rationale why the Congressional Budget Office updates its projections of real potential GDP and the natural rate of unemployment (non-cyclical rate of unemployment) after a recession. Empirically, there is mixed evidence for hysteresis both historically and internationally.

[197] [ADVANCED] WHAT IS THE DIDEROT EFFECT? The Diderot effect is an empirical behavioral argument for cyclical activity that is named for Denis Diderot, who lived between 1713 and 1784 and was a French philosopher and writer. Diderot’s essay, entitled “Regrets on Parting with My Old Dressing Gown,” gave rise to a behavioral response in economics, which empirically may explain one reason for fluctuations in consumer spending. His essay recounted the story of buying a new robe to replace an old, shabby one. Initially, he observed that his old robe was fitting for his wellworn and well-used furniture and house furnishings around him. But his new elegant new robe created an inconsistency with the rest of his possessions in that room. So, slowly he felt compelled to replace the other items, one by one, in the room to be more consistent with that elegant new robe. This idea inspired the economic relationship that certain purchases can trigger a motivation for spending to replace old possessions. This is ex­ emplified by the relationship between home sales (new and existing) growth rates and retail spending, which is portrayed in the graph below. This relationship also has another

131

132 MACROECONOMIC THINKING AND TOOLS

FIGURE 6.10

Home Demand as a Trigger for the Diderot Effect

characteristic that makes it important for macroeconomics, which is that home sales tend to have a cyclical leading indicator relationship with retail spending as shown in Figure 6.10.

[198] ARE THERE CONSISTENT SETS OF INTERNATIONAL LEADING CYCLICAL ECONOMIC INDICATORS TO TRACK AND QUALITATIVELY FORECAST THE GLOBAL BUSINESS CYCLE? Yes, numerous research groups compile consistent sets of composite cyclical indicators to track the global business cycle, including The Conference Board, the Economic Cycle Research Institute (ECRI, which was founded by Dr. Geoffrey H. Moore, who was an NBER business cycle scholar and passed away in March 2000), and the OECD. The OECD, for example, compiles monthly composite leading indicators for the following countries: Australia, Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Mexico, Netherlands, Norway, Poland,Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States, Brazil, China (People’s Republic of), India, Indonesia, Russia, and South Africa.

[199] WHY IS LONG-TERM ECONOMIC GROWTH IMPORTANT? The World Bank offered a succinct rationale why long-term economic growth is so important. They highlighted two key reasons:35 •

Economic growth is the main driver of higher living standards and economic development. - Economic growth is highly correlated with measures like the United Nation’s Human Development Index and median incomes.

BUSINESS CYCLES AND TRENDS

•

An absence of growth creates problems such as debt crises, unemployment, and social unrest. Long-term economic growth that is “sustained and inclusive” is central to the World Bank’s “twin goals” for nations. Those World Bank goals are: - Ending extreme poverty, and - Boosting shared prosperity among countries.

[200] WHAT DOES THE EMPIRICAL RECORD ON U.S. ECONOMIC GROWTH AND OTHER KEY ECONOMIC STATISTICS TELL ABOUT LONG-TERM TRENDS? Several key observations about long-term U.S. economic growth can be gleaned even from a casual analysis of the record. First and foremost, the economy has been growing at a slower pace in more recent years than in the early post-WWII times. That slower pace is from various components—government spending, private investment, business inventories, and exports—which all, on average, have been growing slower than in the earlier postWWII period. With a slower underlying pace of economic growth, business cycles have been affected as well with expansion-period growth a bit more moderate and recessions a bit deeper. Some of these key indicators and patterns are shown in the Table 6.12.

[201] IS THE LONG-TERM TREND OF THE ECONOMY INFLUENCED BY THE SHORT-TERM FLUCTUATIONS IN THE ECONOMY? The brief answer is that short-term fluctuations can affect the long-term trend (as also dem­ onstrated in advanced question 196 on hysteresis)—especially if the economic shock is per­ manent. This is also why the Congressional Budget Office updated its estimate of potential output following the 2020 recession. For example, the January 2020 CBO projection of “trend” or potential output growth for the U.S. economy was 2.02% for 2021, but following the 2020 recession, the estimate was trimmed to 1.85%.

[202] WHAT IS THE “STEADY-STATE” ECONOMY? This idea of a steady-state economy is tied to the “gap analysis” view of economic activity incorporated into the aggregate demand and aggregate supply model. A steady state in the economy occurs when the gap between actual and its goals in key variables of unemployment, inflation, growth, and interest rates all vanish, and that steady-state eco­ nomic environment is sustained. This theoretical long-term equilibrium in the economy is portrayed in the Table 6.13.

133

Real GDP Real Personal Consumption Expenditures Real Gross Private Domestic Investment Real Private Inventories Real Government Consumption Expenditures and Gross Investment Real Exports of Goods and Services Real Imports of Goods and Services Personal Consumption Expenditures: Chain-Type Price Index Personal Consumption Expenditures Excluding Food and Energy (Chain-Type Price Index) Non-farm Payroll Employment Non-farm Business Sector: Unit Labor Costs for All Employed Persons Real Disposable Personal Income Personal Interest Payments (Market Value) Non-financial Corporate Business: Net Interest Payments (Market Value)

2.8% 3.1 4.9 2.3 1.5 6.1 6.3 3.5 3.4 1.6 3.2 3.0 6.8 7.2

6.2 7.1 3.3 3.2 1.7 2.9 3.2 7.3 9.1

1969Q1− 2022Q3

3.2% 3.3 5.6 2.5 2.8

1948Q1− 2022Q3

All Periods

2.6 3.0 3.3

0.9 1.9

2.0

4.0 4.4 2.1

2.1% 2.5 3.4 1.6 1.1

2000Q1− 2022Q3

3.4 8.2 8.5

2.6 2.8

3.1

7.7 9.4 3.2

4.2% 4.0 9.2 3.2 2.8

1948Q1− 2022Q3

3.1 7.8 6.8

2.3 2.9

3.2

7.8 8.6 3.4

3.7% 3.7 8.2 2.8 1.3

1969Q1− 2022Q3

Only Expansion Periods Between Dates

Long-Trend Growth Rates and Cyclical Percentage Change in the U.S. Economy

Long-Trend Growth Rates and Cyclical Percentage Change in the U.S. Economy

Economic Measure

TABLE 6.12

2.2 1.4 12.9

−3.3 3.8

4.4

−3.1 −6.5 4.3

−2.6% −0.4 −15.7 −1.4 2.4

1948Q1− 2022Q3

2.3 0.8 9.9

−3.1 5.3

4.7

−4.8 −8.6 4.6

−2.9% −1.1 −16.4 −1.2 2.5

1969Q1− 2022Q3

Only Recession Periods Between Dates

134 MACROECONOMIC THINKING AND TOOLS

BUSINESS CYCLES AND TRENDS TABLE 6.13

The “Steady-State” Economy The “Steady-State” Economy

Concept

Condition

Jobs

Actual Unemployment Rate = Natural Rate (Full-Employment Unemployment Rate, NAIRU, or Non-cyclical Unemployment Rate) Actual Inflation Rate = Expected Inflation Rate Actual Real GDP = Potential Real GDP Actual Short-Run Interest Rate = r*

Inflation Growth Interest Rates

[203] IS ACTUAL LONG-TERM ECONOMIC GROWTH EQUAL TO LONG-TERM POTENTIAL OUTPUT? Not necessarily—the two measures may be similar, but not identical. Between 1950Q1 and 2022Q1, U.S. real GDP grew by an average 3.17% year-over-year quarterly pace, while the CBO’s estimate of real potential output grew by an average 3.13% year-over-year quarterly pace. However, between 2000Q1 and 2022Q1, real GDP grew by a year-over-year average 2.02% per quarter, while real potential GDP grew by 2.13%. As a theoretical concept, trends in real potential output depend on how this supply-side concept is calculated. Nonetheless, over the long-haul real potential output provides an estimate of non-inflationary long-term eco­ nomic growth, while actual real GDP growth makes no claim that long-term economic growth is without inflationary pressure or economic fluctuation.

[204] [ADVANCED] WHAT IS MEANT BY GROWTH “REVERTING TO THE MEAN”? Economists have long applied this concept to forecasting where economic growth, inflation, or the unemployment rate—among other economic activities—will return to their previous trend, sooner or later, after some shock to the economy that causes a deviation from the existing trend—whether that is from a business cycle recession or some other type of shock. This idea is the opposite of hysteresis, where the trend has been permanently changed.

[205] WHAT IS THE “TREND” IN U.S. ECONOMIC GROWTH? Although this question about trend economic growth is straightforward, the answer is not. The trend to an economist depends on the time horizon, it depends on whether it is extrapolated from the past or projected based on changing factors in the economy (such as real potential GDP). The table below presents various “trends” for quarterly growth in U.S. real GDP based

135

136 MACROECONOMIC THINKING AND TOOLS on a few different periods and concepts. The selection of any of these (or even other possible estimates) as a representative forward trend in the economy makes various assumptions. For example, if an economist implicitly or explicitly believes that the ten-year pattern of growth is the most likely trend for the future, then the underlying assumption is that the factors pro­ ducing that growth rate over the past ten years will largely be the same in the future period. This assumption is similar for any historic period used for extrapolation. The use of real potential GDP as the “trend” has some conceptual advantages and assumes no business cycle fluctuation. If the economist, however, is thinking about the trend as just “long-term growth” then long-term growth will inevitably experience periods of boom and recession, which will likely affect one’s estimate of that future long-term growth projection. Trend economic growth from today’s perspective looking into the future may or may not be the same as longterm growth over some past period and may or may not be the same as potential economic growth, but it is unlikely that future trend growth will be equal to both long-term growth over some past period and potential economic growth over some future period. TABLE 6.14

What Is Trend Growth in the U.S. Economy? What Is Trend Growth in the U.S. Economy? Calculated Based on 2022Q1 as “Present” Actual Real GDP

Real Potential GDP

Extrapolated from Past 15 Years

Extrapolated from Past 10 Years

Extrapolated from Past 5 Years

Extrapolated from Past 3 Years

Upcoming 5 Years

Upcoming 10 Years

1.9% (60 Qtrs)

2.3% (40 Qtrs)

2.6% (20 Qtrs)

2.5% (12 Qtrs)

1.9% (20 Qtrs)

1.8% (40 Qtrs)

[206] WHAT DETERMINES LONG-TERM ECONOMIC GROWTH? As noted earlier in Chapter 5, labor, capital, and technology—which are the inputs into an aggregate production function—will drive long-term economic growth. Generally, im­ proving technology is highlighted by most economists as the main contributor to economic growth among those factors, largely due to the influential study by Robert Solow and his growth model.36 However, another influential theory about how economic growth unfolds is through “innovation diffusion,” which was part of the late Harvard University Prof. Joseph Schumpeter’s theory of economic growth. Innovation was the dynamic in Schumpeter’s 1942 theory, which resulted in one of five outcomes: (1) the introduction of a new product; (2) the application of a new production process/method; (3) the opening of a new market; (4) tapping of new supply of raw materials; and (5) implementation of a new way of organizing a business. In Schumpeter’s own words: “The function of entrepreneurs is to reform or

BUSINESS CYCLES AND TRENDS

revolutionize the pattern of production by exploiting an invention or, more generally, an untried technological possibility for producing a new commodity or producing an old one in a new way, by opening up a new source of supply of materials or a new outlet for products, by reorganizing an industry and so on.”37

[207] HOW DOES ECONOMIC DEVELOPMENT DIFFER FROM ECONOMIC GROWTH? University of Bath Prof. James Copestake—who is also the co-director of the University’s Centre for Development Studies—observed that, “The term ‘economic development’ refers to long-term changes in systems of production and distribution of goods and services affecting human welfare. In contrast to ‘economic growth’ it involves changes in the form as well as the scale of economic activity. In common usage, development is usually assumed to be by def­ inition a good thing. However, students of development cannot assume this. Economies development is almost always fickle in its effects – some benefit at others’ expense, long-term gain may require short-term pain (and vice versa), and one person’s indicator of progress may be another’s indicator of regress.”38

[208] [ADVANCED] WHAT IS “ECONOMIC MOBILITY” AND HOW IS IT TIED INTO ECONOMIC GROWTH? Economists and sociologists have generally defined economic mobility as the degree of opportunity in an economy that allows a person to move up or down the economic ladder within a lifetime or from one generation to the next.39 There are two types of economic mobility: absolute and relative. The Brookings Institution’s Isabel Sawhill—a leading expert on this subject—wrote, “A growing economy ensures that each generation is better off than the previous one. [Therefore,] economic growth is an important source of [absolute] upward mobility.”40 A second concept of economic mobility is the ability and degree of opportunity that a person is able to move up or down the economic strata (largely income strata) or steps in the ladder relative to others. This second concept of relative economic mobility is “especially relevant during period[s] in which inequalities of income and wealth are on the rise.”41 One common measure of economic mobility in studies is the intergenerational income elasticity, which measures the relationship between a parent’s income and their children’s income over their lifetime. The Pew Charitable Trusts has an ongoing initiative to study American economic mobility, which it notes is influenced by a variety of factors including education, neighborhoods, savings, and family structure. Moreover, Pew’s “robust and nonpartisan” research is aimed at helping to inform policymakers seeking to “foster equality of opportu­ nity” in the economy.

137

138 MACROECONOMIC THINKING AND TOOLS

Issues to Think About In the late 1960s, economists wondered if the business cycle was dead, only to discover the hard way it was not with the advent of the 1969−1970 recession. But recessions have different characteristics—some mild, some severe based on their depth, duration, and diffusion. Meanwhile, understanding the economic trends help policymakers to know what growth is possible. • • •

•

Which concept of the business cycle—the Jevons or Mitchell view—is a better paradigm for understanding the cycle today? Why? If you were to develop a theory of the business cycle, what key facts would be imortant to explain? IMF economists Valerie Cerra and Sweta C. Saxena argue “all types of recessions … lead to permanent output losses,” which they suggest has “far-reaching conceptual and policy implications.” If this finding is true, why is that significant? In thinking about long-term trends in the economy, how would you forecast the trend in real GDP growth over the upcoming ten years? Should that trend be the goal for policymakers?

NOTES 1 Victor Zarnowitz, “What is a Business Cycle?,” NBER Working Paper No. W3863, National Bureau of Economic Research, October 1991, p. 1. 2 J.D. Sachs, “Alternative Approaches to Financial Crises in Emerging Markets,” in M. Kahler, ed., Capital Flows and Financial Crises, (Cornell University, Ithaca, NY, 1998), pp. 247–262. 3 Michael P. Niemira and Thomas L. Saaty, “An Analytic Network Process Model for Financial-Crisis Forecasting,” International Journal of Forecasting, vol. 20 (2004), p. 573. 4 Arthur F. Burns and Wesley C. Mitchell, Measuring Business Cycles, National Bureau of Economic Research (1946), p. 3. 5 Julius Shiskin, “The Changing Business Cycle,” The New York Times (December 1, 1974), Section 3, p. 12. 6 The mnemonics in the FRED database are: real GDP (GDPC1) and real GDI must be calculated as 100 × GDI/GDPDEF, which is nominal GDI (GDI) divided by the GDP deflator (GDPDEF). 7 Bert G. Hickman, “Diffusion, Acceleration, and Business Cycles,” The American Economic Review, vol. 49, no. 4 (September 1959), pp. 535−565. 8 Michael P. Niemira and Philip A. Klein, Forecasting Financial and Economic Cycles (John Wiley & Sons, New York, 1994). 9 Ibid., pp. 40−41. 10 Ilse Mintz, Dating Postwar Business Cycles: Methods and Their Application to Western Germany, 1950–67, NBER, 1969, p. 15. 11 See, https://www.businesscycle.com/ecri-business-cycles/international-business-cycle-dates-chronologies. 12 U.S. real GDP goods for had an average growth rate of 4.1% (quarter-to-quarter annualized rate) from 1947-Q1 to 2022-Q2, while real GDP for services grew by an average pace of 3.0%. From

BUSINESS CYCLES AND TRENDS

13

14 15 16 17 18

19 20 21 22 23

24 25 26 27 28

29

30

1970-Q1 through 2022-Q2, real GDP for goods was still averaging 4.1% growth per quarter, but growth in real GDP for services dipped to 2.4% per quarter. Based on the research by Geert Bekaert, Eric Engstrom, and Andrey Ermolov, “Aggregate Demand and Aggregate Supply Effects of COVID-19: A Real-time Analysis,” Finance and Economics Discussion Series 2020−049, Board of Governors of the Federal Reserve System (Washington, DC, 2020), https://doi.org/10.17016/FEDS.2020.049. For a detailed discussion of the process of compiling a composite index, see: Michael P. Niemira and Philip A. Klein, Forecasting Financial and Economic Cycles (John Wiley & Sons, 1994). Sergio Rebelo, “Real Business Cycle Models: Past, Present, and Future,” Working Paper (Northwestern University, March 2005). Edward Prescott, “Theory Ahead of Business-Cycle Measurement,” Carnegie-Rochester Conference Series on Public Policy, vol. 25 (1986), pp. 11−44. Real business cycle models generate exogenous (external) “impulse responses” to measure the impact of shocks on the economy using statistical methods (mainly vector autoregressive or VAR models). Critics of this approach argue that the real business cycle theory, which assumes that cyclical fluctuations arise from productivity shocks and generating persistent changes to output, as a supply-side explanation of the business cycle “suffers from the criticism of implausibility that a country or the world would suddenly lose productivity on a massive scale, with technology suddenly regressing and then remaining at a lower level to rationalize permanently lower output.” See: Valerie Cerra and Sweta C. Saxena, “Booms, Crises, and Recoveries: A New Paradigm of the Business Cycle and Its Policy Implications,” IMF Working Paper WP/17/250, International Monetary Fund, November 2017. Stefan Erik Oppers, “The Austrian Theory of Business Cycles: Old Lessons for Modern Economic Policy?,” IMF Working Paper WP/02/2, International Monetary Fund, January 2002. Fred E. Foldvary, “The Austrian Theory of the Business Cycle,” The American Journal of Economics and Sociology, vol. 74, no. 2 (March 2015), p. 295. Tyler Cowen, Risk and Business Cycles: New and Old Austrian Perspectives (Routledge, London, 1997), p. 14. Arthur F. Burns and Wesley C. Mitchell, Measuring Business Cycles, National Bureau of Economic Research (New York, 1946), p. 460. Mark Knell observed in his article abstract that “Hyman Minsky pioneered the idea of the financial instability hypothesis to explain how swings between robustness and fragility in financial markets generate business cycles in the economic system.” See: Mark Knell, “Schumpeter, Minsky and the Financial Instability Hypothesis,” Journal of Evolutionary Economics, vol. 25 (2015), pp. 293−310. Hyman P. Minsky, John Maynard Keynes (Columbia University Press, New York, 1975), p. 61. Hyman P. Minsky and Mark D. Vaughan, “Debt and Business Cycles,” Business Economics, vol. 25, no. 3 (July 1990). Hyman P. Minsky, “The Financial Instability Hypothesis: An Interpretation of Keynes and an Alternative to ’Standard’ Theory,” Nebraska Journal of Economics and Business, vol. 16, no. 1 (Winter, 1977), p. 9. “Diminished Expectations” (republished as “Economists Still Lack a Proper Understanding of Business Cycles,” The Economist, vol. 427, no. 9088 (April 21, 2018), p. 66. House of Commons Official Report (also known as Hansard), vol. 720: debated on Wednesday 17 November 1965, page 1,165. https://hansard.parliament.uk/Commons/1965-11-17/debates/ 06338c6d-ebdd-4876-a782-59cbd531a28a/EconomicAffairs?highlight=stagflation#contribution2c3e32e7-7c5b-47b8-bfd3-16c3dbd2a001. Martin Bronfenbrenner, “Elements of Stagflation Theory,” Zeitschrift für Nazionalökonomie (Journal of Economics), Wien, vol. 36 (January 1, 1976), pp. 1−8. This article shows stagflation using the tradi­ tional aggregate demand/aggregate supply models. Robert B. Barsky and Lutz Kilian, “Do We Really Know that Oil Caused the Great Stagflation? A Monetary Alternative,” in Ben Bernanke and Kenneth Rogoff, eds., NBER Macroeconomics Annual 2001 (MIT Press, 2001), pp. 137−183.

139

140 MACROECONOMIC THINKING AND TOOLS 31 Ibid, p. 137. This is the standard perspective based on the aggregate demand/aggregate supply model. 32 See, for example, Alan S. Blinder and Jeremy B. Rudd, “The Supply-Shock Explanation of the Great Stagflation Revisited,” Princeton’s Center for Economic Policy Studies (CEPS) Working Paper No. 176 (November 2008). 33 Gernot Kohler, “A General Theory of Stagflation,” Alternatives, vol. VIII (Summer 1982), pp. 49−77. 34 See: A. Belke and M. Göcke, “Interest Rate Hysteresis in Macroeconomic Investment under Uncertainty,” Working paper No. 2019/3, King’s Business School, London, March 2019. Also, Danny Yagan, “Employment Hysteresis from the Great Recession,” NBER Working Paper 23844, National Bureau of Economic Research (September 2017), http://www.nber.org/papers/ w23844. 35 Steven Pennings, “Long-Term Growth in Developing Countries,” World Bank, Development Economics Presentation (December 14, 2021), https://thedocs.worldbank.org/en/doc/ ca2ae029a11f90a944d23211769730a2-0050022021/original/Pennings-PolicyResearchTalkLongTermGrowth.pdf. In late 2021, the World Bank unveiled its “Long Term Growth Model,” which is available for download in EXCEL. This model is based on the Solow-Swan growth model; see https://www.worldbank.org/en/research/brief/LTGM 36 One of the recent controversies with this growth model is whether or not total factor productivity (TFP) is “exponential” or “additive.” The idea that TFP—which has been interpreted as “techno­ logical improvement”—is exponential means that it has a bigger impact on output (as most theory and studies have embraced) than if it was just an “additive” factor. In a recent paper by NYU Prof. Thomas Philippon, he argues that “TFP growth is not exponential. New ideas add to our stock of knowledge; they do not multiply it.” See: Thomas Philippon, “Additive Growth,” unpublished NYU working paper, May 2022, https://pages.stern.nyu.edu/~tphilipp/papers/AddGrowth_macro. pdf. The implication of this, according to Philippon is that it helps to explain the recent slowdown experienced in TFP. If this is true, economists emphasis that technology is the biggest factor de­ termining economic growth may be overstated. 37 Joseph A. Schumpeter, Capitalism, Socialism and Democracy (Routledge, London, 2013) (reprinted), p. 132. 38 James Copestake, “Theories of Economic Development,” UNESCO Encyclopedia of the Life Sciences (2nd Draft), August 1999. 39 Pablo A. Mitnik and David B. Grusky, Economic Mobility in the United States, A Report from The Pew Charitable Trusts and the Russell Sage Foundation, July 2015. 40 Isabel V. Sawhill, “Overview,” in Ron Haskins, Julia B. Isaacs, and Isabel V. Sawhill, eds., Getting Ahead or Losing Ground: Economic Mobility in America, Brookings Institution, Washington, DC, February 20, 2008, p. 2. 41 Ibid., p. 4.

CHAPTER

7

A Macroeconomic Toolbox for Descriptive Analysis

LEARNING OBJECTIVES This chapter introduces you to core analytical measures that are broadly called “exploratory data analysis” or “EDA” to be able to describe patterns in economic growth, economic trends, and changes in the economy, which is the “bread and butter” of economic analysis. Wesley Mitchell—the first research director of the National Bureau of Economic Research—opined that “the quickest way to attain reliable results is to take great care in measuring the phenomena exhibited.” Great care does not necessarily mean applying the latest statistical method—more often than not, basic analysis provides the insights needed to answer most empirical questions before applying more sophisticated statistical methods—if necessary. You will learn: • • • • • • •

How to use descriptive statistics to analyze macroeconomic data trends, cycles, and volatility. Why some data is annualized and other data is not. Alternative ways to calculate growth rates. Simple ways to “filter” data to bring out the message and reduce the “noise.” The basics of how economic data are adjusted for seasonality and the importance of the seasonal cycle. Why volatility is important to understanding changes in the economy. About useful economic databases and websites.

[209] WHAT IS MEANT BY “EXPLORATORY DATA ANALYSIS”? The term “exploratory data analysis” comes from the late mathematician and statistician John Tukey’s 1977 book1 of the same name. Tukey said of his approach that it is “about looking at data to see what it seems to say [using] simple arithmetic and easy-to-draw pictures.” Tukey further observed that there are a considerable variety of simple techniques for looking at data DOI: 10.4324/9781003391050-8

142 MACROECONOMIC THINKING AND TOOLS effectively. In 1962, Tukey noted that it is “far better [to get] an approximate answer [using simple techniques] to the right questions [than] an exact answer to the wrong questions” using sophistical methods. John Maynard Keynes concurred with that view—though he lived earlier than Tukey—when Keynes said, “It is better to roughly right than precisely wrong.”

[210] WHY IS EXPLORATORY DATA ANALYSIS (EDA) IMPORTANT IN ECONOMICS? EDA has become the backbone of the data science field today, however, economists have long embraced this idea that the starting point to investigate data is to summarize the main characteristics of the data and often visualizing it as well. IBM data scientists2 suggest that EDA is “primarily used to see what data can reveal [before any] formal modeling or hypothesis testing task and provides a better understanding of data set variables and the relationships between them. It can also help determine if the statistical techniques you are considering for [deeper] data analysis are appropriate.”

[211] WHAT ARE SOME OF THE BASIC EXPLORATORY DATA ANALYSIS TECHNIQUES USED IN ECONOMICS? Economists start data exploration by determining how the data should be looked at—as levels (such as interest rates or the unemployment rate), as changes (such as with inventories), as growth rates (such as with employment, inflation, real GDP), as episode/event changes (such as from a cyclical peak to a cyclical trough), as period changes (such as decade growth rates), or based on some other meaningful measure of economic change or trend—such as expressed as a “natural logarithm.” Then EDA methods are applied. The core descriptive statistics of EDA that are widely used in economics fall into three categories: (1) measures of central tendency—such as, the mean, median, and mode; (2) measures of variability—such as the range of the data or spread (the difference between the largest and smallest observation), interquartile range (difference between the 25th and 75th percentile), and deviation (spread of the data about the mean)—such as absolute deviation or standard deviation; and (3) the frequency distribution of the observations (the number of occurrences of each outcome)—such as a normal bell curve (a centered and symmetric distribution) or other distributions that could have multiple peaks, be skewed to one side of the center or the other side, be uniform, have gaps, or have outliers. Assembling these EDA pieces of information about the data set and its patterns is likely to prompt questions about the economic activity under study. For example, if the distribution has outliers, then some questions that would be asked are when did those outliers occur and what might have triggered them, and so on. This is how economic stories, hypothesis, and then economic theories begin.

[212] WHAT IS A GROWTH RATE? A growth rate measures the percentage amount of change relative to a base period. Since macroeconomics is about economic growth and trying to explain that dynamic, this concept is integral to that discussion and explanation.

TOOLBOX FOR DESCRIPTIVE ANALYSIS

[213] WHAT GROWTH-RATE FORMULAE ARE RELEVANT TO ANALYZE ECONOMIC ACTIVITY? There is an endless number of growth-rate formulae that may be used in economics, but the more popular ones are discussed and then summarized in the table. •

Simple Percentage Change: This is the most common growth rate that compares two points in time—the later period (denoted as time “t”—the period now—in the summary table) and the earlier period (denoted as in time “t−1”—the prior period observation) and takes the difference (the later minus the earlier value) and compares that difference to the earlier period, then it is multiplied by 100. An alternative—and computationally a bit faster if done by hand—is the take the later observation (in period “t”) and divide it by the earlier observation (in period “t−1”) and subtract 1 from that ratio and finally multiply by 100. Although it is rarely thought about, there is a natural log approximation to growth rates, which was once was very commonly used in econometric models and some theories. This calculation is to either take the natural log of the ratio of the later observation (in period “t”) divided by the earlier observation (in period “t−1”) and multiply by 100. Or, as a property of logarithms, this can be calculated as the difference of the natural log of the later observation minus the natural log of the earlier observations and the result multiplied by 100. A lot of economic statistics are reported using a simple percentage change, such as the Consumer Price Index, Producer Price Index, industrial production, to name a few. - Example of a Simple Percentage Change of Monthly Data: TABLE 7.1

Example of Calculating Month-over-Month Growth Rates Calculating Month-over-Month Growth Rates Sep 2021

Oct 2021

CPI Less Food and 280.017 281.705 Energy % Change Monthover-Month % Change Using Logs

Nov 2021

Dec 2021

283.179 284.77

Jan 2022

Feb 2022

Mar 2022

Apr 2022

286.431

287.878 288.811 290.455

---

0.60

0.52

0.56

0.58

0.51

0.32

0.57

---

0.60

0.52

0.56

0.58

0.50

0.32

0.57

Step 1: Divide the later-dated value by the earlier-dated observation Step 2: Subtract 1 from that ratio Step 3: Multiply Step 2 result by 100 Note that the dash in the table indicates that the August 2021 data was not available in the table to calculate the September 2021 growth rate. Also remember that the growth rate period calculated is determined by the numerator time-period—so if the “later” observation in the numerator is for December 2021, for example, then that is a growth rate for December 2021. The natural log approximation also is shown, but the steps listed are for the standard formulation.

•

Annualized or Compounded Percentage Change: Two versions of this will be discussed— one for monthly data and one for quarterly data. The concept is the same, but the annualization or compounding factor depends how many periods within the year. This only difference between this formula and the simple percentage change is the need to

143

144 MACROECONOMIC THINKING AND TOOLS raise the ratio of the current month (“t”) to the prior month (“t−1”) to either 12, if you are using monthly data, or 4, if you are using quarterly data. The common application of this annualization is with reported real GDP growth. The U.S. Commerce Department’s Bureau of Economic Analysis annualizes the growth rate as a convenience for comparison of annual growth with quarterly growth. Although the Bureau of Labor Statistics does not annualize the CPI anymore, it once did. However, you will notice that annualization of small moves can make monthly fluctuation seem more substantial, which is why the BLS stopped the annualization of the CPI years ago. - Examples for Monthly and Quarterly Annualization: Example of Calculating Month-over-Month and Quarter-over-Quarter Annualized Growth Rates

TABLE 7.2

Calculating Month-over-Month Annualized Growth Rates Sep 2021 CPI Less Food and Energy

Oct 2021

280.017 281.705

Nov 2021

Dec 2021

Jan 2022

Feb 2022

Mar 2022

Apr 2022

283.179

284.77

286.431

287.878

288.811 290.455

% Change Month-over- --Month at an Annualized Rate

7.48

6.46

6.95

7.23

6.23

3.96

7.05

% Change Using Logs

7.21

6.26

6.72

6.98

6.05

3.88

6.81

---

Step 1: Divide the later-dated value by the earlier-dated observation Step 2: Raise the result from Step 1 to the power of 12 (the compounding effect) Step 3: Subtract 1 from that ratio Step 4: Multiply Step 3 result by 100 Calculating Quarter-over-Quarter Annualized Growth Rates Q2–2020 Q3–2020 Q4–2020 Q1–2021 Q2–2021 Q3–2021 Q4–2021 Q1–2022 Real GDP (Billions of 2012 Dollars)

17258.2 18560.8

18767.8 19055.7

19368.3 19478.9 19806.3

19731.1

% Change Quarterly at an Annualized Rate

---

33.8

4.5 6.3

6.7

2.3

6.9

−1.5

% Change Using Logs

---

29.1

4.4 6.1

6.5

2.3

6.7

−1.5

Step 1: Divide the later-dated value by the earlier-dated observation Step 2: Raise the result from Step 1 to the power of 4 (the compounding effect ) Step 3: Subtract 1 from that ratio Step 4: Multiply Step 3 result by 100

•

Year-over-Year Percentage Change (12-Month or 4-Quarter Percentage Change): Two versions of this formula will be discussed as above. This calculation will produce a growth rate with the later-dated observation always compared to the observation for the same period from one year earlier (t−12 for monthly data, or t−4 for quarterly data). Also, note that a year-over-year growth rate of monthly or quarterly data does not mean annual data (that is, a single growth rate)—but a percentage change from the prior year for each month or each quarter of the year relative to the prior year.

TOOLBOX FOR DESCRIPTIVE ANALYSIS

-

Examples for Percentage of Change from Same Period of Period Year:

TABLE 7.3

Example of Calculating Year-over-Year Monthly and Quarterly Growth

Rates Calculating Year-over-Year Monthly Growth Rates

CPI Less Food and Energy

Jan 2021

Feb 2021

Mar 2021

Apr 2021

270.114

270.522

271.347 273.669

Jan 2022

Feb 2022

Mar 2022

Apr 2022

286.431

287.878

288.811 290.455

% Change from Same Month of Prior Year

---

---

---

---

6.0

6.4

6.4

6.1

% Change Using Logs

---

---

---

---

5.9

6.2

6.2

6.0

Step 1: Divide the later-dated value by the 12-month earlier-dated observation Step 2: Subtract 1 from that ratio Step 3: Multiply Step 2 result by 100

Calculating Year-over-Year Quarterly Growth Rates Q2–2020 Q3–2020 Q4–2020 Q1–2021 Q2–2021 Q3–2021 Q4–2021 Q1–2022 Real GDP (Billions of 2012 Dollars)

17258.2 18560.8 18767.8 19055.7 19368.3 19478.9 19806.3 19731.1

% Change Quarterly at an Annualized Rate

---

---

---

---

12.2

4.9

5.5

3.5

% Change Using Logs

---

---

---

---

11.5

4.8

5.4

3.5

Step 1: Divide the later-dated value by the 4-quarter earlier-dated observation Step 2: Subtract 1 from that ratio Step 3: Multiply Step 2 result by 100

•

Smoothed 6-Month and Two-Quarter Percentage Changes: These smoothed growth-rate formulae are not commonly used but were derived by Geoffrey H. Moore and Victor Zarnowitz to address well-known problems of using growth rates with either short-period growth rates or long-period growth rates for cyclical timing. The short-period growth rates can be very volatile (“noisy”) and difficult to read; the long-period growth rates smooth the growth-rate pattern, but at the expense of shifting the turning point dates in growth-rate cycles. Being the first to seeing a changing trend can be useful, which was the motivation of Moore and Zarnowitz (who tied this type of calculation into a signaling system for countercyclical policy). Although most times the differences may be minor, there are times when a non-traditional growth rate might be useful, such as looking at the CPI in the 2008–2011 period as displayed in the graph (Figure 7.1). Notice that the sixmonth smoothed growth rate clearly did a better job in highlighting the growth rate turning points—especially in 2008.

With this motivation, the formulae are a bit more tedious—however, easy to do in spread­ sheets and other software packages.

145

146 MACROECONOMIC THINKING AND TOOLS

CPI-U Growth Rate Comparison: 12-Month % Change vs. Six-Month Smoothed Annualized Rate

FIGURE 7.1

The six-month smoothed annualized rate (SMSAR) and the two-quarter smoothed annualize rate have a common structure—however, one is oriented to monthly data and the other quarterly. The elements of both formulae are: (1) it uses the latest month (“t”) in the numerator; (2) it uses an average of the past year’s observations (12 past observations for monthly, and 4 past observations for quarterly); (3) it is annualized by an exponential factor derived as the ratio of the number of observations in the year (12 or 4) divided by the half of the full-range of data covered (the mid-point of the number of observations). For monthly data the observations are the current month (“t”) over observations that extend back 12 months—“t−1” through “t−12”—which extends totally over 13 months. Therefore, the exponent used to annualize the growth rate is 12 divided by 6.5, which is 13 divided by 2. Similarly for quarterly data the observations are the current quarter (“t”) over the observations that extend back 4 quarters—“t−1” through “t−4”—which totally extends over 5 quarters. As such, the quarterly formula exponent is “4” for the frequency divided by 2.5, which is 5 divided by 2. Although these formulae are much more complicated, they are a good reminder how different spans can affect the turning point dates. Finally, although the SMSAR and TQSAR growth rates can be approximated by natural log forms, this is not included in the Table 7.4. -

Example of the Six-Month Smoothed Growth Rate and Comparison: Example of Calculating Six-Month Smoothed Annualized Rate (SMSAR) Growth (with Comparison to Year-over-Year Growth Rate)

TABLE 7.4

Calculating Six-Month Smoothed Annualized Rate (SMSAR) Growth (with Comparison to Year-over-Year Growth Rate)

CPI Less Food and Energy

Sep 2021

Oct 2021

Nov 2021

Dec 2021

Jan 2022

Feb 2022

Mar 2022

Apr 2022

280.017

281.705

283.179

284.77

286.431

287.878

288.811

290.455

(Continued )

TOOLBOX FOR DESCRIPTIVE ANALYSIS TABLE 7.4

(Continued)

Calculating Six-Month Smoothed Annualized Rate (SMSAR) Growth (with Comparison to Year-over-Year Growth Rate)

CPI Less Food and Energy (PRIOR 12-Month Average)

Sep 2021

Oct 2021

Nov 2021

Dec 2021

Jan 2022

Feb 2022

Mar 2022

Apr 2022

272.973

273.878

274.909

276.023

277.255

278.615

280.061

281.516

SMSAR Growth

4.8

Year-over-Year Percentage Change

---

5.3 ---

5.6 ---

5.9 ---

6.2

6.2

5.8

5.9

6.0

6.4

6.4

6.1

Step 1: Divide the later-dated value by the prior 12-month average Step 2: Raise the result from Step 1 to the power of 12 divided by 6.5 Step 3: Subtract 1 from that ratio Step 4: Multiply Step 3 result by 100

•

Finally, the table below presents a complete listing of all the formulae discussed showing all the variants.

TABLE 7.5

Summary of Common Growth Rate Formulae Used in Economics Summary of Common Growth Rate Formulae Used in Economics

Type of Change

Standard Growth Rate Formula

Natural Log Approximation

Percentage Change (Simple)

= 100 × (Xt – Xt−1)/ Xt−1 = 100 × ((Xt/Xt−1) − 1) = 100 × (((Xt/Xt−1)4) − 1)

= = = =

Month-over-Month Percentage Change (Annualized or “Compound Annual Rate)

= 100 × (((Xt/Xt−1)12) − 1)

= 1200 × LN(Xt/Xt−1) = 1200 × (LN(Xt) − LN(Xt−1))

Year-over-Year Percentage Change of Monthly Data (% Change from Year Ago)

= 100 × ((Xt/Xt−12) − 1)

= 100 × LN(Xt/Xt−12) = 100 × (LN(Xt) − LN(Xt−12))

Year-over-Year Percentage Change of Quarterly Data (% Change from Year Ago)

= 100 × ((Xt/Xt−4) − 1)

= 100 × LN(Xt/Xt−4) = 100 × (LN(Xt) − LN(Xt−4))

Multiple-Year Percentage Change (Annualized) Six-Month Smoothed Annual Rate (SMSAR) Percentage Change

= 100 × ((Xt/Xt−n)(1/n) − 1) where n= span of years = 100 x (((X/((Xt−1 + Xt−2 + Xt−3 + Xt−4 +

= (100/n) × LN(Xt/Xt−n) = (100/n) × (LN(Xt) − LN(Xt−n))

Quarterly Percentage Change (Annualized or “Compound Annual Rate”)

Two-Quarter Smoothed Annual Rate (TQSAR) Percentage Change

Xt−5 + Xt−6 + Xt−7 + Xt−8 + Xt−9 + Xt−10 + Xt−11 + Xt−12)/12))(12/6.5))−1) = 100 x (((X/((Xt−1 + Xt−2 + Xt−3 + Xt−4)/4))(4/2.5))−1)

100 100 400 400

× × × ×

LN(Xt/Xt−1) (LN(Xt) − LN(Xt−1)) LN(Xt/Xt−1) (LN(Xt) − LN(Xt−1))

147

148 MACROECONOMIC THINKING AND TOOLS

[214] WHY DO ECONOMISTS USE THE NATURAL LOG TRANSFORMATION? The natural log transformation is based on the number “e” (Euler’s number), which is equal to 2.71828. It is tied to exponential growth as demonstrated in the first chart. Taking the natural logarithm of those data (on the y-axis) will transform the data into a more linear pattern (or an exact linear in this specific example). This transformation is sometimes used for long-term growth studies on a measure such as real GDP, as shown in the third chart. Visually, comparing the level and the natural log of the actual real GDP data (chart 3) may not seem too different. However, between 1929 and 2021, U.S. real GDP in level terms grew by 1666.3% (100 × (19609.91/1110.206)−1) cumulatively, but the natural log (LN) transformation of those data grew by 124.7% (100 × LN (19609.91)−LN(1110.206)). Consequently, this natural log transformation of the data may lend itself to a “linear regression” (using a log-linear model), (Figures 7.2–7.4). Finally, one author puts the relationship between ex and the LN(x) this way: ex lets us plug in a time span (x) and get the cumulative growth with 100% compounding. LN(x) lets us plug in growth (ex) and get the time it would take.

Y-Axis (Economic Measure)

• •

200000 150000 100000

59874

50000 148 0 5

FIGURE 7.2

Y-Axis (Economic Measure)

162755

14 12 10 8 6 4 2 0

5

403

1097

6

7

2981

8103

8 9 X-Axis (Time)

22026 10

11

12

Example of Economic Variable with Exponential Growth

6

7

8

9

10

11

12

X-Axis (Time)

Natural Log Transformation of Data Characterized by Exponential Growth into Linear Pattern

FIGURE 7.3

TOOLBOX FOR DESCRIPTIVE ANALYSIS

FIGURE 7.4

Real GDP: Level and Natural Log Transformation

Using the real GDP example above, “x” represented the number of years between 1929 and 2021, which was 92 years then, • •

e92 = 9.01763E+39, which is the compounded amount of growth in real GDP between 1929 and 2021. LN(9.01763E+39) = 92, which gives us the number of years it will take to accumulate that amount with 100% compounding.

Of course, 100% compounding is not realistic. More realistically is a question that this approach may assess is how long will it take for real GDP to, say, double in size? For example, how long would it take for real GDP to double (or some multiple) if its growth rate remained steady with its recent trend over a selected historical period, say at its 2000–2021 average pace, which was 1.926% per year? To answer this question, the time span (x) in ex is really a “time span” multiplied by a “rate,” which initially was 1.0 (100%). Therefore, substitute (rate as a decimal) multiplied by (time span) for “x” to determine how long it will take economic growth to increase by some multiple of itself. •

To do this, the “time span” will equal the natural log of the “growth multiple” desired divided by the growth rate for real GDP as a decimal (0.01926). For example, if the targeted growth multiple was 1.2 times the current level of real GDP, then the natural log of 1.2 is 0.18. Then, that natural log is divided by the growth rate as a decimal—that is, 0.18 divided by 0.1979, which tells us that it will take nine years to increase real GDP by 1.2 times. These figures are in the table along with other growth multiples. Realistically, real GDP multiples will be low—well under four times—with growth rates that are plausible around 2% and time frames that are reasonable to comprehend (about half a century).

149

150 MACROECONOMIC THINKING AND TOOLS TABLE 7.6

How Long Will It Take for Real GDP to Multiply? How Long Will It Take for Real GDP to Multiply?

Growth Multiple

Natural Log of Growth Multiple

Time to Accomplish (Years)

1.2 1.4 1.6 1.8 2x 2.2 2.4 2.6 2.8 3x

0.18 0.34 0.47 0.59 0.69 0.79 0.88 0.96 1.03 1.10

9 17 24 31 36 41 45 50 53 57

x x x x x x x x

While this analysis, on the surface, is not extremely useful, it does provide a benchmark for how long it might take (nine years) with uninterrupted growth around 2% to increase real GDP by a modest 1.2 times.

[215] HOW SHOULD ECONOMIC STABILITY BE MEASURED? Stability is an economic goal, but the measurement of it is done as the absence of stability based on statistical measures of “dispersion” of data. The most common measure is standard deviation (note that the standard deviation is the square root of the “variance”) and economic studies have used that concept to assess whether economic growth and inflation have become tamer over time. Standard deviation—which is easily calculated in a spreadsheet3—is denoted with the symbol σ (the Greek letter sigma) and calculated using the formula: =

1 N

N i =1

(xi

µ)2

where, N = The number of data points or observations xi = Each observation μ = (the Greek letter “mu”) is the mean of all values The symbol Σ (the Greek letter “sigma”) tells us to add all of the terms from 1 to N. The steps involved in calculating standard deviation are: •

Calculate the average (that is, mean, μ);

TOOLBOX FOR DESCRIPTIVE ANALYSIS

FIGURE 7.5

• • • • •

8-Quarter Moving Standard Deviation of Real GDP and GDP Price Deflator

Calculate the deviation, which is the difference of the data observation (xi) minus the average (μ); Square the deviation—that is, raise it to the second power; Sum up the squared deviations for N observations; Divide the squared deviation sum by the number of observations in the population (N); Take the square root—which is the same thing as raising it to the power of ½.

A slight variation on this is “moving standard deviation,” which rolls the period of analysis through time—such as, using an eight-quarter (or any period) window on how volatility is changing over time. For example, using such an eight-quarter moving standard deviation of the real GDP and the GDP price deflator—extrapolated back to 1875 based on the NBER historical estimates—the chart demonstrates how the U.S. economy was far more volatile (less stable) in the pre-WWII period than after that time (Figure 7.5). There are a lot of implications of these patterns, which are discussed in detail in Chapter 15: The Great Moderation Followed by the Great Volatility.

[216] WHAT IS AN ECONOMIC “TIME SERIES”? Although earlier chapters have discussed economic times series—such as, employment, inflation, or GDP—there was not a formal conceptual definition offered for the measures. It now is useful for further measurement discussions to offer that definition. An economic time series is a sequence of time-ordered observations of an economic statistic. Thus, every observation is associated with a unique time period and each period refers to an equal time unit (for example, months, quarters, or years). Observations are sequenced by the time unit.

[217] WHAT IS PERSONS’ METHOD OF TIME-SERIES DECOMPOSITION? In 1923, Harvard University Prof. Warren M. Persons suggested one of the earliest methods of analyzing a time series by segmenting it into “four types of variation.”4 Today, this concept is

151

152 MACROECONOMIC THINKING AND TOOLS accepted without question and is the standard approach used by statistical agencies around the world. But in 1923, Persons had to explain why it was useful and how this segmentation might be done in his quest to determine relationships between two or more time series. Persons explained that these four variations are: •

• • •

Seasonal: “Variations which occur within each year as a consequence … of the seasons [where] certain weeks or months are regularly higher or lower than those for other weeks of the year.” Trend: “A long-time movement or secular trend covering a considerable period of years by which the average [change shows] permanent gain or suffers a permanent loss.” Cycle: “Wave-like or cyclical movements—which may or may not be periodic—connected with the ebb and flow of business.” Irregular (or Remainder): “Irregular fluctuations resulting from wars, panics, strikes, etc.”

The methods used for this segmentation have been developed considerably since Persons’ early attempt. Characteristically, this approach is built upon “deterministic” components (that is, non-random elements—trends, cycles, and seasonals) and the segmentation of the components of the time series are “separable” (uniquely different without overlap).5

[218] WHAT IS MEANT BY “SEASONALLY ADJUSTED” DATA? Economic activity is affected by religious and national holidays, seasonal travel, school years, and other intra-year events. These events are recurring each year and with them recurring patterns of economic activity at roughly the same time within the year. For example, the Christmas holiday has a large rise in holiday gift spending in the period leading up to the holiday—as shown in the chart below (Figure 7.6). The volatile thin line represents the unadjusted retail sales spending, and

FIGURE 7.6

U.S. Retail Sales Excluding Food Services Seasonally Adjusted vs. Unadjusted

Source: U.S. Census Bureau

TOOLBOX FOR DESCRIPTIVE ANALYSIS

the thick line represents the seasonally-adjusted retail sales spending. Even during the 2020 pandemic, the holiday spending pattern was noticeable in the unadjusted data. Generally, economists focus on seasonally adjusted statistics to better assess the underlying pattern. Seasonally adjusted time series only remove the seasonal component, leaving the trend, cycle, and irregular components intact. The seasonally adjusted series will be smoother than the unadjusted measure because it dampens seasonal highs and boosts seasonal lows during the year. Thus, the main reason for adjusting a time series to remove seasonality is a broader objective that the resulting metric will enhance the users’ ability to recognize, interpret, and react to the more significant non-seasonal movements—the business cycle principally, and the trend secondarily.

[219] IS THERE SIMPLE LOGIC TO UNDERSTAND HOW TO ADJUST A TIME SERIES FOR SEASONALITY? A basic way to think about the seasonal pattern is to first array the data (in a table) by month6 and year and look at each month’s relative share of its annual performance. Then take an average for all years of those relative shares for each month. Consider this example: the original unadjusted data for U.S. retail sales is shown in this table from 2000 to 2021. The next step is to divide each month of each year by that year’s average. This yields a January ratio of sales to its full year, a February ratio of sales to its full year, a March ratio of sales to its full year, and so forth. Do this for every year, as shown in the next table, and take the median, mean, and standard deviation of each column (that is for each month). That median ratio (or the average, if you prefer) for each of the 12 months represents a “crude” seasonal factor, which can be used to adjust the pattern during any year. To show this, a seasonally adjusted estimate of retail sales using this crude seasonal factor (divide the unadjusted data by the seasonal factor for that month) is presented at the bottom of the table for the year 2021 along with the actual seasonally adjusted retail sales data calculated by the Census Bureau for comparison. Below the table, the two seasonally adjusted estimates of the 2021 retail sales data are graphed (Figure 7.7). This exercise is not to suggest this method as a replacement for what the Census Bureau does—since the census seasonally adjustment accounts for more issues, such as “trading days” within months (a longer or shorter number of days in a month); shifting holidays, such as Easter; and ensuring that the seasonal pattern is stable (based on a range of statistical tests). However, this simple exercise should provide the conceptual motivation behind the adjustment process.

[220] [ADVANCED] WHAT METHODS ARE USED BY STATISTICAL AGENCIES AND INTERNATIONAL ORGANIZATIONS TO DETERMINE SEASONAL-ADJUSTMENT FACTORS, WHICH IN TURN ARE USED TO REMOVE THE INTRA-YEAR SEASONAL FLUCTUATION IN TIME SERIES? There are two broad approaches (which have been intersecting more and more) to remove the identifiable recurrent seasonal pattern in an economic time series:

153

Jan

213709 226791 230546 242271 252818 263469 286152 295284 307576 273998 279044 298626 315540 333695 340476 350304 351772 369081 389740 399359 416285 462558

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

227087 223971 228084 233478 253689 265320 282417 290065 308171 264465 275566 299920 331470 332486 337481 341856 363527 365592 380598 384977 413203 438933

Feb

253717 253439 257133 264532 287944 306384 326153 335917 334416 290068 321305 345052 368502 374171 383567 393110 404996 423002 444925 447160 428285 565764

Mar 239051 249062 257357 265990 284325 302054 316526 321981 331002 292041 316940 339014 349194 362964 383816 387375 394743 407240 420209 442704 375242 557874

Apr

U.S. Retail Sales (2000–2021)

Year

TABLE 7.7

257581 268658 271682 281482 296253 311292 337393 353201 357277 307481 324820 348979 373129 389681 408288 409482 413036 434506 464271 475656 460253 567461

May 255066 260315 260385 271242 289664 317375 330844 338189 339791 306050 319183 346620 356083 369316 385482 397821 410071 423066 443732 448224 478667 560679

Jun 244445 251504 266795 279323 294875 316887 325905 333815 344158 308847 320915 341381 351520 376974 394700 406554 405965 416906 441832 461642 492259 554845

Jul 257487 266460 277716 285212 294133 321409 339155 349191 342443 314505 322319 352224 372986 388227 401450 404920 416489 431804 456445 472891 487134 553822

Aug 243624 236210 246350 265331 282974 300439 310775 317145 313308 288071 307638 333642 342582 352680 374401 381992 394200 412546 417510 428334 474284 532840

Sep

Millions of Dollars, Not Seasonally Adjusted

U.S. Retail Sales

245167 265188 259945 273781 287468 302213 312976 331073 311422 300360 315059 337067 355823 369773 388391 392604 397394 417773 442256 455035 494134 557737

Oct 252145 262004 263738 272396 294278 311715 323089 341848 299238 303850 328381 351517 368593 378686 391502 396058 413901 441933 460034 467311 491813 579687

Nov

294197 298666 308821 327693 354627 370726 380188 387473 346513 362735 386878 408910 416807 430585 451097 464035 482002 496765 493873 518979 558834 632849

Dec

248606 255189 260713 271894 289421 307440 322631 332932 327943 301039 318171 341913 358519 371603 386721 393843 404008 420018 437952 450189 464199 547087

Average

154 MACROECONOMIC THINKING AND TOOLS

Jan

0.860 0.889 0.884 0.891 0.874 0.857 0.887 0.887 0.938 0.910 0.877 0.873 0.880 0.898 0.880 0.889 0.871 0.879 0.890 0.887

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

0.913 0.878 0.875 0.859 0.877 0.863 0.875 0.871 0.940 0.879 0.866 0.877 0.925 0.895 0.873 0.868 0.900 0.870 0.869 0.855

Feb 1.021 0.993 0.986 0.973 0.995 0.997 1.011 1.009 1.020 0.964 1.010 1.009 1.028 1.007 0.992 0.998 1.002 1.007 1.016 0.993

Mar 0.962 0.976 0.987 0.978 0.982 0.982 0.981 0.967 1.009 0.970 0.996 0.992 0.974 0.977 0.992 0.984 0.977 0.970 0.959 0.983

Apr 1.036 1.053 1.042 1.035 1.024 1.013 1.046 1.061 1.089 1.021 1.021 1.021 1.041 1.049 1.056 1.040 1.022 1.034 1.060 1.057

May 1.026 1.020 0.999 0.998 1.001 1.032 1.025 1.016 1.036 1.017 1.003 1.014 0.993 0.994 0.997 1.010 1.015 1.007 1.013 0.996

Jun 0.983 0.986 1.023 1.027 1.019 1.031 1.010 1.003 1.049 1.026 1.009 0.998 0.980 1.014 1.021 1.032 1.005 0.993 1.009 1.025

Jul 1.036 1.044 1.065 1.049 1.016 1.045 1.051 1.049 1.044 1.045 1.013 1.030 1.040 1.045 1.038 1.028 1.031 1.028 1.042 1.050

Aug 0.980 0.926 0.945 0.976 0.978 0.977 0.963 0.953 0.955 0.957 0.967 0.976 0.956 0.949 0.968 0.970 0.976 0.982 0.953 0.951

Sep

Divide Each Month of Each Year By Its Average for That Year

How to Adjust a Time Series for Seasonality

Year

TABLE 7.8

0.986 1.039 0.997 1.007 0.993 0.983 0.970 0.994 0.950 0.998 0.990 0.986 0.992 0.995 1.004 0.997 0.984 0.995 1.010 1.011

Oct 1.014 1.027 1.012 1.002 1.017 1.014 1.001 1.027 0.912 1.009 1.032 1.028 1.028 1.019 1.012 1.006 1.024 1.052 1.050 1.038

Nov 1.183 1.170 1.185 1.205 1.225 1.206 1.178 1.164 1.057 1.205 1.216 1.196 1.163 1.159 1.166 1.178 1.193 1.183 1.128 1.153

Dec

(Continued )

1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

Average

TOOLBOX FOR DESCRIPTIVE ANALYSIS

155

Feb 501579

503650

Jan 522307

513269

Adjusted Series Using Crude Seasonal Seasonally Adjusted As Reported

0.875

0.886

Crude Seasonal Based on Median

0.890 0.802 0.875 0.878 0.027

0.897 0.845 0.886 0.884 0.019

2020 2021 Median Mean Standard Deviation

Feb

Jan

(Continued)

Year

TABLE 7.8

558369

Mar 563130

556531

Apr 569445

547116

May 546437

550638

Jun 553220

543355

Jul 547014

1.060 1.014 1.014 1.014 0.020

Jul

546946

Aug 530889

1.049 1.012 1.043 1.039 0.013

Aug

Sep

Oct

552617

Sep 550728

562296

Oct 560616

0.995

1.031 1.025 1.013 1.012 0.013

Jun

Using the “Crude Seasonal Factor” to Adjust 2021 Data 1.005 0.980 1.038 1.013 1.014 1.043 0.968

0.991 1.037 1.038 1.039 0.021

May 1.064 1.019 0.995 0.998 0.023

0.808 1.020 0.980 0.974 0.040

Apr 1.022 0.974 0.968 0.966 0.019

0.923 1.034 1.005 0.999 0.024

Mar

Divide Each Month of Each Year By Its Average for That Year

565690

Nov 567334

1.022

1.059 1.060 1.022 1.020 0.030

Nov

556810

Dec 536058

1.181

1.204 1.157 1.181 1.176 0.036

Dec

1.00 1.00

Average

156 MACROECONOMIC THINKING AND TOOLS

TOOLBOX FOR DESCRIPTIVE ANALYSIS

Comparison of 2021 U.S. Retail Sales Adjusted Using “Crude” Seasonal Factor vs. Census Bureau Method

FIGURE 7.7

•

•

Moving-Average (Filtering) Methods: “Methods in the first group derive the seasonally adjusted data by applying a sequence of moving average filters to the original series and its transformations. These methods are all variants of the X-11 method, originally developed by the U.S. Census Bureau. The current version of the X-11 family is the “X-13ARIMASEATS” (X-13A-S) version.”7 Statistical Model-Based Methods: “Model-based methods derive the unobserved compo­ nents in accord with specific time series models, primarily autoregressive integrated moving average (ARIMA) models. The most popular model-based seasonal adjustment method is TRAMO-SEATS, which was developed by the Bank of Spain.”8

[221] [ADVANCED] IS THE SEASONAL CYCLE SIMPLY JUST SOMETHING TO ELIMINATE FOR ANALYSIS? Not necessarily. A seminal article by Barsky and Miron9 asked: What is the relationship between the business cycle and the seasonal cycle? Instead of just dismissing seasonal fluctuations as mere “intra-year noise” in the data, these researchers argued that seasonal fluctuations were “worthy of study in their own right.”10 After documenting basic facts about the seasonal cycle, they ex­ amined the similarities between the business cycle and the seasonal cycle and concluded the two cycles shared many common traits. The study established new stylized facts about macro­ economic fluctuations. “The two crucial points are that seasonal fluctuations are a dominant source of short-term variation in economic activity and that seasonal fluctuations display qual­ itatively many of the same characteristics as business cycle fluctuations. Both facts should be challenging to researchers aiming at a deeper understanding of macroeconomic fluctuations. The similarity of the seasonal cycle and the business cycle presents a challenge because [it suggested a possibility of a common explanation] for both business cycles and seasonal cycles. By trying to

157

158 MACROECONOMIC THINKING AND TOOLS understand precisely why the seasonal and the conventional business cycles are so similar, we may be able to shed considerable light on all aggregate fluctuations.”11 There was a flurry of additional studies around this topic following their study, but research in this area has since faded. Their point, however, is still well taken, especially since over the last twenty years (2002Q1–2022Q1) the seasonal cycle alone has accounted for about 70% of real GDP growth volatility with the other 30% by the combined effects of the trend, cycle, and irregular fluctuations.12

[222] WHAT IS A TREND? A trend is defined in the dictionary as the general direction of movement over time. It is thought of as an evolving pattern that is slowly changing over a long-term horizon (period). Note that economists use the word “trend” interchangeably with the word “secular,” or sometimes “secular trend”—which may be redundant. A secular pattern is one that persists over an extended period.

[223] HOW SHOULD ECONOMIC TRENDS BE ANALYZED? There is no one answer for how an economic trend should be measured. However, de­ termining a trend in a time series is first and foremost dependent on whether the trend is historic or prospective. Historic trends tend to be easier to deal with than forward-looking trends. •

Measuring Historic Trends: To describe historic trends in a time-series, initially a time period of interest is selected, and some simple descriptive statistic is calculated. The choice of the time period will affect the trend calculation. Some typical calculations are: - Starting-Point to End-Point: Calculate the growth rate per period between the first observation and the last. Then, use that growth rate over the entire period as the trend (compound-growth factor) by incrementing each period starting from the second period forward based on the actual level of the starting observation for the initial period. - Average Growth Rate: Calculate the growth rates for all periods (you will likely lose some initial periods based on the span for your growth rate). Then take a geometric average of those growth rates—an arithmetic average (simple average) is conceptually inaccurate for growth rates because of a mathematical bias.13 Finally, use the derived growth rate to develop an average-growth rate trend linked to its first observation in level terms.14 - Estimating Trends Based on a “Time” Trend: A time trend (t) is a sequentially ordered set of numbers (integers), such as t = (1, 2, 3, 4, 5, … ), that represent time periods. For every element of that time trend there will be a unique correspondence to an observation in the time series. For example, the table aligns each time trend element from 1 to 301 with real GDP for each period from 1947Q1 to 2022Q1.

TOOLBOX FOR DESCRIPTIVE ANALYSIS

Year/Qtr

Time Trend

Real GDP

1947Q1 1947Q2 1947Q3 1947Q4 1948Q1 1948Q2 1948Q3 1948Q4 1949Q1 1949Q2 1949Q3 . . . 2019Q1 2019Q2 2019Q3 2019Q4 2020Q1 2020Q2 2020Q3 2020Q4 2021Q1 2021Q2 2021Q3 2021Q4 2022Q1

1 2 3 4 5 6 7 8 9 10 11 . . . 289 290 291 292 293 294 295 296 297 298 299 300 301

$2,034.5 $2,029.0 $2,024.8 $2,056.5 $2,087.4 $2,121.9 $2,134.1 $2,136.4 $2,107.0 $2,099.8 $2,121.5 . . . $18,833.2 $18,982.5 $19,112.7 $19,202.3 $18,952.0 $17,258.2 $18,560.8 $18,767.8 $19,055.7 $19,368.3 $19,478.9 $19,806.3 $19,731.1

The graph of shows the time trend and real GDP portrays that relationship (Figure 7.8). There is a 1-to-1 relationship between the time trend and real GDP. This is a linear (straight line) relationship that could be estimated (in a spreadsheet, for example) by a simple ordinary least squares (OLS) regression, or calculated based on the ratio of the two means of the samples. In this example, the mean of real GDP between 1947Q1 and 2022Q1 was $8,996.68, and the mean of the time trend variable was 151. Then the linear trend of real GDP would be equal to ($8,996.68/151) multiplied by the time trend. This would give GDP = 59.6 × Time. But if a linear-trend relationship does not represent the long-term pattern in the data, then a higherorder relationship such as a quadratic (such as GDP = 45.6 × Time + 0.06 × Time2) or some other polynomial form relationship might be used. Some examples are discussed. •

Example 1: What is the long-term historic trend between 1947-Q1 and 2022-Q1? Three answers are shown based on the starting point-to-end point growth over the entire sample, based on the geometric-average growth rate over the same period, and an exponential growth trend

159

160 MACROECONOMIC THINKING AND TOOLS

FIGURE 7.8

•

•

Time Trend and Real GDP

(a log-linear model). The first method based on the starting-point to end-point growth rate was 3.06527% at an annualized rate; the geometric average growth rate was 3.07565% at an annual rate; and the estimated trend based on a log-linear or exponential model (consistent with the growth rate calculations above) was 3.14392%. Each of these produced a slightly different growth rate—but over an extended period seemingly small differences may become large over time. Graphically, the point-to-point estimate is virtually indistinguishable from the geometric growth-rate average, while the exponential version “takes off” after 2007 (Figure 7.9). These results could be very different for different time periods. Example 2: Consider looking at a historic deterministic trend in real GDP between 1980 through early 2022 based on a linear (straight-line) trend that is simply estimated by an ordinary least-squares regression15 and displayed in the graph (Figure 7.10). Does that adequately represent the trend? The answer depends on how it is used. If it is used simply as a summary of the long-term pattern for that period, then it probably is appropriate. If it is used to extrapolate back in time or forward in time, then it may not be appropriate. But it does raise a question: Was economic growth in the early 2000s when the actual pattern was generally above this trend for an extended period consistent with the longer-run trend, or did the trend change over time? If you conclude that the period from 2000 to about 2007 seemed to be a “different trend” within that longer period, then you may not be dealing with a deterministic trend or possibly the trend is not linear and may be better described by some higher-order polynomial relationship between real GDP and a time trend. Example 3: Now consider calculating deterministic trends for real GDP based on historical data from 1980 to 2007 and then extrapolating that trend through 2022. Four possible trends are looked at: (1) the linear trend as in example 2, (2) an exponential trend,

TOOLBOX FOR DESCRIPTIVE ANALYSIS

Log-Linear/Exponenal Trend

Actual

Geometric Average Trend slightly above Point-to-Point Trend

Example: U.S. Real GDP with Alternative Historic Trends (Estimated 1947: Q1 to 2020: Q1)

FIGURE 7.9

FIGURE 7.10

Example: U.S. Real GDP with Linear Deterministic Trend (1980–2022)

(3) a quadratic trend, and (4) a rolling or trailing moving average of real GDP over the past 10 years (40 quarters).16 These trends along with the actual data are displayed in the graph (Figure 7.11). Although several of these trends might describe the long-term pattern up until 2007, the projected trends are quite different relative to the actual pattern after 2007.

161

162 MACROECONOMIC THINKING AND TOOLS

FIGURE 7.11

•

•

Example: U.S. Real GDP with Alternative Trends (Estimated 1980–2007)

This highlights the risk of extrapolating trends without understanding if the trend is shifting and what factors might be causing that shift. Projecting Prospective Trends: Anticipating a future trend poses a more difficult set of measurement problems and questions to forecast a trend into the future. Does the forecast horizon affect the pattern of the trend—or put another way—is there a new dynamic determining the trend? Or is the trend likely to be unchanged from the past? If the trend is likely to change, what factors will affect that change? Of course, even if the future trend is likely to change, it is still possible to project an historic trend into the future as a “benchmark” to assess the future period’s pattern against the past. A simple technique to project a future trend is to base it on a demographic measure, such as population (if it makes sense conceptually), which is widely projected by various government statistical offices well into the future. Deterministic or Stochastic Trends: As just alluded to, a trend can be non-changing, or it can be changing over time—regardless of whether it is a historic or prospective trend. If a trend has a non-changing or fixed relationship (such as a linear trend or some polynomial trend), then it is said to be “deterministic.” This also would apply if the trend is based on historic or future projections of, say, the population, which may change the pattern over time—however, the relationship between the input (popula­ tion) and the output, say real GDP, would be fixed and so it also would be a deterministic trend. However, if an underlying trend is formed by both a trend component—that may be either fixed or changing (a deterministic trend)—plus a random component, then the trend is termed “stochastic.” This distinction becomes important in time-series decomposition where the approach introduced by Warren

TOOLBOX FOR DESCRIPTIVE ANALYSIS

Persons and embraced by Wesley Mitchell and the early work of the National Bureau of Economic Research was built largely upon deterministic trends, whereas methods that developed in the 1980s were based on stochastic trends. Walter Enders posed the question: “A difficult conceptual issue arises if the trend is stochastic. For example, suppose you are asked to measure the current phase of the business cycle. If the trend in [real GDP] is stochastic, how is it possible to tell if [real GDP] is above or below trend?”17 If the data pattern in a time-series is characterized by a stochastic or changing trend, then economists often will transform that data by looking at the data in firstdifferences (period-to-period changes) to analyze, model, and project the pattern in its “stationary form.” Alternatively, there are a few popular “pre-filtering” computational procedures used for detrending a time series.

[224] [ADVANCED] ARE THERE ANY OTHER METHODS ECONOMISTS USE TO DECOMPOSE A TIME SERIES? Yes. According to Low and Anderson,18 the three most popular techniques for time-series decomposition in the macroeconomic literature are: (1) the Hodrick–Prescott19 filter for trend-cycle decomposition, (2) the Beveridge–Nelson20 decomposition, and (3) Harvey’s unobserved component (UC) model21 decomposition. However, there are numerous other methods in the literature. All methods share one common characteristic of decomposing the time series into a permanent (“trend”) and a transitory component (“cycle”).22 However, each approach uses different methods and assumptions to derive those trends and cycles and those estimates of the trend and cycle from different approaches are often quite different and bear little resemblance to the classical NBER/Wesley Mitchell concept of the business cycle. Fabio Canova23 offered a powerful warning to applied macroeconomists to recognize the differences between these time-series approaches and how they yield differing results and “facts” about the business cycle. Canova put his warning this way: In the past the representation and extraction of the secular component was handled in a very simple way. The trend was represented with deterministic polynomial functions of time, assumed to be independent of the cyclical component and extracted using simple regression methods. More recently, following Nelson and Plosser’s (1982) findings, Beveridge and Nelson (1981), Watson (1986), Hamilton (1989) and Quah (1992) have proposed alternative definitions of the trend, different assumptions about the relationship between the trend and the cycle and novel methods for estimating the two components. Since the issue of what is an ‘appropriate’ statistical representation of the trend cannot be solved in small samples and since the choice of the relationship between the cyclical and secular components is arbitrary, statistical based approaches to detrending raise questions about the robustness of certain ‘facts’. As Singleton (1988, p. 372) observes, “The stylized facts motivating recent specifications of the business cycle models may have been distorted by pre-filtering procedures.” Moreover, it is now clear that different statistical representations for the trend embed different economic concepts of business cycle fluctuations and choosing one detrending method over another implies selecting one

163

164 MACROECONOMIC THINKING AND TOOLS particular economic object over another. Documenting the properties of different types of business cycles may therefore help us, on one hand, to provide a more exhaustive description of the data, and, on the other, to highlight the sense in which they are economically different.24

[225] HOW DO ECONOMISTS ANALYZE VERY VOLATILE TIME SERIES? Some economic statistics—such as new orders—can be very choppy even after seasonal adjustment. One of the simplest and effective ways to bring out the story in the data and reduce the choppiness is to use a moving average—which is generically known as data “filters.” For example, the BLS reports a three-month moving average of the monthly change in payroll employment, because those one-month data can be choppy and possible unrepresentative of the short-term pattern. This is simply the sum of the last three months divided by number of observations or months, which is three. Although this is just an example, there are endless variations on moving averages and there are moving averages of moving averages, weighted moving averages (where more recent data may be given a higher weight than older periods—or some other weighting scheme appropriate for the analysis). But keep in mind one important fact about using moving averages: What is known as “trailing moving averages,” where the moving average is put in the last month of the moving-average span, will shift the cyclical timing. This is demonstrated with payroll employment changes versus a 12- and 24-month “trailing” moving average of one-month changes in payroll employment (Figure 7.12). To alleviate this problem, “centered” moving averages will retain the correct timing relationship with the original cycle, as demonstrated in the next graph (Figure 7.13).

U.S. Payroll Employment Changes Month-over-Month Change and 12- and 24-Month “Trailing” Moving Averages

FIGURE 7.12

TOOLBOX FOR DESCRIPTIVE ANALYSIS

U.S. Payroll Employment Changes Month-over-Month Change and 12- and 24-Month “Centered” Moving Averages

FIGURE 7.13

However, the problem with centered moving averages is the loss of data at the end of the timeseries (one-half of the centered moving average duration). It is possible to extrapolate the end values, which would be tentative, as is done with a popular “filter” used by the National Weather Service that also has found its way into economics. This “binominal filter” is a nine-month centered weighted moving average, which also extrapolates the missing observations at the end of the time period using shorter duration averages with the “binominal” weights. And example of this with the annualized growth rate of the monthly CPI is shown in the next graphic (Figure 7.14).

Annualized Growth Rates for the U.S. Consumer Price Index With “Binominal Filtered” Growth Rate

FIGURE 7.14

165

166 MACROECONOMIC THINKING AND TOOLS

[226] [ADVANCED] WHAT IS MEANT BY A TIME SERIES IS STATIONARY? Stationarity means that a time series has both a constant mean and variance over time.25 The value of stationarity for economic analysis is that it allows economists to analyze a time series’ frequency distribution (such as a bell curve or some other distribution) instead of analyzing individual values and their variance and to infer broader insights about the economic process. Basically though, most economic statistics are not stationary.

[227] [ADVANCED] HOW DO YOU KNOW IF A TIME SERIES IS STATIONARY OR NON-STATIONARY? There are statistical tests for stationarity (for example, the Dickey Fuller test or the Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) test for trend stationarity), but visualization can answer the question too. If you cannot decern in the plot of the data a trend or changing levels in the data, a seasonal pattern, a growing cycle (however, cycles without a trend, or range bound, will be stationary), or changing variance over time (either more volatility or less) then you probably are looking at a stationary series. Examples of time series with stationary and non-stationary means and/or variances are shown in Figure 7.15.

