Why Fiscal Stimulus Programs Fail, Volume 1: The Limits of Accommodative Monetary Policy in Practice [1 ed.] 3030656748, 9783030656744

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Why Fiscal Stimulus Programs Fail, Volume 1: The Limits of Accommodative Monetary Policy in Practice [1 ed.]
 3030656748, 9783030656744

Table of contents :
Preface
Contents
List of Figures
List of Tables
Part I Introductory Chapters
1 Introduction
1.1 The Crowd Out Problem and Accommodative Monetary Policy
1.2 Individual Chapter Contents and Findings
1.3 Summary of Key Findings
References
2 Literature Review
2.1 Summary of Findings
2.1.1 Stocks and Bonds
2.1.2 GDP
2.1.3 Inequality
2.2 Detailed Findings
2.2.1 Assessment of Monetary Policy Effectiveness in the Business Press
Stock Market Effects
Bond Market Effects
GDP Effects
Inequality Effects
2.2.2 Assessment of Monetary Policy Effectiveness in the Academic/Professional Literature
Stock Market Effects
Bond Market Effects; Interest Rate Effects
GDP Effects
Effects on Inequality
2.2.3 Comparisons of Findings of the Professional and Business Press
2.3 A Comparison of Cowles, DSGE, and VAR Methodologies Used in Literature Review
References
3 Methodology
3.1 General Methodological Issues
3.1.1 The Importance of Replicating Results Before Publication
3.2 Other Methodological Issues Specific to This Study
3.2.1 GDP Deflator Methodological Adjustments
3.2.2 Reconciling Differences in Signs, Significance Levels of Tests in Different Time Periods
3.2.3 Mixing Periods of Budget Deficit (Crowd Out) Increase and Decrease
3.2.4 Statistical Insignificance Caused by Lack of Variation in the Data
3.2.5 Left-Out Variables
3.2.6 Multicollinearity
3.2.7 Insufficient Sample Size
3.2.8 Spurious Results Indicating Insignificance
3.3 How Should a Change in Loanable Funds Be Distributed to Tax and Spending Deficits
References
Part II Theory of Crowd Out and Accommodative Monetary Policy
4 Theory of Crowd Out and Accommodative Monetary Policy
4.1 How, and Under What Conditions, Can Federal Reserve Purchases of Government Securities Stimulate the Economy
4.1.1 Overview
4.1.2 Detailed Analysis of the Crowd Out and Accommodative Monetary Policy Processes
Accommodative Federal Reserve Purchases from Depository Institutions
Federal Reserve Purchases from Non-depository Institutions
4.2 A Formal Model of the Effects of Fiscal Stimulus Programs, Their Crowd Out Effects, and Accommodative Monetary Policy
4.2.1 Crowd Out Effects of Deficit Financing
4.2.2 How Accommodating Monetary Policy Offsets Crowd Out Effects
4.2.3 Different Crowd Out Effects of Tax Cut and Spending Deficits
Alternative Ways of Modeling Crowd Out Effects
4.2.4 Declining Deficits Create “Crowd in” Effects
4.2.5 Should We Use Accommodate Monetary Policy to Offset Crowd Out?
References
5 A Simplified Balance Sheet View of How Open Market Operations to Stimulate the Economy, When Dominated by Primary Dealers, Actually Stimulate Securities Markets, not the Real Economy
5.1 When the FR Goes into the Open Market and Buys $1000 in Treasuries (T) from a Dealer/Broker (Usually a “Primary Dealer”), The Dealer May Be Paid by Check Drawn on the FR (FRck) (If Dealer Is Paid Electronically by Fed Transfer of Funds to Dealer’s Bank, Skip Steps 5.1–3 and Go to Step #5.4)
5.2 Dealer #1Deposits FR Check in Dealer’s Own Bank
5.3 Bond Dealer’s Bank Cashes in the FRck at the Fed. Assume Required Reserve Ratio (RR) = 10% and Let Excess Reserves = (ER)
5.4 Bond Dealers Make Their Money Buying and Selling Bonds. Bond Dealers (Generally) Would Have no Incentive to Sell Treasuries to the FR, Except to Obtain the Funds Needed to Buy Another Security (Alt Sec) Expected to Pay a Higher Return. This Is Bought from Dealer #2 by Dealer #1 and Paid for with DD (Step Involving Check Payment, and Conversion to Reserves not Shown)
5.5 Bond Dealer #2 (Generally) Only Sold Alt Sec to the First Dealer Because Dealer #2 Needed the Liquidity to Buy Another Security (Alt Sec2) that Looked More Promising, Which Dealer #2 then Bought from Bond Dealer #3 Using the Proceeds of the Sale of Alt Sec to Dealer #1 to Finance the Purchase of Alt Sec2. the Cycle Continues in Perpetuity Until no Other Dealers Wish to Sell Securities at This Time. Results for Dealers #2 and #3 and #4 Are Shown Below (with Some Check & Reserves Movement Intermediate Steps Missing)
5.6 The Final Result Is Shown Below, After All Intermediate Steps Above Are Cancelled Out, and Assuming Bond Dealer #4 Cannot or Does not Want to Find Any Other Dealer/Broker with Desirable Securities to Buy
References
6 A Money Multiplier Approach to How Open Market Operations Stimulate Securities Markets and the Real Economy
6.1 Simple Money Multiplier
6.2 A More Sophisticated Money Multiplier
References
Part III The Effectiveness of Accommodating Monetary Policy Mechanics
7 The Role of Primary Dealers in Federal Reserve Efforts to Change the Money Supply
7.1 Primary Dealers Dominate Auctions
7.2 What Type of Bank Does the Federal Reserve Purchase Securities from: Investment or Depository?
7.3 The Failure of Federal Reserve Securities Purchases During “QE” to Reduce Depository Institutions Holdings of Government Securities, Which Would Have Increased Their Loanable Funds
7.4 Primary Dealers and the Business They Are in: Selected Years 1960–2014
7.5 Loss of Efficiency When Using Investment Banks and Brokerages to Implement Accommodative Monetary Policy
7.6 Primary Dealers Who Are Domestic Vs. Foreign Corporations
References
8 The Failure of Accommodative Monetary Policy Before Quantitative Easing (QE) and Its Success After; the “Pushing on a String Problem”
8.1 Effectiveness of Accommodative Monetary Policy 1960–2007
8.2 Effectiveness of Accommodative Monetary Policy 2008–Present
8.3 Does “Pushing on a String” During QE Apply to M1 as Well as Total Loanable Funds?
8.4 Conclusions
References
9 The Failure of U.S. Loanable Funds to Grow as Much as Federal Reserve Securities Purchases During QE: The Role of Foreign Banks
9.1 The Textbook Equivalence of Increases in FR Securities Purchases and Increases in Loanable Reserves
9.2 The Effects of Fed Purchases of Securities from Foreign Dealer/Brokers
9.3 Trends Since 1960 in M1, Excess Reserves and Currency in Circulation
References
Part IV Increases in M1—Effects on Stock and Bond Markets and the GDP
10 Effect of FR Purchases of Government Securities on M1
10.1 Relationship of M1 Growth to Growth in Securities Purchased by the Fed
10.2 More Sophisticated Models of the Relationship Between FR Securities Purchases and M1
10.3 Tests of the Relationship Between M1 and Excess Reserves and FR Securities Purchases
10.4 Relationship of Growth in M1 to Growth in the Monetary Base
10.5 The Most Theory Consistent Model of M1’s Determinants
10.6 Summary of Results of Tests of Relationship of Changes in FR Purchases to Changes in M1
References
11 Effect of Increases in Loanable Funds or M1 on the GDP
11.1 Simple Tests
11.2 More Sophisticated Tests of the Effects of FR Security Purchases on Real GDP
11.2.1 Summary of Table 11.1 Findings
11.3 Testing Housing and Consumer Services Demand for Sensitivity to FR Securities Purchases
11.3.1 Housing Investment Effects
11.3.2 Lagged Consumer Services Spending Effects
11.3.3 Total Consumer and Investment Spending Effects
11.3.4 Full GDP Effects
11.4 Summary of Results and Conclusions
References
12 Effect of FR Security Purchases and M1 on Stock, Bond, and Mortgage Markets
12.1 Effect of FR Open Market Operations on the Stock Market
12.2 Effect of FR Open Market Operations on Bond and Mortgage Markets
12.3 Do FR Open Market Operations also Affect GDP
12.4 Summary of Findings and Conclusions
References
Part V Does Crowd Out Really Occur?
13 Does Crowd Out Really Occur? Initial Empirical Evidence: One Time Period
13.1 Consumption
13.2 Investment
13.3 Conclusion
References
14 Does Crowd Out Really Occur? Empirical Evidence: Replication in Many Time Periods
14.1 The Heim (2017b) Study
14.2 The Heim (2017a) Study
14.3 Crowd Out Findings in This Study
References
Part VI Increases in Total Loanable Funds (S+FB)—Do They Reduce Crowd Out?
15 Initial Tests of Whether Crowd Out Can Be Offset by Increases in Loanable Funds
15.1 Methodology for Testing Increases in Loanable Funds as an Offset to Consumption Crowd Out
15.2 Taxes: Another Variable That Has Both Positive and Negative Effects on Consumption
15.3 Methodology for Testing Increases in Loanable Funds as an Offset to Investment Crowd Out
15.4 Conclusions
References
16 Which Models Best Explain How Changes in Loanable Funds Offset Crowd Out?
16.1 Effects on the Consumption Function
16.2 Effects on the Investment Function
References
17 Do Loanable Funds Modify the Crowd Out Effects of the One-Variable Deficit (T - G)?
17.1 Consumption Results When also Including (S + FB) as a Separate Variable
17.2 Consumption Results When not Including (S + FB) as a Separate Variable
17.3 Investment Results When also Including (S + FB) as a Separate Variable
17.4 Investment Results When not Including (S + FB) as a Separate Variable
17.5 Comparing the Effects of Exogenous (FR Purchases Induced) and Endogenous (Economic Driven Change Induced) Loanable Funds Growth
17.5.1 Effects on Consumption
17.5.2 Effects on Investment
17.6 Conclusions
Reference
18 Do Loanable Funds Modify the Crowd Out Effects of the Two-Variable Deficit (T), (G)?
18.1 Testing the Two-Variable Deficit Consumption Model
18.1.1 Mixing Crowd Out and Crowd in Periods May Distort Results
Adding a Separate, Stand Alone Loanable Funds Variable to a Crowd Out Model
Can Table 18.1 Results Be Replicated in Other Samples?
Heteroskedasticity (or Heteroscedasticity) and Autocorrelations
18.1.2 Comparing One-Variable and Two-Variable Deficit Results
18.2 Consumption Models Without Stand-Alone (S + FB)
18.3 Crowd Out Effects on Investment Using Stand Alone Loanable Funds Variable
18.4 Crowd Out Effects in Investment Models Without a Stand Alone Loanable Funds Variable
18.5 Chapter Summary
References
19 Does M1 or Total Loanable Funds Better Measure Offset Effects to Crowd Out?
19.1 Comparing Unmodified, LF Modified, and M1 Modified Deficit Variables
19.2 Adding a Separate, Stand-Alone M1 Variable to the Model
19.3 A Note on the Relationship of National Savings to M1
19.4 Summary of Results and Conclusions
References
Part VII Determining M1 Effects on Crowd Out
20 Does M1 or Total Loanable Funds More Accurately Define the Extent to Which Crowd Out Can Be Modified?
20.1 Testing the Consumption Model
20.2 Testing the Two—Variable Deficit Investment Model
20.2.1 Investment Models with a Stand-Alone Loanable Funds Modifier
20.2.2 Investment Models Without a Stand-Alone Loanable Funds Modifier
20.2.3 Investment Models Without a Stand-Alone Loanable Funds or M1 Modifier, but with a Business Cycle Control Variable
20.3 Comparing Model Results with (Table 20.5) and Without (Table 20.4) GDP Control
20.4 Summary of Chapter 20 Results
Reference
Part VIII Non-Black Box Models: Structural Mechanisms Through Which Loanable Funds Affects Consumption and Investment
21 Do Consumer Borrowing, Inflation, and Prime Interest Rate Increase When M1 Is Increased?
21.1 The Consumer Borrowing Equation
21.2 The Inflation Equation
21.3 The Prime Interest Rate Determination Equation
21.4 Summary of Findings and Conclusions
Reference
22 Effects on Consumer and Business Borrowing of Loanable Funds and M1
22.1 Mechanisms Through Which Loanable Funds Changes Affect Business Borrowing, a Determinant of Investment Demand
22.2 Effect of Business Borrowing on Investment Demand
22.3 Mechanisms Through Which Loanable Funds Changes Affect Consumer Borrowing, and Through Which Consumer Borrowing Affects Consumer Demand
22.4 Effect of Consumer Borrowing on Consumer Demand
22.5 Summary of Chapter Results and Conclusions
Reference
23 Effects on Inflation of Loanable Funds and M1
23.1 Testing for the Effects of Loanable Funds on Inflation
23.2 Loanable Funds Relationship to M1
Reference
24 Effects on the Prime Interest Rate in Keynesian Models of Loanable Funds and M1
24.1 In the Keynesian Interest Rate Model, Do Changes in Loanable Funds Explain Changes in the Prime Interest Rate as Well as Changes in M1?
24.2 Summary of Results and Conclusions
Part IX Summary Chapters
25 Summary of Introductory, Literature Review, and Methodology Chapters (Chapters 1–3)
25.1 Chapter 1 Overview of Deficits, Their Crowd Out Effects, and Accommodative Monetary Theory
25.1.1 The Crowd Out Problem
25.1.2 Actual Accommodative Monetary Policy—Chapters 4–9
25.1.3 Accommodative Monetary Science Chapters 10–24
25.2 Chapter 2 Summary—Literature Review
25.3 Chapter 33: Methodology
Reference
26 Summary of Crowd Out and Accommodative Monetary Policy Theory (Chapters 4–6)
26.1 Chapter 4: Theory of Crowd Out and Accommodative Monetary Policy
26.2 Chapter 5: Balance Sheet Presentation of Theory of Crowd Out and Accommodative Monetary Policy
26.3 Chapter 6: Money Multiplier Explanation of Theory of Crowd Out and Accommodative Monetary Policy
26.4 Chapter 7: The Role of Primary Dealers in Open Market Attempts to Increase Loanable Funds and the Money Supply
26.5 Chapter 8: Negative Effects of Excess Reserves and Increased Cash Holdings on the QE Monetary Stimulus Program: The “Pushing on a String” Policy Problem
26.6 Chapter 9: Why Increases in Loanable Funds Are Less Than Increases in FR Security Purchases: The Role of Foreign Banks
27 Summary of the Science Underlying the Conclusion that “Crowd Out” Is a Serious Problem and Accommodative Monetary Policy Can Offset It
27.1 Do Federal Reserve Security Purchases Change the Money Supply or the Monetary Base? (Chapter 10)
27.2 Do Changes in the Money Supply Affect the GDP or Its Components? (Chapters 11, 20–21)
27.3 Do Changes in the Money Supply or Monetary Base Affect Prices in the Stock or Credit Markets? (Chapter 12)
27.4 Does Stimulative Fiscal Policy Create a “Crowd Out” Problem that Reduces Consumer and Investment Spending, Causing the Fiscal Policies to Be Ineffective? (Chapters 13, 14, 17, 18)
27.5 Does Growth in Loanable Funds Offset Crowd Out Better Than Growth in M1? (Chapters 20, 21)
27.6 Do Increases in Total Loanable Funds Eliminate the Crowd Out Effects Caused by Deficits? (Chapters 15–18)
27.7 Which Part of Total Loanable Funds Growth, the Endogenous (Economy Driven) or Exogenous (Federal Reserve Policy Driven) Part Most Effects the Real Economy and Financial Markets? (Chapters 17, 22–24)
References
28 Summary of Engineering Equations in This Book
28.1 Effect of FR Securities Purchases on M1
28.2 Does M1 Affect GDP?—Simple Model (St. Louis Equation) Results
28.3 Do Changes in M1 or Loanable Funds Affect the GDP More—Using Scientifically Valid Models (Table 20.5)
28.4 Do Deficits Really Cause Crowd Out?
28.5 Which Is a Better Measure of Consumer Crowd Out? the Deficit, or the Deficit Reduced by Any Same-Period Growth in the Pool of Loanable Funds (1 and 2 Variable Deficit Model)?
28.6 Which Is a Better Measure of Investment Crowd Out? the Deficit, or the Deficit Reduced by Any Same-Period Growth in the Pool of Loanable Funds (1 and 2 Variable Deficit Model)?
28.7 Do Endogenous or Exogenous Increases in Loanable Funds Have the Most Success in Reducing Crowd Out?
28.8 Do Increases in Loanable Funds Increase Consumer and Business Borrowing? Does Increased Business Borrowing Decrease Consumer Borrowing?
28.9 Effects of Increases in M1 on Inflation
28.10 Effect of M1 on Prime Interest Rate
Reference
29 Definitions of Acronyms Used
Part X Overall Conclusions
30 Overall Conclusions
Index

Citation preview

Why Fiscal Stimulus Programs Fail, Volume 1 The Limits of Accommodative Monetary Policy in Practice John J. Heim

Why Fiscal Stimulus Programs Fail, Volume 1

John J. Heim

Why Fiscal Stimulus Programs Fail, Volume 1 The Limits of Accommodative Monetary Policy in Practice

John J. Heim Department of Economics State University of New York Albany, NY, USA

ISBN 978-3-030-65674-4 ISBN 978-3-030-65675-1 (eBook) https://doi.org/10.1007/978-3-030-65675-1 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Palgrave Macmillan imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

To Roger Porter, brilliant economist, policy analyst and advisor to presidents, and also one of Harvard’s best teachers.

Preface

This is the third of a five book series on the science underlying Keynesian mechanics and the scientific basis for its policy prescriptions. In all these books, results found in testing in one period of time, are discarded unless they can be replicated in most others as far back as 1960, ensuring that our results are good science, not just attractive—sounding theories or random statistical results resulting from testing one model on one period of time. The need for a series of science books like this is obvious: over 80 years ago Keynes coined his famous theory that the economy was fundamentally demand, not supply, driven, and that because of this, deficit financed government fiscal spending and tax cut programs could stimulate the economy. Yet, to this day, there is no unanimity of agreement within the economics profession as to whether they work. This, I believe, is because positions economists hold on the issue are not generally empirically based at all, but based on theory alone, or based on endless numbers of sophomoric empirical studies of (say) investment, with no two models tested containing the same variables, or for more than one time period. Nary a thought given to the fact that you don’t have a scientific result unless you have replicated; i.e., verified that the same model yields the same results in all or almost all time periods. If this continues to be the way economics is done, evaluation of whether economics policies work will not have a sufficiently scientific basis

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to be anything but an intellectualized version of “he said/she said” arguments heard in divorce courts. You can also think of it as an upgraded version of the “talking heads” economics we hear on talk radio and TV. By contrast, of the three science books mentioned above, the first is a well-tested, well replicated, 56 equation empirical model of the economy (“An Econometric Model of the U.S. Economy”, 2017), which compares well known DSGE, VAR and Keynesian model of the economy during the same period of time an concludes the Cowles (Keynesian) Model best explains the variation in the economy since 1960 and does so equally well for each decade since 1960! And similar results are found for the key equations in the Keynesian model: consumption and investment, where the models explain approximately 90% of the variation in those variables since 1960. And again, the same models explain the data as well in one decade of the 1960–2010 period as another, indicating the model is good science, not just “talking heads” economics. The second book, Crowding Out Fiscal Stimulus (2017), uses the standard economic models for investment and consumption from the first book to test whether deficit financed Keynesian stimulus programs worked over the 50 year period, 1960–2010, controlling for all other variables which could also affect consumption and investment levels. It found little or no evidence they did, and that the reason for the failure was that deficit financing “crowded out” borrowing-based private consumption and investment due to the need to finance the deficit from the existing, limited, pool of loanable funds. Again, results were found replicable in several time periods sampled, and were also replicable using a variety of models. The third book, the book presented here, using from 6 to 18 different sample periods, reiterates the testing to see if deficit—finances stimulus programs cause crowd out, and affirms they do. But the main focus of this third book is on why accommodative monetary policy by the Federal Reserve did not offset this crowd out problem and ensure attempts at Keynesian stimulus did work. After all, the empirical soundness of the underlying demand—driven theory of GDP determination was well established in the first of these books, and it implied stimulus programs should work. And later additions to the theory established that should crowd out problems arise, they could be offset by “accommodative monetary policy” by the Federal Reserve. This means the stimulus programs would work if the Fed increased the pool of loanable funds available to private borrowers by the amount it was reduced