[228] [ADVANCED] IF THE SERIES IS NON-STATIONARY, WHAT CAN BE DONE? If the time series is non-stationary, there are some common ways to transform a non-stationary time series into a stationary one. Those methods are: (a) Difference-stationarity: differencing the time series (creating a new time series that is the difference between two periods of time—that is, (Xt – Xt−1). If this does not create a stationary series, then a second difference most likely will.26 (b) Trendstationarity: removing a trend using some method to derive that trend and then subtracting it from the original time series; and (c) Other-stationarity: transform the original series into some other form, such as natural logarithm or growth rates that may stabilize the variance of a series.

[229] [ADVANCED] WHY DO WE CARE ABOUT TIME-SERIES STATIONARITY? For purely descriptive analysis, this may not get you much. However, a key reason that econ­ omists econometrically test for stationarity in time series is that if that condition exists, economic shocks affecting that variable will have “permanent effects,” which also are referred to as “persistent” impacts. If the economic statistic, however, is determined to be stationary around a deterministic trend then the variable (such as GDP) is mean-reverting around that deterministic trend—that is, it will fluctuate around that trend. It has been argued that understanding whether a series (such as inflation or real GDP) is stationary (trend or difference stationary) is important for

TOOLBOX FOR DESCRIPTIVE ANALYSIS

(a)

mean

variance

Stationary Mean, Stationary Variance

(b)

mean variance Non-Stationary Mean, Stationary Variance

(c)

mean

variance

Stationary Mean, Non-Stationary Variance FIGURE 7.15

Examples of Different Types of Mean and Variance Stationary

policymakers, as well, who need to assess how shocks may play on different economic statistics. A considerable amount of literature over the last 40 years has been improving methodology to better examine questions whether a shock is permanent or transitory.

[230] HOW DO ECONOMISTS ANALYZE A BUSINESS CYCLE? Conceptually, the traditional (applied) NBER approach for studying the business cycle has been to use reported economic time series that have been seasonally adjusted and to analyze the cycle in that form of the data—which essentially is the combined trend, cycle, and irregular components of a time series. As earlier discussed, this is the so-called “rec-rec” or recessionrecovery business cycle patterns analysis technique, which is applied by arraying the data into business-cycle segments of common duration, before and after a turning point date, and then simply calculating each business-cycle segment’s relative share of that segment’s turning point

167

168 MACROECONOMIC THINKING AND TOOLS value. For this example, the monthly pattern of U.S. consumer inflation (CPI-U) will be examined 12 months prior to each business turning-point date and 18 months subsequent to it for the post-WWII periods. • •

• •

Step 1: Arrange the data in segments before and after the turning point of interest for each business cycle segment. Step 2: Divide each business cycle segment (column) by its turning-point observation. For example, the 1949 business cycle turning-point observation for the CPI was 23.67, which is used to divide all of the data in that column. Therefore, the trough value will be 23.67 divided by 23.67, which equals 1.00; a representative calculation for T-12 (the first-row observation) would be 24.31 divided by 23.67, which would be 1.027. Do the same for the whole segment (column) and then do the same for each of the other segments. Step 3: Graph the segments for visual comparison (Figure 7.16). Step 4: Describe the pattern. What does this presentation show? One thing it shows is after 18 months of recovery, the 2020 business cycle recovery pattern saw relatively high U.S. consumer inflation compared to prior post-WWII U.S. recoveries at the same number of months from the business cycle reference turning point. Only the 1949, 1973, and 1980 recoveries/expansion periods saw more inflationary pressure. Observe also that the earlier

FIGURE 7.16

Business Cycle Recovery Patterns U.S. Consumer Price Index

T-12 T-11 T-10 T-9 T-8 T-7 T-6 T-5 T-4 T-3 T-2 T-1 Trough T+-1 T+-2 T+-3

24.31 24.16 24.05 24.01 23.91 23.91 23.92 23.91 23.92 23.70 23.70 23.75 23.67 23.70 23.61 23.51

26.70 26.77 26.79 26.85 26.89 26.95 26.85 26.87 26.94 26.99 26.93 26.86 26.93 26.94 26.86 26.85

27.93 28.00 28.11 28.19 28.28 28.32 28.32 28.41 28.47 28.64 28.70 28.87 28.94 28.94 28.91 28.89

29.41 29.41 29.54 29.57 29.61 29.55 29.61 29.61 29.75 29.78 29.81 29.84 29.84 29.84 29.81 29.84

37.50 37.70 37.90 38.10 38.30 38.50 38.60 38.80 38.90 39.00 39.20 39.40 39.60 39.80 39.90 39.90

47.80 48.10 48.60 49.00 49.30 49.90 50.60 51.00 51.50 51.90 52.30 52.60 52.80 53.00 53.10 53.50

73.00 73.70 74.40 75.20 76.00 76.90 78.00 79.00 80.10 80.90 81.70 82.50 82.60 83.20 83.90 84.70

93.80 94.10 94.40 94.70 94.70 95.00 95.90 97.00 97.50 97.70 97.70 98.10 98.00 97.70 97.90 98.00

128.60 128.90 129.10 129.90 130.50 131.60 132.50 133.40 133.70 134.20 134.70 134.80 134.80 135.10 135.60 136.00

174.20 174.60 175.60 176.00 176.10 176.40 177.30 177.70 177.40 177.40 178.10 177.60 177.50 177.40 177.70 178.00

217.46 219.02 218.69 218.88 217.00 213.15 211.40 211.93 212.71 212.50 212.71 213.02 214.79 214.73 215.45 215.86

(Continued )

255.16 255.33 255.36 255.90 256.18 256.60 257.31 257.79 258.26 258.68 259.01 258.17 256.09 255.94 257.22 258.54

Months 1949M10 1954M05 1958M04 1961M02 1970M11 1975M03 1980M07 1982M11 1991M03 2001M11 2009M06 2020M04 Before (-) and After (+) Business Cycle Turning Point (‘T for Trough’)

(Business Cycle Reference Trough Dates)

Consumer Price Index Segments Before and After Business Cycle Troughs

TOOLBOX FOR DESCRIPTIVE ANALYSIS

169

T+-4 T+-5 T+-6 T+-7 T+-8 T+-9 T+-10 T+-11 T+-12 T+-13 T+-14 T+-15 T+-16 T+-17 T+-18

23.61 23.64 23.65 23.77 23.88 24.07 24.20 24.34 24.50 24.60 24.98 25.38 25.83 25.88 25.92

26.81 26.72 26.78 26.77 26.77 26.82 26.79 26.79 26.77 26.71 26.76 26.72 26.85 26.82 26.88

28.94 28.91 28.91 28.95 28.97 29.01 29.00 28.97 28.98 29.04 29.11 29.15 29.18 29.25 29.35

29.84 29.92 29.94 29.98 29.98 29.98 30.01 30.04 30.11 30.17 30.21 30.24 30.21 30.22 30.28

40.00 40.10 40.30 40.50 40.60 40.70 40.80 40.90 41.00 41.10 41.20 41.40 41.40 41.50 41.60

54.00 54.20 54.60 54.90 55.30 55.60 55.80 55.90 56.00 56.10 56.40 56.70 57.00 57.30 57.60

85.60 86.40 87.20 88.00 88.60 89.10 89.70 90.50 91.50 92.20 93.10 93.40 93.80 94.10 94.40

98.10 98.80 99.20 99.40 99.80 100.10 100.40 100.80 101.10 101.40 102.10 102.60 102.90 103.30 103.50

136.20 136.60 137.00 137.20 137.80 138.20 138.30 138.60 139.10 139.40 139.70 140.10 140.50 140.80 141.10

178.50 179.30 179.50 179.60 180.00 180.50 180.80 181.20 181.50 181.80 182.60 183.60 183.90 183.20 182.90

216.51 217.23 217.35 217.49 217.28 217.35 217.40 217.29 217.20 217.61 217.92 218.28 219.04 219.59 220.47

259.58 260.19 260.35 260.72 261.56 262.20 263.35 265.03 266.73 268.60 270.96 272.18 273.09 274.21 276.59

Months 1949M10 1954M05 1958M04 1961M02 1970M11 1975M03 1980M07 1982M11 1991M03 2001M11 2009M06 2020M04 Before (-) and After (+) Business Cycle Turning Point (‘T for Trough’)

(Business Cycle Reference Trough Dates)

Consumer Price Index Segments Before and After Business Cycle Troughs

170 MACROECONOMIC THINKING AND TOOLS

T-12 T-11 T-10 T-9 T-8 T-7 T-6 T-5 T-4 T-3 T-2 T-1 Trough T+-1 T+-2 T+-3

1.027 1.021 1.016 1.014 1.010 1.010 1.011 1.010 1.011 1.001 1.001 1.003 1.000 1.001 0.997 0.993

0.991 0.994 0.995 0.997 0.999 1.001 0.997 0.998 1.000 1.002 1.000 0.997 1.000 1.000 0.997 0.997

0.965 0.968 0.971 0.974 0.977 0.979 0.979 0.982 0.984 0.990 0.992 0.998 1.000 1.000 0.999 0.998

0.986 0.986 0.990 0.991 0.992 0.990 0.992 0.992 0.997 0.998 0.999 1.000 1.000 1.000 0.999 1.000

0.947 0.952 0.957 0.962 0.967 0.972 0.975 0.980 0.982 0.985 0.990 0.995 1.000 1.005 1.008 1.008

0.905 0.911 0.920 0.928 0.934 0.945 0.958 0.966 0.975 0.983 0.991 0.996 1.000 1.004 1.006 1.013

0.884 0.892 0.901 0.910 0.920 0.931 0.944 0.956 0.970 0.979 0.989 0.999 1.000 1.007 1.016 1.025

0.957 0.960 0.963 0.966 0.966 0.969 0.979 0.990 0.995 0.997 0.997 1.001 1.000 0.997 0.999 1.000

0.954 0.956 0.958 0.964 0.968 0.976 0.983 0.990 0.992 0.996 0.999 1.000 1.000 1.002 1.006 1.009

0.981 0.984 0.989 0.992 0.992 0.994 0.999 1.001 0.999 0.999 1.003 1.001 1.000 0.999 1.001 1.003

1.012 1.020 1.018 1.019 1.010 0.992 0.984 0.987 0.990 0.989 0.990 0.992 1.000 1.000 1.003 1.005

(Continued )

0.996 0.997 0.997 0.999 1.000 1.002 1.005 1.007 1.008 1.010 1.011 1.008 1.000 0.999 1.004 1.010

Months 1949M10 1954M05 1958M04 1961M02 1970M11 1975M03 1980M07 1982M11 1991M03 2001M11 2009M06 2020M04 Before (-) and After (+) Business Cycle Turning Point (‘T for Trough’)

(Business Cycle Reference Trough Dates)

Consumer Price Index Segments Before and After Business Cycle Troughs

TOOLBOX FOR DESCRIPTIVE ANALYSIS

171

T+-4 T+-5 T+-6 T+-7 T+-8 T+-9 T+-10 T+-11 T+-12 T+-13 T+-14 T+-15 T+-16 T+-17 T+-18

0.997 0.999 0.999 1.004 1.009 1.017 1.022 1.028 1.035 1.039 1.055 1.072 1.091 1.093 1.095

0.996 0.992 0.994 0.994 0.994 0.996 0.995 0.995 0.994 0.992 0.994 0.992 0.997 0.996 0.998

1.000 0.999 0.999 1.000 1.001 1.002 1.002 1.001 1.001 1.003 1.006 1.007 1.008 1.011 1.014

1.000 1.003 1.003 1.005 1.005 1.005 1.006 1.007 1.009 1.011 1.012 1.013 1.012 1.013 1.015

1.010 1.013 1.018 1.023 1.025 1.028 1.030 1.033 1.035 1.038 1.040 1.045 1.045 1.048 1.051

1.023 1.027 1.034 1.040 1.047 1.053 1.057 1.059 1.061 1.063 1.068 1.074 1.080 1.085 1.091

1.036 1.046 1.056 1.065 1.073 1.079 1.086 1.096 1.108 1.116 1.127 1.131 1.136 1.139 1.143

1.001 1.008 1.012 1.014 1.018 1.021 1.024 1.029 1.032 1.035 1.042 1.047 1.050 1.054 1.056

1.010 1.013 1.016 1.018 1.022 1.025 1.026 1.028 1.032 1.034 1.036 1.039 1.042 1.045 1.047

1.006 1.010 1.011 1.012 1.014 1.017 1.019 1.021 1.023 1.024 1.029 1.034 1.036 1.032 1.030

1.008 1.011 1.012 1.013 1.012 1.012 1.012 1.012 1.011 1.013 1.015 1.016 1.020 1.022 1.026

1.014 1.016 1.017 1.018 1.021 1.024 1.028 1.035 1.042 1.049 1.058 1.063 1.066 1.071 1.080

Months 1949M10 1954M05 1958M04 1961M02 1970M11 1975M03 1980M07 1982M11 1991M03 2001M11 2009M06 2020M04 Before (-) and After (+) Business Cycle Turning Point (‘T for Trough’)

(Business Cycle Reference Trough Dates)

Consumer Price Index Segments Before and After Business Cycle Troughs

172 MACROECONOMIC THINKING AND TOOLS

TOOLBOX FOR DESCRIPTIVE ANALYSIS

cited NBER study that distinguished business cycles based on whether the recession was aggregate demand or aggregate supply driven classified both the 1973 and 1980 recessions mainly as supply driven. Unfortunately, that research did not extend the analysis to business cycles prior to 1969, so it is difficult to infer how much of the 1949 recession story was due to aggregate supply using that same methodology. However, even this simple analysis builds a picture of relationships since that study did find the second phase of the 2020 recession due to aggregate supply impacts. Although it is not the intention here to provide a comprehensive interpretation of these data patterns, it is the intention to highlight why this is done, which is to help provide those comparisons or analogs for possible inference about what business cycle history might imply for the current cycle.

[231] IS THE “IRREGULAR” COMPONENT OF A TIME SERIES ANALYZED? Generally, not. Traditional NBER/Persons decomposition of a time series will calculate that irregular component, which is simply the unexplained residual after subtracting the discrete trend, cycle, and seasonal components from the original time series. It is not ana­ lyzed. Nonetheless, it could be useful for this discrete decomposition to indicate a possible problem with the trend, cycle, and seasonal calculation. If, for example, that the irregular component has some discernable pattern (which conceptually it should not) then it might be signaling a methodology problem. On the other hand, the more recent decomposition methods largely assume that the expected value of that irregular component is zero and will ignore it.

[232] WHAT LESSONS HAVE BEEN LEARNED AND SHARED FOR APPLYING MEASUREMENT TECHNIQUES? To paraphrase the old adage, “let the user beware” in applying methods to analyze economic time series for seasonal swings, cyclical fluctuations, and trends. Use them but be aware of their limitations, and remember the economic principle known as “Occam’s razor” (sometimes, “Ockham’s razor” and even sometimes referred to as the “law of parsimony”) that was put forth by philosopher William of Ockham (who lived between 1285 and 1347). Ockham said, “plurality should not be posited without necessity.” This has come to mean that given competing options (theories or methods), the simplest should be preferred.

[233] WHAT ARE SOME KEY U.S. AND INTERNATIONAL ECONOMIC DATA SOURCES FOR ANALYSIS? There are lots of domestic, international, and multi-national data sets available for economic analysis. Some of the major data agencies include the following.

173

https://www.bea.gov https://www.census.gov/

U.S. Department of Commerce Bureau of Economic Analysis Bureau of the Census

Internal Revenue Services

U.S. Department of the Treasury Federal Finance

Federal Reserve Board of Governors

U.S. Department of Transportation Bureau of Transportation Statistics

U.S. Council of Economic Advisers

https://www.bls.gov

U.S. Department of Labor Bureau of Labor Statistics

Consumer prices, employment costs, employment, unemployment, and wages, producer prices, productivity and costs, import and export prices.

Major Types of Data

https://www.irs.gov/statistics

https://home.treasury.gov/

https://www. federalreserve.gov/

https://www.bts.gov/

Interest rates (yield curves), federal government receipts and outlays (“Treasury statement”), national debt, quarterly refunding of debt, Treasury International Capital (TIC) data–U.S. debt held by foreign investors. Tax statistics – individual and business taxes, statistics of income (SOI) data–including income distribution data.

Industrial production and capacity utilization, money supply, interest reates, business finance, exchange rates and international data, financial accounts for the United States (Flow of Funds), household finance.

Highway travel, air travel, truck tonnage, freight rail traffic, air cargo, water/ seaport transportation, border crossings.

GDP and its components, personal income and savings, profits. Population and demographic, housing, retail spending and inventories, foreign trade, manufacturing orders, shipments, and inventories, wholesale spending and inventories, and business formation counts. https://www.govinfo.gov/app/collection/econi/ Consolidated source of data on: (1) total output, income, and spending; (2) employment, unemployment, and wages; (3) production and business activity; (4) prices; (5) money, credit, and security markets; (6) federal finance; (7) international statistics.

Website

Key U.S. Federal Government and Selected International Sources of Economic Data

Agency

TABLE 7.9

174 MACROECONOMIC THINKING AND TOOLS

http://data.un.org/

United Nations Statistics Division

International Monetary Fund https://data.imf.org/

Organisation for Economic Co-operation and Development (OECD) https://data.oecd.org/ https://stats.oecd.org/

https://www.cbo.gov/

Congressional Budget Office

U.S. Department of Energy U.S. Energy Information Administration https://www.eia.gov/

Balance of payments, consumer prices, government finance, international financial statistics, financial soundness indicators, international reserves and foreign currency liquidity, monetary and financial statistics.

Composite leading indicators, consumer prices, employment, growth and economic well-being, international trade statistics (G20 trade), unemployment rate, quarterly national accounts and productivity statistics.

Global information on population, international migrants and refugees, fertility, life expectancy and mortality, national accounts (GDP and GDP per capita), education, labor markets–labor force and employment, prices and production indices, international merchandise trade, energy, science and technology, finance, environment, and more.

Historical and forecast energy statistics, including petroleum, natural gas, electricity, energy consumption, coal, renewable & alternative fuels, nuclear & uranium. Estimates and forecasts of real and nominal potential GDP, the noncyclical unemployment rate, estimates of the “automatic stabilizers,” long-range budget and economic projections.

TOOLBOX FOR DESCRIPTIVE ANALYSIS

175

176 MACROECONOMIC THINKING AND TOOLS

[234] ARE THERE FREE ECONOMIC DATABASES OF STATISTICS AVAILABLE FOR ANALYSIS? Yes. Consolidated databases for “open data” provide a convenient way to access, track, download, and work with a wide range of economic statistics. These data sources are not as comprehensive as paid data services but offer a considerable variety of data to choose from. Two of the most popular databases are: •

•

FRED (fred.stlouisfed.org): The St. Louis Federal Reserve Bank created its Federal Reserve Economic Database or FRED in 1991, which it says was “an offshoot of the long-running legacy at the Federal Reserve Bank of St. Louis of providing monetary data to help better understand the Fed’s policy decisions. Before the popularization of the World Wide Web, the data were provided in list form on a dial-in, electronic bulletin board system. The data were organized into categories containing roughly 300 data series and expanded from there … It grew over time in a very organic way. St. Louis Fed staff who were involved either directly with the FRED project or working on the periphery developed tools for the database in an independent, ad hoc manner.” Today, the database includes over 800,00 U.S. and international time series from 108 sources. The areas covered include: (1) money, banking, and finance; (2) population, employment, and labor markets; (3) national accounts; (4) production and business activity; (5) prices; (6) international data; (7) U.S. regional data; and (8) academic data. This system allows users to graph data and modify the chart using various presentations, including formula with multiple statistics. Data can be downloaded as well. DataZoa (www.datazoa.com): This database of over 3 billion statistics is supported by a regional network of mainly state government and university people and uses technology to access and maintain data from primary data sources. The system allows users to create “dashboards” of data to track regularly and download, to chart data, and to use formulae to transform the raw data into growth rates and other presentations. Data areas covered are agriculture, banking, companies, economy, energy, environ­ ment, finance, foreign exchange, health, industry, international trade, law enforcement and public safety, lobbying and government influence, patents, population/ demographics/well-being, ports, real estate and construction, transportation, and weather. U.S. regional statistics and some regional Federal Reserve Bank statistics are included as well. This database also consolidates data from foreign sources, such as the Australian Bureau of Statistics, Statistics Canada, China National Bureau of Statistics, Banque de France, INSEE (France), Bank of Japan (BOJ), Banco de Mexico (Bank of Mexico), Bank of England, Deutsche Bundesbank (Germany), European Central Bank, Eurostat, United Nations, the World Bank, and more. This database was developed by Boston-based Leading Market Technologies, or LMT, and the software was launched in April 2012.

TOOLBOX FOR DESCRIPTIVE ANALYSIS

Issues to Think About Albert Einstein famously said, “In theory, theory and practice are the same. In practice, they are not.” Macroeconomic theory and practice are both built on experience. Moreover, that experience is shaped by accurate measurement which informs the practitioner about economic conditions and phenomena to act or react to, while those same statistical insights are culled and packaged by the theorist into a set of principles to explain those economic facts and phenomena. But either way, the practitioner and the theorist both are dependent on descriptive analytics—either basic or advanced techniques. •

•

Sometimes it seems that the economics profession is eager to latch onto a new statistical method and then is in search of an application—because new methods offer the researcher a journal publishing opportunity. However, sometimes those new methods do lead to new insights. On the other hand, the practitioner has long held to the “kiss” principle—“keep it simple, stupid,” which is said to come from the U.S. Navy in 1960. How do you balance these two approaches? Is the Persons’ decomposition concept of a time series into four variations the best way to think about a time series, given that theory suggests the seasonal and business cycles may be uniquely reinforcing and that trends affect business cycle amplitude? (That is, can we really separate these variations?) Or should there be a more holistic approach? (That is, is there a better way to understand a time series?)

NOTES 1 John W. Tukey, Exploratory Data Analysis (Addison-Wesley Publishing Company, Reading, MA, 1977). 2 “Exploratory Data Analysis,” IBM Cloud Education, August 25, 2020, https://www.ibm.com/cloud/ learn/exploratory-data-analysis. 3 In EXCEL, this is done for you using the function “stdevpa.” 4 Warren M. Persons, “Correlation of Time Series,” Journal of the American Statistical Association, vol. 18, no. 142 (June 1923), pp. 713–726. 5 Today, this decomposition can be “additive” (that is, time series yt = Seasonal + Trend + Cycle + Irregular fluctuations) or multiplicative (that is, time series yt = Seasonal x Trend x Cycle x Irregular fluctuations). The method applied depends on the type of data. Data that is bounded (such as, the unemployment rate or some ratio) will use an additive decomposition, but data that is not bounded (such as, employment, GDP, population) will use a multiplicative decomposition. Over the years, some business forecasting methods have developed around this decomposition where the components (seasonal, trend, cycle, and irregular) were projected individually and then rolled into an aggregate projection. 6 Although this discussion is presented in terms of monthly data, the process is identical for quarterly data.

177

178 MACROECONOMIC THINKING AND TOOLS 7 Quarterly National Accounts Manual: 2017 Edition, International Monetary Fund, Washington, DC, 2018, p. 128. These moving-average seasonal-adjustment methods incorporate the ratio-to-movingaverage approach that was developed in the 1920s by Frederick R. Macaulay at the National Bureau of Economic Research. 8 Ibid. The acronym “TRAMO” stands for “Time Series Regression with ARIMA Noise, Missing Observations and Outliers.” The acronym “SEATS” stands for “Seasonal Extraction in ARIMA Time Series.” 9 Robert B. Barsky and Jeffrey A. Miron, “The Seasonal Cycle and the Business Cycle,” Journal of Political Economy, vol. 97, no. 3 (June 1989), pp. 503–534. 10 Ibid., p. 504. Barsky and Miron also noted that their approach was a return to an older NBER tradition, exemplified by Simon Kuznets work, that treated both seasonal and business cycles as important subjects for investigation. 11 Ibid., p. 529. The authors further concluded that there was evidence of a “seasonal business cycle”—which is that hybrid concept where the seasonal and business cycles have characteristics that are very closely aligned and not independent of each other which traditional time-series decom­ position would suggest. For an international application of this idea, see: J. Joseph Beaulieu and Jeffrey A. Miron, “A Cross Country Comparison of Seasonal Cycles and Business Cycles,” The Economic Journal, vol. 102, no. 413 (July 1992), pp. 772–788. 12 The Barsky and Miron finding had an even larger estimate of the seasonal impact by more than 85% based on earlier data for real GNP between 1948:Q2 and 1985:Q4. 13 The easiest way to do this is to take the ratio of current observation to the prior observation for all periods and then a geometric average is the multiplication of all of those ratios and then taking the nth root (where n equals the number of terms) and calculate the result into a growth rate. Because a geometric average cannot be calculated if a growth rate is negative, working with ratios rather than growth rates initially ensures positive numbers (above and below 1.00). A spreadsheet can handle the calculation of a geometric average of growth rates quite easily. 14 As above, using the actual starting point is a “level adjustment” to apply that average growth rate factor to. Think of the process as two-different steps: (1) TREND = Actual Data ONLY for the initial value; then (2) for each subsequent TREND observation, it is derived as follows: TREND = TRENDt-1 x (1 + (growth rate/100)). In words, the process is the current trend observation equals the prior trend observation multiplied by the growth-rate factor. Keep in mind, this assumes the growth rate is not expressed as a decimal, but as a percent point (%). Also that the growth rate is appropriately calculated for the frequency of the data. That means if you have an annualized growth rate based on quarterly data, then the growth rate must be quarter-to-quarter without annualization. 15 The actual equation is: Real GDP = −4344.35 + 79.76*TIME, where the TIME trend equaled zero in 1947:Q1 and increased by 1 for every subsequent quarter. This was model was estimated between 1980Q1 and 2022Q1. 16 Linear Trend: (−5102.35 + 84.1*TIME); Exponential or Log-Linear Trend: EXP(7.74 + 0.008*TIME)); Quadratic Trend: (4839.2 − 25.19*TIME + 0.29*TIME^2); and 10-Year MovingAverage: MOVAVG(REALGDP, 40 Quarters). TIME is defined as in footnote above. 17 Walter Enders, Applied Econometric Time Series (John Wiley & Sons: New York, 1995), p. 186. 18 Chin Nam Low and Heather M. Anderson, “Economic Applications: The Beveridge–Nelson Decomposition,” in Rob Hyndman, Anne Koehler, Keith Ord, Ralph Snyder, eds., Forecasting with Exponential Smoothing: The State Space Approach (Springer: Berlin, 2008), pp. 325–337. 19 R. Hodrick and E. Prescott, “Post-War U.S. Business Cycles: An Empirical Investigation,” Carnegie Mellon University, manuscript, 1980. There is no ideal method for smoothing data and even this popular Hodrick-Prescott filter for extracting trends from a time-series has been criticized by Hamilton because it could create spurious results that have no relationship with the so-called true “data-generating process.” See: James D. Hamilton, “Why You Should Never Use the HodrickPrescott Filter,” NBER Working Paper 23429, May 2017.

TOOLBOX FOR DESCRIPTIVE ANALYSIS 20 S. Beveridge and C. Nelson, C., “A New Approach to Decomposition of Economic Time Series into Permanent and Transitory components with Particular Attention to Measurement of the Business Cycle,” Journal of Monetary Economics, vol. 7 (1981), pp. 151–174. 21 A.C. Harvey, “Trends and Cycles in Macroeconomic Time Series,” Journal of Business and Economic Statistics, vol. 3 (1985), pp. 216–227. 22 Low and Anderson noted that the terminology derived from Milton Friedman’s 1957 concept of “permanent” and “transitory” income in his consumption theory was the inspiration for the terms used by Beveridge and Nelson in 1981. 23 Fabio Canova, “Detrending and Business Cycle Facts,” Journal of Monetary Economics, vol. 41 (1998), pp. 475–512. 24 Ibid., p. 476. 25 A variable with a constant variance is called homoscedastic. 26 If a time series can be made stationary by differencing, it is said to contain a unit root. This means the current value of a series yt is equal to its last value yt−1 plus an error ϵt, that is, yt = ayt−1 + ϵt where the absolute value of “a” equals 1, that is, |a|=1.

179

CHAPTER

8

Understanding the Aggregate Demand (AD) and Aggregate Supply (AS) Model LEARNING OBJECTIVES The aggregate demand and aggregate supply model is one of the core paradigms that is used for learning the theoretical foundation of macroeconomics. In this chapter, you will learn about: • • • •

The history of the aggregate demand/aggregate supply idea and its relation­ ship with the concept of potential output. How the model is constructed and how you might apply that model to different economic periods. What takeaways about recession and inflation could be gleaned from applying the model. What the limitations of the model are.

[235] WHAT IS THE AD/AS MODEL? The AD/AS model portrays the economic relationship between the average price level in the economy (not individual prices and not inflation) and output, accounting for both short-run actual and potential output. Graphically, the model portrays the price level on the vertical or y-axis and output on the horizontal or x-axis—following the same graphical convention used for the microeconomics concept of firm-level supply and demand.

[236] WHO INVENTED THE CONCEPT? It was John Maynard Keynes who wrote about aggregate demand and aggregate supply in his 1936 General Theory of Employment, Interest and Money, but Keynes did not develop the AD-AS model paradigm. It was developed by those interpreting Keynesian ideas. The AD-AS model DOI: 10.4324/9781003391050-9

AGGREGATE DEMAND AND AGGREGATE SUPPLY

with the price-output coordinates first appeared in Kenneth Boulding’s 1948 textbook Economic Analysis, according to Prof. A. K. Dutta,1 followed by O. H. Brownlee’s 1948 article entitled, “Money, Credit, and Monetary Policy,” which appeared in Applied Economic Analysis and edited by Francis Boddy. Dutta further observed that it was Brownlee, who has “the distinction of being the first to present a published version of a complete AD-AS in priceoutput space, without referring to any earlier work on the IS-LM model.”2 Brownlee’s 1950 article appeared in the Journal of Political Economy and was entitled: “The Theory of Employment and Stabilization Policy.”

[237] WHAT ECONOMIC DATA ARE USED AND DISPLAYED IN THE AD/AS MODEL? The AD/AS model uses inflation-adjusted or real GDP (which is conceptually equal to inflation-adjusted or real gross domestic income), the GDP price index, and inflation-adjusted or real potential GDP.

[238] WHAT IS THE DEFINITION OF REAL POTENTIAL GDP? Real or inflation-adjusted potential GDP, which also is known as the natural real GDP or fullemployment real GDP, measures trend or sustainable economic output from the supply-side factors (demographics, labor, capital, technology), without the boom-bust effects from the business cycle, where inflation is steady, and the economy is operating at full employment. This estimated concept of real potential GDP is the long-run aggregate supply (LRAS). Additionally, at that long-run aggregate supply level, the theoretical short-term interest rate—which is dubbed r-star (r*) or the “neutral” or “natural real rate of interest”—is con­ sistent with the full use of economic resources and steady inflation near the Fed’s target (if set appropriately). Therefore, r* is neither putting inflationary or deflationary pressure on the economy through aggregate demand or short-run aggregate supply and neither putting upward or downward unemployment pressure on the economy, as well. •

•

The economic literature on potential output is extensive, mainly about how to measure the concept. Generally, Arthur Okun’s work in 1962 is considered the starting point for the measurement of potential output. However, economists have since pointed out earlier work on “gap theories” began in the late 1920s.3 Arthur Okun wrote: “Potential [output] is a supply concept, a measure of productive capacity. But it is not a measure of how much output could be generated by unlimited amounts of aggregate demand. The nation would probably be most productive in the short run with inflationary pressures pushing the economy … The full employment goals must be understood as striving for maximum production without inflationary pressure; or more precisely, as aiming for a point of balance between more output and greater stability, with appropriate regard for the social valuation of these two objectives.”4

181

182 MACROECONOMIC THINKING AND TOOLS •

•

•

•

Summarizing more recent attempts to measure potential output, a St. Louis Federal Reserve Review article, defined the concept as; “Potential output is an unobserved construct often thought to be synonymous with the maximum level of sustainable output. Because it is unobserved, potential output is generally constructed from observed data such as gross domestic product (GDP). Some definitions of potential output are based on theoretical or empirical relationships that are imposed on the data. For example, one might believe in both a Phillips curve-type relationship (between unemployment and inflation) and an Okun’s law-type relationship (between output and unemployment) and construct potential output from a three-variable New Keynesian model.”5 Attempts to improve the measurement of potential output continue, as reflected in a 2020 article appearing in the journal Structural Change and Economic Dynamics. The authors of that article start with this definition: “Potential output, generally understood as the highest level of output that may be attained without putting inflationary pressures upon the economy, is a crucial notion in the current design and management of macroeconomic policies. Empirical measures of this notion and of the distance between it and actual output – the so-called output gap – play a fundamental role in determining the expansionary or contractionary stance of both monetary and fiscal policy, and the margins for their use. Mainstream economic theory sees potential output as determined exclusively by supply forces, leaving to aggregate demand only the role of causing temporary deviations from it – precisely, the output gaps.”6 Generally, a readily available and continually updated estimate of real potential GDP is produced by the U.S. Congress’ Congressional Budget Office (CBO), which has made this source the “go to” place for the concept. In a nutshell, “To construct its estimates of potential output, CBO relies on a standard modeling framework for analyzing longerterm trends in economic growth and draws from a variety of sources of historical data to estimate components of supply in different sectors of the economy.”7 At the end of the day, this concept of potential real GDP is important because as Okun rhetorically asked, “How much output can the economy produce under conditions of full employment?” Okun then opined that “it is a question with policy significance because the pursuit of full employment (or ‘maximum employment’ in the language of the Employment Act) is a goal of policy.”8 The answer to Okun’s question is captured in the AD/AS model.

[239] HOW IS THE MODEL STRUCTURED? The AD/AS model is based on three curves: •

•

Aggregate demand (AD), which is defined as real GDP and represents aggregate expenditures (that is, the sum of consumption, investment, government spending, and net exports). This AD curve is portrayed in Figure 8.1. Short-run aggregate supply (SRAS) is defined as real gross domestic income (GDI) and represents the payments to the factors of production (that is, wages, rent, interest payments, etc.). This is an upward-sloping curve representing a positive relationship (moves in same

AGGREGATE DEMAND AND AGGREGATE SUPPLY 130.0

Price Level

125.0

120.0

115.0

110.0

105.0 17000

FIGURE 8.1

17500

18000

18500 19000 19500 Real GDP/Real GDI

20000

20500

21000

Aggregate Demand Curve

direction) between the price level and output. By the National Income and Product Accounts (NIPA) definition, real GDP equals real GDI (plus a small statistical discrepancy term due to the differences in data sources). Effectively, real GDP equals real GDI ex post after all transactions settle. This SRAS curve is portrayed in Figure 8.2. Long-run aggregate supply (LRAS), which is defined as real potential gross domestic product, is based on estimates from the Congressional Budget Office (CBO). LRAS is the same as potential real GDP and may be thought of as “trend-growth.” In the long run, all factors of production are assumed variable and there is no imperfect information in the labor market (which is a factor contributing to the upward sloping SRAS curve. As such, potential real GDP occurs with stable inflation, the actual short-run interest rate equal to r* (the theoretical real interest rate concept, sometimes referred to as the “neutral” rate, which is the

•

130.0

Price Level

125.0

120.0

115.0

110.0

105.0 17000

FIGURE 8.2

17500

18000

18500 19000 19500 Real GDP/Real GDI

Short-Run Aggregate Supply Curve

20000

20500

21000

183

184 MACROECONOMIC THINKING AND TOOLS 130.0

Price Level

125.0

120.0

115.0

110.0

105.0 17000

FIGURE 8.3

17500

18000

18500 19000 19500 Real GDP/Real GDI

20000

20500

21000

Long-Run Aggregate Supply Curve (Potential Output)

real short-term interest rate consistent with output equaling potential and stable inflation), and at the full-employment unemployment rate (which is the same as the NAIRU—that is, the non-accelerating inflation rate of unemployment). The LRAS is theoretically portrayed as a vertical line (although we shall see empirically that it may not be perfectly vertical). Nonetheless, this is considered the potential output under normal circumstances. Figure 8.3 portrays the LRAS or potential real GDP line.

[240] WHAT IS THE TIME PERIOD FOR THE AD/AS MODEL? The AD/AS model is a static picture, that is, a snapshot of the economy at a point in time.9 This model is shown with a time element only by overlaying other snapshots that represent changes over time. Alternatively, these snapshots could be separate and shown as different graphs for those different time periods.

[241] WHAT DATA POINTS ARE CAPTURED IN THE MODEL? This AD/AS model implicitly distinguishes between ex post and ex ante aggregate demand and short-run aggregate supply. The ex post observations represent the actual transaction settlement data for the price level, real GDP and real GDI. These are actual data observations available from the U.S. Commerce Department’s GDP report. However, the ex ante points on the AD and SRAS curves are unobserved and must be inferred from statistical analysis and theory. Keep in mind to develop the AD and SRAS curves both the ex post and ex ante observations are needed. This is so because the ex post observation is a single point and you cannot draw a curve with a single observation—you need a minimum of two points for a straight line.

AGGREGATE DEMAND AND AGGREGATE SUPPLY

[242] WHAT DETERMINES THE SHAPE OF THE AD CURVE? The AD curve is downward sloping, moving from left to right, and is argued due to three factors. The reasons assumed for that downward-sloping aggregate demand (or inverse rela­ tionship between price and output) are: (1) the wealth effect; (2) the interest-rate effect; and (3) the exchange-rate effect. These effects work behind the scenes (that is, before all transactions settle and are ex post). •

•

•

•

•

The wealth effect (also known as the real balances effect and the Pigou effect) argues that if the price level is higher, then you can afford less—everything else held constant. The reverse is true as well, and this wealth effect leads to an inverse relationship between the price level and aggregate demand. The interest-rate effect (also known as the Keynes effect) argues that if interest rates rise, then investment (both residential and non-residential) will be lower. This works in reverse if interest rates decline. Again, all of these intermediate adjustments on aggregate demand play out in the ex ante stage, prior to the ex post transaction price level and output. The exchange-rate effect argues that a stronger domestic currency (such as the U.S. dollar) will increase the demand for imports (since the domestic currency is cheaper relative to the foreign country, which is producing the goods and services that will be imported. In turn, this will weaken foreign demand for the domestic economy’s exports (because they cost more to the foreigners). This too works the other way for a depreciation in the domestic currency. In all three cases, there are price-level impacts on aggregate demand, but only with regard to ex ante price levels. Hence, the price-level is viewed as a reaction to some change—not the catalyst for that change.10 Remember that this price level is not inflation, but the value of the market basket of goods and services at a point in time. Changes in aggregate demand are not caused by changes in the price level. Instead, they are caused by changes in the demand for any of the components of real GDP, changes in the demand for consumption goods and services, changes in investment spending, changes in the government’s demand for goods and services, or changes in the demand for net exports.

[243] [ADVANCED] WHAT ARE THE THEORETICAL UNDERPINNINGS OF THE MODEL? The theoretical foundation for the aggregate demand curve is based on the equilibrium points between the “goods or product market” (sometimes also called the “commodity market”) and the “money market.” The “goods market” is captured by the investment-savings (IS) curve, which is dependent on the interest rate that determines where investment equals savings (an ex post relationship in the NIPA tables). The “money market” is captured by the equilibrium where money demand (M) equals money supply (“L” for liquidity) at a given interest rate, which is the LM curve. Then the intersection of the IS and LM curves for some interest rate and real output (real GDP) combination would theoretically represent those points along the AD curve.

185

186 MACROECONOMIC THINKING AND TOOLS •

•

•

•

As the late Sir John Hicks, who was an originator of the IS-LM model, noted about the IS-LM construct:11 “it is only the point of intersection of the [IS and LM] curves which makes any claim to representing what actually happened … Other points on either of the curves … surely do not represent, make no claim to represent, what actually happened. They are theoretical constructions, which are supposed to indicate what would have happened if the rate of interest had been different.”12 Therefore, it is easy to see how Hick’s discussion relates the point of intersection between the IS and LM curves to “ex post” or actual data observations, while the “other points” on the IS and LM curves are “ex ante” or theoretical options. Sir John Hicks said of this IS-LM construct: “The only way in which IS-LM analysis usefully survives [other than] as a classroom gadget [is to incorporate] causal analysis.”13 Indeed, the IS-LM model and the AD-AS model each rest on the discussion of the causal economic linkages that yield an equilibrium point for a snapshot in time. Although the IS-LM model (which is graphed with the interest rate on the vertical axis and real GDP on the horizontal axis) is the theoretical foundation for aggregate demand (which is graphed with the price level on the vertical axis and real GDP on the horizontal axis) the IS-LM construct is not absolutely necessary as a rationale for the aggregate demand curve. However, Hicks’ observation that the causal linkages are a necessary ingredient or rationale to make the IS-LM model useful applies equally well to the AD-AS model. In recent literature, the LM curve has largely given way to a monetary policy rule (such as a Taylor rule) that reflects the shift by major central banks, including the Federal Reserve, to target interest rates directly, instead of the money supply.14

[244] WHAT DETERMINES THE SHAPE OF THE SRAS CURVE? The short-run aggregate supply (SRAS) curve, which measures the relationship between the price level of goods supplied to the economy and the quantity of the goods supplied, is an upward-sloping relationship, moving from left to right. Some authors tie the rationale for the upward-sloping relationship between the price level and output supplied to the idea that at a higher price level that would encourage higher output because producers would hope to gain higher profits. Others consider the price level to be the result of changes in factors of production and, therefore, the price level itself is not the catalyst for SRAS change. This perspective (which might be more consistent and clearer) argues that the primary cause of short-run aggregate supply is a change in input prices (which affects profits and also may be affected by productivity) and economic growth.

[245] HOW CAN THOSE UNOBSERVED EX ANTE AD POINTS IN THE MODEL BE ESTIMATED? It has been argued by the neo-classical school of economic thought that aggregate demand is unit elastic15 (−1.00), that is a 1% change in price is matched by a 1% change in aggregate demand in the opposite direction to the price change, or for any like percentage amount with

AGGREGATE DEMAND AND AGGREGATE SUPPLY

respect to the price level.16 On the other hand, the Keynesian school of economic thought would argue that the AD price elasticity is more variable based on where the economy is along the AD curve. This concept of price elasticity of aggregate demand might be useful to derive estimates of ex ante points along the AD curve. Empirically, Niemira’s estimate of that price elasticity of aggregate demand was -0.90, on average, between 2001 and 2020.17 This estimate is relatively consistent with theory and can be used to derive theoretical points along the AD curve.

[246] HOW CAN THOSE UNOBSERVED EX ANTE SRAS POINTS IN THE MODEL BE ESTIMATED? It also has been argued that the price elasticity of aggregate supply is “fairly elastic” in the short run, but the price elasticity of supply is “fairly inelastic” (a relatively steep or vertical line) in the long run. Here too there is some differences in how the neo-classical school and the Keynesian school would envision the short-run aggregate supply curve. Although the neo-classical school embraces a more traditional upward sloping short-run supply curve, the Keynesian perspective gives rise to a “styl­ ized” SRAS that is shaped as a “Lazy L” or an L-shaped curve on its backside (this SRAS shape also is sometimes referred to as the Kaleckian/Post-Keynesian Model and is portrayed in Figure 8.4.18 The Keynesian perspective therefore (at the extreme) views a two-state economy where the horizontal portion of the Keynesian SRAS curve represents no inflationary implications from expanding AD, if the AD/SRAS is below the economy’s potential (as represented by the longrun aggregate supply). Alternatively, this Keynesian SRAS suggests if the economy is operating at potential, then the consequence of expanding AD further is higher inflation, but no higher real GDP (output). Nonetheless, the empirical support is closer to the neo-classical school rather than the stylized L-shaped Keynesian SRAS. An estimate of the price elasticity of short-run aggregate supply between 2010 and 2018 was 1.48 (elastic),19 but a long-run estimate between 1960 and 135.000

131.109

Price Level (2012=100)

130.000 125.000 119.190 120.000

119.190

115.000 110.000 105.000 100.000 16500

FIGURE 8.4

17000

17500 18000 18500 19000 19500 20000 Real GDP/Real GDI (Billions of 2012 Dollars)

Keynesian SRAS

20500

21000

187

188 MACROECONOMIC THINKING AND TOOLS 2018 was less than one (inelastic). Therefore, these empirically derived short-run and long-run price elasticities of supply are broadly supportive of the theory, yet, the price elasticity of the LRAS does not appear to be zero (which is implied by a vertical line).

[247] HOW CAN WE USE THOSE PRICE ELASTICITY ESTIMATES TO DEVELOP ADDITIONAL POINTS ALONG THE AD OR THE SRAS CURVES? We need to have at least two pairs of price and output coordinates to construct the AD and SRAS curves and have the actual data as one pair of coordinates for each curve. Then we can use those elasticities to find a second or third point. •

AD curve: Assuming we are given the actual price level and real GDP observation for the third quarter of 2021, let’s show how we might use that price elasticity of aggregate demand, which is −0.90 (as noted above), to determine another two points on the aggregate demand curve for that period. To accomplish this, we assume a 10% increase in price level and a 10% decrease in the price level and use that elasticity to determine what the real GDP points along the AD curve would be. This is shown in the table. Therefore, we have three points along the AD curve that could be graphed. The ex post (or actual) point is at the price-quantity coordinates of (119.237, $19,469.40). One of the ex ante points (an unobserved point that is inferred from the elasticity estimate) when the price level rises by 10%, would be at the price-quantity coordinates of (131.2, $17,714.74). Notice that at the higher price the aggregate demand is considerably lower than the ex post point. Similarly, we have another point along the AD curve when the price level declines by 10%. This ex ante point has the price-quantity coordinates of (107.3, $21,224.06). Hence, this allows us to graph the AD curve with those three points. The AD curve is displayed in Figure 8.5. Calculation of the Aggregate Demand Curve Based on Actual Data and the Elasticity of Aggregate Demand

TABLE 8.1

ACTUAL

Data GDP Price Index (Index numbers, 2012 = 100, Seasonally Adjusted) Real Gross Domestic Product (GDP) [Billions of chained (2012) dollars] Seasonally Adjusted at Annual Rates

BASED ON ELASTICITY ESTIMATE

Ex Post Observation

Ex Ante Estimate

Ex Ante Estimate

2021-Q3 119.237

2021-Q3 131.2

2021-Q3 107.3

$19,469.40

$17,714.74

$21,224.06

(Continued )

AGGREGATE DEMAND AND AGGREGATE SUPPLY TABLE 8.1

(Continued) ACTUAL

GDP Price Index (Index numbers, 2012 = 100, Seasonally Adjusted) GDP Price Index (Index numbers, 2012 = 100, Seasonally Adjusted) Real Gross Domestic Product (GDP) [Billions of chained (2012) dollars] Seasonally Adjusted at Annual Rates Real Gross Domestic Product (GDP) [Billions of chained (2012) dollars] Seasonally Adjusted at Annual Rates

BASED ON ELASTICITY ESTIMATE

Ex Post Observation

Ex Ante Estimate

Ex Ante Estimate

INDEX = (1+(10/ 100))∗119.237

131.2

10.0%

INDEX = (1+(−10/ 100))∗119.237

107.3

−10.0%

$Billions = (1+(−9/100))∗$19469.4=

$17,714.74

−9.0%

$Billions = (1+(9/100))∗$19469.4=

$21,224.06

9.0%

135.0

Price Level (2012=100)

130.0

125.0

120.0

115.0

110.0

105.0

100.0 $16,500 $17,250 $18,000 $18,750 $19,500 $20,250 $21,000 $21,750 $22,500 $23,250

Real GDP/Real GDI (Billions of 2012 Dollars) FIGURE 8.5

Aggregate Demand for Q3 2021

189

190 MACROECONOMIC THINKING AND TOOLS •

SRAS curve: Assuming we are given the actual price level and real GDP (or real GDI) observation for the third quarter of 2021, let’s show how we might use that price elasticity of aggregate supply, which is +1.48 (as noted above), to determine another two points on the short-run aggregate supply curve for that period. This is shown in the table below. Although this average-elasticity approach produces a linear or straight-line relationships between output and price for AD and SRAS, it is possible to develop a non-linear relationship (curve) based on more assumptions—though the benefit is likely to be minor.20 Allowing for real GDP equal to real GDI, then we can use the same number in this exercise to determine the price-quantity coordinators for the short-run aggregate supply curve. Therefore, the ex post (actual) price-quantity coordinates are (119.237, $19,469.40), which is the same point as found on the aggregate demand curve. Hence, that also represents the equilibrium between the aggregate demand and short-run aggregate supply.21 One of the ex ante points (an unobserved point that is inferred from the elasticity estimate) when the price level rises by 10% (an arbitrary growth rate to evaluate the impact on quantity) would be at the price-quantity coordinates of (131.2, $22,526.10). Another of the ex ante points when the price level declines by 10% would at the price-quantity coordinates of (107.3, $16,412.70). Notice that the higher the price level, the higher the supply, and vice versa. This gives you an upward sloping line as displayed in Figure 8.6.

Calculation of the Aggregate Supply Curve Based on Actual Data and the Elasticity of Aggregate Supply

TABLE 8.2

ACTUAL

Data GDP Price Index (Index numbers, 2012 = 100, Seasonally Adjusted) Real Gross Domestic Product (GDP) [Billions of chained (2012) dollars] Seasonally Adjusted at Annual Rates GDP Price Index (Index numbers, 2012 = 100, Seasonally Adjusted) GDP Price Index (Index numbers, 2012 = 100, Seasonally Adjusted) Real Gross Domestic Product (GDP) [Billions of chained (2012) dollars] Seasonally Adjusted at Annual Rates Real Gross Domestic Product (GDP) [Billions of chained (2012) dollars] Seasonally Adjusted at Annual Eates

Price Elasticity of Supply

Ex Post Observation

Ex Ante Estimate

Ex Ante Estimate

2021-Q3 119.237

2021-Q3 131.2

2021-Q3 107.3

$19,469.40

$22,353.40

$16,585.40

INDEX = (1+(10/ 100))∗119.237

131.2

10.0%

INDEX = (1+(−10/ 100))∗119.237

107.3

−10.0%

$Billions = (1+(14.8/100))∗ $19469.4 =

$22,353.40

14.8%

$Billions = (1+(−14.8/100))∗ $19469.4=

$16,585.40

−14.8%

AGGREGATE DEMAND AND AGGREGATE SUPPLY 135.0

130.0 SRAS

Price Level (2012=100)

125.0

120.0

Equilibrium (Ex Post)

115.0

AD = SRAS

110.0 AD 105.0

100.0 $16,500 $17,250 $18,000 $18,750 $19,500 $20,250 $21,000 $21,750 $22,500 $23,250 Real GDP/Real GDI (Billions of 2012 Dollars) FIGURE 8.6

Aggregate Demand and Short-Run Aggregate Supply for Q3 2021

[248] WHAT HAPPENS WHEN AGGREGATE DEMAND AND SHORT-RUN AGGREGATE SUPPLY ARE INITIALLY NOT IN EQUILIBRIUM? The off-equilibrium values associated with the price level (that is, the ex-ante points on the AD and SRAS curves for a given price level) represent the adjustment phase where there may be either excess aggregate demand or excess aggregate supply. Excess aggregate demand exists where the price level is lower than the equilibrium value, then aggregate demand (the amount of real GDP) is higher than the aggregate supply (the amount of real GDI). On the other hand, if the price level is higher than the equilibrium value, then aggregate supply is higher than aggregate demand, which is said to be excess aggregate supply. The adjustment process through the factors that effect aggregate demand and those that effect aggregate supply will move the move AD and SRAS to the macro equilibrium.

[249] HOW IS THE LRAS DETERMINED? The last step is to put into the graph the LRAS curve, which is a vertical line at the potential real GDP level. Retrieving the real potential GDP data from the Congressional Budget Office estimates, we find the 2021-Q3 estimate is $19,796 billion in 2012 dollars. This price-quantity coordinate for the LRAS would be any price paired with the $19,796. Since we need two points to graph this vertical line, we can take the price at the high and the low. Therefore, one point on the LRAS would be at the price-quantity coordinates of (131.2, $19,796) and a

191

192 MACROECONOMIC THINKING AND TOOLS 135.0

130.0

Price Level (2012=100)

LRAS 125.0

SRAS

120.0

115.0

110.0 AD 105.0

100.0 $16,500 $17,250 $18,000 $18,750 $19,500 $20,250 $21,000 $21,750 $22,500 $23,250 Real GDP/Real GDI (Billions of 2012 Dollars)

Full Model of Aggregate Demand (AD), Short-Run Aggregate Supply (SRAS) and Long-Run Aggregate Supply (LRAS) for Q3 2021

FIGURE 8.7

second point would be at the price-quantity coordinates of (107.3, $19,796). The LRAS curve is added into the AD/AS model and displayed as a vertical line as shown in Figure 8.7.

[250] ONCE THE MODEL IS TOGETHER, WHAT IS NEXT? Now that all of the relevant components of the model are assembled for the Q3–2021 AD/AS snapshot, it must be interpreted. Is the economy in recession? Is the economy booming? Is the economy facing building inflationary pressures? Is the economy facing increasing unemployment? For this specific period, we see that the equilibrium point for AD = SRAS is less than real potential GDP (LRAS), but we know the economy is in expansion after the 2020 recession and heading towards full employment and potential output. Of course, the closer it gets to that LRAS, the likelihood is elevated that inflationary pressures may increase. Additionally, this then raises the question about how fiscal and monetary policy has gotten the economy to that point and what policy changes might occur in the future.

[251] HOW CAN RECESSION BE DETERMINED IN THE MODEL? The AD/AS model portrays recession based on the output or GDP gap, which measures the deviation of actual real GDP from potential real GDP (LRAS) as a share of potential real GDP, that is (Figure 8.8):

AGGREGATE DEMAND AND AGGREGATE SUPPLY 130.000 AD

PRICE INDEX (2012=100)

125.000

SRAS

LRAS

120.000

115.000

Output Gap or GDP Gap

110.000

105.000 100.000 $16,500.0

FIGURE 8.8

$17,000.0 $17,500.0 $18,000.0 $18,500.0 $19,000.0 REAL GDP/REAL GDI (BILLIONS OF 2012 DOLLARS)

$19,500.0

Output Gap (Also known as GDP Gap)

Output Gap = 100 × ((Real GDP

Real Potential GDP ))/(Real Potential GDP )

A positive output gap (or “inflationary output gap”) occurs when real GDP exceeds real potential GDP and implies a booming economy. A negative output gap (or “recessionary output gap”) occurs when the real GDP is below real potential GDP and represents an economy that might be in recession, an economy that might be growing, but very slowly or just moderately, or an economy that might be coming out of recession. Textbooks suggest that when the output gap is “large,” then the economy is likely in recession. Most textbook discussion of this output gap in the AD/AS model leaves the interpretation of large in the eye of the beholder. However, it is possible to pin-down the idea of “large” based on empirical information of the output gap, as shown below. The mean recessionary output gap between 1949 and 2020 was a negative 2.0% of real potential output, while the median recessionary output gap was a negative 1.5%. Therefore, one interpretation of “large” is an output gap of at least –1.5%. The two largest recessionary gaps historically occurred in 1982 (when it was –7.8%) and 2020 (at a negative 10.8%) as displayed in Figure 8.9. During business cycle expansion, the empirical U.S. record shows that the mean ex­ pansionary output gap was a negative 0.3%, while the median expansionary output gap was a negative 0.5%. On balance, fiscal and monetary policies that help to guide the output gap close to zero helps to stabilize the economy.22 Therefore, empirically, the deviation of the equilibrium (AD = SRAS) can be evaluated relative to real potential GDP (LRAS) since those data are available to graph the AD/AS model. For example, if the output gap is calculated for the Q3 2021 period (as portrayed in the full AD/AS model graphic above), based on actual real GDP and the estimated real potential GDP, then the gap is a negative 1.6%, derived as 100 × ([19,469.4 – $19,795.8]/$19,795.8). Obviously, the output gap remained large in Q3 2021, but we further know that the economy

193

194 MACROECONOMIC THINKING AND TOOLS

FIGURE 8.9

U.S Output Gap, 1950–2021

was out of the 2020 pandemic-induced recession by Q3 2021 and it was on the mend. This is a good example of understanding the narrative of the backstory, which provides us with an interpretation of what the AD/AS model really is telling us for that point in time.

[252] CAN ACTUAL OUTPUT EXCEED POTENTIAL? At times, when the economy is booming the equilibrium output between AD and SRAS might occur at a level that is higher than (to the right of) potential real GDP (LRAS). This can occur because potential real GDP is determined based on normal production, but there are times (for example, during wars, a national emergency, or an overheated economy) that might result in temporarily “stretched” production capacity. For example, during periods of rising COVID-19 cases during the 2020–2022 pandemic, many hospitals exceeded their normal capacity by cre­ ating temporary intensive care unit (ICU) space and extended work hours. This is a short-run situation only and not sustainable, because it puts upward pressure on the price level.

[253] HOW IS INFLATION CAPTURED IN THE AD/AS MODEL? Economists generally distinguish two sources of inflation: (1) demand-pull inflation; and (2) costpush inflation. Both types of inflationary pressure are captured in the AD/AS model as changes in aggregate demand or short-run aggregate supply (that is, allowing for one or more snapshots to develop over time). Discussing inflation in the AD/AS model requires more than one price level (remember that the price level is just an estimate of the cost of a market basket of goods and services and inflation represents a change in that price level or the change in the price of that market-basket of goods and services). Therefore, inflation is captured in the AD/AS model through changes over multiple time periods.

AGGREGATE DEMAND AND AGGREGATE SUPPLY

[254] HOW IS DEMAND-INDUCED INFLATION CAPTURED IN THE AD/AS MODEL? Demand-pull inflation can occur when there is a rightward shift in the AD curve towards or beyond potential output (LRAS).

[255] HOW IS SUPPLY-INDUCED INFLATION CAPTURED IN THE AD/ AS MODEL? Cost-push inflation can occur when there is a leftward shift in the SRAS curve away from potential output (LRAS).

[256] HOW IS OUTPUT GROWTH CAPTURED IN THE AD/AS MODEL? Similar to the need to have multiple snapshots (AD/AS models) to infer what the inflation rate is over time, this also is true for discussing economic growth. An individual AD/AS model for a point in time portrays a static output in billions of real dollars. If you want to discuss how strong or weak real GDP growth was (that is, adding a dynamic element to your discussion), then you need to have multiple AD/AS snapshots and determine the percentage change between those snapshots for output. Graphically, in the AD/AS model this is portrayed by overlaying a second or third or more snapshots, which represent different time periods, and the change often is shown with arrows or sequentially numbered subscripts to the notation for AD or SRAS, which imply the direction of change. The example below shows the change between the third-quarter 2020 and third-quarter 2021. How much slack (calculate the output gap) was there in the economy in 2020 versus 2021? Why did AD shift more than AS? (See Figure 8.10.) Finally, it is useful to calculate annualized growth rates between those shifting AD, SRAS, and the LRAS curves over time to understand the strength of short-run growth, long-run growth, and inflation—especially relative to historic averages.

[257] WHAT IS THE SIGNIFICANCE OF THE SHAPE OF THE SRAS CURVE? Textbooks tend to discuss “three zones” of the non-linear SRAS curve—which are (1) the neo-classical zone (the relatively inelastic or vertical portion), (2) the Keynesian zone (the relatively elastic or flat portion), and (3) the intermediate zone (which as the name implies in between the other two). One reason for this discussion is that it attempts to conceptually incorporate the Phillips curve trade-off (to some degree) between wages/prices and the unemployment rate (through the output level). Moreover, in the intermediate zone of that SRAS this Phillips curve trade-off is still viewed conceptually valid (to a slightly greater degree than in the other two zones). The Phillips curve (or an earlier theoretical discussion of it by Irving Fisher in a 1926 paper) became popular in the late 1950s and into the 1960s and gave rise to the view that policy could be used to trade-off inflation for unemployment and vice

195

196 MACROECONOMIC THINKING AND TOOLS 135.0

130.0

Price Level (2012=100)

LRAS: Q3-2020

SRAS: Q3-2021

LRAS: Q3-2021

125.0

SRAS: Q3-2020

120.0

115.0

AD: Q3-2021

110.0

105.0 AD: Q3-2020 100.0 $16,500 $17,250 $18,000 $18,750 $19,500 $20,250 $21,000 $21,750 $22,500 $23,250 Real GDP/Real GDI (Billions of 2012 Dollars)

Showing Change in Aggregate Demand (AD), Short-Run Aggregate Supply (SRAS) and Long-Run Aggregate Supply (LRAS) between Q3 2020 and Q3 2021

FIGURE 8.10

versa. This idea is still largely embedded in macroeconomics and certainly within the AD/AS model, though empirically that trade-off is largely nil or non-existent in recent years. (Policymakers tend to describe this weakening empirical trade-off between wages/inflation and the unemployment rate as having “flattened,” which also may hint at how the curvature of the AD and SRAS curves may have changed over time.) The three zones of the SRAS are shown in Figure 8.11. SRAS

Price Level

Neo-Classical Zone

Intermediate Zone Keynesian Zone

Output FIGURE 8.11

Three “Zones” of the Short-Run Aggregate Supply Curve

AGGREGATE DEMAND AND AGGREGATE SUPPLY

[258] WHAT “COMPONENT” FACTORS WILL CAUSE A CHANGE IN THE AD/AS EQUILIBRIUM? The “dynamic” process of economic change demonstrated using the AD/AS model is to shown by linking together the change for multiple time periods. These changes can occur in aggregate demand, short-run aggregate supply, and long-run aggregate supply. The factors that move aggregate demand are the components of real GDP—those aggregate expenditures on consumption, investment, government spending, and net exports. Changes can occur in shortrun aggregate supply, which conceptually are the factors of payments for production—such as wages, non-labor production costs (such as energy costs, material costs, etc). Changes can occur in long-run aggregate supply and the CBO’s projections capture that potential economic growth largely through technology and demographics.

[259] WHAT IS PARTIAL-EQUILIBRIUM ANALYSIS AND HOW IS IT USED WITH THE AD/AS MODEL? The idea of partial-equilibrium analysis is attributed to British economist Alfred Marshall, who lived between 1842 and 1924, and is considered the “father of microeconomics.” To segment effects or factor changes, Marshall proposed examining individual changes impacting demand or supply by holding other things equal or unchanged—which is the assumption referred to by the Latin expression ceteris paribus. Therefore, partial-equilibrium analysis is an analytical approach that simplifies how the process of change unfolds through each factor. Although rarely thought of as a macroeconomics approach, the partial-equilibrium approach is often implicitly used in­ structionally in macroeconomics to show specific effects on aggregate demand, short-run aggregate supply, or long-run aggregate supply while ignoring other changes (including time period changes). For example, authors often will show shifts in short-run aggregate supply from changes in productivity without reference to any period changes or even the potential indirect impact on labor demand. Another example is when authors look at the impact of an interest rate change on aggregate demand, in isolation of any impact on short-run aggregate supply. •

In the eighth edition preface to Alfred Marshall’s Principles of Economics, he summarized the analytical methods as follows: “The forces to be dealt with are … so numerous, that it is best to take a few at a time; and to work out a number of partial solutions as auxiliaries to our main study … We reduce to inaction all other forces by the phrase “other things being equal”: we do not suppose that they are inert, but for the time we ignore their activity. This scientific device is a great deal older than science: it is the method by which, consciously or unconsciously, sensible men have dealt from time immemorial with every difficult problem of ordinary life.”

[260] HOW IS THE ANALYSIS OF A “CAUSAL” FACTOR CHANGE IN AD OR SRAS TRACED THROUGH THIS MODEL? Initially, it is important to identify and distinguish “component factors” (as discussed in Question 258) from “causal factors” effecting either AD or SRAS. Causal factors are those actions that

197

198 MACROECONOMIC THINKING AND TOOLS cause changes in aggregate demand through consumption, government spending, invest­ ment, or net exports, or cause changes in short-run aggregate supply through its components—labor compensation, profits, rental income, and interest payments. A causal factor is not a component itself, but actions that impact a component. For example, you might expect changes in tax rates and income tax deductions—personal and business, tariffs on trade, changes in defense or non-defense spending priorities by Congress, changes in consumer preference for savings, etc. all to have some cause-and-effect impact on com­ ponents of aggregate demand. Similarly, there are a host of cause-and-effect factors that impact components of SRAS—such as, changes in the minimum wage, oil prices, interest rates, productivity, technology, etc. Once a causal factor is identified and its direction and magnitude of change assessed then partial-equilibrium analysis (as discussed in Question 259) can be used to narratively trace the causal flow or channel through the model.

[261] HOW DO SHOCKS SHIFT SHORT-RUN AGGREGATE SUPPLY (SRAS)? In a paper by Behaert et al., they define aggregate supply shocks as “shocks that move inflation and real activity in the opposite direction.”23 Supply shocks (positive/beneficial or negative/ adverse) represent those changes to the determinants of the supply-side or income.24 This would include any factor that affects real gross domestic income—which captures payments to the factors of production (that is, the sum of incomes earned and costs incurred in the pro­ duction of GDP). Some typical examples of supply shocks are: (1) changes in oil price, (2) changes in tax rates, (3) increase in the minimum wage, (4) adverse weather impacting a broad area of the country, and (5) the COVID-19 global pandemic effect on the global supply-chain and production costs. Because these supply shocks are not directly visible or portrayed in the AD/AS graphic, it is essential to discuss the “backstory” why the SRAS curve is shifting. That story of the change or shock impact is likely to be segmented into its component parts/factors that are adjusting (using a partial-equilibrium analysis discussion). For example, assuming WTI crude oil prices per barrel rise from $70 to $100 per barrel over the three-month (one-quarter) period of analysis due to global tensions and its potential disruption on oil supply, then it would be expected that the SRAS curve would shift to the left because of a sequence of impacts from this oil-price shock: •

•

The upward oil price shock directly would increase the cost of energy prices purchased by businesses (the consumer impact would likely show up on personal consumption—which is a component of aggregate demand, not aggregate supply). The evidence that could be cited for this causal link to higher business costs is to look at the U.S. BLS’s Producer Price Index (PPI) for energy prices. These PPI data “measure the average change over time in the selling prices received by domestic producers for their output.” Higher energy prices would likely mean the financial markets would anticipate higher inflation, which may cause market interest rates to rise, as well. The evidence for this is captured in interest rate data reported by the Federal Reserve in its H.15 release “Selected Interest Rates.” Lenders would benefit from higher interest rates that they could charge, but borrowers would be hurt by higher interest cost.

AGGREGATE DEMAND AND AGGREGATE SUPPLY

•

• • • •

•

Higher interest rates and higher oil prices would likely mean that business costs are higher, and in turn productivity is lower. The evidence for this is captured in U.S. BLS’s Productivity and Costs report for what is termed, “unit nonlabor costs”25 or “unit nonlabor payments,”26 depending on the sector breakdown. Total unit costs are the shareweighted sum of unit labor costs (about two-thirds) and nonlabor costs (about one-third). The statistical correlation between total unit costs and productivity is inversely related by a factor of −0.4 (estimated between 1960 and 2021). Therefore, higher nonlabor business costs will curb productivity. Therefore, a jump in oil prices leads to a leftward shift in the short-run aggregate supply curve, which now intersects with the original AD curve at a higher price level. Of course, this oil-price jump would have an additional impact on aggregate demand too. If interest rates moved higher, then borrowing costs to invest and build inventories would increase as well, which, in turn, may scale back aggregate demand. If interest rates moved higher, then there also could be an impact on foreign exchange rates causing a stronger U.S. dollar versus other foreign currencies, which in turn may scale back aggregate demand through lower exports. The evidence for this might be captured in the U.S. Census Bureau’s monthly U.S. International Trade in Goods and Services report. Finally, rising interest rates and a stronger U.S. dollar would likely cause a leftward shift in the AD curve, as well, which might limit the price level increase from the supply shock at the expense a larger reduction in real GDP/real GDI.

By explaining each factor impacted, partial-equilibrium analysis allows us to examine the backstory of a shock that would shift short-run aggregate supply, which would lower short-run aggregate supply (real GDI) and raise the price level. Qualitatively, this adjustment from a supply (or demand) shock can be traced out, but the AD/AS model alone does not provide the magnitude of change for which economists rely on more sophisticated methods to provide those answers.

[262] HOW DO SHOCKS SHIFT AGGREGATE DEMAND (AD)? Demand shocks (positive or negative) represent those changes to the determinants of the demand-side or expenditure. This would include any factor that affects real gross domestic product, which is the sum of expenditures by consumers, investment, government spending, and net exports. Some typical examples of demand shocks are: (1) changes government spending or tax rates, (2) changes in interest rates, (3) changes in foreign exchange rates, and (4) changes in either business or consumer expectations. Economists generally have used statistical models, such as vector autoregressive (VAR) models, to infer the magnitude and path of aggregate supply and aggregate demand shocks on output and the price level. Demand shocks that are positive or beneficially influence both real GDP and prices will boost real GDP and dampened price pressure.27 Most econometric studies suggest that demand shocks also are temporary (that is, do not have a lasting impact on real GDP and prices)—though there is some push-back on this empirical finding.

199

200 MACROECONOMIC THINKING AND TOOLS

[263] HOW ARE RECESSIONS RELATED TO AGGREGATE DEMAND (AD) SHOCKS AND SHORT-RUN AGGREGATE SUPPLY (SRAS) SHOCKS? The Bekaert et al., study characterized recessions as either predominately supply or demand driven, or a combination of both. Their findings were largely consistent with how other researchers characterized the supply or demand dynamic leading to past business cycle recessions in the United States. Based on their results, “The 1980 recession did not feature negative cumulative demand shocks but all the other recessions did, with the 1981–82 recession and the [2008–2009 Global Financial Crisis] recession featuring the largest negative demand shocks. All recessions except the 1981–1982 [episode] featured negative supply shocks, with the largest negative shocks occurring in the 1969–1970 and 1973–1975 recessions. On a relative basis, the first three recessions were predominantly supply driven whereas three of the last four were more demand driven (the exception being the 1990–91 recession).”28 With regard to the 2020 COVID-19-induced recession, their “calculations show[ed] that the 2020:Q1 real GDP growth shock was largely due to an aggregate demand shock, while the staggeringly large shock in 2020:Q2 was due to both aggregate demand and aggregate supply shock, but with the latter contributing somewhat more to the decline.”29

[264] CAN THE AD/AS MODEL EXPLAIN A FORECAST? The discussion up until now and followed by most textbooks tends to conclude with simply explaining the AD/AS and, maybe demonstrating what the model might tell us about some historical economic episode. But possibly a more interesting question and application might be: Can the AD/AS model be used for forecasting? In a limited way, the answer is yes. Let’s explore how the AD/AS model could be used for forecasting and, in turn, anticipate and understand what monetary and fiscal policymakers may be concerned about in the near future. To use the AD/AS model, we need to have forward estimates for the period in question (say, the fourth-quarter of the year ahead). We know that the Congressional Budget Office using real potential GDP as a benchmark for its ten-year economic and budget forecasting, which means the CBO has forecasts of real potential GDP over the upcoming ten years. So, that means we have an estimate for LRAS. How about estimates for real GDP and the price level? A lot of economists forecast these measures. You can use a specific firm’s forecast for the future economic period of interest, or you can rely upon a consensus forecast of many forecasters as the needed ingredients for this application. One such consensus eco­ nomic forecast is the Survey of Professional Forecasters (which was originally compiled by the late University of Chicago Prof. Victor Zarnowitz for the National Bureau of Economic Research in the 1960s and 1970s, but later continued by the Federal Reserve Bank of Philadelphia). This quarterly survey provides consensus forecasts for real GDP and the GDP price index, which are the needed elements to complete the AD/AS model forecast. At this point, the mechanics of deriving the forecast AD, forecast SRAS, and forecast LRAS are identical to the historical periods as discussed above.

AGGREGATE DEMAND AND AGGREGATE SUPPLY

[265] [ADVANCED] CAN AGGREGATE DEMAND EVER SLOPE UPWARD? Yes, this is akin to asking can higher interest rates be associated with rising demand; this situation can occur in short periods (maybe even up to a couple of years) when interest rates may be historically low, but rising too slowly, and/or the Federal Reserve’s monetary policy is depressing interest rates too long, which may show up as negative real interest rates. When negative real interest rates last too long, this can result in raising housing and financial asset wealth and may, in turn, trigger an “asset bubble”30 as would-be homeowners and commercial real estate investors and stock-market/commodity market investors chase those assets and bid up their prices, which trigger even more investor demand.31 This idea of perverse-shaped curves—relative to theory—has been extended to include a downward-sloping short-run aggregate supply curve too.

[266] WHAT IS SOME CRITICISM OF THE AD/AS PARADIGM? From its earliest discussions, the economic foundations and use of the AD-AS model have been challenged based on “inconsistencies” with existing economic theories.32 Various economists have ascribed different characteristics to AD and/or AS—some less defensible than others. For example, the AS concept and shape has been developed based on assumptions of perfect competition or monopolistic competition in the economy. Or, that the AD concept is conceptually built on just output and interest rates. However, the AD-AS paradigm may be much more robust than its foundations and assumptions ascribed to it, which is why it con­ tinues to exist and is not a retired concept in economics. Moreover, this paradigm is seemingly best served by what Yale Prof. Robert Shiller calls narrative economics33—the stories that give rise to the AD/AS snapshot. In this way, the paradigm can and has evolved to, maybe, free itself from overly restrictive theoretical assumptions about AD or AS—even if the model borders on the criticism that it is “measurement without theory.” Nonetheless, this more eclectic approach allows for narratives of the underlying factors affecting or effecting AD and AS that may lead to new theories about how the economy works today and not be hamstrung by yesterday’s thinking.

[267] [ADVANCED] HOW DOES THE ADJUSTMENT PHASE OF THE AD/AS MODEL CONCEPTUALLY WORK? Because this adjustment process is theoretical or an ex ante adjustment, it often can be difficult to conceptualize the “back story.” Of course, one way to view this is to go to the foundational model, which is the IS-LM model. As noted by Bofinger:34 “In all textbooks this [IS] curve is explicitly presented as the locus of goods market equilibria for different interest rates. But for teaching purposes it would be more intuitive to label it as a DS curve (demand equals supply) instead as an IS (investment equals saving curve) which, of course, is also a correct interpre­ tation.” The LM curve [the supply and demand for money] poses a different challenge. Bofinger again weighs in, “Today, monetary theory and policy are characterized by strategies

201

202 MACROECONOMIC THINKING AND TOOLS of interest rate targeting. Therefore, [it] seems more convenient to substitute the LM curve [with] an interest rate line (IR curve).” Alternatively, Bofinger suggests a non-standard ADSRAS presentation relating planned (ex ante) to actual (ex post) expenditure as a more informative description of the adjustment process from the planned output possibilities (on the y-axis) to the actual output (on the x-axis).35 He presents this type of graphic where the Keynesian model is explicitly showing that demand creates the supply, that is, supply is dependent on the level of aggregate demand. Of course, if you wanted to show the Classical model, you could swap the position of the SRAS and AD curves on this planned-actual output graphic—which is not an AD/AS curve, per se, since the y-axis is not the price level. Bofinger recommends this as a better starting point to think about the back story. He opined, “Together with the introduction of a full employment or natural output level it shows the student right from the start that a short-term equilibrium on the goods market is possible at the aggregate level while the production potential of an economy is not com­ pletely exhausted.” So, if this planned-actual AD-AS equilibrium is initially at E1 as shown in the graphic (which is based on actual data for Q3 2021), ask yourself what are some demand changes that might be happening in the economy that would shift the AD curve either closer to the LRAS or further away from the LRAS by the end of the period (which, in this case, would be Q4 2021)? This is not a theoretical question as much as a storyline about what really is going on. Obviously, we can think of the same process for the SRAS curve as the catalyst for change. Again, the point of this type of graphic is to help you conceptualize the ex ante adjustments between plan or expectation of AD (or AS) and the actual AD and AS, given the price level (Figure 8.12).

$21,500 AD

Real Gross Domestic Product (Billions of 2012 Dollars) –AD (Planned)

$21,000

LRAS SRAS

$20,500 $20,000 E1

$19,500 $19,000

Output Gap

$18,500 $18,000 $17,500 $17,000 $16,500

45°

$16,500 $17,000 $17,500 $18,000 $18,500 $19,000 $19,500 $20,000 $20,500 $21,000 $21,500

Real Gross Domestic Income (Billions of 2012 Dollars) –AS (Actual) FIGURE 8.12

Demand-Side Determination of SRAS

AGGREGATE DEMAND AND AGGREGATE SUPPLY

[268] [ADVANCED] HOW DOES THE AD-AS MODEL SHOW THE LONG-RUN NEUTRALITY OF MONEY? Money neutrality is a long-run concept that an increase in the supply of money affects only prices, without impacting the real economy. In order to demonstrate this, you need to assume that the LRAS does not change (this assumes there is no trend growth—which may be unrealistic, but it shows the process without the complication of economic growth). Then there are three equilibrium points (three periods) that show this in the AD-AS model. Initially, the AD and AS is in equilibrium at E1, at the initial price level. Step 2 occurs when the money supply is expanded, which in turn pushes AD to the right and will increase output primarily (this would be graphed as a horizontal shift in the equilibrium point to E2. However, SRAS and LRAS remain the same. This is the initial money illusion case. However, if the new equilibrium (E2) is to the right of the LRAS, then Step 3 occurs, in the subsequent period, to force AD to the LRAS, which would lower real GDP to the long-run potential level. However, the net effect is higher inflation and a lower output level (relative to E2), which is the new equilibrium, E3. Moreover, if the initial equilibrium was already at the LRAS, then it would mean that the final step’s output would return to its initial level with only a gain in inflation.

[269] [ADVANCED] ARE AD AND AS “SHOCKS” OR CHANGES INDEPENDENT OF EACH OTHER? Generally, economists have thought of changes, or what is termed “shocks,”36 in aggregate demand and short-run aggregate supply as independently generated and flow through or are contained to either the demand or supply side—though some underlying factors that change such as expectations or interest rates, for example, can affect both aggregate supply and aggregate demand. However, some recent thinking to understand the COVID-19 pandemic-induced business shutdowns posited a new concept of a Keynesian supply shock. This idea put forth by Guerrieri et al., defines Keynesian supply shocks37 as “shocks to aggregate supply that, in turn, lead to a shock to aggregate demand that is even larger than the original supply shock, so that the demand shock dominates the macroeconomic dynamics. Put another way, a Keynesian supply shock is a supply shock with a traditional demand-side multiplier.”38 In addition to the COVID-19 closure impacts on aggregate supply and aggregate demand, another example of a Keynesian supply shock offered by Engelhardt in his 2021 paper is the 2007–2009 financial crisis where a housing contraction spread to the broader economy. If this new concept becomes part of the established macroeconomic doctrine about how the AD/AS model operates, Engelhardt observed, “This new concept calls into question the separability of aggregate supply and aggregate demand.”39

[270] [ADVANCED] WHAT IS THE LUCAS SUPPLY CURVE? Robert Lucas reformulated an aggregate supply model with an emphasis on imperfect infor­ mation.40 The Lucas (short-run) supply curve is generally of the form: Y = YFE + α(P − Pe) in

203

204 MACROECONOMIC THINKING AND TOOLS which Y is output (real GDP or real GDI), YFE is full-employment output (real potential GDP), and P is the price level. Pe represents the price expectations under the assumption of sticky wages and a misperceived price level (which form the typical rationale for an upward sloping short-run aggregate supply curve). The parameter α identifies the degree to which the difference between the actual (or ex post) P and the (expected price level) Pe influences output. Lucas’ model incorporates rational expectations in the formation of inflation expectations. Instead of the inflation expectations based on past rates of inflation (which is widely referred to as adaptive expectations), Lucas posits inflation forecasts by experts as the process for how rational expectations are formed.41

[271] HOW SHOULD THE AD/AS MODEL BE USED? Prof. Jeffrey Parker of Reed College in Oregon opined in an unpublished note that an alternative to the static AS/AD model might be to recast that model in terms of growth rates. So, the y-axis would show the inflation rate and not the price level, and the x-axis would show real GDP/GDI growth and not the level of output.42 Then, the long-run equilibrium or steady-state economy would be shown as growth rates for output and inflation that would be steady or constant. He observed that although the growth-rate approach for aggregate demand and aggregate supply might seem appealing on the surface, “there are pitfalls with modeling rates of change … Putting the graphical analysis entirely in terms of growth rates means that there is no information on the graph about the level of output. Suppose that last year’s growth rate was unusually low, so the current level of output in the economy is below its long-run growth trend.” A growth-rate AD/AS presentation would not convey this catch-up well. On the other hand, the traditional AD/AS graph can easily show how the level of “aggregate supply or aggregate demand (or both) must shift to the right when output is below trend in order to increase growth” and, in turn, move closer to real potential output. His point is that there really is no perfect way to capture the economy’s complex dynamic in a two-dimensional graph. Nonetheless, the conventional AD/AS model (presented in most macroeconomics textbooks), with all its limitations and some inconsistencies, still can work to capture the story of how the economy works, but it requires looking beyond the simple graph to see the inner workings of the economy. Without a doubt, the AD/AS model is more of a stylistic paradigm (framework), which is primarily used as a pedagogical tool, and relies on stories about what is happening. For example, you will note that an expansionary monetary policy can look identical to an expansionary fiscal policy in the AD/AS portrayal, which means the narrative is important to describe what is the cause of the change or why the ex post equilibrium (at that specific point in time) looks like it does. A more formal framework than the AD/AS model to capture the economic and financial relationships in the economy (which is beyond the bounds of this discussion) would be to portray the economic dynamic using an econometric model. An econometric model could be used to evaluate planned or considered policies (evaluating the impact of alternative monetary or fiscal policy scenarios), and it could be used to understand why the economy behaved as it did during different historical periods, such as the Great Depression or the 2020 pandemic-induced recession. An econometric model also can be used to

AGGREGATE DEMAND AND AGGREGATE SUPPLY

forecast where the economy is likely heading. But the development and use of econometric models does not mean that the concepts embodied in the aggregate demand and aggregate supply model are jettisoned—quite the contrary—they are just reformulated in a different way.

[272] HOW WOULD THE AD/AS MODEL BE STRUCTURED BASED ON GROWTH RATES VERSUS GAP ANALYSIS? Some economists are blurring the structure of the AD/AS model using the inflation rate rather than the price level compared to output. But the problem with that approach is that the model has a static element (output in a specific period) compared to a dynamic element (inflation over some period of time). Conceptually, this is problematic. Some economists also have built the AD/AS model with inflation and the output gap. This too is problematic since it compares a dynamic element (inflation) with a static gap (actual output minus potential output for a specific period in time). Consider differences in the AD/AS model based on “gap analysis”–which is the traditional foundation versus one based on “growth analysis.” The first graph shows the traditional AD/AS model, which is a static picture of the economy that incorporates ex post and ex ante (unobserved) elements and focuses on the gap between actual and potential output, which affects the price level (Figure 8.13).

Long-run aggregate supply

Short-run aggregate supply

Price Level

Aggregate demand

Gap

Output or Income

Static Perspective: Constructed with Ex Post (Actual) and Ex Ante (Estimates) for a Specific Period

FIGURE 8.13

205

206 MACROECONOMIC THINKING AND TOOLS Building the AD/AS model using only growth rates is portrayed in the next graph, which is Figure 8.14. This version of the model will turn the model into a dynamic perspective—­ however, it does raise a question about what the growth rate period should cover—one quarter, one year, or what. The growth-analysis version of the AD/AS model would eliminate the ex ante values since it focuses on actual (ex post) period changes. The target inflation rate and the real potential growth rate would be graphed, which creates quadrants for the inflation-growth rate co­ ordinates. Those four outcomes are where: (1) actual growth is below potential output with inflation above its target, (2) actual growth is above potential output with inflation above its target, (3) actual growth is below potential growth with inflation below its target, and (4) actual growth is above potential growth with inflation below its target. This is portrayed on the graph with sample points in each of those quadrants. If used in conjunction with the gapanalysis version, then the growth-rate version of the AD/AS model may add a perspective, but alone the growth-analysis version does not have any absolute interpretation or benchmarks since it is possible that any of those four combinations for inflation and output may exist irrespective of whether the output gap is positive or negative.

Real Potential Growth

If here, actual growth above potential growth with inflation above target.

If here, actual growth below potential growth with inflation above target.

Target Inflation

Inflation

If here, actual growth below potential growth with inflation below target.

If here, actual growth above potential growth with inflation below target.

Output % or Income %

Dynamic Perspective: Growth Rate of Actual (Ex Post) Data Only (Points on Chart) Compared with Estimate of Potential Growth and Target Inflation Rate Between Two Periods

FIGURE 8.14

AGGREGATE DEMAND AND AGGREGATE SUPPLY

[273] HOW CAN THE AD/AS MODEL BE USED TO INFORM ABOUT A SPECIFIC PERIOD? As an example, let’s look at how the 2008–2009 financial crisis in the United States can be por­ trayed through the AD/AS model. First, there is a lot written about that financial crisis—which was global and seemingly triggered internationally by roughly the same basic causes as in the United States: low interest rates for too long causing an asset bubble in the housing and commercial real estate markets. In the aftermath of the crisis, Congress set up a commission—the Financial Crisis Inquiry Commission—to review what, why, and how the crisis happened. The Commission reported its findings in January 2011, but it was not asked by the Congress for recommendations to prevent future crises, rather it was just a fact-finding commission. In its report, it said: The crisis was the result of human action and inaction, not of Mother Nature or computer models gone haywire. The captains of finance and the public stewards of our financial system ignored warnings and failed to question, understand, and manage evolving risks within a system essential to the well-being of the American public. Theirs was a big miss, not a stumble. While the business cycle cannot be repealed, a crisis of this magnitude need not have occurred. To paraphrase Shakespeare, the fault lies not in the stars, but in us. Despite the expressed view of many on Wall Street and in Washington that the crisis could not have been foreseen or avoided, there were warning signs. The tragedy was that they were ignored or discounted. There was an explosion in risky subprime lending and securitization, an unsustainable rise in housing prices, widespread reports of egregious and predatory lending practices, dramatic increases in household mortgage debt, and exponential growth in financial firms’ trading activities, unregulated derivatives, and short-term “repo” lending markets, among many other red flags. Yet there was pervasive permissiveness; little meaningful action was taken to quell the threats in a timely manner. The prime example is the Federal Reserve’s pivotal failure to stem the flow of toxic mortgages, which it could have done by setting prudent mortgage-lending standards. The Federal Reserve was the one entity empowered to do so and it did not. The record of our examination is replete with evidence of other failures: financial institutions made, bought, and sold mortgage securities they never examined, did not care to examine, or knew to be defective; firms depended on tens of billions of dollars of borrowing that had to be renewed each and every night, secured by subprime mortgage securities; and major firms and investors blindly relied on credit rating agencies as their arbiters of risk. What else could one expect on a highway where there were neither speed limits nor neatly painted lines?43 So with that background from the Commission finding, the next step is to assemble some economic data44 for the AD/AS model snapshots, which in this example will be for the fourthquarter 2007 (ahead of the crisis) and the fourth-quarter 2008 (at the beginning of the crisis). Clearly, it is not the intent to delve so deeply into the causes of this specific event, but to discuss what happened between the final quarter of 2007 and the final quarter of 2008 relying on a range of economic data that show the economic performance between those two periods. Consider this table, which shows two periods, the fourth-quarter of 2007 and the fourthquarter of 2008, along with 20-year historical averages for comparison.

207

Unemployment Rate

Full-Unemployment Unemployment Rate (Natural Rate)

CPI Inflation Rate (Year-Year % Growth)

CPI Core Inflation Rate (Year-Year % Growth)

10-Year Breakeven Inflation Rate

10-Year Govt Note Yield

3-Month Treasury Bill Rate

CPIAUCSL

CPILFESL

T10YIE

DGS10

DGS3MO

Economic Variables

UNRATE

Initial Point: 2007Q4

GDP Price Deflator

GDPDEF

NROU

93.327

GDP Gap (pp.)

CALCULATED

3.5%

4.3%

2.3%

0.3%

3.2%

0.6%

2.0%

1.6%

4.0%

2.3%

4.9%

6.9%

Terminal Point: 2008Q4

4.9%

4.8%

Price Level

Price Level 95.065

−3.4

0.9

$15,902.96

GDP Level

$15,621.98

GDP Level

Real Potential GDP

GDPPOT

GDP Level $15,366.61

GDP Level $15,767.15

Real GDP

GDPC1

Terminal Point: 2008Q4

Initial Point: 2007Q4

From 2007Q4 to 2008Q4

% Growth Rate

1.9%

1.8%

−2.5%

% Growth Rate

−3.2 pp.

−1 pp.

−1.7 pp.

−0.3 pp.

−2.4 pp.

0 pp.

2.1 pp.

Change

−4.3

Change

1.19%

2.9%

2.0%

2.0%

2.2%

5.0%

5.9%

1991Q1–2021Q4 Historic Average

2.0%

1.4 pp.

2.4%

2.4%

1991Q1–2021Q4 Historic Average Growth (Yearover-Year Percentage Change, Unless Otherwise Noted)

5.11%

5.1%

2.6%

4.7%

6.4%

5.7%

13.1%

1991Q1–2021­ 2021Q4 Historic High

4.6%

10.8 pp.

4.3%

12.2%

1991Q1– 2021Q4 Historic High Growth

0.01%

0.7%

0.6%

0.6%

−1.6%

4.5%

3.6%

1991Q1– 2021Q4 Historic Low

0.1%

−2.2 pp.

1.3%

−9.1%

1991Q1– 2021Q4 Historic Low Growth

The Fourth-quarter of 2007 and the Fourth-quarter of 2008, Along with 20-year Historical Averages for Comparison

FRED database mnemonics

TABLE 8.3

208 MACROECONOMIC THINKING AND TOOLS

Federal Funds Rate

R-Star (NY Fed Calc, HLW version)

Government Spending (Current Dollars, Year-Year % Growth)

Federal Govt Deficit/ Surplus (Current Dollars in Billions)

Nonfarm Productivity Y-Y % Growth

Compensation Y-Y % Growth (Employment Cost Index)

Crude Oil Price (WTI, per bbl)

Average New Home Price (Year-Year Growth Rate)

FEDFUNDS

https://www. newyorkfed.org/ research/policy/ rstar

FGEXPND

FGLBAFQ027S

OPHNFB

ECIALLCIV

WTISPLC

ASPNHSUS

1.2%

$90.85

3.3%

2.4%

−$597.4

7.2%

2.4%

4.5%

−9.2%

$58.37

2.6%

0.1%

−$1,039.4

6.7%

0.9%

0.5%

−35.8% −10.4 pp.

−$32.48

−0.7 pp.

−2.3 pp.

−$442.0

−0.5 pp.

−1.5 pp.

−4 pp.

5.4%

$64.17

2.7%

1.8%

−$675.0

6.1%

1.80%

1.28%

20.8%

$123.96

3.9%

6.0%

$701.2

92.2%

3.44%

5.26%

−12.6%

$21.61

1.4%

−0.6%

−$6,656.2

−17.7%

0.03%

0.06%

AGGREGATE DEMAND AND AGGREGATE SUPPLY

209

210 MACROECONOMIC THINKING AND TOOLS The data highlighted in yellow will be needed directly for the AD/AS model. The right three columns provide some historical perspective for the data, and the rows below the yellow section provide a range of data that effects or is affected by AD and AS. So, for example, we can see from that table that over the course of that year, the unemployment rate (on a quarterly average basis) rose from 4.8% to 6.9%, which was a 2.1 percentage point increase. Inflation—as measured by the CPI slowed from a 4.0% pace to 1.6%. Interest rates fell over that year with the Federal Reserve’s policy rate—the federal funds rate—dropping from 4.5% to 0.5%, which was a hefty four percentage point decline. The financial market’s expectation for inflation over the upcoming ten-years weakened dra­ matically, as well, from an average of +2.3% per year to +0.6% per year. Other economic indicators showed input prices for labor slowing from 3.3% to 2.6%, while energy prices—as reflected by the WTI crude oil price—declined by $32 per barrel. Home prices over that year dropped sharply. Now armed with some additional information about the performance of the economy, one can put together those snapshots of aggregate demand, short-run aggregate supply, and

LRAS: Q4-2007

AD: Q4-2008

103.0

LRAS: Q4-2008

Price Level (2012=100)

GDP Gap is -3.4%

SRAS: Q4-2007

98.0 GDP Gap is +0.9%

E2008

E2007

Ex Post Increase in Price Level is +1.9%

93.0

SRAS: Q4-2008

Ex Post Decrease in Real GDP is -2.5%

88.0

83.0 $13,000

$13,750

$14,500

$15,250

$16,000

AD: Q4-2007

$16,750

$17,500

$18,250

Real GDP/Real GDI (Billions of 2012 Dollars)

Aggregate Demand (AD), Short-Run Aggregate Supply (SRAS) and LongRun Aggregate Supply (LRAS) Changes between 2007Q4 and 2008Q4

FIGURE 8.15

AGGREGATE DEMAND AND AGGREGATE SUPPLY

long-run aggregate supply curves for Q4–2007 and Q4–2008. Because we are looking at the change over time, this provides us with a dynamic picture of the economy over that one-year horizon. These curves are portrayed in the following graph. What is the story that the AD/AS model tells for the economy between the endquarters of 2007 and that of 2008? First, we see that the equilibrium point where AD equals SRAS in Q4–2007 (designated as E2007 in the graph) was higher than potential output (the LRAS curve). Theory also would suggest that the unemployment rate was likely lower than the natural rate of unemployment (or the full-employment unemployment rate). Here, too, the actual data echoes that with the actual unemployment rate at 4.8% in the fourth quarter of 2007, while the natural rate of unemployment was at 4.9%. Theory also would suggest that inflation was likely building and indeed, consumer inflation was running at 4.0%–considerably higher than its 20-year trend of 2.2%. Over the course of the next year, the economy was in recession (as reflected by the output gap) and the Federal Reserve was slashing its short-term policy rate. By the end of 2008, we see that AD and SRAS have both shifted to the left (lower). One reason for the SRAS shift is the weakening in productivity—as shown in the data table. The AD curve shifted to the left also, but not as much as SRAS. Why is this? Well, look at the federal budget deficit, which ballooned over that year and helped to offset weakening aggregate demand. Although this discussion using the AD/AS model may not capture every aspect of that or any period, it does portray the themes of what happened which are fortified with economic statistics and accounts of what was happening (Figure 8.15).

[274] CAN THIS AD/AS MODEL BE APPLIED INTERNATIONALLY? Conceptually, the AD/AS model has applicability around the world. However, to apply this model, which assesses the gap between actual and potential output, estimates of potential output are needed. Some countries have developed their own measure of potential output—such as the United Kingdom—but most do not. The World Bank developed a methodology45 to estimate potential output for 159 developing countries using its “production function method,” and the European Union has created some studies of potential output for the European Union. However, with the global rise of independent financial councils (such as the U.S. Congressional Budget Office or the U.K. Office of Budget Responsibility), which use this medium-to-long-term concept of potential or the “sustainable level” of output to judge the country’s path of public sector spending and debt, it is likely to see more official estimates of potential output for more countries in the years ahead.

211

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Issues to Think About The aggregate demand/aggregate supply model attempts to capture the causeand-effect impacts on components of the economy from the expenditure and income sides. The model is static—which means it shows a point in time. The dynamic aspect only comes from comparing changes over multiple time periods. Although the model has a lot of limitations, its value is in conceptualizing the channels of influence or economic linkages through macroeconomic narratives. •

• •

•

Why do economists use this model with the ex ante (unobserved) price and quantity coordinates, if the only point that really matters (as Sir John Hicks observed) is the ex post (actual) equilibrium? Are there better ways to convey the concepts of AD and AS? As this chapter demonstrated, with a few assumptions about the slope and elasticity of the curves, the AD/AS model can actually be applied for historical and forecast periods. In applying this model, what are some of the key takeaways that you can gain? Although theory generally suggests a downward-sloping aggregate demand and an upward-sloping aggregate supply relationship, some economists have argued the possibility that the reverse also can be true at times. empirically there are very limited estimates of the slope of the AD and AS curves and elasticities. (More advanced studies often impose some slope restrictions.) Does this all suggest that the value of the AD/AS model should be questioned and maybe recast?

NOTES 1 Amitava Krishna Dutta, “Aggregate Demand-Aggregate Supply Analysis: A History,” History of Political Economy, vol. 34, no. 2 (2002), pp. 321–363. 2 Ibid, p. 335. 3 Qingwei Wang Hans-Michael Trautwein Andreas Schrimpf Margit Kraus Marcus Kappler Friedrich Heinemann Sebastian Hauptmeier, Projecting Potential Output: Methods and Problems, ZEW Economic Studies (Physica-Verlag, Springer, Berlin, 2009). 4 Arthur M. Okun, “Potential GDP: Its Measurement and Significance,” American Statistical Association, Proceedings of the Business and Economics Statistics Section, 1962, pp. 98–204. 5 Amy Y. Guisinger, Michael T. Owyang, and Hannah G. Shell, “Comparing Measures of Potential Output,” Federal Reserve Bank of St. Louis Review, Fourth Quarter 2018, p. 299. This reference to the typical three equation of the New Keynesian Model (NKM) has been described as “the work­ horse of modern macroeconomics. From either empirical observation or theoretical deduction, the usual approach defines demand as a negative function of the expected real rate of interest (the IS curve), inflation as a positive function of economic activity (the Phillips curve), and the interest rate as a positive function of inflation (the monetary rule). When we put the three things together, the result is a system of equations in which the central bank, under normal conditions, can stabilize the economy.” (see: Nelson H. Barbosa-Filho, “A Post-Keynesian version of the New-Keynesian

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6

7

8 9

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11 12 13 14 15

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Model” Working Paper, February 13, 2021, https://economiapoliticaunb.com.br/wp-content/ uploads/2021/02/TD5.pdf.) The NKM incorporates expectations of future output, inflation, and interest rates in determining current economic outcomes. It also typically includes the GDP (output) gap and a monetary rule that is a Taylor Rule or variant. Another discussion of this model is found in Wendy Carlin and David Soskice, “Teaching Intermediate Macroeconomics using the 3-Equation Model,” Contributions to Macroeconomics, vol. 5, no. 1 (2005). Claudia Fontanari, Antonella Palumbo, Chiara Salvatori. “Potential Output in Theory and Practice: A Revision and Update of Okun’s Original Method,” Structural Change and Economic Dynamics, vol. 54 (September 2020), p. 247. Robert Shackleton, “Estimating and Projecting Potential Output Using CBO’s Forecasting Growth Model,” Working Paper 2018–03, Working Paper Series, Congressional Budget Office, Washington, DC, February 2018, p. 1. Okun, p. 98. John Maynard Keynes’ view of the time horizon was what he referred to as a “short-period,” which he quantified as, maybe, a year. Sir John Hicks’ view differed from Keynes; Hicks suggested the time horizon should be an “ultra-short-period” (maybe a week) or a moment in time where little changes--especially expectations about the economy. For this applied perspective based on actual data that period is three-months (a quarter of a year). This means the adjustment phase occurs up to the final snapshot, which represents the quarter’s final transaction settlements. Another way of thinking about the price level (that is, the cost of a basket of goods and services) in this model is that an ex ante increase (or decrease) in the price level may not have any effect on either the ex post aggregate supply output (real GDI) or the ex post aggregate demand output (real GDP), if there is a compensating increase (decrease) in the nominal wages and other costs of production. John Hicks, “IS-LM”: An Explanation, Journal of Post Keynesian Economics, vol. 3, no. 2 (Winter 1980–1981), pp. 139–154. Ibid, p. 152. Ibid, p. 152. David Romer, “Keynesian Macroeconomics without the LM Curve,” Journal of Economic Perspectives, vol. 14, no. 2 (Spring 2000), pp. 149–169. Price elasticity is defined as the percentage change in output divided by the percentage change in price. There are five relevant zones or points for price elasticity: (1) When the elasticity is greater than 1, it is considered to be elastic meaning that for every percentage change in price the percentage change in quantity is larger; (2) When the elasticity equals one, it is said to be unitary elasticity meaning that for every percentage change in price the percentage change in quantity is proportional (or the same); (3) When price elasticity is greater than zero, but less than one, elasticity is considered to be inelastic meaning that for every percentage change in price the percentage change in quantity is smaller; (4) When the elasticity is equal to zero than it is considered to be perfectly inelastic meaning that for every percentage change in price the percentage change in quantity is zero. This case is represented by a vertical line in the price- output space; and (5) When the elasticity is equal to infinity than that is said to be perfectly elastic when a small percentage change in price results in an infinite change in quantity. This is often represented by a horizontal line (although technically and mathe­ matically if the percentage change in price is exactly zero that ratio is undefined, therefore the representation of a horizontal line and the calculation of a price elasticity equal to infinity is not consistent, which is why authors would often have caveats). It has been argued by Kyer and Maggs that, “the price-level elasticity of aggregate demand is a concept which has been overlooked in both macroeconomic textbooks and the research literature.” See: Ben L. Kyer and Gary E. Maggs, “A Macroeconomic Approach to Teaching Supply-Side Economics,” The Journal of Economic Education, vol. 25, no. 1 (Winter 1994), pp. 44–48. Most at­ tempts to estimate the price-level elasticity have been done based on econometric models. However, in another paper by Kyer and Maggs, they note however that “The literature on the price-level elasticity of aggregate demand is indeed scarce … A few estimates [for example, by L.R. Klein] seem

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214 MACROECONOMIC THINKING AND TOOLS to suggest that aggregate demand in the United States is inelastic with respect to the general price level were derived by estimating the IS-LM cores of various large-scale macroeconomic models. Kyer and Maggs, however, have also estimated this elasticity for the United States and concluded that aggregate demand was price-level elastic for most of the time period from 1955 to 1991.” See: Ben L. Kyer and Gary E. Maggs, “Inflation and the Exchange Rate: The Role of Aggregate Demand Elasticity,” International Advances in Economic Research, vol. 22 (2016), pp. 1–9. A study by Apergis and Eleftheriou observed that, “The Classical school of thought argues that aggregate demand is unit elastic with respect to the price level. By contrast, Keynesian economists support an aggregate demand with variable elasticity, whereas in the extreme case of a liquidity trap, aggregate demand is shown to be perfectly price inelastic. Despite the importance of the value of price elasticity, only a handful of empirical estimates of price elasticity have been carried out, mostly for the United States.” Their study for Greece estimated that the average price elasticity of aggregate demand between 1961 and 1995 was a negative 0.173 (with some wide variance per year), which is highly inelastic. See: Nicholas Apergis and Sophia Eleftheriou, “Aggregate Demand in Greece: 1961–1995,” Public Finance Review, vol. 28, no. 5 (September 2000), pp. 452–467. Attempts to empirically determine the price-level elasticity of aggregate demand generally are derived in the price-output space (AD) by solving some version of the IS-LM model for the interest rate. However, these price-level elasticity estimates clearly are dependent on the model specification and period of estimation. 17 Rather than base this estimate on a specific model, it was approximated by calculating the mean elasticity of the aggregate demand relationship from quarterly NIPA data, assuming a downward sloping aggregate demand curve. Technically, this does not assume causality between the price level and output (which a true price elasticity does), but just a computational and theoretical relationship. That is, for example, if the average growth rate of real GDP (aggregate demand) was 2.98% and the average growth of the GDP price level was 3.28%, then the theoretical computation is that elasticity is a negative 2.98% divided by 3.28%, which gives an implied price elasticity of aggregate demand of −0.91. Since this approximation only is used to portray unobserved and unused ex ante points, it will suffice. Another approach to derive the price elasticity of aggregate demand would be a nationwide survey (though this would likely be of households--not the other players in aggregate demand). Nonetheless, a survey question could ask: How much would you likely scale back your expenditures, if the price level of all goods rose by 10% and there was no change in your income? A second question could ask: How much would likely expand your expenditures, if the price level of all goods fell by 10% and there was no change in your income? This would be a way to assess the sensitivity of changes in the price level on output (for at least two points) without developing an econometric model and inferring the elasticity from that model. Yet, another rough gauge to help find those ex ante aggregate demand curve points might be to estimate the relationship between real GDP growth and the percentage change in the GDP Price Deflator, controlling for the trend. Based on annual NIPA data from 1961 to 2021, the estimated coefficient on the price term is −0.32, which would suggest that a 10% increase in the price level would yield a 3.2% drop in real GDP. Armed with that relationship and a price elasticity of demand of −0.32, it is also possible to generate the ex ante points along the aggregate demand curve, assuming that those points behave as historical relationships might suggest. Here, of course, this estimate would imply an even more inelastic relationship. 18 A variant on the Keynesian SRAS is a completely horizontal short-run aggregate supply curve (perfectly elastic) which reflects a fixed-price assumption that might occur in the very short term when firms respond to a change in aggregate demand without any adjustment in prices. Although not realistic, some textbooks show this as a “what if,” where only output is affected. 19 This is based on annual data for the mean change in real gross domestic income between 2010 and 2018 (2.478%) and the GDP price index (1.673%). It is possible to use the maximum and minimum percentage changes in those two measures to derive a proxy for the range of the SRAS price elasticity. With these data, that elasticity range would be 0.367 to 3.59 around the mean of 1.481. That is, 0.367 equals (0.878/2.390)—which is the minimum growth rate for real GDI over that period divided by

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25 26

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28 29 30

the maximum growth rate for the GDP price index. Similarly, the upward elasticity would be cal­ culated as 3.59468% divided by 1.00034%. The use of the range of elasticities in the calculation would produce a non-linear relationship. Again, these data are not actually elasticities (which suggest causal relationships), but these empirical relationships measure relative rates of percentage change. Use of the average price elasticity of short-run aggregate supply will produce a straight line. However, it is possible to be guided by theory and use a range of elasticities, which would be consistent with the zones of the SRAS curve. For example, it is possible to assume the lower values of the price are associated with a much higher elasticity (say, 10—which is far more elastic) and the higher values of the price term are associated with a much lower elasticity (say, 0.75—which is far more inelastic). Although for our purpose the intersection of AD and SRAS is regarded as the ex post or equilibrium point, Keynes called that point “effective demand.” Keynes described effective demand as the point where the amount of employment is determined for the economy and also where entrepreneurs’ expectation of profits is maximized. Although the focus in this chapter is mainly oriented towards the application of the AD/AS model based on data for the United States, other countries produce estimates of real potential output for analytical purposes, which can aid in the application of this model internationally. For example, the U.K. government’s Office of Budget Responsibility (OBR)—which was created in 2010—has developed estimates of the U.K. output gap back to the first quarter of 1972. Those estimates show an average gap of −0.1% between 1972:Q1 and 2021:Q4 with the largest positive gap of 8.3% in 1973:Q2 and the largest recessionary gap of −4.1% in 2009:Q2. For a discussion of the OBR‘s methodology, see: Briefing Paper No. 2: Estimating the Output Gap, U.K. Office of Budget Responsibility, April 2011. Geert Bekaert, Eric Engstrom, and Andrey Ermolov, “Aggregate Demand and Aggregate Supply Effects of COVID-19: A Real-time Analysis,” Finance and Economics Discussion Series 2020–049, May 26, 2020, Board of Governors of the Federal Reserve System, https://doi.org/10.17016/ FEDS.2020.049. Keating opined that, “Macroeconomists frequently assert that ‘aggregate supply shocks’ and ‘per­ manent shocks to output’ are equivalent. Under certain structural conditions this equivalence is justified. For example, aggregate supply disturbances are the only source of permanent movements to real output in simple theoretical models found in most introductory and intermediate-level macro­ economics texts. However, there are also many economic theories which permit disturbances originating from aggregate demand to have permanent effects on output.” See: John W. Keating, “What Do We Learn from Blanchard and Quah Decompositions of Output if Aggregate Demand May Not be Long-Run Neutral?,” Journal of Macroeconomics, vol. 38 (2013), p. 203. Unit nonlabor costs include consumption of fixed capital, taxes on production and imports less subsidies, net interest and miscellaneous payments, and business current transfer payments. Unit nonlabor payments include profits, consumption of fixed capital, taxes on production and imports less subsidies, net interest and miscellaneous payments, business current transfer payments, rental income of persons, and the current surplus of government enterprises. See, for example, Ionuț Jianu, “A comprehensive view on the manifestations of aggregate demand and aggregate supply shocks in Greece, Ireland, Italy and Portugal,” Theoretical and Applied Economics, vol. XXIII, no. 2 (607), (Summer 2016), pp. 207–224. Geert Bekaert, Eric Engstrom, and Andrey Ermolov, p. 12. Ibid., p. 16. Asset bubbles are not easy to measure—there is no widely accepted asset price index to monitor and track asset inflation. Robert Shiller has offered a definition of a bubble as follows: “A situation in which news of price increases spurs investor enthusiasm which spreads by psychological contagion from person to person, in the process amplifying stories that might justify the price increases and bringing in a larger and larger class of investors, who, despite doubts about the real value of an investment, are drawn to it partly through envy of others’ successes and partly through a gambler’s excitement.” (See: Robert J. Shiller, “Speculative Asset Prices,” The American Economic Review, vol. 104, no. 6 (June 2014), pp. 1486–1517.

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216 MACROECONOMIC THINKING AND TOOLS 31 For a theoretical rationale of this situation see: Leon Podkaminer, “Downward-Sloping Aggregate Supply Functions, Upward-Sloping Aggregate Demand Functions,” Journal of Post Keynesian Economics, Winter, 1997–1998, vol. 20, no. 2 (Winter, 1997–1998), pp. 301–308. Another attempt to rationalize an upward-sloping aggregate demand curve is in a paper looking at the Albanian economy, Naqellari argues that a more appropriate theoretical model of aggregate demand (AD) and aggregate supply (SRAS) has both AD and SRAS upward-sloping (with different slopes) and that there is not necessarily an intersection of AD and SRAS, yet there still can be an “unstable” equilibrium, which presumably is at the ex post real GDP point for a moment in time. However, the author acknowledges that an intersection also can happen in a particular case. Real GDP is portrayed as a line between the aggregate demand curve and the aggregate supply curve. The problem, of course, is that author does not make a convincing case why the aggregate demand curve and the aggregate supply curve both are upwardly sloping, nor why real GDP is likely to be totally distinct from AD and SRAS, although he may have embraced a Paul Samuelson idea that aggregate demand represents total planned or desired output. Then AD is always a theoretical concept (and so too aggregate supply). See: Alqi Naqellari, “Positive Slope Model of Aggregate Demand,” Academic Journal of Interdisciplinary Studies, vol. 7, no. 3 (November 2018), pp. 63–85. Finally, Hoover Institution economist John Cochrane, who previously was at the University of Chicago, argued that there was an upward-sloping IS curve, rather than the standard view that it was downward-sloping. This controversial view is important because the IS-LM curves form the theoretical underpinnings of aggregate demand. Prof. James Bradford DeLong of the University of California at Berkeley provided perspective on Cochrane’s model, which has its origin in Sir John Hick’s 1937 three-commodity macro model with liquid cash money stock, the bond stock, and spending on currently-produced goods and services. Then “we draw a graph with the interest rate (the inverse of the price of bonds in terms of money) on the vertical axis and the rate of spending in terms of money on the horizontal axis. We then draw the money-spending equilibrium as an upward-sloping ‘LM’ curve--with a higher interest rate people want to shift from holding cash to spending on currently-produced goods and services. We then draw the bonds-spending equili­ brium as a downward-sloping ‘IS’ curve--with a higher interest rate businesses do not issue as many bonds, and so spenders feel short of wealth and cut back on spending to try to build up their asset stocks.” DeLong goes on to say that John Cochrane has “a twist on this model. Cochrane’s model has the standard ‘LM’ curve built off of the money demand function and the money-spending equili­ brium condition. It has an ‘IS’ curve built off of a bonds-spending equilibrium condition. But its ‘IS’ curve is not downward but upward sloping: A higher interest rate lowers the attractiveness of the fixed stock of government debt. Spenders then try to dump their government bonds in order to purchase more of currently-produced goods and services instead. And so the higher the interest rate, the higher is the flow of spending needed to maintain bonds-spending equilibrium. In this model contractionary monetary policy–i.e., the Federal Reserve increasing interest rates–is expansionary as higher interest rates induce wealth holders to dump their bonds and spend more on currentlyproduced goods and services instead. And in this model the economy is very volatile: small shocks either to the money demand curve or to interest rate spreads produce huge shifts in this equilibrium. Thus by playing with this model Cochrane can get small changes to have huge effects–after all, when both curves slope upwards any shift in one curve will move the economy’s equilibrium a very long way.” The importance of this here is that if you buy into this upward-sloping IS curve, it supports the idea of an upward sloping aggregate demand curve too. (See: “Does the IS Curve Slope Upwards?”, https://delong.typepad.com/sdj/2011/07/does-the-is-curve-slope-upwards.html). 32 See, for example, David Colander, “The Stories We Tell: A Reconsideration of AS/AD Analysis,” Journal of Economic Perspectives, vol. 9, no. 3 (Summer 1995), pp. 169–188. Colander offers a reinterpretation of the AD/AS model by introducing a “new ingredient,” which he calls a “pricelevel flexibility curve and reformulates the AS concept” to accommodate the dynamics implicit in the Keynesian model, and more complicated dynamics as well. At a root level, this change involves modifying the specification of the underlying aggregate production function. One way to incorporate complicated dynamics into the production function that is consistent with recent developments in

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macro theory is to separate out the coordination function needed in the economy from the pro­ duction process and to specify it directly in the production function. Aggregate output depends on production technology, inputs, and ‘coordination technology’—the institutions that coordinate in­ dividuals’ actions.” (pp. 181–182). Then this flexibility curve “reflects the institutionally-imposed degree of price-level flexibility on individuals and firms. A flat curve would illustrate inflexible prices, while a vertical curve would show perfect flexibility … The price flexibility curve describes an economy that has a 50–50 split between price level and real output flexibility” (p. 184). However, the addition of the price-level flexibility curve seemingly acts like a short-run aggregate supply curve that intersects with the aggregate demand curve, which sets the price level. However, the illustration also includes an inelastic short-run aggregate supply curve, which does not seem to serve much purpose, which is in part due to the Keynesian idea that “aggregate supply depends on aggregate demand, or at least on expected demand, the revised production function behind the AS curve must have expected demand as a component. The Keynesian model becomes a supply model in which aggregate demand, or more specifically expectations of aggregate demand, influences aggregate supply” (p. 182). Although this may be preferable theoretically, it has not been ingrained into standard economic textbook presentations. Moreover, it seems that addressing the implicit elasticity of aggregate demand and aggregate supply (as discussed here) effectively captures this price-level flexibility. Another critic of the standard AD/AS presentation is Peter Bofinger, “Teaching macroeconomics after the crisis,” W.E.P. - Würzburg Economic Papers, No. 86, University of Würzburg, Department of Economics, Würzburg, 2011. In his 2011 paper, he reformulates the SRAS curve as an upward-sloping 45-degree curve relating planned aggregate demand and planned aggregate supply (p. 6). In essence, his basic model formulation has the aggregate demand function specified as equal to consumption, which is a function of aggregate supply, and an exogenous investment component. Then the aggregate supply equation is set equal to aggregate demand. Finally, long-run aggregate supply is set equal to the natural level of output (real potential GDP). The idea of relating actual output to planned output in this formulation is appealing since it highlights the adjustment process “within the time period” before all transactions settle for the quarterly observation. But this approach complicates the simple presentation and has not been integrated into the standard approach either. Shiller—who received a Nobel-prize in economics—expanded on this idea of narratives to explain how stories can go viral and shape economic events. But his key “lesson here is that we can’t avoid using our human judgment about narratives for optimal understanding of economic events.” See: Robert J. Shiller, Narrative Economics (Princeton University Press, 2019), p. XII. Bofinger, p. 9. When graphing planned and actual expenditures, the use of a 45-degree line represents a one-to-one relationship between the value of the planned output and the value of actual output. This type of presentation is also used in the foundational IS-LM framework. A very straight-forward definition of “shocks,” with reference to the COVID-19 pandemic impact on the economy was provided by Pedro Brinca, Joao B. Duarte, and Miguel Faria e Castro, “Is the COVID-19 Pandemic a Supply or a Demand Shock?,” Economic Synopses, St. Louis Federal Reserve Bank, No. 31, 2020, https://doi.org/10.20955/es.2020.31. The authors write: “A supply shock is anything that reduces the economy’s capacity to produce goods and services, at given prices. Lockdown measures preventing workers from doing their jobs can be seen as a supply shock. A demand shock, on the other hand, reduces consumers’ ability or willingness to purchase goods and services, at given prices. People avoiding restaurants for fear of contagion is an example of a demand shock.” Veronica Guerrieri, Guido Lorenzoni, Ludwig Straub, and Iván Werning, “Macroeconomic Implications of COVID-19: Can Negative Supply Shocks Cause Demand Shortages?” NBER Working Paper Series 26918, April 2020. https://www.nber.org/papers/w26918. For a fuller discussion, see: Lucas M. Engelhardt, “Keynesian Supply Shocks and Hayekian Secondary Deflations,” The Quarterly Journal of Austrian Economics, vol. 24, no. 3 (Fall 2021), pp. 431–449. Ibid., p. 432. See: Lucas, Robert E. Jr., “Expectations and the Neutrality of Money.” Journal of Economic Theory,

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vol. 4, no. 2 (1972), pp. 103–124. Also, Lucas, Robert E. Jr., “Some International Evidence on Output‐Inflation Trade‐Offs.” American Economic Review, vol. 63 (1973), pp. 326–334. For more detailed discussion of this approach, see: N. Gregory Mankiw and Ricardo Reis, “Imperfect Inflation and Aggregate Supply,” Handbook of Monetary Economics, Edited by Benjamin M. Friedman and Michael Woodford, Elsevier, 2010, pp. 183–229. See also, Clive Bull and Roman Frydman, “The Derivation and Interpretation of the Lucas Supply Function,” Journal of Money, Credit and Banking, vol. 15, no. 1 (February 1983), pp. 82–95. Another alternative that was used by Frederic Mishkin (who was a former Federal Reserve Board governor appointed on September 5, 2006 and resigned on August 31, 2008) in his textbook, Macroeconomics: Policy and Practice (Second Edition), Pearson, 2015, was to define the AD/AS model as a relationship between the inflation rate (not the price-level) and the level of aggregate output (real GDP/real GDI). Mishkin argues that in this version, aggregate demand is downward sloping because an increase in the inflation rate causes monetary policy to raise interest rates. The Financial Crisis Inquiry Report, Submitted by The Financial Crisis Inquiry Commission Pursuant to Public Law 111–21 (January 2011), p. xvii. Most of the data was extracted from the Federal Reserve Bank of St. Louis’ FRED database, which is an open access and free consolidated database resource, which is found at: https://fred.stlouisfed.org/ Andrew Burns, Theo Janse Van Rensburg, Kamil Dybczak, and Trung Bui, “Estimating Potential Output in Developing Countries,” Journal of Policy Modeling, vol. 36 (2014), pp. 700–716.

AGGREGATE DEMAND AND AGGREGATE SUPPLY

APPENDIX – WORKED EXAMPLE OF FORMING AN AD/AS MODEL •

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Assume a downward-sloping aggregate demand (AD) curve and an upward-sloping shortrun aggregate supply (SRAS) curve with an AD elasticity of average price of −0.90 and a SRAS elasticity of average price of +1.48. Given the following data: Frequency: Quarterly

•

•

observation date

Real Gross Domestic Real Potential Gross Product (Billions of Domestic Product Chained 2012 Dollars) (Billions of Chained 2012 Dollars) GDPC1 GDPPOT

Gross Domestic Product: Implicit Price Deflator (2012 = 100) GDPDEF

2019-Q1 2019-Q2 2019-Q3 2019-Q4 2020-Q1 2020-Q2 2020-Q3 2020-Q4 2021-Q1 2021-Q2 2021-Q3 2021-Q4 2022-Q1 FRED Mnemonic

18833.2 18982.5 19112.7 19202.3 18952.0 17258.2 18560.8 18767.8 19055.7 19368.3 19478.9 19806.3 19735.9 GDPC1

111.514 112.152 112.517 112.978 113.346 112.859 113.888 114.439 115.652 117.413 119.115 121.188 123.545 GDPDEF

18879.0 18971.4 19064.1 19157.1 19250.2 19340.2 19424.2 19512.0 19602.5 19697.4 19795.8 19898.3 20003.7 GDPPOT

Graph the AD, SRAS, and LRAS curves using data for the second-quarter 2020. (Developing two pairs are sufficient for a straight line, but we will develop three pairs so that you can easily see if the relationship is direct or inverse—remember the AD relationship between price and output is inversely related and the SRAS relationship is directly related). Step 1: For that period, fill in the values of LRAS in the table for given price levels, which will be plotted in the AD/AS model. 2020-Q2

Price Level

Long-Run Aggregate Supply

Ex Ante Point 1

124.145 (10% Above Equilibrium Price) 112.859 (Actual Price-Level) 101.573 (10% Below Equilibrium Price)

$19,340.2 (Same for All Prices Because it is a Vertical Line) $19,340.2 (Real Potential Output) $19,340.2 (Same for All Prices Because it is a Vertical Line)

Ex Post Equilibrium Point Ex Ante Point 2

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Step 2: For that period, fill in the values of AD, based on the assumed demand elasticity measure (–0.90), in the table for given price levels, which will be plotted in the AD/AS model. 2020-Q2

Price Level

Aggregate Demand

Ex Ante Point 1

124.145 (10% Above Equilibrium Price) 112.859 (Actual Price-Level) 101.573 (10% Below Equilibrium Price)

$15,705.0 1 + ((Elasticity x [+10])/ 100) x Actual Real GDP $17,258.2 (Actual Real GDP) $18,811.4 1 + ((Elasticity x [−10])/ 100) x Actual Real GDP

Ex Post Equilibrium Point Ex Ante Point 2

Be sure to notice that the price-output coordinates show an inverse relationship—that is, the higher price level is associated with the lower output and vice versa. Also remember that the choice of two additional price points is arbitrary—but were chosen for the ease of cal­ culation. If, however, you choose the price level that is 1% higher and 1% lower (instead of 10% higher and lower), then the calculated ex ante would not be adjusted for +10 for the higher price and a –10 for a lower price, but +1 and –1, which means no impact. Similarly, if you choose 5% higher and 5% lower price levels, then a +5 and a –5 has to be accounted for in the derivation of the ex ante AD points, and so forth. In words, a 10% increase in the price level will decrease the real GDP (output) by 9% and vice versa (Figure 8.16). •

Step 3: For that period, fill in the values of SRAS, based on the assumed short-run supply elasticity measure (+1.48), in the table for given price levels, which will be plotted in the AD/AS model. Aggregate Demand/Aggregate Supply Model for Second-Quarter 2020 130.000

PriceIndex, 2012=100

125.000 120.000 AD

115.000

SRAS 110.000

LRAS

105.000 100.000 $14,500.0 $15,500.0 $16,500.0 $17,500.0 $18,500.0 $19,500.0 $20,500.0 Real GDP, Billions of 2012 Dollars FIGURE 8.16

Complete Aggregate Demand/Aggregate Supply Model for 2020Q2

AGGREGATE DEMAND AND AGGREGATE SUPPLY

2020-Q2

Price Level

Short-Run Aggregate Supply

Ex Ante Point 1

124.145 (10% Above Equilibrium Price) 112.859 (Actual Price-Level)

$19,812.4 1 + ((Elasticity x [+10])/ 100) x Actual Real GDP $17,258.2 (Actual Real GDI = Actual Real GDP) $14,704.0 1 + ((Elasticity x [−10])/ 100) x Actual Real GDP

Ex Post Equilibrium Point Ex Ante Point 2

• •

101.573 (10% Below Equilibrium Price)

Step 4: Graph the price-output coordinates for each of the three curves. Step 5: Explain what the graphic tells us. Calculate the output gap—what is the value? What does it mean? Since this is a static picture of the economy, the only way you can look at the economic dynamic is to develop another AD/AS model for a different period and discuss the change.

Test your knowledge • • •

•

•

Continue this example and develop the AS/AD model for the first-quarter 2022 (repeat the steps above). Describe the change in the output gap between those two periods. Calculate the annualize compound growth rate between 2020-Q2 and 2022-Q1 for real GDP. This will give you the annualized economic growth per quarter between those two points in time. Calculate the annualize compound growth rate between 2020-Q2 and 2022-Q1 for the price level. This will give you the annualized inflation rate per quarter between those two points in time. Now explain what changes the economy went through over that period. Remember the AD/AS model helps to tell useful and simple yet coherent stories about the transmission of monetary, fiscal, and other shocks through the economy. Your job is to tell that story—the model itself is not the story. To do this, think about changes between 2020-Q2 and 2022-Q1 that occurred in the component and causal factors of both AD and SRAS, many of which are summarized in the table below. Aggregate Demand Component Factors

Causal Factors

Aggregate Expenditures = Real GDP Consumption

Wealth, Expectations about future prices and income, Interest rates, and Personal Income taxes

Short-Run Aggregate Supply Component Factors

Causal Factors

Aggregate Income = Real GDI Compensation of Employees

Human capital productivity, Minimum wages changes, Labor Union strength, Availability of labor (Continued )

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222 MACROECONOMIC THINKING AND TOOLS Aggregate Demand Component Factors

Causal Factors

Aggregate Expenditures = Real GDP

Short-Run Aggregate Supply Component Factors

Causal Factors

Aggregate Income = Real GDI

Investment

Interest rates, Expectations about future sales, and Business taxes.

Net Interest Payments

Interest rates, Availability of credit

Government Spending

Changes in revenues (taxes), Changes in spending priorities

Rental Income

Fixed and variable costs of commercial property rent, including property prices, interest rates, costs of maintaining the property, landlord insurance premiums, utilities, property taxes, etc.

Net Exports

Foreign real national income and Exchange rates

Corporate Profits

Business taxes, Productivity, Cost of inputs, Business expectations

Proprietors’ Income

CHAPTER

9

Money and Banking

LEARNING OBJECTIVES This chapter introduces the special role that money and the financial system play in the economy. You will learn: • • • • • • • • •

About the three functions of money (conceptually). Whether or not credit or debit cards are money. Whether or not cryptocurrencies are money. What central bank digital currency is. How the central bank defines measures of money. Understand the importance of the money supply for the macro economy today and in the past. How banks operate. About the growth and role of the non-bank financial intermediation sector. How the banking system creates money through fractional reserves.

[275] WHAT IS THE IMPORTANCE OF MONEY IN ECONOMIC THEORY? The role of money in the economy—believe it or not—was not always considered important in the early political economy theories, as documented by Wesley Mitchell’s 1916 article.1 Mitchell went on to credit Alfred Marshall’s influential declaration that money “is the center around which economic science clusters,” as a turning point in the profession. Marshall’s view, however, was that it was important not because it was a motivation for economic activity, but Alfred Marshall felt that money was a “convenient means of measuring human motive on a large scale.” Mitchell more significantly advocated developing theories around “money,” which he felt would promise to provide a better framework for economic theories and to offer a more realistic study of human behavior. Today’s theories, of course, provide that central role for money, not just from a motivation factor but also as a propagating of economic growth and cycles. DOI: 10.4324/9781003391050-10

224 MACROECONOMIC THINKING AND TOOLS

[276] WHAT IS MONEY? Economists define money as any asset that is generally accepted in payment for goods and services or in the repayment of debts and has legal status granted by a government. Conceptually, money has three functions. It serves as a: (a) medium of exchange, (b) unit of account, and (3) store of value.

[277] WHY IS MONEY A “MEDIUM OF EXCHANGE”? Money as a medium of exchange or means of payment simply means it is an efficient way for people to trade. It minimizes the transaction costs of exchange. If money did not exist, people would have to barter; that is exchange physical goods.

[278] WHY IS MONEY A “UNIT OF ACCOUNT”? Money as a unit of account means it is a convenient way to measure the value of goods and services. Again, think about the alternative with physical trading (barter). How do you really know that the horse that someone is trading is worth 20 bales of hay? Or whatever the going rate was. Money standardized that relationship.

[279] WHY IS MONEY A “STORE OF VALUE”? Money as a store of value means that money holds its value over time. Of course, money is not the only financial asset that is a store of value, but this function of money allows buyers and sellers to transfer purchasing power from today to tomorrow, or next week, or whenever, through the medium of money.

[280] HOW ARE THOSE THREE FUNCTIONS OF MONEY RELATED? In a 2018 speech on the future of money by then Bank of England Governor Mark Carney, he discussed the common conceptual functions of money as a hierarchy, as depicted in Figure 9.1.2 Carney said, “These functions of money operate in a hierarchy. There are many assets that people view as stores of value, houses, for instance, that are not used as media of exchange. By comparison, an asset can only act as a medium of exchange if at least two people are prepared to treat it as a store of value, at least temporarily. And for an asset to be considered a unit of account, it must be able to be used as a medium of exchange across a variety of transactions over time between several people. The hierarchy points to the reality that money is a social convention. We accept that a token has value whether made of metal, polymer or code because we expect that others will also do so readily and easily.”

MONEY AND BANKING

Medium of exchange

Unit of account

Store of value

FIGURE 9.1

Three Functions of Money

Source: Mark Carney, “The Future of Money,” Speech to Scottish Economics Conference, Edinburgh University, Bank of England, March 2, 2018

[281] HOW DID THE U.S. CURRENCY GET NAMED? On January 28, 1791, Secretary of the Treasury Alexander Hamilton submitted the final version of the Report on the Establishment of a Mint3 to Congress as ordered by the U.S. House of Representatives. In the report, the Secretary of Treasury determined that the United States’ national currency would be called the “dollar” and he accepted Thomas Jefferson’s suggestion that the unit should be expressed in decimals.4

[282] IS A CREDIT CARD MONEY? No. A credit card is a method to move money and is not money, per se. A credit card immediately transfers money from the credit card company’s checking account to the seller, and at the end of the month the user owes the money to the credit card company. Effectively, a credit card is a short-term loan.

[283] IS A DEBIT CARD MONEY? No. A debit card is similar to a check, which is an instruction to the user’s bank to transfer money directly for the debit card holder’s bank account to the seller.

[284] IS A PREPAID CARD MONEY? No. A prepaid card stores a certain amount of money on the card, which can be used to make purchases. This is simply a way to move money and not money, per se.

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[285] ARE CRYPTOCURRENCIES, SUCH AS BITCOIN, MONEY? No, because as noted by the Bank of England’s Mark Carney, cryptocurrencies are: (1) poor short-term stores of value and (2) not an effective medium of exchange.5 But the IMF also noted there is another important requirement missing to be “money,” which is whether or not it is “legal tender.” Nevertheless, in early 2021, El Salvador became the first country to adopt Bitcoin as a national currency, despite criticism and concerns by the IMF.

[286] WHAT IS MEANT BY “LEGAL TENDER”? Legal tender is any lawful form of money that cannot be refused in payment of debt. In the United States, The Coinage Act of 1965 specifies that “United States coins and currency (including Federal Reserve notes and circulating notes of Federal Reserve banks and national banks) are legal tender for all debts, public charges, taxes, and dues.” However, the legal authority of the Federal Reserve to issue legal tender notes existed since 1933. The govern­ mental authority to issue legal tender notes has not generally been available to a central bank at its founding, according to the IMF. TABLE 9.1

The Origins of Central Bank Powers The Origins of Central Bank Powers

Country

Year Central Bank Founded

Year Given Monopoly Over Note Issuance

Year Central Bank Notes Made Legal Tender

France Germany Japan Italy United Kingdom United States Canada

1800 1875 1882 1893 1694 1913 1934

1848 1875 1884 1893 1844 1913 1935

1878 1909 1885 1893 1833 1933 1935

Source: “Virtual Currencies and Beyond: Initial Considerations,” IMF, January 2016

[287] WHAT IS THE “MONEY SUPPLY” OF A NATION? The money supply is commonly defined to be a group of safe assets that households and businesses can use to make payments or to hold as short-term investments. This typically would include cash and balances in bank accounts. The money supply, historically, has been defined in terms of its relationship with economic activity.

MONEY AND BANKING

[288] WHAT IS THE HISTORIC RELATIONSHIP OF MONEY SUPPLY AND ECONOMIC ACTIVITY? As observed by the Federal Reserve, “historically, measures of the money supply have exhibited fairly close relationships with important economic variables, such as nominal gross domestic product and the price level. Based partly on these relationships, some economists—Milton Friedman being the most famous example—have argued that the money supply provides important information about the near-term course for the economy and determines the level of prices and inflation in the long run. Central banks, including the Federal Reserve, have at times used measures of the money supply as an important guide in the conduct of monetary policy.”6

[289] WHAT IS THE RECENT RELATIONSHIP OF MONEY SUPPLY AND ECONOMIC ACTIVITY? As also opined by the Federal Reserve, “Over recent decades … the relationships between various measures of the money supply and variables such as GDP growth and inflation in the United States have been quite unstable. As a result, the importance of the money supply as a guide for the conduct of monetary policy in the United States has diminished over time.”7 As the relationship between the money supply and economic activity diminished, the central bank shifted directly to interest rate targets, rather than money supply targets as its policy approach. This has been the experience in other major industrialized countries, as well.

[290] WHY DID THE MONEY SUPPLY RELATIONSHIP WITH ECONOMIC ACTIVITY CHANGE? The typical explanation for why the money supply and economic activity relationship has changed over time has been attributed to financial innovations. Under this moniker of financial innovations are lots of changes depending on how far one wants to look back in time. Among the changes are the rise of the non-financial banking intermediation sector (hedge funds, private equity, etc.), new financial instruments, evolving financial technology that has spanned everything from online banking to more sophisticated money management systems, and much more.

[291] [ADVANCED] HOW HAVE COMPONENTS OF THE MONEY SUPPLY BEEN CHOSEN? Historically, the selection of components of the money supply was determined based on statistical relationships with economic measures, such as nominal GDP. However, the Austrian school was critical of that approach, arguing a money supply definition cannot be established based on a statistical correlation between the component and some measure of economic activity, but the component selection required a solid theoretical rationale.8

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[292] [ADVANCED] WHAT RESEARCH IS BEING DONE TO REFINE THE MONEY SUPPLY MEASUREMENT CONCEPTS? Today, there is not much research on redefining concepts of the money supply. This is because central banks, especially the Federal Reserve, have jettisoned the money supply as a policy target and focus directly on interest rates. However, the Austrian school seems to be one of the last groups of economists still thinking about this issue. In Rothbard’s essay on defining the money supply,9 he argued that “There are several common arguments for not including savings deposits in the money supply: (1) they are not redeemable on demand, the bank being legally able to force the depositors to wait a certain amount of time (usually thirty days) before paying cash; (2) they cannot be used directly for payment. Checks can be drawn on demand deposits, but savings deposits must first be redeemed in cash upon presentation of a passbook; (3) demand deposits are pyramided upon a base of total reserves as a multiple of reserves, whereas savings deposits (at least in savings banks and savings and loan associations) can only pyramid on a one-to-one basis on top of demand deposits, since such deposits will rapidly “leak out” of savings and into demand deposits.” Note that in the Fed’s definition of M1, savings deposits, are included.

[293] WHAT MONEY SUPPLY MEASURES EXIST FOR THE UNITED STATES? Today, the Federal Reserve defines M1 and M2 as its primary measures of the U.S. money stock. Up until March 2006, the Federal Reserve also had another measure of the money supply called M3. The Federal Reserve ceased compiling M3 because it said, “M3 does not appear to convey any additional information about economic activity that is not already embodied in M2 and has not played a role in the monetary policy process for many years”10 (Figure 9.2).

FIGURE 9.2

M1 Money Stock Measure

Source: Federal Reserve Board of Governors

MONEY AND BANKING

FIGURE 9.3

M2 Money Stock Measure

Source: Federal Reserve Board of Governors

•

•

M1 supply of money includes: ‐ Currency - Currency consists of Federal Reserve notes and coin outside the U.S. Treasury, Federal Reserve Banks, and the vaults of depository institutions. ‐ Demand deposits - Checkable deposits in banks that are available to the customer by making a cash withdrawal or writing a check (physical or electronic through a debit card). Other liquid deposits – Savings deposits plus other checkable deposits (Figure 9.3). M2 supply of money includes: ‐ All M1 types. ‐ Small-denomination time deposits – deposits are those issued in amounts of less than $100,000. Individual retirement account (IRA) and Keogh account balances at depository institutions are subtracted from small-denomination time deposits. ‐ Retail money market funds – IRA and Keogh account balances at money market funds are subtracted from retail money market funds.

[294] WHY DO DIFFERENT COUNTRIES DEFINE THEIR MONEY-SUPPLY AGGREGATES DIFFERENTLY? Money supply measure definitions are dependent on a country’s financial system, which means that the money stock definitions are different in different countries. The United Kingdom, for example, has an M-0 and an M-4 measure. In Japan, there are four money stock metrics, M1, M2, M3, and L. “L” is broadly defined liquidity.

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[295] WHAT IS “HIGH-POWERED” MONEY? High-powered money refers to the monetary base, which is highly liquid funds including notes, coinage, and current bank deposits. When the Federal Reserve creates new funds to purchase bonds from commercial banks, the banks see an increase in their holdings, which causes the monetary base to expand (and vice versa). The monetary base is called high-powered money because the Fed exercises complete and direct control over it through open market operations and through its extension of discount loans to banks. The monetary base (MB) equals the total reserves in banking system (R) plus currency in circulation (C): MB = R + C (Figure 9.4).

FIGURE 9.4

U.S. Monetary Base

Source: Federal Reserve Board of Governors

[296] WHY DOES THE CENTRAL BANK ONLY CONTROL THE MONETARY BASE AND NOT ALL MONETARY AGGREGATES? Depositors and banks themselves have direct influence and control over parts of the monetary aggregates, such as M1 and M2. Depositors will determine how much currency they wish to hold (which is a component of M1), for example. The depositors and banks will determine how much reserves are available to be loaned out, as well. These players will determine the money supply along with the central bank.

[297] [ADVANCED] WHY HAVE SOME ECONOMISTS ARGUED FOR PRIVATE-SECTOR ISSUANCE OF MONEY, SUCH AS CRYPTOCURRENCY, RATHER THAN CENTRAL BANK ISSUANCE? The 1974 Nobel Memorial prize winner in economics, Friedrich A. Hayek—a prominent Austrian school economist—argued, “The government monopoly on money [nonconvertible fiat

MONEY AND BANKING

currency] must be abolished to stop the recurring bouts of acute inflation and deflation,” instability in the economy, and undisciplined state expenditure.11 The IMF notes that although Hayek’s idea was rejected at the time by leading monetary economists, such as Milton Friedman, Anna Schwartz, and Stanley Fischer (who held positions as vice chair of the Federal Reserve Board and governor of the Bank of Israel—the central bank of Israel), other researchers have explored “laissez-faire monetary regimes, and there has also been extensive theoretical work on the feasi­ bility and optimality of privately issued money under monopoly or competition.”12 A key reason advanced for this “denationalization of money” position, as advocated by Hayek, is that central banks can issue as much money as deemed appropriate and that can be a source of inflation and instability, but private-sector issuance of money would be limited and viewed as a cap on inflation.

[298] WHAT DOES “FINANCIAL INTERMEDIATION” MEAN? The term “financial intermediation” means a financial institution, which operates as an “in­ termediary” or middleman, that obtains funds from lenders (deposits) and provides funds to borrowers (loans).

[299] WHAT TYPES OF FINANCIAL INTERMEDIARIES EXIST? Generally, there are three types: •

•

•

Depository institutions: Deposit-taking institutions accept and manage deposits and make loans, including commercial banks, credit unions, savings and loans, and mortgage loan companies. Contractual institutions: Insurance companies (also known as “risk pooling” institutions), pension funds, and money market funds (the last two are also known as “contractual savings” institutions). Investment institutions: Investment banks, underwriters, and brokerage firms.

Contractual and investment institutions have become known as the non-bank financial inter­ mediation (NBFI) sector.

[300] GIVEN THE DIFFERENT TYPES OF FINANCIAL INSTITUTIONS, DOES THIS SUGGEST DIFFERENT CONCEPTUAL RISKS OF MONEY BASED ON ITS SOURCE? Yes, according to the U.S Federal Reserve, which distinguishes three “types of money” by its source:13 •

Central bank money is a liability of the central bank. In the United States, central bank money comes in the form of physical currency issued by the Federal Reserve and digital balances held by commercial banks at the Federal Reserve.

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232 MACROECONOMIC THINKING AND TOOLS • •

Commercial bank money is the digital form of money that is most used by the public. Commercial bank money is held in accounts at commercial banks. Nonbank money is digital money held as balances at nonbank financial service providers (such as money market mutual fund shares). These firms typically conduct balance transfers on their own books using a range of technologies, including mobile apps.

The Fed described the importance of this perspective because different types of money carry different amounts of credit and liquidity risk. “Commercial bank money has very little credit or liquidity risk due to federal deposit insurance, the supervision and regulation of commercial banks, and commercial banks’ access to central bank liquidity. Nonbank money lacks the full range of protections of commercial bank money and therefore generally carries more credit and liquidity risk. Central bank money carries neither credit nor liquidity risk and is therefore considered the safest form of money. Central bank money serves as the foundation of the financial system and the overall economy. Commercial bank money and nonbank money are denominated in the same units as central bank money (i.e., U.S. dollars) and are intended to be convertible into central bank money.”14

[301] HOW MANY COMMERCIAL BANKS ARE THERE IN THE UNITED STATES? The number of commercial banks has been shrinking for years, with consolidation of the industry. As of mid-2022, there were just a tad under 4,200 commercial banks operating in the United States and another 600 savings institutions (Figure 9.5).

FIGURE 9.5

Number of Commercial Banks in the United States, 1934–2022

MONEY AND BANKING

[302] HOW DOES A BANK WORK? One of the most succinct descriptions of how a bank operates was provided by Paul McCulley, who was a managing director of the investment management firm PIMCO, in his congressional testimony before the Financial Crisis Inquiry Commission. McCulley put it this way: “Banking is fundamentally defined as the business of transforming savings into investment in our economy, while simultaneously acting as the nation’s payments system. Traditionally, we think of this activity in the context of conventional banks, which issue deposits and then turn them into loans. Technically, this activity is called maturity, liquidity, and quality transformation. Simply put, this means that banks transform their deposit base into loans that are of longer maturity, less liquidity and lower credit quality than their liabilities.”15 This process earns profits for banks.

[303] [ADVANCED] WHAT ARE THE SOURCES OF FINANCIAL RISK FOR A BANK? Typical sources of risk for a bank are: (1) interest rate risk, which can occur from the impact of market changes in interest rates relative to the securities held in the bank’s portfolio; (2) liquidity risk, which can be either from “funding liquidity risk,” which is when liquidity is insufficient to meet redemptions due to mismanagement by the bank’s asset–liability committee (ALCO) – which sets the bank’s policy on lending and charges to match the flow of assets to liabilities – or from “market liquidity risk,” which is when market volatility forces the bank to sell securities below the mark-to-market price in order to meet large unexpected withdrawals; and (3) credit risk, which can be from two sources: (a) “default risk,” which is the failure to repay on securities and loans held, and (b) “downgrade risk,” which is the risk that a credit agency rating on a security or issuer is reduced. These risks affect bank’s credits, which are the bank’s asset holdings of U.S. Treasury and agency securities, “other securities,” commercial and industrial loans, real estate loans, consumer loans, and other loans and leases.

[304] [ADVANCED] HOW DOES A BANK MANAGE OR MITIGATE ITS RISK? Typical ways banks will mitigate their financial risks include: •

•

•

Mitigating interest rate risk: The risk from higher market interest rates can be mitigated by shifting towards a shorter “weighted average maturity” or duration of the bank’s credit portfolio. Mitigating liquidity risk: To minimize funding risk, banks can hold higher levels of reserves, including overnight cash balances, having an appropriate “ladder of security maturities,” and having a diversified depositor base. To minimize market liquidity risk, a bank can reduce its longer-dated security holdings, especially of less liquid securities (which will help to minimize the impact of price volatility), and increase its holdings of money market funds. Mitigating credit risk: The use of external and internal credit research will help to mitigate this source of risk.

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[305] HOW DOES A BANK BECOME INSOLVENT? A bank must match its assets (loans) to its liabilities (deposits) plus net worth (which represents “bank capital”) considering day-to-day cash-flow requirements of its customers. This is a fun­ damental accounting relationship that a company (including a bank) must pay for everything it owns (its assets) by borrowing money (taking on liabilities – including deposits for a bank) and getting money from investors (by the issuance and sale of shareholder equity or stock). If the value of a bank’s assets is below the value of its liabilities plus net worth, then a bank becomes insolvent. There are two types of bank insolvency: (1) a solvency crisis and (2) a liquidity crisis. •

•

A solvency crisis is when large numbers of customers default on their loans (such as during the 2007–2008 financial crisis) causing a bank’s assets to be less than its liabilities. Capital requirements in the banking system are generally viewed as the backstop for loan defaults. A liquidity crisis occurs when a bank is unable to meet the demand for its liabilities (withdrawals), even if the bank’s assets are greater than its liabilities and net worth. A bank facing a liquidity crisis may try to sell some of its loan portfolio – but if investors require a lower price to purchase those loans, then the value received will be below the “book” value of the loans. Or a bank may be forced to sell-off its other assets at a “fire-sale” to raise funds, such as what Silicon Valley Bank did in 2023. On March 8, 2023, Silicon Valley Bank lost $1.8 billion on the sale of about $21 billion in securities to offset its withdrawals. Reserve requirements – which no longer exist in the United States – generally have been viewed as the backstop against sudden withdrawals.16

In either case, financial regulators step in to take over a “failed” bank. When the Federal Deposit Insurance Corporation (FDIC) takes over a failed bank, it will try to find a buyer immediately but, if not, it will create a bridge bank. The FDIC notes that “A bridge bank is a chartered national bank that operates under a board appointed by the FDIC. It assumes the deposits and certain other liabilities and purchases certain assets of a failed bank. The bridge bank structure is designed to ’bridge’ the gap between the failure of a bank and the time when the FDIC can stabilize the institution and implement an orderly resolution,” which is an eventual sale or closure.