PREFACE

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to finance the deficit. This book concludes the main reason fiscal stimulus programs haven’t worked was the systematic decisions by the Fed, from 1960 to 2007, not too increase bank reserves, i.e., implement accommodative monetary policy, to anywhere near the extent needed to offset the deficit. Other problems limiting the effectiveness of the Feds accommodative actions were its stubborn insistence on implementing the accommodative policy though securities purchases mainly from investment banks, who sell securities mainly to raise money to buy other securities, not to finance consumer and business loans to buy real goods and services, which is the business of retail commercial and savings banks. This third book also looks at whether endogenous growth in the loanable funds pool, due to rising incomes and saving, or an increasing marginal propensity to save, could help offset the crowd out problem caused by deficits, and concludes it can. This explains why in some periods, fiscal stimulus programs seem to work, despite inadequate Federal Reserve action to increase the pool, while in others they don’t. Hence, depending on the period picked, it provides good evidence for economists on either side of the argument about the effectiveness of Keynesian stimulus programs. It helps provide an explanation why the economics profession has found it so hard to become of one mind on this topic. Two additional books are planned for the future, with a goal of providing in the five books strong enough empirical evidence on what works and what doesn’t, to constitute an engineering—quality manual on how the economy works, thereby getting macroeconomics out of this deductive/faux empirical quagmire it has been in the last few decades. The fourth book, which is nearly finished, explores in great detail how well different combinations of increases in loanable funds, some by the Fed, some endogenous, actually offset crowd out problems. It also tests different definitions of loanable funds to see which works best. It concludes the total national savings plus foreign borrowing provides the best definition of loanable funds. It also concludes endogenous growth is generally much more effective than Federal Reserve-induced (exogenous) growth, presumably because it is more likely to be used for loans to purchase real goods and services, and not the result of liquidity added to the system mainly used to buy other securities. Th fifth book, not yet stated, will be an effort to determine if the “neoclassical synthesis”, the theory most commonly used to show how the

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PREFACE

economy melds from the Keynesian short run into the classical long run can be empirically verified to exist. When completed, I hope these five books will provide good reason to reclassify macroeconomics from a series of different, essentially deductive philosophies, poorly grounded in empirics, into a branch of engineering. Toward that end, we may be making progress: The first citation of the 56 equation econometric model book was in an engineering journal sponsored by the Institute of Physics and dealt with production functions. Engineers do a lot of economics. It is a good sign the engineering field is finding it can confidently rely on replicable economic studies developed using the scientific method. Albany, USA September 2020

John J. Heim

Contents

Part I 1

2

Introductory Chapters

Introduction 1.1 The Crowd Out Problem and Accommodative Monetary Policy 1.2 Individual Chapter Contents and Findings 1.3 Summary of Key Findings References Literature Review 2.1 Summary of Findings 2.1.1 Stocks and Bonds 2.1.2 GDP 2.1.3 Inequality 2.2 Detailed Findings 2.2.1 Assessment of Monetary Policy Effectiveness in the Business Press Stock Market Effects Bond Market Effects GDP Effects Inequality Effects 2.2.2 Assessment of Monetary Policy Effectiveness in the Academic/Professional Literature Stock Market Effects

3 3 6 10 12 13 13 14 14 15 16 16 16 17 17 18 19 19

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Bond Market Effects; Interest Rate Effects GDP Effects Effects on Inequality 2.2.3 Comparisons of Findings of the Professional and Business Press 2.3 A Comparison of Cowles, DSGE, and VAR Methodologies Used in Literature Review References 3

Methodology 3.1 General Methodological Issues 3.1.1 The Importance of Replicating Results Before Publication 3.2 Other Methodological Issues Specific to This Study 3.2.1 GDP Deflator Methodological Adjustments 3.2.2 Reconciling Differences in Signs, Significance Levels of Tests in Different Time Periods 3.2.3 Mixing Periods of Budget Deficit (Crowd Out) Increase and Decrease 3.2.4 Statistical Insignificance Caused by Lack of Variation in the Data 3.2.5 Left-Out Variables 3.2.6 Multicollinearity 3.2.7 Insufficient Sample Size 3.2.8 Spurious Results Indicating Insignificance 3.3 How Should a Change in Loanable Funds Be Distributed to Tax and Spending Deficits References

Part II 4

21 23 32 33 34 37 41 44 50 51 53

53 55 69 72 73 74 75 75 77

Theory of Crowd Out and Accommodative Monetary Policy

Theory of Crowd Out and Accommodative Monetary Policy 4.1 How, and Under What Conditions, Can Federal Reserve Purchases of Government Securities Stimulate the Economy

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CONTENTS

4.1.1 4.1.2

Overview Detailed Analysis of the Crowd Out and Accommodative Monetary Policy Processes Accommodative Federal Reserve Purchases from Depository Institutions Federal Reserve Purchases from Non-depository Institutions 4.2 A Formal Model of the Effects of Fiscal Stimulus Programs, Their Crowd Out Effects, and Accommodative Monetary Policy 4.2.1 Crowd Out Effects of Deficit Financing 4.2.2 How Accommodating Monetary Policy Offsets Crowd Out Effects 4.2.3 Different Crowd Out Effects of Tax Cut and Spending Deficits Alternative Ways of Modeling Crowd Out Effects 4.2.4 Declining Deficits Create “Crowd in” Effects 4.2.5 Should We Use Accommodate Monetary Policy to Offset Crowd Out? References 5

A Simplified Balance Sheet View of How Open Market Operations to Stimulate the Economy, When Dominated by Primary Dealers, Actually Stimulate Securities Markets, not the Real Economy 5.1 When the FR Goes into the Open Market and Buys $1000 in Treasuries (T) from a Dealer/Broker (Usually a “Primary Dealer”), The Dealer May Be Paid by Check Drawn on the FR (FRck) (If Dealer Is Paid Electronically by Fed Transfer of Funds to Dealer’s Bank, Skip Steps 5.1–3 and Go to Step #5.4) 5.2 Dealer #1Deposits FR Check in Dealer’s Own Bank 5.3 Bond Dealer’s Bank Cashes in the FRck at the Fed. Assume Required Reserve Ratio (RR) = 10% and Let Excess Reserves = (ER)

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83 83 84

87 89 90 94 96 102 103 105

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108 108

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5.4

Bond Dealers Make Their Money Buying and Selling Bonds. Bond Dealers (Generally) Would Have no Incentive to Sell Treasuries to the FR, Except to Obtain the Funds Needed to Buy Another Security (Alt Sec) Expected to Pay a Higher Return. This Is Bought from Dealer #2 by Dealer #1 and Paid for with DD (Step Involving Check Payment, and Conversion to Reserves not Shown) 5.5 Bond Dealer #2 (Generally) Only Sold Alt Sec to the First Dealer Because Dealer #2 Needed the Liquidity to Buy Another Security (Alt Sec2) that Looked More Promising, Which Dealer #2 then Bought from Bond Dealer #3 Using the Proceeds of the Sale of Alt Sec to Dealer #1 to Finance the Purchase of Alt Sec2. the Cycle Continues in Perpetuity Until no Other Dealers Wish to Sell Securities at This Time. Results for Dealers #2 and #3 and #4 Are Shown Below (with Some Check & Reserves Movement Intermediate Steps Missing) 5.6 The Final Result Is Shown Below, After All Intermediate Steps Above Are Cancelled Out, and Assuming Bond Dealer #4 Cannot or Does not Want to Find Any Other Dealer/Broker with Desirable Securities to Buy References 6

A Money Multiplier Approach to How Open Market Operations Stimulate Securities Markets and the Real Economy 6.1 Simple Money Multiplier 6.2 A More Sophisticated Money Multiplier References

Part III 7

109

109

110 113

115 115 116 122

The Effectiveness of Accommodating Monetary Policy Mechanics

The Role of Primary Dealers in Federal Reserve Efforts to Change the Money Supply 7.1 Primary Dealers Dominate Auctions

125 126

CONTENTS

What Type of Bank Does the Federal Reserve Purchase Securities from: Investment or Depository? 7.3 The Failure of Federal Reserve Securities Purchases During “QE” to Reduce Depository Institutions Holdings of Government Securities, Which Would Have Increased Their Loanable Funds 7.4 Primary Dealers and the Business They Are in: Selected Years 1960–2014 7.5 Loss of Efficiency When Using Investment Banks and Brokerages to Implement Accommodative Monetary Policy 7.6 Primary Dealers Who Are Domestic Vs. Foreign Corporations References

xv

7.2

8

9

The Failure of Accommodative Monetary Policy Before Quantitative Easing (QE) and Its Success After; the “Pushing on a String Problem” 8.1 Effectiveness of Accommodative Monetary Policy 1960–2007 8.2 Effectiveness of Accommodative Monetary Policy 2008–Present 8.3 Does “Pushing on a String” During QE Apply to M1 as Well as Total Loanable Funds? 8.4 Conclusions References The Failure of U.S. Loanable Funds to Grow as Much as Federal Reserve Securities Purchases During QE: The Role of Foreign Banks 9.1 The Textbook Equivalence of Increases in FR Securities Purchases and Increases in Loanable Reserves 9.2 The Effects of Fed Purchases of Securities from Foreign Dealer/Brokers 9.3 Trends Since 1960 in M1, Excess Reserves and Currency in Circulation References

126

131 134

140 141 145

147 147 153 157 159 161

163

164 165 166 174

xvi

CONTENTS

Part IV 10

11

12

Increases in M1—Effects on Stock and Bond Markets and the GDP

Effect of FR Purchases of Government Securities on M1 10.1 Relationship of M1 Growth to Growth in Securities Purchased by the Fed 10.2 More Sophisticated Models of the Relationship Between FR Securities Purchases and M1 10.3 Tests of the Relationship Between M1 and Excess Reserves and FR Securities Purchases 10.4 Relationship of Growth in M1 to Growth in the Monetary Base 10.5 The Most Theory Consistent Model of M1’s Determinants 10.6 Summary of Results of Tests of Relationship of Changes in FR Purchases to Changes in M1 References Effect of Increases in Loanable Funds or M1 on the GDP 11.1 Simple Tests 11.2 More Sophisticated Tests of the Effects of FR Security Purchases on Real GDP 11.2.1 Summary of Table 11.1 Findings 11.3 Testing Housing and Consumer Services Demand for Sensitivity to FR Securities Purchases 11.3.1 Housing Investment Effects 11.3.2 Lagged Consumer Services Spending Effects 11.3.3 Total Consumer and Investment Spending Effects 11.3.4 Full GDP Effects 11.4 Summary of Results and Conclusions References Effect of FR Security Purchases and M1 on Stock, Bond, and Mortgage Markets 12.1 Effect of FR Open Market Operations on the Stock Market

177 177 187 191 196 198 199 205 207 207 212 217 220 221 222 222 224 226 232 233 234

CONTENTS

Effect of FR Open Market Operations on Bond and Mortgage Markets 12.3 Do FR Open Market Operations also Affect GDP 12.4 Summary of Findings and Conclusions References

xvii

12.2

Part V 13

14

16

Does Crowd Out Really Occur?

Does Crowd Out Really Occur? Initial Empirical Evidence: One Time Period 13.1 Consumption 13.2 Investment 13.3 Conclusion References

249 251 253 254 254

Does Crowd Out Really Occur? Empirical Evidence: Replication in Many Time Periods 14.1 The Heim (2017b) Study 14.2 The Heim (2017a) Study 14.3 Crowd Out Findings in This Study References

255 255 256 258 260

Part VI 15

237 242 244 245

Increases in Total Loanable Funds (S+FB)—Do They Reduce Crowd Out?

Initial Tests of Whether Crowd Out Can Be Offset by Increases in Loanable Funds 15.1 Methodology for Testing Increases in Loanable Funds as an Offset to Consumption Crowd Out 15.2 Taxes: Another Variable That Has Both Positive and Negative Effects on Consumption 15.3 Methodology for Testing Increases in Loanable Funds as an Offset to Investment Crowd Out 15.4 Conclusions References Which Models Best Explain How Changes in Loanable Funds Offset Crowd Out? 16.1 Effects on the Consumption Function 16.2 Effects on the Investment Function References

263 264 269 270 273 273 275 278 285 290

xviii

17

18

CONTENTS

Do Loanable Funds Modify the Crowd Out Effects of the One-Variable Deficit (T − G)? 17.1 Consumption Results When also Including (S + FB) as a Separate Variable 17.2 Consumption Results When not Including (S + FB) as a Separate Variable 17.3 Investment Results When also Including (S + FB) as a Separate Variable 17.4 Investment Results When not Including (S + FB) as a Separate Variable 17.5 Comparing the Effects of Exogenous (FR Purchases Induced) and Endogenous (Economic Driven Change Induced) Loanable Funds Growth 17.5.1 Effects on Consumption 17.5.2 Effects on Investment 17.6 Conclusions Reference Do Loanable Funds Modify the Crowd Out Effects of the Two-Variable Deficit (T), (G)? 18.1 Testing the Two-Variable Deficit Consumption Model 18.1.1 Mixing Crowd Out and Crowd in Periods May Distort Results Adding a Separate, Stand Alone Loanable Funds Variable to a Crowd Out Model Can Table 18.1 Results Be Replicated in Other Samples? Heteroskedasticity (or Heteroscedasticity) and Autocorrelations 18.1.2 Comparing One-Variable and Two-Variable Deficit Results 18.2 Consumption Models Without Stand-Alone (S + FB) 18.3 Crowd Out Effects on Investment Using Stand Alone Loanable Funds Variable 18.4 Crowd Out Effects in Investment Models Without a Stand Alone Loanable Funds Variable 18.5 Chapter Summary

291 291 298 301 307

310 310 312 315 322 323 323 338 346 348 360 362 364 368 377 384

CONTENTS

References 19

Does M1 or Total Loanable Funds Better Measure Offset Effects to Crowd Out? 19.1 Comparing Unmodified, LF Modified, and M1 Modified Deficit Variables 19.2 Adding a Separate, Stand-Alone M1 Variable to the Model 19.3 A Note on the Relationship of National Savings to M1 19.4 Summary of Results and Conclusions References

Part VII 20

21

389 391 395 400 405 407 410

Determining M1 Effects on Crowd Out

Does M1 or Total Loanable Funds More Accurately Define the Extent to Which Crowd Out Can Be Modified? 20.1 Testing the Consumption Model 20.2 Testing the Two—Variable Deficit Investment Model 20.2.1 Investment Models with a Stand-Alone Loanable Funds Modifier 20.2.2 Investment Models Without a Stand-Alone Loanable Funds Modifier 20.2.3 Investment Models Without a Stand-Alone Loanable Funds or M1 Modifier, but with a Business Cycle Control Variable 20.3 Comparing Model Results with (Table 20.5) and Without (Table 20.4) GDP Control 20.4 Summary of Chapter 20 Results Reference

Part VIII

xix

413 414 424 424 431

433 437 438 446

Non-Black Box Models: Structural Mechanisms Through Which Loanable Funds Affects Consumption and Investment

Do Consumer Borrowing, Inflation, and Prime Interest Rate Increase When M1 Is Increased? 21.1 The Consumer Borrowing Equation

449 450

xx

CONTENTS

21.2 The Inflation Equation 21.3 The Prime Interest Rate Determination Equation 21.4 Summary of Findings and Conclusions Reference 22

23

24

Effects on Consumer and Business Borrowing of Loanable Funds and M1 22.1 Mechanisms Through Which Loanable Funds Changes Affect Business Borrowing, a Determinant of Investment Demand 22.2 Effect of Business Borrowing on Investment Demand 22.3 Mechanisms Through Which Loanable Funds Changes Affect Consumer Borrowing, and Through Which Consumer Borrowing Affects Consumer Demand 22.4 Effect of Consumer Borrowing on Consumer Demand 22.5 Summary of Chapter Results and Conclusions Reference Effects on Inflation of Loanable Funds and M1 23.1 Testing for the Effects of Loanable Funds on Inflation 23.2 Loanable Funds Relationship to M1 Reference Effects on the Prime Interest Rate in Keynesian Models of Loanable Funds and M1 24.1 In the Keynesian Interest Rate Model, Do Changes in Loanable Funds Explain Changes in the Prime Interest Rate as Well as Changes in M1? 24.2 Summary of Results and Conclusions

455 457 462 466 467

468 473

476 485 488 490 491 491 495 506 507

507 511

Part IX Summary Chapters 25

Summary of Introductory, Literature Review, and Methodology Chapters (Chapters 1–3) 25.1 Chapter 1 Overview of Deficits, Their Crowd Out Effects, and Accommodative Monetary Theory 25.1.1 The Crowd Out Problem

517 517 517

CONTENTS

Actual Accommodative Monetary Policy—Chapters 4–9 25.1.3 Accommodative Monetary Science Chapters 10–24 25.2 Chapter 2 Summary—Literature Review 25.3 Chapter 33: Methodology Reference

xxi

25.1.2

26

27

Summary of Crowd Out and Accommodative Monetary Policy Theory (Chapters 4–6) 26.1 Chapter 4: Theory of Crowd Out and Accommodative Monetary Policy 26.2 Chapter 5: Balance Sheet Presentation of Theory of Crowd Out and Accommodative Monetary Policy 26.3 Chapter 6: Money Multiplier Explanation of Theory of Crowd Out and Accommodative Monetary Policy 26.4 Chapter 7: The Role of Primary Dealers in Open Market Attempts to Increase Loanable Funds and the Money Supply 26.5 Chapter 8: Negative Effects of Excess Reserves and Increased Cash Holdings on the QE Monetary Stimulus Program: The “Pushing on a String” Policy Problem 26.6 Chapter 9: Why Increases in Loanable Funds Are Less Than Increases in FR Security Purchases: The Role of Foreign Banks Summary of the Science Underlying the Conclusion that “Crowd Out” Is a Serious Problem and Accommodative Monetary Policy Can Offset It 27.1 Do Federal Reserve Security Purchases Change the Money Supply or the Monetary Base? (Chapter 10) 27.2 Do Changes in the Money Supply Affect the GDP or Its Components? (Chapters 11, 20–21) 27.3 Do Changes in the Money Supply or Monetary Base Affect Prices in the Stock or Credit Markets? (Chapter 12)

518 518 519 519 520 521 521 522

522

522

522

524

525

526 528

531

xxii

CONTENTS

27.4

Does Stimulative Fiscal Policy Create a “Crowd Out” Problem that Reduces Consumer and Investment Spending, Causing the Fiscal Policies to Be Ineffective? (Chapters 13, 14, 17, 18) 27.5 Does Growth in Loanable Funds Offset Crowd Out Better Than Growth in M1? (Chapters 20, 21) 27.6 Do Increases in Total Loanable Funds Eliminate the Crowd Out Effects Caused by Deficits? (Chapters 15–18) 27.7 Which Part of Total Loanable Funds Growth, the Endogenous (Economy Driven) or Exogenous (Federal Reserve Policy Driven) Part Most Effects the Real Economy and Financial Markets? (Chapters 17, 22–24) References 28

Summary of Engineering Equations in This Book 28.1 Effect of FR Securities Purchases on M1 28.2 Does M1 Affect GDP?—Simple Model (St. Louis Equation) Results 28.3 Do Changes in M1 or Loanable Funds Affect the GDP More—Using Scientifically Valid Models (Table 20.5) 28.4 Do Deficits Really Cause Crowd Out? 28.5 Which Is a Better Measure of Consumer Crowd Out? the Deficit, or the Deficit Reduced by Any Same-Period Growth in the Pool of Loanable Funds (1 and 2 Variable Deficit Model)? 28.6 Which Is a Better Measure of Investment Crowd Out? the Deficit, or the Deficit Reduced by Any Same-Period Growth in the Pool of Loanable Funds (1 and 2 Variable Deficit Model)? 28.7 Do Endogenous or Exogenous Increases in Loanable Funds Have the Most Success in Reducing Crowd Out? 28.8 Do Increases in Loanable Funds Increase Consumer and Business Borrowing? Does Increased Business Borrowing Decrease Consumer Borrowing? 28.9 Effects of Increases in M1 on Inflation

532 535

536

543 547 549 550 550

552 554

556

558

560

561 562

CONTENTS

29

xxiii

28.10 Effect of M1 on Prime Interest Rate Reference

562 562

Definitions of Acronyms Used

563

Part X Overall Conclusions 30

Overall Conclusions

Index

569 573

List of Figures

Graph 2.1 Graph 10.1 Graph 10.2 Graph 10.3 Graph Graph Graph Graph Graph Graph

10.4 11.1 11.2 11.3 11.4 19.1

Comparisons of Japan, Canada and the U.S M1 regressed on FR securities purchases 1955–2016 M1 regressed on FR securities purchases 1955–2016 (W/O constant term) M1 regressed on FR purchased securities securities 1955–2007 M1 regressed on FR purchased 1955–2017 GDP regressed on M1 1961–2017 GDP regressed on M1 (W/Constant) 1961–2017 GDP regressed on M1 1961–2017 GDP regressed on M1 (W/Constant) 1961–2017 Savings = ƒ(M1, FB)—predicted and actual

24 182 183 185 186 208 208 210 211 406

xxv

List of Tables

Table 3.1 Table 3.2 Table 3.3 Table 3.4 Table 3.5 Table 3.6 Table 3.7 Table 3.8 Table 3.9 Table 3.10 Table 3.11 Table 3.12 Table 3.13 Table 3.14