[306] [ADVANCED] WHAT IS MEANT BY A NATION’S PAYMENTS SYSTEM? A payments system is part of a broader financial market infrastructure that includes payment, clearing, and settlement mechanisms to facilitate domestic and international monetary and other financial transactions. According to the NY Federal Reserve Bank, payment systems themselves are of two types: (1) Large-value payment systems, such as Fedwire (FedNow), National Settle Service (NSS), Clearing House Interbank Payments System (CHIPS), and continuous linked settlement bank; and (2) retail systems, such as the automated clearing house (ACH) transfer system, check clearing system, and the credit and debit card system. Examples of retail credit card payment systems are those by Visa, Mastercard, American Express, UnionPay International, and JCB. A NY Fed payments survey found that ACH electronic transfers accounted for 19% of the

MONEY AND BANKING

number of transactions in 2020, but almost 70% of the value of transactions. On the other hand, as shown in the table, credit and debit cards accounted for the lion’s share of the number of transactions, but about 8% of the value of the total retail transactions. TABLE 9.2

Percent Share of Retail Non-Cash Payments, 2020 Percent Share of Retail Non-Cash Payments, 2020

Total Retail Payments Credit and Debit Cards Systems Automated Clearinghouse (ACH) transfer system Check Systems

Number of Transactions (% of Total)

Value of Transactions (% of Total)

100.00 74.25 19.24

100.00 7.84 69.25

6.51

22.91

Source: Federal Reserve Payments Study (FRPS), NY Federal Reserve Bank, January 14, 2022

[307] WHY ARE PAYMENT SYSTEMS IMPORTANT? Domestic and international payment networks are vital to “promote development, support financial stability, and help expand financial inclusion,” according to The World Bank.

[308] WHY IS THE NON-BANK FINANCIAL INTERMEDIATION (NBFI) SECTOR IMPORTANT? The NBFI sector has been termed the “shadow banking” sector. The term “shadow bank” was coined in a 2007 speech at the annual financial symposium hosted by the Kansas City Federal Reserve Bank in Jackson Hole, Wyoming, by economist Paul McCulley, who was a managing director and portfolio manager with the investment management firm PIMCO. •

•

•

Shadow banking describes a large segment of financial intermediation that is outside the balance sheets of regulated commercial banks and other depository institutions. Shadow banks are defined as financial intermediaries that conduct functions of banking “without access to central bank liquidity or public sector credit guarantees.” Shadow banking funding includes commercial paper and other short-term borrowing (bankers acceptances), repo, net securities loaned, liabilities of asset-backed securities issuers, and money market mutual fund assets. The four key aspects of intermediation used by NBFI’s are: (1) Maturity transformation: obtaining short-term funds to invest in longer-term assets; (2) liquidity transformation: a concept similar to maturity transformation that entails using cash-like liabilities to buy harder-to-sell assets such as loans; (3) leverage: employing techniques such as borrowing money to buy fixed assets to magnify the potential gains (or losses) on an investment; and (4) credit risk transfer: taking the risk of a borrower’s default and transferring it from the originator of the loan (or the issuer of a

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236 MACROECONOMIC THINKING AND TOOLS bond) to another party. These are the same functions of a traditional bank, except for the backstop lending guarantee from a central bank or public-sector credit guarantee.

[309] WHY DO WE CARE ABOUT THE NON-BANK FINANCIAL INTERMEDIATION (NBFI) SECTOR (SHADOW BANKING)? There are several reasons why the shadow banking sector has come out from the shadows: •

• • • •

The size of the liabilities held by the shadow banks (market-based financing) has grown tremendously relative to traditional bank financing. See the graph for the United States (Figure 9.6). Shadow banking problems played a critical role in the financial crisis of 2008–2009. The rise of non-bank finance has important implications for monetary policy transmission. Non-bank financial intermediaries (NBFIs) can make the financial system more efficient, but also more unstable. Globally, according to the Financial Stability Board (FSB)17—an international body set up in 2010 to monitor the global financial system—the shadow banking sector accounts for a tad above 48% of all global financial assets in 2020. The FSB estimate of the size of the NBFI sector includes insurance corporations, pension funds, money market funds, investment funds, real estate investment trusts and real estate funds (REITs), hedge funds, finance companies, broker-dealers, structured finance vehicles, trust companies, captive financial institutions and money lenders, and central counterparties (Figure 9.7).

FIGURE 9.6

Shadow Bank Liabilities versus Traditional Bank Liabilities

Sources: Board of Governors of the Federal Reserve System, “Flow of Funds Accounts of the United States”; Zoltan Pozsar, Tobias Adrian, Adam Ashcraft, and Hayley Boesky, “Shadow Banking,” Economic Policy Review, New York Federal Research Bank, Vol. 19, No. 2 (December 2013)

MONEY AND BANKING

Non-bank Financial Intermediation (NBFI) Sector Assets as Percent of Total Global Financial Assets

FIGURE 9.7

[310] [ADVANCED] IS SHADOW BANKING REALLY BANKING? This question was the focus of an article by Federal Reserve Bank of St. Louis’ economists Bryan Noeth and Rajdeep Sengupta in which they concluded, “the shadow banking system can be viewed as a parallel system—one that is a complement to and not a substitute for traditional banking.”18

[311] WHAT ARE SOME EXAMPLES OF FIRMS REPRESENTED IN THE NBFI SECTOR? One U.S. example of a firm that is part of the non-bank financial intermediation sector is Rocket Mortgage (formerly Quicken Loans and is America’s largest mortgage lender). That company reported it originated $351.2 billion of mortgages in 2021—which represented about 39% of all mortgages funded, according to the Mortgage Bankers Association. Some other examples of NBFI firms include the California State Teachers’ Retirement System, BlackRock Capital Investment Corporation, Blackstone Inc., American Honda Finance Corp., GE Capital US Holdings, Inc., Brookfield U.S. Holdings Inc., and KKR & Co. Inc.

[312] WHAT IS CENTRAL BANK DIGITAL CURRENCY (CBDC)? Central bank digital currency (CBDC) is a digital or electronic version of cash. There is no one format for a CBDC. It could take the form of a state-endorsed token on a blockchain (crypto­ currency) or an account directly held at a central bank, which would be available to both individuals

237

238 MACROECONOMIC THINKING AND TOOLS and firms for retail payments. An early adopter of one form of CBDC was China’s central bank (The Central Bank of the People’s Republic of China—PBOC). The PBOC has been testing its digital yuan (its currency), which is also known as a digital currency electronic payment (DCEP), the digital RMB, and more commonly the e-CNY. China first started to explore a digital currency in 2014. The digitization of banknotes and coins in circulation constitutes part of China’s monetary base, which is expected to be used mainly for high-frequency small-scale retail transactions. Keep in mind that the digital yuan is not a cryptocurrency, which is banned in China. The European Central Bank is anticipated to launch its digital euro by 2025. The U.S. Federal Reserve Board has been studying the idea of a digital dollar and will proceed only if Congress authorizes it to do so. The International Monetary Fund is backing CBDC. The IMF believes: • •

•

•

•

Central bank digital currencies (CBDCs) can co-exist with privately issued cryptocur­ rencies as the dual monetary system evolves. Unlike private solutions, such as Bitcoin and other cryptocurrencies, an official digital currency would be backed by the central bank, making it risk-free like banknotes and coin. “Central bank currency – along with regulation, supervision, and oversight – will continue to be essential to anchor stability and efficiency of the payment system. And privately issued money can supplement this foundation with innovation and diversity.”19 Private-sector cryptocurrencies are susceptible to be used to facilitate illicit transactions. (This recently led the Central Bank of Nigeria to ban banks from facilitating transactions done with cryptocurrencies.) “The option of redemption into central bank currency is essential for stability, inter­ operability, innovation, and diversity of privately-issued money, be it a bank account or other. A system with just private money would be far too risky. And one with just central bank currency could miss out on important innovations. Each form of money builds on the other to deliver today’s dual money system—a balance that has served us well.”20

[313] [ADVANCED] WHY SHOULD WE CARE IF MONETARY AUTHORITIES DEVELOP CBDC? The IMF observed that, “Some academic scholars view CBDC as a means to enhance the transmission of monetary policy. They argue that an interest-bearing CBDC would increase the economy’s response to changes in the policy rate. They also suggest that CBDC could be used to charge negative interest rates in times of prolonged crisis (thus breaking the ‘zero lower bound’ constraint), to the extent cash [holdings] were made costly.”21

[314] WHAT IS MEANT BY “FRACTIONAL-RESERVE” BANKING? The concept of fractional-reserve banking—which is employed globally—developed hundreds of years ago when goldsmiths held customer deposits and realized that not everyone would withdraw one’s holdings at the same time. This ultimately led goldsmiths to use some of those

MONEY AND BANKING

deposit to issue loans at high interest rates. This practice of holding only a portion of deposits and lending on the rest was established as a banking practice in Sweden in 1668 when its central bank was given the authority to regulate commercial banks and to set reserve requirements, which are the share of deposits that must be held.

[315] WHAT IS THE HISTORY OF A BANKING “RESERVE REQUIREMENT” IN THE UNITED STATES? In 1863, Congress passed the National Bank Act, which established a “reserve requirement” to ensure liquidity was on-hand to satisfy customers’ cash demands. The law required banks “at all times have on hand, in lawful money of the United States, an amount of money amount equal to at least twenty-five per centum of the aggregate amount of its outstanding notes of circulation and its deposits; and whenever the amount of its outstanding notes of circulation and its deposits shall exceed the above-named proportion for the space of twelve days, or whenever such lawful money of the United States shall at any time fall below the amount of twenty-five per centum of its circulation and deposits, such associ­ ation shall not increase its liabilities by making any new loans or discounts otherwise than by discounting or purchasing bills of exchange, payable at sight, nor make any dividend of its profits, until the required proportion between the aggregate amount of its outstanding notes of circulation and its deposits and lawful money of the United States shall be restored.”

[316] IS A RESERVE REQUIREMENT THE ONLY WAY TO ENSURE DAY-TODAY TRANSACTION LIQUIDITY IN THE BANKING SYSTEM? No. Some countries, such as Canada, the United Kingdom, Australia, Sweden, and New Zealand, do not impose reserve requirements on its banking system. Instead, lending in those countries is constrained by capital requirements. In 2020, the Federal Reserve Board reduced its 10% reserve requirement on large deposits at U.S. banks to zero. The Federal Reserve Board may not reinstitute that reserve requirement, instead relying on the discretion of the banking institution to hold excess reserves at the Fed (which, as of 2008, the Fed is permitted to pay interest on those reserves) as a safety measure in the event of massive cash withdrawals by customers due to some crisis or period of economic uncertainty.

[317] WHY DO WE CARE ABOUT THIS PRACTICE OF FRACTIONALRESERVE BANKING? The simple answer why fractional-reserve banking is important—regardless of whether the reserve requirement is non-existent or some share of deposits—is that this mechanism is how money is created by the banking system.

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240 MACROECONOMIC THINKING AND TOOLS

[318] WHAT IS THE MAXIMUM AMOUNT OF MONEY THAT CAN BE CREATED BY A FRACTIONAL-RESERVE BANKING SYSTEM? The maximum amount of money that could be generated by the lending process is what is known as the money multiplier, which is determined as one over the reserve requirement ratio. If the reserve requirement share was 10% of deposits that would imply the maximum increase in the money supply would be ten times the original deposit (that is, 1/(0.10) equals 10). If, however, the reserve requirement is set at zero, then the theoretical maximum expansion of the money supply would be infinite (that is, 1/0 which is infinity). However, other prudent and legal constraints ensure that loan expansion is not infinite.

[319] HOW DOES THE BANKING SYSTEM CREATE MONEY? The starting point to understand the process of how the banking system creates money through its lending (creation of a “demand deposit” is by definition included in the narrow concept of money—M1). Initially, consider the financial account (or T-account) of a bank’s balance sheet. As is true for all businesses, assets will equal liabilities and net worth. A customer’s deposit at a bank is a liability of the bank (since it is owed to the customer), but that generates a reserve on the bank’s balance sheet as an asset. Those reserves get loaned out (which is how the bank earns money). One bank loan then becomes another bank demand deposit, which continue to cascade through the banking system. If reserve requirements exist, then the monetary expansion is limited by the money multiplier, however, without a reserve requirement the process can continue. •

Example of Money Creation through Lending: The starting point for this money creation begins with a bank deposit. In this example, it is assumed that the “First Bank of the People” (FBP) receives a $10 million deposit—which is shown below in step 1—as a liability and as an asset of the bank. Assuming there is no reserve requirement on bank lending, then the bank management will decide how much of that reserve will be loaned. In step 2, FBP decides to loan $7 million of that deposit to “Business 1.” In turn, that cuts FBP’s reserves by that amount to $3 million. Then (for simplicity) assume that “Business 1,” which received the loan deposits it into its checking account (demand deposit) at the Bank of the Nation’s Business (BNB), which increases BNB’s deposits and reserves by that $7 million—which is shown in step 3. Next, assume that BNB decides to loan out $2 million to “Business 2” of that new deposit it received (step 4). Now, assume that “Business 2” deposits that loan in its bank, “Bank of All Business” (BAB)—which adds $2 million to BAB’s assets and liabilities. Finally, assume that BAB decides to loan out $1 million of that incremental deposit on its balance sheet to “Business 3,” which deposits the loan into its bank FBP (step 6). Although this process could go on, this should be sufficient to show the impact on the money supply. Initially, that $10 million demand deposit adds directly to the narrow measure of the money supply. But then each successive loan creates additional demand deposits, which in turn adds incrementally to the money supply. Then after these three loans that initial $10 million deposit expanded the money supply cumulatively by twice the initial deposit to $20 million. This is the power of fractional reserves.

MONEY AND BANKING TABLE 9.3

How the Banking Sector Creates Money How the Banking Sector Creates Money First Bank of the People (FBP) (Millions of Dollars) Assets Liabilities & Net Worth

Step 1 Reserves

Step 2

$10

Deposit

$10

$7

Deposit (increase)

$7

$5 $2

Deposit (increase)

then becomes: $17

$7

Bank of the All Business (BAB) (Millions of Dollars) Assets Liabilities & Net Worth Reserves (increase) Loan to Business 3

Step 6

$10

Bank of the Nation’s Business (BNB) (Millions of Dollars) Assets Liabilities & Net Worth Reserves (increase) Loan to Business 2

Step 5

$3 $7

Bank of the Nation’s Business (BNB) (Millions of Dollars) Assets Liabilities & Net Worth Reserves (increase)

Step 4

$10

First Bank of the People (FBP) (Millions of Dollars) Assets Liabilities & Net Worth Reserves Loan to Business 1

Step 3

Deposit

Money Supply Change (Millions of Dollars) Initial increase:

$1 $1

Deposit (increase)

$2

First Bank of the People (FBP) (Millions of Dollars) Assets Liabilities & Net Worth Reserves (increase)

$1

Deposit (increase)

$1

then becomes: $19

then becomes: $20

[320] [ADVANCED] WHAT IS THE “CHICAGO PLAN” FOR BANKING SYSTEM REFORM? In March 1933, University of Chicago economists G. V. Cox, Aaron Director, Paul Douglas (later U.S. senator from Illinois between 1949 and 1967), A. G. Hart, Frank Knight, L. W. Mints, Henry Schultz, and Henry C. Simons drafted a proposal for a 100% reserve requirement for the banking system plus it addressed deposit safety, the separation of

241

242 MACROECONOMIC THINKING AND TOOLS investment and commercial banks, and inflation.22 Shortly thereafter, this idea was em­ braced by Yale University Professor Irving Fisher who became a champion of the idea and promoted it with members of Congress and the Roosevelt administration – though this proposed banking reform was never implemented by the federal government. Nonetheless, the 100% reserve requirement idea continues to be discussed from time-to-time usually after some banking crisis. Allen23 described the core idea of this plan as: The essential effect of imposing 100 percent reserves would be to separate the lending function of financial institutions–along with money‐holding, money‐shifting, and currency‐deposit convertibility processes–from money‐creation, control of the size of the money stock then being solely a governmental function. Replacing fractional‐ reserve banking with reserves required to be equal to demand liabilities of banks would eliminate the ability of banks to create (and to destroy) money and to do so in multiples of changes in reserves. Demand deposits would be fully liquid and convertible into currency, with the aggregate size of the community’s money supply determined by government policy. In 2012, IMF researchers Jaromir Benes and Michael Kumhof 24 revisited the idea of a 100% reserve requirement noting that Irving Fisher claimed the Chicago Plan would provide “(1) Much better control of a major source of business cycle fluctuations, sudden increases and contractions of bank credit and of the supply of bank-created money. (2) Complete elim­ ination of bank runs. (3) Dramatic reduction of the (net) public debt. (4) Dramatic reduction of private debt, as money creation no longer requires simultaneous debt creation.” Their IMF study supported all four of Fisher’s claims with an additional benefit of boosting output gains in the economy and subduing inflation. This idea for a 100% reserve requirement in the banking system again was circulated after the 2023 banking crisis, but there also are critics of this proposal as impractical to implement and it potentially would dramatically restrain credit needed for economic growth.

[321] [ADVANCED] WHAT IS MEANT THAT THE U.S. DOLLAR IS AN INTERNATIONAL “RESERVE CURRENCY”? The U.S. dollar as a “reserve currency” means that the U.S. dollar accounts for the largest share of foreign official (international reserves25) and private-sector holdings and transactions of any non-domestic currency. As such, the U.S. dollar serves as an international store of value and as an international medium of exchange. “The dollar rose to prominence after the financial crisis associated with World War I, then solidified its international role after the Bretton Woods Agreement in 1944,”26 replacing the British pound as the predominant currency for world trade. This prominent international position for the U.S. dollar as a reserve currency is due to “the U.S. economy’s stability and openness to trade and capital flows, and strong property rights and the rule of law. [Moreover,] the depth and liquidity of U.S. financial markets

MONEY AND BANKING

is unmatched, and there is a large supply of extremely safe dollar-denominated assets.”27 Thus, U.S. dollar denominated assets (including U.S. Treasuries) account for the lion’s share of: (1) foreign official (central bank and other official government entities) reserves; and (2) inter­ national trade and capital flows. A few statistics highlight the importance of the U.S. dollar: (1) In 2021, global official foreign reserves denominated in U.S. dollars accounted for a 60% share—though down from 71% in 2000. The euro, in 2021, accounted for the next highest share of foreign-currency denominated official holdings with 21%. (2) The U.S. dollar is the world’s more frequently used currency for international trade transactions. “Over the period 1999–2019, the dollar accounted for 96% of trade invoicing in the Americas, 74% in the Asia-Pacific region, and 79% in the rest of the world. The only exception is Europe, where the euro is dominant.”28 (3) Many foreign economies use the U.S. dollar to stabilize their own currencies (“anchoring” their currency to the U.S. dollar). A 2020 study estimated that about half of non-U.S. world GDP in 2015 was produced by countries that anchored their currency to the U.S. dollar. In a speech by the Federal Reserve Board Chairman Jerome Powell, he opined that, “The dollar’s international role holds multiple benefits. For the United States, it lowers transaction fees and borrowing costs for U.S. households, businesses, and the government. Its ubiquity helps contain uncertainty and, relatedly, the cost of hedging for domestic households and businesses. For foreign economies, the wide use of the dollar allows borrowers to have access to a broad pool of lenders and investors, which reduces their funding and transaction costs. The benefits of the dollar as the dominant reserve currency have generated an extensive academic literature.”29

[322] [ADVANCED] WHAT IS MEANT BY “DOLLARIZATION” OF AN ECONOMY? Dollarization means that a country other than the United States uses the U.S. dollar (or any country that adopts a currency of a foreign nation or multiple currencies of foreign nations) as its official domestic currency and legal tender. This also is referred to as “formal or full” dollarization versus informal dollarization where residents of a country diversify their financial assets into owning currency and/or financial assets of another country to protect holdings from their own economy’s high inflation and/or political instability. The IMF suggests that the advantages of dollarization are: (1) A government’s full adoption of dol­ larization avoids currency and balance of payments crises; (2) It forces a “closer integration” with the U.S. and global economies; and (3) It eliminates the possibility of inflationary finance through monetarization of domestic government debt and it may strengthen domestic financial institutions. The downside of dollarization is the political resistance of using a foreign currency (including paper money with pictures of U.S. presidents and not domestic leaders). It also eliminates what is known as “seigniorage revenues” to the gov­ ernment, which are central bank profits from the issuance of paper currency that costs less to create than its face value. Finally, dollarization relinquishes control of monetary and ex­ change rate polices to a foreign country, which is the most significant economic impact. Countries that have adopted full dollarization include Panama (1904), Ecuador (2000), El Salvador (2001), and Zimbabwe (2009–2019).

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244 MACROECONOMIC THINKING AND TOOLS

Issues to Think About For years the banking system has been consolidating and evolving, but still plays a vital role in the economy, especially since it is the system through which the money supply multiples by originating loans. • •

• •

However, with that consolidation, it has given rise to the public concern that some banking institutions are “too big to fail.” Should this be a concern? With the growth of the non-banking financial sector, has the overall financial system become riskier since those institutions are not back-stopped by any government entity in the event of a financial crisis? Is there value for central banks to develop a digital currency? Friedrich Hayek opined, “the cause of waves of unemployment is not ‘capitalism’ but governments denying enterprise the right to produce good money.” Should there be a “denationalization of money” as Nobel Laureate Hayek argued for?

NOTES 1 Wesley C. Mitchell, “The Role of Money in Economic Theory,” American Economic Review, vol. 6, no. 1 (March 1916), pp. 140–161. 2 Mark Carney, “The Future of Money,” Speech to Scottish Economics Conference, Edinburgh University, Bank of England (March 2, 2018). 3 “Final Version of the Report on the Establishment of a Mint, [28 January 1791],” Founders Online, National Archives, https://founders.archives.gov/documents/Hamilton/01-07-02-0334-0004. 4 Eric Brothers, “Forging the U.S. Mint from Words of Alexander Hamilton,” Financial History, Museum of American Finance, no. 141, (Spring 2022), pp. 23–27. 5 Mark Carney, Governor of the Bank of England, “The Future of Money,” Speech to Scottish Economics Conference (Edinburgh University, Bank of England, March 2, 2018). 6 “What is the Money Supply? Is it Important?,” Frequently Asked Questions, Federal Reserve Board of Governors (December 16, 2015), https://www.federalreserve.gov/faqs/money_12845.htm. 7 Ibid. 8 Frank Shostak, “The Mystery of the Money Supply Definition,” Quarterly Journal of Austrian Economics, vol. 3, no. 4 (Winter 2000), pp. 60–76. 9 Murray N. Rothbard, “Austrian Definitions of the Supply of Money,” in Louis M. Spadaro, ed., New Directions in Austrian Economics, (Sheed Andrews and McMeel, Kansas City, 1978), pp. 143–156. https://mises.org/library/austrian-definitions-supply-money. 10 “Discontinuance of M3,” Federal Reserve Statistical Release (H.6) Money Stock Measures, Federal Reserve Board, Washington, DC (March 9, 2006). 11 F.A. Hayek, Denationalisation of Money, Hobart Papers, The Institute of Economic Affairs (IEA), London (1976). 12 Dong He, Karl Habermeier, Ross Leckow, Vikram Haksar, Yasmin Almeida, Mikari Kashima, Nadim Kyriakos-Saad, Hiroko Oura, Tahsin Saadi Sedik, Natalia Stetsenko, and Concepcion Verdugo-Yepes, Virtual Currencies and Beyond: Initial Considerations, IMF Staff Discussion Note, International Monetary Fund, Washington, DC (January 2016), p. 11.

MONEY AND BANKING 13 Money and Payments: The U.S. Dollar in the Age of Digital Transformation, Federal Reserve Board of Governors, Washington, DC (January 2022). 14 Ibid., p. 5. 15 Paul A. McCulley, Statement before the Financial Crisis Inquiry Commission, U.S. Congress (May 6, 2010). 16 Although reserve requirements historically have been implemented as either a flat percentage or a graduated percentage based on certain types of deposits and other liabilities of depository institutions, more sophis­ ticated reserve requirements might be designed by regulators based on tiers determined by various factors, such as the degree of industry or geographic concentration or diversity of the loan portfolio, the duration of the loan portfolio, as well as the size of the institution’s liabilities. Note that prior to the March 2020 elimination of the reserve requirement by the Federal Reserve, the Federal Reserve implemented reserve requirement ratios “based on the amount of net transactions accounts at the depository institution. A certain amount of net transaction accounts, known as the ‘reserve requirement exemption amount’, was subject to a reserve requirement ratio of zero percent. Net transaction account balances above the reserve requirement exemption amount and up to a specified amount, known as the ‘low reserve tranche’, were subject to a reserve requirement ratio of 3 percent. Net transaction account balances above the low reserve tranche were subject to a reserve requirement ratio of 10 percent.” Moreover, each year, the Federal Reserve also adjusted the reserve requirement exemption amount and the low reserve tranche for inflation. For additional history, see: https://www.federalreserve.gov/monetarypolicy/reservereq.htm. 17 https://www.fsb.org/. 18 Bryan J. Noeth and Rajdeep Sengupta, “Is Shadow Banking Really Banking?,” Regional Economist, Federal Reserve Bank of St. Louis (October 1, 2011). 19 Tobias Adrian and Tommaso Mancini-Griffoli, “Public and Private Money Can Coexist in the Digital Age,” IMF Blog, International Monetary Fund, Washington, DC (February 18, 2021), https://blogs.imf. org/2021/02/18/public-and-private-money-can-coexist-in-the-digital-age/. 20 Ibid. 21 Tobias Adrian and Tommaso Mancini-Griffoli, “Central Bank Digital Currencies: 4 Questions and Answers,” IMF Blog, International Monetary Fund, Washington, DC (December 12, 2019), https:// blogs.imf.org/2019/12/12/central-bank-digital-currencies-4-questions-and-answers/. 22 For a history of the Chicago Plan, see: Ronnie J. Phillips, “The ‘Chicago Plan’ and New Deal Banking Reform,” Working Paper No. 76, The Jerome Levy Economics Institute of Bard College, June 1992. 23 William R. Allen, “Irving Fisher and the 100 Percent Reserve Proposal”, The Journal of Law & Economics, vol. 36, no. 2 (Oct. 1993), pp. 703–717. 24 Jaromir Benes and Michael Kumhof, “The Chicago Plan Revisited”, IMF Working Paper, International Monetary Fund: Washington, DC, August 2012. 25 The IMF defines international reserves as “those external assets that are readily available to and controlled by monetary authorities for meeting balance of payments financing needs, for intervention in exchange markets to affect the currency exchange rate, and for other related purposes (such as maintaining confidence in the currency and the economy, and serving as a basis for foreign bor­ rowing)” See: International Reserves and Foreign Currency Liquidity: Guidelines for a Data Template, International Monetary Fund, Washington, DC, p. 3, https://www.imf.org/external/np/sta/ir/ IRProcessWeb/pdf/guide.pdf. 26 Carol Bertaut, Bastian von Beschwitz, and Stephanie Curcuru, “The International Role of the U.S. Dollar,” Fed Notes, Federal Reserve Board of Governors Washington, DC (October 6, 2021), https:// www.federalreserve.gov/econres/notes/feds-notes/the-international-role-of-the-u-s-dollar-20211006.htm. 27 Ibid. 28 Ibid. 29 Jerome H. Powell, “Welcoming Remarks at the “International Roles of the U.S. Dollar,” A Research Conference Sponsored by the Federal Reserve Board, Washington, DC (June 17, 2022).

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CHAPTER

10

Monetary Policy—Goals, Theories, Rules, Discretion, and Implementation LEARNING OBJECTIVES Monetary policy is determined by the central bank, but its long-term goals are generally established by government. Such is the case with the Federal Reserve’s policy goals, which have been set by Congress. Monetary policies are imple­ mented through the banking system. You will learn: • • • • • • • • • • • •

About the history of the central bank in the United States. How the Federal Reserve’s operational policy goals have been evolving. How r-star plays a role in monetary policy. What the term structure of interest rates is and means. How monetary policy effects the economy and inflation. About some monetary theories. How monetary theories have embraced rules-of-thumb for setting interest rates. How the Federal Reserve is structured and how policy is implemented. The policy tools available for central banks. About the role of large-scale asset purchases and sales. How monetary policy has been impacted by market-based financing and may be affected by central bank digital currencies. How broader social issues are affected by monetary policy.

[323] WHAT IS THE HISTORY AND IMPORTANCE OF A CENTRAL BANK IN THE UNITED STATES? Alexander Hamilton—the first secretary of the Treasury—was influenced by the economic role played by the Bank of England (United Kingdom’s central bank). The Bank of England, which was created by an act of Parliament in 1694, was instrumental as the government’s banker and debt manager. (The Bank of England was the second-oldest central bank in the DOI: 10.4324/9781003391050-11

MONETARY POLICY

world after the Swedish Riksbank, which was founded in 1668.) The Bank of England was founded to “help meet the need for greater liquidity in the national marketplace, fuel the increasing overseas trade and meet the needs of businesses and newly-urban private individuals, as well as the political and military needs of the Crown.”1 Embracing the need for a central bank, Hamilton promoted the idea for the United States, but Thomas Jefferson—the first secretary of state—was opposed to its creation. However, Hamilton managed to convince President George Washington, who threw his support behind the idea, and legislation creating the bank was passed by Congress. However, the bank’s charter was provisional and had to be renewed in 1811. In 1811, Congress reversed its position on the need for a central bank and failed to renew its charter. Finally, by 1913, Congress recognized the necessity for a central bank and passed the Federal Reserve Act, which created a permanent central bank for the United States.

[324] WHAT ARE THE FEDERAL RESERVE’S POLICY GOALS FOR THE U.S. ECONOMY? In legislation enacted by Congress, The Federal Reserve Reform Act of 1977 set forth those monetary policy goals for the central bank. The legislation said, “The Board of Governors of the Federal Reserve System and the Federal Open Market Committee shall maintain long run growth of the monetary and credit aggregates commensurate with the economy’s long run potential to increase production, so as to promote effectively the goals of maximum em­ ployment, stable prices, and moderate long-term interest rates.”2

[325] WHY DOES THE FEDERAL RESERVE DISCUSS ITS “DUAL MANDATE” FROM CONGRESS AND NOT ITS “TRIPLE MANDATE”? “Maximum employment, stable prices, and moderate long-term interest rates,” are the mandates from Congress for the central bank’s economic policy goals. However, former Federal Reserve Board Governor Frederic S. Mishkin succinctly summarized why the Fed’s triple mandate has become a dual mandate. He said, “Because long-term interest rates can remain low only in a stable macroeconomic environment, these goals are often referred to as the dual mandate; that is, the Federal Reserve seeks to promote the coequal objectives of maximum employment and price stability.”3 The Federal Reserve has routinely noted in its statements over the years that “the inflation rate over the longer run is primarily determined by monetary policy, [while] the maximum level of employment is largely determined by nonmonetary factors, such as the pace of technological innovation and the structure of the labor market, that may change over time and that may not be directly measurable.”4

[326] WHAT IS INFLATION TARGETING? Inflation targeting is a rule that the central bank is required to keep inflation low. Inflation targeting, a common practice in central banking today, aims to move the expected rate of inflation towards its target, regardless of its past levels.

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248 MACROECONOMIC THINKING AND TOOLS

[327] WHAT IS THE FEDERAL RESERVE’S FLEXIBLE AVERAGE INFLATION-TARGETING STRATEGY (FAIT)? In 2020, the Federal Reserve recast its inflation-targeting strategy from an ongoing goal of achieving a 2% inflation to what was dubbed a new framework that adopted a flexible average inflation-targeting strategy (FAIT) that “seeks to achieve inflation that averages 2 percent over time in order to ensure longer-term inflation expectations are well anchored at 2 percent. Under a FAIT strategy, appropriate monetary policy aims to achieve inflation moderately above 2 percent for some time to make up for shortfalls during a period when it has been running persistently below 2 percent.”5 The strategy of average-inflation targeting frameworks, according to a San Francisco Federal Reserve Bank study, can alleviate downward pressures on inflation and output and thus anchor inflation expectations closer to, or even at, the target. Average-inflation targeting is one approach policymakers could use to help address these challenges. Considering previous periods of below-target inflation, average-inflation targeting overshoots to bring the average rate back to target over time. If the public perceives it to be credible, average-inflation targeting can help solidify inflation expectations at the 2% inflation target by providing a better inflation anchor and thus maintain space for potential interest rate cuts. It importantly can help lessen the constraint from the effective lower bound in recessions by inducing policymakers to overshoot the inflation target and provide more accommodation in the future.6

[328] WHICH MEASURE OF INFLATION DOES THE FEDERAL RESERVE TARGET? The Federal Reserve currently targets two measures of inflation: (1) the Personal Consumption Expenditure price index; and (2) the Personal Consumption Expenditure price index less food and energy. Historically, the Federal Reserve embraced these measures of inflation – rather the Consumer Price Index (CPI) – because of widely perceived upward biases in the fixed-weighted CPI.7 Yet, it might be expected that sometime in the future the FOMC’s inflation metric may shift to the chain-CPI, which addresses those earlier biases in the fixed-weighted CPI-U and CPI-W. Indeed, on February 26, 2004, former Federal Reserve Board Chair Alan Greenspan even suggested to the U.S. House Budget Committee that it might consider switching the annual cost-of-living adjustment in Social Security payments from the fixed-weighted CPI to the (then relatively new) chain-weighted CPI (C-CPI-U), compiled by the BLS, because it gave lower inflation readings and would mean smaller payment increases.

[329] WHAT MEASURE OF EMPLOYMENT DOES THE FEDERAL RESERVE TARGET? On November 3, 2021, Federal Reserve Board Chair Jerome Powell opined on this question by saying, “maximum employment is [a] broad-based and inclusive goal that’s not

MONETARY POLICY

directly measurable and changes over time due to various factors. You can’t specify a specific goal. So, it’s taking into account quite a broad range of things, and of course, employment, levels of employment, participation are a part of that. But in addition, there are other measures of what’s going on in the labor market, like wages is a key measure of how tight the labor market is. The level of quits. The amount of job openings. The flows in and out of various states. So, we look at so many different things, and you make an overall judgment.”8

[330] ARE THE FEDERAL RESERVE’S MANDATES BEING BLURRED? There are critics of the Federal Reserve’s shift in what is meant exactly by its mandates. For example, former Philadelphia Federal Reserve Bank President Charles Plosser weighed in on the Federal Reserve’s recent interpretations of its goals. On the employment mandate, Plosser wrote, “The new interpretations elevated and broaden the employment mandate to mean maximum ‘inclusive’ employment shifting away from the previous interpretation where the Fed focused on something akin to the ‘natural rate’ of unemployment. This new interpretation stresses the level of employment and adds a vague and unprecedented distributional element to the Fed’s objective. In the absence of more quantitative guidance, it offers the Fed enormous flexibility to interpret and justify its actions and makes it more difficult for the public to judge its success and hold it accountable. It also risks introducing dangerous political under currents to its decision-making process.” Plosser further addressed the shifting inflation goal, “On the inflation side of the mandate, the Fed replaced its inflation targeting (IT) regime with [a flexible] average inflation target (FAIT). Vice chair Clarida referred to this new target as an ‘aspiration’ and is not to be construed as an arithmetic average but a ‘flexible’ concept. In addition, the Fed articulated an asymmetric element to its strategy by intentionally over­ shooting its aspirational inflation objective to make-up for any shortfalls in inflation while emphasizing that it does not intend to similarly seek to offset overshoots, thus reinforcing the idea that the ‘average’ target is in no sense an average of expected outcomes. The Fed has not offered any quantitative guidance as to how this strategy will be implemented. Here again, the lack of clarity allows for a wide range of discretionary policy actions by the Fed, increasing policy uncertainty.”9

[331] WHAT IS R-STAR? The theoretical concept of r-star (r*) is the short-term interest rate that would exist when the economy is at full employment and stable inflation, such that monetary policy is neither contractionary nor expansionary. This term is interchangeable with “the long-run equilibrium interest,” (because it is the interest rate associated with the LRAS), the “natural” rate—Knut Wicksell’s concept that “there is a certain rate of interest on loans which is neutral in respect to commodity prices and tends neither to raise nor to lower them”10—and the “neutral” rate (or the “long-term neutral rate”). This measure is unobservable, but various statistical estimates

249

250 MACROECONOMIC THINKING AND TOOLS exist. R-star also is considered to be a real interest rate, because it exists when the economy is at its long-term potential with stable inflation and full employment. Interchangeable Terms for the Theoretical Interest Rate (R*) at Full Employment r-star or r*

Long-run equilibrium interest rate

Natural rate

Neutral rate

[332] IS R-STAR REALLY A SHORT-TERM OR LONG-TERM INTEREST RATE? The conventional approach is to think of r-star as a short-term policy rate of the central bank that exists when the economy is at full-employment/real potential output.11 However, Roberts offers a “new measure [and interpretation of r-star, which] is an estimate of the equilibrium value of the long-term interest rate—in particular, the ten-year Treasury yield.”12 He further noted this his approach “is in contrast to many other estimates that are focused on the equilibrium value of short-term interest rates, such as the federal funds rate, an overnight rate.”

[333] [ADVANCED] HOW IS R-STAR ESTIMATED? The derivation of r-star is simply described as: “The point of departure for the analysis is the textbook IS curve, which states that, other things equal, higher … interest rates should imply weaker economic activity. A simple model that captures this idea is: xgapt =

xgapt

1

(R t

R tn )

(10.1)

where xgap is the output gap, Rt is the real long-term interest rate, and Rtn is the neutral rate of interest. Equation 10.1 says that when the long-term interest rate rises, the output gap will fall. [Note that this formulation could apply to the short-term interest rate, instead of a long-term rate, depending on one’s point of view.] The equation allows for some lag in the effect of interest rates on the output gap. The neutral rate of interest is one type of equilibrium interest rate; it is defined to be the value of Rt that will lead to GDP growing at potential (and thus to no change in the output gap) provided the output gap is initially zero. An implication of equation 1 is that when (real long-term) interest rates are higher than their neutral level, the output gap will be negative. [Next,] to derive an estimate of the neutral rate of interest, Equation 1 can be re-arranged as, R tn = Rt + (xgapt

xgapt 1)/ .

(10.2)

To use equation 10.2 to infer the neutral rate of interest, the requirements are: (a) data on the output gap and the real long-term [or real short-term, depending on the perspective,] interest rate and (b) assumptions about the two model parameters,”13 which is important for statistical

MONETARY POLICY

estimation. The Roberts’ paper shows the estimates of η = 0.75, and σ = 0.75. A direct estimate of those r-star parameters by the New York Federal Reserve Bank using the federal funds rate as Rt and the Holston, Laubach, and Williams estimates of the natural rate of interest, Rtn, yielded estimates of η = 0.50, and σ = 0.50.

[334] ARE THERE EMPIRICAL TIME-SERIES ESTIMATES OF R-STAR? Yes, there are empirical estimates of r-star, based on Laubach and Williams14 and Holston, Laubach, and Williams15 and a few other economists. Although this has become an important guidepost for monetary policy, its measurement is fraught with problems. The NY Federal Reserve Bank’s “real-time” estimates of r-star were halted on November 30, 2020, when the methodology created a problem in the face of the COVID-19 pandemic. Nonetheless, the broad trend up until that point gave monetary policymakers a perspective of a declining trend in r-star in the United States, Canada, the United Kingdom, and the Euro Area. The U.S. estimates of r-star are shown based on three approaches (Figure 10.1).

FIGURE 10.1

NY Federal Reserve Bank Estimates of R-Star (Three Approaches)

[335] WHY SHOULD WE CARE ABOUT R-STAR? Former Dallas Federal Reserve Bank President Robert S. Kaplan provided an important rationale why the neutral rate of interest was important. He said, “Monetary policy is accommodative when the federal funds rate is below the neutral rate. When this is the case, economic slack will tend to diminish—the economy will tend to grow faster, the unemployment rate should decline and inflation should tend to rise. When the federal funds rate is above the neutral rate, monetary policy is restrictive. When this is the case, economic slack will tend to increase—economic growth will tend to slow, the unemployment rate should tend to rise and the rate of inflation should be more muted or decline. The challenge for the Federal Reserve is managing this process and getting the balance right.”16 The federal funds rate was below various estimates of the natural rate or r-star between

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252 MACROECONOMIC THINKING AND TOOLS 2009 and 2016 and again between 2020 and early 2022 (the latest perspective), which describes monetary accommodation. Moreover, former Federal Reserve Board Vice Chair Richard H. Clarida in a 2020 speech on the new monetary policy framework said, “Perhaps the most sig­ nificant change since 2012 in our understanding of the economy is our reassessment of the neutral real interest rate, r*, that, over the longer run, is consistent with our maximumemployment and price-stability mandates. In January 2012, the median FOMC participant projected a long-run r* of 2.25 percent, which, in tandem with the inflation goal of 2 percent, indicated a neutral setting for the federal funds rate of 4.25 percent. However, in the eight years since 2012, members of the Committee—as well as outside forecasters and financial market participants—have repeatedly marked down their estimates of longer-run r* and, thus, the neutral nominal policy rate. Indeed, as of the most recent Summary of Economic Projections (SEP) released in June, the median FOMC participant currently projects a longer-run r* equal to just 0.5 percent, which implies a neutral setting for the federal funds rate of 2.5 percent. Moreover, as is well appreciated, the decline in neutral policy rates since the Global Financial Crisis (GFC) is a global phenomenon that is widely expected by forecasters and financial markets to persist for years to come.”17

[336] [ADVANCED] WHAT IS THE FED’S “SEP”? The Fed’s SEP is its “Summary of Economic Projections.” This summary includes economic projections of Federal Reserve Board members and Federal Reserve Bank presidents, under their individual assumptions of projected appropriate monetary policy. Members provide projections for real GDP, the unemployment rate, personal consumption expenditures (PCE) price index, the PCE price index less food and energy, and a projected path for the federal funds rate. Of note is the implication for r*. Given the Fed’s consensus view for the federal funds rate and the PCE inflation rate, then the difference of those two provide the Fed’s thinking on r*—the “neutral” short-term interest rate. For example, the June 2022 SEP re­ ported a longer run expectation of 2.5% for the federal funds rate and a 2.0% expectation for PCE inflation then implies a “neutral rate” of 0.5%. However, in that same report the current year’s expectation for the neutral rate was −1.8% (inflation exceeding the funds rate—the policy rate is well-below neutral).

[337] WHAT IS MONETARY POLICY? Monetary policy involves managing interest rates and credit conditions, which influences the level of economic activity and inflation.

[338] HOW DOES MONETARY POLICY EFFECT THE MACROECONOMY? The central bank of Mexico—Banco de Mexico—offered a flowchart, which is generic for all central banks, highlighting the typical flow how monetary policy actions effect economic

MONETARY POLICY

FIGURE 10.2

The Effects of Monetary Policy on the Economy

Source: Bank of Mexico

activity and inflation. Most of the impact of monetary policy acts through aggregate demand. A little bit spills over to aggregate supply through how the central bank’s actions alter expectations and secondarily through the exchange rate channel. If the central bank raises interest rates, for ex­ ample, that will likely strengthen the domestic economy’s foreign exchange rate, which will cause foreign buyers to pay more for the country’s exports. In turn, that will likely reduce foreign demand (an aggregate demand effect), but also will likely reduce the domestic price of foreign purchases of inputs for production (an aggregate supply impact). Slowing aggregate demand will ease pressures on inflation. (It should be noted for the United States a stronger dollar does not affect some international prices, such as oil, which largely is denominated in U.S. dollars.) If the central bank reduces interest rates, then the reverse situation unfolds (Figure 10.2).

[339] HOW LONG DOES IT TAKE FOR A MONETARY POLICY CHANGE IN INTEREST RATES TO AFFECT THE ECONOMY? Different studies have come up with a range of estimates of a monetary impact on real GDP based on different background conditions and time horizons. However, it is fair to say that the typical impacts of a 100 basis point (1 percentage point) increase in the federal funds rate, will, over the course of the subsequent four quarters, be estimated to reduce real GDP by between 0.25% and 0.50%. A similar-sized impact ripples through the economy in the subsequent four quarters of year two, but by the third year the impact is nearly zero. The reverse holds for a decrease in the federal funds rate. Of course, most changes in the federal funds rate are not a singular change, but a series of changes. Nonetheless, this result is based on model simulations

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254 MACROECONOMIC THINKING AND TOOLS to assess a short-term interest rate effect on economic growth and provides some guidance on the magnitude effects.18

[340] WHAT IS THE “YIELD CURVE”? The yield curve, also known as the “term structure of interest rates,” is the relationship (often shown graphically) between yields of similar-quality bonds against their maturities, ranging from shortest to longest.

[341] WHAT IS THE “NORMAL SHAPE” OF THE YIELD CURVE? The normal shape of the yield curve is upward sloping, because it embodies the idea that further out in time that the risks of holding an asset are higher (the “risk premium”), which should be compensated for by a higher interest rate (Figure 10.3).

FIGURE 10.3

“Normal” U.S. Treasury Yield Curve (February 2018)

[342] WHAT IS AN “INVERTED” YIELD CURVE? When short-term maturities of the similar-quality bonds (such as the U.S. Treasury securities) are above the longer-term maturities the yield curve is said to be inverted (Figure 10.4).

[343] WHY DOES THE YIELD CURVE INVERT? An inversion of the term structure of interest rates can occur either from the long end or short end of the term structure. If long-term bond investors expect that current long-term interest

MONETARY POLICY

FIGURE 10.4

“Inverted” U.S. Treasury Yield Curve (September 2019)

rates are likely to decline, money flows into those long-term assets to lock in the higher rates that cause long-term yields to decline. An inverted yield curve occurring in the long end of the term structure is often followed by an economic slowdown or an outright recession, and ultimately, if the slowdown or recession materializes, lower interest rates along all maturity durations of the yield curve. If the Fed is raising short-term interest rates, this also may cause an inversion in the yield curve as long-term investors at some point may anticipate an economic slowdown causing the Fed to reverse that higher interest rate policy. With central bank implementation of LSAPs (a quantitative ease), this can put downward pressure on the yields of longer-maturity assets. A reduction of central bank holdings of those long-term assets (a quantitative tightening) will likely have the opposite effect on longer-maturity assets. These purchases or sales may temporarily affect the shape of the yield curve as monetary policy transitions and in those cases may provide less inferred insight about financial-market psy­ chology of an economic slowdown or recession.

[344] HOW DOES THE YIELD CURVE CHANGE OVER THE BUSINESS CYCLE? A steep yield curve generally occurs at the beginning of an economic expansion as long-term interest rates tend to edge up faster than short-term interest rates that are anchored by the central bank’s monetary policy. This may be best viewed as the difference between a long-term and short-term interest rate—such as the difference between the ten-year U.S. Treasury note yield and the three-month U.S. Treasury bill rate. As the economic expansion matures and typically inflation pressures build, short-term interest rates begin to rise (nudged up by the central bank to guide the economy towards sustainable growth), which narrows the spread between the long-term and short-term rates (Figure 10.5).

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256 MACROECONOMIC THINKING AND TOOLS

The Spread Between the Ten-Year U.S. Treasury Yield and the Three-Month U.S. Treasury Bill Rate

FIGURE 10.5

[345] [ADVANCED] HOW IS MONETARY POLICY VIEWED IN “MODERN MONETARY MACROECONOMICS” OR THROUGH THE “NEW KEYNESIAN MODEL”? Carlin and Soskice observe that “Modern monetary macroeconomics is based on what is increasingly known as the 3-equation New Keynesian model: IS curve, Phillips curve and a monetary policy rule equation. This is the basic analytical structure of Michael Woodford’s book Interest and Prices published in 2003 and, for example, of the widely cited paper ‘The New Keynesian Science of Monetary Policy’ by Clarida et al. … These authors are concerned to show how the equations can be derived from explicit optimizing behavior on the part of the individual agents in the economy in the presence of some nominal imperfections [such as, sticky wages and prices].”19 The New Keynesian School builds microeconomic foundations capture the price and/or wage rigidity.

[346] [ADVANCED] HOW DOES THE “NEW KEYNESIAN MODEL” RELATE TO THE AD/AS MODEL? The three-equation New Keynesian model, which is based on an IS relationship, Phillips curve, and a monetary policy rule, corresponds to the aggregate demand/aggregate supply model as follows, according to Poutineau, Sobczak, and Vermandel: “First, the AS curve is represented by the New Keynesian Phillips curve that relates inflation to the output gap. Second, the AD component of the model combines a dynamic IS curve [that relates the evolution of the output gap to the interest rate] and a MP [monetary policy] schedule [that describes how the nominal interest rate is set by the central bank] following fluctuations in the output gap and in the inflation rate. This model is based on agents’ micro founded decision rules where consumers maximize their welfare subject to an intertemporal budget constraint and where firms maximize their

MONETARY POLICY

profit, subject to nominal rigidities, characterizing the imperfect adjustment of prices on the goods market.”20

[347] [ADVANCED] WHAT POLICY RECOMMENDATIONS WOULD BE ADVOCATED BY THE NEW KEYNESIAN MONETARY THEORIES? In a research article21 by Bank of Canada economist Steve Ambler, he states that “New Keynesian macroeconomic models have become the workhorses for monetary policy analysis by academic economists and central banks.” With regard to inflation, Ambler opines that these models have three channels through which inflation works through the economy. 1

2

3

Since firms set prices at different times, there is price dispersion across firms. This price dispersion increases at higher rates of trend inflation and entails a loss of efficiency in production. [This idea seemingly is connected to the empirical Katona effect—but not explicitly linked.] Since firms set prices under monopolistic competition, their prices are higher than their marginal costs of production. The rate of trend inflation has an effect on the average markup set by firms, and therefore on the size of the distortion that results from monopoly power, which constitutes an additional source of inefficiency. At higher levels of trend inflation, firms’ pricing decisions are relatively less sensitive to their marginal costs. Monetary policy acts via its effects on aggregate demand, which in turn is related to firms’ real marginal costs. Therefore, monetary policy becomes less effective at higher rates of inflation. This leads to a higher variability of inflation, which is also costly.22

Therefore, monetary policymakers facing high inflation would effectively advocate the same policy option as with the Keynesian approach, which is to dampen aggregate demand.

[348] WHAT IS THE QUANTITY THEORY OF MONEY? The quantity theory of money is a framework suggested by classical economists to link eco­ nomic activity, inflation, and the money supply.23 American economist Irving Fisher gave a clear exposition of this theory in his influential book, The Purchasing Power of Money, published in 1911. Fisher’s formulation of the quantity theory of money equated it with the equation of exchange. The equation of exchange is the relationship of the money supply times the velocity of money equals the price level times the quantity of output (also known as nominal GDP), and is written: MV = PQ, where M, Q, and P, respectively, denote measures of the nominal quantity of money, real transactions or physical output per period, and the price level, with V then being the corresponding monetary “velocity.” Classical economists, who held the view that prices and wages were flexible, argued that this framework suggested that goods, services, and factor prices (P) would fully adjust to the level that equates the supply and demand for a particular good or service (Q) in the long run, on the assumption that velocity was relatively

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258 MACROECONOMIC THINKING AND TOOLS

FIGURE 10.6

Velocity of M2

Source: Federal Reserve Board of Governors

fixed. But, alas, velocity is not fixed, though in the 1960s there was a relatively narrow band for M2 velocity, which allowed the monetarist movement that believed in this relationship (though not the flexibility of wages and prices) to flourish. The changing velocity of money ultimately led to the diminished popularity of monetarist theories (which morphed into a new framework) (Figure 10.6).

[349] WHAT IS MEANT BY THE VELOCITY OF MONEY? The velocity of money is the frequency at which one unit of currency (M1 or M2, for example) is used to purchase domestically produced goods and services within a given time period. This means the number of times a dollar turns over or is used to purchase goods and services during a given period of time. Velocity of money is calculated as nominal GDP divided by the monetary aggregate.

[350] [ADVANCED] HAVE THERE BEEN ANY ATTEMPTS TO UNDERSTAND WHY VELOCITY OF MONEY HAS DECLINED? Yes. Many of the reasons for changes in velocity are tied to factors motivating spending (GDP) versus savings. Factors boosting spending will raise the velocity and vice versa. Among those factors, it is believed that the velocity of money is affected by the level of interest rates since higher interest rates encourage savings over spending. Thus, higher interest rates are thought to reduce the velocity of money and vice versa. However, this argument works against the explanation for the decline in velocity over the past 20 years. A second argument is that economic growth expectations may affect future growth. Thus, if businesses expect faster

MONETARY POLICY

economic growth, then they would be more likely to invest in additional capital equipment, which boosts GDP spending. This too seems to work against the empirical evidence of the last 20 years. Another argument made for the decline in velocity is financial innovations that accelerate the payment process and hence speed up the turnover of money. This argument seemingly can contribute to an explanation for the decline in velocity. Finally, it might be argued that the money supply is not properly measured (as the Austrian school has argued for years). Aside from these suggestions, one attempt to understand why the quantity theory of money empirically failed because of changing velocity of money was offered by U.K. Prof. Richard Werner, who was the director of the Centre for Banking, Finance, and Sustainable Development at the University of Southampton.24 Werner observed that the original for­ mulation of the quantity theory (as earlier stated by the late monetarist and University of Chicago Prof. Milton Friedman, as well) was not based on nominal GDP equaling “PQ,” but rather conceptually based on “all transactions,” which included goods and services transaction plus asset transactions. Werner pointed out that although even the Nobel Prize–winning economist Milton Friedman acknowledged that, the reality is GDP does not represent all transactions—only goods and services transactions. This led Werner to reformulate this foundational Quantity Theory of Money into his Quantity Theory of Credit.25 If asset trans­ actions exist (as they do), Werner reformulated the fundamental link between money and the economy into two parts where MV (money multiplied by velocity of money) equals MV for the goods and services transactions plus MV for the asset transactions. He then reformulated PQ (aggregate price multiplied by the real quantity of transactions) to equal PQ for goods and services (which would be nominal GDP) plus PQ for asset transactions. Werner rhetorically asked, “How can money ‘M’ be separated into a portion used for goods and services and a portion used for asset transactions—something that this researcher opined Irving Fisher, John Maynard Keynes, nor Milton Friedman were able to do?” To do this, Werner argues that “M” should be defined as credit—not some metric of the money stock. He opined that “Using total bank credit as the measure of the ‘money supply’ (M) … has the advantage that (a) credit always represents effective purchasing power, as no borrower will take out a loan if there is no plan to use the money for transactions; (b) it becomes possible to define effective purchasing power clearly—namely not bank liabilities, but bank assets of private sector liabilities to the bank sector; and (c) credit aggregates are available by economic sector and hence provide us with additional information about the direction of purchasing power—something that deposit aggregates cannot tell us.”26 In his next step, Werner used econometrics with data for the Japanese economy to disaggregate transactions into goods and services (nominal GDP) and asset transactions (proxied by real estate transactions). The importance of this work was that Werner’s reformulated model attempted to explain why the traditional measure of velocity of money (without the goods and services versus asset distinction) “gave the illusion of a velocity decline.” Empirically he showed for Japan (and separately for Spain and the United Kingdom) that the velocity of money remained relatively constant for goods and services, if segmented as he proposed. His work showed a high correlation between the growth in “productive” credit supporting nominal GDP and growth of “financial” or “speculative” credit used to purchase financial assets (and he further argued that “speculative credit” can lead to asset inflation, asset bubbles, and banking crises). There has been little supplemental research to

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260 MACROECONOMIC THINKING AND TOOLS expand this Quantity Theory of Credit to other economies. Moreover, this also highlights a probable need for economy-wide benchmarks of “asset prices,” “asset quantities,” and credit aggregates (maybe, “C1,” “C2,” and “C3,” and so forth, with increasing expansive coverage—similar in design of monetary aggregates) to be developed and maintained by sta­ tistical agencies.

[351] IF MONETARY THEORIES ARE LACKING STRONG EMPIRICAL SUPPORT TO GUIDE POLICY, THEN WHAT IS HELPING TO FILL THE GAP? As monetary theories became more empirically challenged, “rules of thumb” began to fill in the void. These rules offer systematic and pragmatic monetary policy implementation. For example, the European Central Bank’s monetary approach has been described as “rule-based, but not rule-bound.”

[352] WHAT IS THE TAYLOR RULE FOR SETTING THE FEDERAL FUNDS RATE? One of the most quoted rules for setting monetary policy was published in 1993 by John Taylor, an economist from Stanford University. The Taylor rule27 is a simple equation—essentially, a rule of thumb—that is intended to describe the interest rate decisions of the Federal Reserve’s Federal Open Market Committee (FOMC). The Taylor rule states that the short-term interest rate should be determined according to three factors: • • • •

Where actual inflation is relative to the targeted level that the Fed wishes to achieve, How far economic activity is above or below its “full employment” level (actual real GDP vs. potential), and What the level of the short-term interest rate (r-star) is that would be consistent with full employment. Taylor argued that the Federal Reserve should set its policy rate in the following manner: ‐ Policy Rate = 2 + Inflation + ½ (Inflation – 2) + ½ (Real output – Potential output) ‐ The policy rate is the federal funds rate, the initial “2” represents an estimate of the neutral interest rate, inflation is the growth rate in the PCE price index, real output is real GDP growth rate, and potential output is real potential GDP growth rate. ‐ There is no consensus about the size of the coefficients in the policy rule. Taylor assumed that the 2% figure for the target rate of inflation (which has become the standard) and ½ “seemed right” for the coefficient of the GDP gap, and 2 real funds rate, or neutral rate, was thought reasonable). ‐ The rule recommends a relatively high federal funds rate when monetary policy is tight and a relatively low federal funds rate when monetary policy is easy.

MONETARY POLICY

‐

No rule-of-thumb is a perfect guide for policy since the original Taylor rule would suggest negative interest rates during the global financial crisis and during the COVID-19 pandemic (Figure 10.7).

FIGURE 10.7

Taylor “Original” Monetary-Policy Rule for Setting the Federal Funds Rate

[353] [ADVANCED] WHAT IS THE TAYLOR “INERTIAL” RULE FOR SETTING THE FEDERAL FUNDS RATE? This version of the Taylor rule28 is based on growth rates and is defined as: ffrt = [0.75 × ffrt−1] + 0.25 × [(1.5 × (pt − pt-4 − 2) + rgdpt − rgdppott)], where ffr is the federal funds rate, p is the PCE price inflation rate, rgdp is real GDP growth, and rgdppot is real potential GDP growth. The subscript “t” represents the time period. Note that 75% of the current estimate of the federal funds rate is determined by the prior period and only 25% is determined by the inflation rate gap and the output gap. Also note that the “2” represents the target inflation rate (Figure 10.8).

FIGURE 10.8

Taylor “Inertial” Monetary-Policy Rule for Setting the Federal Funds Rate

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262 MACROECONOMIC THINKING AND TOOLS

[354] [ADVANCED] WHAT OTHER RULES-OF-THUMB EXIST FOR SETTING THE FEDERAL FUNDS RATE? Since Taylor’s 1993 seminal work on monetary policy rules, there have been a number of proposed rules some of which the Federal Reserve examined.29 • • • • •

The balanced-approach rule is similar to the Taylor rule except that the coefficient on the resource utilization gap is twice as large as in the Taylor rule. The effective-lower-bound (ELB)-adjusted rule recognizes a lower bound constraint and suggests setting the policy rate at the ELB, whenever this rule prescribes a rate below the ELB. The inertial rule prescribes a response of the federal funds rate to economic developments that is spread out over time. The first-difference rule, like the inertial rule, relates the current value of the federal funds rate to its previous value. The St. Louis Fed’s modernized monetary policy rule, which uses a low value for the short-term real interest rate (“r-star”), attenuates the feedback parameter from the real economy to inflation by a factor of ten and replaces the inflation gap with an inflation expectations gap.

[355] WHAT IS THE VALUE AND DRAWBACK OF MONETARY POLICY RULES? St. Louis Federal Reserve Bank President James Bullard argued, “Monetary policy rules are useful because they yield a recommended level for the policy rate as a function of current conditions in the economy.” And “this helps set a baseline for the monetary policy debate.” Additionally, Milton Friedman felt that when the Fed follows a discretionary policy, the policy tends to be implemented too slowly, thus a policy rule might be helpful to accelerate the reaction. Although the Federal Reserve notes that “Simple monetary policy rules, which relate a policy interest rate to a small number of other economic variables, can provide useful guidance to policymakers,”30 they can be problematic since they do not take into account a broader range of circumstances and for this reason discretionary monetary policy is vital.31 Moreover, the Federal Reserve also notes that these simple rules do not account for any policy tool other than the federal funds rate and ignore, for example, large-scale asset purchases.

[356] [ADVANCED] WHAT IS THE M C CULLUM RULE? In 1959, Milton Friedman suggested that the Fed should increase the money supply by a constant 4% rate to avoid inflation and imbalances in the economy. This is considered a non-activist rule or passive rule. However, other rules—such as the Taylor rule—are considered activist rules since they require the Federal Reserve to adjust the federal funds rate to changing economic condi­ tions. Additional activist policy rules that have been suggested include one by Stanford’s Robert Hall and Harvard’s Gregory Mankiw based on a forecast of future nominal income, and one

MONETARY POLICY

proposed by Harvard’s Martin Feldstein and James Stock that uses the M2 money stock to target nominal GDP.32 However, in 1988, Bennett McCallum of Carnegie-Mellon University pro­ posed a monetary policy rule that gained considerable popularity at the time. The McCallum rule operates through the monetary base and has three components: “(1) the target for current growth of nominal GDP [GDP at market value]; (2) a moving-average adjustment for changes in velocity; [and] (3) the difference between the target and actual nominal GDP.”33 The target of nominal GDP could be nominal potential GDP, as calculated by the Congressional Budget Office. However, the two quantitative ease episodes in 2008 and 2020, which vastly inflated the monetary base, have all but eliminated the value of this monetary base rule.34

[357] [ADVANCED] HOW DOES THE M C CULLUM RULE COMPARE TO THE TAYLOR RULE? A study by Razzak for the pre-quantitative ease period in the U.S. found the original Taylor and the McCallum rules told similar policy stories.35 Another study for the European Central Bank by Jung also found that “both rules were fairly close to actual policy” and neither was superior to the other.36

[358] [ADVANCED] WHAT IS “GOODHART’S LAW”? Monetary policy theorist Charles Goodhart, who spent most of his career at the Bank of England and the London School of Economics, “jokingly” said at a conference in Sydney, Australia that, “any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.” His statement was later rephrased by British anthropologist Marilyn Strathern that “When a measure becomes a target, it ceases to be a good measure.”37 The idea is closely related to the Lucas Critique, which is relatively ingrained in macro­ economics. Goodhart’s Law has been used as a partial explanation (along with financial innovation) why the monetary aggregates became unreliable policy tools as the primary transmission mechanism from monetary policy to the real economy.38

[359] WHAT IS THE ROLE OF A CENTRAL BANK? A central bank—such as the Federal Reserve—is responsible for implementing monetary policy, promoting stability in the financial system (and functions as the “lender of last resort”), and to provide banking services for commercial banks and the federal government.

[360] WHAT IS MEANT BY THE “LENDER OF LAST RESORT”? The term “lender of last resort” implies that the central bank will provide emergency loans to financial institutions during a financial crisis.

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[361] HOW IS THE FEDERAL RESERVE SYSTEM STRUCTURED? The Federal Reserve (“Fed”) is structured as a regional system with elements of public and private oversight. This system is structure under the Board of Governors of the Federal Reserve System, headquartered in Washington, D.C. The board has seven members, each of which serves a maximum term of 14 years or the remainder of an unexpired term. Each governor is appointed by the president of the United States and confirmed by the Senate. The chairman, who must be a member of the Board of Governors, serves a four-year, renewable term. The system is comprised of 12 regional Federal Reserve banks, each of which is responsible for supporting the commercial banks and economy generally in its district (Figure 10.9).

FIGURE 10.9

The Twelve Federal Reserve Banks

Source: Federal Reserve Board of Governors

The determination of the location of the regional banks is historic, based on hubs of business activity in the early 1900s when the Federal Reserve System came into existence. The regional banks were the physical check-clearing facilities for the system—largely a legacy role.

[362] WHAT IS THE FEDERAL OPEN MARKET COMMITTEE? The Federal Open Market Committee (FOMC) is the monetary policy arm of the Federal Reserve Board. The FOMC usually regularly meets eight times a year (but can have special meetings, if warranted). The FOMC consists of seven members of the Board of Governors, the president of the Federal Reserve Bank of New York (since the NY Fed executes FOMC

MONETARY POLICY

decisions), and the presidents of four other Federal Reserve banks (on a rotating basis). The chairman of the Board of Governors presides as the FOMC chairman. The FOMC (along with the Federal Reserve Board itself) currently implements monetary policy changes through administered interest rates (the federal funds rate, the interest rate paid by the Fed on reserve balances, and the overnight reverse repurchase agreement interest rate). This is supplemented by changes in the discount rate, through “open market operations,” and several other policy instruments it has available to impact liquidity in the economy.

[363] WHAT IS MEANT BY OPEN MARKET OPERATIONS? Open market operations are asset purchases or sales that occur for three reasons: (1) to maintain “ample levels” of banking system reserves, which also support interest rate control through the Federal Reserve’s floor and subfloor interest rate bands around the federal funds rate; (2) to affect financial conditions (quantitative easing or tightening); and (3) to smooth out market disruptions. The New York Federal Reserve Bank’s Trading Desk conducts asset purchases or sales, as directed by the FOMC, by executing trades in the fixed‐income market with member banks to purchase or sell bonds (such as Treasury securities and agency mortgage‐backed securities (MBS)): • •

When the Fed buys bonds, the monetary base increases and that puts downward pressure on interest rates. When the Fed sells bonds, the monetary base decreases and that puts upward pressure on interest rates.

[364] WHY IS IT CALLED OPEN MARKET OPERATIONS? The Federal Reserve’s trading activity is termed open market operations, because the price of securities that are purchased or sold by the Fed are determined by competitive bidding among the banks. It does business with its member banks, which are called primary dealers of which today there are two dozen. Primary dealers include U.S. based and foreign based banks with a U.S. branch. Some of the primary dealers are Bank of America securities, BMO capital markets, Citi group global markets, Goldman Sachs, Deutsche Bank securities, Nomura Securities International, and Wells Fargo securities, to name a few. A primary dealer functions as a trading party of the New York Fed in its implementation of monetary policy. Remember that the Federal Reserve System implements its open market operations through its New York branch.

[365] WHAT IS MEANT BY THE “FEDERAL FUNDS RATE”? The federal funds rate is the short-term interest rate, controlled by the Federal Reserve, at which depository institutions lend reserve balances to other depository institutions overnight.

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[366] HOW ARE OPEN MARKET OPERATIONS PERFORMED? Open market operations (OMOs) operate through the banking system the same way as any deposit or withdrawal from the private sector, which adds to or subtracts from bank reserves (but with a bigger bang). OMOs are conducted by the New York Federal Reserve Bank under instructions from the FOMC. The key role of OMOs is to provide adequate reserves for the banking system—irrespective of a change in monetary policy. This means the Fed economists need to estimate the banking system’s daily liquidity need and adjust reserves accordingly. There are lots of reasons why reserve needs change daily based on seasonal needs, tax payments, quarterly government bond refunding purchases, etc. This activity is not related to policy changes. However, OMOs are also used to build up or reduce the size of the Federal Reserve’s balance sheet (quantitative easing or quantitative tightening/nor­ malization). Historically, OMO is how typical monetary policy changes were implemented. However, today when the FOMC changes its monetary policy stance, the change is im­ plemented in what is known as an “ample reserves” environment where the Federal Reserve will guide the federal funds rate (its main policy rate) using a “floor” (which is the interest rate on reserve balances) and a “subfloor” interest rate (which is the overnight reverse repurchase agreement rate). Both of those interest rates are administered rates set by the Federal Reserve Board.

[367] WHAT ARE THE FEDERAL RESERVE’S MONETARY POLICY TOOLS? The full range of Federal Reserve policy tools include: • •

• • • • • •

Federal funds rate. Discount rate: The interest rate charged by the central bank on the loans that it gives to other commercial banks. (Set by the Federal Reserve Board—not the FOMC—and any change must be requested by a regional Federal Reserve Bank) Rate of interest on reserve balances. Reserve requirement. Large-scale-asset purchases, or LSAPs, and sales. Also referred to as “balance sheet policy.” Yield-curve control. Direct lending facilities. Forward guidance.

Some of these policy instruments in the Fed’s tool kit are no longer used or have not been used, such as yield curve control (where the Federal Reserve could purposefully impact both short-term and long-term interests through its holdings of long-term assets). The Bank of Japan uses yield-curve control. The Bank of Japan also is the only central bank that holds domestic stock (ETFs – exchange traded funds—a basket of securities that mirror market movement and not individual stocks).

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[368] WHAT IS MEANT BY LARGE-SCALE ASSET PURCHASES OR SALES BY THE CENTRAL BANK? Large-scale asset purchases, which is more commonly referred to as a “quantitative ease,” occur when a central bank purchases long-term government and private mortgage-backed securities to make credit available to the economy in hopes of stimulating aggregate demand. The reverse of this process (sales of long-term securities) is dubbed a “quantitative tightening” (QT) or “normalization.”

[369] WHAT IS THE HISTORY OF THE TERM “QUANTITATIVE EASE”? In a July 2013 newsletter by the United Kingdom’s Royal Economic Society,39 Richard Werner described the history of the term “quantitative ease” based in his 1995 research on the Japanese monetary system. Werner wrote, “Since the expression ‘credit creation’ was considered difficult to understand in Japanese, [he] prefaced the standard Japanese expression for monetary stimulation (‘monetary easing’ or ‘easing’) with the word ‘quantitative’ to declare that ‘Quantitative Easing’, defined as credit creation for GDP transactions, would create a recovery.” Werner’s original dis­ cussion of the concept appeared in the Nikkei newspaper and was based on his ongoing economic research, which embraced the conceptual and empirical foundation that “money is credit.”40

[370] WAS THE 2020 QUANTITATIVE EASE GLOBALLY DUE TO THE PANDEMIC? Yes. These graphs highlight the surge in central bank assets among some of the key global economies because of massive liquidity injections into their respective banking systems (Figures 10.10–13).

FIGURE 10.10

Bank of Canada: Total Assets

Source: Bank of Canada

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268 MACROECONOMIC THINKING AND TOOLS

FIGURE 10.11

European Central Bank: Total Assets

Source: European Central Bank

FIGURE 10.12

U.S. Federal Reserve System: Total Assets

Source: Federal Reserve Board of Governors

MONETARY POLICY

FIGURE 10.13

Bank of Japan: Total Assets

Source: Bank of Japan

[371] WHEN WAS A QUANTITATIVE EASE STRATEGY FIRST USED? The Bank of Japan was the first central bank to implement a quantitative ease in 2001.

[372] HOW DO LARGE-SCALE ASSET PURCHASES (LSAP) OR SALES BY THE CENTRAL BANK EFFECT INTEREST RATES? The short answer is as the central bank buys those assets, the central bank pushes down interest rates on those assets, and vice versa when the central bank sells them. More formally, a New York Fed study41 put it this way: “The primary channel through which LSAPs appear to work is by affecting the risk premium on the asset being purchased. By purchasing a particular asset, a central bank reduces the amount of the security that the private sector holds, displacing some investors and reducing the holdings of others, while simultaneously increasing the amount of short-term, risk-free bank reserves held by the private sector. In order for investors to be willing to make those adjustments, the expected return on the purchased security has to fall. Put differently, the purchases bid up the price of the asset and hence lower its yield. This pattern was described by Tobin and is commonly known as the ‘portfolio-balance’ effect.”42

[373] HOW MUCH OF AN INTEREST RATE IMPACT ON LONG-TERM INTEREST RATE ASSETS DID THE FEDERAL RESERVE’S QUANTITATIVE EASE HAVE IN THE 2008 EPISODE (QE1 THROUGH QE3)? The Federal Reserve launched three episodes of quantitative easing to counter the global financial crisis of 2008. On November 25, 2008, QE1, was announced by the Fed to

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270 MACROECONOMIC THINKING AND TOOLS purchase up to $600 billion of agency mortgage-backed securities (MBS) and agency debt (Fannie Mae and Freddie Mac). On March 18, 2009, the FOMC announced that the program would be expanded by an additional $750 billion in purchases of agency MBS and agency debt and $300 billion in purchases of Treasury securities. The second episode of quantitative ease, QE2, began on November 3, 2010, when the Fed announced pur­ chases of $600 billion of longer dated treasuries at a pace of $75 billion per month. The third episode, QE3, was launched in September 2012 with Fed purchasing $40 billion of additional MBS per month, which was increased in December 2012 to $85 billion per month. That quantitative ease ended in October 2014. An evaluation by the NY Fed of the interest rate impact of these QE programs (only) through March 2010 found that the ten-year term interest rate was reduced by between 30 and 100 basis points.43 Presumably, the cumulative effect was even larger since the QE program went on for another four years.

[374] HOW DOES YIELD-CURVE CONTROL WORK? Although the Federal Reserve has not embraced yield-curve control at this point in time, it is employed by the Bank of Japan and may find greater acceptance globally over time. Essentially, there are four yield-curve control strategies that might be employed by a central bank. Those are targeting the level of a short-term or long-term interest rate, the change in the level of a short-term or long-term interest rate, the spread between a long-term and short-term interest rate, or the change in the slope of the spread between the long-term and short-term interest rate. Many of these strategies have not been fully explored theoretically or practically. TABLE 10.1

Setting Monetary Policy with Yield-Curve Control Strategies Setting Monetary Policy with Yield-Curve Control Strategies

Policy Variable

Absolute Position

Relative Position

Interest Rates (either short-term or long-term rate)

Level

Change in Level

Interest-Rate Spreads between the long-term and short-term interest rate

Slope

Shift in Slope

[375] WITH THE GROWTH OF MARKET-BASED FINANCING THROUGH SHADOW BANKING RATHER THAN TRADITIONAL BANK FINANCING, HAS MONETARY POLICY BEEN DIMINISHED? No. An ECB study found that its monetary policy actions through its policy interest rates “remain a powerful policy instrument also in a world in which market-based finance has expanded measurably.”

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[376] WHAT ARE THE BENEFITS OF CENTRAL BANK DIGITAL CURRENCIES (CBDC) FOR CENTRAL BANKS? Generally, it is argued that CBDC could create a higher degree of financial inclusion (more people having access to digital currency) would make monetary policy more effective since it would be able to reach more parts of the economy. Additionally, the CBDC would provide more information for the central bank where the money is going and where debt may be accumulating. It is also argued that CBDC could eliminate the “effective lower bound on interest rates” with the demise of traditional paper and coin currency.

[377] WHAT ARE THE CONCERNS OF CENTRAL BANK DIGITAL CURRENCIES (CBDC) FOR CENTRAL BANKS? Two major concerns about the use of CBDC are: (1) If consumers and businesses use CBDC, why would they need to use a commercial bank? Would this then weaken the “maturity and liquidity transformation” that banks engage in to make a profit? (2) Will there be acceptance of CBDC given that users will lose the anonymity inherent in using cash for purchases?

[378] SHOULD THE U.S. MONETARY POLICY GOALS OF HIGH EMPLOYMENT AND LOW INFLATION BE EXPANDED TO INCLUDE SOCIAL OBJECTIVES? In thinking about the role and impact of monetary policy on income inequality, the Bank of England noted that a monetary expansion would tend to benefit: (1) financial market participants; (2) households whose financial liabilities exceed their assets; (3) borrowers; and (4) households that are more exposed to unemployment. On the other hand, a monetary policy expansion would likely harm: (1) low-income households vulnerable to inflation; (2) households with short-term assets; and (3) savers. Generally, the thinking is that a monetary expansion would likely lead to more inflation. Policymakers have been aware of these distributional impacts. Witness, for example, the former president of the Kansas City Federal Reserve Thomas Hoenig, who as a FOMC member opposed the quantitative easing policy in 2008, which he feared would lead to inflation but also would have undesirable “allocative” impacts on the economy. By allocative impacts, Hoenig, argued that the Fed’s quantitative ease policy would encourage financial-market speculation, cause asset prices to rise, and shift wealth to the rich, who owned more of those financial assets.

[379] IS MONETARY POLICY THE RIGHT TOOL TO ADDRESS INCOME-DISTRIBUTION ISSUES IN THE ECONOMY? In 2016, then–Bank of England’s Governor Mark Carney, who also was previously a former head of the Bank of Canada, rhetorically asked, “has monetary policy robbed savers to pay

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272 MACROECONOMIC THINKING AND TOOLS borrowers, [and acted as] Robin Hood in reverse?”44 He answered with a solid “no,” and went on to explain that “all monetary policy has distributional effects, but it is rightly the role of elected governments to take measures to offset some or all of those”45 distributional effects—not the monetary authorities. But still, monetary policymakers need to be mindful of how monetary policy can affect income inequity. To this end, a 2018 BOE study opined that a monetary policymaker “needs to understand the distributional consequences of its policy decisions.”46 The study suggested that whether monetary policy should be responsible for income-distribution equity has a lot to do with how effective it might be in this nontraditional role, which historically has been the realm of fiscal policy. •

•

The BOE researchers isolated six channels through which a change in the policy rate would filter through the economy, and ultimately to the income distribution. Assuming a cut in interest rates (or a quantitative ease) then the study found: ‐ Net interest income would be reduced on in interest payments and lower interest income, which means that borrowers are made better off, but savers are made worse off. ‐ Net financial wealth would increase in the value of equities and other assets. Holders of these assets gain. ‐ Employment and wages would be impacted through looser monetary policy, which would likely boost employment and wage growth by stimulating demand. The impact of this change on households is likely to vary by age and education. ‐ Real value of nominal debts and deposits would tend to be lower as higher inflation reduces the real value of debts and deposits that have fixed nominal interest rates. ‐ Net property wealth would increase. Property wealth is affected through stronger housing demand that bids up home prices, which in turn would increase the value of housing wealth. Existing homeowners gain, but future home-owners are made worse off as the cost of future housing increases. ‐ Net pension wealth would see an increase in the value of pension assets and claims. The overall impact on income distribution will depend on the type of pension, and how near one is to retirement. ‐ Clearly, the magnitude of these income distributional effects would be determined based on a host of factors, such as age, homeownership, amount of savings, amount of debt, and so forth. The upshot of this Bank of England study was that it found, “monetary policy has the potential to affect income and wealth inequality in the short run. [Empirically,] changes in interest payments and receipts, higher financial asset prices, higher house prices and better labor market outcomes from a stronger economy have all been important channels through which monetary policy has affected the distribution of income and wealth, [but] our results suggest that the marginal contribution [to changes in income distribution] from monetary policy was small.”47 In the United States, fiscal policy may not be addressing social issues because of the political in-fighting in Congress. As a result, this seemingly has given rise to proposals,

MONETARY POLICY

•

such as one from then–U.S. presidential candidate Joe Biden, to have monetary policy also address some social issues. But, monetary policy is neither the most suitable policy to address this inherently long-run problem, nor is monetary policy best suited to do little more than to shift income distributions marginally in the short run, as various studies from the Bank of England, the Bank for International Settlement, and the International Monetary Fund found.48

[380] IS MONETARY POLICY THE RIGHT TOOL TO ADDRESS RACIAL AND ECONOMIC EQUITY IN THE ECONOMY? The Federal Reserve’s monetary policy is quite limited in its ability to address racial and economic equity in the economy—which often is viewed as a role for fiscal policy, however, the Federal Reserve’s regulatory role over the banking industry may be more likely to effectuate that social objective. Nonetheless, the U.S. Congress has advanced this role for the Federal Reserve. On June 15, 2022, with a vote of 215 to 207 the U.S. House of Representatives passed, with the White House’s support, H.R. 2543—Federal Reserve Racial and Economic Equity Act.49 The House legislation, which is not given a high chance of passage in the Senate, directs “the Board of Governors of the Federal Reserve System and the Federal Open Market Committee [to] exercise all duties and functions in a manner that fosters the elimination of disparities across racial and ethnic groups with respect to employment, income, wealth, and access to affordable credit, including actions in carrying out: (1) monetary policy; (2) regulation and supervision of banks, thrifts, bank holding companies, savings and loan holding companies, and nonbank financial compa­ nies and systemically important financial market utilities designated by the Financial Stability Oversight Council; (3) operation of payment systems; (4) implementation of the Community Reinvestment Act of 1977; (5) enforcement of fair lending laws; and (6) community development functions.” The Federal Reserve is directed further to report to Congress on “disparities in employment, income, and wealth across racial and ethnic groups as well as other specific segments of the population; and plans, activities, and actions of the Board and the Federal Open Market Committee to minimize and eliminate disparities across racial and ethnic groups with respect to employment, wages, wealth, and access to affordable credit.” According to the Congressional Budget Office, already “under current law, the Federal Reserve includes certain demographic information by race and ethnicity in several of its major reports, including in its semi-annual testimony and monetary policy report to the Congress,”50 but the Fed’s analysis of these issues would have to be ex­ panded. This version of the U.S. House of Representative’s legislation that was passed was much more expansive than the original bill introduced, but with regard to the Federal Reserve’s role may unrealistically conflate the Fed’s monetary policy with its regulatory policy.

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Issues to Think About Today, monetary policy is influenced theoretically by the new Keyesian model. the New Keynesian model, which has gained popularity among central banks, encompasses a forward-looking “is” equation (technically, the “is” equation is where savings equals investment, but practically it means where aggregate demand equals aggregate supply for various interest rates) and is characterized as aggregate demand, a Phillips curve describing aggregate supply, and a rule for the central bank’s principal policy tool, the short-term interest rate. •

•

•

To paraphrase, the chief economist of the bank of England, who asked in 2022: “Is it sufficient for the central bank’s policy rate alone to be the focus of monetary theories and models, or should other measures of monetary and financial conditions be required to understand the monetary transmission?” The Bank of Japan uses “yield-curve control” strategies to manage both short-term and long-term interest rates through market operations. should the federal reserve adopt such a strategy? currently, the fed says that its primary policy rate is the federal funds rate. what are the pros and cons for the fed to use yield-curve control strategies? According to the late monetarist Milton Friedman and the quantity theory of money, inflationary consequences of rapid money supply growth will certainly begin within one or two years after the rise. is monetary policy in the major economies the main cause of the 2022 surge in inflation?

NOTES 1 Halley Goodman, “The Formation of the Bank of England: A Response to Changing Political and Economic Climate, 1694,” Penn History Review, University of Pennsylvania, vol. 17, no. 1 (Fall 2009), p. 10. 2 Public Law 95–188, November 16, 1977. 3 See Frederic S. Mishkin, “Monetary Policy and the Dual Mandate,” address at Bridgewater College, Bridgewater, VA, April 10, 2007. 4 “The FOMC’s Longer-Run Goals and Policy Strategy,” Federal Reserve Board of Governors, December 13, 2011, p. 197. 5 Lael Brainard, “Full Employment in the New Monetary Policy Framework,” Speech at the Inaugural Mike McCracken Lecture on Full Employment Sponsored by the Canadian Association for Business Economics, January 13, 2021, https://www.federalreserve.gov/newsevents/speech/brainard20210113a.htm. 6 Renuka Diwan, Sylvain Leduc, and Thomas M. Mertens, “Average-Inflation Targeting and the Effective Lower Bound,” FRBSF Economic Letter, Federal Reserve Bank of San Francisco, August 10, 2020. 7 In the Federal Reserve’s January 25, 2012 statement, “Federal Reserve issues FOMC statement of longer-run goals and policy strategy,” it stated the FOMC target for inflation is “measured by the annual change in the price index for personal consumption expenditures, [which] is most consistent over the longer run with the Federal Reserve’s statutory mandate.”

MONETARY POLICY 8 Jerome Powell’s press conference after the FOMC meeting, November 3, 2021. 9 Charles Plosser, “The Fed’s Risky Experiment,” Economics Working Paper 21116, Hoover Institution, Stanford University, June 18, 2021, p. 1. 10 See: Knut Wicksell, Interest and Prices, London: Macmillan, 1936. Translation of 1898 edition by R.F. Kahn, p. 102. 11 See, for example, Robert Barsky, Alejandro Justiniano, and Leonardo Melosi, “The Natural Rate of Interest and Its Usefulness for Monetary Policy,” American Economic Review, 104(5), 2014, pp. 37–43; Kathryn Holston, Thomas Laubach, and John C. Williams, “Measuring the Natural Rate of Interest: International Trends and Determinants,” Journal of International Economics, 108, May 2017, S59–S75, https://www.frbsf.org/economic-research/publications/working-papers/2016/wp2016-11.pdf; Michael T. Kiley, “What Can the Data Tell Us About the Equilibrium Real Interest Rate?” Finance and Economics Discussion Series 2015–077. Board of Governors of the Federal Reserve System 2015; Thomas Laubach and John C. Williams, “Measuring the Natural Rate of Interest,” Review of Economics and Statistics, 85(4), November 2003, pp. 1063–1070. 12 John M. Roberts, “An Estimate of the Long-Term Neutral Rate of Interest, Board of Governors of the Federal Reserve System, September 5, 2018, https://www.federalreserve.gov/econres/notes/ feds-notes/estimate-of-the-long-term-neutral-rate-of-interest-20180905.htm. 13 Ibid. 14 Thomas Laubach and John C. Williams, “Measuring the Natural rate of Interest,” The Review of Economics and Statistics, vol. 85, no. 4 (Nov. 2003), pp. 1063–1070. 15 Kathryn Holston, Thomas Laubach, and John C. Williams, “Measuring the Natural Rate of Interest: International Trends and Determinants,” Working Paper 2016–11, Federal Reserve Bank of San Francisco, December 2016. http://www.frbsf.org/economic-research/publications/workingpapers/ wp2016-11.pdf. 16 https://www.dallasfed.org/news/speeches/kaplan/2018/rsk181024.aspx#appendix. 17 Richard H. Clarida, “The Federal Reserve’s New Monetary Policy Framework: A Robust Evolution,” Speech at the Peterson Institute for International Economics, Washington, D.C., August 31, 2020, https://www.federalreserve.gov/newsevents/speech/clarida20200831a.htm. 18 See, for example, Glenn Rudebusch, “What Are the Lags in Monetary Policy?,” FRBSF Weekly Letter, Federal Reserve Bank of San Francisco, No. 95–05, February 3, 1995. 19 Wendy Carlin and David Soskice, “Teaching Intermediate Macroeconomics using the 3-Equation Model,” Contributions to Macroeconomics, vol. 5, no. 1, 2005. The foundational reference for this approach is: Michael Woodford, Interest and Prices: Foundations of a Theory of Monetary Policy. Princeton University Press, Princeton, 2003. An additional discussion is found in Richard Clarida, Jordi Gali and Mark Gertler, “The Science of Monetary Policy: A New Keynesian Perspective,” Journal of Economic Literature, vol. 37, no. 4 (1999), pp. 1661–1707. 20 Jean-Christophe Poutineau, Karolina Sobczak, and Gauthier Vermandel, “The analytics of the New Keynesian 3-equation Model,” 2015, ffhal-01194642f, p. 4. 21 Steve Ambler, “The Costs of Inflation in New Keynesian Models,” Bank of Canada Review, Winter 2007–2008, pp. 5–14. 22 Ibid, pp. 5–6. 23 It might be noted that this theoretical link between money and the economy (that is, MV = PQ) was held by Classical economists, Keynesian economists, Monetarists, the New Classical economists (who advocated rational expectations or real business cycles theories), and the Post Keynesian economists. Only the New Monetary Policy Consensus economists jettisoned the idea and focused attention on interest rates directly and suggested that money does not matter. 24 Richard A. Werner, “Towards a New Monetary Paradigm: A Quantity Theorem of Disaggregated Credit, with Evidence from Japan,” Kredit und Kapital, vol. 30 (no. 2), July 1997, pp. 276–309. 25 Richard A. Werner, “Towards a New Research Programme on ‘Banking and the Economy’— Implications of the Quantity Theory of Credit for the Prevention and Resolution of Banking and Debt Crises,” International Review of Financial Analysis, vol. 25 (2012), pp. 1–17.

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276 MACROECONOMIC THINKING AND TOOLS 26 Richard A. Werner, “Towards a New Monetary Paradigm: A Quantity Theorem of Disaggregated Credit, with Evidence from Japan,” Kredit und Kapital, vol. 30, no. 2 (Duncker & Humblot, Berlin, July 1997), p. 283. 27 John B. Taylor, “Discretion versus Policy Rules in Practice,” Carnegie-Rochester Conference Series on Public Policy 39, North-Holland (1993), pp. 195–214. 28 This is based on quarterly data. John B. Taylor, “Staggered Price and Wage Setting in Macroeconomics,” In J. B. Taylor and M. Woodford, eds., Handbook of Macroeconomics, (NorthHolland, Amsterdam, 1999), pp. 1010–1050. 29 See: https://www.federalreserve.gov/monetarypolicy/policy-rules-and-how-policymakers-use-them.htm. 30 Monetary Policy Report (to Congress), Board of Governors of the Federal Reserve System (June 17, 2022), p. 4. 31 Ben S. Bernanke, “The Taylor Rule: A Benchmark for Monetary Policy?,” The Brookings Institution, Washington, D.C., April 28, 2015, https://www.brookings.edu/blog/ben-bernanke/ 2015/04/28/the-taylor-rule-a-benchmark-for-monetary-policy/. 32 Dean D. Croushore and Tom Stark, “Evaluating McCallum’s Rule for Monetary Policy,” Business Review, Federal Reserve Bank of Philadelphia, January/February 1995, pp. 3–14. 33 Ibid., p. 7. 34 Alexander Jung, “Does McCallum’s Rule Outperform Taylor’s Rule during the Financial Crisis?,” The Quarterly Review of Economics and Finance, vol. 69 (2018), pp. 9–21. 35 W. Razzak, “Is the Taylor rule really different from the McCallum rule?”, Contemporary Economic Policy, vol. 21, no. 4 (2003), pp. 445–457. 36 Alexander Jung, p. 19. 37 Peter Coy, “Goodhart’s Law Rules the Modern World. Here Are Nine Examples,” Bloomberg Businessweek, (March 26, 2021, https://www.bloomberg.com/news/articles/2021-03-26/goodharts-law-rules-the-modern-world-here-are-nine-examples. 38 Huw Pill, “What Did the Monetarists Ever Do for Us?,” Speech given at Walter Eucken Institut / Stifung Geld und Währung Conference – Inflation and Debt: Challenges for Monetary Policy after Covid-19, Bank of England (June 24, 2022). 39 Richard A. Werner, “Quantitative Easing and the Quantity Theory of Credit,” Royal Economic Society Newsletter (July 2013), pp. 20–22, https://www.res.org.uk/resources-page/july-2013-newsletterquantitative-easing-and-the-quantity-theory-of-credit.html. 40 Richard A. Werner, Keiki kaifuku, ryōteki kinyū kanwa kara, (“How to Create a Recovery through ‘Quantitative Monetary Easing’), The Nihon Keizai Shinbun (Nikkei), ‘Keizai Kyōshitsu’ (‘Economics Classroom’), 2 September 1995 (morning edition), p. 26; English translation by T. John Cooke (November 2011), https://eprints.soton.ac.uk/340476/1/Translation_Werner_QE_Nikkei_Sep_ 1995_final1.pdf. 41 Joseph Gagnon, Matthew Raskin, Julie Remache, and Brian Sack, “Large-Scale Asset Purchases by the Federal Reserve: Did They Work?,” FRBNY Economic Policy Review, Federal Reserve Bank of New York, May 2011, pp. 41–59. 42 Ibid., p. 42. 43 Ibid., p. 59. 44 Mark Carney, “The Spectre of Monetarism,” Roscoe Lecture, Liverpool John Moores University, Bank of England, December 5, 2016. 45 Ibid, p. 10. 46 Haroon Mumtaz and Angeliki Theophilopoulou, “The Impact of Monetary Policy on Inequality in the UK: An Empirical Analysis,” Bank of England presentation, May 2017. 47 Ibid. 48 The Bank for International Settlement (BIS) wrote in its 2021 Annual Report, “The long-term rise in economic inequality since the 1980s is largely due to structural factors, well outside the reach of monetary policy, and is best addressed by fiscal and structural policies.” The BIS concluded, “Monetary policy can most effectively contribute to a more equitable society by fulfilling its mandate,

MONETARY POLICY which addresses two key factors causing inequality at shorter horizons. This requires keeping inflation low and limiting the incidence and duration of macroeconomic and financial instability, which disproportionately hurt the poor.” Adding to this point, an IMF study concluded, “The existing literature reviewed in this paper is still evolving but mostly find small net effects of monetary policy on inequality.” Valentina Bonifacio, Luis Brandao-Marques, Nina Budina, Balazs Csonto, Chiara Fratto, Philipp Engler, Davide Furceri, Deniz Igan, Rui Mano, Machiko Narita, Murad Omoev, Gurnain Kaur Pasricha, and Hélène Poirson, “Distributional Effects of Monetary Policy,” IMF Working Paper WP/21/201, International Monetary Fund, July 2021. 49 See: https://www.govtrack.us/congress/bills/117/hr2543/text. 50 “At a Glance: H.R. 2543, Federal Reserve Racial and Economic Equity Act,” Congressional Budget Office, May 26, 2022, https://www.cbo.gov/system/files?file=2021-05/hr2543.pdf.

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CHAPTER

11

Government Spending, Taxation, and Fiscal Policy

LEARNING OBJECTIVES This chapter introduces you to the practical side of economic fiscal policy. You will learn about: • • • • • • • • • • • • •

How fiscal policy works. Adam Smith’s theory of optimal taxation. The types of taxes that exist. The types of spending—discretionary and mandatory. The distinction between federal deficits and the national debt. More countries are establishing oversight advisory functions to help govern­ ment policymakers better handle fiscal spending and taxation. The U.S. process of funding the federal government and some problems in implementation. How automatic stabilizers work in the economy. What a “sustainable” fiscal policy is. The need or not of a balanced budget. Some theories of government finance. How government borrowing affects private saving. A theory of government deficits and inflation.

[381] HOW DOES FISCAL POLICY WORK? The IMF notes that “governments influence the economy by changing the level and types of taxes, the extent and composition of spending, and the degree and form of borrowing.”1 The overarching goal of the government’s expenditures and taxes is to promote stable and sus­ tainable economic growth. DOI: 10.4324/9781003391050-12

FISCAL POLICY

[382] WHAT ARE THE “PRINCIPLES OF OPTIMAL TAXATION”? Adam Smith—who is considered the father of economics—opined that everyone in the nation ought to contribute to the support of the government. Smith wrote the revenue-generating mechanism should be based on a tax that is equitable, certain, convenient, and efficient. • • • •

Equitable: An equitable tax is broadly crafted to be proportional to one’s ability to pay. Certain: A certain tax is one that is not arbitrary, but “clear and plain.” It also should have a well-defined time and manner of payment. Convenient: A tax should have a payment date that is convenient to pay. Efficient: An efficient tax is one where the cost of administering the tax collection is as low as possible relative to the amount collected.

[383] HOW ARE TAXES STRUCTURED TO COLLECT REVENUE? Taxes can be structured generally in one of three forms: progressive, proportional, or regressive. A progressive tax—similar to the U.S. personal income tax—is one that collects a higher share of revenue from those with higher income through a graduated tax rate system (that is, higher marginal tax rates as income rises). A proportional tax is one that collects the same proportion from each taxpayer. A flat tax—say, a 10% tax on all income—is an example of this type of tax. Finally, a regressive tax is structured, such that lower-income taxpayers will pay a higher tax than a higher-income taxpayer. A regressive tax for comparison is the reverse of a progressive tax. The U.S. Internal Revenue Service (IRS) notes that, a regressive tax may at first appear to be a fair way of taxing citizens because everyone, regardless of income level, pays the same dollar amount. By taking a closer look, it is easy to see that such a tax causes lower income people to pay a larger share of their income than wealthier people pay. Though true regressive taxes are not used as income taxes. They are used as taxes on tobacco, alcohol, gasoline, jewelry, perfume, and travel. The IRS goes on to say, user fees often are considered regressive because they take a larger percentage of income from low-income groups than from high income groups. These include fees for licenses, parking, admission to museums and parks, and tolls for roads, bridges, and tunnels.

[384] WHAT ECONOMIC ACTIVITIES ARE TAXES LEVIED UPON? The Tax Foundation2 suggests that most taxes can be classified as falling into one of three buckets (without specific reference to any country) based on earnings, purchases, and own­ ership. The Tax Foundation further observed that a fundamental difference between these types of taxes is when they are paid following a taxation sequence associated with income first

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280 MACROECONOMIC THINKING AND TOOLS being earned, then spent, and then accumulated. Specific types of taxes are included with each of the three buckets, as shown below: •

•

•

Taxes on Earnings: ‐ Individual Income Taxes ‐ Corporate Income Taxes ‐ Payroll Taxes ‐ Capital Gains Taxes Taxes on Purchases: ‐ Sales Taxes ‐ Gross Receipts Taxes ‐ Value-Added Taxes ‐ Excise Taxes Taxes on Ownership: ‐ Property Taxes ‐ Tangible Personal Property (TPP) Taxes ‐ Estate and Inheritance Taxes ‐ Wealth Taxes

[385] WHAT IS A WEALTH TAX? Sarah Perret3 of the OECD Centre for Tax Policy and Administration wrote that Net wealth taxes [based on financial and non-financial assets minus debts] are recurrent taxes [or potentially a “one-off tax”] on individual [or corporation] net wealth stocks. They are distinct from taxes on capital income, which are levied on the flow of income generated by assets (e.g. dividends, capital gains, interest income). They can also be distinguished from other taxes on property. They differ from inheritance or estate taxes, which are only levied when wealth is inherited by heirs. Compared with recurrent taxes on immovable property, they are taxes on a broad range of movable and immovable property, net of debt. Finally, unlike sporadic capital levies, net wealth taxes are levied on a regular (annual) basis.4 Generally, these taxes are levied on wealth above some threshold level. In 2022, there were three OECD countries that levy annual wealth taxes: Norway, Spain, and Switzerland. However, in 1990, there were 12 OECD countries (the above list, plus Austria, Denmark, Germany, Netherlands, Finland, Iceland, Luxembourg, Sweden, and France) that had wealth taxes of which nine countries ultimately repealed them.

[386] WHAT ARE THE PROS AND CONS OF A WEALTH TAX? The main reasons wealth taxes have been advocated are to raise revenue, but more impor­ tantly, to address inequality. The main reasons wealth taxes have been avoided are the difficulty

FISCAL POLICY

of accurately valuing wealth, the cost of administering a wealth tax, the potential negative impact on the economy, and the potential for wealth tax avoidance. One example of how wealth avoidance has occurred in the past is when residents renounce their citizenship and relocate to another country that does not tax wealth. (In her proposal for a wealth tax, U.S. Sen. Elizabeth Warren included a 40% “exit tax” on the net worth above $50 million of any U.S. citizen who renounces their citizenship.)5

[387] [ADVANCED] WHAT IS MEANT BY “TAX SALIENCE”? Australian National University Prof. Peter Varela wrote: Tax salience is a relatively new field of economic research, which emphasizes that the way in which taxes are displayed can affect how they influence the economy. In particular, it emphasizes that people are more likely to change their behavior in response to highly visible and highly salient taxes. As tax salience implies that people respond to factors other than net tax liability, such as the way that taxes are displayed and the tax payment mechanism, it can be seen as an application of behavioral economics to the field of taxation.6 An important study by Chetty, Looney, and Kroft examined this issue. These researchers con­ ducted a grocery store experiment where they “posted tax-inclusive prices” for 750 products and discovered that demand for those products was reduced by about 8%. A second case that was examined was the effect of a state increase in an alcohol excise tax versus a similar-sized increase in state sales taxes (excise taxes boost the price on the shelf) in which they found that the excise tax increase reduced demand more than the sales tax increase (which the consumer only saw at the register).7

[388] CAN THE U.S. FEDERAL GOVERNMENT TAX STATE GOVERNMENTS AND VICE VERSA? In a law review article, Mahoney8 observed that no provision of the Constitution prohibits either federal taxation of state instrumentalities, or state taxation of federal instrumentalities, and no provision expressly preserves the power of either sovereign to tax the other. Intergovernmental tax immunities have nonetheless been implied by the federal courts.9 Federal immunity from state taxation was first set forth in the Supreme Court’s 1819 decision in McCulloch v. Maryland and further established in the Weston v. City Council decision in which Chief Justice of the Supreme Court John Marshall crafted the doctrine of absolute federal tax immunity. Although later curtailed a bit, these cases established the precedent. The doctrine of state immunity from federal taxation originated in the Supreme Court’s 1871 decision in Collector v. Day.10

281

282 MACROECONOMIC THINKING AND TOOLS

[389] WHICH BRANCH OF THE U.S. GOVERNMENT DETERMINES THE FEDERAL GOVERNMENT’S SPENDING? The U.S. Constitution gives Congress the authority over federal government spending. In Article 1 of the Constitution, it states that “No money shall be drawn from the Treasury, but in Consequence of Appropriations made by Law.” Although Congress is required to pass legislation to allow spending, the president still must agree to the spending for it to become law. However, if the president vetoes the spending bills or tax legislation, Congress still can attempt to override the president’s veto.

[390] WHAT IS MEANT BY THE TERM “FEDERAL BUDGET”? Congress does not enact a single piece of legislation that is known as the federal budget. Instead, the process is much more decentralized with multiple pieces of spending legislation that con­ stitutes the federal government’s spending authority. The “federal budget” is 12 separate bills that get consolidated into an appropriations act. Those 12 bills, which originate in the House, are written individually by the Agriculture, Commerce-Justice-Science, Defense, Energy-Water, Financial Services, Homeland Security, Interior-Environment, Labor-HHS-Education, Legislative Branch, Military Construction-Veterans Affairs, State-Foreign Operations, and Transportation-HUD committees, which have jurisdiction over those areas.

[391] HOW DOES THE U.S. FEDERAL BUDGET PROCESS WORK? As required by the Budget and Accounting Act of 1921, the federal budget process begins with the president submitting a proposed budget to Congress, usually by the first Monday in February. The president’s budget is a statement of the administration’s priorities (crafted by the ad­ ministration’s Office of Management & Budget—OMB11), but Congress is under no obli­ gation to embrace the administration’s view. The CBO will submit to Congress a report on the outlook for the budget and the economy in February. By about April 15, both the House and Senate approve a “budget resolution,” which is its broad “blueprint” for top-line or aggregate spending targets to be used to assess individual appropriations and revenue legisla­ tion. The respective committees of the House and Senate with jurisdiction over areas of the budget (which are known as the “authorizing committees”) will begin work to develop a budget for those areas under its jurisdiction. Authorizing committee legislation, if passed by the full Senate or full House, then needs to be reconciled with the counterpart authorizing leg­ islation passed by the other body of Congress and this is done through a joint House and Senate member conference committee, if the legislation is not identical between the two bodies of Congress. If reconciled, a reconciliation bill will need to be adopted by the full House and Senate. This process occurs for each of the 12 budget bills making up the full federal budget. Assuming that these bills are completed roughly at the same time, a “consolidated appropriations” act is created that rolls all of these separate bills into a final consolidated bill, which will be put to a vote in the House and Senate, and if passed, then sent to the president

FISCAL POLICY

for approval or veto. The passage of the consolidated appropriations is considered the current fiscal year’s “regular appropriations.”

[392] WHAT MECHANISMS ARE EMPLOYED BY CONGRESS FOR FISCAL DISCIPLINE? Over time, Congress has used different methods to attempt to have some fiscal discipline over federal government spending. At times, deficits have been reined in by compromise or legisla­ tion, such as the 1990 Budget Enforcement Act or the Omnibus Budget Reconciliation Act of 1993. The 1990 Budget Enforcement Act established caps on discretionary spending. Those budget spending caps were placed on three parts of discretionary spending (defense, international, and domestic spending) and were set to expire in 1995 but were extended in 1993 and 1997 and later allowed to expire in 2002. Then in 2011, Congress passed the Budget Control Act, which brought back discretionary spending caps through 2021. However, those caps were raised in 2013, 2015, and in 2018. The use of statutory spending limits or caps on discretionary spending have been viewed by the CBO and others as an effective way to rein in budget deficits.

[393] WHAT IS THE ROLE OF THE CONGRESSIONAL BUDGET OFFICE IN THIS BUDGET PROCESS? The Congressional Budget Office was created by the Congressional Budget and Impoundment Control Act of 1974 to empower the Congress, along with the existing House and Senate Congressional budget committees, to more efficiently perform its constitutional charge of funding the federal government. The CBO is an independent nonpartisan office of the Congress to impartially analyze budget legislation—not opine on the legislation’s merits, only its revenue and outlay impact. To this end, the CBO uses “dynamic scoring” to assess the legislation’s budget impact.

[394] WHAT IS “DYNAMIC SCORING”? Dynamic scoring, as opposed to static revenue loss or gain, is the estimate of the net cost of a legislative initiative accounting for economic feedback effects. The CBO or the Joint Committee on Taxation of the U.S. Congress will analyze the budget impact of legislation, approved by an authorizing committee, incorporating the direct and indirect macroeconomic effects of that legislation over a forecasted 10-year horizon.

[395] WHAT IS THE “FISCAL MULTIPLIER”? Ingrained in the dynamic scoring process is the idea that certain expenditures may have a larger or smaller feedback effect through the economy. The measurement of the feedback effects is

283

284 MACROECONOMIC THINKING AND TOOLS captured by the fiscal multiplier, which measures the change in a nation’s economic output (GDP) generated by each dollar of the budgetary cost of a change in fiscal policy. This multiplier has a direct and an indirect effect. The direct effect is the initial (or first round) expenditure. The indirect effect is the circular flow or feedback effect.

[396] WHAT TYPES OF FISCAL SPENDING HAVE THE HIGHEST FISCAL MULTIPLIERS? Over the years, numerous studies have assessed the type of federal government spending and its cumulative effect on the economy. In the late 1960s, Michael K. Evans—who founded Chase Econometrics in 1969 and sold 80% ownership to Chase Manhattan Bank in 1971—found that defense spending had a higher fiscal multiplier than non-defense spending based on his econo­ metric model simulations.12 These simulation studies, of course, need to be updated regularly and the CBO has provided a number of these estimates, under different conditions, with a range of estimates—as shown in the Table 11.1. Any expenditure that has a multiplier of more than one times its expenditure (1x) suggests that the spending has ripple effects beyond the direct outlay. TABLE 11.1

Fiscal Multipliers as Estimated by the CBO Fiscal Multipliers as Estimated by the CBO

Type of Activity

Estimated Multiplier Range

Purchases of Goods and Services Transfer Payments to State and Local Governments for Infrastructure Transfer Payments to State and Local Governments for Other Purposes Transfer Payments to Individuals One-Time Payments to Retirees Two-Year Tax Cuts for Lower- and Middle-Income People One-Year Tax Cut for Higher-Income People Extension of First-Time Homebuyer Credit Corporate Tax Provisions Primarily Affecting Cash Flow Infrastructure Spending Corporate Tax Cuts Payroll Tax Cuts Aid to the Unemployed Paycheck Protection Program Enhanced Unemployment Insurance

0.5x to 2.5x 0.4x to 2.2x 0.4x to 1.8x 0.4x to 0.2x to 0.3x to 0.1x to 0.2x to 0.0x to 0.4x to 0.0x to 0.3x to 0.7x to 0.36x 0.67x

2.1x 1.0x 1.5x 0.6x 0.8x 0.4x 2.2x 0.4x 1.3x 1.9X

Sources: Charles J. Whalen and Felix Reichling, “The Fiscal Multiplier and Economic Policy Analysis in the United States,” Working Paper 2015–02, Congressional Budget Office, Washington, DC, February 2015. Also, “Comparing Fiscal Multipliers,” Committee for a Responsible Federal Budget, Washington, DC, October 6, 2020, https://www. crfb.org/papers/comparing-fiscal-multipliers

FISCAL POLICY

Summarizing the findings from various CBO fiscal multipliers estimates, the Committee for a Responsible Federal Budget offers this summary: In general, the following criteria result in higher multipliers: • • • • • •

Funding for direct government purchases; Transfers or tax cuts for those likely to spend the money rather than save it; Funding that can be spent quickly; Policies that are credibly temporary; Policies that encourage rather than discourage work and capital investment; and Policies that target structural damage in the economy.13

[397] WHAT IS THE FISCAL YEAR OF THE FEDERAL GOVERNMENT? A fiscal year is a 12-month accounting period. The fiscal year for the federal government begins on October 1 and ends on September 30. The fiscal year is designated by the calendar year in which it ends; for example, fiscal year 2022 is the year beginning October 1, 2021, and ending September 30, 2022. The fiscal year was not always this 12-month period. Originally, the federal government’s fiscal year coincided with the calendar year up until 1842. But in 1842, President John Tyler signed legislation changing the fiscal year to a July 1 through June 30 cycle, which continued until fiscal year 1977. Beginning with fiscal year 1977, the Congressional Budget and Impoundment Control Act of 1974 changed the fiscal year to its current cycle. Both 1842 and 1974 legislated changes to the fiscal year were because Congress was unable to complete its budget by the start of its new fiscal year, and this change was to allow Congress more time to complete its budget work.

[398] WHAT HAPPENS IF CONGRESS CANNOT COMPLETE ITS NEW FISCAL-YEAR BUDGET AUTHORIZATION LEGISLATION BEFORE THE START OF THE CYCLE? When Congress cannot complete passage of its new fiscal-year budget authority before the new fiscal year starts, Congress can authorize federal government spending to continue temporarily by passing a “continuing resolution (CR).” A continuing resolution is an appropriation act that provides spending authority to continue the operations of the federal government based on the prior fiscal year’s appropriations acts (and levels), which are extended into the new fiscal year.

[399] IS CONGRESS GENERALLY FINISHING ITS BUDGET WORK ON TIME? No. Since fiscal year 1998, the average number of continuing resolutions passed by Congress has been five, with an average delay before regular appropriations are in place for the current fiscal year of 137 days late. Moreover, the CBO observed that Congress could not even pass a

285

286 MACROECONOMIC THINKING AND TOOLS budget resolution (the “blueprint” for the fiscal-year budget) ten times since 1974 and no minority party members have voted for a budget resolution since 2008. This has led to concerns that the congressional budget process is not working efficiently or effectively. Some observers have suggested that Congress should follow the lead of some states that have a biannual budget cycle, which extends the funding period for two-year periods and allows more lead time to complete the budgeting process. However, Congress does not seem likely to embrace a change. Fiscal Year Budgets: Number of Continuing Resolutions, Date of Regular Appropriations Legislation Signed by the President, Days after New Fiscal Year

TABLE 11.2

Fiscal Year (October 1– September 30)

Number of Continuing Resolutions Passed

Implementation of Regular Appropriations

Days Late (Final Passage of Regular Appropriations AFTER the New Fiscal Year Began)

FY1998 FY1999 FY2000 FY2001 FY2002 FY2003 FY2004 FY2005 FY2006 FY2007 FY2008 FY2009 FY2010 FY2011 FY2012 FY2013 FY2014 FY2015 FY2016 FY2017 FY2018 FY2019 FY2020 FY2021 FY2022 FY2023

6 6 7 21 8 8 5 3 3 4 4 2 2 8 5 2 4 5 3 3 5 3 2 5 4 3

November 26, 1997 October 21, 1998 December 2, 1999 December 21, 2000 January 10, 2002 February 20, 2003 January 31, 2004 December 8, 2004 December 31, 2005 September 30, 2007 December 31, 2007 March 11, 2009 December 18, 2009 September 30, 2011 December 23, 2011 September 30, 2013 January 18, 2014 March 6, 2015 December 22, 2015 May 5, 2017 March 23, 2018 February 15, 2019 December 20, 2019 December 27, 2020 March 11, 2022 December 29, 2022

57 21 63 82 102 143 123 69 92 365 92 162 79 365 84 365 110 156 83 216 173 138 81 88 162 90

Source: Congressional Research Service

Days Days Days Days Days Days Days Days Days Days Days Days Days Days Days Days Days Days Days Days Days Days Days Days Days Days

FISCAL POLICY

[400] WHAT ARE THE PROBLEMS OF LATE ENACTMENT OF A REGULAR APPROPRIATIONS BUDGET BY CONGRESS? At one extreme, if there is no passage of a regular appropriations budget, nor interest in passing a continuing resolution, the federal government would face a shutdown. At the other end of the spectrum, with a series of continuing resolutions it makes it harder for federal agencies to function and plan their programs and expenditures.

[401] WHAT IS MEANT THAT THE BUDGET HAS “AUTOMATIC STABILIZERS”? The term automatic stabilizers means that there will be changes to federal revenues and outlays that will result due to the stage of the business cycle (slowdown or recession). The CBO describes this process as: When output falls below its potential amount and the unemployment rate rises above the natural rate of unemployment, revenues fall and outlays rise. Those changes help support the economy by cushioning the fall in after-tax income. When output rises above its potential and the unemployment rate falls below the natural rate, the rise in revenues and fall in outlays act to restrain the economy. Examples are unemployment insurance and food stamps.

[402] HOW SENSITIVE IS THE U.S. FEDERAL BUDGET TO CHANGES IN ECONOMIC CONDITIONS? To properly assess how economic conditions will impact the federal budget, it is important to know the starting point (initial conditions) and the overall level of the revenues and outlays of the federal government, including the level of debt service. Nonetheless, with caveats and cautions, the Congressional Budget Office has provided some rules-of-thumb for how changes in some key economic factor would likely effect the budget. The table below presents four economic factors—productivity growth, labor force growth, interest rates, and inflation—each assuming a 0.1 percentage point change per year (less for pro­ ductivity and labor force growth and more for interest rates and inflation). The CBO es­ timates the impact per year over a ten-year horizon, though this table only presents the initial first year impact and the average annual impact over the full ten years. The average impact after ten years is about the same for a sustained reduction in productivity growth by 0.1 percentage point per year and a 0.1 percentage point higher interest rate or inflation rate per year, which is about $26–$29 billion per year. All of these impacts start out low per year and accelerate over the ten-year period.

287

−0.1 percentage point each year −0.1 percentage point each year +0.1 percentage point each year

+0.1 percentage point each year

Productivity Growth Labor Force Growth Interest Rates

Inflation

Lower real GDP growth by an average −0.12 pp per year Lower real GDP growth by an average −0.07 pp per year Inflation is held constant in this scenario, so the corresponding rule of thumb shows only effects from higher real interest rates. All economic indicators that are measured as nominal values, such as GDP and taxable income, increase in response to higher inflation.

Main Economic Impact

$7 billion lower balance (or larger deficit)

$3 billion lower balance (or larger deficit) $1 billion lower balance (or larger deficit) $8 billion lower balance (or larger deficit)

Magnitude Impact on Government Deficit/ Surplus after One-Year (Initial Impact)

Source: How Changes in Economic Conditions Might Affect the Federal Budget: 2022 to 2032, Congressional Budget Office, June 2022

Change Relative to Baseline

$26.2 billion lower balance (or larger deficit)

$29.2 billion lower balance (or larger deficit) $12.8 billion lower balance (or larger deficit) $28.5 billion lower balance (or larger deficit)

Magnitude Impact on Government Deficit/ Surplus Average per Year Impact after Ten-Years

CBO Rules-of-Thumb for How Changes in Economic Factors Affect Federal Budget Balance Based on 2022 Baseline

CBO Rules-of-Thumb for How Changes in Economic Factors Affect Federal Budget Balance Based on 2022 Baseline

Economic Factor

TABLE 11.3

288 MACROECONOMIC THINKING AND TOOLS

FISCAL POLICY

[403] [ADVANCED] WHAT IS AN “INDEPENDENT FISCAL INSTITUTION”? Increasingly there has been a worldwide trend for nations to create non-partisan fiscal councils to provide independent analysis of public finances and fiscal policies. The Congressional Budget Office of the U.S. Congress is one such independent fiscal institution, but the International Monetary Fund reports there are about four dozen fiscal councils around the world. Those institutions and the dates they were set up are: • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

Australian Parliamentary Budget Office (2012) Austrian Fiscal Advisory Council (2002) Belgian Federal Planning Bureau (1959) Canadian Parliamentary Budget Office (2008) Croatian Fiscal Policy Commission (2014) Cyprus Fiscal Council (2014) Czech National Budget Board (2017) Danish Economic Council (1962) Estonian Fiscal Council (2014) Fiscal Council of Bulgaria (2015) Fiscal Council of Hungary (2009) French Fiscal Council (2013) Georgia Parliamentary Budget Office (1997) Greece Parliamentary Budget Office (2010) Independent Advisory Board to the German Stability Council (1963) Iranian Public Sector Directorate of Parliament (Majlis) Research Center (1991) Irish Fiscal Advisory Council (2011) Italian Parliamentary Budget Office (2014) Japan Fiscal System Council (2001) Kenyan Parliamentary Budget Office (2007) Korean National Assembly Budget Office (2003) Latvian Fiscal Council (2014) Lithuanian National Audit Office (2015) Luxembourg National Council of Public Finance (2014) Malta Fiscal Advisory Council (2015) Mexican Center for Public Finance Studies (1999) National Audit Office of Finland (2013) Netherlands Bureau for Economic Policy Analysis (1945) Netherlands Council of State (2014) Peruvian Fiscal Council (2015) Portuguese Public Finance Council (2012) Republic of Slovenia Institute of Macroeconomic Analysis and Development (2000) Romania Fiscal Council (2010) Scottish Fiscal Commission (2017)

289

290 MACROECONOMIC THINKING AND TOOLS • • • • • • • • •

Serbian Fiscal Council (2011) Slovak Republic Council for Budget Responsibility (2012) Slovakian Council for Budgetary Responsibility (2014) South African Parliamentary Budget Office (2014) Spanish Independent Authority for Fiscal Responsibility (2014) Swedish Fiscal Policy Council (2007) Ugandan Parliamentary Budget Office (2001) United Kingdom Office of Budget Responsibility (2010) United States Congressional Budget Office (CBO) (1974)

[404] WHAT IS MEANT BY THE FEDERAL BUDGET’S “ON-BUDGET” AND “OFF-BUDGET” RECEIPTS AND EXPENDITURES? Federal receipts and outlays are segmented for budget analysis into “on-budget” and “offbudget,” which are those spending and revenues that are “walled off” from political inter­ ference and those that are not walled off. The off-budget items are those that are segmented or walled off and include the Social Security trust funds (the Old-Age and Survivors Insurance Trust Fund and the Disability Insurance Trust Fund), the Postal Service Fund, and a few other agencies, such as the Federal Reserve. However, the so-called “unified budget” includes both on- and off-budget spending. Researchers at the Tax Policy Center, a Joint Center of the Urban Institute, and the Brookings Institution have suggested that this distinction between the on- and off-budget spending categories has less relevance today, because Congress treats spending categories as interchangeable whether or not they are walled off, and Congress consequently focuses on a unified budget perspective (See Table 11.4).

[405] WHAT IS MEANT BY THE FEDERAL GOVERNMENT’S “DISCRETIONARY” AND “MANDATORY” EXPENDITURES? Discretionary spending is the budget authority, which is provided and controlled by appropriation acts that the current Congress authorizes. The key phrase to focus upon is “current Congress.” On the other hand, mandatory spending, which also is known as direct spending or entitlement spending, represents expenditures that have been authorized by previous Congresses. These expenditures are legal obligations of the federal government to make payments to a person or group of people, business, unit of government, or similar entities that meet the eligibility criteria set in law and for which the budget authority is not provided in advance in an appropriation act. Spending for entitlement programs is con­ trolled through those programs’ eligibility criteria, and benefit or payment rules. The bestknown entitlement programs are the government’s major benefit programs, Social Security and Medicare (See Figure 11.1).

2,153.6

2,406.9 2,568.0

2,524.0

2,105.0 2,162.7

2,303.5

2,450.0 2,775.1

3,021.5

3,249.9 3,268.0

3,316.2

3,329.9 3,463.4

3,421.2

4,047.1 4,436.6

4,638.2

2005.

2006. 2007.

2008.

2009. 2010.

2011.

2012. 2013.

2014.

2015. 2016.

2017.

2018. 2019.

2020.

2021. 2022 (estimates).

2023 (estimates).

5,792.0

6,822.4 5,851.6

6,553.6

4,109.0 4,447.0

3,981.6

3,691.9 3,852.6

3,506.3

3,526.6 3,454.9

3,603.1

3,517.7 3,457.1

2,982.5

2,655.1 2,728.7

2,472.0

2,292.8

−1,153.9

−2,775.3 −1,415.0

−3,132.4

−779.1 −983.6

−665.4

−442.0 −584.7

−484.8

−1,076.6 −679.8

−1,299.6

−1,412.7 −1,294.4

−458.6

−248.2 −160.7

−318.3

−412.7

Outlays Surplus or Deficit (−)

3,537.6

3,094.8 3,389.4

2,455.7

2,475.2 2,549.1

2,465.6

2,479.5 2,457.8

2,285.9

1,880.5 2,101.8

1,737.7

1,451.0 1,531.0

1,865.9

1,798.5 1,932.9

1,576.1

1,345.4

Receipts

4,605.3

5,818.6 4,763.7

5,598.0

3,260.5 3,540.3

3,180.4

2,948.8 3,077.9

2,800.2

3,019.0 2,821.1

3,104.5

3,000.7 2,902.4

2,507.8

2,233.0 2,275.0

2,069.7

1,913.3

−1,067.8

−2,723.8 −1,374.3

−3,142.3

−785.3 −991.3

−714.9

−469.3 −620.2

−514.3

−1,138.5 −719.2

−1,366.8

−1,549.7 −1,371.4

−641.8

−434.5 −342.2

−493.6

−568.0

Outlays Surplus or Deficit (−)

On-Budget

[Billions of Dollars]

1,100.6

952.3 1,047.2

965.4

854.7 914.3

850.6

770.4 810.2

735.6

569.5 673.3

565.8

654.0 631.7

658.0

608.4 635.1

577.5

534.7

1,186.7

1,003.8 1,087.9

955.6

848.6 906.6

801.2

743.1 774.7

706.1

507.6 633.8

498.6

517.0 554.7

474.8

422.1 453.6

402.2

379.5

−86.1

−51.5 −40.7

9.8

6.2 7.7

49.4

27.3 35.5

29.5

61.9 39.5

67.2

137.0 77.0

183.3

186.3 181.5

175.3

155.2

Surplus or Deficit (−)

Off-Budget

Receipts Outlays

Federal Receipts, Outlays, and Debt

Sources: Department of the Treasury and Office of Management and Budget Note: Data (except as noted) are from Budget of the United States Government, Fiscal Year 2023, issued March 31, 2022

1,880.1

2004.

Receipts

Total

Federal Receipts, Outlays, and Debt

Fiscal Year or Period

TABLE 11.4

32,593.2

28,385.6 31,291.9

26,902.5

21,462.3 22,669.5

20,205.7

18,120.1 19,539.5

17,794.5

16,050.9 16,719.4

14,764.2

11,875.9 13,528.8

9,986.1

8,451.4 8,950.7

7,905.3

7,354.7

Gross Federal

26,033.3

22,284.0 24,836.2

21,016.7

15,749.6 16,800.7

14,665.4

13,116.7 14,167.6

12,779.9

11,281.1 11,982.7

10,128.2

7,544.7 9,018.9

5,803.1

4,829.0 5,035.1

4,592.2

4,295.5

Held by the Public

Federal Debt (End of Period)

FISCAL POLICY

291

292 MACROECONOMIC THINKING AND TOOLS

11.1 Composition of U.S. Federal Government Discretionary & Net Interest Shares, FY1962–2021

FIGURE

Spending:

Mandatory,

[406] HOW ARE DISCRETIONARY AND MANDATORY EXPENDITURES RELATED TO ON- AND OFF-BUDGET FEDERAL GOVERNMENT SPENDING? The relationship between discretionary/mandatory spending and on-/off-budget expenditures is best viewed in the table below. There is a fair amount of regrouping between the concepts. Firstly, federal outlays are the sum of mandatory, discretionary, and net interest payments. Secondly, mandatory outlays tend to account for about two-thirds of federal government outlays, with discretionary outlays about a quarter to about 30% and the remainder is net interest is about 5%–8% of the expenditures. Obviously, different years can change these shares and the heavy pandemic-related expenditures in 2020 and 2021 certainly affects the shares. On the other hand, on-budget expenditures about 80%–85% of the total with the remainder accounted for by off-budget expenditures.

[407] WHAT IS THE “PRIMARY DEFICIT”? This concept of a primary federal deficit is defined as the total deficit less the net interest outlays. For example, in fiscal year 2018, the total U.S. federal deficit was $779.1 billion of which $325.0 billion was expenditures to service the federal debt. Excluding the net interest outlays, then the primary deficit would be $779.1 billion plus $325.0 billion, or $454.2 billion. It is “added back” to exclude it from the deficit because it is an outlay, and the deficit is revenue minus outlays. This concept plays into macroeconomic theories and “budget goals.”

FISCAL POLICY TABLE 11.5

U.S. Federal Budget by Category, Fiscal Years 2018–2021 U.S. Federal Budget by Category, Fiscal Years 2018–2021 (in Billions of Dollars) 2018

2019

1,683.5 1,170.7 204.7 270.9

1,717.9 1,243.1 230.2 272.1

1,608.7 1,310.0 211.8 290.7

2,044.4 1,314.1 371.8 316.8

TOTAL On-budget Off-budget Outlays Mandatory Discretionary Defense Non-defense Net Interest

3,329.9 2,475.2 854.7

3,463.4 2,549.1 914.3

3,421.2 2,455.7 965.4

4,047.1 3,094.8 952.3

2,522.4 1,261.6 622.7 638.9 325.0

2,734.1 1,337.7 676.4 661.3 375.2

4,580.3 1,627.8 713.8 914.0 345.5

4,833.7 1,636.4 741.6 894.8 352.3

TOTAL On-budget Off-budget Total Deficit On-budget Off-budget Primary Deficit

4,109.0 3,260.5 848.6 −779.1 −785.3 6.2 −454.2

4,447.0 3,540.3 906.6 −983.6 −991.3 7.7 −608.4

6,553.6 5,598.0 955.6 −3,132.4 −3,142.3 9.8 −2,787.0

6,822.4 5,818.6 1,003.8 −2,775.3 −2,723.8 −51.5 −2,423.0

Revenues Individual income taxes Payroll taxes Corporate income taxes Other

2020

2021

[408] WHAT IS THE DIFFERENCE BETWEEN THE FEDERAL GOVERNMENT DEFICIT AND THE FEDERAL GOVERNMENT DEBT? The federal government deficit (or surplus) is what is incurred each year, while the debt is the accumulated deficits over time. Another way of thinking about this is that the deficit is the “flow,” and the debt is the “stock.”

[409] WHAT CONSTITUTES FEDERAL DEBT? Federal government debt is the total value of outstanding bills, notes, bonds, and other securities issued by the Treasury and other federal agencies. • •

Gross Federal Debt = Held by Federal Government Accounts + Held by Public Debt held by government accounts is debt issued for internal government transactions, to trust funds and other federal accounts, and not traded in capital markets.

293

294 MACROECONOMIC THINKING AND TOOLS

Debt Held by FRB 26.5% $22.3 trillion 73.5% Debt Held by the Public, Excluding FRB

Shares of U.S. National Debt Publically Held by the Federal Reserve (FRB) and Non-FRB Public

FIGURE 11.2

Source: U.S. Department of Treasury

•

•

Debt held by the public consists mainly of securities that the Treasury issues to raise cash to fund the operations and pay off the maturing liabilities of the federal government that tax revenues are insufficient to cover. This debt is held by outside investors, including individuals, corporations, state or local governments, Federal Reserve Banks, foreign governments, and other entities outside the U.S. government less Federal Financing Bank securities. Total U.S. national debt was $28.4 trillion as of fiscal year 2021. The share held by the public was $22.3 trillion, of which $5.9 trillion was held by the Federal Reserve System (a record high).

[410] WHAT IS MEANT BY “DEBT MONETIZATION?” Debt monetization is tied conceptually to the independence of the central bank and to the practice of quantitative easing. Thornton observed in a St. Louis Fed article that the phrase “monetizing the debt” developed immediately after World War II since the Federal Reserve had a tacit commitment to the U.S. Treasury to stabilize the Treasury’s cost of financing the war debt. After the war, individuals began liquidating their holdings of Liberty Bonds. Because of its agreement with the Treasury, the Federal Reserve purchased substantial amounts of government debt.14 As a result, these purchases increased bank reserves and the money stock. Thornton observed that this term was born at that time and referred to Federal Reserve’s monetarization (or the purchase) of the federal debt, which held down interest rates on government debt. Fast forward to the 2008 and 2020 quantitative easing episodes by the Federal Reserve, which had the same outcome on bank reserves, the money supply, and interest rates. However, in the post-WWII period, concern about this role for the central bank to finance government spending led to a March 1951 accord between the Federal Reserve and the U.S. Treasury, which established the

FISCAL POLICY TABLE 11.6

Fiscal Year

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Total U.S. National Debt Held by the Federal Reserve System Federal Debt (End of Period, Billions $)

As Percentage of GDP

Gross Federal

Held by the Public

Gross Federal

Held by the Public

6,760.0 7,354.7 7,905.3 8,451.4 8,950.7 9,986.1 11,875.9 13,528.8 14,764.2 16,050.9 16,719.4 17,794.5 18,120.1 19,539.5 20,205.7 21,462.3 22,669.5 26,902.5 28,384.5

3,913.4 4,295.5 4,592.2 4,829.0 5,035.1 5,803.1 7,544.7 9,018.9 10,128.2 11,281.1 11,982.7 12,779.9 13,116.7 14,167.6 14,665.4 15,749.6 16,800.7 21,016.7 22,287.0

59.9 61.1 61.6 62.0 62.6 67.5 82.1 90.9 95.5 99.6 100.3 102.4 100.2 105.4 105.0 105.8 107.2 128.4 126.9

34.7 35.7 35.8 35.4 35.2 39.2 52.1 60.6 65.5 70.0 71.9 73.6 72.5 76.4 76.2 77.6 79.4 100.3 99.7

Source: U.S. Office of Management & Budget (OMB)

central bank’s independence. In that agreement, the Federal Reserve was “free to pursue its policy objectives independent of the debt financing needs of the Treasury.”15 However, as the 2008 and 2020 experiences showcased, central bank independence from government spending objectives may be blurred during national emergencies.

[411] HOW MUCH OF THE U.S. FEDERAL DEBT IS OWNED BY FOREIGNERS? In 2021, about a quarter of total U.S. federal government debt was held by foreigners, which was down from about 34% in 2015. About half of those foreign holdings of U.S. federal debt are by foreign officials, which include foreign governments and central banks, as well as a small number of international organizations and government investment funds which have accounts at the FRBNY, including the IMF and the BIS (Figure 11.3). Among the countries with the largest U.S. Treasury security holdings of U.S. federal debt are Japan and mainland China, which each own nearly 18% of the total foreign holdings. The United Kingdom holds about 5% of U.S. Treasury securities and a full list of foreign holders by year appears in the Table 11.7.

295

296 MACROECONOMIC THINKING AND TOOLS

FIGURE 11.3

Share of U.S. Federal Government Debt Owned by Foreigners

Source: U.S. Department of Treasury

[412] WHY IS THERE A FEDERAL GOVERNMENT DEBT CEILING? The U.S. Constitution (Article I, Section 8) states that Congress has the power “to borrow money on the credit of the United States.” But to do so, Congress must legally authorize the borrowing through legislation. Prior to Congress establishing a debt ceiling, it was required by law to approve issuance of debt in a separate piece of legislation. A debt ceiling was first enacted in 1917 through the Second Liberty Bond Act, which was used to finance the U.S. entry into World War I. The legislation set the ceiling at $11.5 billion to simplify the federal government’s spending process and provide borrowing flexibility for the U.S. Treasury’s debt management by dropping “certain limits on the maturity and redemption” of government bonds from prior laws.16 In 1939, Congress created its first aggregate debt limit covering nearly all of government debt, which was set at $45 billion—about 10% higher than the total debt at the time.17

[413] HOW DOES THE GOVERNMENT PAY OFF ITS DEBT? A government (similar to a private business or consumer) will pay off its existing debt either by refinancing (rolling over the debt, issuing new debt to pay the old) or amortizing (paying it off from surpluses in tax revenue).

[414] WHAT IS THE ROLE OF THE DEBT CEILING TODAY? The debt ceiling does not authorize new spending commitments, but it simply allows the federal government to borrow to finance existing legal obligations that Congresses and

FISCAL POLICY TABLE 11.7

Major Foreign Holders of U.S. Treasury Securities Major Foreign Holders of U.S. Treasury Securities (in Billions of Dollars) Calendar Year Averages

Country

2015

2016

2017

2018

2019

2020

2021

Average 7-Year Share of Total

Japan

1,192.3

1,131.9

1,099.9

1,039.3

1,119.7

1,,264.5

1,290.1

17.8

China, Mainland

1,257.6

1,187.2

1,136.8

1,163.4

1,107.5

1073.0

1,076.2

17.5

United Kingdom

198.8

217.0

230.8

265.9

361.2

441.2

525.0

4.9

Ireland

222.0

266.8

309.7

301.0

273.7

309.3

318.2

4.4

Brazil

257.1

253.3

266.0

298.4

304.0

266.7

251.5

4.1

Switzerland

219.0

234.9

242.0

236.7

230.1

248.5

278.9

3.7

Luxembourg

183.8

219.9

215.1

221.6

239.6

264.6

299.8

3.6

Cayman Islands

218.2

261.5

240.9

192.4

228.3

222.7

246.2

3.5

Hong Kong

187.2

192.3

195.3

193.3

226.4

249.5

225.4

3.2

Taiwan

173.5

186.3

183.0

165.1

178.9

210.2

239.7

2.9

Belgium

199.8

141.7

106.1

152.0

198.3

220.8

234.3

2.7

India

111.4

120.3

131.0

146.1

156.8

189.3

210.8

2.3

Saudi Arabia

109.1

104.5

134.5

163.2

176.7

141.2

126.0

2.1

Singapore

112.1

106.1

114.3

124.3

140.7

157.3

181.5

2.0

Canada

69.8

79.9

78.9

95.6

127.1

133.6

154.3

1.6

France

60.6

62.9

73.2

98.0

125.5

135.7

183.0

1.6

Korea, South

71.0

84.3

96.9

107.1

117.5

120.8

126.9

1.6

Germany

76.8

92.7

72.6

74.6

79.7

78.2

80.0

1.2

Norway

69.3

59.4

52.9

55.1

97.2

91.6

107.0

1.2

Russia

79.5

86.0

101.2

37.4

Thailand

33.2

51.1

68.6

64.1

88.4

85.1

63.5

1.0

Bermuda

54.3

64.4

61.7

64.9

68.6

69.7

69.1

1.0

Turkey

73.4

56.8

57.4

United Arab Emirates

73.4

63.7

59.1

58.4

48.4

31.9

51.1

0.8

Netherlands

40.0

51.2

50.2

43.9

53.9

69.3

67.4

0.8

Mexico

80.9

56.1

40.0

41.8

47.6

47.6

48.9

0.8

Sweden

39.5

40.0

42.3

45.1

47.7

39.6

41.8

0.6

Italy

33.0

39.5

35.8

38.4

44.7

43.0

40.2

0.6

Philippines

40.8

40.5

36.9

30.1

30.5

44.4

50.0

0.6

Australia

32.0

32.9

37.4

38.1

41.5

42.7

46.6

0.6

Kuwait

31.3

30.5

33.8

41.6

42.6

44.9

46.1

0.6

Poland

28.8

33.7

36.1

40.3

34.8

41.6

53.1

0.6

Israel

19.6

27.4

30.8

29.4

39.6

48.0

62.3

0.6

Spain

32.4

38.4

37.3

34.4

42.6

41.5

27.3

0.6

Colombia

36.4

32.1

28.6

27.5

30.4

29.8

34.0

0.5

Chile

27.8

31.0

27.6

29.4

29.8

29.8

37.5

0.5

Indonesia

20.9

21.0

25.5

24.1

26.6

24.1

23.4

0.4

Iraq

25.6

15.3

16.7

27.0

34.0

27.4

19.2

0.4

Vietnam

13.2

12.9

13.8

19.7

25.6

30.9

41.1

0.3

Kazakhstan

26.9

22.5

21.1

13.6

Peru

11.3

12.4

13.5

17.6

20.4

22.7

28.3

0.3

Denmark

17.8

21.7

18.0

17.2

16.9

17.4

16.5

0.3

Oman

22.4

16.7

14.3 17.1

21.3

12.6

14.0

17.6

South Africa All Other Grand Total

9.7

1.2

1.0

0.3

0.3 0.2

242.2

246.1

245.8

293.4

336.0

340.8

314.5

4.4

6,135.2

6,169.4

6,165.1

6,224.1

6,685.4

7,048.0

7,406.2

100.0

Source: U.S. Treasury International Capital (TIC) reporting system, U.S. Treasury

297

298 MACROECONOMIC THINKING AND TOOLS presidents (past and present) have established. This includes paying interest on its debt, which is held domestically and by foreigners. Failure to raise the debt ceiling, if it will be breached, will restrict the issuance of future debt and may result in defaulting on the federal government’s debt service obligations (“failure to pay”).

[415] [ADVANCED] IS ALL GOVERNMENT DEBT SUBJECT TO THE “STATUTORY LIMIT”? Almost, but not quite all. Debt that is subject to the statutory limit is debt held by the public (which includes the Federal Reserve) and debt held by government accounts. However, there are some minor components that are not subject to the debt limit, which include debt of the Federal Financing Bank, Hope bonds, and “unamortized discount.” However, concep­ tually, it is fair to effectively consider all federal debt subject to the statutory limit.

[416] [ADVANCED] WHAT MEASURES HAVE THE U.S. TREASURY EMPLOYED TO PREVENT BREACHING THE DEBT LIMIT AND ALLOW CONGRESS TIME TO ADJUST THE DEBT LIMIT? The U.S. Treasury can take “extraordinary measures”—short-term measures to prevent a default on government debt. Those extraordinary measures18 mainly include the use of: •

•

• •

The G-Fund of the Thrift Savings Plan (Government Securities Investment Fund)—Each day, the U.S. Treasury may temporarily reduce the amount of debt held by this fund, which holds government bonds for federal employee retirement accounts. The Civil Service Retirement and Disability Fund (CSRDF)—Treasury may postpone new investments in this pension fund. The CSRDF measure is most useful in June, September, and December, when major interest credits and reinvestments of maturing securities typically occur. The Exchange Stabilization Fund (ESF)—Each day, the Treasury may temporarily reduce the amount of debt held by this fund, which is used to facilitate foreign exchange transactions. Exchange Federal Financing Bank securities, which do not count against the debt limit, for Treasury securities held by the CSRDF.

[417] WHAT ARE THE BENEFITS OF A FEDERAL GOVERNMENT DEBT LIMIT? According to a Congressional Research Service study,19 •

Congress has always placed restrictions on federal debt. Limitations on federal debt have helped Congress assert its constitutional powers of the purse, of taxation, and of the initiation of war.

FISCAL POLICY

• •

The debt limit provides Congress with the strings to control the federal purse, allowing Congress to assert its constitutional prerogatives to control spending. The debt limit also imposes a form of fiscal accountability that compels Congress and the president to take visible action to allow further federal borrowing when the federal government spends more than it collects in revenues.

[418] WHAT ARE THE DRAWBACKS OF A FEDERAL GOVERNMENT DEBT LIMIT? According to a Congressional Research Service study,20 • •

• •

The debt limit can hinder the Treasury’s ability to manage the federal government’s finances. Some budget experts have advocated elimination of the debt limit, arguing that other controls provided by the modern congressional budget process established in 1974 have superseded the need for a debt limit, and that the limit does little to alter spending and revenue policies that determine the size of the federal deficit. The Obama Administration even proposed allowing increases in the debt limit to occur subject to congressional disapproval. Congress can suspend it at will. For example, under the Bipartisan Budget Act of 2018, the statutory debt limit was suspended through March 1, 2019. Then the statutory debt limit was permanently increased effective March 2, 2019, to $21,987,705,611,407.70.

[419] WHAT PROPOSALS ARE AVAILABLE SO AS TO LEVERAGE THE DEBT CEILING FOR BETTER DISCIPLINE IN FEDERAL BUDGETING? The bipartisan Committee for a Responsible Federal Budget has suggested budgeting reforms for Congress that would: • • • •

Link changes in the debt limit to achieve responsible fiscal targets, so that Congress would not need to increase the debt ceiling if fiscal targets are met. Have debate about the debt limit when Congress is making decisions on spending and revenue levels, not after those decisions have been made. Apply the debt limit to more economically meaningful measures, such as debt held by the public or debt as a share of GDP. Replace the debt limit with limits on future obligations.21

[420] WHY IS THERE CONCERN THAT THE FEDERAL GOVERNMENT MUST EMBRACE FISCAL DISCIPLINE? The Congressional Budget Office has addressed this issue of fiscal discipline many times. The key concerns that the CBO has raised are:

299

300 MACROECONOMIC THINKING AND TOOLS •

•

• •

•

•

•

In recent years, the CBO’s annual long-term projections for the federal government deficits have largely told the same story, which is that the deficits tend to grow over the upcoming ten-year horizon—based on trends in the current services revenues and outlays. For example, in the CBO’s 2022 projections, it notes that, “the projected shortfall increases to 6.1% of GDP in 2032—significantly larger than the 3.5% of GDP that deficits have averaged over the past 50 years.”22 Additionally, the CBO also warns about the growth in federal debt. For example, also in the CBO’s 2022 projections, it notes that, “as deficits increase in most years after 2023 in CBO’s projections, debt steadily rises, reaching 110% of GDP in 2032—higher than it has ever been—and 185% of GDP in 2052.”23 The Congressional Budget Office projects that rising debt would dampen economic output over time. The CBO also argues that this high and rising debt is ultimately unsustainable, and could have severe adverse consequences, including slower income growth, a weakened ability to respond to the next recession or emergency, a larger burden on future generations, and heightened risk of a fiscal crisis. As such, if left unchecked, this situation will threaten the standard of living for all Americans. Interest on the debt is the fastest growing part of the federal budget. As debt rises, it will crowd out important investments in areas like education, infrastructure, and research that can help grow the economy. Also, rising interest costs associated with that debt would increase the federal government’s interest payments to foreign debt holders and thus reduce the income of U.S. households by increasing amounts. Getting the debt under control will be very beneficial for generations to come from higher wages to increased investment to lower borrowing costs for families and businesses.

[421] WHAT IS MEANT BY “CROWDING OUT” OF INVESTMENT? Crowding out occurs when government borrowing and spending results in higher interest rates, which reduces business investment and household consumption. In a speech on fiscal sustainability, former Federal Reserve Board Chair Ben S. Bernanke said: A large and increasing level of government debt relative to national income risks serious economic consequences. Over the longer term, rising federal debt crowds out private capital formation and thus reduces productivity growth. To the extent that increasing debt is financed by borrowing from abroad, a growing share of our future income would be devoted to interest payments on foreign-held federal debt. High levels of debt also impair the ability of policymakers to respond effectively to future economic shocks and other adverse events.24 Theoretically, economists have discussed three scenarios: (a) no crowding out, (b) incomplete or partial crowding out, and (c) complete crowding out.

FISCAL POLICY

[422] WHAT IS THE RELATIONSHIP BETWEEN FEDERAL GOVERNMENT BORROWING AND THE INTERNATIONAL TRADE BALANCE? The national saving and investment identity from the National Income Accounts shows the relationship between the federal deficit and the trade deficit—which has been dubbed the “twin deficits.” That identity is that the quantity supplied of financial capital will equal (ex post) the quantity demanded of financial capital as given by the equation: S + (M – X) = I + (G – T), where S = saving by individuals and firms; (M – X) = imports (M) - exports (X) = trade deficit (or surplus); I = private sector investment; G = government spending; and T = taxes collected. Therefore, this shows that when the federal government borrowers in the financial markets, there are three possible sources for those funds from a macroeconomic point of view: (a) households might save more; (b) private firms might borrow less; and/or (c) more foreign financial investment will be made from outside the country. This is seen by rearranging that identity’s terms as follows: (T – G) = I – S + (X – M). Thus, if there is no change in investment or savings when the deficit is larger (that is, T minus G is larger) then the adjustment must come totally from the trade deficit. When the federal government budget deficit rises or the budget surplus falls, then the trade deficit must rise (or the trade surplus will fall). In reality, of course, there is likely to be some adjustment in investment and saving so the whole adjustment is not totally borne by the trade balance. Indeed, the CBO’s Policy Growth Model estimates that an increase in the federal deficit would be financed by all three factors with savings increasing to offset 43% of the incremental federal deficit, investment reduced to offset another 33% of that incremental deficit, and an increase in net foreign investment to offset the remaining 24% of that increase in the federal deficit.25 Moreover, one of the key takeaways of this identity is that it shows how growing federal deficits will likely create a larger international trade deficit.