Calculating 2013–2017 real GDP using estimated values of the base year 2005 chain deflator Simulated regression data Changes in regression coefficients and t-statistics associated with loanable funds changes Average yearly increases (+)/decreases (−) in deficits by decade Yearly changes in the deficit in the 1990s Simulation of deficit and surplus effects on sign of government spending coefficients Effects of spending deficit growth on consumption Effects on consumption of loanable funds growth less than spending deficit Effects on consumption of loanable funds growth greater than spending Effects on consumption of declining spending deficit Trends in crowd out significance and movement in other variables Ratio of standard deviation/average yearly change in standard consumption function variables Single variable deficit significance in standard consumption model (multi-decade samples) Coefficients of modified T ,G variables, and stand-alone (S + LF) when (S + LF) distribution varies

54 56 59 60 62 65 67 67 67 68 71 72 74

76

xxvii

xxviii

LIST OF TABLES

Table 6.1 Table 7.1

Table 7.2

Table 7.3 Table 7.4

Table 7.5 Table 7.6 Table 7.7 Table 8.1

Table 8.2

Table 8.3

Table 8.4

Table 8.5

Table 9.1 Table 9.2 Table 9.3 Table 9.4 Table 9.5

Money expansion resulting from a $430.8 billion increase in national saving Firms from which permanent treasury security purchases were made as part of the quantitative easing program (Q1: 2016; Q1–4: 2014, 2012; and Q3–4: 2010) Permanent treasury security purchases as part of the quantitative easing program (selected periods Q3: 2010–Q1: 2016) Depository institution holdings of treasury, agency and GSE securities Total treasury, agency and GSE securities held by depository institutions during the QE program years Primary dealers and the business they are in: selected years 1960–2014 Q4: 2014 purchases of US securities by the Federal Reserve: $10,517.5 million FR purchases of treasury and agency securities and growth of the monetary base (billions) Excess reserves in U.S. depository institutions during recessions and non-recessionary periods (billions) Real yearly changes in the deficit (T – G) and FR security purchases (Tr + A) (billions of 2005 dollars) Levels of accommodating monetary policy compared to fiscal deficits in recession years during 1959–2017 (nominal values) Levels of real accommodating monetary policy compared to fiscal deficits in recession years during 1959–2017 FR security holdings, reserves, M1 and monetary base; average values before and during QE (nominal values, billions) M1 trends, as a percent of GDP Excess reserves, currency in circulation and M1 Additional excess reserves held during recessions (billions) Excess reserves/total reserves ratio Credit market borrowing 2004–2011 all sectors, by instrument (billions of dollars)

122

129

130 132

133 135 142 144

148

151

154

156

157 167 168 170 171 173

LIST OF TABLES

Table 9.6

Table 10.1 Table 10.2

Table 10.3 Table 10.4

Table 10.5

Table 10.6 Table 10.7

Table 10.8 Table 11.1 Table 11.2 Table 12.1 Table 12.2

Table 12.3

Table 14.1 Table 16.1 Table 16.2

Average yearly declines in borrowing average annual borrowing in 2004–2007 compared to 2008–2011 (billions of dollars) Historical data: nominal treasuries held by FRB, nominal M1 and real GDP (billions) Relationship of real FR purchases of securities to real M1 growth in five time periods controlling for other determinants of M1 growth (2005 = 100) Money multiplier estimates from Table 10.2 model Changes in real M1 associated with same period changes in real excess reserves and two year average changes in real FR securities purchases Changes in real M1 associated with same period changes in real excess reserves and two year average changes in real FR securities purchases Ratio of changes in real M1 to changes in real monetary base Relationship of FR purchases of securities to monetary base growth in five time periods controlling for other determinants of monetary base growth This chapter summary table effects of changes in FR security purchases on M1 Marginal effects of changes in FR Purchases and (M1 − FR Purchases) on Real GDP Chapter 11 summary table: Effects of changes in M1 on GDP Marginal effects of FR purchases on the NYSE composite index Marginal effects of FR purchases on the NYSE composite index, controlling for endogenous changes to M1 Marginal effects of changes in endogenous M1 and FR securities purchases on bond and mortgage market yields and stock market prices Unmodified effects of deficits (crowd out) consumption and investment Effects of different loanable funds offset models on crowd out in consumption models Results of different investment models of the effects of loanable funds on crowd out

xxix

173 178

189 190

193

195 196

197 200 214 227 235

237

239 259 281 287

xxx

LIST OF TABLES

Table 17.1A Table 17.1 Table 17.2

Table 17.2A Table 17.3

Table 17.4

Table 17.5 Table 17.6 Table 17.7

Table 17.8 Table 17.9 Table 17.10 Table 17.11

Table 18.1D Table 18.1B Table 18.1

Table 18.2

Growth in explained variance when adding unmodified crowd out to a standard model Crowd out effects on consumption, with and without offsetting changes in loanable funds Here crowd out effects on consumption, with and without offsetting changes in loanable funds (no stand-alone S + FB) Growth in explained variance when adding crowd out to a standard model Effects of crowd out on investment, with and without loanable funds modification of the deficit; stand alone variable included Effects on investment of crowd out, with and without same-period changes in loanable funds Endogenous and exogenous changes in loanable funds: effects on consumption crowd out Endogenous and exogenous changes in loanable funds: effects on investment crowd out Endogenous and exogenous changes in loanable funds: effects on crowd out (GDP control added to Table 17.6 model) Total loanable funds deficit modifier, W/WO separate (S + FB) control variable Total loanable funds modifier, W/WO separate (S + FB) control variable Separate (S + FB − TR – A) an (Tr + A) deficit modifiers, with stand alone (S + FB) control variable Separate (S + FB − TR − A) an (Tr + A) deficit modifiers, no stand alone (S + FB) control variable Growth in explained variance when adding crowd out to a standard model Base line model with only deficit variables added: estimates of consumption crowd out Comparing Robustness Over Time of Effects on Consumption of Crowd out, With and With out Compensating Loanable Funds (Separate Stand-Alone S + FB Variable Included) Changes in regression coefficients and t-statistics associated with loanable funds changes

294 297

299 304

306

308 311 313

314 316 317 319

321 329 331

335 340

LIST OF TABLES

Table 18.3 Table 18.4 Table 18.5 Table 18.6 Table 18.7 Table 18.8 Table 18.9 Table 18.10A Table 18.10

Table 18.10B Table 18.11

Table 18.12 Table 18.13 Table 19.1

Table 19.2

Table 19.3 Table 19.4 Table 19.5 Table 19.6

Effects of adding “crowd out” and “Crowd In” Periods Simulated results of combining statistically significant “Crowd In” and “crowd out” samples Effects of adding a separate, sand alone loanable funds variable to a crowd out model Crowd out variable coefficients, t-statistics and R 2 in different sample periods* Annual Growth (+)/Decline (−) in Deficits 1990–2000 Annual Growth (+)/Decline (−) in Deficits 1960–2000 Robustness of effects of crowd out on consumption (No Stand-Alone Loanable Funds Control Variable) Growth in explained variance when adding crowd out to a standard model Effects on investment of crowd out, with and without modification by loanable funds (Stand Alone (S + FB) and GDP Variables Included) Base line model with deficit variables added: estimates of investment crowd out Estimates of investment of crowd out, with and without modification by loanable funds (No Stand Alone (S + FB); GDP variable included) Cptr. 18 Consumption Summary Table Cptr. 18 Investment Summary Table Regression estimates of crowd out effects using different definitions of loanable funds crowd out effect per $ of deficit and (t-statistic) shown Regression estimates of crowd out effects using different definitions of loanable funds (Table 19.1 Models With Stand Alone M1 Variable Added)  Investment regression results for all variables included in the one and three variable M1 models  Consumption regression results for all variables included in the one M1 and three M1 models Summary table standard consumption model with LF, M1 deficit modifiers Summary table standard investment model with LF, M1 deficit modifiers

xxxi

342 344 346 349 357 359 366 371

375 379

381 386 388

396

401 404 404 408 409

xxxii

LIST OF TABLES

Table 20.1

Table 20.2

Table 20.3

Table 20.4

Table 20.5

Table 20.6

Table 20.7

Table 20.8

Table 20.9

Table 21.1

Table 21.2 Table 22.1 Table 22.2

Effects on Standard Consumption Model of an additional separate variable, with and without also adding it as a deficit modifier Robustness over time of effects on consumption of crowd out, with and without offsetting loanable funds (no stand-alone modifying variables were used) Comparing robustness over time of effects on investment of crowd out, with and without accommodating loanable funds modification Comparing robustness over time of effects on investment of crowd out, with and without loanable Funds and M1 modification (no stand-alone modifier) Effects on investment of crowd out, with and without loanable funds and M1 modification (no stand-alone modifier, GDP control added) Chapter 20 Consumption Summary Table 1 (S + FB), (S + FB + M1), M1, and (M1 + FR purchases) deficit modifier and stand-alone models Chapter 20 Consumption Summary Table 2 (S + FB), (S + FB + M1), M1, and (M1 + FR purchases) deficit modifier (no stand-alone offset) models Chapter 20 Investment Summary Table 3 (S + FB), (S + FB + M1), M1, and (M1 + FR Purchases) Deficit Modifier and Stand-Alone Models Chapter 20 Investment Summary Table 4 (S + FB), (S + FB + M1), M1, and (M1 + FR Purchases) Deficit Modifiers; (No Stand Alone, GDP Added) Models Comparisons of statistical significance of M1 effects on the prime interest rate in Keynesian and Taylor Rule interest rate determination models Summary Table: Effects of Growth in M1, (Tr + A), (M1 − r + A) on CBor , Interest Rates, and Inflation Effects of crowd out and loanable funds on business borrowing (controlling for total loanable funds) Effects of crowd out and loanable funds on business borrowing (controlling for endogenous and exogenous loanable funds separately)

417

421

427

429

435

439

441

443

445

461 463 470

472

LIST OF TABLES

Table 22.3 Table 22.4 Table 22.5 Table 22.6 Table 22.7 Table 22.8 Table 23.1 Table 23.2 Table 23.3 Table 23.4 Table 24.1 Table 26.1 Table 27.1

Effects of business borrowing on P&E investment Effects of crowd out and loanable funds on consumer borrowing Effects of crowd out and loanable funds on consumer borrowing Effects of business borrowing on consumer borrowing Effects of consumer borrowing on consumer demand Chapter 22 Summary Table: Effects of growth in (S + FB = LF), (Tr + A), (LF − Tr − A) on C Bor ,I Bor Effects of lagged loanable funds on inflation Estimated relationship of loanable funds to the M1 money supply M1 = ƒ(GDP, loanable funds) Effects of growth in (S + FB = LF), (Tr + A), (LF – Tr – A) on inflation Summary table M1 as a function of loanable funds, controlling only for GDP Comparing M1 and FR purchases as determinants of the prime interest rate US banking system excess reserves (% of total reserves) Explanator Power of Models With Total Loanable Funds (LF) Variables, compared to Baseline Deficit Model With no Loanable Funds Variable Included

xxxiii 475 478 481 483 487 489 494 497 499 505 512 523

534

PART I

Introductory Chapters

CHAPTER 1

Introduction

1.1 The Crowd Out Problem and Accommodative Monetary Policy John Maynard Keynes (1936) created a revolution in macroeconomics, arguing that in periods of economic recession or depression, governments could stimulate the economy and help restore full employment by cutting taxes or increasing government spending. Almost 50 years later (1983), one of Harvard’s greatest economists, Otto Eckstein, noted with great disappointment that economists had been arguing for decades about whether these Keynesian stimulus programs worked, and had failed to reach any general agreement. Even more disappointing, thirty-four years later after Eckstein, in 2017, this author’s own studies indicated the continuing (and embarrassing) lack of consensus among macroeconomists on this issue most central to the importance of their own discipline. There clearly was a need for a science-based work that would resolve the question once and for all. Toward that end, in 2017, a book containing what is probably the most exhaustive scientific study ever done of the effects of Keynesian fiscal stimulus programs, and their effect on the economy, was published (Heim 2017). Its statistical testing of hundreds of models and time periods concluded Keynesian stimulus programs:

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. J. Heim, Why Fiscal Stimulus Programs Fail, Volume 1, https://doi.org/10.1007/978-3-030-65675-1_1

3

4

J. J. HEIM

1. Could stimulate the economy as Keynes argued, but that unfortunately, 2. The stimulative effects were commonly offset by reduced private spending due to the “crowd out” problem caused by deficits (which Keynes never addressed). The need to borrow money to fund the deficits created by Keynesian stimulus programs reduces (“crowds out”) what is available for consumers and businesses to borrow, reducing their spending. This reduction has a negative effect on spending that offsets the positive Keynesian stimulus effect on spending, leaving the stimulus ineffective. 3. (1) and (2) left the impression Keynesian stimulus programs didn’t work, when the truth was, they did, but were offset by crowd out, a factor Keynes had failed to address in his analysis. The study found overwhelming empirical proof that because of the “crowd out” problem fiscal stimulus programs generally did not work. Keynesian economists have long argued that the “crowd out” problem is not fatal; that Federal Reserve “accommodative” monetary policy, undertaken at the same time the Keynesian stimulus programs are undertaken, eliminates the “crowd out” problem and allows stimulus programs to work as Keynes intended. This is done by increasing the loanable funds available for consumers and businesses to borrow to offset what is borrowed by the government to fund the deficit. The purpose of this book is to comprehensively test the assertion that accommodative monetary policy can eliminate the “crowd out” problem, allowing fiscal stimulus programs to stimulate the economy as intended. It is intended to be the largest scale scientific test ever performed (hundreds of separate statistical tests covering 50 years) of whether accommodative monetary policy, which increases the pool of loanable resources (or natural growth in this pool), can offset the crowd out problem. The book, employing the best scientific methods available to economists, concludes it could have, but until the quantitative easing program, Federal Reserve efforts to accommodate fiscal stimulus programs were not large enough to offset more than 1/8 to 1/4 of any one year’s crowd out problem. That provides the science part of the answer as to why accommodative monetary policy didn’t accommodate; too little was tried. There was also a monetary policy part that contributed to the Federal Reserve’s failure to effectively accommodate. It involves flaws in the actual methods used by the Federal Reserve as it attempts to increase bank

1

INTRODUCTION

5

reserves, so as to replace lost borrowing power due to “crowd out,” i.e., enact accommodative monetary policy. 1. Federal Reserve purchases of securities were done from banks, but the wrong type of banks (investment banks rather than commercial and savings banks), and 2. Much of the Federal Reserves securities purchases were from foreign banks and in some cases have increased the loanable funds pool in foreign countries, where it was of little use in stimulating the US economy. The Fed mostly buys securities from investment banks and brokerages, who sell the Fed securities mainly so they can buy other securities. Securities purchases by investment bankers do not in any direct way increase the GDP or reduce unemployment. Fed securities purchases would have been much more effective at restoring private borrowing lost to crowd out, if the Fed had restricted its purchases to retail banks, i.e., commercial and savings banks, whose main line of business is loaning to those that wished to buy houses, cars, machines, and factories. Such purchases do increase the GDP and lower unemployment. Worse, many of the purchases were from foreign banks, with no guarantee payment by the Fed would be deposited and spent in the U.S. at all. If not, loanable funds in the U.S. are not increased, and no offset to the crowd out problem occurs. Hence, the fiscal stimulus does not work. In one period sampled (2014), about 40% of all Fed purchases were from foreign banks and brokerages. This is Book 1 of a two-book-related series. Book 1 explores the effect of total loanable funds and M1 on the economy. The second book, Why Fiscal Stimulus Programs Fail, Volume 2: Statistical Tests Comparing Monetary Policy and Growth Effects is more science oriented and less policy analysis oriented. It contains nine additional econometric chapters and less policy analysis chapters. The nine econometric new chapters focus on testing the exogenous part of loanable funds generated by Federal Reserve securities purchases and the endogenous part generated by economic growths causing a natural growth in saving. The objective is to determine which of these two parts of total loanable funds is the most effective in reducing crowd out’s negative effect on consumer and business spending.

6

J. J. HEIM

1.2

Individual Chapter Contents and Findings

A summary of how these science and policy issues are examined in the remaining chapters of this study is presented below, as are the findings of each chapter. In Chapter 2, we look at previous literature on the subject. We find that the business press nearly unanimously agrees that FR security purchases during the quantitative easing period helped Wall Street, the bond and stock markets, by pushing up stock and bond prices, but did not much affect Main Street, i.e., the GDP and unemployment. The academic and professional literature agreed QE had a positive effect on Wall Street stock and bond market prices, but was split on whether there was any positive effect on the GDP and unemployment to go along with it. Chapter 3 discusses the methodology used in this study. The models tested are fairly standard models of consumption and investment’s determinants (income, interest rates, profits, etc.), with deficit variables added to measure crowd out effects, and either loanable funds, M1 or M2 variables added to measure accommodative monetary policy. Well over 1000 tests of specific consumption and investment models and time periods were undertaken in volume I and II of this book. Initial findings for any model are tested in multiple time periods to ensure results are replicable. The importance of replication in any economic study (preferably before publication, so as not to waste the reader’s time reading spurious results), is discussed. Techniques used to control for typical time series regression problems are discussed, including stationarity, endogeneity, multicollinearity, serial correlation and heteroskedasticity. Particular difficulties obtaining significant results when mixing data from “crowd out” and “crowd in” periods, or mixing periods in which there is no movement in key deficit variables with periods in which there is, are discussed. Guidelines for acceptable percentages of the data being of one category or the other are suggested. Circumstances under which creating a loanable funds-modified deficit variable is an appropriate way of modeling the effectiveness of changes in loanable funds in reducing crowd out are discussed, as well as circumstances in which a stand-alone variable representing loanable funds should also be used. In short, in consumption models, an increase in loanable funds has two effects: a positive crowd out effect and a negative effect stemming from the ceteris paribus (partial derivative) nature of the process of estimating marginal effects using regression; that is,

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it measures the effect of an increase in loanable funds on consumption holding income and other variables in the model (taxes, government spending, etc.) constant. That being so, an increase in savings, i.e., loanable funds, can only occur if there is a reduction in consumption. Two separate effects require two separate variables if one is to see each effect separately. Chapters 4–6 present the theory of how crowd out occurs when budget deficits occur, how crowd out negatively affects the GDP, and how changes in M1 or loanable funds can offset it. Chapter 4 presents the basic theory in both literary and mathematical form; Chapter 5 presents the same theory in balance sheet form. Chapter 6 presents the same theory in money multiplier form. Chapters 7–9 of this study examine the Federal Reserve’s (FR) use of accommodative monetary policy. Chapter 7 examines whether using investment banks, brokerages, and foreign banks reduces the effectiveness of accommodative monetary policy (compared to using only U.S. commercial and savings banks), and concludes it does. Test results showing this are presented. Chapter 8 examines the success of accommodate monetary policy before and after the quantitative easing program was introduced. Data is presented to show it was a failure 1960–2007, but successful after that due to the huge size of the accommodation program after the QE program started in 2008. This chapter also examines the “pushing on a string” problem that developed in the QE era. This problem limits of the FR’s ability to stimulate the economy when loanable funds are increased far in excess of the current demand for loans. It concludes large increases in reserves created by the FR during the QE period went unused because of insufficient demand, something tied strongly to the overall size of the economy. Chapter 9 shows that FR purchases of securities are not always matched by an equal increase in loanable funds in U.S banks; it often is less. The best explanation for this is that the FR buys substantial amounts of securities from foreign banks, increasing the loanable funds available in foreign countries, but not increasing loanable funds in the U.S. Also examined are the effects of Federal Reserve (FR) security purchases on M1 growth and on the GDP (Chapters 10–11). Chapter 10 statistically examines the relationship of FR security purchases to M1 growth and finds a strong relationship. Chapter 11 examines the effect of M1 and loanable funds availability on GDP and concludes FR purchases have a positive effect, and that the effect if felt mainly through changes

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M1’s effect on housing investment and 2 years later, changes in demand for consumer services. Effects of FR Security Purchases on Stock, and Bond Prices Chapter 12 examines the effect of FR security purchases on stock and credit market prices and always finds a strong positive relationship with bond market prices, but never with stock market prices except for the enormous increase in FR security purchases during the QE years. Estimating Crowd Out’s Actual Effects Chapter 13 tests to determine if the crowd out problem actually occurs. It does. Chapter 14 extends the initial tests in Chapter 13 to different time period to ensure initial results were worthy of publishing, i.e., could be replicated in other time periods and models. The goal was to create results, so persuasive analysts would no need for further studies in this area. Total Loanable Funds as a Crowd Out Modifier Several chapters statistically test to demonstrate how effectively growth in the total pool of loanable funds can offset crowd out (Chapters 15– 18). Chapter 15 develops the methodology to test the extent to which the negative effects of crowd out can be offset by changes in the size of the loanable funds pool. It concludes that the increase in loanable funds is large enough, it can completely offset crowd out. Chapter 16 tests different ways of modeling the combined effects of deficits and loanable funds offsets, either as one “modified” deficit variable or as separate deficit and loanable funds variables. As was noted earlier in discussing methodology, for consumption models, modifying the deficit variable and including a stand-alone modifier variable are the best, but that just including a stand-alone variable produced the same net results and therefore is an equivalent alternative. For investment, just the deficit modifier may be enough. Chapters 17 and 18 verify that Chapter 15 results can be (and were) replicated in multiple time periods and models. M1 as a Crowd Out Modifier Chapter 19 determines whether M1 or loanable funds best explain how crowd out is offset, and concludes total loanable funds are the better measure. Chapters 20 tests additional M1 models. It examines whether the deficit, modified by growth in M1, is a more accurate measure of how much crowd out effects can be modified than deficits modified by changes

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in total LF. Total LF was found to be a better measure of how much crowd out can be offset than M1. LF Effects on Consumption and Investment Indirectly through Their Structural Determinants Tests described above in Chapters 10–21 test LF or M1 variables by just adding them to the model as new separate variables or by using them to modify the deficit, or both. No structural mechanism is specified for how they bring about a change in consumption or investment; they are “black box” models, in the sense used to describe money’s effect on the GDP in the St. Louis Equation decades ago. Chapters 22–24 attempt to correct this. They test the hypotheses that the change in loanable funds directly affects borrowing, inflation, and interest rates, all of which subsequently are determinants of consumption and investment. Chapter 22 tests the relationship of consumer and business borrowing to changes in total loanable funds (LF) and finds changes in LF significantly, positively effect business borrowing. Changes in the endogenous portion of LF were the most important. Total loanable funds are positively related to borrowing, but neither part alone was consistently significant. Chapter 23 tests the relationship of inflation to changes in M1 and LF in Phillips curve inflation models. M1 was found significantly related to inflation, but not LF or either of its two components, even though we show changes in LF are positively related to changes in M1. The lack of relationship appears to be an econometric problem related to adding variances, not a substantive finding. Evidence indicated M1 was mostly driven by changes in the exogenous part (FR purchases) of LF, but less systematically to growth in the endogenous part. Chapter 24 examines the relationship of the Prime interest rate to variation in M1. In Taylor Rule models, M1 was not significant in most periods sampled. But it may be because the inflation variable in Taylor models is already affected by M1; hence, adding it separately may be redundant, and the reason it found is insignificant. We show that even though LF and M1 are significantly related, and M1 and the Prime rate are also significantly related, it is possible that the regression of LF on the Prime rate will be insignificant because both relationships, though significant, have low R 2 s, but that doesn’t mean they are not linked. By comparison, Keynesian models show significant liquidity effects and, after a lag, inflation effects of a change in M1 on interest rates are

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found in all time periods sampled. The same was true for total LF and its two parts. Chapters 25–29 summarize the results of all earlier chapters. Chapter 25 summarizes the introductory, literature, and methodology Chapters (1–3). Chapter 26 summarizes the theory Chapters (4–6) and the Chapters (7–9) analyze the mechanics of implementing accommodative monetary policy, and how successful it has been. Chapter 27 summarizes the tests undertaken to determine if crowd out exits, and if so, how best to scientifically measure the extent to which changes in loanable funds, or reasonable variants of them, can offset it and which appear to work best (Chapters 10–24). Chapter 28 summarizes the equations found reliable enough to be considered engineering equations, available for reliable use by policymakers and analysts. Chapter 29 provides definitions for the multitude of acronyms used in the book.

1.3

Summary of Key Findings

To conclude this introduction, let us briefly summarize the book’s major findings, taken from Chapter 30. Even in the summary form presented in Chapters 25–27, this book’s findings are highly detailed, and it can be difficult to tell the forest from the trees. To summarize this book’s major findings in as a brief way as possible (a summary of summaries), we repeat the major empirical findings of this book here:, 1. Deficit-driven fiscal stimulus programs positively stimulate the economy, but simultaneously create negative “crowd out” problems related to financing the deficit out of the existing pool of loanable funds. Financing the deficit reduces private borrowing, and therefore spending, out of the pool. The combined effects of stimulus and crowd out usually leave fiscal stimulus programs having a zero or near-zero net effect. 2. If the pool of loanable funds grows sufficiently, which is policy controllable by the Federal Reserve, it can offset negative crowd out completely, leaving fiscal stimulus programs effective. There is considerable evidence this occurred during the quantitative easing (QE) period, but not before.

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3. Total loanable funds are a better measure of the actual crowd out modifying effect than either its endogenous part or its exogenous part (FR security purchases) alone, though the endogenous part explains most of the variation that total loanable funds do. Total loanable funds, as a deficit modifier, also explain more variation in consumption and investment than M1. 4. While the level of loanable funds is policy controllable by the Federal Reserve, it is not likely that its current methods of exercising this control through investment banks have much positive effect on the GDP or lowering unemployment. The Federal Reserve historically has relied on purchasing securities from investment banks and brokerages. These institutions typically only sell securities to the Fed (or anybody else) to obtain funds to buy other securities. Securities trading is what they do for a living. This helps inflate bond market prices, helping Wall Street. The Federal Reserve, historically, has relied on purchasing securities from investment banks and brokerages. Such institutions most typically only sell securities to the Fed (or anybody else) to obtain funds to buy other securities. After all, securities trading is what they do for a living. This helps inflate bond market prices, helping Wall Street, but does little to increase the demand for real goods and services necessary to raise GDP and lower unemployment, which is what is needed if Federal Reserve actions are to help Main Street. If this hypothesis is correct, the results should indicate a smaller marginal effect on consumption and investment of a dollar’s increase in loanable funds due to FR security purchases than by a dollar’s increase due to growth in the endogenous portion of the loanable funds pool. And this is exactly what we see. For consumption, in 6 of 6 periods tested, the estimated marginal effect is lower for increases in FR purchases than for increases in the endogenous part of the loanable funds pool. For investment, the marginal effect of an increase in loanable funds is lower for FR purchases compared to endogenous growth in 5 of 6 periods tested (Chapter 17, Tables 17.5 and 17.6). The Federal Reserve’s purchases of securities would more likely stimulate the GDP and reduce unemployment if its purchases of securities were restricted to purchases from U.S. commercial and

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savings banks. It is these banks, not investment banks and brokerages, that are in the business of directly lending money to consumers and businesses that want to buy, cars, houses, machinery, and other goods and services, the very actions which will raise GDP and reduce unemployment. 5. In addition, many of the investment banks used are foreign banks with less incentive to invest Federal Reserve money in the U.S. than U.S. banks would have. 6. Finally, here is a major policy issue involved in deciding what to do with any growth in loanable funds that occur, whether by endogenous or exogenous means. With no deficit, growth in the loanable funds pool, if borrowed, increases the GDP by increasing private investment and spending. With a deficit created by a fiscal stimulus program, the increase in loanable funds goes to offset crowd out effects (i.e., keep private spending at old levels). The increase in GDP due to the fiscal stimulus is likely to be more oriented toward production of public goods than the no-deficit increase in private spending characterizing that leads to that increase in GDP. Hence doing deficits amounts to policy decision about private vs public goods.

References Eckstein, O. (1983). The DRI Model of the U.S. Economy. New York: McGrawHill Book Company. Heim, J. J. (2017). Crowding Out Fiscal Stimulus. Hoboken: Palgrave Macmillan. Keynes, J. M. (1936). The General Theory of Employment, Interest and Money. London: Macmillan.

CHAPTER 2

Literature Review

This literature review is based on an exhaustive survey of recent literature on the effect of accommodative monetary policy on: 1. The stock and bond markets, 2. The GDP, and 3. Inequality. Findings are presented in two sections: first, in a summary of findings from the literature reviewed, and second, in a larger, more detailed form, for those who wish to investigate the literature more thoroughly. The more detailed review includes each author’s own statement of their findings and how to interpret them, as well as a description of each study’s methodology.

2.1

Summary of Findings

The business press concludes the main beneficiaries of the quantitative easing (QE) program have been the owners of stocks and bonds, who have seen the prices of those assets rise dramatically due to QE, and that this has increased inequality (see, for example, Warsh, K., former FR Board member, WSJ, August 24, 2016).

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. J. Heim, Why Fiscal Stimulus Programs Fail, Volume 1, https://doi.org/10.1007/978-3-030-65675-1_2

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The business press was also virtually unanimous in assessing the Fed’s attempts to raise GDP and lower unemployment using QE to stimulate the economy, were a failure. Professional and academic press studies tend to find monetary policy has had a positive effect on both the stock and bond markets, and GDP. This is particularly true since the beginning of the QE period in 2008. 2.1.1

Stocks and Bonds

All studies of the effects of the huge increases in FR security purchases during the QE program, without exception, in the business press and academic/professional papers reviewed, found that bond interest rates were lowered (bond market prices increased). A one trillion USD purchase of long-term bonds reduced 10-year U.S. Treasury yields and low-grade corporates by about 30 to 50 basis points while MBS yields declined by 66 basis points and mortgage rates fell further still…. (Krishnamurthy and Vissing-Jorgensen 2011). 1 trillion in bond purchases by Fed reduced treasury and corporate bond rates 0.3–0.5%; MBS rates declined by 0.66% (Krishnamurthy and Vissing-Jorgensen 2011). Bond purchases equal to 10% of GDP lowered interest rates on an average of 0.68% in 28 studies reviewed (Gagnon 2016). Mortgage rate dropped 1.3% (Caixa Bank Research 2018), Klein and Evans (1968), Eckstein (1983). Fair (2004) and Heim (2017) found the same result for bond and mortgage markets, but did not test for stock market effects. Federal Reserve security purchases effectively funded 55% of treasury debt issued during the Obama presidency, compared to 10% during World War II (Gramm and Saving 2017). Bank reserves have grown as a result of quantitative easing to 13.07 for every dollar they are required to hold and have not expanded bank lending (Gramm and Saving 2017), indicating a “pushing on a string” effect of FR purchases increases during the QE period. 2.1.2

GDP

Most professional/academic papers reviewed found stimulative monetary policy has positive effects on GDP or in reducing unemployment. Some papers also found a positive effect on inflation. Only 3 found no effect on GDP of QE or earlier efforts by the Fed to increase asset

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purchases through open market operations. (Business Press articles consistently concluded just the opposite: massive FR security purchases during the QE period had no effect on the GDP or unemployment.) A summary of professional literature findings indicates: QE:

0% effect on inflation or economic growth (Williamson 2017). QE lowered unemployment rate 1% …(Wu and Xia 2016). $40 billion in asset purchases increases output 0.4% Bhattarai et al. (2015). Asset purchase equal to 1% of GDP raises GDP 0.58% Weale and Wiedelak (2016). A$600 billion increase in asset purchases increases GDP 0.13% (Chen et al. 2012). Doubling the size of FR balance sheet increases GDP by 0.45% for QE2 and 0.12% for QE3 (Bhattarai et al. 2015). QE1 was estimated to raise GDP 1.0%. Accommodating monetary policy had a positive effect on GDP, but of negligible size: only 0.4% of the size of the combined fiscal and monetary stimulus itself (Heim 2017), Klein and Evans (1968), Eckstein (1983). Fair (2004) also found the same result for effect of monetary policy on GDP.

Long-term multiplier effects of monetary policy have been small (0.1– 0.4) (Leeper et al. 2017). Multiplier is 1.5 when monetary policy is at the zero lower bound (ZLB), less above it (Wataru et al. 2018). The output multiplier is 0.5–1.9, depending on the shadow interest rate (Wataru et al. 2018). Conclude: The professional/academic literature concludes Federal Reserve asset purchases between $40 billion and roughly $1 trillion increased GDP from near 0.00% to 0.58%, with no correlation of study results with purchase size. Multiplier results were similarly mixed, with multiplier effects of monetary stimulus generally varying from 0.5 to 1.9. Business Press articles consistently concluded just the opposite: they found that massive FR security purchases during the QE period had no effect on the GDP. 2.1.3

Inequality

Summary of Effects: All three professional press studies surveyed found changes in monetary policy increased inequality. However, the results of one study indicated contractionary monetary policy changes did

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it, while the other two said expansionary monetary policies increased inequality. Business press reports found FR security purchase programs (QE) increased inequality.

2.2 2.2.1

Detailed Findings

Assessment of Monetary Policy Effectiveness in the Business Press

It is commonly asserted in the business press that when the FR adopts a stimulative monetary policy, i.e., purchases securities in the open market, primarily, it results in a financial markets gain, but not a real economy gains, as noted in the quotes below. Stock Market Effects …The Federal Reserve’s main ministration for a weak recovery, after all has been stoking a “wealth effect”. By levitating the stock portfolios of the top 1%, jobs and wage growth for the other 99% would be stimulated. “Higher stock prices will boost consumer wealth and help increase confidence” once explained ex-Fed chief Ben Bernanke. It hasn’t worked. The only confidence simulated has been the confidence of hedge funds that stocks might be a good bet in the short term if central banks are printing money…. (Jenkins, H., WSJ, November 7, 2014)

and …Easy money is driving up the prices of stocks, bonds, houses…central banks are unleashing easy money to fight an imaginary villain, consumer price deflation, at the risk of feeding a real monster, asset price inflation…. (Sharma, R., WSJ, May 12, 2015)

and …Since the Fed began aggressive monetary easing in 2008,…nearly 60% of stock market gains have come on those days, once every six weeks, that the Federal Open Market Committee announces its decisions…Mr. Trump was basically right in saying that Fed policy has done more to boost the prices of financial assets-including stocks, bonds and housing-than it has done to help the economy overall…Much of the Fed’s easy money has gone into financial engineering, as companies borrow billions of dollars

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to buy back their own stock…That kind of finance does more to increase asset prices than to help the middle class…. (Sharma, R. Chief Global Strategist, Morgan Stanley Investment Management. WSJ, September 29, 2016, p. A13) …We were skeptical of the later bouts of QE…Asset prices are up and the wealthy are better off, but the working stiff is still waiting for the economic payoff…. (Editorial, WSJ, May 1, 2015a) …the Fed’s great monetary experiment since the recession ended in 2009 looks increasingly like a failure. Recall the Fed’s theory that quantitative easing (bond buying) and near-zero interest rates would lift financial assets, which in turn would lift the real economy. But while stocks have soared, as have speculative assets like junk bonds and commercial real estate, the real economy hasn’t. This remains the worst economic recovery by far since World War II…. (WSJ editorial, August 23, 2015b)

Bond Market Effects …Easy money is driving up the prices of stocks, bonds, houses…central banks are unleashing easy money to fight an imaginary villain, consumer price deflation, at the risk of feeding a real monster, asset price inflation…. (Sharma, R., WSJ, May 12, 2015) …We were skeptical of the later bouts of QE…Asset prices are up and the wealthy are better off, but the working stiff is still waiting for the economic payoff…. (Editorial, WSJ, May 1, 2015a) …quantitative easing (QE)…zero to negative interest rates and detailed guidance on future monetary policy amount to…(bond)…market manipulation on a grand scale. (Ip, G. WSJ July 19, 2017)

GDP Effects …We were skeptical of the later bouts of QE…Asset prices are up and the wealthy are better off, but the working stiff is still waiting for the economic payoff…. (Editorial, WSJ, May 1, 2015a) …the Fed’s great monetary experiment since the recession ended in 2009 looks increasingly like a failure. Recall the Fed’s theory that quantitative easing (bond buying) and near-zero interest rates would lift financial assets,

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which in turn would lift the real economy. But while stocks have soared, as have speculative assets like junk bonds and commercial real estate, the real economy hasn’t. This remains the worst economic recovery by far since World War II…. (WSJ editorial, August 23, 2015b) …From the beginning of 2008 to the present, more than half the increase in the value of the S&P 500 occurred on the day of Federal Open Market Committee decisions…it appears to make monetary policy with the purpose of managing financial asset prices, …bolstering the share prices of public companies…. (Warsh, K., former FR Board member, WSJ, August 24, 2016)

And …So massive were the Fed purchases of treasury debt and mortgagebacked securities that the central bank effectively funded 55% of the treasury debt issued during Mr. Obama’s presidency, as compared with less than 10% during World War II….Recall that the Fed’s bloated balance sheet is the mirror image of bank reserves, which have swollen as a result of the central bank’s various quantitative easing programs…to $13.07 for every dollar they are required to hold…These massive excess reserves have not expanded bank lending or the money supply because the Fed now pays interest on them…in essence converting them to interest-bearing Fed securities…. (Gramm, P. and Saving, T., WSJ, May 18, 2017)

And …The 2007-08 financial crisis was also followed by vast monetary expansion…The Fed’s expansion featured a dramatic rise in excess reserves…Remarkably, the strong monetary growth came without inflation. The absence of inflation is surprising but may have occurred because weak opportunities for private investment motivated banks…to hold the Fed’s added obligations…(i.e., reserves)…despite the negative real interest rates paid…the key factor is the flight to quality stimulated by the heightened perceived risk in private investment…. (Barro, R., WSJ, September 20, 2016)

Inequality Effects …(The Fed)…expresses grave concern about income inequality while refusing to acknowledge that its policies unfairly increased asset

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inequality…From the beginning of 2008 to the present, more than half the increase in the value of the S&P 500 occurred on the day of Federal Open Market Committee decisions…it appears to make monetary policy with the purpose of managing financial asset prices, including bolstering the share prices of public companies…. (Warsh, K., former FR Board member, WSJ, August 24, 2016)

2.2.2

Assessment of Monetary Policy Effectiveness in the Academic/Professional Literature

Stock Market Effects …we find that fund investors in countries with decreased real interest rates shift their portfolio investment out of the money market and into the riskier equity market. This produces the strongest equity price increase in countries where domestic institutional investors hold a large share of the countries’ stock market capitalization.… (Haur and Lai 2014). Method: Regression of asset fund inflows on short term interest rates using lagged fund values and lagged market values as controls. …We show that event studies provide very strong evidence that U.S. unconventional policy announcements have strongly influenced international bond yields, exchange rates, and equity prices in the desired manner…. (Krishnamurthy and Vissing-Jorgensen 2011) Method: Event Study …$1 trillion purchase … raised stock prices by perhaps 1-1.5 percent…. (Neely 2015) Method: Event Study; Kiley (2018) …the cumulative financial market impact of the FED’s LSAP program is equivalent to an unanticipated cut in the federal funds target rate that ranges between zero (for three-month yields and 197 basis points with the response of stock prices…within this interval (for ten-year yields)…. (Rosa 2012) Method: Event Study ….This paper investigates the impact of the unconventional policies implemented by the Federal Reserve, the Bank of England, the European Central Bank, and the Bank of Japan on the returns on a broad class of assets…for some economies and periods we also find an impact on …stock prices…. (Hosono and Isobe 2014) Method: Event Study

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…Monetary policy actions since 2008 have influenced long-term interest rates through forward guidance and quantitative easing. I propose a strategy to identify the comovement between interest rate and equity price movements induced by monetary policy when an observable representing policy changes is not available. A decline in long-term interest rates induced by monetary policy statements has a larger positive effect on equity prices prior to 2009 than in the subsequent period. This change appears to reflect the impact of the zero lower bound on short-term interest rates…A decline in long-term interest rates induced by monetary policy statements prior to 2009 is accompanied by a 6- to 9-percent increase in equity prices. This association is substantially attenuated in the period since the zero lower bound has been binding - with a policy-induced 100 basis-point decline in 10-year Treasury yields associated with a 1½- to 3-percent increase in equity prices. (Kiley 2018) …Unlike other debt, most bank loans have floating rates mechanically tied to monetary policy rates. Hence, monetary policy can directly affect the liquidity and balance sheet strength of firms through existing loans. We show that firms—especially financially constrained firms—with more unhedged loans display a stronger sensitivity of their stock price, cash holdings, inventory, and fixed capital investment to monetary policy. This effect disappears when policy rates are at the zero lower bound, revealing a new limitation of unconventional monetary policy. The floating-rate channel is at least as important as the bank lending channel operating through new loans…. Using market-based monetary policy surprise measures as in Kuttner (2001) and Gurkaynak et al. (2005), we find that while a typical stock price decreases about 4 to 5 percent in response to a 100 basis point (bp) surprise increase in the federal funds rate, the stock price of a firm that has one standard deviation more bank debt relative to assets decreases about 1.6 percent more. (Ippolito et al. 2018) Method: DSGE w/ calibration …This study examines the impact of unconventional monetary policies on the stock market when the short-term nominal interest rate is stuck at the zero lower bound (ZLB). Unconventional monetary policies appear to have significant effects on stock prices and the effects differ across stocks…. (Wu 2018) Method: Vector Autoregression …The operation of the portfolio balance channel has been emphasized by monetary policymakers as a key channel through which quantitative easing (QE) policies work. We assess whether the investment behavior of insurance companies and pension funds in the United Kingdom during the

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global financial crisis was consistent with such an effect by analyzing both sectoral and institution-level data. Our results suggest QE led to institutional investors shifting their portfolios away from government bonds toward corporate bonds but did not lead to a shift into equities. (Joyce et al. 2017) Method: Regression of Asset types on central bank securities purchases and control variables for economic conditions.