[423] WHAT IS THE IMPACT OF INCREASES IN FEDERAL GOVERNMENT DEBT ON INTEREST RATE? Different estimation methods and models will produce different responses. The CBO notes, however: According to economic theory, the macroeconomic effects of rising government debt primarily depend on how private investment activity responds to increased federal borrowing. If greater federal borrowing ultimately reduces—or crowds out—private investment below what it would have been without the additional borrowing, the stock of private capital would be lower in the long run. A lower private capital stock would push up the marginal product of capital, increasing the return on capital and the interest rate.26 Moreover, it is not a settled question in the economic literature as to whether it is more appropriate to measure how federal government debt, or how federal government deficits, would affect interest rates. Nonetheless, a study by the CBO estimated that “the effect of federal debt on interest rates range from about 2 to 3 basis points for each percentage-point

301

302 MACROECONOMIC THINKING AND TOOLS increase in the projected debt-to-GDP ratio.”27 So, if the debt-to-GDP ratio goes from the CBO’s estimated 97.9 in 2022 to a projected 109.6 in 2032, this rule-of-thumb would imply that the average interest rate on debt held by the public would increase by about 30 basis points. For example, the interest rate would move from the CBO estimated interest rate of 1.9% in 2022 to 2.2% from the increase in debt-to-GDP ratio alone. Obviously, there are more factors at play in the economy than just that, but this study, at least, provides a benchmark.

[424] SHOULD THE FEDERAL GOVERNMENT FINANCE ITS EXPENDITURES THROUGH TAXATION OR BY BORROWING? This question was examined by the classical economist David Ricardo, who opined that increasing taxation or borrowing will have the same effect (they are equivalent). This has been called the Ricardian equivalence theory. Ricardo argued that debt financing (borrowing) is essentially just future taxation. If a government issues a bond today to avoid raising taxes, it will need to raise taxes tomorrow to pay off the bond when it comes due. According to Ricardo’s argument, it makes no difference to the public whether those increased taxes will come sooner (tax financing) or later (debt financing). This old theory, and mainly its newer restatement, contradicts the Keynesian view that a government can boost aggregate demand. Although Ricardo later questioned and ultimately rejected his own idea of equivalence, it was later updated and modernized by economist Robert Barro in 1974 (and now known as the Barro-Ricardo equivalence proposition), but the Ricardo theory has never been held broadly by economists. The main criticism of Ricardo’s theory is due to what is perceived to be unrealistic assumptions about the existence of perfect capital markets and that individuals are willing to save for future tax increases, which they may or may not see in their lifetime.

[425] WHAT IS THE LINGERING VALUE OF THE RICARDIAN EQUIVALENCE? The simple takeaway from the Ricardian equivalence concept is the fundamental insight Ricardo provided so long ago that the choice of government debt versus taxes is one of timing still remains valid—something to keep in mind when considering government finances and tax policy. However, it is often politically easier for governments to incur more debt then to raise taxes.

[426] WHAT ARE SOME PRACTICAL PROBLEMS TO IMPLEMENT COUNTER-CYCLICAL FISCAL POLICY? Although fiscal policy has been reacting much faster in recent years, the traditional concerns about fiscal policy implementation is the long-and-variable time lags to put a policy in place. Those problems are a result of typically three lags:

FISCAL POLICY

• • •

Recognition lag: The time it takes for Congress or the president to determine that a recession or some other “negative shock” to the economy has occurred. Legislative lag: The time it takes to craft a fiscal policy to address the problem and to get the legislation passed. Implementation lag: The time it takes for federal government spending or a tax policy change to available to effect the economy.

[427] WHAT IS MEANT BY “FISCAL SUSTAINABILITY”? In a speech by then FRB Chair Ben Bernanke, he said: A straightforward way to define fiscal sustainability is as a situation in which the ratio of federal debt to national income is stable or moving down over the longer term. This goal can be attained by bringing spending, excluding interest payments, roughly in line with revenues, or in other words, by approximately balancing the primary budget.28 Thus, the primary federal budget deficit/surplus (that is, the federal budget deficit/surplus without the outlays on net interest on the federal debt) is a metric to develop a goal around.

FIGURE 11.4

Primary Federal Budget Deficit/Surplus

Source: U.S. Department of Treasury

[428] WHY IS FISCAL SUSTAINABILITY IMPORTANT FOR A NATION? According to the IMF, fiscal sustainability29 should be a goal for nations because it is “a requirement for macroeconomic stability and sustainable and inclusive long-term growth.”

303

304 MACROECONOMIC THINKING AND TOOLS

[429] [ADVANCED] WHAT IS THE RELATIONSHIP BETWEEN FEDERAL DEBT AND THE PRIMARY DEFICIT? The total federal debt can be thought by the equation: DEBTt = (1+rt) DEBTt−1 + PDt, where DEBTt is the stock of national debt in real terms outstanding at the end of year “t,” PDt is the government’s primary deficit during year “t,” and rt is the real interest rate on national debt in year “t.” Therefore, a fiscal shock that increases the primary deficit (PD) in year “t” can influence the stock of debt, DEBTt, in several ways (as witnessed in the 2020 global pandemic, for example), including: •

Causing output to change, which will likely lead to adjustments in taxes and spending (either by the automatic stabilizers or through discretionary policies) and, in turn, further impacting the primary deficit; It could also lead to a change in the nominal interest rate on government debt, which affects the real interest rate, rt; and Additionally, it could change in the inflation rate, which also affects the real interest rate rt.

• •

[430] WHAT IS THE FISCAL THEORY OF THE PRICE LEVEL? This theory, largely due to Stanford University economist John Cochrane, argues that the price level sometimes is determined by fiscal policy rather than monetary policy. The theory attempts to show that bond-financed fiscal deficits30 that are not fully backed by future taxes lead to increased federal government debt, which causes the price level to adjust and restore the fiscal equilibrium. Since its inception, this theory has been extremely controversial. The government debt accumulation equation, which the theory rests upon, is expressed as: B/P = present value of future surpluses, where B is the outstanding nominal debt of the government (that is, the nominal value of government liabilities—both debt and money) and P is the price level. Alternatively, (government debt plus money) divided by the price level equals the debt equation. Therefore, the price level is defined as the inverse of the value of government debt. If the government is running huge budget deficits such that it will not be able to pay off its debt obligation in the future from tax revenue (that is, the government runs a persistent structural deficit), then it will pay them off via inflating the debt away.31

Issues to Think About There are three ways that governments have used to pay off government debt. they have borrowed, they have taxed, and they have allowed inflation to eat away at the purchasing power of the debt. • • •

Do you believe one of these strategies is a “best” policy to pursue to fund the government? Should fiscal sustainability be a stated goal for government? Is there a more efficient and effective way for the federal government to fund itself than what is currently done in the U.S. Congress?

FISCAL POLICY

NOTES 1 Mark Horton and Asmaa El-Ganainy, “Fiscal Policy: Taking and Giving Away,” Finance and Development (International Monetary Fund, Washington, DC, February 24, 2020). 2 “The Three Basic Tax Types,” The Tax Foundation, https://files.taxfoundation.org/ 20210823155839/TaxEDU-Primer-The-Three-Basic-Tax-Types.pdf. 3 Sarah Perret, “Why Were Most Wealth Taxes Abandoned and Is This Time Different?,” Fiscal Studies, vol. 42 (2021), pp. 539–563, https://doi.org/10.1111/1475-5890.12278. 4 Ibid., p. 540. 5 For more information on the wealth tax and its design, implication, and problems, see: Sarah Perret, “Why Were Most Wealth Taxes Abandoned and Is This Time Different?,” Fiscal Studies, vol. 42 (2021), pp. 539–563, https://doi.org/10.1111/1475-5890.12278. Nick O’Donovan, “One-Off Wealth Taxes: Theory and Evidence,” Fiscal Studies, vol. 42 (2021), pp. 565–597, https://doi.org/ 10.1111/1475-5890.12277. David Burgherr, “The Costs of Administering a Wealth Tax,” Fiscal Studies, vol. 42 (2021), pp. 677–697, https://doi.org/10.1111/1475-5890.12276. Emma Chamberlain, “Who Should Pay a Wealth Tax? Some Design Issues,” Fiscal Studies, vol. 42 (2021), pp. 599–613, https://doi.org/10.1111/1475-5890.12284. Arun Advani, Helen Miller, and Andy Summers, “Taxes on Wealth: Time for Another Look?,” Fiscal Studies, vol. 42 (2021), pp. 389–395, https://doi.org/ 10.1111/1475-5890.12289. E. Saez and Gabriel Zucman, “How Would a Progressive Wealth Tax Work? Evidence from the Economics Literature,” Working Paper, 2019, https://gabriel-zucman.eu/ files/saez-zucman-wealthtaxobjections.pdf. For a discussion of Sen. Warren’s proposal, see: https:// elizabethwarren.com/plans/ultra-millionaire-tax. 6 Peter Varela, “What Is Tax Salience?,” Tax and Transfer Policy Institute, Crawford School of Public Policy, Australian National University, March 2016. 7 Raj Chetty, Adam Looney, and Kory Kroft, “Salience and Taxation: Theory and Evidence,” NBER Working Paper 13330, National Bureau of Economic Research, August 2007. 8 Maureen Mahoney, “Federal Immunity from State Taxation: A Reassessment,” The University of Chicago Law Review, vol. 45, no. 3 (Spring 1978), pp. 695–730. 9 Ibid., p. 695. 10 William R. Watkins, “The Power of the State and Federal Governments to Tax One Another,” Virginia Law Review, vol. 24, no. 5 (March 1938), pp. 475–506. 11 The Office of Management and Budget was original established by the Budget and Accounting Act of 1921 and that executive office was originally called the Bureau of the Budget but renamed in 1971 to its present designation. 12 Michael K. Evans, Macroeconomic Activity (Harper & Row, New York, 1969). 13 “Comparing Fiscal Multipliers,” Committee for a Responsible Federal Budget, Washington, DC, October 6, 2020, https://www.crfb.org/papers/comparing-fiscal-multipliers. 14 Daniel L. Thornton, “Monetizing the Debt,” Review (Federal Reserve Bank of St. Louis, December 1984), pp. 30–43, research.stlouisfed.org/publications/review/1984/12/01/monetizing-the-debt. 15 Ibid., p. 30. 16 “Q&A: Everything You Should Know about the Debt Ceiling,” Committee for a Responsible Federal Budget, Washington, DC, July 28, 2021, https://www.crfb.org/sites/default/files/managed/ media-documents2022-02/QA_Debt%20Ceiling_July2021.pdf. 17 D. Andrew Austin, “The Debt Limit: History and Recent Increases,” Congressional Reserve Service, November 2, 2015, https://sgp.fas.org/crs/misc/RL31967.pdf. The U.S. Treasury notes that “the nature of the [overall debt] limitation was modified until, in 1941, it developed into an overall limit on the outstanding Federal debt.” (U.S. Treasury’s Bureau of the Fiscal Service). 18 “Federal Debt and the Statutory Limit, November 2021,” Congressional Budget Office, Washington, DC, November 2021. Also see: Government Accountability Office, Debt Limit: Market Response to Recent Impasses Underscores Need to Consider Alternative Approaches, GAO-15-476 (July 2015), www. gao.gov/products/GAO-15-476.

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306 MACROECONOMIC THINKING AND TOOLS 19 D. Andrew Austin, “The Debt Limit: History and Recent Increases,” Congressional Research Service, November 2, 2015. 20 Ibid. 21 “Q&A: Everything You Should Know about the Debt Ceiling,” Committee for a Responsible Federal Budget, Washington, DC, July 28, 2021, p. 6. 22 The Budget and Economic Outlook: 2022 to 2032 (Congressional Budget Office, Washington, DC, May 2022), p. 1. 23 Ibid., p. 2. 24 FRB Chairman Ben S. Bernanke, “Fiscal Sustainability,” A speech to the Annual Conference of the Committee for a Responsible Federal Budget, Washington, DC, June 14, 2011. 25 Aaron Betz and Robert Shackleton, “CBO’s Policy Growth Model,” Congressional Budget Office, April 2021. 26 Edward Gamber and John Seliski, “The Effect of Government Debt on Interest Rates,” Working Paper 2019–01, Congressional Budget Office, March 2019, p. 1. 27 Ibid., p. 13. 28 Bernanke, 2011, p. 3. 29 See, for example, Nigel Chalk and Richard Hemming, “Assessing Fiscal Sustainability in Theory and Practice,” Working Paper 00–81, International Monetary Fund, Washington, DC, April 2000. 30 The deficits or surpluses in this theory are based on what is termed primary balances, which are the difference between tax revenues and government spending less net interest payments on the debt. 31 John H. Cochrane, The Fiscal Theory of the Price Level (Princeton University Press, Princeton, NJ, 2022).

CHAPTER

12

Policy Implications of Macroeconomic Theories

LEARNING OBJECTIVES Economic policies are influenced by macroeconomic theories. In this chapter, you will learn: • • • • •

How economists classify economic policies. What economic policymakers do. What current macroeconomic theories are influencing economic policies. Why the “third wheel” of macroeconomic policy is industrial policy. Why past (and future) economic policy failures based on the latest main­ stream theory will force macroeconomic theory to continue its evolution.

[431] HOW IS ECONOMIC POLICY DETERMINED? Policy prescriptions generally employ one of three methods of analysis, which all can co-exist: (1) a positive or objective approach; (2) a normative or subjective approach; and (3) a politicaleconomy approach. The first two are widely discussed in textbooks, while the third is less common—but has a long-standing history. •

Positive economics: The positive-economics approach is one that focuses on the facts, description, and explanation of the channels effecting an economic phenomenon. This is an objective perspective of an economic situation. John Neville Keynes—the father of John Maynard Keynes—described this positive approach as forming a “body of systematized knowledge concerning what is.”1 Understanding those cause-and-effect relationships and circumstances that create a certain economic outcome allows a policymaker to craft a response to an adverse effect or to develop a policy to reinforce a beneficial economic effect for similar or near-similar circumstances. This perspective also has been thought of as the economic “science.” DOI: 10.4324/9781003391050-13

308 MACROECONOMIC THINKING AND TOOLS •

•

•

•

Normative economics: The normative-economics approach builds “a body of systematized knowledge discussing criteria of what ought to be”—as John Neville Keynes wrote. In other words, this approach is the study of what should be, based on an individual’s or group’s beliefs. It has been noted that in the first part of the twentieth century, most leading economists “devoted a significant part of their research effort to normative issues, notably the definition of criteria for the evaluation of public policies.”2 Issues such as inequality and poverty measurement, welfare economics, the theory of social choice, the theory of bargaining, and of cooperative games are ripe with normative policy recommendations. This approach also has been dubbed the economic “art.” Political economics: The third form of a policy perspective, which is called political economics or the application of economics3 is less commonly discussed in the macroeconomics textbooks. The political-economics approach is viewed as an extension of positive economics but does not exclude normative judgments. Some suggest that this method­ ology attempts to internalize political decision making (the policy choice) and most often includes objectives beyond traditional economics.4 J.N. Keynes opined that political economics must account for “ethical, social, and political considerations that lie outside the sphere” of traditional economic science.5 There has been some criticism in the economic literature that the positive-normative methodology distinction, which has been largely developed by John Stuart Mill and John Neville Keynes, is no longer appropriate because each approach can work together. For example, it has been argued that the positive approach can find the facts that are used “to guide policymakers who, in principle, may hold any theory and any social welfare function” (normative perspective).6 Today, economic policy “draws on all three” methods—as the science, art, and application of economics. “Positive economics remains indispensable to the understanding of the likely effects of public decisions. Normative economics brings intellectual discipline to policy choices and helps address the trade-offs they involve. Both, however, are nowadays aware of their own limits. They are increasingly supplemented by political economics.”7

[432] WHAT DO ECONOMIC POLICYMAKERS DO? Agnès Bénassy-Quéré et al. offered six main tasks of economic policymakers:8 •

• • •

Enforce the rules and legislative objectives. This may include monetary policymakers that are guided by legislative objectives or economic circumstances (such as responding to the COVID-19 pandemic impact on the economy or some financial crisis). Craft tax and spending policies. This would include fiscal policymakers—members of the legislature that shape and control the “government budget.” Issue and manage the country’s currency. In the United States, this function would be handled by the U.S. Department of Treasury. In limited ways and at times, produce goods and services. The areas where governments may be responsible for such production and services include providing healthcare and

MACROECONOMIC POLICIES

• •

educational services or sometimes transport services and energy production—in some economies. Fix problems. This is a generic “catch-all” category of activities. Negotiate trade pacts with other countries.

These authors also correctly observe that economic policy means different things to different people and different things at different times.

[433] CAN MACROECONOMICS INFORM US ABOUT THE “MOST-DESIRABLE” POLICY TO IMPLEMENT TODAY? Yes, with limitations. Macroeconomic theories, empirical techniques, and studies continue to evolve as an economy’s institutions, technology, and demographics also evolve. As such, macroeconomic policy prescriptions for business, monetary, or fiscal policies will change over time. Today’s recommendations may very well not be tomorrow’s recommendations. Nonetheless, the basic macroeconomic policy goals for an economy—with respect to unemployment, inflation, and economic growth—largely have remained unchanged. The most desirable policy, therefore, will be judged against those core macroeconomic goals and some set of criteria such as economic efficiency or economic benefit and cost. However, the “expanded” macroeconomic policy goals may or may not gain broad-based acceptance by policymakers or be thought of as universal policy goals. Witness how the Federal Reserve has expanded its employment mandate to include more detailed aspects of its employment mandate or goal. Or how strong economic growth as an objective has been expanded to include concepts of economic mobility. Any “most-desirable” policy, moreover, still requires a fair amount of value judgment for which economists, politicians, and the public at-large will likely be divided on.

[434] WHAT IS THE RELATIONSHIP BETWEEN MACROECONOMIC THEORIES AND ECONOMIC POLICY? Nobel Prize–winning economist Joseph E. Stiglitz observed that “Keynes rightly pointed out [that] policy is shaped by theories.”9 This view is held throughout the economics profession.

[435] [ADVANCED] HOW IS MACROECONOMIC THEORY SHAPING ECONOMIC POLICIES? Chari and Kehoe10 argued that macroeconomic theories have helped shape or reshape monetary policies, taxation policies, and labor-market policies. They opined further three elements of current macroeconomic theory—the Lucas critique of policy evaluation, the timeinconsistency critique of discretionary policy, and the development of quantitative dynamic sto­ chastic general equilibrium (DSGE) models—are embedded into economic policies today.

309

310 MACROECONOMIC THINKING AND TOOLS •

•

•

Lucas Critique: Robert Lucas argued that traditional macroeconomic models could not correctly predict or explain the impacts of alternative economic policies, because alternative policies essentially would change the structure of the macroeconomic model. Lucas put it this way, “Given that the structure of an econometric model consists of optimal decision rules of economic agents, and that optimal decision rules vary systematically with changes in the structure of series relevant to the decision maker, it follows that any change in policy will systematically alter the structure of econometric models.”11 Goutsmedt et al. noted that as a matter of logic, the Lucas Critique is unquestionable.12 As a result, Rudebusch observed that this “helped reorient macro­ economic research toward models with explicit expectations and ‘deep’ parameters of taste and technology. [And] the Lucas critique helped change the focus of policy evaluation from consideration of alternative paths of the policy instrument to consideration of alternative policy rules, which allowed individual agents to formulate forward-looking dynamic optimization problems.”13 Time-Inconsistency Critique: The 2004 Nobel Prize in economics was presented to Finn E. Kydland and Edward C. Prescott for their “time-consistency” perspective of economic policy. The award recognized Kydland and Prescott14 for “uncover[ing] inherent imperfections—credibility problems—in the ability of governments to imple­ ment desirable economic policies … Kydland and Precsott’s work has transformed academic research in economics, as well as the practice of macroeconomic analysis and policymaking.” The idea is that “governments unable to make binding commitments regarding future policies will encounter a credibility problem … . They showed that the outcome in a rational-expectations equilibrium where the government cannot commit to policy in advance–discretionary policymaking—results in lower welfare than the outcome in an equilibrium where the government can commit.”15 Thus, formal commitments through rule-based policymaking are believed to circumvent this potential credibility problem. Dynamic Stochastic General Equilibrium Models: Dynamic stochastic general equili­ brium (DSGE) models are small to medium sized economic models that are built upon microeconomic foundations. “They are general equilibrium in nature, meaning that prices and interest rates adjust until supply equals demand in every market”—including the goods, assets, and labor markets. “The models are also stochastic, meaning that they incorporate the random components that play an important role in explaining the cyclical behavior of the economy.” This includes demand and supply shocks. “Combining these ingredients—the use of explicit maximizing behavior that is also dynamic in nature and forward- looking rational expectations—makes the output of DSGE models, whether the results of a policy experiment, or the analysis of the sources of economic fluctuations, readily interpretable in terms of economic theory.”16 These models have been used by central banks around the world, including the Swedish Central Bank, the European Central Bank, the Norwegian Central Bank, and the Federal Reserve. The basic DSGE model structure is portrayed in the following graphic from a New York Federal Reserve Bank introductory discussion of this type of model.

MACROECONOMIC POLICIES

FIGURE 12.1

The Basic Structure of DSGE Models

Source: Argia M. Sbordone, Andrea Tambalotti, Krishna Rao, and Kieran Walsh, “Policy Analysis Using DSGE Models: An Introduction,” FRBNY Economic Policy Review, October 2010, p. 25

[436] [ADVANCED] WHAT ECONOMIC POLICIES WERE EMBRACED UNDER MERCANTILISM? Mercantilism was an economic system that existed in Europe between about 1600 and 1800. It was based on the idea that a nation that traded goods with other nations should seek to increase its exports but limit imports to accumulate larger international reserves of gold, which could be used by to build its military might and economic power. However, this idea was challenged by Adam Smith in his Wealth of Nations (1776) and later by British economist David Ricardo in his 1817 book, On the Principles of Political Economy and Taxation, where Ricardo put forth his international trade theory of comparative advantage—which argued a country will benefit from international trade even if the country does not have an absolute cost advantage compared to a trading partner, but only relative cost advantage or relative opportunity cost (that is, costs in terms of other goods given up). Ricardo further showed that a relative cost advantage could benefit both trading partners if each country would specialize in the production of the goods each had the relative cost advantage and then trade with each other for the other goods. Therefore, this theory of comparative advantage offered a strong rationale in favor of free trade and specialization among countries. Ricardo’s view became more prevalent throughout Europe, which caused mercantilism to lose favor as an economic policy.

[437] [ADVANCED] WHY DID MERCANTILISM RE-SURFACE AS NEOMERCANTILISM AND WHAT ARE ITS MAIN TENETS TODAY? Neo-mercantilism returned in the 1880s after Germany’s first Chancellor Otto von Bismarck abandoned free trade in the aftermath of the long 1873 depression and embraced

311

312 MACROECONOMIC THINKING AND TOOLS the protectionist economic policies advocated by German economist Georg Friedrich List (1789–1846). List argued that a nation’s economic power depended on its ability to develop “productive forces” that would provide wealth in the future and to do this, it would be necessary to protect domestic “infant industries.” In addition to protectionist policies, these productive forces also included policies to promote “scientific discoveries, advances in technology, improvements in transportation, the provision of educational facilities, the maintenance of law and order, an efficient public administration, and the introduction of a measure of self- government.”17 List’s ideas also spread to France and even influenced U.S. Treasury Secretary Alexander Hamilton. The Wilson Center’s William Krist also observed that even later a number of countries—including Japan, South Korea, China, and some other countries in the Far East—pursued a neo-mercantilism model in which they [embraced policies] to grow through an aggressive expansion of exports, coupled with a very measured reduction of import barriers.18 Krist further opined that the success of some countries pursuing a neo-mercantilist strategy does not refute [David Ricardo’s] law of comparative advantage. In fact, the reason these countries are successful is that they focus on industries where they have or can create a comparative advantage.19 Modern mercantilism shares the objectives of the original view in pursuing a policy that encourages exports, discourages imports, but also may use capital controls (which are government or central bank imposed policies to restrict the flow of capital and in and out of the domestic economy through regulations, tariffs, taxes, or any other restrictions) and currency intervention by the government or central bank to keep the country’s exchange rate undervalued to make its exports more competitive and imports more expensive. In an article prepared for the World Economic Forum in 2019, author Paul Rawlinson noted that The unforeseen return of [neo-]mercantilism—particularly in the U.S. under President Trump—[was] based on the false premise that one country can only make economic gains at the expense of another. It holds that every deal has a winner and a loser, and nothing can be mutually beneficial.20

[438] WHAT IS INDUSTRIAL POLICY? An industrial policy is a macroeconomic program or plan created by the federal government to promote development for selected industries (or for targeted economic development policies) or the retrenchment of selected industries.21 Harvard Prof. James Robinson

MACROECONOMIC POLICIES

observed that there are many ways in which an industrial policy is implemented, including through the use of tariffs and trade policy, tax relief, government subsidies, export pro­ cessing zones (free-trade zones), and even government ownership of an industry.22 An industrial policy is a political economy application and is a “third wheel” of macroeconomic policy, which operates alongside monetary and fiscal policy. The critics of industrial policy argue that it is not as efficient for a government to determine winners and losers than to allow the market to determine which industries thrive and which industries perish. Lincicome and Zhu further contend that there are several practical obstacles that prevent successful implementation of an effective industrial policy.23 However, a nuanced propo­ nent of industrial policy, Robert Reich argued that protections tariffs to help a domestic industry that is losing its international competitiveness is not always the best policy approach. Instead, Reich advocated for a “managed adjustment,” where government works hand-in-hand with business and its associated labor force to “ease the transition out of declining industries and into emerging ones.”24 Additionally, Robinson’s article surveying industrial policies around the globe concluded, “There are good reasons to believe from economic theory that industry policy may be socially desirable and may stimulate economic growth and development.”25 But still, he opined, that was true only for some countries. However, determining which countries are suitable for an industrial policy is a publicly debatable political question.

[439] HOW DO DIFFERENT MACROECONOMIC THEORIES ADDRESS DIFFERENT ECONOMIC CONDITIONS? Different macroeconomic theories address economic problems based on their framework and emphasis. For example, Keynes’ General Theory addressed high unemployment and the global depression of the 1930s and his theory focused on short-term policies to correct the economic conditions. Similarly, other competing theories that have developed tend to focus upon a primary condition that the theory is trying to correct—such as inflation or recession. Table 12.1 highlights some of the policy perspectives offered by theories today; each condition shown is presented in isolation of any other condition—however actions can be combined. For example, high economic growth may not be a problem, per se, but in combination with demand-pull inflation then economic growth may be “too high” and that high economic growth may be the cause of high inflation. This table summarizes the policy actions of various economic theories that would be advocated for each economic condition. And, of course, not all policy options are dictated by theory, but some are based on what policy tools that a monetary or fiscal policymaker has available. For example, a “short-term” policy option to address cost-push inflation, which is typically thought of as a supplyinduced shock—such as a spike in oil prices or pandemic-induced production shortages resulting in sharply higher prices—and only corrected over the long-term, has been the imposition of wage and price controls (as the Nixon Administration imposed—a short-term fix with long-term consequences).

313

Demand-Pull Inflation

Cost-Push Inflation

Low (Below Central Bank’s target)

Low High

Low

Inflation

Inflation

Inflation

Unemployment Unemployment

Economic Growth

Economic Growth High Theory’s Main Focus

Condition

Remedy Based on Theory

Economic Metric

TABLE 12.1

Reduce aggregate demand through fiscal and/or monetary policy Increase aggregate supply over the long-term through fiscal policy Increase aggregate demand through fiscal and/or monetary policy Do Nothing Increase aggregate demand through fiscal and/or monetary Policy Increase aggregate demand through fiscal and/or monetary policy Do Nothing “Short-run”

Keynesian School/ Modern Monetary Theory

Do Nothing Do Nothing

Do Nothing “Long Run”

Do Nothing “Long Run”

Increase the growth of Do Nothing the money supply

Do Nothing Do Nothing

Increase the growth of Do Nothing the money supply

Reduce the growth of Reduce federal deficits the money supply by increasing taxes

Reduce the growth of Reduce federal deficits the money supply by increasing taxes

Austrian Economics New Fiscal Theory School/ Monetarism

Remedy Based on Theory

Do Nothing Problem triggered by monetary policy mistakes (surprises), advocates a money-supply growth/interest rate rule. Supply-side fiscal approaches to affect preferences, technology, and/or labor productivity. Do Nothing “Long Run”

Problem triggered by monetary policy mistakes (surprises), advocates a money-supply growth/interest-rate rule. Problem triggered by monetary policy mistakes (surprises), advocates a money-supply growth/interest-rate rule. Rule-based monetary growth would increase money-suppy growth (lower interest rates)

New Classical School/New Monetary Consensus

314 MACROECONOMIC THINKING AND TOOLS

MACROECONOMIC POLICIES

[440] HOW DO ECONOMISTS AND ECONOMIC POLICYMAKERS KNOW IF A SHOCK TO AN ECONOMY IS TRANSITORY OR PERMANENT? The simple answer is that neither group knows initially and only time will tell. Obviously, this poses a problem for policymakers since a one-time impact on the economy may not require a policy action, but an ongoing effect might. Witness the Federal Reserve’s initial response to inflationary pressures building in late 2020 and early 2021—they shrugged it off as temporary—then reversed course later as inflationary pressures lingered. As a practical matter, macroeconomics does not offer a lot of help to policymakers who may not have the luxury of waiting to see if a shock is temporary or permanent on aggregate demand and aggregate supply.

[441] HOW IS “NOWCASTING” HELPING ECONOMIC POLICYMAKERS? Economists are increasingly using what is termed “nowcasting” (which is a blending of the words “now” and “forecasting”) to provide real-time estimates of growth and inflation within an economy and offering the potential for faster policy response to changing economic conditions. These estimates are based on statistical models that attempt to cobble together disparate information to provide a very timely reading on the state of the economy. However, unusual times (such as the COVID-19 pandemic) may invalidate or make the model estimates highly uncertain. Witness, for example, the Federal Reserve Bank of New York’s “Nowcast Report” being suspended in 2021. The NY Fed’s research staff wrote: The uncertainty around the pandemic and the consequent volatility in the data have posed a number of challenges to the Nowcast model. Therefore, [the NY Fed] decided to suspend the publication of the Nowcast while [continuing] to work on methodological improvements to better address these challenges. (September 3, 2021) However, the Federal Reserve Bank of Atlanta’s “GDPNow” project26 continued during and in the aftermath of the 2020 pandemic, but warned of some increased estimate uncertainty. The Atlanta Fed’s nowcast model provides ongoing estimates of real GDP growth for the current quarter. There are other nowcasting model estimates being used, including models developed at the International Monetary Fund (IMF). The IMF noted the following: Economists are increasingly turning to new technologies [to] offer better input for policymakers. Nowcasting, or forecasting of the present, is especially promising for developing economies where statistical authorities may not release indicators frequently. [The IMF] developed an approach that pairs high-frequency data with machine learning, a kind of artificial intelligence, to provide economic-growth nowcasts and help policy­ makers make better decisions.27 Or, at least, faster decisions.

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316 MACROECONOMIC THINKING AND TOOLS

[442] AS A FINAL QUESTION, HAS MACROECONOMIC THEORY FAILED POLICYMAKERS? This is a debatable question. However, Prof. Joseph E. Stiglitz has forcefully argued that macro­ economic theory has failed policymakers.28 In a 2014 NBER paper, Stiglitz, who was the 2001 Nobel Memorial Prize in economics winner and who later shared a Nobel Peace Prize in 2007, wrote that macroeconomic theory failed to predict the 2008 global financial crisis or the duration of the sub­ sequent recession. Indeed, he asserted a view held much earlier by the late University of Chicago Prof. Milton Friedman29 that “the test of science is prediction” and on that basis Stiglitz criticized main­ stream macroeconomic theories as flawed. He offered his view for macroeconomic theory to be effective for policymakers, it must accurately answer three basic questions: (1) What is the source of a disturbance or shock in the economy? He further offered his assessment that, “most major downturns are man-made events. The system creates them. And that means it may be possible for us to at least reduce their frequency and depth.” (2) Why do seemingly small shocks have such large effects and can create global contagion? And (3) Why do deep downturns last so long? Or, in other words, why does there seem to be such persistence? To be sure, economists have focused attention on these problems and will continue to search for new answers to these important policy questions.

Issues to Think About All of the economic tools, techniques, theories, and thinking that earlier chapters discussed come together as a guide to macroeconomic policy. • • • • •

Why is the “political economics” approach important? Why is it useful to be aware of some historical ideas, such as mercantilism or neo-mercantilism? How and why does an “industrial policy” fit into governmental macro­ economic policy? What can be done to make macroeconomic theory more useful to policy­ makers? Has macroeconomic theory failed policymakers?

NOTES 1 John Neville Keynes, republished 1955 in The Scope and Method of Political Economy, 4th edition (Kelley and Millman, New York, 1891). 2 Marc Fleurbaey, “Normative Economics and Economic Justice,” In Edward N. Zalta, ed., The Stanford Encyclopedia of Philosophy (Spring 2022 edition), https://plato.stanford.edu/archives/spr2022/ entries/economic-justice/. 3 David Colander, “The Lost Art of Economics,” Journal of Economic Perspectives, vol. 6, no. 3 (Summer 1992), pp. 191–198. A better description of this type of approach may be as an application of eco­ nomics considering broader objectives than economics alone may offer.

MACROECONOMIC POLICIES 4 Agnès Bénassy-Quéré, Benoît Coeuré, Pierre Jacquet, and Jean Pisani-Ferry, Economic Policy: Theory and Practice (Oxford University Press, New York, 2010). 5 John Neville Keynes, p. 34. 6 David Colander and Huei-Chun Su, “Making Sense of Economists’ Positive-Normative Distinction,” Journal of Economic Methodology (2015), p. 10. 7 Ibid., pp. 10–11. 8 Agnès Bénassy-Quéré, Benoît Coeuré, Pierre Jacquet, and Jean Pisani-Ferry, pp. 11–12. 9 Joseph E. Stiglitz, “Reconstructing Macroeconomic Theory to Manage Economic Policy,” National Bureau of Economic Research (NBER) Working Paper 20517, September 2014, p. 24. 10 V. V. Chari and Patrick J. Kehoe, “Modern Macroeconomics in Practice: How Theory Is Shaping Policy,” Journal of Economic Perspectives, vol. 20, no. 4 (Fall 2006), pp. 3–28. 11 Robert Lucas, “Econometric Policy Evaluation: A Critique,” Carnegie-Rochester Conference Series on Public Policy (Issue 1, 1976), p. 41. 12 Aurélien Goutsmedt, Erich Pinzón-Fuchs, Matthieu Renault, and Francesco Sergi, “Reacting to the Lucas Critique: The Keynesians’ Replies,” History of Political Economy, Duke University Press, vol. 51, no.3 (2019), pp. 535–556. 13 Glenn D. Rudebusch, “Assessing the Lucas Critique in Monetary Policy Models,” Journal of Money, Credit & Banking, vol. 37, no. 2 (April 2005), pp. 245–246. 14 See, for example, Finn Kydland and Edward Prescott, “Rules rather than Discretion: The Inconsistency of Optimal Plans,” Journal of Political Economy, vol. 85, no. 3 (June 1977), pp. 473–492. 15 “Finn Kydland and Edward Prescott’s Contribution to Dynamic Macroeconomics: The Time Consistency of Economic Policy and the Driving Forces Behind Business Cycles,” Information on the Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel, The Royal Swedish Academy of Sciences, October 11, 2004, https://www.nobelprize.org/uploads/2018/06/advancedeconomicsciences2004.pdf. 16 These quotes are all from: Michael Dotsey, “DSGE Models and Their Use in Monetary Policy,” Business Review (Philadelphia Federal Reserve Bank, Q2 2013), p. 11. 17 New World Encyclopedia contributors, “Friedrich List,” New World Encyclopedia, https://www. newworldencyclopedia.org/p/index.php?title=Friedrich_List&oldid=1054316 (accessed April 12, 2022). 18 William Krist, “Trade Policy in Crisis–Chapter 3: Trade Agreements and Economic Theory,” Online update of Globalization and America’s Trade Agreements (Woodrow Wilson Center Press/Johns Hopkins University Press, Wilson Center, Washington, DC, 2013), https://www.wilsoncenter.org/ chapter-3-trade-agreements-and-economic-theory. 19 Ibid. 20 Paul Rawlinson, “Confronting Neo-Mercantilism: Why Regulation is Critical to Global Trade,” World Economic Forum (January 7, 2019), https://www.weforum.org/agenda/2019/01/confrontingneo-mercantilism-regulation-critical- global-trade-tariff/. 21 Ellis W. Hawley, “‘Industrial Policy’ in the 1920s and 1930s,” in Claude E. Barfield and William A. Schambra, eds., The Politics of Industrial Policy (American Enterprise Institute, Washington, DC, 1986), pp. 63–86. 22 James A. Robinson, “Industrial Policy and Development: A Political Economy Perspective,” Paper prepared for the 2009 World Bank Conference in Seoul, Korea, June 22–24, May 2009. 23 Scott Lincicome and Huan Zhu, Questioning Industrial Policy: Why Government Manufacturing Plans are Ineffective and Unnecessary (Cato Institute, Washington, DC, 2021). 24 Robert B. Reich, “Making Industrial Policy,” Foreign Affairs, Council on Foreign Relations, vol. 60, no. 4 (Spring 1982), p. 855. 25 James A. Robinson, p. 25. 26 See: https://www.atlantafed.org/cqer/research/gdpnow. 27 Karim Barhoumi, Seung Mo Choi, Tara Iyer, Jiakun Li, Franck Ouattara, Andrew J Tiffin, and Jiaxiong Yao, “Overcoming Data Sparsity: A Machine Learning Approach to Track the Real-Time Impact of COVID-19 in Sub-Saharan Africa,” IMF Working Paper, International Monetary Fund,

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318 MACROECONOMIC THINKING AND TOOLS Washington, DC, May 6, 2022. The authors further note that, “The tool generates nowcasts by incorporating a broad range of increasingly popular machine-learning techniques that use an array of high-frequency economic indicators historically related to change in GDP. Tourist arrivals, for ex­ ample, are more reliable predictors for tourism-dependent countries. For oil-exporting countries, such as Nigeria, GDP tends to move with crude oil prices. Other nontraditional data inputs can include satellite imagery of nighttime lights, which tend to glow with greater intensity as economic activity increases, and of shipping vessels, used to track trade volumes and disruptions.” 28 Stiglitz, 2014. 29 Milton Friedman’s famous quote on this perspective was, “the only relevant test of the validity of a hypothesis is comparison of its predictions with experience,” See: Milton Friedman, “The Methodology of Positive Economics,” in Essays in Positive Economics (University of Chicago Press, Chicago, 1953), pp. 14–15. In a review of Friedman’s book, Michael E. Brady wrote that “after substituting ‘soundness’ for ‘validity’ and ‘theory’ for ‘hypothesis’, we have Milton Friedman’s general criteria for theory appraisal.” See: Michael Emmett Brady, “A Note on Milton Friedman’s Application of His ‘Methodology of Positive Economics,’” Journal of Economic Issues, vol. 20, no. 3 (September 1986), p. 845.

Part 2 Macroeconomic Issues

CHAPTER

13

The Disappearing Worker

LEARNING OBJECTIVES Labor supply is an important ingredient for economic growth. However, the U.S. labor market has seen a declining rate of participation, especially among the prime working-age population. You will learn: • • • •

What is occuring with the U.S. labor supply. Why these trends are of concern to economists and policymakers. What are the presumed causes of the labor supply changes. Suggestions to address the disappearing worker.

[443] WHAT IS MEANT BY A “DISAPPEARING” WORKER? The U.S. labor-force participation rate (that is, the labor force—both employed and looking for work—as a share of the working-age population (16 years and older) hit a peak in 2000 at 67.3% of the population and has been trending lower ever since that time. The COVID-19 pandemic further depressed the participation rate, but it has partially recovered by mid-2022 from that impact of the pandemic yet continues its long-term trend. This decline in the working-age population that is in the workforce has triggered a number of studies to try to understand why potential workers are disappearing from the workforce (See Figure 13.1).

[444] IS THIS DISAPPEARING WORKER A GLOBAL PHENOMENON? No. According to OECD statistical data, the disappearing worker phenomenon is found only in the United States, as shown in Figure 13.2.

DOI: 10.4324/9781003391050-15

322 MACROECONOMIC ISSUES

FIGURE 13.1

U.S. Labor Force Participation Rate

Source: U.S. Bureau of Labor Statistics

FIGURE 13.2 Source: OECD

Labor-Force Participation Rates, 25–64-Year-Olds

THE DISAPPEARING WORKER

[445] WHAT DOES THE DECLINE IN THE U.S. LABOR-FORCE PARTICIPATION RATE MEAN FOR THE EMPLOYMENT-TOPOPULATION RATIO? The employment-to-population ratio shows a related declining pattern to that of the laborforce participation rate.

FIGURE 13.3

U.S. Employment-to-Population Ratio

Source: U.S. Bureau of Labor Statistics

[446] IS THE LABOR-FORCE PARTICIPATION RATE DECLINE MAINLY FROM MEN OR WOMEN DROPPING OUT OF THE LABOR FORCE? The long-term decline in the overall participation rate is largely from men. The data clearly highlights a declining long-term trend in the labor-force participation rate of men (well before 2000) from a post-war high of 87% in 1949 to 68% by mid-2022. On the other hand, the data show the influx of women into the labor force from a low of 32% in the late 1940s to 60% by 2000. The female laborforce participation rate remained relatively steady at 60% through 2009 before edging lower.

[447] [ADVANCED] HOW IS THE U.S. PRIME-WORKING AGE LABOR-FORCE PARTICIPATION RATE PERFORMING? The prime-working age population in the United States is considered to be 25–54 years of age. The trends in labor-force participation among this age group echoes the broader pattern for all men and all women aged 16 years and older, as shown Figures 13-4A and 13-4B below.

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324 MACROECONOMIC ISSUES

FIGURE 13.4A

Labor Force Participation Rate: 25–54 Years, Men

Source: U.S. Bureau of Labor Statistics

FIGURE 13.4B

Labor Force Participation Rate: 25–54 Years, Women

Source: U.S. Bureau of Labor Statistics

THE DISAPPEARING WORKER

[448] WHY DO WE CARE ABOUT THIS INCREASE IN THE U.S. NON-PARTICIPATION RATE? The increase in non-participation in the labor force means that the labor supply is more constrained, accentuates labor shortages, and puts upward pressure on wage rates. In a more philosophical tone, Greszler and Lobel argued that A strong work ethic was fundamental to America’s founding, and it enabled America to become one of the most prosperous nations in the world. That’s why the recent decline in labor force participation in the United States is so troubling.1

[449] WHAT ARE THE SUGGESTED CAUSES OF THE RISE IN NON-PARTICIPATION RATES IN THE LABOR MARKET? Numerous studies have explored reasons for why working-age people are not participating in the labor market.2 The main reasons offered for this trend are: • •

•

•

•

•

Retirement: Workers taking early retirement can affect the non-participation rate. Social programs: An increase of people on Social Security Disability Insurance has been identified as a key reason for working-age men dropping out of the labor force. According to Eberstadt, “Around three-quarters of prime-age non-working men without a highschool diploma are reporting disability benefits.”3 Increase in school enrollment: As more young people stay in school to complete higher academic degrees, this increases the number of young people not in the labor force. “Job polarization”: This view suggests that the demand for middle-skilled workers is increasingly being replaced by technological innovation. This is tied to the discouraged worker concept that individuals without current labor-market skills ultimately drop out of the labor market after unsuccessfully searching for a new job. Higher incarceration rates: This idea is that the rise of incarceration rates will eliminate people from the workforce and make it more difficult for those individuals to return to the labor force once released from jail. The problem with this argument is that the rates of jail incarceration have been trending lower since 2005, according to the U.S. Bureau of Justice Statistics.4 Gender gaps in compensation and career opportunities: This argument is that labor-market inequities for prime-aged married women result in less women entering the workforce.

The Kansas City Federal Reserve study by Didem Tüzemen found that, “the most common personal situation reported among nonparticipating prime-age men was disability or illness, while the least common personal situation was retirement.” Tüzemen also maintains that the reduction in the demand for middle-skill workers accounts for most of the decline in labor force participation among prime-age men.

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326 MACROECONOMIC ISSUES

[450] HOW CAN FISCAL AND MONETARY POLICY RAISE LABORFORCE PARTICIPATION RATES? An IMF study using a New Keynesian model argued that monetary policy could induce a more rapid closure of the participation gap through allowing the unemployment rate to overshoot its long-run natural rate (i.e., unemployment [rate] falls below the natural rate). Quite intuitively, keeping [the] unemployment [rate] persistently low draws cyclical non-participants back into the labor force more quickly.5 Jacobs, in testimony to the Congress, offered some social changes from fiscal policy that might be used to increase labor market participation, which included: (a) developing family-friendly policies to allow more women to enter the labor force; and (b) implement criminal justice reform to eliminate “discriminatory employment practices against those with criminal records.”6 Another suggestion that has been proffered to increase participation rates is to “relax” medical screening for Social Security Disability Insurance participation and lower the program’s income-replacement rates so as to encourage “able-bodied workers” back into the labor market. Finally, more federal spending also has been suggested for “active labor market policies, such as job creation programs, job-search assistance and training programs in the U.S.,” which would likely improve participation rates.7 Obviously, all of these suggestions are controversial and have proponents and opponents.

Issues to Think About In the aftermath of the COVID-19 pandemic, worker shortages were widespread, but those trends in non-participation were in place for years—especially for some segments of the population. • • •

Why do you think this long-term non-participation problem is a bigger problem for the United States than other industrialized countries? Is the short-term non-participation rise in the aftermath of the COVID-19 pandemic intertwined with the long-term trend? Do you think policies need to be implemented to address the labor-market supply? if so, what policies?

NOTES 1 Rachel Greszler and Matthew Lobel, “Why America’s Labor Force Decline Matters beyond Current Supply Shortages, Rising Prices,” The Heritage Foundation, March 29, 2022, https://www.heritage. org/jobs-and-labor/commentary/why-americas-labor-force-decline-matters-beyond-current-supplyshortages.

THE DISAPPEARING WORKER 2 See, for example, Nicholas Eberstadt, “Education and Men without Work,” National Affairs (Winter 2020), pp. 3–17, https://nationalaffairs.com/publications/detail/education-and-men-without-work. Also, Francisco Perez-Arce and María J. Prados, “The Decline in the U.S. Labor Force Participation Rate: A Literature Review,” Journal of Economic Surveys, vol. 35, no. 2 (2021), pp. 615–652. Alan B. Krueger, “Where Have All the Workers Gone? An Inquiry into the Decline of the U.S. Labor Force Participation Rate,” Brookings Papers on Economic Activity (The Brookings Institution, Fall 2017), pp. 1–59. Another study exploring this issue is: Katharine G. Abraham and Melissa S. Kearney, “Explaining the Decline in the US Employment-to-Population Ratio: A Review of the Evidence,” Journal of Economic Literature, vol. 58, no. 3 (2020), pp. 585–643. Didem Tüzemen, “Why Are PrimeAge Men Vanishing from the Labor Force?,” Economic Review (Federal Reserve Bank of Kansas City, First Quarter 2018), pp. 5–30. 3 Eberstadt, p. 13. 4 See, for example, Zhen Zeng and Todd D. Minton, Jail Inmates in 2019 (Bureau of Justice Statistics, March 2021), https://bjs.ojp.gov/content/pub/pdf/ji19.pdf. 5 Christopher J. Erceg and Andrew T. Levin, “Labor Force Participation and Monetary Policy in the Wake of the Great Recession,” IMF Working Paper WP/13/245, International Monetary Fund, July 2013. 6 Elisabeth Jacobs, “The Declining Labor Force Participation Rate: Causes, Consequences, and the Path Forward,” Testimony before the Joint Economic Committee of the U.S. Congress, Washington Center for Equitable Growth, July 15, 2015, https://equitablegrowth.org/declining-labor-forceparticipation-rate-causes-consequences-path-forward/. 7 Alexander W. Richter, Daniel Chapman, and Emil Mihaylov, “Declining U.S. Labor Force Participation Rates Stand Out,” Economic Letter, Federal Reserve Bank of Dallas, vol. 13, no. 6 (April 2018), https://www.dallasfed.org/~/media/documents/research/eclett/2018/el1806.pdf.

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CHAPTER

14

Implications of an Evolving Economy

LEARNING OBJECTIVES Long-term shifts in the economy (from agriculture to manufacturing; then manufacturing to services) have changed the trend growth in the economy. You will learn: • • •

How the evolution of the economy effected economic growth. How those compositional changes affect prices. What compositional changes might affect the economy ahead.

[451] HOW HAS THE U.S. ECONOMY EVOLVED SINCE ITS INCEPTION? One of the earliest historic estimates of the composition of the U.S. economy was by the Conference Board1 and that data suggested agriculture accounted for about 40% of total production in the economy in 1799. However, with the broadening of economic growth across other sectors, the shift away from an agrarian society continued to pick up momentum. According to that Conference Board, agricultural was still the largest sector in the U.S. economy between 1869 and 1879—accounting for about one-fifth of national income (20.5%) over that period. The second-largest sector was trade (15.7%), followed by services (14.7%), and then manufacturing (13.9%). Government held a small share of 4.4%. Over the subsequent ten years, 1879 through 1889, the economy saw the advent of the industrial revolution boost the manufacturing share of the economy (16.6% on average) along with trade (also 16.6%) to become the largest segments of the economy followed by agriculture, which slipped to 16.1%. In the final decade of the 1800s (1889 through 1899), manufacturing was clearly the dominant sector of the U.S. economy, accounting for 18.2% of national income, followed by agriculture (17.1%) and trade (16.8%). In the early 1900s, the manufacturing sector’s dominance grew even further to about 22% by the 1920s. Agriculture, however, slipped to about 12%, on average, over the period from 1919 through 1928. The agricultural share of the economy DOI: 10.4324/9781003391050-16

IMPLICATIONS OF AN EVOLVING ECONOMY

between 1910 and the present is highlighted in the graphic below. The government sector also saw a marked growth in its average share of the economy between 1919 and 1928 accounting for almost 10%, according to estimates from Kuznets.2 That growth in the government sector took an even larger share of the economy during the Great Depression years, with a 14.4% share on average between 1929 and 1938—second only to manu­ facturing’s 19.4% share. Throughout the 20th century, the U.S. economy continued

FIGURE 14.1A

Net Value Added of Farm Output as a Share of GDP (1910–2022)

Sources: U.S. Department of Agriculture (Farm Income and Wealth Statistics); U.S. Bureau of Economic Analysis; National Bureau of Economic Research

FIGURE 14.1B

Share of U.S. GDP (1947–2021)

Source: U.S. Bureau of Economic Analysis

329

330 MACROECONOMIC ISSUES to evolve, this time further away from agriculture and manufacturing towards private-sector services. In 1960, private-sector services broke above the halfway mark for the first time, accounting for 50.2% of the economy. The shift towards a larger percentage of the economy in services continued and by 2021, private services accounted for 70.3% of the economy, as shown in Figure 14.1B.3

[452] WHY DO WE CARE ABOUT THE COMPOSITION OF THE ECONOMY? In the economic literature there are two main schools of thought on how sectoral composition and growth interrelate. The neoclassical view holds that sectoral composition is a relatively unimportant byproduct of growth. However, scholars associated with the World Bank, including [1971 Nobel memorial prize winner in economics] Kuznets [in his Economic Growth of Nations, Total Output and Productive Structure, published in 1971], Rostow [in his Stages of Economic Growth, published in 1971], Chenery and Syrquin [in Patterns of Development 1950–70, published in 1975], and Baumol, Blackman, and Wolf [in Productivity and American Leadership, published in 1989] posit that growth is brought about by changes in sectoral composition.4 Moreover, according to a study by Echevarria, she found that sectoral composition explained 22% (about one-fifth) of the variation in economic growth between 1970 and 1987.5

[453] WHAT DOES THE COMPOSITION OF THE ECONOMY MEAN FOR ECONOMIC GROWTH AND ECONOMIC VOLATILITY? A 2015 paper by Moro6 found that “the composition of GDP represents an important channel shaping both GDP and volatility.”7 Although his paper focused on per capita GDP, his empirical observations hold with regard to real GDP growth as well. To recast the empirical findings in terms of real GDP growth, three observations can be made: • • •

Fact 1: There is a negative relationship between the share of services in GDP and the growth rate of real GDP. Fact 2: The share of services in GDP and real GDP growth rate volatility are negatively related. Fact 3: The share of services in GDP and level of real GDP are positively related.

Table 14.1 supports the original observation by Moro.

[454] HOW MUCH HAS THE LONG-TERM SHIFT IN THE COMPOSITION OF THE ECONOMY CHANGED OVERALL ECONOMIC GROWTH? During the post‐WWII period, the median annual real GDP growth was 3.1% with services growing by 2.6%, goods up by 4.1%, and structures increasing by 2.2%. The three components of GDP—services, goods, and structures—equal the total. This suggests that the long-term shift in

IMPLICATIONS OF AN EVOLVING ECONOMY TABLE 14.1

Relationship between Economic Growth, Volatility, and the Composition of the

Economy Relationship between Economic Growth, Volatility, and the Composition of the Economy Concept

Constant Term

Real GDP Growth Two-Year Moving Standard Deviation of Real GDP Growth Level of Real GDP

8.39 5.11

−29,523

Explanatory Term

Annual Period

Comment on Explanatory Term

−0.09 x Service Share 1948–2021 −0.059 x Service Share 1949–2021

Statistically Significant Statistically Significant

661.3 x Service Share

Statistically Significant

1947–2021

the composition of GDP can affect the rate of growth in the overall economy. This is important since the economy has been shifting towards more services, which are a slower-growing component of GDP and away from goods, which is a faster-growing component of GDP (See Table 14.2). To highlight this point, consider a “what if” question. What if the composition of the U.S. economy did not change from its 1950 sector shares? How much would that change the TABLE 14.2

Median Annual U.S. Growth, 1948–2021 Median Annual U.S. Growth, 1948–2021

Real GDP

Real GDP Services

Real GDP Goods

Real GDP Structures

3.1%

2.6%

4.1%

2.2%

How Much Different Would Real GDP Growth Be If the Economy’s Shares Were Fixed at Its 1950s Shares?

FIGURE 14.2

331

332 MACROECONOMIC ISSUES reported real GDP growth rates? Fixing the weights as they existed in 1950, and recalculating real GDP growth for snapshots at every ten years, except for the inclusion of 2021, suggests that the strong growth of real services in 1960 (+14.7%) and 1970 (+9.2%) would have meant that the overall growth for real GDP in 1960 and 1970 would have been 0.6 percentage points lower in 1960 and 0.9 percentage points lower for 1970 had the service share remained at it 1950 share of 38% rather than 44% in 1960 and 48% in 1970. However, with more mod­ eration in real service GDP growth in more recent times, that compositional change was significant with almost a two percentage-point gap in 2000 and about a one percentage-point gap in 2021, if the economy was compositionally similar to 1950. Of course, this is only hypothetical, but it does bring home the point that sector composition can have significant implication for trend growth (Figure 14.2).

[455] WHAT DOES THAT SHIFTING COMPOSITION OF THE ECONOMY MEAN FOR INFLATION? As the economy shifts towards more services and less goods or commodities, this is likely to inflate the overall trend pace of inflation. •

•

• •

Consumer service inflation tends to higher than consumer commodity inflation. The median CPI for commodities grew by 2.5% between January 1957 and June 2022, while the median CPI for services grew by 3.5%. Consumer service inflation tends to be less volatile than consumer commodity inflation. The standard deviation of the CPI for commodities was 2.87 percentage points between January 1957 and June 2022, while the standard deviation of the CPI for services was 3.57 percentage points. If there is a cyclical relationship, consumer service prices tend to lag commodity prices. Extending this perceptive back to 1936 on an annual basis, the same facts about the relationship between service and commodity inflation exist. The median CPI for commodities between 1936 and 2021 grew by 2.1%, while the median CPI for services grew by 3.3%. The standard deviation of the CPI for commodities was 4.0 percentage points between 1936 and 2021, while the standard deviation of the CPI for services was 2.7 percentage points (Figure 14.3).

[456] WHY DOES A SERVICE-COMMODITY INFLATION GAP EXIST? Since 1936, service inflation has outpaced commodity inflation 80% of the time. This has led economists to wonder about the different dynamic between service and commodity inflation—as observed, for example, by NY Federal Reserve Bank economists Peach, Rich, and Linder,8 who wrote that “the divergent behavior of goods and services inflation raises the possibility that their determinants may also differ.”9 This is not a new observation since Bert Hickman10 discussed this question in 1960. Hickman provided a succinct explanation for the service-commodity price gap. He wrote that service prices

IMPLICATIONS OF AN EVOLVING ECONOMY

FIGURE 14.3

Comparing CPI Service Inflation with CPI Commodity Inflation, 1936–2021

Source: U.S. Bureau of Labor Statistics

are subject to special influences which set them somewhat apart from industrial prices, especially with regard to sensitivity to current shifts of cost or demand. [Consequently, service prices] as a class … are slow to respond to changes in cost or demand, because some are subject to public regulation (utilities) and other are influenced by market imperfections of some sort or another (rent, medical care, personal services, etc.).11 Other reasons offered for this services-commodity price gap include (a) the difference in relative productivity between service-producing and goods-producing industries; (b) the differences in the amount of labor versus capital inputs between service-producing and goods-producing industries; and (c) the shift in the composition of employment towards more services. BLS economists Eldridge and Price12 found that between Q2 2005 and Q4 2014, the median pace of goods-producing industry productivity was 0.1% per quarter, while the service-producing industry productivity was 0.3% per quarter, which challenges the first thesis since the service-goods inflation gap is not explained by lower productivity in the services industries. Kutscher and Marks’ finding13 also challenged the second thesis about labor intensity. These BLS economists found “the assumption that service industries are relatively labor intensive has a strong element of truth about it,” but a ranking of labor intensiveness among all industries (in 1981) found that service-producing industries were no more labor intensive than goods-purchasing industries. Finally, with regard to the final thesis, Kutscher and Marks also found that “the shift in employment between goodsproducing and service-producing industries has had a negligible effect on productivity growth.”14 Finally, the NY Fed conclusion from the Peach, Rich, and Linder study may provide a better explanation of the service-goods inflation gap, which is tied to inflation expectations, the unemployment gap, and import prices. They opined:

333

334 MACROECONOMIC ISSUES

A Comparison of Private Goods-Producting Industry Productivity vs. Private Service-Producing Industry Productivity

FIGURE 14.4

Source: Lucy Eldridge and Jennifer Price, U.S. Bureau of Labor Statistics, June 2016

Thus, while long-run inflation expectations help to determine core services inflation, shortrun inflation expectations influence core goods inflation. In addition, the unemployment gap has a meaningful effect only on price changes in core services, a finding consistent with the view of some commentators that core goods inflation varies with the extent of global— not domestic—economic slack. Finally, core goods inflation depends on relative import price inflation, but the same variable shows no link with aggregate core inflation.15

[457] WHAT DOES THAT SHIFTING COMPOSITION OF THE ECONOMY MEAN FOR THE BUSINESS CYCLE? In a 1987 article,16 business-cycle scholar Geoffrey H. Moore observed that the growth of the service industry in the economy has tended to make recessions shorter, less severe, and the overall economy more stable. Simple statistics tend to reinforce Moore’s point. The standard deviation (volatility) of the annual growth rate of U.S. real GDP for services was 2.2 per­ centage points relative to 3.7 percentage points for the annual growth rate of U.S. real GDP for goods over the 1948 to 2021 period. Even paring the time period to begin in 2000 and end in 2021 shows a standard deviation of those growth rates at 1.8 percentage points for real GDP for services and 3.4 percentage points for goods. Moreover, Geoffrey Moore’s observation is supported, prima facie, given that the lower-volatility service-sector share has grown relative to the goods-sector share of the economy. However, the degree of stability due to the increased service-sector share of the economy may lessen in the future. A study of the Spanish

IMPLICATIONS OF AN EVOLVING ECONOMY

economy by Cuadrado-Roura and Ortiz V.-Abarca17 echoed Moore’s finding for the United States, but also observed that “Services are more and more important inputs for manufacturing, as can be observed through input-output tables.”18 To this end, the researchers note the service sector has become more pro-cyclical than in the distant past. Additionally, this is a global phenomenon that their research found, which likely will characterize future business cycles in the United States and other industrialized countries.

Issues to Think About The composition of an economy matters for economic growth, economic volatility, and inflation. • • • •

What are the benefits and risks for monetary policy from the evolving economic trend towards more service output? Is there a likely maximum share that services can garner of the economy? Why? What are the benefits and risks for domestic fiscal policy from these secular changes? What are the benefits and risks to the global business cycle from these compositional changes in economic activity?

NOTES 1 R.F. Martin, National Income in the United States 1799–1938 (National Industrial Conference Board, New York, 1939). 2 Simon Kuznets, National Income: A Summary of Findings (National Bureau of Economic Research, New York, 1946). 3 Technically, service-producing industries include the government sector. However, for this purpose government was separated out. By definition, the goods-producing industries supersector group include natural resources and mining, construction, and manufacturing. The service-providing industries supersector group include the following sectors: trade, transportation, and utilities, infor­ mation, financial activities, professional and business services, education and health services, leisure and hospitality, other services (except public administration), and government. 4 Cristina Echevarria, “Changes in Sectoral Composition Associated with Economic Growth,” International Economic Review, vol. 38, no. 2 (May 1997), pp. 431–452. 5 Ibid., p. 432. 6 Alessio Moro, “Structural Change, Growth, and Volatility,” American Economic Journal: Macroeconomics, vol. 7, no. 3 (July 2015), pp. 259–294. 7 Ibid., p. 259. 8 Richard Peach, Robert Rich, and M. Henry Linder, “The Parts Are More than the Whole: Separating Goods and Services to Predict Core Inflation,” Current Issues in Economics and Finance, New York Federal Reserve Bank, vol. 19, no. 7 (2013). 9 Ibid., p. 2. 10 Bert G. Hickman, Growth and Stability of the Postwar Economy (Brookings Institution, Washington, DC, 1960).

335

336 MACROECONOMIC ISSUES 11 Ibid., p. 399. 12 Lucy P. Eldridge and Jennifer Price, “Measuring Quarterly Labor Productivity by Industry,” Monthly Labor Review (U.S. Bureau of Labor Statistics, June 2016), https://doi.org/10.21916/mlr.2016.28. 13 Ronald E. Kutscher and Jerome A. Mark, “The Service-Producing Sector: Some Common Perceptions Reviewed,” Monthly Labor Review (April 1983), pp. 21–24. 14 Ibid., p. 24. 15 Richard Peach, Robert Rich, and M. Henry Linder, p. 8. An earlier NY Federal Reserve study in 1987 looked at wage differentials as an explanation of the services-commodities inflation gap. That study found the main reason for the sector wage differentials was “the growth of demand for services against a background of low labor mobility between manufacturing and services.” 16 Geoffrey H. Moore, “The Service Industries and the Business Cycle,” Business Economics, vol. 22, no. 2 (April 1987), pp. 12–17. 17 Juan R. Cuadrado-Roura and Alvaro Ortiz V.-Abarca, “Business Cycle and Service Industries: General Trends and the Spanish Case,” The Service Industries Journal, vol. 21, no. 1 (January 2001), pp. 103–122. 18 Ibid., p. 121.

CHAPTER

15

The Great Moderation Followed by the Great Volatility LEARNING OBJECTIVES This chapter introduces you to longer-term shifts in the economy that have created changes in economic and price stability. You will learn: • • • • • • •

The economy became less volatile in the post–World War II (WWII) period versus the pre-WWII period. Then, another reduction in volatility occurred in the mid-1980s—which has been dubbed the “great moderation.” Which expenditure catagories posted the most and the least volatility change. How output volatility and inflation volatility are empirically related. How output volatility and inflation volatility are theoretically discussed in the “Taylor curve” theory. What the “great volatility” looks like. What the historical record teaches us.

[458] WHAT IS ECONOMIC VOLATILITY? The macroeconomic goal of economic stability is measured in terms of the degree of volatility in the economy. This is measured by the statistical concept of standard deviation (or related concepts such as mean or median absolute deviation). Therefore, economic volatility is the degree of variation in real GDP growth rates (or GDP component growth rates).

[459] HOW MUCH DID ECONOMIC VOLATILITY CHANGE BETWEEN THE PRE- AND POST-WWII PERIODS? Based on four segments of economic history, the pre-WWII quarterly growth rate volatility in output between 1875 and 1945 was about 2½ times as volatile as the post-WWII period DOI: 10.4324/9781003391050-17

338 MACROECONOMIC ISSUES between 1946 and 1985. Beginning in the mid-1980s, another reduced change in growthrate output volatility unfolded and was dubbed the “Great Moderation.”1 The Great Moderation between 1986 and 2019 saw growth-rate volatility reduced by about half of what it was between 1946 and 1985. Although some economists later dismissed this moderation once the 2007–2009 financial-crisis induced recession occurred, empirical research still supports the conclusion.2 Finally, the economy saw a major disruption and historically high volatility return with the advent of the COVID-19-induced recession, disruption, and policy response. That period, which began in 2020 (and measured through the most recent observation, which was third-quarter 2022), might be dubbed the “Great Volatility”—saw volatility about 6½ times the degree experienced during the Great Moderation period.

FIGURE 15.1

Real GDP Growth (1875–2022)

Sources: Robert J. Gordon, National Bureau of Economic Research (1875–1946); U.S. Bureau of Economic Analysis (1947–2022)

TABLE 15.1

Changing Volatility Patterns in the U.S. Economy, 1875–2022

Period

Standard Deviation (Annualized Percentage Points)

Pre–World War II (1875–1945) Post–World War II (1946–1985) “Great Moderation” (1986–2019) “Great Volatility” (2020–2022)

11.8 4.7 2.3 15.0

pp. pp. pp. pp.

GROWTH MODERATION AND VOLATILITY

[460] [ADVANCED] IS REDUCED U.S. ECONOMIC VOLATILITY IN THE POST-WWII PERIOD RELATIVE TO EARLIER PERIODS JUST A FIGMENT OF IMPROVEMENTS IN DATA COLLECTION AND METHODOLOGY? Attempts to create more accurate historical measures of national output before 1929 to determine whether or not the economy experienced greater stabilization in the post-WWII period relative to that of the pre-WWII periods have left the issue somewhat unsettled. Christina Romer, in a series of papers, argued that the volatility of industrial production, unemployment, and real GNP after 1947 is little different than before 1929 based on her reformation of the historical measures.3 However, a detailed data reformulation effort by Balke and Gordon, which created new estimates of real GNP, concluded their finding “reaffirmed the standard conclusion that real GDP was more volatile before 1929 than since 1946.”4 However, volatility changes since the BEA’s more consistent methodology beginning in 1929 on an annual basis and 1947 on a quarterly basis are less likely to be criticized based on data concerns. [See: Chapter 5 for a discussion of the difference between GDP and GNP.]

[461] HOW MUCH DID INFLATION VOLATILITY CHANGE BETWEEN THE PRE- AND POST-WWII PERIODS? Based on four segments of economic history, the pre-WWII quarterly growth rate of the GDP price deflator volatility in output between 1875 and 1945 was about three times as volatile as the post-WWII period between 1946 and 2022. Generally, the reduced output volatility is associated with reduced inflation volatility and has been part of the story that economists have told about the changing volatility in the economy since those two phenomenon largely went hand-in-hand.

FIGURE 15.2

GDP Price Defaltor Growth (1875–2022)

Sources: Robert J. Gordon, National Bureau of Economic Research (1875–1946); U.S. Bureau of Economic Analysis (1947–2022)

339

340 MACROECONOMIC ISSUES TABLE 15.2

Changing Volatility Patterns in the U.S. Inflation, 1875–2022

Period

Standard Deviation (Annualized Percentage Points)

Pre–World War II (1875–1945) Post–World War II (1946–1985) “Great Moderation” (1986–2019) “Great Volatility” (2020–2022)

9.9 4.4 1.0 3.1

pp. pp. pp. pp.

[462] WHY IS REDUCED VOLATILITY IN THE POST-WWII PERIOD VERSUS IN THE PRE-WWII PERIOD IMPORTANT? The benefit to the economy from reduced volatility in the economy was discussed by Ben Bernanke, who provided several key reasons: • • •

Lower volatility of inflation improves market functioning, makes economic planning easier, and reduces the resources devoted to hedging inflation risks. Lower volatility of output tends to imply more stable employment and a reduction in the extent of economic uncertainty confronting households and firms. The reduction in the volatility of output is also closely associated with the fact that recessions have become less frequent and less severe.5

[463] DID REDUCED VOLATILITY ONLY OCCUR IN THE UNITED STATES? No. In former Federal Reserve Board Governor Ben S. Bernanke’s February 2004 speech on the great moderation, he opined that, “Similar declines in the volatility of output and inflation occurred at about the same time in other major industrial countries.”6 The table shows representative real GDP growth-rate volatility in some international economies. The reunification of Germany in October 1990 likely moderated any output volatility benefit relative to other countries. TABLE 15.3

Real GDP Volatility—The Great Moderation and Great Volatility Real GDP Volatility—The Great Moderation and Great Volatility

(Based on Standard Deviation Percentage Points (pp.) in Quarter-to-Quarter Annualized Growth Rates) Country

Pre-1986 *

1986–2019

2020–2022

Canada Germany United Kingdom

4.1 pp. 4.4 pp. 5.2 pp.

2.6 pp. 3.6 pp. 2.4 pp.

19.1 pp. 18.0 pp. 36.0 pp.

Note * Canadian data begin in 1961; German data begin in 1970; and United Kingdom data begin in 1955.

GROWTH MODERATION AND VOLATILITY

[464] WHAT ACCOUNTED FOR THE REDUCED VOLATILITY IN THE 1986–2019 PERIOD? The “Great Moderation” period (1986–2019) compared with the earlier post-WWII period saw output volatility reduced by slightly more than half with nearly every major expenditure component of real GDP becoming more stable—except for investment in nonresidential structures. Although the overall volatility is dependent on the share of the total represented by the expenditure component of real GDP, the components that saw the largest reduction in volatility were: (1) nondefense federal government spending (−80.9% reduction); (2) exports of services (−77.4%); and (3) imports of goods (−69.1%). Among the compo­ nents sharing the least amount of reduction in volatility between those two episodes were: (1) private inventories (−10.3%); (2) personal consumption expenditures for services (−31.4%); and (3) nonresidential equipment investment expenditures (−32.7%). Interestingly, the “Great Moderation” was not due to better inventory control—as many argued. Similarly, in the “Great Volatility” period, inventory volatility—though higher than the earlier episode—played a minor role and was about a third of the increased instability that occurred with real final sales (real GDP less inventories).

[465] WHAT ECONOMIC FACTORS EXPLAIN THE REDUCED VOLATILITY IN THE ECONOMY DURING THE GREAT MODERATION PERIOD? This issue generated a lot of studies testing hypotheses about possible reasons for the reduced volatility. However, there is no single or consensus answer to explain the cause. The ex­ planations include good luck (“Good luck refers to the possibility that the remarkably benign series of economic shocks that have hit the economy in recent years has been the result of nothing other than chance.”7), good policy, less oil dependence, reduced uncertainty, better predictability, better monetary policy, and a structural change in the economy. This structural argument held that the overall economy underwent an underlying change that made the economy less sensitive to economic shocks. Some economists also describe this assumed structural change due to a more subdued “propagation mechanism” (that is, the ripple effect through the economic channels that could be due to financial innovations or better supplychain management of inventories). Other studies describe the structural change in the variance or standard deviation of output and inflation having declined because “boom and recession regimes moved closer together.”