Summary of Business Press and Professional Paper Results: All studies surveyed except one found that QE resulted in increased equity market prices. 60% of stock market gains come on days of FOMC meetings (Charma 2016). More than 50% of S&P 500 gains came on the day of FOMC decisions (Warsh 2016). …$1 trillion purchase … raised stock prices by perhaps 1–1.5 percent…(Neely 2015) Method: Event Study; Kiley (2018). The impact of the FED’s LSAP program on stocks ranges between zero and 197 basis points (Rosa 2012). A decline in long-term interest rates induced by monetary policy statements prior to 2009 is accompanied by a 6- to 9-percent increase in equity prices. This association is substantially attenuated in the period since the zero lower bound has been binding—with a policy-induced 100 basis-point decline in 10-year Treasury yields associated with a 1½- to 3-percent increase in equity prices (Kiley 2018). Typical stock price decreases about 4 to 5% in response to a 100 basis point (bp) surprise increase in the federal funds rate. Worse for firms with much debt (Ippolito et al. 2018). Conclude: Professional/academic literature indicates QE increased stock prices 1–9%; Half the total effect occurred on day of FOMC announcement of stimulative action. Bond Market Effects; Interest Rate Effects Event studies imply that a surprise announcement of a one trillion USD purchase of long-term bonds reduced 10-year U.S. Treasury yields and low-grade corporates by about 30 to 50 basis points while MBS yields declined by 66 basis points and mortgage rates fell further still…. (Krishnamurthy and Vissing-Jorgensen 2011). Method: Event Study …a one standard deviation shock to assets purchases — i.e., 40 billion dollars — reduces 10- year Treasury yields by 10 basis points on impact…. (Bhattarai et al. 2015) Method: Bayesian VAR

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…This paper examines the impact of large scale asset purchases on U.S. asset prices (nominal and inflation indexed bonds, stocks and the dollar spot exchange rates) using an event study with intraday data…Estimation results show that the LSAP news has economically large and highly significant effects on asset prices…For most U.S. asset prices, the effects of asset purchases are not statistically different from an unanticipated cut in the federal funds rate…. (Rosa 2012) Method: Event Study …Table… displays estimated effects on the 10-year government bond yield of a QE bond purchase equivalent to 10 percent of GDP. These studies unanimously conclude that QE lowers bond yields significantly, …. As shown in table, the two most common types of QE studies are event studies and time series studies. The simplest event studies add up movements in bond yields around central bank announcements concerning QE programs. Studies use different sizes of event “windows,” from 30 minutes to 3 days bracketing the announcements. Shorter windows risk missing some of the market reaction; longer windows risk including the effects of other news that is unrelated to QE. By and large, the results are not particularly sensitive to the size of the event window. When QE programs catch markets by surprise, simply adding up the yield movements in the windows is a reasonable way to estimate the total effects of QE on yields…. (Gagnon 2016) Method: Literature Review of Past Empirical Studies …This paper investigates the impact of the unconventional policies implemented by the Federal Reserve, the Bank of England, the European Central Bank, and the Bank of Japan on the returns on a broad class of assets in a comprehensive and consistent manner. Controlling for market expectations, we find that for most economies and periods, policies had the effect of lowering long-term government bond yields…. (Source: Hosono and Isobe 2014) Method: Event Study …“For instance, between 2012 and 2016 …the ECB’s accommodative monetary policy helped to reduce mortgage interest rates by 1.3 pp…. However, this low interest rate environment is also pushing up house prices. It encourages investment in property by lowering returns on financial savings and also makes it easier to buy more expensive housing…. (Source: Caixa Bank Research: “The Impact of Monetary Policy on Housing Prices”. 10 January 2018). Method: Graphical comparison of housing prices and interest rates

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…The operation of the portfolio balance channel has been emphasized by monetary policymakers as a key channel through which quantitative easing (QE) policies work. We assess whether the investment behavior of insurance companies and pension funds in the United Kingdom during the global financial crisis was consistent with such an effect by analyzing both sectoral and institution-level data. Our results suggest QE led to institutional investors shifting their portfolios away from government bonds toward corporate bonds but did not lead to a shift into equities…. (Joyce et al. 2017) Method: Regression of Asset types on central bank securities purchases and control variables for economic conditions

(Klein and Evans (1968), Eckstein (1983). Fair (2004) and Heim (2017) also found positive correlations between monetary policy and credit market assets prices; detailed descriptions of those works are given later in this chapter.) GDP Effects “This paper studies the effects of FOMC forward guidance. We begin by using high frequency identification and direct measures of FOMC private information to show that puzzling responses of private sector forecasts to movements in federal funds futures rates on FOMC announcement days can be attributed entirely to Delphic forward guidance”. (Campbell et al. 2017) Method: Event Study, DSGE …Simulation results using a large-scale model (FRB/US) suggest that QE does not improve economic performance if the steady-state interest rate is high, confirming that such policies were not advantageous from 1960 to 2007. However, QE can offset a significant portion of the adverse effects of the ZLB when the equilibrium real interest rate is low…. (Kiley 2018) Method: simulation using large scale FRB/US model

In January 2013, the Bank of Japan (BOJ) announced that it would pursue a 2% inflation target, and in April 2013 it announced the Quantitative and Qualitative Monetary Easing Program, intended to achieve the 2 percent target within two years. From 2013 to early 2016, the overnight nominal interest rate was close to zero, and it has been negative since early 2016. In Graph 2.1, note that the monetary base in Japan (a measure of total liabilities of the Bank of Japan) increased by about threefold from the beginning of 2013 to May 2017.

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Graph 2.1 Comparisons of Japan, Canada and the U.S

If QE is indeed effective in increasing inflation—the BOJ’s ultimate goal—then surely inflation should have increased in response to this massive QE program. But Graph 2.1 shows that this was not the case, if we look at the consumer price index (CPI) for Japan. CPI indeed increased in 2014, but largely due to an increase of three percentage points in Japan’s consumption tax in April 2014, which fed directly into the CPI measure. But, from mid-2015 to March 2017, average inflation in Japan was roughly zero, obviously far short of the 2% target. Since the financial crisis, central bank interest rate policy has been little different in Canada and the U.S. But, the Bank of Canada did not engage in QE over this period, while the Fed did. As of December 2016, the Bank of Canada’s balance sheet stood at 5.1% of GDP, as compared to 23.6% of GDP for the Fed. Canada and the U.S. are typically subject to similar economic shocks, given their close proximity and similar level

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of economic development; so, if QE were effective in stimulating aggregate economic activity, we should see a positive difference in economic performance in the U.S. relative to Canada since the financial crisis. In Graph 2.1, we show real GDP in Canada and the U.S., scaled to 100 for each country in the first quarter of 2007. The figure shows that there is little difference from 2007 to the fourth quarter of 2016 in real GDP performance in the two countries. Indeed, relative to the first quarter of 2007, real GDP in Canada in the fourth quarter of 2016 was 2% higher than real GDP in the U.S., reflecting higher cumulative growth, in spite of supposedly less accommodative monetary policy. Thus, in these two natural experiments, there appears to be no evidence that QE works either to increase inflation, if we look at the Japanese case, or to increase real GDP, if we compare Canada with the U.S…. (Source: Williamson 2017). Federal Reserve Bank of St. Louis) Method: Graphical comparisons of U.S. vs. Canada growth trends; inflation vs monetary base growth trends. …between 2012 and 2016…this low interest rate environment is also pushing up house prices. It encourages investment in property by lowering returns on financial savings and also makes it easier to buy more expensive housing…. (Source: Caixa Bank Research Monthly Report 2018) “The Impact of Monetary Policy on Housing Prices”. Caixa Bank, Spain. 10 January 2018. Method: Graphical comparison of house prices and interest rates …Our estimates imply that the efforts by the Federal Reserve to stimulate the economy since July 2009 succeeded in making the unemployment rate in December 2013 1% lower, which is 0.13% more compared to the historical behavior of the Fed…. (Wu and Xia 2016) Method: Maximum Likelihood Regression

Neely and Bhattarai (2016) report that …Bhattarai et al. (2015) find that a one standard deviation shock to assets purchases—i.e., 40 billion dollars … increases industrial production and consumer prices by 0.4 and 0.1%, respectively, at a horizon of 10 months. Weale and Wiedelak (2016) similarly used VAR analysis to determine that an asset purchase of 1% of GDP raises GDP and CPI by 0.58 and 0.62%, respectively. Their conditional forecasting analysis implies that QE1 raised GDP and CPI by about 2 percentage points at its peak, while QE2 raised GDP and the CPI by about 6 percentage points. Method: Literature Review.

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…Fiscal Policy can be an effective countercyclical tool if monetary policy accommodates the fiscal expansion…. (Canova and Pappa 2011) Method: Structural VAR … We find clear evidence of positive associations between the degree of monetary ease in advance of fiscal consolidation programs and … programs’ success … Successful consolidations tend to be preceded, or accompanied …by greater loosening of monetary policy…. (Hellebrant et al. 2012) Method: Graphical comparisons of interest rate vs. fiscal consolidation trends.

Neely and Bhattarai (2016, pp. 27–31) report that:…Chen et al. (2012) calibrate their DSGE model that features preferred habitat preferences, and thus a role for the portfolio balance channel, to assess the effects of LSAPs. They find that a $600 billion purchase of long-term government bonds (roughly matched to the announcement of the QE2 program), together with a credible commitment to hold short-term interest rates at zero for four quarters, increases GDP growth by 0.13% and inflation by 3 bp (both annualized). The bulk of the effects however, is due to the credible commitment by the central bank to hold short-term interest rates at zero in future.… Bhattarai et al. (2015) calibrate their DSGE model that features a central bank with balance sheet concerns that conducts policy under discretion,…The results from the calibrated model implies that QE2, which doubles the size of the Federal Reserve’s balance sheet in their calibration without changing much the duration mismatch in Treasury holdings, increased output by about 45 bp. Then they estimate that QE3 or MEP/Operation Twist, which increases the average duration of Treasury bond holdings by around 5 quarters without changing the size of the Federal Reserve’s balance sheet, increased output by about 12 bp. The results suggest that QE2 stimulated the economy more effectively than QE3…. Gambacorta et al. (2014), in an early study, estimate a panel VAR with feasible generalized least squares (FGLS) on monthly output, price, VIX and central bank assets data from 8 countries—Canada, the euro area, Japan, Norway, Switzerland, Sweden, the United Kingdom and the United States—over the sample January 2008 to June 2011….the authors conclude that an exogenous increase in central bank assets, which they consider to be the monetary policy instrument at the zero lower bound, temporarily increases output and prices. …Walentin (2014) uses

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a structural VAR (SVAR) to argue that shocks to mortgage spreads— the mortgage rate less the Treasury rate of the same maturity—which he interprets as credit supply (policy) disturbances, have large macroeconomic effects in the US. Unanticipated increases in this spread reduce house prices, residential investment, consumption, and GDP. Walentin then indirectly estimates the effects of QE1 in a counterfactual exercise that imposes the ZLB on the Fed Funds rates and uses previous empirical estimates of the effects of 30 QE1 on mortgage spreads. The implied effects are economically large: at peak, consumption and GDP increase by 1.18 and 1.02 percentage points respectively, while residential investment and house prices increase by 6.74 and 3.00 percentage points respectively…Bhattarai et al. (2015) also estimate the impact of U.S. QE using a structural Bayesian VAR …Unanticipated fluctuations in Federal Reserve assets produce substantial macroeconomic and financial effects on the U.S. economy…Similarly, Baumeister and Benati (2013) employ a timevarying parameter VAR framework to identify a pure term spread shock that leaves the federal funds rate unchanged but affects the 10-year Treasury yield. The authors estimate that reductions in this interest rate spread produce substantial macroeconomic effects during the ZLB period. Then, using the effects of asset purchases on the interest rate spread, the authors infer the effects of LSAPs on the macroeconomy, finding that such policies successfully averted deflation and output collapse.…Weale and Wieladek (2016) employ a hybrid method that merges LSAP announcement effects approach with a monthly Bayesian VAR to assess the macroeconomic effects of these policies. The paper assumes that the QE policy instrument is the cumulated level of LSAP purchases, scaled by GDP. These authors find that an asset purchase of 1% of GDP raises US (UK) GDP by 0.58% (0.25%) and CPI by 0.62% (0.32%), all of which are statistically significant…Method: Literature Review. Klein and Evans (1968), uses a large scale structural macroeconomic model of 76 equations, 47 of which are behavioral, i.e., econometrically estimated. Klein argues the federal reserve controls both the discount rate and, the level of free reserves through open market operations, and therefore monetary policy can have macroeconomic effects (p. 50). Simulated changes in both of these monetary variables positively affect the GDP. Fiscal policy changes also positively affect GDP in the model and would have an additive effect to the fiscal policy changes. The crowd out problem is not discussed in the book, neither is the role of accommodative

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monetary policy as a factor affecting fiscal policy’s effectiveness. Method: 2SLS Regression of structural IS/LM model equations. The Eckstein (1983) large scale structural econometric model, the GDP determination sub-model contains 212 equations, 64 of which are econometrically estimated. Eckstein shows Federal Reserve control over interest rate policy has an effect on consumption and investment. In this respect, the model is similar to Klein’s. The “crowd out” problem resulting when stimulative fiscal policy is unaccompanied by monetary stimulus to keep interest rates down is discussed, and empirical results of simulations of the effects of fiscal policy with and without accommodating monetary policy shown (pp. 35–40). Monetary policy designed to keep interest rates down or augment bank loanable funds are found to be effective in addressing the crowd out problem, therefore leaving a net positive effect of monetary policy on GDP. Method: 2SLS Regression of structural IS/LM model equations (Author’s note: findings of this current study are the same as Eckstein’s). Fair’s (2004) large scale structural econometric model has 100 equations, of which 30 are econometrically estimated. Though, as was the case with Klein and Eckstein, none of the consumption or investment equations show the money supply as one of the determinants, the consumer services demand model showed services demand to be a determinant of short term interest rates, and both the durables and nondurable consumer goods equations show demand to be in part a negative function of mortgage interest rates, as is the demand for residential housing. The demand for fixed investment is in part determined (negatively) by the bond interest rate. Since the Fed has some control over interest rates, we conclude Fair’s model shows that monetary policy can positively affect GDP. Fair includes an interest rate determination equation for the 3 month U.S. Treasury bill interest rate, which is a function of percentage changes in the M1 money supply and the two Taylor Rule variables: unemployment and inflation. Expectations theory is used to model longer term bond and mortgage rates as functions of current and lagged values of short term interest rates (the 3 month treasury bill rate). Empirical results indicate a change in the treasury bill rate has a positive impact on the GDP (p. 155). There is no discussion of the extent to which fiscal policy might vary in effectiveness with accommodating monetary policy. Method: 2SLS Regression of structural IS/LM model equations.

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The Heim (2017) model is a 56 equation structural econometric model of the macroeconomy containing 38 econometrically estimated equations. The current year prime interest rate was found significant in 6 of 7 models of consumption or its individual subcomponent parts, and in 4 of 7 models of investment and its individual subcomponent parts. Current year M1 was found positively related to housing investment, even after controlling for the positive, statistically significant, effects of declining current year interest rates. (This suggests credit rationing by the FR, as well as FR-induced interest rate changes affect housing investment.) With investment, typically effects were felt after a one or two-year lag. In this study there are two models of the determinants of the prime interest rate: a Keynesian demand for money model, and a Taylor Rule model. The money supply was systematically related to interest rates in the Keynesian model in all time periods tested, and also in the Taylor Rule model, but only for the QE time period tested. Heim’s structural equations allow for indirect effects on interest rates through M1’s effect on inflation. Method: 2SLS Regression of structural IS/LM model equations. For the Heim (2017) model, dynamic simulations using the statistically estimated structural equations showed the positive effects of fiscal stimulus programs (permanent tax cuts and temporary spending stimulus) on GDP were more than fully offset by the crowd out effects. Hence, the net effect was slightly negative, Permanent increases in spending yielded better results, but still only led to essentially no net effect on GDP. However, when accompanied by accommodative monetary policy, the net negative effects of fiscal stimulus programs were fully offset by an accommodating monetary stimulus of the same size as the fiscal stimulus, and the net effect on GDP was positive, but negligibly small, only increasing GDP about (0.4%) of the size of the combined fiscal and monetary stimulus by the time the new equilibrium was reached. The simulation used the Obama fiscal stimulus number ($800 billion), and an equal sized increases in M1 by the FR in the simulation. The small permanent effect is due to the fact that much of the Obama stimulus was a one year only stimulus. …We introduce liquidity frictions into an otherwise standard DSGE model with nominal and real rigidities… We find that the effects of the liquidity shock can be large, and show some numerical examples in which the liquidity facilities of the Federal Reserve prevented a repeat of the Great

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Depression in the period 2008-2009”. (Del Negro et al. 2017) Method: DSGE model simulation …We quantify government spending multipliers in US data using Bayesian prior and posterior analysis of a monetary model with fiscal details and two distinct monetary-fiscal policy regimes…Short-run output multipliers are comparable across regimes—posterior means around 1.3 on impact—but much larger after 10 years under passive money/active fiscal than under active money/passive fiscal—90 percent credible sets of [1.5, 1.9] versus [0.1, 0.4] in present value, when estimated from 1955 to 2016…. (Leeper et al. 2017) Method: Monetary DSGE Model analysis …Using a rich dataset on government spending forecasts in Japan, we provide new evidence on the effects of unexpected changes in government spending when the nominal interest rate is near the zero lower bound (ZLB). The on-impact output multiplier is 1.5 in the ZLB period and 0.6 outside of it. We estimate that government spending shocks increase both private consumption and investment during the ZLB period, but crowd them out in the normal period…. (Wataru et al. 2018) Method: Comparison of DSGE models under different assumptions …The effects of monetary policy are less powerful in recessions, especially for durables expenditure and business investment… We also find evidence that contractionary policy shocks are more powerful than expansionary shocks, but contractionary shocks have not been more common in booms…. (Silvana and Thwaites 2016) Method: Regression of key economic variables on a monetary shock with controls for GDP trends, prior period values of the dependent variable and the federal funds rate …In our baseline experiment intended to capture the effectiveness of the American Recovery and Reinvestment Act of 2009, the output multiplier at the ZLB is 1.9 when the weight on the lagged shadow rate is zero, and 0.5 when the weight is 0.85.” …. (Wataru et al. 2018) Method: New Keynesian DSGE analysis …This paper assesses the macroeconomic effects of unconventional monetary policies by estimating a panel vector autoregression (VAR) with monthly data from eight advanced economies over a sample spanning the period since the onset of the global financial crisis. It finds that an exogenous increase in central bank balance sheets at the zero lower bound leads to a temporary rise in economic activity and consumer prices.… Individual

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country results suggest that there are no major differences in the macroeconomic effects of unconventional monetary policies across countries, despite the heterogeneity of the measures that were taken…. (Gambacorta et al. 2014) Method: VAR …Unlike other debt, most bank loans have floating rates mechanically tied to monetary policy rates. Hence, monetary policy can directly affect the liquidity and balance sheet strength of firms through existing loans. We show that firms—especially financially constrained firms—with more unhedged loans display a stronger sensitivity of their stock price, cash holdings, inventory, and fixed capital investment to monetary policy. This effect disappears when policy rates are at the zero lower bound, revealing a new limitation of unconventional monetary policy. The floating-rate channel is at least as important as the bank lending channel operating through new loans…. (Ippolito et al. 2018) Method: DSGE w/ calibration …The impact of announcements of large-scale purchases of government bonds on real GDP and the CPI in the United Kingdom and the United States is explored with a Bayesian VAR, estimated on monthly data from 2009M3 to 2014M5. Four different identification schemes are used, all leaving the reactions of GDP and CPI unrestricted, and the transmission channels of the policy are examined. An asset purchase announcement of 1% of GDP leads to a statistically significant rise of 0.58% (0.25%) and 0.62% (0.32%) rise in real GDP and CPI for the US (UK). The transmission channels differ in the two countries…. (Weale and Wieladek 2016) Method: Bayesian VAR …The Great Inflation of the 1970s can be understood as the result of equilibrium indeterminacy in which loose monetary policy engendered excess volatility in macroeconomic aggregates and prices. The Federal Reserve inadvertently pursued policies that were not anti-inflationary enough because it did not fully understand the economic environment it was operating in. Specifically, it had imperfect knowledge about the structure of the economy and was subject to data misperceptions. The combination of learning about the economy and the use of mis-measured data resulted in policies, which the Federal Reserve believed to be optimal, but when implemented led to equilibrium indeterminacy…. (Lubik and Matthes 2016) Method: VAR and components of DSGE

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Effects on Inequality • … Whenever the main effect of asset purchases occurs initially and primarily in financial markets and induces a pronounced appreciation of financial asset values, adverse distributional effects may result since primarily wealthier households benefit from it. Adverse distributional effects are likely mitigated once the asset purchases unfold their intended impact on real economic activity and inflation. • Since the ECB’s extended asset purchase program started at a time when long-term interest rates were already low and liquidity in the financial system was abundant, major effects are likely to be observed only in financial markets where they lead to an increase in asset prices and therefore valuation gains for the holders of these assets. • This implies that the ECB’s asset purchase program will most likely, at least in the short-run, exacerbate income and wealth inequalities in the euro area… (Bernoth et al. 2015) Method: Graphical analysis of key data; some literature review …We use data from the Federal Reserve’s Tri-Annual Survey of Consumer Finances (SCF) and look at the evolution of income by quantile between the “Pre-QE period” and the “QE period” analyzing three key impact channels of QE policy on income distribution: (1) the employment channel (2) the asset appreciation and return channel, and (3) the mortgage refinancing channel. … we find that while employment changes and mortgage refinancing were equalizing, these impacts were nonetheless swamped by the large dis-equalizing effects of equity price appreciations. Reductions in returns to short term assets added further to dis-equalizing processes between the periods. Bond price appreciations, surprisingly, had little distributional impact. We cannot know precisely how much of these changes are due to QE as opposed to other influences, but to assess potential causal effects we utilize a counter-factual exercise to assess the quantitative range of impacts of QE on the main channels. We conclude that, most likely, QE was modestly dis-equalizing, despite having some positive impacts on employment and mortgage refinancing. The modestly dis-equalizing impacts were due to both policy choices and deep seated structural problems, such as the long-term deterioration in labor market opportunities for many workers due to globalization and legal and political reductions in labor bargaining power that have contributed to long term wage stagnation. Finally, there is no support in our analysis, for the proposition that raising interest rates would be an efficient mechanism for improving income distribution, because of the likely costs in terms of

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employment and debt refinancing opportunities. ∗(Montecino and Epstein 2015) Method: Recentered Influence Function (RIF) Regressions; OaxacaBlinder decomposition technique …We study the effects of monetary policy shocks on - and their historical contribution to - consumption and income inequality in the United States since 1980 as measured by the Consumer Expenditure Survey. Contractionary monetary policy systematically increases inequality in labor earnings, total income, consumption and total expenditures. Furthermore, monetary policy shocks account for a non-trivial component of the historical cyclical variation in income and consumption inequality. Using detailed micro-level data on income and consumption, we document some of the different channels via which monetary policy shocks affect inequality, as well as how these channels depend on the nature of the change in monetary policy…. (Coibion et al. 2017) Method: Time series analysis of variables constructed from Consumer Expenditures Survey (CEX) data

Summary of Effects: All three studies surveyed found changes in monetary policy increased inequality. However, the results of one study indicated contractionary monetary policy changes did it, while the other two said expansionary monetary policy increased inequality. (Klein and Evans (1968), Eckstein (1983). Fair (2004) and Heim (2017) also found positive correlations between monetary policy and GDP; they are also discussed in the next section of this chapter.) 2.2.3

Comparisons of Findings of the Professional and Business Press

1. Like the business press, the professional press consistently finds open market operations by the FR stimulate the bond and stock markets. 2. Unlike the business press, the professional/academic press finds open market operations by the FR increase the GDP, at least during the QE years. The professional/academic press finds Federal Reserve asset purchases between $40 billion and roughly $1 trillion increased GDP from near 0.0 to 0.58%, with no correlation of study results with purchase size. Multiplier results were similarly mixed, with multiplier effects of monetary stimulus generally varying from 0.5 to 1.9. (Note: this studies concludes that because Federal Reserve securities purchases increased loanable funds so much during the QE period, they easily offset government deficits during that period,

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allowing the stimulus effects of government deficits to work, perhaps for the first time since at least 1960. See Chapters 15–29). 3. All professional studies surveyed indicated stimulative monetary policy affected inequality, but results were mixed as whether it was increased or decreased it. The business press found stimulative monetary policy, at least during the QE years, increased inequality.

2.3 A Comparison of Cowles, DSGE, and VAR Methodologies Used in Literature Review Somehow, in summarizing the findings of past literature on controversial issues, we have to take into consideration the quality of the studies that produced the results. Is does no one any good to say that overall, results were “mixed” if all the best methodological studies point in one direction and all the worst in another. In this book, we wish to both determine the underlying science that allows monetary policy to have an effect on the real economy, and evaluate how well accommodative monetary policy has achieved its objective of stimulating the real economy. Methodologically, how should we go about it? John Taylor, the author of the “Taylor Rule” hypothesis, noted that monetary policy can be evaluated by either historical analysis or the right kind of econometric analysis (structural modeling). He has noted that: … Studying monetary history is, of course, not the only way to evaluate monetary policy. Another approach is to build structural models of the economy and then simulate the models stochastically with different monetary policy rules…. (Source: Taylor, J. “An Historical Analysis of Monetary Policy Rules”. NBER Working Paper No. 6768, October 1998)

The approach taken in this book is to determine the underlying structural relationships of variables in the economy that determine the effectiveness of monetary policy, i.e., structural modeling: Over 1200 separate structural models are tested in volumes I and II, adding to standard models of investment’s and consumption’s determinants, variables including deficit variables and a wide variety of bank reserves and loanable funds variables. Initial findings for each model are retested in from 5 to 17 other, though sometimes overlapping time periods to ensure the initial results were replicable, not

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idiosyncratic. No result can be considered good science unless it is replicable. It is, after all, 400 years since Newton and the scientific revolution. What’s the point, this far into a scientific age, of publishing any nonscientific work? In today’s scientific world, economists need to be able to assure others that their tests of relationships in one model and in one time period are not just spurious, and have been replicated, preferably before publication. When doing scientific testing, using good econometric models is necessary, but not easy, as noted by Neely and Bhattarai: …Studying the effect of unconventional …(monetary)…policy on the macro economy is both more important and more difficult than studying its effects on asset prices and yields. It is more important because the ultimate goals of central banks pertain to output, inflation and, eventually, consumer welfare. It is also more difficult because problems of endogeneity, simultaneity, omitted variables, specification error and measurement errors are much more serious than for financial markets, which are amenable to the use of “event studies” to gauge the effects of policy announcements. To study the effect of unconventional monetary policy on macro variables, one must use low frequency data and control for non-monetary factors…. (Neely and Bhattarai 2016, p. 27)

In surveying the literature of the last few decades, virtually all empirical studies of monetary effects were found to use either the DSGE methodology, or some variant of the VAR methodology. Few employed the type of econometrically-based structural model referred to above by Taylor (1998). These three methods are very different ways for trying to uncover empirical reality. Choice of method is critically important: more often than economists would like to admit; they provide different answers to the same economic questions using the same data. Heim (2017, pp. 48–114) has a 46 page section comparing: 1. The underlying economic theories tested by each, and the strengths and weaknesses of each, and 2. How successfully these three methodologies were when used to develop a model explaining fluctuations in U.S. GDP 1960–2010. 3. How successfully each of the three methodologies, when used to reestimate their own models using only 1960–2000 data, were

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at predicting or explaining actual fluctuations of the GDP in the 10 years following the estimation period, i.e., between 2001–2010. A structural model of the Cowles Commission type was found to be by far the most successful at explaining the actual data. Cowles models are versions of the IS/LM model, a large scale demand driven structural model with equations to explain the determinants of each variable that affects the GDP. In a Cowles model, every structural equation that plays a role in determining GDP (equations expressing the determinants of consumption, investment, determinants of government spending, imports, etc.) is empirically estimated. Results are combined in an IS curve model and used to calculate the GDP. Typically, these models include scores or even hundreds of equations. By comparison, DSGE models, which in some cases are detailed enough to be considered structural models, mainly tend to be largely deductions from first principles: hypotheses considered so self-evident they do not require testing to be considered “true,” e.g., the amount people work is a trade off between income and leisure, what you have to pay people to get them to work, and the utility of leisure. Another example of these first principles is the rational expectations hypothesis, which in many DSGE models translates into an assumption people today have full and accurate information of their future income, and from this assumption deduce (e.g.) that consumer spending levels will always be constant at today’s levels, regardless of the (assumedly known) course of future income. Why? Because that is the only path consistent with the assumption that consumers always maximize utility. Some people consider DSGE to be good theory, but bad science, since the equations in its models are mostly deductively, not inductively (statistically) derived. Others say it is bad theory as well, and models, when tested, rarely explain actual data very well. VAR models, by comparison, are considered by some to be good science (every relationship is empirically derived), but bad theory, since the tested relationships are often devoid of theoretical content, i.e., they are not accurate expressions of any known economic theory. A full examination of the strengths and weaknesses of the DSGE and VAR models used in the accommodating monetary policy literature reviewed above would require many more pages than space here allows, and would just duplicate Heim (2017), readers are referred there for comparisons of how well work relative to one another. A

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briefer comparison is provided in the methodology section of this paper (Chapter 3). The results presented in this book on the effectiveness of monetary policy, and the exact mechanisms through which it works are based on empirically derived standard theoretical models of consumption and investment given in IS curve “structural” models described above. The four major large-scale econometric models of the macroeconomy of the type underlying this study, referred to Cowles Models, are the Klein and Evans Model (1968), the Eckstein Model (1983), the Fair Model (2004), and the Heim Model (2017). All four were described in detail earlier in Sect. 2.2.2.3 of this chapter. All of these models are basically IS/LM curve, demand driven, general equilibrium models.

References Barro, R. (2016, September 9). The Reasons Behind the Obama Non-Recovery. Wall Street Journal. Available at: http://www.wsj.com/articles/the-reasonsbehind-the-obama-non-recovery. Bernoth, K., Konig, P., Beckers, B., & Grazzini, C. (2015). Quantitative Easing—What Are the Side Effects on Income and Wealth Distribution. DIW Berlin: Politikberatung kompakt 99. Bhattarai, S., Chatterjee, A., & Park, W. (2015, November). Effects of US Quantitative Easing on Emerging Market Economies (UNSW Business School Research Paper No. 2015–26). 69. Bhattarai, S., Eggertsson, G. B., & Gafarov, B. (2015). Time Consistency and the Duration of Government Debt: A Signalling Theory of Quantitative Easing (NBER Working Paper No. w21336). Caixa Bank Research Monthly Report. (2018, January 10). The Impact of Monetary Policy on Housing Prices. Caixa Bank, Spain. Campbell, J., Fisher, J., Justiniano, A., & Melosi, L. (2017). Forward Guidance and Macroeconomic Outcomes Since the Financial Crisis. NBER Macroeconomics Annual 2016 (Vol. 31, pp. 283–357). University of Chicago Press. Canova, F., & Pappa, E. (2011, October). Fiscal Policy, Pricing Frictions and Monetary Accommodation. Economic Policy, 26(68), 555, 557–598. Coibion, O., Gorodnichenko, Y., Kueng, L., & Silvia, J. (2017, June). Innocent Bystanders? Monetary policy and inequality. Journal of Monetary Economics, 88, 70–89. Del Negro, M., Eggertsson, G., Ferrero, A., & Kiyotaki, N. (2017, March). The Great Escape? A Quantitative Evaluation of the Fed’s Liquidity Facilities. American Economic Review, 107 (3), 824–857.

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Eckstein, O. (1983). The DRI Model of the U.S. Economy. New York: McGrawHill Book Company. Fair, R. (2004). Estimating How the Macroeconomy Works. Cambridge, MA: Harvard University Press. Gagnon, J. (2016, April). Quantitative Easing: An Underappreciated Success (Policy Brief, Number 16–4). The Peterson Institute for International Economics. Gambacorta, L., Hofmann, B., & Peersman, G. (2014, June). The Effectiveness of Unconventional Monetary Policy at the Zero Lower Bound: A Cross-Country Analysis. Journal of Money Credit and Banking, 48(4). Gramm, P., & Saving, T. (2017, May 18). The Economic Headwinds Obama Set in Motion. Wall Street Journal, p. A15. Gurkaynak, R. S., Sack, B., & Swanson, E. T. (2005, May). Do Actions Speak Louder Than Words? The Response of Asset Prices to Monetary Policy Actions and Statements. International Journal of Central Banking. Hasono, K., & Isobe, S. (2014). The Financial Market Impact of Unconventional Monetary Policies in the U.S., the U.K., the Eurozone, and Japan. Policy Research Institute, Ministry of Finance, Japan. Haur, H., & Lai, S. (2014). Asset Allocation and Monetary Policy: Evidence from the Eurozone. Paper Presented at 2014 Annual Meeting of the American Economic Association. Available at: https://www.aeaweb.org/conference/ 2014/retrieve.php?pdfid=665. Heim, J. J. (2017). An Econometric Model of the U.S. Economy. Hoboken: Palgrave Macmillan. Hellebrant, T., Posen, A., & Tolle, M. (2012). Does Monetary Cooperation or Confrontation Lead to Successful Fiscal Consolidation? (Policy Brief No. BP1208). Peterson Institute for International Economics. Ip, G. (2017, July 19). Markets to Fed: Please Leave Us Alone. Wall Street Journal. Available at: http://www.wsj.com/articles/markets-to-fed-pleaseleave-us-alone. Ippolito, F., Ozdagli, A., & Perez-Orive, A. (2018, May). The Transmission of Monetary Policy Through Bank Lending: The Floating Rate Channel. Journal of Monetary Economics, 95, 49–71. Jenkins, H. (2014, November 7). Does the Fed Read the Election Returns? Wall Street Journal. Joyce, M., Liu, Z., & Tonks, I. (2017, September). Institutional Investors and the QE Portfolio Balance Channel. Journal of Money Credit and Banking, 49(6), 1225–1246. Kiley, M. (2018). Quantitative Easing and the “New Normal” in Monetary Policy (Finance and Economics Discussion Series, 2018-004). Divisions of Research & Statistics and Monetary Affairs Federal Reserve Board, Washington, DC.

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Klein, L., & Evans, M. (1968). The Wharton Econometric Forecasting Model. Philadelphia: Wharton School of Finance and Commerce, University of Pennsylvania. Krishnamurthy, A., & Vissing-Jorgensen, A. (2011, October). The Effects of Quantitative Easing on Interest Rates: Channels and Implications for Policy (*NBER Working Paper No. 17555). Kuttner, K. (2001). Monetary Policy Surprises and Interest Rates: Evidence from the Fed Funds Futures Market. Journal of Monetary Economics., 47 (3), 523– 544. Leeper, Eric M., Traum, Nora, & Walker, Todd B. (2017). Clearing Up the Fiscal Multiplier Morass. American Economic Review, 107 (8), 2409–2454. Lubik, T., & Matthes, C. (2016, September). Indeterminacy and Learning: An Analysis of Monetary Policy in the Great Inflation. Journal of Monetary Economics, 82, 85–106. Montecino, A., & Epstein, G. (2015). Did Quantitative Easing Increase Income Inequality? Available at: https://www.umass.edu/economics/sites/default/ files/Montecino.pdf. Neely, C. (2015). Unconventional Monetary Policy Had Large International Effects. Journal of Banking & Finance, 52(C), 101–111. Neely, C., & Bhattarai, S. (2016). A Survey of the Empirical Literature on U.S. Unconventional Monetary Policy (Working Paper 2016-021A). Federal Reserve Bank of St. Louis, p. 3. Rosa, C. (2012, May). How “Unconventional” Are Large-Scale Asset Purchases? The Impact of Monetary Policy on Asset Prices (Federal Reserve Bank of New York Staff Reports, No. 560). Sharma, R. (2015, May 12). The Federal Reserve Asset Bubble Machine. Wall Street Journal, p. A13. Silvana, T., & Thwaites, G. (2016). Pushing on a String: US Monetary Policy Is Less Powerful in Recessions. American Economic Journal: Macroeconomics, 8(4), 43–74. Taylor, J. (1998, October). An Historical Analysis of Monetary Policy Rules (NBER Working Paper No. 6768). Walentin. (2014). Business Cycle Implications of Mortgage Spreads. Journal of Monetary Economics, 67 (C), 62–77. Warsh, K. (2016, August 24). The Federal Reserve Needs New Thinking. Wall Street Journal. Available at: http://wsj.com/articles/the-federal-reserveneeds-new-thinking-1472076212. Wataru, M., Nguyen, T., & Sergeyev, D. (2018). Government Spending Multipliers Under the Zero Lower Bound: Evidence from Japan. American Economic Journal: Macroeconomics, 10(3), 247–277. Weale, M., & Wieladek, T. (2016, May). What Are the Macroeconomic Effects of Asset Purchases? Journal on Monetary Economics, 79, 81–93.

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Williamson, S. (2017). Quantitative Easing: How Well Does This Tool Work? Regional Economist, 3rd Qtr. 2017. Federal Reserve Bank of St. Louis. Available at: www.stlouisfed.org/publications/regional-economist/third-qua rter-2017/quantitative-easing-how-well-does-this-tool-work. Wu, W. (2018, March–April). The Credit Channel at the Zero Lower Bound through the Lens of Equity Prices. Journal of Money Credit and Banking, 50(3–4). Wu, C., & Xia, D. (2016, March). Measuring the Macroeconomic Impact of Monetary Policy at the Zero Lower Bound. Journal of Money, Credit and Banking. Wiley Online Library. Available at: http://onlinelibrarywiley.com/ doi/full/10.111/jmcb.12300.

CHAPTER 3

Methodology

This study has two objectives: 1. Determine if methods used by the Federal Reserve to implement accommodative monetary policy are effective. This is done in Chapters 7–9 and the methodology for doing it is discussed there, and 2. Econometrically determine if increases in the pool of loanable funds can offset the “crowd out” effects on consumer and business spending that result from government deficits. The methodology for doing that is described in this chapter. Financing government deficits requires borrowing from the pool of loanable funds. It is argued that this reduces what is left for consumers and businesses to borrow, and that reduced access to borrowed money reduces consumer spending, causing “crowd out”. If so, this would cause a negative effect on GDP that offsets the positive stimulative effect of the deficit. The existence of the crowd out effect has been amply statistically documented by Heim (2017a, b). The econometric portion of this study will further verify the existence of this crowd out effect (Chapters 13 and 14) with the exact models used in this study. It will then test to determine if same-period growth in the pool of loanable funds can offset crowd out

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. J. Heim, Why Fiscal Stimulus Programs Fail, Volume 1, https://doi.org/10.1007/978-3-030-65675-1_3

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effects (Chapters 15–19). Finally, this study will test to determine if any alternative possible ways of offsetting crowd out actually seem to work as well at reducing crowd out and changes in the total pool of loanable funds (Chapters 20–24). These alternatives include (1) effects of loanable funds on several of consumption and investment’s determinants and (2) increases in the M1 money supply. The test method is to take standard, commonly accepted models of the determinants of consumption and investment (baseline models), and add to these models any same-period changes in loanable funds (or one of the alternatives) that might have an effect on crowd out. The new variable would be added either as a modification to the deficit variable that reduces the deficits size or as a separate variable, or both. If the addition to the baseline model increases the model’s ability to explain variance in consumption and investment, we conclude the evidence supports the theory that loanable funds (or one of the alternatives) can offset crowd out effects; that is, we reject the null hypothesis. If not, we reject the hypothesis that loanable funds or one of its alternatives can offset the crowd out effects of deficits. As a secondary test, we also look for changes in the significance level of the deficit (crowd out) variable, before and after modification by loanable funds changes (or changes in one of the alternatives). Typically, before adding any loanable funds modifier, the deficit variable is found negatively related to consumption and investment and statistically significant, indicating deficits do cause a crowd out problem. If a modifier is added to the deficit variable, it will modify (generally, reduce) the magnitude of the hypothesized crowd out effect of any deficit from the size of the deficit itself (T − G) to (T − G) + (S + FB). If it does modify crowd out effects, the modified deficit’s statistical significance should rise compared to that of the deficit alone, and the R 2 will rise, indicating that the deficit alone is an imperfect “errors in variables” estimate of crowd out’s actual effect. If the modifier does not actually offset the crowd out effects, as measured by the deficit alone, adding it to the deficit will create an errors in variables problem (Johnston 1963) that reduces the statistical significance of the modified deficit variable, compared to the baseline model. Of course, that’s in a world where testing conditions are ideal. But conditions are never ideal: multicollinearity relationships change with the changed crowd out variable used, or the change in loanable funds may not be fully available to offset crowd out (as we show in Chapter 7), so

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out loanable funds variable itself may have errors in variables problems. These problems and others that can distort regression results are discussed in more detail further below. When evaluating any set of results, they have to be taken into account. We found ourselves continually asking whether insignificant t-statistics resulted from a technical problem or were a substantive result telling us to reject the particular hypothesis being tested. This book’s tests build on the consumption and investment equations found in an empirically tested, 56 equation model of how the U.S. economy works (Heim 2017a), whose results were found replicable in several different time periods. These equations will provide the baseline information on how consumption and investment are affected by changes in variables that are commonly thought to affect them. The use of these models is a critical to this book’s statistical methodology for testing loanable funds effects. The credibility of a study’s findings that a variable is statistically significant or insignificant, or what its true magnitude is, depends heavily on how well the study controlled for all the other variables that also affect the dependent variable (the “left out variables” problem; see Goldberger 1961). The Heim (2017a) study exhaustively tests, in multiple time periods and multiple models, to determine what variables need to be included as controls in any consumption or investment function when testing for the magnitude or significance of any one variable (like loanable funds effects). The tests used in that study to ensure that controls were adequate are discussed in the next section and are our justification for why we used certain variables and not others as controls in our test of crowd out or loanable funds effects. All the controls used in that baseline study are repeated to test any new variables added to the models in this study. We did not retest the variables Heim (2017a) already tested on the grounds that would have been redundant, but all the stationarity, endogeneity, time period robustness, etc., tests used there were repeated for any additional variables tested in those models in this study. Then, using the same variables found in Heim (2017a) to be determinants of consumption and investment, and the same 1960–2010 dataset, we then retest those “standard” models to determine: 1. Whether government deficits have crowded out consumer and business spending 1960–2010.