[466] [ADVANCED] ARE THERE OTHER MEASURES TO ASSESS STABILITY IN THE ECONOMY? The early research on stability focused explicitly on “cyclical variability” changes between the pre- and post-WWII periods. The research tried to understand why greater stability occurred in the postwar period. DeLong and Summers’ study, for example, concluded, “structural changes in the economy, discretionary stabilization policy, and the avoidance of financial panics, probably

341

342 MACROECONOMIC ISSUES did relatively little to enhance stability.”8 However, the research did assert that the cyclical volatility was likely lessened in the post-WWII period due to “greater public and private efforts to smooth consumption and the increasing rigidity of prices, [which they attributed the rigidity] to the increasing institutionalization of the economy.”9 Another study by Diebold and Rudebusch addressed the question using a different approach rather than from evaluating changes in cyclical-output variance. They focused on “duration, or frequency, as opposed to volatility, or amplitude.” The question that they examined was whether there was a shift in the post-WWII period towards business cycle “duration stabilization” rather than “volatility stabi­ lization” that Romer10 first examined. Diebold and Rudebusch found “strong evidence” for longer-duration expansions and shorter-duration recessions, which also is directly seen in the average cycle duration from the NBER’s U.S. business cycle chronology.

[467] [ADVANCED] IS THERE A MACROECONOMIC THEORY TO UNDERSTAND THE CHANGING VOLATILITY IN THE ECONOMY? Prof. John Taylor offered a model and a theory to understand how output volatility and inflation volatility may be related through what is called the “Taylor curve.”11 The Taylor curve is a downward-sloping relationship between output volatility and inflation volatility (as shown stylistically in the chart) that was derived from a model and estimated between 1953 and 1975. Prof. Taylor’s goal for this model was to “calculate optimal monetary control rules to stabilize fluctuations in output and inflation.”12 Then Taylor opined, “over the relevant range of this curve, business cycle fluctuations can be reduced only by increasing the variability of inflation.”13 This Taylor-curve trade-off leads monetary policymakers—which are the only policymakers assumed in his model—to accept “increases in inflation when there is great concern with stabilizing output and little concern with fluctuations in inflation. On the other hand, the optimal policy is extremely non-accommodative when fluctuations in inflation are Output Volality

Taylor Curve #1

Taylor Curve #2

Inflaon Volality FIGURE 15.3

The Taylor Curve Trade-Off

Source: Ben S. Bernanke, “The Great Moderation,” Speech at the Eastern Economic Association, Washington, DC, Federal Reserve Board, Washington, DC, February 20, 2004

GROWTH MODERATION AND VOLATILITY

viewed as very harmful.”14 Finally, if the Taylor curve trade-off shifts inward from location #1 to location #2 on the graph, then this suggests that the trigger points for monetary policy accommodation or non-accommodation will be lower, which stylistically portrays the reduced volatility in the Great Moderation period. But how might that happen? In Federal Reserve Governor Ben Bernanke’s 2004 speech on the Great Moderation, he offered some thoughts about the changing volatility. He suggested (following the Taylor model logic) if monetary policies were not optimal in periods prior to when the reduction in outputinflation volatiles occurred, then that might cause a “combination of output volatility and inflation volatility [to lie] well above the efficient frontier defined by the Taylor curve”—that is at location #1 in the graph. Then, if monetary policy improved and was optimally operating then that could cause an inward shift in the Taylor-curve tradeoff to location #2. Bernanke offered his “second possible explanation” as well. Here he posits that instead of an improvement in monetary policy, the economic environment becomes more stable. Then, “changes in the structure of the economy that increased its resilience to shocks or reductions in the variance of the shocks themselves would improve the volatility tradeoff faced by policymakers.” In turn, this might also cause the inward shift of the Taylor curve to location #2. Take your pick, but it was his working hypothesis that made for a thoughtful and often-quoted Fed speech.

[468] WHAT HAS BEEN LEARNED FROM THESE CHANGES IN VOLATILITY? The first takeaway is that reduced volatility is not permanent. The second point is that increased output volatility does correlate with increased inflation volatility and similarly for decreases. The third observation is that the federal government is a major cause of stability and instability in the economy. The fourth takeaway, which is preliminary, is that going from the Great Moderation to the Great Volatility personal consumption expenditures on services was a major contributor (not surprising considering the industries negatively affected most during the COVID-19induced recession were leisure and hospitality and food services) to the surge in volatility with its roughly 1,100% jump. Other components of real GDP that had outsized increases were nondurable goods consumption (+433%), imports (+341%), and exports of goods (+306%). TABLE 15.4

U.S. Real GDP Volatility by Expenditures U.S. Real GDP Volatility by Expenditures Standard Deviation of Annualized Quarterly Percentage Change 1947–1985

Gross domestic product Personal consumption expenditures Goods Durable goods Nondurable goods Services

4.7 4.1 6.5 19.6 3.7 2.3

1986–2019 2.3 2.0 4.1 9.5 2.4 1.5

2020–2022 15.0 17.5 18.8 34.5 12.8 18.2 (Continued )

343

344 MACROECONOMIC ISSUES TABLE 15.4

(Continued) U.S. Real GDP Volatility by Expenditures Standard Deviation of Annualized Quarterly Percentage Change

Gross private domestic investment Fixed investment Nonresidential Structures Equipment Intellectual property products Residential Exports Goods Services Imports Goods Services Government consumption expenditures and gross investment Federal National defense Nondefense State and local Addendum Private inventories

1947–1985

1986–2019

2020–2022

25.7 11.7 11.3 11.0 16.2 7.6 25.1 22.4 26.9 45.4 22.3 28.9 22.8 9.7

11.7 6.9 7.3 12.7 10.9 5.0 12.4 8.3 9.8 10.3 7.8 8.9 8.8 3.0

34.8 14.6 12.9 12.4 24.3 6.3 26.6 29.6 39.8 21.9 34.2 37.1 34.8 4.0

16.6 20.5 36.8 5.1

5.9 7.6 7.0 2.6

12.3 6.1 34.7 2.7

3.3

2.9

6.7

Issues to Think About A core macroeconomic goal for a nation is economic stability. some of the factors that lead to that stability may be out of the control or influence of policymakers, but some factors may be influenced by policy. • • • •

Should monetary policymakers focus more attention on economic volatility and inflation volatility? Why or why not? Should fiscal policymakers focus more attention on economic volatility and inflation volatility? Why or why not? What policies might induce lower economic volatility and inflation volatility? Is the “Taylor curve” a useful theoretical paradigm? is there really a trade-off between output volatility and inflation volatility or do those metrics just move in lock-step and are a result of some other factor or factors?

GROWTH MODERATION AND VOLATILITY

NOTES 1 The origin of the phrase “Great Moderation” was due to Harvard Prof. James Stock and Princeton Prof. Mark Watson’s 2002 paper. See: James H. Stock and Mark W. Watson, “Has the Business Cycle Changed and Why?,” in M. Gertler and K. Rogoff, eds., NBER Macroeconomics Annual 2002, (MIT Press, 2002). However, their discussion was not new. It was explored in 1994 in Michael P. Niemira and Philip A. Klein, Forecasting Financial and Economic Cycles (John Wiley & Sons, New York, 1994). Niemira and Klein wrote: “Whether or not the business cycle become less volatile or not between the pre- and post-World War II periods, may be a secondary point for understanding and forecasting the business cycle in the 1990s and beyond. Volatility is an issue within the post-World War II period as well” (p. 266). This is arguably one of the earliest references (if not the first) and analysis of this reduced volatility phenomenon and was motivated by the pre- and post-WWII volatility-change literature at the time. 2 María Dolores Gadea, Ana Gómez-Loscos, and Gabriel Pérez-Quirós, “Great Moderation and Great Recession: From Plain Sailing to Stormy Seas?,” International Economic Review, vol. 59, no. 4 (November 2018), pp. 2297–2321. 3 Christina D. Romer, “The Prewar Business Cycle Reconsidered: New Estimates of Gross National Product, 1869–1908,” Journal of Political Economy, vol. 97, no. 1 (February 1989), pp. 1–37. Also see: Christina D. Romer, “Changes in Business Cycles: Evidence and Explanations,” Journal of Economic Perspectives, vol. 13, no. 2 (Spring 1999), pp. 23–44. 4 Nathan S. Balke and Robert J. Gordon, “The Estimation of Prewar Gross National Product: Methodology and New Evidence,” Journal of Political Economy, vol. 97, no. 1 (February 1989), pp. 38–92. 5 Ben S. Bernanke, “The Great Moderation,” Speech to the Eastern Economic Association, Washington, DC, Federal Reserve Board, February 20, 2004. 6 Ibid. Also see: Peter M. Summers, “What Caused The Great Moderation? Some Cross-Country Evidence,” Economic Review (Federal Reserve Bank of Kansas City, Third Quarter 2005), pp. 5–32. 7 Andrea Pescatori, “The Great Moderation: Good Luck, Good Policy, or Less Oil Dependence?,” Economic Commentary (Federal Reserve Bank of Cleveland, March 2008). 8 J. Bradford DeLong and Lawrence H. Summers, “The Changing Cyclical Variability of Economic Activity in the United States,” in Robert J. Gordon, ed., The American Business Cycle: Continuity and Change, (University of Chicago Press, 1986), p. 719. 9 Ibid. 10 Christina D. Romer, “Spurious Volatility in Historical Unemployment Data,” Journal of Political Economy, vol. 95 (February 1986), pp. 1–37. Also see, Christina D. Romer, “Is the Stabilization of the Postwar Economy a Figment of the Data?,” American Economic Review, vol. 76 (June 1986), pp. 314–334. 11 John B. Taylor, “Estimation and Control of a Macroeconomic Model with Rational Expectations,” Econometrica, vol. 47, no. 5 (September 1979), pp. 1267–1286. Taylor refers to his model as a “second moment” of the Phillips curve—which is the trade-off between inflation and the unemployment rate. To understand Taylor’s reference to a “second moment,” first recognize that his model is built on “expectations.” Next, it is helpful to recognize that in math (probability) the mathematical expectation of a discrete variable is the mean of a series of observations multiplied by its respective probability of occurrence, and that result is also called the “first moment.” The “second moment” of that prob­ ability distribution is variance and the square root of variance is standard deviation. Hence, that is how Taylor came to build his model on volatility. 12 Ibid., p. 1282. 13 Ibid., p. 1283. 14 Ibid.

345

CHAPTER

16

The Global Debt Explosion and Worries

LEARNING OBJECTIVES Intensified concerns about the global debt explosion began in the aftermath of the 2008 global financial crisis and further were inflamed by the COVID-19 pandemic spending by governments. You will learn: • • • • • • •

How an economy’s debt is defined. What the empirical record shows for debt accumulation in the United States and globally. What the benefits and risks of debt are for an economy. The history of recent global debt waves. How theories explain the debt-growth nexus. Whether the debt-growth relationship is linear (consistent relatioship regardless of the debt level) or has a global or country-specific tipping point (non-linear). What international organizations suggest to correct debt overhangs in an economy.

[469] HOW ARE CREDIT AND DEBT RELATED? Credit provides potential spending power, and the use of that credit (borrowed money) creates both an expenditure and a debt.

[470] HOW IS TOTAL DEBT COMPRISED? Total debt is the sum of domestic nonfinancial debt plus domestic financial sector debt plus debt owed to the rest of the world, which is sometimes referred to as external debt or foreign debt because it is a liability of residents of the economy that is owed to nonresidents. DOI: 10.4324/9781003391050-18

THE GLOBAL DEBT EXPLOSION AND WORRIES

[471] WHAT IS DOMESTIC NONFINANCIAL DEBT? Domestic nonfinancial debt is the sum of household debt ((including non-profit institutions serving households), business debt, federal government debt, and state and local government debt. Household debt equals residential mortgages and consumer credit. Business debt is the sum of nonfinancial corporate business and nonfinancial noncorporate business debt. “General government” debt (a term often used internationally) is the sum of debt of the federal gov­ ernment and state and local governments.

[472] WHAT IS DOMESTIC FINANCIAL-SECTOR DEBT? Domestic financial debt is the sum of debt of the central bank plus private depository insti­ tutions plus insurance companies plus pension funds plus “other” financial businesses.

[473] HOW MUCH DEBT EXISTS IN THE U.S. ECONOMY? According to the Federal Reserve Board’s flow-of-funds data (officially known as the Financial Accounts of the United States), in 2022, total U.S. debt reached a record $93 trillion. Between 1986 and 2022, annual growth in U.S. debt averaged about 7% per year, but in the aftermath of the 2008 global financial crisis, that growth rate moderated to about a 1% pace. Then, with the advent of COVID-19 pandemic spending by the federal government, 2020 overall U.S. debt jumped by 10.8%—its highest growth rate since 2004 (which was up 11.4%). U.S. debt continued to grow rapidly in 2021 at 6.6%, but moderated to around 5.8% in 2022.

FIGURE 16.1A

Total U.S. Debt, 1986—2022

Source: Federal Reserve Board, Flow-of-Funds

347

348 MACROECONOMIC ISSUES

FIGURE 16.1B

U.S. Debt Shares of Total

Source: Federal Reserve Board, Flow-of-Funds

Compositionally, about three-quarters of U.S. debt is in the nonfinancial sector, about one-fifth in the financial sector, and the remainder in the rest-of-world or external sector.

[474] HOW LARGE ARE U.S. DEBT SHARES RELATIVE TO GDP? According to the Federal Reserve Board’s flow-of-funds data, in 2020, total U.S. debt (private and public sector) reached a high of almost four times nominal GDP but edged off to 367% of GDP by 2022. Between 1986 and 2022, the average level of U.S. debt to GDP was 309% of GDP by 2020. Compositionally, U.S. nonfinancial debt as a share of GDP rose to 293% in 2020 (a record high)—with federal government debt a major source of that upward spike. The financial sector debt rose to its highest share of GDP as a result of the 2008 global financial crisis that pushed the share up to an annual high of 121%, but has stabilized around 80% since 2016, including through the COVID-19 pandemic. The U.S. rest-of-world or external debt share of GDP has edged ever so slightly higher since 2010 but remains low. In 2022, the external debt share as a share of U.S. nominal GDP was 19%.

[475] [ADVANCED] HOW DOES THE PRESENTATION OF U.S. DEBT FIGURES DIFFER FROM THE IMF/WORLD BANK INTERNATIONAL ECONOMIC ACCOUNTING STANDARDS? The international economic accounting standards used to collect and report debt statistics by the International Monetary Fund, World Bank, Bank for International Settlement (BIS), and

THE GLOBAL DEBT EXPLOSION AND WORRIES

FIGURE 16.2

U.S. Debt Shares of GDP

Source: Federal Reserve Board, Flow-of-Funds

General Government Sector

Nonfinancial Corpora!ons Sector

Public

Financial Corpora!ons Sector

Private Private

Nonprofit Ins!tu!ons Serving Households Sector

Public

Public

FIGURE 16.3A

Household Sector

Private

Private

Concordance between Public Sector and Other Institutional Sectors of the

Economy

OECD, rely on expanded financial accounts within the international standard System of National Accounts (SNA). The development of those international financial accounts has been a “top priority” in the aftermath of the 2008 global financial crisis and they are still devel­ oping.1 Under SNA, the sector or “institutional unit” breakdown differs from that used in the U.S. flow of funds accounts. In particular, overall international debt is allocated to private or public institutions or sectors. The main international differences between the U.S. flow-offunds methodology and the international financial accounts is the allocation of the central bank from the domestic financial sector to the public sector for international purposes, and the allocation of the external debt to the private and public sectors. This concordance between the Federal Reserve sector definitions with those of the IMF/World Bank methodology shifts all public‐sector debt–which the Federal Reserve might include with the nonfinancial corpora­ tions or financial corporations sectors–into the “general government” sector for international comparison. This is portrayed in Figure 16.3A.

349

350 MACROECONOMIC ISSUES

FIGURE 16.3B

U.S. Debt Shares of GDP

Source: Federal Reserve Board, Flow-of-Funds

[476] WHAT IS GLOBAL DEBT? Global debt is the total borrowing by governments, businesses, and individuals around the world.

[477] WHERE CAN I FIND STATISTICS ON MULTI-NATIONAL DEBT? The main sources of international debt statistics are: •

•

•

International Monetary Fund: https://www.imf.org/en/Data, external debt statistics; publicsector debt statistics; Quarterly Public Sector Debt Statistics (QPSD) database, jointly developed by the World Bank and the International Monetary Fund, brings together detailed quarterly public sector debt data of selected countries; https://www.imf.org/ external/datamapper/datasets/GDD. The Global Debt Database (GDD) is multiyear data on gross debt of the (private and public) nonfinancial sector for 190 advanced economies, emerging market economies and low-income countries, dating back to 1950. The World Bank: https://www.worldbank.org/en/programs/debt-statistics/statistics, “The World Bank Debt Data Team compiles and publishes a series of comprehensive debt statistics, on an annual, quarterly and monthly basis. The International Debt Statistics (IDS) include annual stocks and flows of external debt. The Debt Service Suspension Initiative (DSSI) include annual external debt stocks and monthly projected debt service data. The Quarterly External Debt Statistics (QEDS), Quarterly Public Sector Debt (QPSD), and Joint External Debt Hub (JEDH) statistics are quarterly.” Bank for International Settlement: https://stats.bis.org/ and https://www.bis.org/statistics/ secstats.htm, statistics on debt securities and debt service ratio by country.

THE GLOBAL DEBT EXPLOSION AND WORRIES

•

•

•

OECD: https://stats.oecd.org/, External debt, central government debt, public-sector debt, and private-sector debt for Australia, Austria, Belgium, Canada, Chile, Columbia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, and United States. International Institute of Finance (IIF): https://www.iif.com/Research/Capital-Flows-andDebt/Global-Debt-Monitor, “The Global Debt Monitor tracks indebtedness by sector across key mature and emerging markets, offering a unique like-for-like comparison across countries. Published quarterly, the Monitor offers a snapshot of key trends using a variety of international and national-level data sources.” AVAILABLE TO IIF MEMBERS ONLY. Note some of these debt statistics are available in the St. Louis Federal Reserve Bank’s FRED database, https://fred.stlouisfed.org/.

[478] WHAT IS THE CONCERN ABOUT AN INCREASE IN GLOBAL DEBT? The global explosion of private- and public-sector debt is one of the most important economic developments in recent years that has elevated the risk to financial stability and economic growth worldwide. The World Bank issued a 2021 study, entitled Global Waves of Debt,2 which explored the risks from bursts of debt accumulation drawing upon the experiences of past global debt waves through 2018—before global public debt soared even further due to the COVID-19 pandemic spending. The World Bank report observed the following: The global economy has experienced four waves of debt accumulation over the past fifty years. The first three debt waves ended with financial crises in many emerging and developing economies. The latest [debt wave beginning in 2010] has already witnessed the largest, fastest and most broad-based increase in debt in these economies.3 In 2020, global debt rose to a record $226 trillion, and the IMF noted that was “the largest one-year debt surge since World War II” with slightly more than half of the increase due to government borrowing. Additionally, “private debt from non-financial corporations and households also reached new highs.”4 More timely tracking of global debt by the Institute of International Finance (IIF)5 found its tally of total global debt rose by $3.3 trillion in Q1 2022 to a new record of over $305 trillion (348% of GDP), mostly due to China (+$2.5 trillion) and the U.S. (+1.5 trillion), while the euro zone debt declined for its third con­ secutive quarter.6 The main concern is that the debt surge “amplifies vulnerabilities,” as the IMF notes, especially with conditions of rising inflation and interest rates in the aftermath of a debt explosion. These vulnerabilities, which have led to financial crises, have been traced over eight centuries by economists Reinhart and Rogoff.7

351

352 MACROECONOMIC ISSUES

[479] WHAT DID THE HISTORICAL RECORD SHOW ABOUT RECENT DEBT CRISES AS IDENTIFIED BY THE WORLD BANK? The World Bank study identified three debt waves since 1970. The first wave, which began in 1970 and ended in 1989 played out as an external debt crisis in Latin America and among low-income countries, which was reflected in large current account and fiscal deficits. Many of these economies also experienced a follow-on currency crisis forcing those countries to devalue their currency. This debt overhang was resolved by the implementation of a plan put forth by President Reagan’s Treasury Secretary Nicholas F. Brady. The Brady plan, as it became known, resolved the Latin American debt crisis by the securitization and re­ structuring of existing loans into bonds with some debt forgiveness such that the Latin American debtors paid what they could afford. The second wave identified by the World Bank began in 1990 and ended in 2001 with the East Asian financial crisis. This debt wave began with private-sector debt accumulation often with short-term maturities and total external debt rose rapidly in Indonesia, Korea, Malaysia, the Philippines, and Thailand. During the peak of the crisis in 1997–1998, “currencies plummeted, inflation soared, and output collapsed. Economies with larger short-term debt, as well as smaller reserves, were most affected.”8 International contagion from the Asian financial crisis contributed to crises in other countries, most notably Russia in 1998, Argentina in 2001, and Turkey in 2001. In response to the spreading crisis, the international community mobilized large loans totaling $118 billion for Thailand, Indonesia, and South Korea, and took other actions to stabilize the most affected countries. Financial support came from the International Monetary Fund, the World Bank, the Asian Development Bank, and governments in the Asia-Pacific region, Europe, and the United States. The basic strategy was to help the crisis countries rebuild official reserve cushions, and buy time for policy adjustments to restore confidence and stabilize economies, while also minimizing lasting disruption to countries’ relations with their external creditors.9 The third wave began in 2002 and ended in 2009 with the global financial crisis. The buildup in debt during this period was greatest in the Europe and Central Asia region, but the United States saw a major impact as well. The debt explosion was primarily ac­ counted for by the private sector, particularly households, as a result of low or falling global interest rates. The World Bank opined that this “crisis also vividly illustrated how cycles in housing markets and credit tend to amplify each other.”10 This global debt surge and crisis was resolved by monetary authorities’ use of quantitative easing and the purchase of distressed debt.

[480] WHAT ARE THE BENEFITS AND COSTS OF DEBT? The World Bank opined:

THE GLOBAL DEBT EXPLOSION AND WORRIES

Debt accumulation offers both benefits and costs. The benefits depend heavily on how productively the debt is used, the cyclical position of the economy, and the extent of financial market development. The costs of debt include interest payments, the possibility of debt distress, constraints that debt may impose on policy space and effectiveness, and the possible crowding out of private sector investment.11

[481] IS THERE AN OPTIMAL LEVEL OF DEBT? The World Bank said the following: There is no generally applicable optimal level of debt, either for advanced economies or for emerging market and developing economies. Optimal levels of debt depend on country characteristics, financial market conditions, the behavior of governments and private agents, and the multiple functions of debt.12 Typically, debt levels are evaluated relative to the country’s GDP.

[482] HOW MUCH GLOBAL DEBT EXISTS AS A SHARE OF GDP? Based on data compiled by the World Bank, total private and public debt is two and a half times as large as GDP in 2020. As shown in the graphic, that share has doubled since 1970.

FIGURE 16.4

Global Debt as a Percent of GDP

Sources: International Monetary Fund; World Bank Note: The estimated ratios of global debt to GDP are weighted by each country’s GDP in U.S. dollars and shown as a three-year moving average

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354 MACROECONOMIC ISSUES

[483] WHY IS IT COMMON TO ASSESS DEBT RELATIVE TO NOMINAL GDP? Dividing debt levels—total, private, or public—by a country’s GDP compares what a country owes to residents and foreigners holding the debt to what economy produces (that is, its productive capacity). This provides a metric of the country’s debt issuers ability to pay back those loans. Normalizing debt relationships (a stock to a flow variable) is common. Another example is that the OECD typically presents global household debt as a percentage of net disposable income.

[484] IS THERE A “TIPPING POINT” IN THE DEBT-TO-GDP RATIO THAT HAS A CONSEQUENCE FOR ITS ECONOMY? Several studies suggest, “yes.” An influential study by Reinhart and Rogoff13 empirically found that the link between growth and debt seems relatively weak at ‘normal’ debt levels [but] median growth rates for countries with public debt over roughly 90% of GDP are about one percent lower than otherwise; average (mean) growth rates are several percent lower. Surprisingly, the relationship between public debt and growth is remarkably similar across emerging markets and advanced economies.14 They also found that “much lower levels of external debt-to-GDP (60%) are associated with adverse outcomes for emerging market growth. Seldom do countries ‘grow’ their way out of debts.”15 This study, however, did not explore the threshold levels for adverse economic effects with high private-debt to GDP ratios. Despite the impact that the Reinhart and Rogoff paper had on the debt-growth nexus research, Herndon, Ash, and Pollin repeated the analysis discovering some “coding errors” with the Reinhart and Rogoff data. These researchers showed that the Reinhart and Rogoff conclusion that public debt loads greater than 90% of GDP would consistently reduce GDP growth was incorrect, and that there was no particular threshold debt-to-GDP share that triggered a negative impact.16 Nonetheless, the Reinhart and Rogoff article set in motion lots of studies using different methodologies to empirically determine the relationship between debt and growth. A broad range of 25 follow-on studies assessing threshold levels of debt that were surveyed by Salmon17 and done by numerous researchers using various methodologies determined there were thresholds of debt that cause lower economic growth, but the debt levels were lower than the Reinhart and Rogoff study (erroneously) determined. For advanced economies, those studies generally found threshold levels for the debt-to-GDP ratio to be 78% for the mean and 82% for the median. For developing countries, those ratios were lower than for advanced economies—61% for the mean threshold and 56% for the median.

THE GLOBAL DEBT EXPLOSION AND WORRIES

[485] WHY ARE MOST DEBT-TO-GDP TIPPING POINT STUDIES FOCUSED ONLY ON PUBLIC DEBT? The Reinhart and Rogoff study aptly put it this way: “reliable data on private domestic debts are much scarcer across countries and time.”18

[486] IS THE DEBT-TO-GDP RELATIONSHIP VALID AFTER THE REINHART AND ROGOFF MEASUREMENT ERROR? Numerous studies have explored the link between debt shares and real GDP growth. One of the best surveys of the entirety of those studies was a review by Salmon in which he reviewed 40 empirical studies—some exploring a link between debt and economic growth (so-called linear relationships) and some exploring a threshold link (so-called non-linear relationships). Salmon found that of the 40 studies surveyed, 36 found either a statistically significant linear or non-linear relationship (based on numerous methodologies) between debt and economic growth.

[487] WHAT IS THE ECONOMIC THEORY BEHIND THE DEBTGROWTH NEXUS? The Salmon survey identified four main channels linking debt to economic growth. •

•

•

•

Crowding Out: This longstanding theory argues that excessive government debt pushes interest rates higher than otherwise and “crowds out” private-sector investment. This explanation is linked only to public-sector debt and not private-sector debt. Elevated Risk Premia: A number of studies argue that an excessive supply of debt elevates the risks in the economy and with it the financial sector’s credit-risk premium that is demanded of borrowers—which in turn pushes up interest rates. This explanation could apply to both public and private debt. Higher Taxes: Another argument in the economic literature suggests that excessive government debt triggers the need for higher taxes to service that debt. This explanation is linked only to public debt and not private debt. Higher Inflation: This idea, known as the fiscal theory of inflation, argues that excessive government debt triggers higher inflation, which in turn triggers higher interest rates and slows economic growth. Although this concept developed only for excessive government debt, the theory conceivable could explain the link between total debt and economic growth as well.

[488] IS THERE RESEARCH SUGGESTING DEBT DOES NOT MATTER FOR ECONOMIC GROWTH? There is some limited thinking by the New Keynesians that excessive debt may not deter economic growth if interest rates are sustained below long-term economic growth (nominal).

355

356 MACROECONOMIC ISSUES Moreover, Keynesian theory largely has argued that increased government spending (even if it produces higher government debt) has a positive relationship on economic growth, while generally ignoring the possibility of too much debt—especially as held by the Modern Monetary Theory.

[489] IS THERE MID-POINT THINKING BETWEEN A POSITIVE DEBTGROWTH NEXUS AND A NEGATIVE DEBT-GROWTH NEXUS?

Threshold Point

Real GDP Growth

Yes, this is the idea of thresholds or “tipping points” that allow for a non-linear relationship between debt and economic growth. Consider the theoretical debt-growth relationship, as shown below, with an inverted “U” curve. This perspective hypothesizes that up until some threshold or tipping point that increased debt will spur on economic growth, as the Keynesian theory would argue, but at some point, debt will becomes drag on economic growth beyond that threshold point. This is the essence of the Reinhart and Rogoff study, which has been tested by others.

Debt-to GDP Rao FIGURE 16.5

Relationship between Debt and Economic Growth

[490] WHAT IS THE CONSENSUS ON THE DEBT-GROWTH RELATIONSHIP? Salmon’s survey of 40 research studies concluded that there is a broadly well-founded conclusion that high levels of public debt have a negative impact on economic growth. The empirical evidence for a nonlinear debt-growth threshold suggests that, while such thresholds might exist, there may not be a common threshold level and they may be largely dependent upon other factors such as a country’s level of development and the quality of its institutions.19 However, there are considerably more issues that Salmon suggests need to be explored and this area of study will likely remain robust in the coming years.

THE GLOBAL DEBT EXPLOSION AND WORRIES

[491] HOW DO ECONOMIES CORRECT THIS DEBT EXPLOSION? The IMF warns that the explosion in debt will mean “debt restructurings are likely to become more frequent and will need to address more complex coordination challenges than in the past owing to increased diversity in the creditor landscape.”20 The World Bank suggested specific policy options to deal with excessive debt will depend on the country itself and its institutions as well as the type of debt accumulation. However, they generally offer four policy options for countries to implement: •

First, governments need to put in place mechanisms and institutions, including sound debt management and high debt transparency, that help them strike the proper balance between the benefits and costs of additional debt. Second, the benefits of stability-oriented and resilient fiscal and monetary policy frameworks and exchange rate regimes cannot be overstated. Third, financial sector policies need to be designed to foster responsible private sector borrowing. Such policies include robust supervisory and regulatory systems as well as corporate and bank bankruptcy frameworks that allow prompt debt resolution to limit the damage from debt distress. Fourth, it is essential to have strong corporate governance practices and effective bankruptcy and insolvency regimes.21

• •

•

Issues to Think About With the interconnection of the global economies, excessive debt and crisis in one country can ripple through the international financial environment, causing contagion and crises beyond the initial country, which is a major concern for international organizations, such as the IMF and World Bank. • •

•

Will global debt crises ever be eliminated? Why or why not? Should international organizations develop better and more specific “exces­ sive debt” guidelines by type of economy to tamp down risky debt-to-gdp accumulation? If so, what do those guidelines need to take into account? Should there be a different concern for excessive private-sector debt vs. public-sector debt? Is one type of debt riskier to economic growth than the other?

NOTES 1 Bruno Tissot, “Development of Financial Sectoral Accounts,” Irving Fisher Committee (IFC) Working Papers, Bank for International Settlements, November 2016, https://www.bis.org/ifc/ publ/ifcwork15.pdf.

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358 MACROECONOMIC ISSUES 2 M. Ayhan Kose, Peter Nagle, Franziska Ohnsorge, and Naotaka Sugawara, Global Waves of Debt: Causes and Consequences, The World Bank Group, Washington, DC, 2021. 3 Ibid, p. 1. 4 Vitor Gaspar, Paulo Medas, and Roberto Perrelli, “Global Debt Reaches a Record $226 Trillion,” IMF Blog: Insights & Analysis on Economics and Finance, International Monetary Fund, Washington, DC, December 15, 2021, https://blogs.imf.org/2021/12/15/global-debt-reaches-a-record-226-trillion/ 5 The Institute of International Finance (IIF) is the global association of the financial industry, with about 400 members from more than 60 countries. 6 Rodrigo Campos, “China, U.S. Lead Rise in Global Debt to Record $305 Trillion,” Reuters News Service (May 18, 1922). 7 Carmen M. Reinhart and Kenneth S. Rogoff, This Time Is Different: Eight Centuries of Financial Folly (Princeton University Press, Princeton, NJ, 2011). The theme of this book is succinctly stated on its book jacket: “Throughout history, rich and poor countries alike have been lending, borrowing, crashing—and recovering—their way through an extraordinary range of financial crises. Each time, the experts have chimed, ‘this time is different’—claiming that the old rules of valuation no longer apply, and that the new situation bears little similarity to past disasters.” But these economists suggest otherwise. 8 Global Waves of Debt: Causes and Consequences, p. 105. 9 See: Michael Carson and John Clark, “Asian Financial Crisis: July 1997–December 1998,” Federal Reserve Bank of St. Louis, November 22, 2013, https://www.federalreservehistory.org/essays/ asian-financial-crisis. 10 Global Waves of Debt: Causes and Consequences, p. 124. 11 Global Waves of Debt: Causes and Consequences, p. 48. 12 Ibid. 13 Carmen M. Reinhart and Kenneth S. Rogoff, “Growth in a Time of Debt,” American Economic Review: Papers & Proceedings, vol. 100, no. 2 (May 2010), pp. 573–578, http://www.aeaweb.org/articles.php? doi=10.1257/aer.100.2.573. 14 Ibid., p. 573. 15 Ibid., p. 577. 16 Thomas Herndon, Michael Ash, and Robert Pollin, “Does High Public Debt Consistently Stifle Economic Growth? A Critique of Reinhart and Rogoff,” Working Paper Series No. 322, Political Economy Research Institute, University of Massachusetts Amherst, April 2013. 17 Jack Salmon, “The Impact of Public Debt on Economic Growth,” Cato Journal, vol. 41, no. 3 (Fall 2021), pp. 487–509. 18 Reinhart and Rogoff, “Growth in a Time of Debt,” p. 577. 19 Salmon, p. 503. 20 Vitor Gaspar and Ceyla Pazarbasioglu, “Dangerous Global Debt Burden Requires Decisive Cooperation,” IMF Blog: Insights & Analysis on Economics and Finance (International Monetary Fund, Washington, DC, April 11, 2022), https://blogs.imf.org/2022/04/11/dangerous-global-debt-burdenrequires-decisive-cooperation/. 21 Global Waves of Debt: Causes and Consequences, p. 209.

CHAPTER

17

Setting Tax Rates

LEARNING OBJECTIVES The setting of tax rates has been an intensely political issue. However, the core of this discussion is apolitical and an exploration into a paradigm offered to conceptualize the setting of an optimal tax rate. You will learn: • • •

Why the Laffer curve has become a paradigm for understanding optimal tax rates. What empirical studies for the United States suggest are the threshold tax rates to maximize tax revenue given the labor market sensitivity to tax rates. What the limitations of these studies are.

[492] WHY IS THE SETTING OF TAX RATES IMPORTANT? In Chapter 11, there was a discussion of the principles of taxation, types of taxation, and the three areas of taxation (earnings, purchases, and wealth). However, once it has been decided to tax earnings, purchases, or wealth, the next question is to assess the balance between the rate of taxation on those areas and any potential for an adverse economic response. Herein lies the public finance question for a policymaker of what level should tax rates be set at to generate the needed revenue or desired policy response, but to have the least adverse effect on economic activity?

[493] IS THERE AN OPTIMAL TAX RATE? One perspective on this question comes from Prof. Arthur Laffer, who may be best known for his drawing on a napkin of what has become known as the Laffer curve. This idea influenced supply-side economics of the Reagan administration, but more importantly the Laffer curve has become a useful paradigm for crafting of tax rates. The basic idea of the Laffer curve, which is stylistically drawn as a bell curve, shows the relationship between tax rates and tax revenues DOI: 10.4324/9781003391050-19

360 MACROECONOMIC ISSUES Tax Rates 100%

Prohibitive or Wrong Side Threshold Rate Correct Side

0%

FIGURE 17.1

Tax Revenue ($)

The Laffer Curve

raised. Laffer argues that every tax rate has two effects on government revenue, the arithmetic effect and the economic effect. The arithmetic effect is simply when tax rates are lowered, tax revenues per dollar of tax base will be lowered by the amount of the decrease in the rate. The reverse is true for an increase in tax rates. The economic effect, however, recognizes the positive impact that lower tax rates have on work output, and employment and thereby the tax base by providing incentives to increase these activities. The arithmetic effect always works in the opposite direction from the economic effect. Thus, the paradigm suggests that the same level of tax revenue can be raised by two different tax rates—one that is in the “correct” side of the Laffer curve, which will have limited adverse eco­ nomic effects, and a second tax rate that is on the “prohibitive” or “wrong” side of the Laffer curve, which will raise the same amount of tax revenue with adverse impacts on future economic activity.

[494] HOW DOES THE LAFFER CURVE WORK? Consider an example of the Laffer curve where there are two tax rates, say one at 25% and one at 75% that will generate the same amount of tax revenue. So if the government can set the rate at 25%, or 75% to generate its needed revenue, which is the better option? Well, it does not take much to conclude the lower one. Laffer would argue the arithmetic option for generating that given revenue is the same at a 25% or 75% tax rate, but the economic choice is clear. But empirically determining that threshold tax rate between the “right side” and “wrong side” of the curve is important.

[495] HAVE THERE BEEN STUDIES TO DETERMINE THAT THRESHOLD TAX RATE? Yes. Economists have addressed this question based on labor-supply estimation. Specifically, estimating the elasticity of labor supply with respect to tax rates provides an estimate of the

SETTING TAX RATES

threshold level in the Laffer curve. From microeconomics, the concept of elasticity of labor supply is the percentage change in the labor income when the tax changes by 1%. Knowing what that elasticity is tells what the average worker’s propensity is to work more, given an increase in the taxes. Or, another way of saying it, is how much of a disincentive is there to work more, if the income tax rate is higher. There are a range of results based on different labor-market studies. One influential study was by MIT economist and 2010 Nobel Prize winner Peter Diamond, and UCLA-Berkeley Prof. Emmanuel Saez, who wrote a Wall Street Journal opinion article1 describing their results. Diamond and Saez also produced a Journal of Economic Perspectives article,2 which reported their results. The economists observed: According to [their] analysis, the revenue-maximizing top federal marginal income tax rate would be in or near the range of 50% to 70%. Thus, we conclude that raising the top tax rate is very likely to result in revenue increases at least until we reach the 50% rate that held during the first Reagan administration, and possibly until the 70% rate of the 1970s. But will raising top tax rates significantly lower economic growth? In the postwar U.S., higher top tax rates tend to go with higher economic growth, not lower. Neither does international evidence support a case for lower growth from higher top taxes. By itself, a suitable increase in the taxation of top earners will not solve our unsustainable long-term fiscal trajectory. But that is no reason not to use this tool to contribute to addressing this problem.3 For context, the U.S. Internal Revenue Service (IRS) notes that for tax year 2022, the top tax rate is 37% for individual single taxpayers with incomes greater than $539,900, or $647,850 for married couples filing jointly. To get to their conclusion, Diamond and Saez, approach the question by estimating the revenue-maximizing tax rate (the threshold level on the Laffer curve), which is found using the ratio of 1 divided by the term of 1 plus the elasticity of labor income. If the elasticity of labor income equals zero, then the revenue-maximizing tax rate is 100% meaning that people will not change the amount of time at all they are willing to work based on the tax rate. If the elasticity equals 0.25, then the revenue-maximizing tax rate is 80%. And so on. It turns out that empirical studies have found that the elasticity is about 0.3. This means that a 10% increase in wages is consistent with about a 3% incremental increase in the amount of time people are willing to work given the tax rate. Additionally, that labor supply elasticity also would imply the maximum revenue tax (the Laffer “threshold”) rate is 77% based on their research.

[496] SHOULD THE MAXIMUM TAX RATE BE SET AT 77% AS THE DIAMOND AND SAEZ STUDY DETERMINED? Not necessarily. Harvard University Prof. Raj Chetty asked in a lecture on this topic that, does it follow from those empirical [studies], that the correct tax rate on high income families [should be set at] 70% [or more]. I think that’s not necessarily the case for two reasons. First of all, notice that this calculation is purely about maximizing revenue. So it’s

361

362 MACROECONOMIC ISSUES putting zero weight on the welfare of people. At the top of the income distribution, it’s saying that their income and their consumption doesn’t enter social welfare at all. And if our goal is simply to raise as much money as we can from the rich, then we want to have a tax rate of maybe up to 70%. Of course, that’s kind of an extreme view that we put no weight at all on the utility of the rich or the income of the rich. So it’s hard to imagine that people would literally take that extreme perspective. But whether that empirical evidence means that we should have high top income tax rates, I think fundamentally goes outside the domain of traditional economics, it depends upon value judgments about the importance of equality. And that’s something that I think people have different opinions on. My own reading of people’s perceptions [is that there’s probably broader public support, especially in the United States,] for equality of opportunity, rather than equality of outcomes. That is to say, when people see lots of different realizations of outcomes, where some people happen to earn a lot of money, and other people don’t, they seem less troubled by that, at least on the face of it, then when you have [an] inequality of opportunity.4

[497] [ADVANCED] WHAT IS THE ECONOMIC THEORY BEHIND SETTING TAX RATES? The theory of optimal taxation is crafted around the idea of maximizing a social welfare function for an economy subject to constraints. After determining an objective function, the next step is to specify the constraints that the social planner faces in setting up a tax system. In a major early contribution, Frank Ramsey5 [in 1927] suggested one line of attack: suppose the planner must raise a given amount of tax revenue through taxes on commodities only. Ramsey showed that such taxes should be imposed in inverse proportion to the representative consumer’s elasticity of demand for the good, so that commodities which experience inelastic demand are taxed more heavily. Ramsey’s efforts have had a profound impact on tax theory as well as other fields such as public goods pricing and regulation.6 Echoing this view, Prof. Joseph Stiglitz wrote that “Frank Ramsey’s classic paper ‘A contri­ bution to the theory of taxation’ gave rise to the modern theory of optimal taxation.”7 However, even as taxation theory develops, Mankiw, et al. observed, “The optimal design of a tax system is a topic that has long fascinated economic theorists and flummoxed economic policymakers.”8

[498] [ADVANCED] SHOULD TAX RATES VARY BY AGE? For nearly a century, Hemel9 opined, economists have embraced the Frank Ramsey per­ spective that taxes should be applied to the least elastic (most inelastic) sources of labor supply.

SETTING TAX RATES

However, following on the idea put forth in Tax by Design10 a report to reform the British tax system led by Nobel Laureate economist James Mirrlees that proposed raising tax rates for taxpayers under age 55 years old and lowering tax rates for taxpayers between 55 and 65 years old, he asked, “Should tax rates decline with age?” This suggestion is tied to research by French11 showing that “the evidence from older individuals indicates that labor supply is responsive to changes in economic incentives,”12 that is it is more inelastic than at younger ages. Hemel concluded that “although the inverse elasticity rule has been a central tenet of optimal taxation theory ... tax law scholars are beginning to realize that the optimal tax system might incorporate age-adjusted rates.”13

[499] WHAT MAKES FOR GOOD TAX POLICY DESIGN? A committee of the Institute of Fiscal Studies under the chairmanship of British economist James Meade, who won the Nobel Prize in economics in 1977, was set up in the late 1970s to review the United Kingdom’s tax system.14 The committee report entitled, The Structure and Reform of Direct Taxation, was published in 1978. It was the first major study since that of the Royal Commission on the Taxation of Profits and Income in 1955. The Meade Committee, as it became known, offered suggestions on reforming the existing U.K. tax system. Although policymakers are unlikely to embrace every recommendation from a committee of experts, periodic reviews of tax structures and design can be helpful for the economy. The U.S. Tax Foundation—which is a nonpartisan, educational organization—suggests that sound tax policy should be based on four cornerstones of tax design: simplicity, transparency, neutrality, and stability. • •

•

•

Simplicity: In this context, the Tax Foundation suggests a standard deduction is sound policy, but itemized deductions are poor policy. Transparency: Clear, concise, and plainly define taxes are the goal. The Tax Foundation considers retail sales taxes as sound policy, but gross receipts taxes as poor policy. They consider employee-side payroll taxes as sound policy, but employer-side payroll taxes as poor policy. Neutrality: The goal here is that taxes should neither encourage nor discourage economic decisions. In this context, the Tax Foundations favors broad-based consumption taxes as sound policy, but digital services taxes as poor policy. Full expensing for capital investments is viewed as sound, but preferential tax credits and exemptions as poor policy. Stability: The goal is to have “consistent and predictable” taxes. Under this banner, sound policies are those tax policies that are permanent, and poor policies are those that are temporary.

To be sure, the application of any tax design criteria will have supporters and detractors. Moreover, tax design issues are difficult to deal with politically, theoretically, and practically for policymakers. However, a Meade-like commission might provide some beneficial tax ideas to better serve the economy.

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364 MACROECONOMIC ISSUES

Issues to Think About The Laffer curve has been used to argue for some maximum tax rate. • •

Based on the same logic of the Laffer curve, should the elasticity of labor supply be used to determine all break points for income? Do you agree with Prof. Chetty’s perspective that equality of opportunity is more important in the economy than the equality of outcomes?

NOTES 1 Peter Diamond and Emmanuel Saez, “High Tax Rates Won’t Slow Growth,” Wall Street Journal (April 23, 2012). 2 Peter Diamond and Emmanuel Saez, “The Case for a Progressive Tax: From Basic Research to Policy Recommendations,” Journal of Economic Perspectives, vol. 25, no. 4 (Fall 2011), pp. 165–190. Also see: Thomas Piketty, Emmanuel Saez, and Stefanie Stantcheva, “Optimal Taxation of Top Labor Incomes: A Tale of Three Elasticities, NBER Working Paper 17616, National Bureau of Economic Research, November 2011, http://www.nber.org/papers/w17616. 3 Peter Diamond and Emmanuel Saez, “High Tax Rates Won’t Slow Growth,” Wall Street Journal (April 23, 2012). 4 Raj Chetty (William A. Ackman Professor of Public Economics Harvard University and Director of Opportunity Insights), “Using Big Data to Solve Economic and Social Problems: Lecture 16, Income Taxation,” Opportunity Insights, May 15, 2019, https://www.youtube.com/watch?v=qr-MUKFn4_w 5 F.P. Ramsey, “A Contribution to the Theory of Taxation,” The Economic Journal, vol. 37, no. 145 (March 1927), pp. 47–61. 6 Mankiw, N. Gregory, Matthew Charles Weinzierl, and Danny Ferris Yagan, “Optimal Taxation in Theory and Practice,” Journal of Economic Perspectives, vol. 23, no. 4 (2009), pp. 147–174, http://nrs. harvard.edu/urn-3:HUL.InstRepos:4263739. 7 Joseph E. Stiglitz, “In Praise of Frank Ramsey’s Contribution to the Theory of Taxation,” The Economic Journal, vol. 125, no. 583 (125th Anniversary Issue, March 2015), pp. 235–268. 8 Mankiw, N. Gregory, Matthew Charles Weinzierl, and Danny Ferris Yagan, p. 147. 9 Daniel Hemel, “Should Tax Rates Decline with Age?,” The Yale Law Journal, vol. 120, no. 7 (May 2011), pp. 1885–1897. 10 James Mirrlees, Stuart Adam, Timothy Besley, Richard Blundell, Stephen Bond, Robert Chote, Malcolm Gammie, Paul Johnson, Gareth Myles, and James Poterba, Tax by Design: The Mirrlees Review (Oxford University Press, Oxford, UK, 2011). 11 Eric French, “The Effects of Health, Wealth, and Wages on Labour Supply and Retirement Behaviour,” The Review of Economic Studies, vol. 72, no. 2 (April 2005), pp. 395–427. 12 Ibid., p. 412. 13 Hemel, p. 1897. 14 Some other influential tax review studies been the 1975 Asprey report for Australia and the Carter report for Canada. See: Paul Tilley, “Post-War Tax Reviews and the Asprey Blueprint,” Tax and Transfer Policy Institute Working Paper 15/2020, The Australian National University, Crawford School of Public Policy, November 2020. Also see, Richard A. Musgrave, “The Carter Commission Report,” The Canadian Journal of Economics/Revue canadienne d’Economique, vol. 1, no. 1, Supplement (February 1968), pp. 159–182.

CHAPTER

18

Measuring Macroeconomic Uncertainty

LEARNING OBJECTIVES This chapter explores the history and measurement of uncertainty, which is a crucial element in macroeconomic theories—though the concept is rarely delved into deeply. You will learn: • • • • • •

The importance of uncertainty in economic theories. What uncertainty means in economics and its relationship to similar concepts. Why uncertainty is intertwined with expectations. How Frank Knight thought about risk and uncertainty. How John Maynard Keynes thought of expectations. The many ways to measure uncertainty.

[500] WHAT IS THE HISTORY OF MACROECONOMIC UNCERTAINTY? For years, the popular press has written stories about nationwide economic uncertainty, eco­ nomic insecurity, and/or the lack of confidence by the consumer or sometimes, by business. Long ago, economic researchers began writing about those ideas as well. In 1927, A.C. Pigou suggested that “waves of optimism and pessimism” played a significant role in economic fluc­ tuations.1 In a slightly different vein, Wesley Mitchell—the first research director of the National Bureau of Economic Research and noted business cycle scholar—also observed, coincidently, in 1927 that economic uncertainty is an all-evading phase of every business undertaking. Its tap root is uncertainty concerning what people will buy at what price. Its lateral roots are uncertainty concerning what competitors, direct and indirect, will sell at what prices; uncertainty what supplies of all needed kinds can be bought at what prices, and uncertainty what will happen within the DOI: 10.4324/9781003391050-20

366 MACROECONOMIC ISSUES enterprise, or within its business connection, to affect its profits. The fruits of uncertainty appear in emotional aberrations of business judgments and competitive illusions.2 And, of course, business uncertainty is intertwined with consumer uncertainty and these two psychological factors have been part of some business cycle theories for years. However, uncertainty is more than just a risky outcome, as Katona observed: For most people the term uncertainty does not have the meaning of simply not knowing what will happen in the future. Uncertainty has definite unfavorable connotations; it implies fear of adverse developments because the feeling of uncertainty arises only when problem solving has been attempted but has not been successful.3

[501] WHY DO WE CARE ABOUT MEASURING UNCERTAINTY IN MACROECONOMICS? There are various reasons why it is important to have a representative measure of uncer­ tainty to track and analyze because it has been suggested that uncertainty affects consumer spending (as the Katona effect highlights), it also affects business investment decisions, and some recent research suggests that increased uncertainty reduces the effectiveness of economy policy.

[502] WHAT ARE THE RELATED CONCEPTS TO UNCERTAINTY IN MACROECONOMICS? Throughout the literature various terms have been used to describe the behavioral response of individuals to the economic environment and vice versa. Confidence, risk, insecurity, mood, anxiety, and uncertainty are but a few of the more common phrases used to describe that behavioral response.

[503] IS THE UNCERTAINTY CONCEPT DIFFERENT FROM CONFIDENCE? Confusing and non-standard terminology used throughout the economics literature makes it difficult to distinguish confidence from the degree of uncertainty. Is the concept of confidence already capturing uncertainty or are they truly distinct? How should uncertainty be defined? Frank Knight, suggested in his classic 1921 book, Risk, Uncertainty and Profit, a distinction between risk and uncertainty. Knight felt that risk was measurable while uncertainty was not.4 A similar view was held by John Maynard Keynes, whose theories in the mid-1930s helped to elevate the role of uncertainty in economics. Keynes believed that uncertainty could not be estimated by the “calculus of probability,” but shared Frank Knight’s view that uncertainty was distinct

MACROECONOMIC UNCERTAINTY

from measurable risk. Keynes found a key role for uncertainty within his path-breaking economic framework, which was posited in his General Theory in 1936.5 Yet throughout the survey-research literature, the terms, uncertainty, and risk, essentially are equivalent and believed to be measurable. Knight’s theoretical delineation for clarity in economic research is viewed as a distinction without difference for survey research purposes. •

•

For the most part, economic confidence is identical to the inverse of economic insecurity. The higher the degree of confidence there is in the economy, then the lower the degree of macroeconomic insecurity. To be sure, fluctuations in the perceptions of noneconomic confidence and security—from political insecurity to personal safety concerns, exist among the public simultaneously with economic confidence and certainty. Moreover, those non-economic aspects of confidence may reinforce or ameliorate the behavioral-macroeconomic link at various times. Nonetheless, the role of those noneconomic issues in shaping economic confidence and uncertainty is beyond the scope of this discussion.6 Confidence is captured by U.S. consumer surveys, such as, the monthly surveys taken by the University of Michigan’s Survey Research Center and the Conference Board.7 Confidence also is captured in business surveys, such as those taken by the National Federation of Independent Business (NFIB—the “small-business survey”), the NY Federal Reserve Business Leaders Survey of regional service firm executives, the U.S. Census Bureau’s Small Business Pulse Survey, the Atlanta Fed’s the Survey of Business Uncertainty (along with the University of Chicago and Stanford University), the Richmond Fed’s CFO Survey, and the Conference Board. Generally, those business surveys tend to be more narrowly focused than the consumer surveys.

[504] HOW IS UNCERTAINTY INTERTWINED WITH EXPECTATIONS? There are various definitions of expectations, but one of the more encompassing is that it is the degree of probability that something will occur. In this context, therefore, the degree of uncertainty or certainty of something occurring is the foundation for the expectations for some future occurrence.

[505] HOW DID JOHN MAYNARD KEYNES VIEW UNCERTAINTY? Stohs8 writes: According to Keynes, uncertainty concerning the future enters into the economic scheme in two ways, (i) as uncertainty concerning the future of the rate of interest and (ii) as uncertainty concerning the effect of the future course of events on the yield of investment in assets.9

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[506] WHAT DID KEYNES MEAN WHEN HE WROTE IN HIS GENERAL THEORY THAT “IT WOULD BE FOOLISH, IN FORMING OUR EXPECTATIONS, TO ATTACH GREAT WEIGHT TO MATTERS WHICH ARE VERY UNCERTAIN”? Although Keynes’ statement may seem somewhat muddling of the terms “expectations” and “uncertainty,” keep in mind that he embraced Frank Knight’s distinction between risk and uncertainty, where risk (and expectations) was measurable, but uncertainty was not. To this end, J.M. Keynes also felt the need to clarify his statement in a footnote, in which he wrote that the term “very uncertain” did not mean “very improbable.” Here improbable means measured risk with a low probability of occurring.

[507] HOW DID JOHN MAYNARD KEYNES INCORPORATE EXPECTATIONS INTO HIS THEORY? John Maynard Keynes10 relied heavily on expectations in his macroeconomic theory— especially about its impact on output and employment—and distinguished between short-term and long-term expectation. •

Short-term expectations: Keynes described short-term expectations in his theory as a concern “with the price which a manufacturer can expect to get for his “finished” output at the time when he commits himself to starting the process which will produce it; output being “finished” (from the point of view of the manufacturer) when it is ready to be used or to be sold to a second party.” Although this is not the clearest definition, it essentially is a short (initial) period in the decision-making process when production plans are formulated.

•

Long-term expectations: “The second type [of expectation] is concerned with what the entrepreneur can hope to earn in the shape of future returns if he purchases (or, perhaps, manufactures) “finished” output as an addition to his capital equipment [which Keynes called] long-term expectation.” Here, too, the definition may not be the clearest, but it essentially is an extended period whereby production decisions and profits are realized.

•

Short-term versus long-term expectations dependency: Keynes believed that “these short-term expectations will largely depend on the long-term (or medium-term) expectations of other parties.”

•

Change in expectations: Modification of expectations was an important element of Keynes theory, as well. He observed, “in general, a change in expectations (whether short-term or long-term) will only produce its full effect on employment over a considerable period.” He opined that the “original expectations” driving an initial economic decision are no longer relevant as expectations are updated (possibly

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frequently), but “every state of expectation has its definite corresponding level of longperiod employment.” Moreover, Keynes felt that “revision of short-term expectation is a gradual and continuous one, carried on largely in the light of realized results; so that expected and realized results run into and overlap one another in their influence.” •

Cyclical impact from changing expectations: Keynes wrote in his General Theory that, “a mere change in expectation is capable of producing an oscillation of the same kind of shape as a cyclical movement, in the course of working itself out.”

[508] IS UNCERTAINTY DIFFERENT FROM CONSENSUS? Victor Zarnowitz drew a distinction between consensus and uncertainty with regard to economic forecasts. He defined consensus as “the degree of agreement among corresponding point predictions by different individuals” and then defined uncertainty as “the diffuseness of the probability distributions attached by the same individuals to their predictions.”11 This distinction is uniquely based on a quarterly expert opinion forecast poll, which was collected by Victor Zarnowitz on behalf of the American Statistical Association and the National Bureau of Economic Research and now is collected by the Federal Reserve Bank of Philadelphia. That survey—known as the Survey of Professional Forecasters—is a compilation of forecasts based on point estimates for economic measures, such as real GDP and inflation, as well as a probability distribution associated with each economic indicator’s forecast.12 However, this type of information is not widely collected in consumer or business surveys and is difficult to implement in consumer or business surveys.

[509] HOW IS UNCERTAINTY RELATED TO MACROECONOMIC FORECASTS? McNees observed that “uncertainty is a key concept in both economic theory and economic forecasting practice … [But] macroeconomic forecasters and forecast users have paid too little attention to defining and measuring a reliable concept of uncertainty.”13 In a broader sense, “behavioral macroeconomic theory”—including concepts of expectations anchoring, over­ confidence, and the irrelevance of history—can provide some vital insights into how the economy and its financial markets really operate.14

[510] HOW HAS UNCERTAINTY BEEN CAPTURED IN MACROECONOMICS? Historically, economists have generally preferred the simple approach of using a “cer­ tainty equivalent” or an “expected value,” which allows one to ignore the problem rather than trying to measure the concept and assess how uncertainty might impact the economy. In recent years, however, some theoretical research has dispensed with the expected-value approach and attempted to incorporate “risk aversion” into consumer expenditure models, which at least recognizes the importance of the concept.15 Additionally, the New Classical or Keynesian theories explicitly incorporate a forward

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370 MACROECONOMIC ISSUES expectation that is not based on the past. But econometric and statistical approximations of uncertainty may be inappropriate for this uniquely behavioral response to the economic environment. Survey-based measures of uncertainty seem more appropriate to capture this response because of the confluence of various factors impacting it, which might range from recent history through announced changes in government policy. Thomas Juster of the University of Michigan’s Survey Research Center made this point in his 1981 testimony to the U.S. Congress. Juster observed that economists often have used historical trends as a proxy for expectations. However, he cautioned, “In real life, as in behavioral-relevant theory, that pro­ cedure simply will not work.”16

[511] WHAT ARE THE COMMON MEASURES OF UNCERTAINTY? The economic literature discusses the following measures of uncertainty: (a) direct evaluation, (b) standard deviation over time, (c) absolute deviation of responses within the same time period, (d) entropy—a statistical measure of uniformity of survey responses within the same time period, and (e) the “cumulative marginals.” Only the set of first measures are directly captured by surveys, while the remaining measures are calculated or inferred based on some reported measure or measures of economic activity.

[512] WHAT ARE THE DIRECT MEASURES OF UNCERTAINTY? Dominitz and Manski17 took a direct-measurement approach to assess the degree of insecurity using the Survey of Economic Expectations (SEE). SEE was initiated in 1994 as part of a national telephone survey conducted by the University of Wisconsin-Madison’s Letters and Science Survey Center, but it ended in 2002. They asked three survey questions to assess the public’s economic and social insecurity. The questions covered three areas: (1) health insurance cov­ erage, (2) the chances of being a robbery victim, and (3) the chances of losing one’s job. The researchers used a probability-rating scale in their questionnaire and asked the respondents to use a 12-month horizon for the evaluation. From those responses, the researchers constructed a classification scheme of “relatively secure,” “relatively insecure,” and “highly insecure.” They found that economic insecurity as determined by those three factors tended to decline with age and the amount of schooling one has, which does not seem to be very startling given the lifecycle hypothesis of earnings held throughout the economics profession. But beyond that finding, the Dominitz and Manski study had little macroeconomic implication. Moreover, it is not clear that an overall measure of macroeconomic uncertainty should be limited to the three concepts of health insurance, crime, and job security, which was done by the SEE. Another example of direct measurement of economic uncertainty was reported by Newport and Saad18-based Gallup polls. Newport and Saad examined longer term trends in the consumers’ evaluation of various factors affecting confidence—including the consumers’ evaluation of job security (percentage of Americans worried that they or their spouse would lose a job in the

MACROECONOMIC UNCERTAINTY

next few years), personal finances (“Would you say that you are financially better off now than you were a year ago, or are you financially worse off now?”), and the economy (“How would you rate economic conditions in this country today: excellent, good, only fair or poor?”). They observed that economic uncertainty in the early 1990s was lower than the popular perception and not particularly worrisome compared to history. In 1992, Gallup found 36% of the public worried about losing a job, personally or for one’s spouse, and four years later, the percentage of the American public worrying about that same issue had not changed. Moreover, Newport and Saad interpreted those results even more optimistically over those four years. They suggested that since the forecast horizon was lengthened in the 1996 Gallup job-insecurity question (“looking at the next few years”) from the 1992 question (“looking at the next twelve months”), that implied the American public was less worried in 1996. However, those findings from the Gallup polls were in stark contrast to the International Survey Research Corporation (ISR) composite surveys taken between 1992 and 1996, which reported increased job insecurity of managers at large corporations. In 1992, ISR found that 31% of the employees surveyed agreed with the statement, “I am frequently concerned about being laid off.” The percentage agreeing with that statement climbed to 38% in 1993, 44% in 1994, and leveled off at 46% in 1995 and 1996. The New York Times National Economic Insecurity Survey provided another window on this issue. That survey, which was conducted between December 3 and December 6, 1995, found that 9% of the public felt “very insecure” and 28% were “somewhat insecure.” However, that question (as well as the similar one by Gallup) was criticized by Dominitz and Manski, who thought the question and response choices were “loosely-defined.” Without a doubt, direct measurement of insecurity or uncertainty can be problematic. Echoing this point was a study by Garner, Stinson, and Shipp19 in which they observed, “the effective use of subjective questions must absolutely depend upon (1) clear, precise, and unambiguous language, (2) non-arbitrary response categories, and (3) clearly defined concepts.”20 Further reflecting the difficulty of direct measurement of uncertainty was George Katona’s observation that, “Many people, when they say that the nearterm outlook is uncertain, mean to say that it is unfavorable.”21 Another attempt to pin down the significance of job insecurity for the economy was undertaken by Otoo.22 Using the University of Michigan survey, Otoo defined a measure of worker anxiety based on the answer to six questions, which are routinely included in the Michigan survey. Those questions covered the consumer’s evaluation of his/her financial situation, whether the consumer felt it was a good/bad time to buy a house, whether the consumer felt it was a good/bad time to sell a house, whether it was a good/bad time to buy a major household item, whether it was a good/bad time to buy a car in the next 12 months, and whether it was a good/bad time to buy a pickup, van, or jeep-type vehicle in the next 12 months. Otoo developed a cross-sectional model to explain worker anxiety, which was estimated for the final seven months (June– December) of 1995 data and for the final seven months of 1988. Her conclusions were: (1) In 1995, 27% of the sample of households were concerned with job security—which was between the Gallup and New York Times estimates and the University of Wisconsin’s Survey of Economic Expectations findings—compared to 15% of households that were “anxious” in 1988; (2) anxious

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372 MACROECONOMIC ISSUES households were more likely to be pessimistic about the overall economic environment; (3) job anxiety was heavily influenced by reports of job loss (reports of “bad news”); and (4) worker anxiety did not affect the consumer’s willingness to use household savings for major purchases. The Federal Reserve Bank of Atlanta, in partnership with Steven Davis of the University of Chicago Booth School of Business and Nicholas Bloom of Stanford University, created the Atlanta Fed/Chicago Booth/Stanford Survey of Business Uncertainty (SBU), which began in 2014.23 The SBU is a nationwide survey of firms and measures the one-year-ahead expectations and uncertainties that firms have about their own employment and sales. This survey requires the participant to answer a set of questions for each variable. For example, the sales revenue questions are: (1) For the current quarter, what would you estimate the total dollar value of your sales revenue will be? (2) Looking back, over the last 12 months, what was your approximate percentage sales revenue growth rate? (3) Looking ahead, from now to four quarters from now, what approximate per­ centage sales revenue growth would you assign to each of the following scenarios? (3a) The lowest percentage sales revenue growth rate would be about? (3b) A low percentage sales revenue growth rate would be about? (3c) A middle percentage sales revenue growth rate would be about? (3d) A high percentage sales revenue growth rate would be about? (3e) The highest percentage sales revenue growth rate would be about? (4) Please assign a percentage likelihood to the sales revenue growth rates you entered (questions 3a–3e). Those probabilities sum to 100%.

[513] WHAT ARE SOME MEASURES OF UNCERTAINTY BASED ON INFERRED VOLATILITY? An early attempt to define a survey-based measure of aggregate uncertainty was suggested by Lazarsfeld, Berelson, and Gaudet.24 They wrote, “If the [period-to-period survey response] turnover is large, it indicates that the opinion or behavior is unstable. [Therefore, we infer] that people feel uncertain.” These researchers defined uncertainty as the variability of opinion. This view was echoed by McNees,25 who wrote: In light of the difficulty of modeling uncertainty, a plausible and easily obtainable measure of uncertainty has been the dispersion of individuals’ forecasts. This procedure presumes that when different individuals have unusually great dispersion among their point estimate forecasts, then uncertainty is high. It should be clear that this measure is, at best, only a crude proxy for uncertainty … No logically necessary connection exists between a forecasters’ degree of uncertainty and the degree of uniformity of the point estimates of different forecasters.26 McNees’ point is well taken and should be underscored. Nonetheless, it is common to assume that a high degree of consensus is tantamount to certainty of opinion. As a result, the concept of economic confidence, per se, is definitionally different from economic uncertainty. One can be very confident but highly uncertain in that expectation and vice versa.

MACROECONOMIC UNCERTAINTY

[514] [ADVANCED] WHAT IS MEANT BY “TAIL RISK”? Plotting the distribution of historic event outcomes will likely show that some events do not occur very often, which is represented by the likelihood or frequency of those events being low and in the “tail” of the distribution (think about a bell-curve with the tails representing low occurrence).

[515] [ADVANCED] HOW IS VOLATILITY MEASURED AS AN ESTIMATE OF ECONOMIC UNCERTAINTY? A typical measure of uncertainty is to equate it with a moving-period standard deviation or variance. One form of this measure, for example, can be defined as a 12-month moving or rolling standard deviation (which is the square root of the moving variance measure), as follows: MStdDev =

x xbar )2

i =12 i =1 ( i

12

where xbar is the 12-month moving average.

The use of standard deviation (or variance) as a measure of risk is rooted in finance and is an integral part of such theories as the capital asset pricing model. However, its information content for assessing the degree of macroeconomic uncertainty has been subject to some criticism. A more recent variant of this volatility-based measure of uncertainty is the standard deviation of a distribution of expected values as determined by some mathematical model or possibly, survey of expectations. But as Golob27 observed, different statistical models of uncertainty are likely to result in different empirical conclusions. Furthermore, George Katona noted that “observed volatility may be interpreted in different ways; it may or may not reflect uncertainty.”28 Supporting the Katona observation was a survey designed to explicitly measure the degree of inflationary uncertainty that existed among Swedish households,29 which con­ cluded that the standard deviation of responses was different than the conclusion reached from direct measurement of uncertainty. The survey asked three questions—first using a “percep­ tion” question and later changing that question to an “expectation” wording. Comparing the survey results to the calculated standard deviation, the study concluded, the standard deviation of the point estimates across respondents of different degrees of certainty do not indicate that the standard deviation rises with growing uncertainty as suggested by the use in some studies of the standard deviation as a proxy variable for … uncertainty.30 Hence, although the standard deviation (or variance) concept is simple to apply, it is not clear that volatility over time necessarily should be equated with uncertainty. An example of var­ iance as a measure of uncertainty is shown in Figure 18.1 for the University of Michigan’s Index of Consumer Sentiment and its 12-month moving variance. This measure seemingly shows uncertainty spikes associated with recession periods.

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374 MACROECONOMIC ISSUES

University of Michigan’s Index of Consumer Sentiment and Variance Measure of Uncertainty

FIGURE 18.1

[516] [ADVANCED] HOW IS AGGREGATE DISPERSION MEASURED AS AN ESTIMATE OF ECONOMIC UNCERTAINTY? To counter the confusion between volatility and uncertainty, George Katona defined uncer­ tainty as aggregate dispersion of the components of the University of Michigan consumer sen­ timent survey. Katona’s logic for this measure was given as follows: At a time when almost everybody in a representative sample expresses optimistic expectations, or almost everybody expresses pessimistic expectations, we may say that the people are optimistic or pessimistic. On the other hand, when a substantial proportion is optimistic and a similar proportion is pessimistic, the people as a whole may be viewed as uncertain in their expectations about future developments. Thus the smaller the difference between expecting good or better times and expecting bad or worse times, the greater the uncertainty on the aggregate or macro level. This measure [is] constructed irrespective of whether optimists or pessimists are more frequent … It indicates not only … that growing optimism dispels uncertainty but also that growing pessimism dispels uncertainty.31 Hence, viewed from a cyclical perspective, Katona’s logic would argue that uncertainty should be a lagging indicator of consumer confidence. Katona’s consumer uncertainty index (CUI) can be defined notationally as follows for the University of Michigan’s consumer sentiment survey:

MACROECONOMIC UNCERTAINTY

CUIT =

2 3 × CUIN + × CUIF 5 5

diN | for i = 1 to 2 and CUIF = j |uFj dFj | for j =1 to 3, and the where CUIN = i |uiN superscripts designate the present situation (N), expectations (F), and the weighted average of the two (T). Practically, the CUIN is the sum of the absolute value of the percentage reporting that jobs are plentiful minus the percentage reporting jobs hard to get and the absolute value of the business conditions good percentage minus the percentage reporting business conditions are bad. Similarly, CUIF is based on expectations six months; hence, for business conditions, employment, and income. The University of Michigan’s weighting scheme gives two-fifths of the aggregate weight to present situation indicators and threefifths of the weight to expectations indicators. Moreover, the weighting scheme for CUIT is identical for either the University of Michigan’s Index of Consumer Sentiment or the Conference Board’s composite Consumer Confidence Index, which is a weighted sum of Present Situation (based on two components for business conditions and employment) and Expectations (based on three components for business conditions, employment, and income)—CUIT. An example of this type of uncertainty measure is shown below. This example shows that higher the consumer confidence seemingly is associated with greater dispersion and consequently higher uncertainty by inference. This seems to go against Katona thought that higher or lower confidence drives out uncertainty, which does seem to be supported by the variance or standard deviation measures.

University of Michigan’s Index of Consumer Sentiment and Dispersion Measure of Uncertainty

FIGURE 18.2

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376 MACROECONOMIC ISSUES

[517] [ADVANCED] HOW IS RELATIVE ENTROPY MEASURED AS AN ESTIMATE OF ECONOMIC UNCERTAINTY? Relative entropy is defined as observed entropy divided by the maximum possible entropy for the number of outcomes considered, where entropy is the sum of the probability of a particular 32 outcome multiplied by the logarithm of the probability, that is, A variation on i pi ln(pi ). 33 this formula is to define consensus as: Consensus = 1

N i =1

pi ln(pi )/ ln

1 N

,

where pi is the relative frequency or proportion in the ith category, ln is the natural logarithm, and N is the number of categories. Then, the consensus metric approaches the value 1 when entropy approaches its maximum

ln

( ). A relative-entropy consensus value of zero would result if the 1 N

survey answers were equally distributed across all possible response options. This measure, which represents a degree of consensus, ranges between 0 (no consensus) and 1 (perfect consensus).34 Based on the University of Michigan’s consumer sentiment survey, the relative entropy consensus for the two present situation components will be calculated over the categories (for example, N = 5, if the categories are: good times, uncertain, bad times, don’t know, and no answer) and the three ex­ pectations components and the consensus estimates are averaged for the number of components. The combined measure of relative entropy consensus is based on the weighted average of the present and future conditions is: Consensus (Total) = (2/5) × Consensus (Present) + (3/5) × Consensus

University of Michigan’s Index of Consumer Sentiment and Relative Entropy Measure of Uncertainty

FIGURE 18.3

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(Expectation). Studies by Ricketts and Shoesmith35 and Alston, Kearl, and Vaughan36 are examples of the use of this type of measure. Unfortunately, entropy is undefined at the point when the response percentage equals zero since the log of zero is undefined. An example of the relationship between confidence and uncertainty is shown in the chart for the University of Michigan’s confi­ dence index and its derived measure of the relative entropy measure of uncertainty. Note that confidence and uncertainty are directly related with more certainty—that is, when the confidence measure is at its highest so too is certainty, and vice versa.