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2. Whether crowd out is most accurately measured by the deficit’s size, or by the deficit as reduced by any offsetting increases in loanable funds that occur at the same time. 3. Whether dividing changes in total loanable funds into its exogenous and endogenous parts indicates that one is more important in offsetting crowd out than the other. The exogenous part is defined as the part generated by FR purchases of government securities, including agency and mortgage-backed bonds. The endogenous part of loanable funds is taken to be the part that moves up and down with the economy, i.e., total loanable funds (national savings plus foreign borrowing, or S + FB) minus federal reserve security purchases. However, we show later in this book (Chapter 7) that evidence suggests not all, in fact very little, of the proceeds received by banks from selling government securities to the Federal Reserve is used by them to lend to those who wish to borrow so they can buy real goods and services, like cars and furniture, which will increase the GDP. Much of the money received from selling securities to the FR may go to buying other securities. After all, most sellers of securities to the Fed are in the business of selling and buying securities, not selling securities and buying real goods and services. In this case, adding the FR purchases modifier may just create an error in variables problem that lowers the model’s R 2 and the statistical significance of the deficit variable. We could argue the deficit variable, once modified, turns insignificant because crowd out has been eliminated. However, it is shown elsewhere in this study that FR purchases were never big enough to offset most crowd out that occurred. They have only been big enough to offset a small portion of the yearly deficits occurring, about 44% as large on average from 1960 to 2000, and about 23% as large from 2000 to 2007. Hence, generally, FR purchases shouldn’t have turned the crowd out effect variables insignificant because they were not big enough to offset more than a small part of the deficit’s crowd out effects. There are other reasons that can cause the deficit variables to look statistically insignificant which were noted above.

3.1

General Methodological Issues

As noted earlier, the reliability of marginal effect and statistical significance estimates for any variable in a regression is largely determined by

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how accurately the analyst determines what other variables need to be controlled for in the model to be tested. Below, we explain how we deal with these issues were treated in the baseline models tested. Since these baseline models are taken from Heim (2017a), we repeat below the methods used there to determine which variables should be in the baseline models, i.e., what variables need to be included as controls in this study’s tests of loanable funds and other possible crowd out modifiers. Methods for treating endogeneity, stationarity, serial correlation, multicollinearity, and heteroskedasticity in any new variables added in this study are also the same as were used in the baseline model and are included in the discussion below Specific results for endogeneity, stationarity, etc., tests for any particular new variable added to the baseline model are discussed at the beginning of the chapter dealing with that alternative and are only discussed generically here. Parameter estimates for the models tested were developed using 1960– 2010 annual data. All data were obtained from the Economic Report of the President (2002 and 2011) and the Federal Reserve’s Flow of Funds Accounts (2011). The baseline economic models used were “standard models” in the sense that all variables commonly cited in the literature as determinants of consumption, investment, etc., were used in the initial hypotheses tested in this study. Variables initially tested for the baseline model were listed in Chapter 1.3 of Heim (2017a) and again in the appropriate chapter of Heim (2017a) detailing test results for each equation in the model (Chapters 4–16), and in the summary and conclusions chapter, Chapter 19, of that book. To this list of variables were added the variables tested in this study. Some preliminary testing was done to weed out variables cited only occasionally in the literature and found to be statistically insignificant in our initial tests. However, when variables commonly theorized to be significant factors were found insignificant (e.g., interest rates and measures of consumer confidence in some consumption and investment functions), they were left in the model even though insignificant. This was done if it was not clear if the insignificance was a substantive finding or the result of a technical problem such as multicollinearity, small sample size, or some other econometric problem. Here, we follow both Otto Eckstein’s (1983) practical advice (see, for example, his multi-family housing demand equation) and that of statistician M. Triola (2011) who notes that in the absence of statistical significance when evaluating the mean of a sample, the best estimate is the sample mean, not zero (if there

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is a theoretical reason to think, the variable’s coefficient is not zero). In addition, it serves as a reminder that the research agenda for the future must include further work to determine if the lack of significance is indicating something substantive about the variable, or only some technical problem with the data and how it is used. When dealing with which (if any) lagged values of a variable to use, economic theory typically provides little if any guidance. In the baseline models presented in Heim (2017a), if theory indicated one variable was a determinant of another, numerous lags were tested, and we adopted the lag level of the determinant found most significantly related to the first variable, unless of course, theory specified a specific lag level. This approach follows that of Tinbergen (1939). Generally, the 2017a study followed the same process in defining the values to include when calculating aggregates and averages. It did not use “Life Cycle” or “Permanent Income” averages of income over time as the definition of the “right” income variable to use as a determinant of consumption, as many economists do. Prior testing of different sized averages strongly indicated that current income, or something very close to it, explained the most variance. The evidence was overwhelming, and sounds the death knell for DSGE economics, which requires that current consumption be based on knowlege of average income over exended periods of time. Similarly, exchange rate testing indicated that the average of the current rate and the past three years’ rates was the lag combination most systematically (significantly) related to consumption and investment, so that is the rate used in our consumption and investment models. All baseline models were initially estimated using OLS. However, testing was then done to determine if endogeneity was present among the variables in the model tested. If so, two-stage least squares (2SLS) was used, using instruments to replace the endogenous variable(s). This was done to eliminate simultaneous equations bias caused by identification problems arising from endogeneity. The process used to identify endogeneity and replace endogenous variables with strong, non-endogenous instruments was as follows, following a process used by Griffiths et al. (2008): • Hausman endogeneity tests were used to determine what needed to be instrumented. • Wald weak instrument tests were used to ensure the instrument was a reasonable proxy for the variable it replaced, i.e., was not a weak instrument.

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• Sargan “Valid Instrument” tests were used to ensure the chosen instrument was free of any endogeneity with the dependent variable. The same process was used to test for and resolve endogeneity issues of any new variables added in this study. In addition, a reasonably objective way of arriving at suitable instruments is needed to ensure instrument components were not picked to ensure the instrument selected would obtain some desired result (e.g., the right sign on itself or other variables). Here again, the method we have used reflects Griffiths et al. (2008), as well as that of Pindyck and Rubinfeld (1998): all exogenous and lagged variables in the system were used as the initial components of the instrument. If tests indicate it is a weak instrument, additional lagged values of variables already in the instrument are added. If that is not enough, efforts shift to removing instrument components that have very low statistical significance as a way of increasing t-statistics on remaining variables and the instrument’s F statistic until the standard Wald criteria are met (at least one t-statistic greater than 3.3 or an F -statistic greater than 10.0). All variables in this study and the baseline study were tested for stationarity. If found nonstationary, a variable was detrended unless it was found cointegrated with the dependent variable in the model in which it was used. • ADF (Augmented Dickey–Fuller) Unit Root Tests Used to determine Stationarity. • DF (Dickey–Fuller) Test Used to Determine if Nonstationary variables were Cointegrated. • Detrending Done to Those Not Cointegrated. All models were tested extensively to ensure the replicability (robustness) of the findings. This is particularly important when using nonexperimental techniques like regression analysis where left-out variables or even moderate levels of multicollinearity in any one period can severely distort estimates of a variable’s impact. In developing the baseline model, every model result obtained was tested for robustness in two different ways:

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• Robust to Time Period Sampled: • Robust to Model Specification:

Four Different Time Periods tested (6–18 in this study) Test Sensitivity to two Significant Changes in Variables specified in the model tested

Generally, the key coefficients in the equations included in this large-scale model were strong enough to be very robust to time period sampled and choice of regression technique (we typically find more difference between weak and strong instrument results than between OLS and 2SLS strong instrument results). There were occasional exceptions, noted in the testing sections for each baseline model. Testing for robustness to model specification in the baseline model was more complicated. In a model (already) well specified, i.e., explaining roughly 85% or more of the dependent variable’s variance with theoretically sound variables and lags, adding or subtracting variables rarely will lead to significant changes in other variables’ parameter estimates, except perhaps for variables making only marginal contributions to the model. In this circumstance, the stability of results occurs because most of the possible effects of direct multicollinearity or left-out variables which are collinear with variables in the model have already been accounted for by including the variables in the model. But, respecifying a model by deleting a variable already in it which accounts for much of the variance in the model is to court disaster. The variable dropped is likely to be multicollinear with many other variables in the model, since many variables tend to move together, at least partially. Dropping it will change the estimated effect of other variables in the model on the variable of interest’s coefficient, since each variable’s coefficient is a function of their collinearity levels with other variables in the equation. Similarly, adding variables to a model which explains little or only moderate amounts of variance will often significantly change the estimated effects of variables already in the model, particularly if the variable added adds much to explained variance. This is because the newly-added variable is likely somewhat collinear with variables already in the model, and when entered, will be assigned some of their explanatory power. Hence, tests for specification robustness will be taken to be successful if variables making minor contributions to explained variance can be added or subtracted without changing other variables’ coefficients much.

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However, given the nature of correlational tools, we will actually expect that removal of major variables in a model, say the income variable in the consumption function, will result in major distortions to coefficients on the remaining variables. Hence, we do not test for specification robustness this way. (Put another way, be suspicious of the coefficients in any model with an R 2 below roughly 80%.) Hence, the key criteria for evaluating what belongs in an equation are the equation’s ability to explain variance (R 2 ), the statistical significance of a variable’s regression coefficient, and in some tests, comparison of an equation’s mean square error with that of another equation. All models in both the current and baseline studies were tested in first differences, not levels. This has many advantages when dealing with time series data. In our case, testing in first differences: • Reduced By Almost Half the Number Of Variables found Nonstationary. • Raised Virtually All Durbin–Watson Serial Correlation Statistics to 1.6–2.2 Range. • Reduced Multicollinearity Effects Substantially: Median Correlation Coefficient Fell from ~.80 to ~.40. • Regression Coefficients Far More Stable When Model Changes were made. Newey–West Standard Errors were used throughout to address heteroskedasticity problems. Generally, when testing “standard” models to determine if deficits were related to crowd out, we simply added the deficit variable to the baseline model, and noted the increase in R 2 . If the increases was less than two percent, we used changes in adjusted R 2 . Only if their was also increase in adjusted R 2 did we conclude the added variable was truly a significant determinant of the model’s dependent variable. The same procedure was used when adding a loanable funds variable to the standard models (with deficits) to determine if loanable funds significantly increased the model’s explanatory power.

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3.1.1

The Importance of Replicating Results Before Publication

There are investment and consumption models we take as the “standard” models in tests throughout this book. The models are Eqs. 4.4 or 5.4.TR taken from Heim (2017a). We use “standard” as a statement of what other variables need to be controlled for when we test for the effects of accommodative monetary policy, or other changes in the loanable funds pool, on deficit-induced crowd out effects. It may seem that some variables commonly thought to influence consumption, like consumer confidence, have been left out. Many other variables were initially tested to determine if they were determinants of consumption or investment, but were only found significant in one or two periods tested. The criteria used in (Heim 2017a) to determine if a variable was to be included in the standard model were that they had to be found significant 3 of 4 different, though somewhat overlapping, time periods between 1960 and 2010. Once variables met this replicability test, the robustness of regression coefficients in the baseline model were subject to a model modification test. Their coefficient values could vary no more than 30% when an additional two variables were added or subtracted from the model. Needless to say, these are difficult standards to meet. Many variables thought (by many economists) to be determinants of consumption or investment could not meet this standard and were excluded. New variables added in this study were tested in 6–18 time periods and only considered significant if they added variance in ¾ or more of the periods tested. We justify such exclusions on the following grounds: good science absolutely requires the ability to replicate initial results in different time periods and models. To ensure a study’s results are worth reading, replication should be required before publication wherever possible. After all, this is the twenty-first century. The scientific revolution has been with us for 350 years. In the previous section, we have described the replication efforts that were made before baseline model results were published. In this book, we add loanable funds and other variables to these models and reestimate the models to determine if changes in loanable funds, or these other variables can offset crowd out.

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3.2 Other Methodological Issues Specific to This Study Chapters 13 and 14 test the hypothesis that financing government deficits causes a “crowd out” problem to occur when deficits arise, ceteris paribus. By crowd out, we mean that government borrowing of loanable funds in the U.S. reduces, or “crowds out” the loanable funds available to private consumers and businesses to borrow, reducing the level of consumer and investment spending. These negative effects on the economy may offset any stimulus effects of (Keynesian) tax cuts or government spending increases designed to stimulate the economy. Prior statistical (Heim 2017a, b) studies have determined that this crowd out effect is real, but this study again retests the hypothesis to further reaffirm its validity. This study also tests for the best way to test deficits for the crowd out effect. Specifically, we test whether the government deficit alone, measured by the one variable (T − G), or as two separate variables, (T ) and (G), provides the best definition of crowd out effects. Either one measures crowd out by assuming there is always a dollar-for-dollar decline in private consumer or investment spending when a deficit occurs. But this is a hypothesis incorporated into our modeling, not a fact. We will test it by comparing it with deficits modified by loanable funds or other modifiers. Other factors may reduce the crowd out effect below the size of the deficit itself. If the deficit increases by $100, and during the same period, the pool of loanable funds increases by $100 due to business cycle effects or due to FR security purchases increasing bank reserves, the increase in loanable funds may offset the crowd out effect created by the deficit. If so, the net crowd out effect may appear to be zero. To determine if this happens is the second key objective of this book (along with determining if Federal Reserve accommodating monetary policy has been effective in replacing loanable funds lost to consumers and businesses by the need to finance deficits). To see if this is the case, we tested different modifications of the deficit. For example, after testing the one-variable deficit (T − G) alone for crowd out effects, we reran the same model using the modified form (T − G) + (S + FB) to determine how effectively changes in loanable funds reduced crowd out. In the standard consumption and investment models, the (T − G), or (T ) and (G) variable(s) were tested in both unmodified and loanable

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funds—modified forms. The model which had the largest R 2 and (generally, but not always) significant t-statistic(s) on the crowd out variable(s), i.e., the model which best fits the data, is taken to most accurately describe the size of crowd out effects. Loanable funds are defined as national saving plus foreign borrowing. The Economic Report of the President, 2013, Table B32 defines total gross saving, or national savings, whose components are • personal savings (personal income not spent on taxes or personal consumption expenditures included in the GDP), • corporate saving (undistributed profits), • depreciation allowances, and • government savings (T − G). Gross savings are calculated on a GNP, not a GDP basis, so it is most accurately defined as “national” savings, which we designate as (S). Foreign borrowing will be designated as (FB). Total loanable funds (S + FB) are defined as the total loanable funds pool. If increases in loanable funds can offset crowd out, we consider (S + FB) to be the most theoretically justified definition of the variable that may do the offsetting. That said. We also tested a number of different possible offsets to the deficit. There is theoretical justification for some of the alternatives, but in other cases, we just tested them just because they were likely to be of interest to others. We desired our tests and results to be sufficiently comprehensive that others will see all the plausible alternative options have been fully explored. In some cases, the endogenous (S + FB − Tr − A) and exogenous (Tr + A) components of the loanable funds pool are also tested separately to determine which of the two actually offsets crowd out the most. We distinguish between exogenous and endogenous changes to the loanable funds pool as: 1. Exogenous component: increases in loanable funds due to deposits in banks of proceeds received from selling the FR treasury, agency, and mortgage-backed securities (Tr + A) to the Federal Reserve (FR). 2. Endogenous component: business cycle fluctuation-based variation in loanable funds borrowed related to variation in the money multiplier from one phase of the business cycle to the next, increases in

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savings stemming from increases in aggregate income, or changes in mpc. A range of other problems arising in some chapters but not all are also examined. Details of the testing procedures used to deal with these problems are discussed in detail in the first chapter where they occur. These issues include: • • • • • •

Mixing Periods of Deficit Increase and Decrease, Statistical Insignificance Caused by Lack of Variation in the Data, Left–Out Variables, Multicollinearity, Insufficient Sample Size, Spurious Results Indicating Insignificance. 3.2.1

GDP Deflator Methodological Adjustments

The GDP chain deflator given in the annual Economic Report of the President is used in various parts of this paper to convert nominal values of the GDP and other variables to “real” values. After 2012, the base year was changed from 2005 to 2009. To allow continued estimation of the real GDP in 2005 dollars from 2013 to 1017, we took advantage of the fact that data was available in Table B3 of both the ERP 2013 and ERP 2018 for the GDP chain deflator using both the 2005 and 2009 base years. On average, for the last three years, the 2005 deflated values were available (2010–2012), the value of the 2005-based deflator was 1.0848 times the value of the 2009-based deflator. Hence, the actual 2013–2017 chain deflator values based on the 2009 base year were adjusted upward by 1.0848 to obtain estimated values of the chain deflator for those years using 2005 as the base year. Table 3.1 shows the relationship. Results of models tested using these techniques are presented in Chapters 10–11 below. 3.2.2

Reconciling Differences in Signs, Significance Levels of Tests in Different Time Periods

Originally, we intended to use two criteria, R 2 and t-statistics, to determine the success of a model.

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R 2 —Did the new variable being added to the standard model (i.e., the deficit, or a modifier) increase R 2 ? If not, we rejected the variable as a significant determinant of consumption or investment; if it did increase R 2 , we compared the increase to that obtained in other models using other definitions of the deficit or other modifiers. If the increases were larger than when the other modifiers were tested, we concluded that this way of modifying the deficit best explained the real level of crowd out resulting from the deficit. t-statistic—Did adding a modifier to a deficit variable increase the tstatistic on the crowd out variable? It was initially assumed that if the baseline model contained statistically significant deficit variables, modifying them by a loanable funds or M1 variable would reduce or eliminate the modified deficit variable’s statistical significance, since it had offset the crowd out effect of the deficit. This did not turn out to be an appropriate criteria for evaluating the modifier’s effect on crowd out. In many cases, adding one of the loanable funds alternative modifiers to the deficit changed the deficit variable from significant to insignificant, but left R 2 reduced. This result suggested the modifier had no effect on crowd out and distorted the real effect given by the deficit variable alone. This anomaly occurred because a modifier that had no real effect on crowd out was subtracted from a variable that did (the deficit), creating an “errors in variable’s” problem (Johnston 1963). Table 3.1 Calculating 2013–2017 real GDP using estimated values of the base year 2005 chain deflator Year

2010 2011 2012 2013 2014 2015 2016 2017

Nominal GDPa $14,924 billion 15,518 16,155 16,692 17,428 18,121 18,625 19,387

Real GDP (2005-based chain deflator) $13,481 billion 13,688 14,002 14,210 14,465 14,623 14,810 15,075

Base 2005 chain deflator 110.00 113.37 115.38 117.46 120.48 123.92 125.76 128.60

Base 2009 chain deflatora

Actualb Actualb Actualb Est. Est. Est. Est. Est.

a Economic Report of the President ERP (2018, Table B3); b ERP (2012, Table B3)

102.53 104.17 106.49 108.28 111.06 114.24 115.93 118.55

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It just added random errors to the true magnitude of crowd out, which reduces the true variable’s significance. An effective modifier more likely would leave the level of significance of the deficit variable unchanged or increased, since adding the modifier to the deficit made it a much more accurate measure of real crowd out effects. As a result, in evaluating the effects of different modifiers, we rely primarily on whether adding the modifier increases, leaves unchanged, or reduces R 2 . If modifying the deficit only definition of crowd out creates a modified deficit variable that increases R 2 , we conclude it gives a more accurate picture of true magnitude of crowd out effects than just the deficit alone. If it leaves R 2 unchanged, we conclude it has no effect on crowd out, even if it reduces the crowd out variable to insignificance. This problem is discussed in more detail as it arises in various chapters of the book. 3.2.3

Mixing Periods of Budget Deficit (Crowd Out) Increase and Decrease

Fundamentally, a regression coefficient (b) represents the average way that changes in (X ) from observation-to-observation are related to changes in (Y ) in a sample of data tested using the model Y = ƒ(X ). The standard formula representing this relationship in a simple regression is typically shown as: b=

Σ(xi yi ) Σ xi2

(3.1)

(where lower case xi , yi are deviations of observations (i) from their means) Let y = x, and assume our data set has but two observations on each variable. Then b=

x1 x1 + x2 x2 y Σ(xi yi ) = = 1.00 = average x1 x1 + x2 x2 x Σ xi2

If y = 2x b=

Σ(xi yi ) x1 2x1 + x2 2x2 y = = 2.00 = average x1 x1 + x2 x2 x Σ xi2

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Table 3.2 Simulated regression data Decade 1: (strong positive correlation) Year 1 2 3 4 5 6 7 8 9 10

Decade 2: (strong negative correlation)

Decade 3: (no correlation)

Y

X

Year

Y

X

Year

Y

X

1 2 3 4 5.5 6 7 8 9 10

1 2 3 4 5 6 7 8 9 10

1 2 3 4 5 6 7 8 9 10

1 2 3 4 5.5 6 7 8 9 10

10 9 8 7 6 5 4 3 2 1

1 2 3 4 5 6 7 8 9 10

1 2 3 4 5.5 6 7 8 9 10

1 1 2 1 1 1 1 2 1 1

Suppose in another sample y = −2x b=

x3 (−2x3 ) + x4 (−2x4 ) Σ(xi yi ) = = −2 2 x3 x3 + x4 x4 Σ xi

average

y x

If you reestimate b combining both data sets into one larger (i = 1–4) data set, adding the last two samples together, the coefficient collapses to zero if the value of (x 3 , x 4 ) = (x 1 , x 2 ). If not precisely equal, a much smaller net effect (regression coefficient) will likely show carrying the sign of the dominant data (Table 3.2).