[518] [ADVANCED] HOW IS “MOOD” MEASURED AS AN ESTIMATE OF ECONOMIC UNCERTAINTY? Stimson37 offered yet another uncertainty concept, which he dubbed “mood” that was intended to track changes in public sentiment over time. Mood, Stimson observed, “is intended to be a scientific alias for what Lippman [in his 1922 book] called, with some skepticism, the ‘spirit of the age.’ It connotes shared feelings that move over time and circumstance.”38 Operationally, mood is defined as a cumulative sum of the “marginal” responses, which is different from some direct survey measures of public mood, such as the semiannual “mood survey” taken in South Africa, which asks respondents to score their level of happiness, hopefulness, satisfaction and safety for the present and expectations of those same variables over the next six months.39 Stimson provides the following rationale for looking at his version of this concept: [S]pecificity leads us astray in public opinion work … We want to believe that the crisp response to sharply framed query is a response to the query itself. And so we catalog opinions according to the questions that produced them and in the process forget that there might be something like public opinion (singular) that is far more powerful and far more interesting than the questions that are its indicators.40 Based on the University of Michigan’s survey, mood can be defined for optimism (designated by the U superscript) and pessimism (designated by the D superscript) as a weighted average of the present situation (N) and expectation (F) components (using the same weighting scheme that the University of Michigan’s consumer sentiment measure uses to compile the overall consumer confidence index): “Upbeat or Optimistic Mood” = MU =

( )× 2 5

uiN +

and “Downbeat or Pessimistic Mood” = MD =

( )×

( )× 2 5

3 5

diN +

j = 1 to 3. Then, taking the cumulative sum, D MOODtD = MOODD t 1 + M t and U U MOODU t = MOODt 1 + M t MOODNETt = MOODtU MOODD t

uFj for i = 1 to 2, j = 1 to 3,

( )× 3 5

d Fj for i = 1 to 2,

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FIGURE 18.4

Mood: Cumulative Net Mood

where initial values for MOODD and MOODU are set to an arbitrary value of 100. In this framework, uncertainty would be defined as swings in cumulative net mood. An example of this type of mood metric is shown in the chart above.

[519] HOW DO THE CALCULATED MEASURES OF UNCERTAINTY COMPARE? Table 18.1 presents some of the common calculated measures of uncertainty in the economic literature. More often than not, standard deviation is the most popular metric used to proxy the concept.

[520] [ADVANCED] ARE THERE ECONOMETRIC ESTIMATES OF ECONOMIC UNCERTAINTY? Yes. One such econometric study of economic uncertainty was by Jurado, Ludvigson, and Ng,41 who explored the following question: “How important is time-varying economic uncertainty and what role does it play in macroeconomic fluctuations?” The authors noted that “uncertainty is typically defined as the conditional volatility of a disturbance that is un­ forecastable from the perspective of economic agents.”42 The researchers attempt to develop an “objective” measure of uncertainty, which they define as free of both a theoretical model structure and any individual observable economic indicators. For their study, they define “macroeconomic uncertainty” as “uncertainty that may be observed in many economic

Easy to understand and interpret. Easy to measure.

Relatively easy to measure. Equates variability of opinion in the current period with uncertainty. Equates the degree of the consensus of opinion in the current period with certainty. Equates changes over time to mood swings.

Direct Measurement (Surveys)

Standard Deviation

Dispersion of Responses (Katona)

Cumulative Net Marginals (Mood)

Relative Entropy

Benefits

Comparison of Calculated Uncertainty Measures

Concept

TABLE 18.1

Not well-defined for current period uncertainty.

Undefined at one point; Difficult to interpret.

Not necessarily addressing the root cause of uncertainty or the changing nature of it. Equates volatility over time to uncertainty and is dependent on the span of the time horizon. Some studies suggest that standard deviation is inconsistent with direct measurement of uncertainty at points in time. Dependent on number of components used.

Limitations

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379

380 MACROECONOMIC ISSUES indicators at the same time, across firms, sectors, markets, and geographic regions.”43 The conclusion of this work is that they argue their measure showed that “macro uncertainty is strongly countercyclical, explaining a much larger component of total uncertainty during recessions than non-recessions.”

[521] ARE THERE ANY OTHER METHODOLOGIES USED TO MEASURE UNCERTAINTY? Yes. Advances in computing power, artificial intelligence (AI) methodology, and increasing amounts of information available online (news, blogs, economic bulletins, etc.) have accel­ erated the development of textual-analysis algorithms to search newspapers, audio files, and video files to compile text-based or “natural language processing” (NLP) measures. These state-of-the-art techniques are still evolving. A recent IMF application to develop text-based sentiment indicators44 used what an IMF study termed “linguistically-determined word clusters” to capture sentiment: Unlike survey-based sentiment measures, and different from several ‘uncertainty’ measures recently developed in the literature, [the IMF uses] ‘semantic clustering’ techniques, as opposed to ‘lexical’ or ‘bag-of-words’ approaches (which typically rely on predetermined, and sometimes narrow, sets of words). Relying on word vector representation techniques, semantic clustering enables us to identify the appropriate set of words (e.g., semantic cluster) that capture a specific sentiment.45

[522] WHAT IS THE “ECONOMIC POLICY UNCERTAINTY” MEASURE? Baker, Bloom, and Davis46 constructed sets of international text-based indicators based on newspaper stories (from ten newspapers: USA Today, the Miami Herald, the Chicago Tribune, the Washington Post, the Los Angeles Times, the Boston Globe, the San Francisco Chronicle, the Dallas Morning News, the Houston Chronicle, and the Wall Street Journal), federal tax code ex­ piration, and economic forecaster disagreement, which they called an economic policy uncertainty (EPU) index,47 based on articles containing words relating to the concepts of uncertainty, economy, and policy. The EPU for the United States (as shown in Figure 18.5), based on a cursory review, had the highest statistical correlation with the standard deviation metric used to measure uncertainty.

[523] HOW DO TEXT-BASED INDICATORS OF UNCERTAINTY COMPARE TO THE SURVEY OR STATISTICALLY DERIVED MEASURES? Gbohoui48 in his study suggested that conceptually uncertainty metrics can be divided into two groups—those that are backward-looking and those that are forward-looking. The researcher argued that

MACROECONOMIC UNCERTAINTY

FIGURE 18.5

U.S. Economic Policy Uncertainty Metric (Three Components)

Source: “Measuring Economic Policy Uncertainty” by Scott Baker, Nicholas Bloom and Steven J. Davis at www.PolicyUncertainty.com

Backward-looking measures … are less appropriate to measure real-time uncertainty because the data that are the key inputs to the forecasting models are observable with delays. Existing forward-looking measures capture different dimensions of uncertainty and include stock market volatility, Google News-based indexes of economic uncertainty and economic policy uncertainty indexes based on newspaper coverage frequency, as well as the subjective uncertainty about future business growth and the disagreement among professional forecasters about the future dynamic of economic variables.49

[524] OF THE THREE TYPES OF UNCERTAINTY MEASURES BASED ON TEXT-BASED TECHNIQUES, DIRECT MEASUREMENT BY SURVEYS, AND STATISTICALLY DERIVED MEASURES, ARE THERE KEY TAKEAWAYS FOR MACROECONOMICS? A lot more research needs to be done to determine which measure of uncertainty is truly the most conceptually and functionally appropriate for economic theory, empirical analysis, and for forecasting. But today a lot of research is being directed towards more text-based indicators. New findings are being offered regularly and many international organizations, such as the International Monetary Fund, and central bank research staffs are advancing the exploration. However, it is by no means settled that text-based indicators offer more insight than traditional statistics, surveys, or statistically derived measures. Nonetheless, these timely text-based met­ rics, along with other “big data” measures, may be a welcome complement to the perspective derived from conventional economic statistics.

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Issues to Think About Understanding the linkage between macroeconomic uncertainty for the con­ sumer and for business provides a deeper understanding of a motivation for consumer spending, business investment, and the interaction of the various parts of the economy. • • • •

Is there a best measure of uncertainty? What are the benefits of incorporating uncertainty into macroeconomics? What are the drawbacks of incorporating uncertainty into macroeconomics? Are text-based measures of uncertainty truly “forward-looking” or just timely?

NOTES 1 A.C. Pigou, Industrial Fluctuations (Macmillan, London, 1927). 2 Wesley C. Mitchell, Business Cycles: The Problem and Its Setting (National Bureau of Economic Research, New York, 1927), pp. 156–157. 3 George Katona, The Powerful Consumer (McGraw-Hill Book, New York, 1960), p. 59. 4 Frank H. Knight, Risk, Uncertainty and Profit (Houghton Mifflin, Boston, 1921). 5 In Chapter 12 of Keynes’ General Theory he wrote that, “It would be foolish, in forming our expectations, to attach great weight to matters which are very uncertain. It is reasonable, therefore, to be guided to a considerable degree by the facts about which we feel somewhat confident, even though they may be less decisively relevant to the issue than other facts about which our knowledge is vague and scanty.” See: John Maynard Keynes, The General Theory of Employment, Interest, and Money (Macmillan, London, 1936). Accessed online, https://www.files.ethz.ch/isn/125515/1366_KeynesTheoryofEmployment.pdf, p. 75. 6 It has been argued by proponents of “institutional economics” (Rutherford, 1996) that institutions may provide a “security of expectations,” which means that institutions provide the rules for resolving conflicts of self-interest. Therefore, countries and cultures shape the role and importance of mac­ roeconomic uncertainty. Hofstede (1984) carried out an international survey across 66 countries, which led to four main characteristics of cultures. One of those factors was uncertainty avoidance. That study suggested cultures that were less sensitive to uncertainty were also more likely to tolerate change. The macroeconomic behavioral response to uncertainty may be different in different societies and countries. Moreover, another lesson from the Hofstede study is that uncertainty may not be detrimental to society after all, if it is an engine of progress and change. See: Malcolm Rutherford, Institutions in Economics (Cambridge University Press, New York, 1996). Also: Gert Hofstede, Culture’s Consequences: International Differences in Work-Related Values (Sage, Beverly Hills, CA, 1984). 7 Another U.S. consumer confidence survey is conducted by TechnoMetrica Market Intelligence and reported by Investor’s Business Daily, which is its IBD/TIPP Economic Optimism Index. This na­ tionwide monthly survey compiles its results based on three questions: (1) In the next six months, do you think that economic conditions in the country will be better, worse, or about the same as now? (2) In the next six months, do you think that your personal financial situation will be better, worse, or about the same as now? (3) How satisfied are you with the current federal economic policies meant to keep the economy going in the right direction: Very satisfied, somewhat satisfied, not very satisfied, or not at all satisfied?

MACROECONOMIC UNCERTAINTY 8 Mark Hoven Stohs, “‘Uncertainty’ in Keynes’ General Theory,” History of Political Economy, vol. 12, no. 3 (September 1980), pp. 372–382. 9 Ibid., p. 374. 10 Keynes, General Theory, Chapter 5. 11 Victor Zarnowitz, Business Cycles: Theory, History, Indicators, and Forecasting (University of Chicago Press, Chicago, 1992), p. 517. 12 Note that the European Central Bank began a similar survey of professional forecasters in 1999 with forecasts of probability distributions. See: Juan Angel Garcia, An Introduction to the ECB’s Survey of Professional Forecasters, Occasional Paper No. 8, European Central Bank, Frankfurt am Main, Germany, September 2003. 13 Steven K. McNees (with the assistance of Lauren K. Fine), “Forecast Uncertainty: Can It Be Measured?,” paper presented at Federal Reserve Bank of Philadelphia and University of Pennsylvania’s Conference on Expectations in Economics, October 1996, p. 18. Although this paper takes the perspective of measuring forecast uncertainty based on probability distributions, the author recognized the broader importance of the concept of uncertainty in economics. A related concept to forecast uncertainty is forecast “surprises,” which is generally the difference between the market consensus forecast and the actual. In this sense, uncertainty might be defined as the sum of the probability of a negative surprise plus the probability of a positive surprises. 14 Robert J. Shiller, “Human Behavior and the Efficiency of the Financial System,” Recent Developments in Macroeconomics Conference, Federal Reserve Bank of New York, February 27–28, 1997. In this paper, Shiller provided a survey of these behavioral factors, which may result in financial market anomalies. 15 For a brief discussion of this work and further references, see: Angus Deaton, Understanding Consumption (Oxford University Press, New York, 1992), p. 20. 16 Thomas Juster, Statement to Congress, Expectations and the Economy, Joint Economic Committee, Congress of the United States, December 11, 1981, pp. 130–138. 17 Jeff Dominitz and Charles F. Manski, “Perceptions of Economic Insecurity: Evidence from the Survey of Economic Expectations,” Public Opinion Quarterly, vol. 61, no. 2 (Summer 1997), pp. 261–287. 18 Frank Newport and Lydia Saad, “The Economy and the Election,” The Public Perspective (Roper Center, April/May 1996), pp. 1–4. 19 T. Garner, L. Stinson, and S. Shipp, “Measuring Subjective Economic Well-Being: Economic Foundations and Cognitive Methods,” paper presented at the American Association of Public Opinion Research Annual Conference, Norfolk, VA, May 15, 1996. 20 Ibid., p. 5. 21 George Katona, Psychological Economics (Elsevier, New York, 1975), p. 202. 22 Maria Ward Otoo, “The Sources of Worker Anxiety: Evidence from the Michigan Survey,” Federal Reserve Board of Governors, Division of Research and Statistics, Working Paper, September 1997. 23 David Altig, Jose Maria Barrero, Nicholas Bloom, Steven J. Davis, Brent H. Meyer, and Nicholas Parker, “Surveying Business Uncertainty,” Working Paper 25956, National Bureau of Economic Research, Cambridge, MA, February 2020. 24 P.F. Lazarsfeld, B. Berelson, and H. Gaudet, The People’s Choice (2nd ed.) (Columbia University Press, New York, 1948). 25 Steven K. McNees (with the assistance of Lauren K. Fine), “Diversity, Uncertainty, and Accuracy of Inflation Forecasts,” New England Economic Review (July/August 1994), pp. 33–44. 26 Ibid., p. 36. 27 John E. Golob, “Inflation, Inflation Uncertainty, and Relative Price Variability: A Survey,” Working Paper (RWP 93–15), Federal Reserve Bank of Kansas City, November 1993. 28 George Katona, “Toward a Macropsychology,” American Psychologist, vol. 34, no. 2 (February 1979), p. 122. 29 Lars Jonung, “Uncertainty about Inflationary Perceptions and Expectations,” Journal of Economic Psychology, vol. 7 (1986), pp. 315–325.

383

384 MACROECONOMIC ISSUES 30 Ibid., pp. 324–325. 31 Katona (1979), p. 122. 32 Özlem Ege Oruç, Emel Kuruoglu, and Özgül Vupa, “An Application of Entropy in Survey Scale,” Entropy, vol. 11 (2009), pp. 598–605. 33 Rulon D. Pope and Arne Hallam, “A Confusion of Agricultural Economists?: A Professional Interest Survey and Essay,” American Journal of Agricultural Economics, vol. 68, no. 3 (August 1986), pp. 572–594. 34 Ibid., p. 577. Some studies do not subtract the relative entropy from 1, but in doing so, this associates “no consensus” with zero and a “perfect consensus” with one, which is a logical way of interpret the output. 35 Marin Ricketts and Edward Shoesmith, “British Economic Opinion: Positive Science or Normative Judgment?,” American Economic Review (May 1992), pp. 210–215. 36 Richard M. Alston, J.R. Kearl, and Michael B. Vaughan, “Is There a Consensus among Economists in the 1990s?,” American Economic Review (May 1992), pp. 203–209. 37 James A. Stimson, Public Opinion in America: Moods Cycles, and Swings (Westview Press, Boulder, Colorado, 1991). 38 Ibid., p. 18. 39 A discussion of the mood survey is found in H. Boshoff, “Political Forecasting Using Opinion Surveys as an Aid to Business Cycle Forecasting,” in K. Oppenlander and G. Poser, eds., The Explanatory Power of Business Cycle Surveys, (Avebury, Aldershot, England, 1994), pp. 190–215. 40 Stimson, pp. xix–xx. 41 Kyle Jurado, Sydney C. Ludvigson, and Serena Ng. “Measuring Uncertainty,” American Economic Review, vol. 105, no. 3 (March 2015), pp. 1177–1216. 42 Ibid., p. 1177. 43 Ibid., p. 1212. 44 Chengyu Huang, Sean Simpson, Daria Ulybina, and Agustin Roitman, “News-Based Sentiment Indicators,” IMF Working Paper WP/19/273, International Monetary Fund, Washington, DC, December 2019. 45 Ibid., p. 5. 46 Scott R. Baker, Nicholas Bloom, and Steven J. Davis, “Measuring Economic Policy Uncertainty,” Quarterly Journal of Economics, vol. 131, no. 4 (November 2016), pp. 1593–1636. 47 For the Baker, Bloom, and Davis metrics, see: http://www.policyuncertainty.com/. Also see: Luca Barbaglia, Sergio Consoli, Sebastiano Manzan, Luca Tiozzo Pezzoli, and Elisa Tosetti, “Sentiment Analysis of Economic Text: A Lexicon-Based Approach,” May 2022, https://papers.ssrn.com/sol3/ papers.cfm?abstract_id=4106936. These authors discuss their measure of economic pessimism created based on a “dictionary” of search terms and found it to be strongly pro-cyclical and a useful predictor of recessions. For more depth on these text-based indicators, see: Sergio Consoli, Diego Reforgiato Recupero, and Michaela Saisana, editors. Data Science for Economics and Finance: Methodologies and Applications (Springer, 2021). Additionally, see: Nicholas Bloom, “Fluctuations in Uncertainty,” The Journal of Economic Perspectives, vol. 28, no. 2 (Spring 2014), pp. 153–175. 48 William Gbohoui, “Uncertainty and Public Investment Multipliers: The Role of Economic Confidence,” IMF Working Paper WP/21/272, International Monetary Fund, Washington, DC, November 2021. 49 Ibid., p. 9.

Part 3 Macroeconomics Reshaped

CHAPTER

19

The Future of Macroeconomics

FACING THE CHALLENGE The prior chapters covered conventional macroeconomic theories, tools, and empirical observations, but this discussion goes beyond all of that and challenges you, based on some critics of the economics teachings, to question and think about what you have learned. The challenge is to imagine new or revised paradigms for macroeconomics through your imagination: new theories, new insights, new methods to analyze the macroeconomy can unfold! And, just maybe, better macroeconomic policies.

[525] WHY IS MACROECONOMICS FACING A CHALLENGE TODAY? MIT Profs. Banerjee and Duflo, who jointly won the 2019 Nobel Prize in economics, opined that various political, social, and economic forces coalesced to “finally shatter the stranglehold [on] ‘basic’ economics. Higher minimum wages, massive levels of deficit financing (in a crisis), and public debt levels that were unthinkable till recently suddenly became acceptable and responsible tools of economic management.”1

[526] HAS THIS “SHATTERING” OR CHALLENGE TO SOME OF THE BASIC ECONOMIC FOUNDATIONS LED TO MORE HETERODOXY IN ECONOMICS? This challenge to basic economic foundations, which Banerjee and Duflo observed, has led to a very limited rise in economic heterodoxy. There does not seem to a widespread change proposed for macroeconomics that might be characterized as “throwing out the baby with the bathwater.” Instead, it may be more akin to adding fresh warm water to the bath with economists proposing new goals to incorporate within the economic framework. DOI: 10.4324/9781003391050-22

388 MACROECONOMICS RESHAPED

[527] WHY IS THERE A FOCUS ON NEW BENCHMARKS OF ECONOMIC PERFORMANCE? Economist Joseph Stiglitz opined that “measurement matters.” He further argued, We live in a world of metrics, where we are constantly quantifying our progress, our success. What we measure affects what we do. If we measure the wrong thing, we will do the wrong thing. If we don’t measure something, it becomes neglected, as if the problem didn’t exist.2 A major focus of this movement to find broader benchmarks of economic performance is to improve policy.

[528] SHOULD “ECONOMIC WELL-BEING” REPLACE ECONOMIC GROWTH (REAL GDP) AS THE NATION’S MACROECONOMIC GOAL? The Wellbeing Economy Alliance—a global alliance of over 200 organizations—argues that the ultimate aim of well-being-oriented metrics is to replace GDP as the ‘key performance indicator’, with those metrics that measure performance in terms of contribution to wellbeing, sustainability and equity. These alternative metrics would guide governments, companies, citizens and organizations to ‘manage’ their activities differently, i.e., implementing a new type of policymaking, replacing policies that are narrowly focused on economic growth. These alternative metrics will also contribute to shifting dominant societal narratives from ‘economic growth is good’, to narratives that reinforce the goals of the well-being economy.3 However, there is no correct or incorrect answer to whether this should happen—this is a value judgement, but the first step in that direction is to have widely accepted metrics of wellbeing. To this end, measuring economic well-being is gaining traction among statistical agencies, such as at the OECD (with its “Beyond GDP” initiative),4 the European Commission, the UK’s Office of National Statistics, and even the U.S. Bureau of Economic Analysis (the BEA’s project is known as “GDP and Beyond”). But there are no well-being measures that have broad-based support as a policy goal and the nascent research has a long way to go. However, the logic is simple, as described by the OECD: In recent years, concerns have emerged regarding the fact that macro-economic statistics, such as GDP, don’t provide a sufficiently detailed picture of the living conditions that ordinary people experience … Societal progress is about improvements in the well-being of people and households. Assessing such progress requires looking not only at the functioning of the economic system but also at the diverse experiences and living conditions of people. [The OECD framework] is built around three distinct components: current well-being, inequalities in well-being outcomes, and resources for future well-being.5

THE FUTURE OF MACROECONOMICS

[529] IS THERE PRECEDENCE FOR “WELL-BEING” AS A CENTRAL ECONOMIC GOAL FOR AN ECONOMY? Attempts to cast the concept of well-being as a key macroeconomic objective, instead of real GDP, may appear recent, but it is far from new to economics. English economist William Stanley Jevons argued along a similar—though not identical—path. In 1874, Jevons wrote, maximizing aggregate “pleasure and [minimizing aggregate] pain are undoubtedly the ultimate [objectives of an] economy.” Jevons further clarified his view by noting, “In other words, to maximize comfort and pleasure is the problem of the economy.” Of course, what was considered pleasure and comfort in the late 1800s is quite different than what is assumed to generate it in the 21st century. Therefore, the concept of economic well-being—if it is measurable—is likely to be continually evolving over time.

[530] IF AN ECONOMIC WELL-BEING MEASURE COULD BE DEVELOPED, HOW SHOULD IT BE USED? The history of economic “gap models” has existed for well over a hundred years. One might imagine this approach could be extended to well-being. If a broad-based consensus could be achieved for a well-being goal, then a gap-type model could be useful to examine policies that could close the gap between the well-being measure “target” and the current reading.

[531] HOW SHOULD AN ECONOMIC WELL-BEING METRIC BE COMPILED? Conceptually, the Wellbeing Alliance opined that “there is not one blueprint for the wellbeing economy … the shape, institutions, and activities that get us there will look different, both across countries and between different communities within countries.” This, of course, implies that there is no single metric either. Stiglitz—who has been working with the OECD—argues for a “dashboard” of a limited set of indicators of well-being.6 He observed that “The challenge [is] to make the dashboard small enough to be easily comprehensible, but large enough to include a summary of what we care about the most.”7

[532] IS INEQUALITY MEASUREMENT AND ECONOMIC THEORY WELL-DEVELOPED TO ANSWER TODAY’S POLICY QUESTIONS? Minneapolis Federal Reserve Bank economists opined that “inequality is a complex and multidimensional subject.” Accounting for some aspect of inequality “probably still [is] beyond the limits both of existing theory and of the available computational technologies.”8

389

390 MACROECONOMICS RESHAPED

[533] SHOULD THE AGGREGATE DEMAND/AGGREGATE SUPPLY MODEL PARADIGM BE ABANDONED AND REPLACED BY A NEW PARADIGM? In Kate Raworth’s Doughout Economics,9 she challenges readers to change the macroeconomic goals that are widely embraced around the world by fiscal and monetary policymakers and to replace the circular flow concept (which is tied into aggregate demand equals aggregate supply) with what she calls an “embedded economy.” An embedded economic flow intertwines all parts of society (including social, environmental, income distribution, and more) with the economy.

[534] SHOULD A “HIGH-DEGREE OF ECONOMIC MOBILITY” REPLACE THE “LOW UNEMPLOYMENT RATE” AS AN ECONOMIC GOAL? Again, this is a value judgment. Harvard Prof. Raj Chetty argues that, according to some studies, the public prefers a high degree of economic mobility generally more than the desirability of a more equal income distribution. However, the ability to move up the economic ladder is a longer-run focus than the shorter-run goal of a low unemployment rate, so those two objectives could co-exist.

[535] HOW SHOULD ECONOMIC MOBILITY BE MEASURED? Paul Winfree opined that Economic mobility is a dynamic process. As people move from one rung of economic achievement to another, their earnings, income, and wealth do not necessarily hold constant. Thus, the three measures of economic mobility must be analyzed together to explain an individual’s economic condition … There is no comprehensive measure of economic inequality and mobility, but several metrics, used together, can give a more complete picture. Looking exclusively at income inequality or mobility is not enough. Income, earnings, and wealth all contribute to an accurate assessment of inequality and mobility in America.10

[536] FINALLY, IS THERE AN ECONOMIC APPROACH THAT CAN ENCOMPASS NEW OBJECTIVES BY BLENDING TRADITIONAL ECONOMIC GOALS WITH SOCIAL, ENVIRONMENTAL, AND OTHER PUBLICLY HELD POLICY AIMS? Yes. The original institutional approach argued exactly for a broader set of objectives and considerations to be taken into account in developing economic theory and empirical insights.

THE FUTURE OF MACROECONOMICS

Issues to Think About John Maynard Keynes famously opined:

The ideas of economists and political philosophers, both when they are right and when they are wrong, are more powerful than is commonly understood. indeed, the world is ruled by little else. Practical men, who believe themselves to be quite exempt from any intellectual influence, are usually the slaves of some defunct economist. Madmen in authority, who hear voices in the air, are distilling their frenzy from some academic scribbler of a few years back. I am sure that the power of vested interests is vastly exaggerated compared with the gradual encroachment of ideas. • • • •

Was Keynes correct? Does macroeconomic theory need a new foundation? If so, what should it include? Does macro policy need new objectives? If so, what should they be? Is it time for the original or “old” institutionalist approach to regain prominence in macroeconomics?

NOTES 1 Abhijit V. Banerjee and Esther Duflo, Good Economics for Hard Times (Public Affairs Books, 2019). 2 Joseph E. Stiglitz, Measuring What Counts: The Global Movement for Well-Being (The New Press [Kindle Edition], 2019), p. 12. 3 Rutger Hoekstra, Measuring the Wellbeing Economy: How to Go Beyond GDP (Wellbeing Economy Alliance, October 2020). Also, see: Rutger Hoekstra, Replacing GDP by 2030: Towards a Common Language for the Well-being and Sustainability Community (Cambridge, Cambridge University Press, 2019). 4 This global initiative has been led by the OECD’s High-Level Expert Group on the Measurement of Economic Performance and Social Progress, which most recently is co-chaired by Prof. Joseph E. Stiglitz of Columbia University, Prof. Jean-Paul Fitoussi at Sciences-Po (Paris) and LUISS University (Rome), and Martine Durand who is the chief statistician of the OECD. 5 See: https://www.oecd.org/wise/measuring-well-being-and-progress.htm. 6 The UK’s Office of National Statistics (ONS) has embraced this “Beyond GDP” initiative in 2017 and developed a prototype of such as an economic dashboard, but the coverage is a work-in-progress. See: https://www.ons.gov.uk/peoplepopulationandcommunity/personalandhouseholdfinances/incomeandwealth/bulletins/economicwellbeing/quarter1jantomar2017#economic-well-being-indicatorsat-a-glance. 7 Stiglitz, p. 12. 8 Santiago Budria Rodriguez, Javier Diaz-Gimenez, Vincenzo Quadrini, and Jose-Victor Rios-Rull, “Updated Facts on the U.S. Dristributions of Earnings, Income, and Wealth,” Quarterly Review (Federal Reserve Bank of Minneapolis, Summer 2002), pp. 2–35. 9 Kate Raworth, Doughnut Economics: Seven Ways to Think Like a 21st-Century Economist (Chelsea Green Publishing, 2018). 10 Paul Winfree, “Analyzing Economic Mobility: Measuring Inequality and Economic Mobility,” The Heritage Foundation, May 31, 2007, https://www.heritage.org/poverty-and-inequality/report/ analyzing-economic-mobility-measuring-inequality-and-economicmobility.

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Epilogue—Developing and Interpreting Macroeconomic Policy Choices The various schools of economic thought have their own perspective on how to address economic problems that largely follow from their assumptions, theories, and interpretation of the economic situation. This makes it difficult for the public or even the policymaker to know what the “best” path is to address an economic problem. It is not the intent of this book to say that there is one clear winner in the schools of economic thought for every economic problem—clearly there is not. More often than not, different theories and channels of economic influence simply stress one factor more than others as the economic catalyst of the problem and the remaining are “supporting factors.” Then the “solution” to the problem is tied to that presumed catalyst. Some economists recommend following conventional thinking (the “settled theory and practice”) and its policy recommendations; some recommend a more radical approach; some recommend a more laissez-faire approach and bet the economic system will correct itself. This book is about the economic tools, techniques, and thinking that define macroeconomics today, but it does not give you that singular pathway to an economic utopia, which simply does not exist. So how do you weigh the pros and cons of an economic theory and policy to decide on which is the most effective path to address a problem of concern? Some economic problems are unquestionably addressed in the simplest manner by macroeconomics—such as overall issues of unemployment, inflation, and economic growth. However, some economic issues are only tangentially addressed by macroeconomics—such as environmental issues, asset inflation, labor-market bargaining power, the minimum wage, income inequality, gender and racial wage gaps, and many other issues. Most economic problems need, and will benefit by, an original “institutionalist” and a “political economics” perspective—as John Neville Keynes opined. But, alas, those political institutionalist policy options are harder to develop than to simply apply some economic recommendation from an “academic scribbler” of the past. The road to successfully develop economic policy options necessarily begins by assembling the stories and facts about the economic situation. An integral part of this story-building process is to tease out of the economic statistics the stories they tell or do not tell. Moreover, just as the American institutionalists of long ago recognized a need to develop new economic measures to capture and evaluate the evolving performance of the economy, this need continues to exist today. New macroeconomic metrics can help provide new insights on economic problems and, hopefully, better guide today’s economic policy.

EPILOGUE

Once the stories and facts are assembled, it is important to clearly define the economic goals—what is the policy trying to accomplish? Do not be hamstrung by yesterday’s policy goals but be realistic and comprehensive. These stated objectives will be needed to evaluate and determine when and if the policy is successful. Answer the following questions to better flesh out a policy option: • • • • •

•

Is the economic problem a short-term or a long-term issue, or maybe a little of both? Is the problem best addressed by monetary or fiscal policy (or even an industrial policy), or both? Is the policy addressing a new problem or one that is repeated from the past? Are there existing policy tools to address the problem? If not, what is needed? Understand that a policy response needs to address the Congressional Budget Office’s concepts of “totals, timing, and targets.” How much money or magnitude change of a policy should be directed to the problem? How quickly will the spending or policy impact the situation? What are the targets for that spending or policy? What are the pros and cons of the policy?

Once answers to these questions are evaluated and a policy choice is made, then it is necessary to package the economic story of how and why the policy is needed to achieve the policymaker’s goal. It still does not mean that everyone will “buy into” the policy, but it will offer a thoughtful rationale why the policy is considered the most effective strategy. Also, be aware that even the most well-thought out economic policy still may have some “unintended consequences” since the economy is evolving and people may change their behavioral response to a given policy in some unexpected way. The financial markets’ reaction to announced economic policy proposals (through selloffs or asset buying) offers a quick test of how successful and compelling the “policy story” is. There are plenty of examples of this both positively and negatively. Witness, for example, the negative reaction in late 2022 to the former U.K. Chancellor of the Exchequer Kwasi Kwarteng’s “mini-budget” proposal for cutting the top income tax rate to 40% from 45%, bringing forward a planned cut in the basic rate of income tax from 20% to 19% to April 2023, eliminating the planned rise in the corporate tax, VAT-free shopping for overseas visitors to the United Kingdom, capping household energy bills at £2,500, and cancelling the United Kingdom’s planned health and social care tax levy. Kwarteng’s stated goal for these policies, which would have increased the U.K. deficit, was to “turn the vicious cycle of stagnation into a virtuous cycle of growth.” But the former Chancellor’s story and policy proposal was not well received and set into motion a devastating reaction for the U.K. currency, which required the Bank of England to step in and attempt to “repair” the damage. Or witness the stock market’s sharp drop when then President Donald Trump announced in December 2018 that the United States would impose tariffs on Chinese imports. The examples can go on and on, but these two cases highlight policymakers need to be mindful of a convincing and thoughtful economic story to go along with any proposed policy. In the final analysis, even if you are not a policymaker (and most of us are not), it is hoped that this book has given you the insight to evaluate those macroeconomic stories behind economic policies more critically, objectively, and purposefully.

393

394 EPILOGUE Lastly, John Maynard Keynes opined in his tribute to his teacher Alfred Marshall, which was published in The Economic Journal in September 1924: The master-economist must possess a rare combination of gifts. [One] must be mathematician, historian, statesman, philosopher—in some degree. [One] must understand symbols and speak in words. [One] must contemplate the particular in terms of the general and touch abstract and concrete in the same flight of thought. [One] must study the present in the light of the past for the purposes of the future. No part of [a person’s] nature or [one’s] institutions must lie entirely outside [one’s] regard. [One] must be purposeful and disinterested in a simultaneous mood; as aloof and incorruptible as an artist, yet sometimes as near to earth as a politician.1 It is hoped your journey through these pages whet your interest in macroeconomics and that you, too, achieve the status of a master-economist in knowledge, even if it is not your career.

NOTE 1 John Maynard Keynes, “Alfred Marshall: 1842–1924,” The Economic Journal, September 1924, pp. 321–322. Reprinted by permission of Oxford University Press on behalf of the President and Fellows of Harvard College.

Index

Abraham, Katharine G. 97, 97n15, 327n2 absolute cost advantage 311 absolute federal tax immunity 281 ACH (automated clearing house) 234–35 Adam, Stuart 364n10 AD/AS model 21, 180–82, 184, 186, 190–97, 199–201, 203–7, 210–12, 215–16, 218–21, 256 Adrian, Tobias 236, 245n19, 245 aggregate demand 6–8, 10–11, 13, 20–22, 26, 30, 68, 72, 173, 180–207, 209–11, 213–22, 253, 257, 267, 274, 314 aggregate demand curve 183, 185–86, 188, 190, 214, 216–17 aggregate income 4, 24, 26, 221–22 aggregate supply 8, 10–11, 13, 18, 20–21, 23–24, 30, 40, 99, 111, 130, 173, 180–207, 209, 211–21, 274, 314–15 Alchian, Armen 58 Allen, George 47 Allen, William R. 245n23 Almeida, Yasmin 244n12 Alston, Richard M. 384n36 Altig, David 383n23 Ambler, Steve 275n21 Anderson, Heather M. 163, 178n18, 179n22 Apergis, Nicholas 214n16 ARIMA 157, 178 Ash, Michael 354, 358n16 Ashcraft, Adam 236 asset price index 60, 215 asset-price inflation 58, 60, 62, 73 asset prices 3, 58, 60, 74, 260 ATMs (automated teller machines) 87 Austin, Andrew 305n17, 306

Austrian school 8, 11, 94, 123, 126, 129, 139, 217, 227–28, 230, 244, 259, 314 automated clearing house (ACH) 234–35 automatic stabilizers 175, 287, 304 availability of credit 222 average-inflation targeting 274 Baker, Scott R. 380–81, 384, 384n47 Balke, Nathan S. 339, 345n4 Banerjee, Abhijit 387, 391n1 bank: bridge 234; failed 234; shadow 235–36; traditional 236 banking system 223, 230, 234, 239–42, 246, 266 Bank of Japan (BOJ) 176, 266, 269–70, 274 bank reserves 115, 266, 294 Barattieri, Alessandro 66, 74n26 Barbaglia, Luca 384n47 Barbosa-Filho, Nelson H. 212n5 Barfield, Claude E. 317n21 Barhoumi, Karim 317n27 Barrero, Jose Maria 47, 383n23 Barro-Ricardo equivalence proposition 302 Barsky, Robert B. 129, 139n30, 157, 178n9, 178, 178n12, 275n11 basket of goods and services 49–50, 213 BEA (Bureau of Economic Analysis) 18–19, 22–24, 56, 64, 76–78, 81–82, 97, 106, 174, 329, 338–39, 388 Beaulieu, Joseph 178n11 Bernanke, Ben 139n30, 276n31, 300, 303, 306, 306n28, 340, 342, 343, 345n5 Bertaut, Carol 245n26 Besley, Timothy 364n10 Betz, Aaron 306n25

396 INDEX Beveridge full-employment ratio 44–47, 163, 178–79 big data 87, 97, 364, 381 Bitcoin 226, 238 Blanchard and Quah decompositions of output 215 Blinder, Alan S. 6, 16n4, 140n32 Bloom, Nicholas 47n1, 372, 380–81, 383n23, 384, 384n46 BLS (Bureau of Labor Statistics) 31–39, 41, 44–45, 47, 49–50, 54, 56–58, 60, 88, 93, 107–8, 144, 164, 174, 322–24, 333–34, 336 Blundell, Richard 364n10 Boddy, Francis 181 Boesky, Hayley 236 Bofinger, Peter 201–2, 217n32, 217 BOJ. See Bank of Japan Bond, Stephen 364n10 Bonifacio, Valentina 277n48 Bordalo, Pedro 75n37 Boskin, Michael J. 73n10 Boskin Commission 57, 64 Boulding, Kenneth 181 brackets, federal tax 63 Brady, Dorothy S. 73n8 Brady, Michael E. 318n29 Brady plan 352 Brainard, Lael 274n5 Brandao-Marques, Luis 277n48 Bretton Woods Agreement 242 Brinca, Pedro 217n36 British tax system 363 Bronfenbrenner, Martin 129, 139n29 Brunner, Karl 7 Bryan, Michael F. 60, 66, 74n24 Brynjolfsson, Erik 91, 98n17 Budina, Nina 277n48 Buffett ratio 62 Bull, Clive 218n41 Bureau of Economic Analysis. See BEA Bureau of Justice Statistics 325, 327 Bureau of Labor Statistics. See BLS Burgherr, David 305n5 Burns, Andrew 218n45 Burns, Arthur F. 4, 16n1, 100–101, 107–8, 119, 127, 138, 139n22 business cycles 4, 7–10, 16, 37, 40–41, 99–105, 107–19, 123–39, 153, 157–58, 163–64, 167–68, 173, 177–79, 181, 334–36, 342, 345, 382–83;

classical 100–101, 107–9; classical NBER/Wesley Mitchell concept 163; history 114–17, 173; theories 8, 126, 366 Campos, Rodrigo 358n6 Canova, Fabio 163, 179n23 capital-to-labor ratio 89 Carlin, Wendy 213n5, 256, 275n19 Carney, Mark 224–26, 244n2, 244, 271, 276n44 Carson, Michael 358n9 Carter, Joseph R. 97n2, 364n14 CBDC (Central Bank Digital Currency) 237–38, 271 CBO (Congressional Budget Office) 43–44, 80, 131, 133, 182–83, 200, 211, 213, 263, 273, 277, 282–85, 287–88, 290, 299–302, 305–6 CBO Rules-of-Thumb 288 CBO’s Policy Growth Model 306 C-CPIU 63 Cecchetti, Stephen G. 60 Cerra, Valerie 139n18 certainty equivalent 369 Cette, Gilbert 97n15 Chalk, Nigel 306n29 Chamberlain, Emma 305n5 Chapman, Daniel 327n7 Chetty, Raj 281, 305n7, 361, 364, 364n4, 390 Chicago Plan 245 Chote, Robert 364n10 circular flow: accounting concept 78; national income and product accounting 30 Clarida, Richard H. 249, 252, 256, 275n17 Clark, John 358n9 classical school theory 17 Cochrane, John H. 70, 75, 75n35, 216n31, 216, 304, 306n31 Coeuré, Benoît 317n4, 317 coincident indicators 107, 117–18 Colander, David 216n32, 216, 316n3, 317n6 command-basis GDP 81–82 commercial banks 116, 230–32, 239, 242, 263–64, 266, 271 Commercial Mortgage-Backed Securities (CMBS) 116 Congressional Budget Office. See CBO consumer commodity inflation 332 consumer price index. See CPI consumer service inflation 332

INDEX control, yield-curve 266, 270 Cooke, John 276n40 Copestake, James 137, 140n38 COVID-19 46, 111, 139, 198, 203, 215, 217, 276, 317 COVID-19-induced recession 117 Cowen, Tyler 139n21 Coy, Peter 241, 276n37 CPI (consumer price index) 49, 56–59, 61, 63–65, 67, 71, 73–74, 84, 98, 143–45, 168, 210, 248, 332; chain-weighted 248; fixed-weighted 248; trimmed-mean 65 CPS (current population survey) 31–32, 36, 45 Crafts, Nicholas 97n13 credit conditions 252 credit contraction 128 credit crunch 127–28 credit expansion 128 credit risk 233 crises 16, 100, 128, 139, 207, 217, 239, 317, 352, 357, 387 Croushore, Dean D. 276n32 Cuadrado-Roura, Juan R. 335, 336n17 Curcuru, Stephanie 245n26 currency crisis 100, 245, 352 currency intervention 312 current population survey. See CPS cycle chronology 119 cycle comparisons 119 cycle fluctuations 102 cycle of business cycles 127 cycles 99–104, 110–14, 117–19, 125, 127, 129, 138, 141, 152–53, 157–58, 163, 166–67, 169–73, 177, 179, 223, 285; boom-bust 114; economic 108–9, 126, 138–39, 345; step 109; stock market 109 cycle sequences 110 Davis, Steven J. 47n1, 372, 380–81, 383n23, 384n46 debt 6, 8, 11, 16, 128, 174, 224, 226, 271–72, 276, 280, 291, 293–94, 296, 298–300, 302, 304–6, 346–48, 351–58; national 174, 278, 294–95, 304 debt ceiling 296, 298–99, 305–6 debt limit 298–99, 305–6 debt monetization 294 debt waves 351–52 decomposition: multiplicative 177; trend-cycle 163 deficits, twin 29–30, 301

DeLong, James Bradford 216n31, 216, 341, 345, 345n8 demand: effective 215; expected 217 demand deposits 228–29, 240, 242 demand-pull inflation 125, 194–95, 313–14; increasing 125 demand-shock dominated cycles 111–14 demand shocks, aggregate 199–200 Denison, Edward F. 73n8 Diamond, Peter 361, 364, 364n3 Diaz-Gimenez, Javier 391n8 Diderot, Denis 131 Diderot effect 131–32 digital currency 223, 237–38, 244, 246, 271 digital yuan 238 dollarization: formal or full 243; informal 243 Dolmas, Jim 74n21 Dominitz, Jeff 370–71, 383n17 Dotsey, Michael 317n16 Doughnut Economics 391n9 Douglas, Paul 241 DS curve 201 DSGE (dynamic stochastic general equilibrium) 309–11, 317 Duarte, Joao B. 217n36 Duflo, Esther 387, 391n1 Dulberger, Ellen R. 73n10 Dutta, Amitava Krishna 181, 212n1 Dybczak, Kamil 218n45 ease, quantitative 255, 267, 269–70, 272 Eberstadt, Nicholas 325, 327, 327n3 Echevarria, Cristina 330, 335n4 economic goals 11, 79, 96, 150, 390, 393 economic growth, long-term 132–33, 135–36, 355 economic mobility 99, 137, 140, 309, 390–91 economic policies 307–12, 317, 392–93 economic uncertainty 239, 340, 365, 370–74, 376–78, 381 economic volatility 330, 335, 337, 339, 344 economic well-being 175, 389 EDA (Exploratory Data Analysis) 141–42, 177 effective-lower-bound (ELB) 262 elasticity 188–89, 212–15, 220–21, 361, 364 elasticity of aggregate demand 188 elasticity of aggregate supply 190 elasticity of labor supply 360–61, 364

397

398 INDEX ELB (effective-lower-bound) 262 Eldridge, Lucy P. 334, 336 Eleftheriou, Sophia 214n16, 214 El-Ganainy, Asmaa 305n1 Engelhardt, Lucas M. 203, 217n38 engines for growth 91 Erceg, Christopher J. 327n5 Evans, Michael K. 284, 305n12 Exchange Stabilization Fund (ESF) 298 excise taxes 115–16, 280–81 expected rate of inflation 247 expenditure multiplier 284 ex post and ex ante observations 26, 183–86, 188–90, 202, 204–6, 210, 213, 215–16, 219–21, 301 external debt 346, 348–52 factor productivity, total 91, 140 FDIC (Federal Deposit Insurance Corporation) 234 federal budget deficit/surplus 303 federal budget process 282 federal debt 291–95, 298, 300–301, 303–5 federal deficits 8, 278, 292, 299, 301, 314; primary 292 Federal Deposit Insurance Corporation (FDIC) 234 federal funds rate 13, 115, 209–10, 250–53, 260–62, 265–66, 274 Federal Open Market Committee. See FOMC Federal Price Statistics Review Committee 57 Federal Reserve Act 247 Federal Reserve Board 4, 12, 107, 127, 218, 238–39, 244–45, 252, 264, 266, 342, 345, 347–50, 383 Federal Reserve Board Chairman Alan Greenspan 72, 93, 248 Federal Reserve Board Chairman Jerome Powell 243, 248 Federal Reserve Board of Governors 60, 108, 174, 228–30, 244–45, 258, 264, 268, 274 Federal Reserve’s policy goals 246–47 Federal Reserve’s policy interest rate 210 Feldstein, Martin 263 Fellner, William 107 Fernald, John 97n12 filter: binominal 165; Hodrick-Prescott 178 filtering data 141, 157 financial crises 13–14, 100, 127–28, 138, 203, 207, 234, 236, 242, 244, 263, 276, 308, 351–52, 358 financial-crisis induced recession 338

Financial Crisis Inquiry Commission 207, 233, 245 financial effects of inflation 72 financial fragility 128 financial markets 10, 47, 71, 116–17, 139, 198, 242, 252, 301, 369, 393 financial system 7, 117, 207, 223, 229, 232, 236, 244, 263, 383 Fine, Lauren K. 383n13, 383 fiscal multipliers 283–85 fiscal policy tools 12 fiscal theory of inflation 48, 68, 70, 355 Fischer, Stanley 231 Fisher, Irving 50, 52, 56, 58, 70, 73n1, 73, 242, 245n23, 257 Fisher price index 49–50, 52, 56, 73 Fleurbaey, Marc 316n2 Flexible-Price CPI 66 flowchart, circular 18–19, 26–28, 30, 79, 252, 390 fluctuations 68, 101, 123, 126, 131, 153, 342, 367; aggregate 158; cyclical 126, 139, 173 Foldvary, Fred E. 126, 139n20 FOMC (Federal Open Market Committee) 247–48, 260, 264, 266, 270, 273–74 fractional reserves 240 framework, foundational IS-LM 217 Fratto, Chiara 277n48 FRED database 138 French, Eric 364n11 frictional unemployment 40–43, 47 Friedman, Benjamin M. 75n39, 218n41 Friedman, Milton 7, 70, 109, 179n22, 227, 231, 259, 262, 316, 318, 318n29 Fullwiler, Scott 8 Gagnon, Joseph 276n41 Gali, Jordi 275n19 Gamber, Edward 306n26 Gammie, Malcolm 364n10 gap, services-commodity price 333 gap theories 68, 96, 133, 181, 205, 389 Gbohoui, William 384n48 GDI (gross domestic income) 18–21, 24–26, 76, 78–79, 82, 106, 138, 181–82, 198, 202, 214 GDP (gross domestic product) 18–23, 26–28, 74, 76–86, 96–97, 159, 174, 177, 182, 188–90, 258–59, 263, 300, 318, 329–31, 339, 343, 348–51, 353–54, 388, 391

INDEX GDP price indexes 80, 181, 200, 214–15 Gennaioli, Nicola 75n37 global debt 350–51, 353, 358 GNP 77–78, 97, 178, 339 Golob, John E. 373, 383n27 Gómez-Loscos, Ana 345n2 Goodhart’s Law 263, 276 Gordon, Robert J. 73n10, 338, 339, 345 Gottschalk, Peter 66, 74n26 Goutsmedt, Aurélien 310, 317n12 Gravelle, Jane G. 16n3 great moderation period 151, 337–38, 340–43, 345 Greenspan, Alan 72, 75n40, 94, 98n19 Greszler, Rachel 326n1 Griliches, Zvi 73n10 gross domestic income (GDI). See GDI gross domestic product. See GDP gross output 77, 97 growth accounting 88–89, 97 growth cycle chronology 110 Grusky, David B. 140n39 Guerrieri, Veronica 203, 217n37 guidance, forward 266 Guisinger, Amy Y. 212n5 Habermeier, Karl 244n12 Haksar, Vikram 244n12 Hall, Robert 262 Hallam, Arne 384n33 Hamadeh, Nada 98n21 Hamilton, Alexander 244, 246–47, 312 Hamilton, James D. 163, 178n19, 178 Hamilton, Walton 8 Haskins, Ron 140n40 Hawley, Ellis W. 317n21 Hayek, Friedrich 217n38, 230–31, 244 HCI (Human Capital Index) 86, 97 He, Dong 244n12 Heberling, Michael E. 97n2 Hemel, Daniel 362–63, 364n9, 364 Herndon, Thomas 354, 358n16 Hickman, Bert G. 107, 138n7, 332, 335n10 Hicks, Sir John 6, 68, 100, 125, 186, 212–13, 213n11, 216n31 Hoagland, John H. 97n2 Hodgson, Geoffrey M. 9–10, 16, 16n12 Hodrick-Prescott Filter 163, 178

Hoenig, Thomas 271 Hofstede, Gert 382n6, 382 Holston, Kathryn 251, 275n11, 275 Hood, Robin 272 Human Capital Index (HCI) 86, 97 Human Development Index and median incomes 132 Humphrey, Thomas M. 68, 75n32 hypothesis: business-cycle 128; financial instability 139; life-cycle 370 hysteresis 99, 130, 133, 135 Iain Macleod 129 IFC (Irving Fisher Committee) 357 Igan, Deniz 277n48 IMF (International Monetary Fund) 78, 139, 175, 178, 226, 238, 242–45, 273, 277, 289, 295, 303, 305–6, 315, 317, 348, 350–53, 357–58, 380–81, 384 implicit price deflator 20–21 index, chained 22, 53–54 index numbers 49, 73 industrial policy 307, 312–13, 317, 393 industrial production diffusion index 113 inflation 5, 7–8, 10, 13–14, 39–40, 42, 45–46, 48–75, 78–80, 111, 115–16, 129–30, 135, 150–51, 180–82, 194–95, 203–6, 210–14, 227, 231, 245–49, 251–53, 256–57, 260, 262, 271, 274–78, 287–88, 313–15, 332, 340–42; actual 260; acute 231; aggregate core 334; aspirational 249; asset 5, 58, 259, 392; below-target 248; cause 114; conquering 7; cost-push 194–95, 314; defined core 64; stable 63, 183–84, 249–50; unanticipated 64, 67 inflation and unemployment 14, 130 inflation expectations 48, 64, 69, 71–73, 115, 204, 333; market-inferred 71, 208 inflation gap 262; service-goods 333 inflation rate 42, 46–49, 65, 72, 86, 195, 204–5, 218, 247, 251, 256, 287, 304 inflation target 63, 248 inflation theories 68 inflation volatility 337, 342–44 innovations 9, 87–88, 94, 136, 238 institutional economics 9–10, 16, 382 institutionalists 9, 124, 392 interest rates, long-run equilibrium 250

399

400 INDEX Internal Revenue Service. See IRS International Monetary Fund. See IMF Ionuț Jianu 215n27 irregular fluctuations 152, 158, 177 IRS (Internal Revenue Service) 63, 174, 279, 361 Irving Fisher Committee (IFC) 357 IS-LM model 10, 181, 186, 201, 213–14 Iyer, Tara 317n27 Jacobs, Elisabeth 327n6 Jacquet, Pierre 317n4, 317 J-curve 91 Jefferson, Thomas 225, 247 Jevons, William Stanley 99–100, 138, 389 Johnson, Paul 364n10 Jonung, Lars 383n29 Jorgenson, Dale 73n10 Katona, George 67, 366, 373–74, 379, 382, 383n21, 383, 384n31 Katona Effect, empirical 257 Kearney, Melissa S. 327n2 Keating, John W. 215n24, 215 Kehoe, Patrick J. 309, 317n10 Kelton, Stephanie 8 Keynes, John Maynard 3–4, 6, 14, 16–17, 40–42, 47, 64, 66, 68, 99–101, 139, 142, 180, 213, 215, 307–9, 366–69, 382–83, 391, 394 Keynes, John Neville 307–8, 316n1, 317, 392 Keynes’ General Theory 40, 313, 382–83 Keynesian macroeconomics 6–7, 16, 68, 213, 302, 317 Keynesian school 6, 187, 202, 214, 216–17 Keynesian SRAS, stylized L-shaped 187 Keynesian supply shock 203 Kiku, Dana 74n19 Kiley, Michael T. 275n11 Klein, Benjamin 58 Klein, Lawrence 6, 213n16 Klein, Philip A. 138n8, 139, 345n1 Knell, Mark 139n23, 139 Knight, Frank H. 241, 366–68, 382n4 Knoop, Todd A. 14, 16n15 Koehler, Anne 178n18 Kose, Ayhan 358n2 Krist, William 312, 317n18 Kroft, Kory 281, 305n7

Krueger, Alan B. 327n2 Kumhof, Michael 242, 245n24 Kutscher, Ronald E. 333, 336n13 Kuznets, Simon 329–30, 335n2 Kydland, Finn E. 310, 317, 317n15 labor demand 5, 9, 16, 31–32, 35–37, 40–41, 45–46, 175–76, 183, 197, 247, 249, 310, 321, 325–26 labor force: growth 287; participation 325 Laffer curve 359–61, 364 laissez-faire monetary regimes 231 large-scale-asset purchases. See LSAPs Laspeyres price index 49–52, 56, 73 Laubach, Thomas 251, 275, 275n11, 275 Leamer, Edward E. 14, 16n16 Leckow, Ross 244n12 Lee, Don 47n3 Leeper, Eric M. 70, 75, 75n35 legislative lag 303 lender of last resort 263 Levin, Andrew T. 327n5 life-cycle hypothesis 74 Lincicome, Scott 313, 317n23 Linder, Henry 332–33, 335, 336n15 liquidity 10–11, 13, 116–17, 124, 185, 233, 239, 242, 247; crisis 234; defined 229; risk 232–33; trap 214 LM curves 185–86, 201–2, 213 Lobel, Matthew 326n1 logarithms 143, 376 Long-Run Aggregate Supply. See LRAS Long-Term Total-Factor Productivity 90 Looney, Adam 281, 305n7 Lord Beveridge 47n5 LRAS (Long-Run Aggregate Supply) 181, 183–84, 188, 191–96, 200, 202–3, 210, 219–20, 249 LSAPs (large-scale-asset purchases) 13, 246, 255, 262, 266–67, 269, 276 L-shaped curve 187 Lubik, Thomas A. 74n31 Lucas, Robert 7, 203–4, 217n40, 218, 310, 317n11 Lucas Supply Function 203, 218 Ludvigson, Sydney C. 378, 384n41 Macaulay, Frederick R. 178n7 Macleod, Iain 129 macroeconomic: goals 5–6, 15, 337, 390; policies 182, 307–17; theories 6, 15, 21, 31, 47, 69, 72,

INDEX 87–88, 123, 292, 307, 309, 316, 365, 368, 387, 391; uncertainty 365, 367–83 Maggs, Gary E. 213n16, 213–14 Mahoney, Maureen 281, 305n8 Mallatt, Justine 97n15 Malthus, Robert 6 Mankiw, Gregory 218n41, 262, 362, 364, 364n8 Manski, Charles F. 370–71, 383n17 Mark, Jerome A. 336n13 Marshall, Alfred 14–16, 197, 223 Marshall’s dictum 17 Martine Durand 391 Matoba, Kyle 97n12 MBS. See mortgage-backed securities McCallum, Bennett 263 McCallum rules 263, 276 McCarthy, Philip J. 73n8 McCulley, Paul A. 233, 245n15 McNees, Steven K. 369, 372, 383n13, 383 Meade Committee 363 median CPI 65, 332 medium of exchange 224 Melosi, Leonardo 275n11 Meltzer, Allan 7 Menger, Carl 8 Mertens, Thomas M. 274n6 methods, moving-average seasonal-adjustment 178 Metreau, Eric 98n21 Meyer, Brent H. 66, 74n24, 383n23 Mill, John Stuart 6, 308 Miller, Helen 305n5 Minsky, Hyman P. 128, 139n23, 139 Minton, Todd D. 327n4 Mintz, Ilse 108–9, 138n10 Miron, Jeffrey A. 157, 178n9, 178 Mirrlees, James 363, 364n10 Mishkin, Frederic S. 5, 16n2, 218n42, 218, 247, 274n3 Mitchell, Wesley 100–101, 119, 124–25, 127, 129, 141, 163, 223, 365 Mitchell, William 8, 16n6 Mitchell’s price-cost-profit framework 129 Mitnik, Pablo A. 140n39 MMT (Modern Monetary Theory) 8, 11, 16, 356 mobility 390 Modern Monetary Theory. See MMT Modigliani, Franco 6

Monetarism 7, 68, 258, 276, 314 monetarization 243 monetary base (MB) 230, 263, 265 monetary rule 212–13 money demand 185 money market funds 229, 231, 233, 236 money supply 7, 41, 68, 174, 185–86, 203, 223, 226–30, 240–42, 244, 257, 259, 262–63, 294, 314; and economic activity 227 Moore, Geoffrey H. 334 Moro, Alessio 330, 335n6 mortgage-backed securities (MBS) 116, 267, 270 Mosler, Warren 8 moving-average seasonal-adjustment 178 multiplier 284 Musgrave, Richard A. 364n14 Nagle, Peter 358n2 NAIRU (Non-Accelerating Inflation Rate of Unemployment) 31, 41–42, 47, 135, 184 National Federation of Independent Business 367 National Income and Product Accounts. See NIPA natural rate of interest 68, 74–75, 251, 275 natural rate of unemployment 31, 41, 43–45, 47, 131, 211, 287 NBER (National Bureau of Economic Research) 88, 101–4, 106–8, 110, 117–18, 138–41, 151, 200, 316–17, 335, 339, 342, 365, 369; chronology 102; reference cycle 119 NBFI (non-bank financial intermediation) 223, 231, 235–37 NBFI firms 235–37 Nelson decomposition 163, 178–79 neo-classical zone 195 neo-mercantilism 311, 316 neutral rate, long-term 249 neutral rate of interest 250–51 New Classical School/New Monetary Consensus 314 New Keynesian Model (NKM) 212–13, 256, 275 New Keynesian 3-equation Model 275 Newport, Frank 370–71, 383n18 Ng, Serena 378, 384n41 NIPA (National Income and Product Accounts) 18–19, 21, 25, 27–30, 36, 50, 53, 76, 78, 83, 94–96, 183, 303, 328, 335 NKM. See New Keynesian Model Noeth, Bryan J. 237, 245n18

401

402 INDEX Non-Accelerating Inflation Rate of Unemployment. See NAIRU non-bank financial intermediation. See NBFI North, Douglass C. 16 nowcasting 315, 318 Occam’s razor 173 O’Donovan, Nick 305n5 OECD (Organisation for Economic Co-operation and Development) 78, 97, 109, 132, 175, 321–22, 349, 351, 354, 388–89, 391 off-budget 290–93 Office of Budget Responsibility (OBR) 211, 215, 290 Office of Management and Budget 291, 305 Okun, Arthur M. 67, 74n27, 181–82, 212n4, 213 Okun’s law-type relationship 182 Omnibus Budget Reconciliation Act 283 on-budget 290–91, 293 OPEC (Organization of Petroleum Exporting Countries) 115 OPEC oil embargo and surging oil prices 129 open market operations 13, 230, 265–66 optimal taxation theory 363 optimism, economic 125 Ord, Keith 178n18 Organisation for Economic Co-operation and Development. See OECD output gap 69, 86, 108, 182, 193–95, 205–6, 211, 215, 221, 250, 256, 261 output of DSGE models 310 output volatility 337–43 overnight reverse repurchase agreement facility 13 Owyang, Michael T. 212n5 Paasche price index 49–53, 73 Palley, Thomas I. 16n6 Papageorgiou, Chris 16n4 Parker, Jeffrey 204 Parker, Nicholas 383n23 PCE. See Personal Consumption Expenditure PCE price index 56, 64, 67, 252, 260 Peach, Richard 332–33, 335n8, 336n15 Pennings, Steven 140n35 Perez-Arce, Francisco 327n2 Pérez-Quirós, Gabriel 345n2 Perrelli, Roberto 358n4

Perret, Sarah 280, 305n3, 305 Personal Consumption Expenditure (PCE) 22, 56, 63, 65, 134, 248, 252, 274, 341, 343 Persons, Warren M. 151, 177n4 Pescatori, Andrea 345n7 Philippon, Thomas 140n36, 140 Phillips, Ronnie J. 245n22 Phillips curve 10, 48, 68–69, 75, 130, 182, 195, 212, 256, 274, 345 Pigou’s writings 14–17, 126, 185, 365, 382 Piketty, Thomas 364n2 PIMCO 233, 235 Pinzón-Fuchs, Erich 317n12 Pisani-Ferry, Jean 317n4, 317 Plosser, Charles 163, 249, 275n9 Podkaminer, Leon 216n31 Poirson, Hélène 277n48 political economy 6, 69, 75, 178, 181, 212, 316–17, 345, 383 Pollin, Robert 354, 358n16 Ponzi finance 128 population 7, 36, 38–39, 43, 99, 151, 162, 174–77, 273, 321, 326; prime-working age 323; working-age 321 Poterba, James 364 Pozsar, Zoltan 236 PPP. See purchasing power parity Prescott, Edward C. 126, 139n16, 163, 178n19, 310, 317n14 Price, Jennifer 334, 336n12 price and wage controls 12 price level 4, 7, 48–49, 70–71, 75, 81, 180, 183–91, 194, 196, 199–200, 202, 204–5, 208, 210, 213–14, 217, 219–21, 227, 304, 306 prices: commodity 249; domestic 253; export 174; flexible 66; foreign 84; import 333; industrial 333; inflexible 217; international 253 Price Statistics Review Committee 73 price volatility 233 private-sector debt 351, 355 productivity growth, total factor 91 profits 4, 25, 124–25, 174, 186, 198, 215, 233, 239, 257, 271, 363, 366, 368, 382 purchasing power parity (PPP) 83–85, 95 QE programs 269–70 Quadrini, Vincenzo 391n8

INDEX Quah decompositions of output 215 quantitative easing (QE) 13, 255, 267, 269–70, 272, 276 quantitative tightening 117, 267 quantity of output 257 quantity theory of credit 259–60, 275–76 quantity theory of money 7, 68, 70, 257, 259, 274 Ramsey, Frank 362, 364n5 Raskin, Matthew 276n41 rate of interest on reserve balances 266 Rawlinson, Paul 312, 317n20 Raworth, Kate 391n9 Real GDP, potential 80, 135, 182–83, 192, 194 real GDP and real GDI 106, 184 real potential GDP 86, 131, 135–36, 181–82, 192–93, 200, 204, 208, 217 Rebelo, Sergio 126, 139n15 Rees, Albert 73n8 Reich, Robert B. 313, 317n24 Reinhart, Carmen M. 354–55, 358, 358n18 Reinhart and Rogoff study 354–56 REITs (real estate investment trusts) 115, 236 Remache, Julie 276n41 Renault, Matthieu 317n12 Ricardian equivalence theory 302 Ricardo, David 6, 302, 311–12 Rich, Robert 335n8, 336 Richter, Alexander W. 327n7 Roberts, John M. 250–51, 275n12 Robinson, James A. 312, 317, 317n25 Robinson, Joan 16n13, 313 Rock, Daniel 91, 98n17 Rocket Mortgage 237 Rogoff, Kenneth S. 139n30, 345n1, 351, 354–56, 358, 358n18 Roitman, Agustin 384n44 Romer, Christina D. 339, 345, 345n10 Romer, David 213n14 Roosevelt administration 242 Rothbard, Murray N. 228, 244n9 r-star 251 Rudd, Jeremy B. 140n32 Rudebusch, Glenn D. 275n18, 310, 317n13, 342 Ruggles, Richard 73n8 Rutherford, Malcolm 16n5, 9, 16, 16n11, 382n6, 382

Saad, Lydia 370–71, 383n18 Saaty, Thomas L. 138n3 Sachs, Jeffrey D. 138n2 Sack, Brian 276n41 Saez, Emmanuel 305n5, 361, 364, 364n3 Salmon, Jack 358n17 Salvatori, Chiara 213n6 Samuelson, Paul Anthony 6, 17n21, 17, 107 Sargent, Thomas J. 7, 75n35 savings 4, 27–30, 36, 67, 74, 96–97, 127, 137, 174, 185, 198, 228, 231, 258, 272–74, 301 Sawhill, Isabel V. 140n40, 140 Schambra, William A. 317n21 Schultz, Henry 241 Schumpeter, Joseph A. 94, 136, 139, 140n37 Schwartz, Anna 109, 231 seasonal adjustment 141, 152–53, 156–58, 164, 173, 177–78, 266 Second Liberty Bond Act 296 segments, business-cycle 11, 101, 167–68, 273, 326, 337, 339 Seliski, John 306n26 Sengupta, Rajdeep 237, 245n18 Sergi, Francesco 317n12 Shackleton, Robert 213n7, 306n25 shadow banking 235–37, 245 Shaliastovich, Ivan 74n19 Shapiro, Adam 67 Shell, Hannah G. 212n5 Shiller, Robert J. 14, 16n17, 61–62, 74n15, 201, 215, 215n30, 217, 217n33, 383n14, 383 Shiskin, Julius 41, 102, 138n5 shocks 111, 126, 130, 135, 139, 167, 198–200, 203, 216–17, 221, 315–16, 343; demand-induced 111; oil price 126, 198 Shoesmith, Edward 377, 384n35 Short-Run Aggregate Supply. See SRAS short-run determinants of inflation 73 short-term interest rates 249–50, 255, 260, 265, 270, 274 Shostak, Frank 244n8 Silicon Valley Bank 234 Simons, Henry C. 241 Simpson, Sean 384n44 Sims, Christopher A. 70, 75, 75n39 Sir John Hicks 6, 68, 100, 125, 186, 212–13, 213n11, 216n31

403

404 INDEX Smith, Adam 6, 278–79, 311 SMSAR (Six-Month Smoothed Annualized Rate) 146–47 SNA (System of National Accounts) 78, 82, 97, 349 Snyder, Ralph 178n18 Sobczak, Karolina 256, 275n20 soft landing 110 Solow, Robert M. 6, 89, 97n11, 136 Solow-Swan growth model 140 Soskice, David 213n5, 275n19 SRAS (Short-Run Aggregate Supply) 20–21, 26, 181–84, 186–88, 190–204, 210–11, 214–17, 219–22 SRAS price elasticity 214, 219 stabilization policy 11, 181 stage of development 96 stagflation: cause of 130; periods 129 Stantcheva, Stefanie 364n2 Stark, Tom 276n32 Stetsenko, Natalia 244n12 Sticky Price Consumer Price Index 66, 74 Stigler, George J. 57, 73n8 Stigler committee 57 Stiglitz, Joseph E. 309, 316–18, 362, 364n7, 388–89, 391n2, 391, 391n7 Stimson, James A. 377, 384, 384n40 Stock, James H. 263, 345n1, 345 Summary of Economic Projections (SEP) 252 Summers, Andy 305n5 Summers, Lawrence H. 345n8 Summers, Peter M. 345n6 Swedish Riksbank 247 Swerling, Boris C. 73n8 System of National Accounts. See SNA Syverson, Chad 91, 98n17 T-account 19, 240 target rate of inflation 260 tariffs 117, 198, 312–13, 393 taxation 25, 115, 278–79, 281, 360–64 Tax Foundation 279, 305, 363 tax rates 12, 64, 198–99, 359–63 tax salience 305 Taylor, John B. 260–62, 276n27, 276, 342, 345n11, 345 Taylor curve 337, 342–44 Taylor rule 8, 186, 213, 260–63, 276

technology shocks 126 telework 35–36 Tenreyro, Silvana 14, 16n14 theory of government deficits and inflation 278 Thornton, Daniel L. 294, 305n14 TIC (Treasury International Capital) 174, 297 Tilley, Paul 364n14 time series 151–53, 158, 163, 166–67, 173, 177, 179 tipping point 346, 354, 356 Tissot, Bruno 357n1 Tobin, James 6, 61, 269 Törnqvist price index 49–50 Tosetti, Elisa 384n47 total-factor productivity (TFP) 88–90, 92, 96–97 TQSAR growth rates 146 trade cycle 100 trade deficit 29, 301 TRAMO 178 TRAMO-SEATS 157 treasury yield curve 254–55 Trimmed-Mean Consumer Price Index 74 Trimmed Mean PCE Inflation Rate 65, 74 Trung Bui 218n45 Tukey, John W. 141–42, 177n1 turning point dates 102, 106, 108, 117, 119, 145–46, 167 turning points 100, 105–8, 110, 118–19, 145, 167–72, 223; cyclical 100, 104, 106; lower 118, 125 Tüzemen, Didem 325 Ulybina, Daria 384n44 uncertainty 72, 116–17, 128, 140, 243, 315, 365–83; economic policy 380 unemployment 5, 7, 10, 14, 17, 31, 33, 35–47, 129–31, 133, 174, 182, 211, 244, 249, 309, 314, 326; actual 41–42; cyclical 41–42; fullemployment 211; gap 43, 333–34; involuntary 40, 42; non-accelerating inflation rate of 31, 41, 47, 184; non-cyclical rate of 41, 43, 47, 131; structural 42–43, 47; theoretical components of 42; voluntary 40, 42 unemployment rate, actual 5, 10, 31–32, 36–38, 41–47, 68–69, 86, 104, 107, 118, 127, 129, 135, 142, 175, 177, 195–96, 208, 210–11, 251–52, 287 unemployment rate gap 45–47 union membership 33

INDEX United Kingdom Office of Budget Responsibility 290 unit of account 224 Upbeat or Optimistic Mood 377 Varela, Peter 281, 305n6 Vaughan, Michael B. 128, 139n25, 384n36 velocity 7, 257–59, 263; M2 258 Verdugo-Yepes, Concepcion 244n12 Vermandel, Gauthier 275n20 volatility 55, 61, 74, 118, 141, 151, 166, 330–31, 334–35, 337–39, 341–43, 345, 373–74; changing 330, 337, 339, 343; price-level 67 wage rigidity 256 wages 7, 10, 25, 32, 36, 40, 49, 54–55, 66, 69–70, 115, 174, 182, 197, 249, 257–58, 272–73; sticky 66–67, 74, 204, 256 Wallace, Neil 7 Warren, Elizabeth 162, 281, 305n5 Watkins, William R. 305n10 Watson, Mark 163, 345, 345n1 Watts, Martin 16n6 waves of optimism and pessimism 365 wealth 32, 63, 74, 97, 137, 216, 221, 272–73, 280–81, 305, 312, 359, 364, 390–91 wealth effect 63, 185 Wealth of Nations 311 wealth tax, main reasons 280 well-being 55, 207, 388–89, 391 Wellbeing Economy Alliance 388–89, 391

Werner, Richard A. 259, 267, 275n24, 275–76, 276n39, 276n40, 276 Wesley Mitchell’s research 124 Whalen, Charles J. 284 Wicksell, Knut 68, 74n30, 249, 275n10 Wicksell’s interest-rate gap model of inflation 68 Williams, John C. 251, 275n11, 275 William Stanley Jevons 99–100, 138, 389 Winfree, Paul 390, 391n10 Woltjer, Pieter 88–89, 97n13 Woodford, Michael 75n39, 218n41, 275n19, 276 Wooldridge, Adrian 94, 98n19 work, remote 35, 125 worker anxiety 371–72, 383 workers: agricultural 32; discouraged 41 World Bank 74, 84–86, 98, 132–33, 140, 176, 211, 235, 330, 348, 350–53, 357 Wray, Randall 8, 16n6, 16 X-11 method 157 X-13 ARIMA-SEATS 157 Yagan, Danny 140n34 Yaron, Amir 74n19 yield curve 254–55 yield-curve control strategies 270, 274 Zarnowitz Victor 100, 138n1, 145, 200, 369, 383n11 Zhen Zeng 63, 74n18, 327n4 Zucman, Gabriel 305n5 Zvi Griliches 73n10

405