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A simple example shows this. Suppose we have the following 3 decades of data. After regressions are calculated for each decade and the combined decades, Decade Decade Decade Decade Decade Decade

1 Only Regression Coefficient (t-stat.): 2 Only Regression Coefficient (t-stat.): 3 Only Regression Coefficient (t-stat.): 1&2 Combined Regression Coefficient (t-stat.): 2&3 Combined Regression Coefficient (t-stat.): 1&3 Combined Regression Coefficient (t-stat.):

+.997(54.1) −.997(−54.1) −.063(−0.0) .000(0.0) −.466(−2.3) +.466(2.3)

The effect of mixing samples is clear: combine a sample of data with a statistically significant positive effect, with one that has a statistically significant negative effect, and the combined sample will have a smaller, or zero, coefficient, and which will not likely be statistically significant (though this depends on how different the magnitudes of the numbers in one subsample versus the other). This was a common result for some periods sampled below in Chapter 18. For example, in Table 18.1.A, we added data from a statistically significant “crowd in” (i.e., declining deficit) decade of the 1990s to data from the earlier statistically significant “crowd out” (i.e., increasing deficit) decades. For example, adding the 1990s data to (1960–1989) data tended to eliminate the statistical significance. Though simple on its face, this is an extremely important finding for us. It will help us interpret the sign and lack of statistical significance on our regression coefficients for our deficit variables when testing samples of data that include both crowd out and crowd in periods. In such periods, the lack of a finding that deficit variables had a statistically significant crowd out effect on consumption or investment may not mean there is none; it may mean we were not careful to avoid mixing crowd in and crowd out periods in the same sample. The part of the loanable funds pool available to consumers and businesses declines in years in which government deficits increase because of the need to finance the deficit, ceteris paribus, causing a “crowd out” problem. Similarly, in years, in which government deficits decline, ceteris paribus, the decline increases the portion of the loanable funds pool available to private borrowers. This causes “crowd in.” There is good evidence “crowd in” normally will have a positive effect on consumption

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and investment. It increases the pool of privately available loanable funds, and that increases consumer and business borrowing. For example, statistical tests indicate increases in consumer borrowing increase consumer spending (Heim 2017a, eq. 4.4.TR). There is considerable evidence demand for loans since 1960 may have chronically exceeded the supply of loanable funds until 2008, when the QE program hugely increased the pool of loanable funds leaving it far larger than the demand for loans (Chapters 8 and 9). Excess reserves in U.S. banks indicate very small balances of excess reserves; through the whole 1960–2007 period, they varied between only 1 and 5% of total reserves, and averaged only 2.2%. This consistently small size, in good times and bad, suggests any excess funds during that period were kept for precautionary purposes, not because of a lack of borrower demand. This suggests the demand for loanable funds typically was probably greater than the supply. The “quantitative easing” (QE) years starting in 2008 were an exception to this. QE years were years in which the Federal Reserve engaged in a huge securities purchasing program, massively flooding of banks with increased loanable reserves (Chapter 8). Hence, if we sampled only a decade in which deficits were increasing, like the 1980s, we would expect a negative sign on the regression coefficient describing the way increased government spending affected consumption and investment. For the same reason, we would expect a positive sign on the coefficient indicating how years in which deficits caused by cuts in taxes (since the cuts are negatively signed) also had a negative effect on consumption and investment due to crowd out. However, there are circumstances when the spending deficit variable will have the wrong sign, i.e., a positive sign. A cut in government spending causing deficit reduction would show as a negative change in (G). This negative change in government spending would, when multiplied by the negative signed regression coefficient on government spending, show a positive effect on consumption or investment. Similarly, if the deficit declines due to positive changes in tax collections (increases), we would expect this positive change, times the positive coefficient on the taxes variable (T ) to increase consumption or investment. Consider a more typical case. Suppose government spending was rising, but the loanable funds pool was also rising. If the deficit causing rise in government spending was larger than the change in loanable funds,

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the net “crowd out” effect G − (S + FB) would be greater than zero. If the increase in loanable funds (S + FB) was greater than (G) in the same period, the result is a “crowd in” effect. Not only does the deficit variable (the increase in government spending) not cause a crowd out problem, the growth in G is positively associated with growth in consumption. If this “crowd in” effect continued for several years, a regression of C = ƒ(G) will show a positive relationship; that is, the sign on the government spending variable may change from negative to positive. For example, using some hypothetical values for G and (S + FB), if the normal way the spending deficit enters into the consumption function is C = . . . . . . − (G − (S + FB)) = −(100−(150)) = +50 . . . . . . . . . (3.2) In a regression for this time period only, we would see the deficit variable (G) positively correlated with consumption, giving the regression coefficient a positive sign. Note in Table 3.3 the general (though not always) tendency when Table 3.3 Changes in regression coefficients and t-statistics associated with loanable funds changes Period

Loanable funds growth net of the govt. deficit during the decade

( T − G) deficit Coef.( t)

T only deficit Coef.( t)

G only deficit Coef.( t)

2000–2010 Average: 1960–1980 Average: 1980–1990 Average: 1960–1970 Average: 1970–1980 Average: 1990–2000 Average:

$ −184.7 Billion

+.42(4.2)

.45(2.4)

−.56(−1.3)1

$ −27.8 Billion

+.45(3.3)

.72(5.3)

−.28(−2.8)

$ −17.8 Billion

+.02(0.1)

.10(0.2)

−.20(−0.2)

$ + 6.6 Billion

+.53(2.4)

.82(12.0)

−.42(−7.7)

$ + 6.9 Billion

+.22(1.4)

.22(0.9)

−.20 − 0.8)

$ +132.9 Billion

−.09(0.5)

1.18(3.7)

+.50(6.5)

Regression results use standard regression model, including a control for changes in loanable funds. Data taken from Table 18.1A

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growth in loanable funds exceeds the deficit (like the 1970s and 1990s), for the regression coefficient on the deficit variable to be small or positive, and to be statistically insignificant or perhaps even show a positive, statistically significant “crowd in” effect. By comparison, in periods in which there was much larger growth in deficits than in loanable funds, like the 1960–1980 and 2000–2010 periods, we generally have highly statistically significant negative crowd out effects. Hence, we conclude that often the coefficients and significance levels on the spending crowd out variable change from negatively significant when crowd out prevails in the sample period, to either negative and insignificant, or sometimes positive and significant, when crowd in prevails. In addition to growth in loanable funds, decline in deficits is an additional reason the (G) deficit variable can sometimes appear insignificant, or have a positive sign, is the declining deficits of the in the 1990s decade shown in Table 3.4. In our statistical tests, during the 1990s, accompanying the declining deficit shown above, we see “crowd in” as represented by a positive sign on the government spending deficit variable and higher than usual positive coefficients on the tax variable. During this decade, the U.S. experienced a third of a trillion dollar decline in the deficit. This decline is also shown in Table 3.3 single variable deficit (T − G) coefficient of (−.09). The negative sign is consistent with our expectation that a decline in the deficit should result in a positive change in consumption. This is because deficit reduction increases the portion of the loanable funds pool available for private borrowing. There was also a positive sign on the government spending variable, indicating that even though government spending increased, the deficit was declining, i.e., (T ) was growing faster than (G). This stimulated consumption at the same time Table 3.4 Average yearly increases (+)/decreases (−) in deficits by decade

Decade 1960–1970 1970–1980 1980–1990 1990–2000 2000–2010

Average Increase(+)/decrease(−) $ + 10.3 Billion ($ 2005) +17.3 +32.6 −39.0 +125.1

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(G) was growing, causing the positive correlation, but not a causal relationship between increased government spending and increased consumer spending. The equivalent effect on the tax variable (T ) is shown in the larger coefficient in that decade than in others. In Chapter 18, Table 18.1.A, we undertake 90 separate tests of the standard consumption model which differ only by slight differences in the length of the time period tested. Results are consistent with the theory that the cause of most statistically insignificant government spending or tax cut crowd out results in regressions is mixing the declining deficits of the 1990s (crowd in) with data for decades characterized by crowd out. In tests, where the 1990s data was a large part of the data set tested, the sign on the government spending variable will actually turn positive. In Table 18.1.A, models including the 1970s and 1980s data were found to have significant crowd out effect for both variables. When the 1990s data was added and the model reestimated, the tax variable coefficients fell, as did the spending variable coefficients (in absolute value), as our analysis above indicated should happen because coefficients are just averages of yearly effects in a sample. In addition, results for both variables turned from significant to insignificant. Similarly, when the 2001–2010 data was added to the 1990s data, the 1990s’ coefficients for taxes fell, and the coefficient for spending returned to negative by the time all 10 years of data for 2001–2010 were added in. Adding the 2001–2010 data to the 1990s data turned the statistically significant deficit effects to statistical insignificance, again, as expected from our previous analysis. The large deficit declines occurring in many years in the 1990s far exceeded the smaller growth in some other years during that decade, leaving a net decline of about $337.3 billion (2005 dollars) during the 1990–2000 period, as shown in Table 3.4. These negative changes, times a negative regression coefficient on the regression for this decade (Table 3.2), show a positive effect of these deficit declines on consumer spending during this period. This was because each yearly decline, though not increasing the total pool of loanable funds, added to the portion of the total pool available for consumer and business borrowing. Hence, from the standpoint of the loanable funds available to private borrowers, the (declining) deficits variable is measuring “crowd in,” yielding a change in the coefficient on the government spending variable from negative to positive and increasing the coefficient on the tax deficit variable when the two components of the deficit are modeled as separate variables. Table 3.5

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Table 3.5 Yearly changes in the deficit in the 1990s

Year

Change in deficita

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Total Deficit Decline 1990–1999

$ + 96.2 Billion +68.1 +104.3 −58.4 −115.6 −22.0 −95.5 −133.0 −128.0 −53.3 $ −337.3 Billion

a Consolidated U.S., State and Local government Budgets. Billions

of 2005 Dollars. Economic Report of the President 2012

shows individual year’s deficit declines during the 1990s, mostly related to generally good economic conditions except at the beginning of the decade, as well as a tax increase. In the decade by decade samples (Table 3.2), the statistical insignificance of the government spending deficit variable for the 1970s seems principally associated with small sample size and high multicollinearity with another variable. For the 1980s, the insignificance appears mainly associated with lack of variation in the government spending variable, compounded by small sample size. Let us assume the crowd out effects of deficits, i.e., (T − G) < 0, are reduced, on a dollar-for-dollar basis by any growth in the loanable funds pool (S + FB). Assume further that FR open market operations as well as business cycle fluctuation can increase or decrease the loanable funds (LF) pool, by increasing bank excess reserves. Then, based on our statistical findings presented later in Chapter 18 of this study, and this chapter, there are several possibilities: 1. No change in loanable funds occurs in the sample period in which the deficit changes (i.e., no FR accommodative policy, or endogenous growth in LF). Then, the crowd out variables will have their expected sign: the coefficient on the taxes variable will have a positive sign indicating a cut in taxes will negatively affect

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consumption or investment; the coefficient on the government spending deficits will have a negative sign, indicating an increase in government spending, ceteris paribus, will have a negative effect on consumption or investment. Both types of deficits typically are found to be statistically significant, though significance levels on government spending deficits tend to be lower, apparently due to less variation in the data in a typical decade than is the cases for variation in government revenues. Less variation of this type is typically associated with lower significance levels and should not be taken as a sign spending deficits have less crowd out than tax deficits. 2. There is an increase in loanable funds in the years in the period sampled, but it is smaller than the increase in the deficit in those years. If the increase in loanable funds is small relative to the increase in the deficit, the crowd out variables should retain their expected signs, and roughly the same coefficients and statistical significance (if most or all of the increase in LF is used to purchase real goods and services, not other securities). 3. There is an increase in loanable funds, equal in size to the increase in the deficit: If the increase in loanable funds equals or nearly equals the increase in the deficit each year in the years sampled, the crowd out variable coefficients should decline to approximately zero and become statistically insignificant, since the year to year data on changes in T + (S + FB) and G − (S + FB) should equal/nearly equal zero for each year in the sample. If there is no variation in the variable during a period, it cannot be significantly related to a dependent variable. 4. There is an increase in loanable funds in the years sampled, and it is greater than the increase in the deficit in those years. If the increase in loanable funds is only marginally larger than the increase in the deficit, the crowd out variables should have signs opposite to their expected signs, but the variables should be statistically insignificant. The sign change represents a “crowd in” effect. For the modified tax cut deficit variable, T + (S + LF), if the change in (T ) is negative, and the change in (S + FB) positive, will give a positive data series for this variable for the time sampled, and it will be associated with a positive effect on consumption or investment. Hence, the sign will stay positive. For spending deficits, the regression coefficient on the modified government spending variable G − (S + FB) should change

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from negative (the expected sign for the effect of G) to positive as (S + FB) exceeds growth in the deficit (G). If the negative values are significantly positively correlated with changes in consumption or investment, the effect should be statistically significant as well. This, of course, is what we see with the 1990s data. Regression Example of Sign Changes on the Spending Deficit Variable A simple example can show how the regression can assign a negative sign to the (G) variable in deficit periods and a positive sign in surplus years. Suppose the true relationship between consumption and the deficit was C = 1.00 T − 1.00 G +1.00 Y (Income). Then, in government deficit years where the G is larger than the T and growing, e.g., G = 8, 9, or 10, we have (Table 3.6). Arithmetic Examples of Sign Changes on the Spending Deficit Variable We can also show the same result arithmetically as well as with regression coefficients, with a simple “partial derivative” example of the effects of (G) and (S + FB) on consumption, showing the effect of changes in consumption that result from changes in the deficit stemming from increased government spending and any same-period change in loanable funds. Let C = −1.00*(G − (S + FB)) show the effects of crowd out, and crowd out reduced by changes in loanable funds. Then, if G = 1–5 and the (S + FB) = 0, for 5 periods (t ) to (t + 4), we have Table 3.7. Table 3.7 shows a clear crowd out theory consistent pattern of growing (G) deficits being associated with declining private consumer spending. (Similar results would result using the tax deficit variable, and the real effect on consumption would be the sum of the two effects in any period.) Smaller increases in loanable funds, which do not completely offset crowd out (the spending deficit), show a negative relationship between changes in (C ) and changes in (G − (S + FB)), as shown in Table 3.8. Next, again let C = − 1.00(G − (S + FB)) show the effects of spending deficit crowd out on consumption. Let the spending deficit’s crowd out effect be reduced by changes in loanable funds. Let G = 5, and let (S + FB) take on different, but larger values than the deficit, for the 5 periods (t = 0 to 4). Then, “crowd in”

C

T

G

t (97) (0) (3) t + 1 (197) (2) (6) t + 2 (296) (3) (7) Regression results C = + 1.50 T − 0.25 G +0.98 Y and examples in government budget surplus years using the same Small surplus years example Model: C = + 1.00 T − 1.00 G +1.00 Y + e Hypothetical data Period C T G t (99) (0) (1) t + 1 (200) (2) (1) t + 2 (301) (3) (2) Regression results

Period

Hypothetical data

0 +1 0

Y 100 200 300 −1

ea

model, the results are:

100 200 300

Y ea

T

G

Y

0 +1 0

ea

(continued)

Large surplus years example C = + 1.00 T − 1.00 G + 1.00 Y + e Hypothetical data C T G Y ea (109) (10) (1) 100 (211) (13) (1) 200 −1 (313) (15) (2) 300 Regression results

(90) (0) (10) 100 (190) (2) (13) 200 (288) (3) (15) 300 Regression results C = + 2.67 T −0.67 G +0.97 Y

C

Hypothetical data

Large government deficit years example C = + 1.00 T − 1.00 G + 1.00 Y + e

Model: C = + 1.00 T − 1.00 G +1.00 Y + e

Simulation of deficit and surplus effects on sign of government spending coefficients

Small government deficit years example

Table 3.6

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C

T

Y ea

G

T

G

Y

C = + 0.88 T +0.88 G +0.99 Y

C

Hypothetical data ea

method shown is heuristicand not a complete solution to the positive sign problem; results are sensitive to sign of error term

a Size of error term tested between (1–50); sign of government spending variable stayed as shown above, but magnitude changed with error size. The

C = + 2.00 T +2.00 G +0.97 Y

Period

Hypothetical data

Large government deficit years example C = + 1.00 T − 1.00 G + 1.00 Y + e

Model: C = + 1.00 T − 1.00 G +1.00 Y + e

(continued)

Small government deficit years example

Table 3.6

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Table 3.7 Effects of spending deficit growth on consumption (C) =

Period t t t t t

+ + + +

−1.00 (G) −1.00 −1.00 −1.00 −1.00 −1.00

(−1) (−2) (−3) (−4) (−5)

1 2 3 4

(1) (2) (3) (4) (5)

Table 3.8 Effects on consumption of loanable funds growth less than spending deficit Period

(C) =

−1.00 (G − (S + FB))

= − 1.00(G + (S + FB))

t t t t t

(−1) (−2) (−3) (−4) (−5)

−1.00 −1.00 −1.00 −1.00 −1.00

= = = = =

+ + + +

1 2 3 4

Table 3.9 Effects on consumption of loanable funds growth greater than spending

(5 (5 (5 (5 (5

− − − − −

4) 3) 2) 1) 0)

−1.00 −1.00 −1.00 −1.00 −1.00

(1) (2) (3) (4) (5)

Period

(C) =

−1.00 (G − (S + FB))

= (−G) + (S + FB)

t t t t t

(+1) (+2) (+3) (+4) (+5)

−1.00 −1.00 −1.00 −1.00 −1.00

= = = = =

+ + + +

1 2 3 4

(5 (5 (5 (4 (5

− − − − −

6) 7) 8) 9) 10)

(−5) (−5) (−5) (−5) (−5)

+ + + + +

(6) (7) (8) (9) (10)

will result from the net growth in growth in loanable funds relative to (G) (Table 3.9). Table 3.9 shows a clear positive relationship between (C ) and a change in (G) accompanied by an even bigger change in (S + FB). Here, crowd out effects are eliminated by increasing loanable funds through either accommodative monetary policy or some endogenous change in the economy until the growth in loanable funds exceeds the size of the government spending deficit (G) during the same period.

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Table 3.10 Effects on consumption of declining spending deficit

Period

(C) =

−1.00 (G − (S + FB))

= −G + (S + FB)

t t t t t

(+0) (+1) (+2) (+3) (+4)

−1.00 −1.00 −1.00 −1.00 −1.00

= = = = =

+ + + +

1 2 3 4

(5 (4 (3 (2 (1

− − − − −

5) 5) 5) 5) 5)

(−5) (−4) (−3) (−2) (−1)

+ + + + +

(5) (5) (5) (5) (5)

Finally, we can show that a decline in the spending deficit that leaves it lower than the level of loanable funds also results in a positive relationship between changes in (C ) and changes in the loanable funds modified deficit variable (G − (S + FB)). This is a simulation of our “1990s” case discussed earlier (Table 3.10). Clearly, declining deficits are associated with growing private spending (consumption) as crowd out theory predicts. In the same fashion, we can show tax cut deficits (T < 0) result in positive changes in consumption if they more than offset the change in the deficit, and negative changes in consumption if they are too small to fully offset the tax cut’s negative effects on consumption. Conclusions: the sign on the spending deficit variable switches from negative to positive when the size of the deficit falls below the same-period change in the level of loanable funds (the 1990s case), or when the change in level of loanable funds is greater than the growth in the spending deficit (the quantitative easing period case). Similar effects hold for tax cut deficits when the growth in loanable funds is less or more than the tax cut deficit, but the sign on the coefficient stays positive. 5. Finally, for samples including periods in which decades of deficit growth were much larger than savings growth (like the 1980s or 2000s), that also include periods in which the opposite occurred (like the 1990s—see Table 21.1.B), the sign on the modified deficit variables will depend on which period dominates the sample. Regression coefficients conceptually are, (after all), nothing but averages of yearly changes in the data in time series models. Standard errors, and therefore t-statistics, are conceptually nothing more than a measure

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of the average way in which individual year data observations vary from that average (e.g., yearly modified deficits). If large parts of the sample have the expected crowd out effects, and large parts have crowd in effects, regressions combining periods of both crowd out and crowd in typically will show the spending deficit variable’ s coefficient as statistically insignificant. The coefficient (~the average effect of yearly changes) for the whole sample will likely be small, because it averages a lot of pluses and minuses. The standard error will likely be large, because half the data shows positive and half negative yearly effects. Hence, whatever the sign on the coefficient, it is likely to be statistically insignificant, indicating what large standard deviations always do: that the average (the coefficient) is not at all indicative of the individual values of the underlying data that got averaged to make it. 3.2.4

Statistical Insignificance Caused by Lack of Variation in the Data

Regressions associated with the simulated data in Table 3.2 clearly show that mixing data for a time period which shows a significant positive relationship between two variables with data for the same variables, but a period when there was no change in one of them, reduces the coefficient and statistical significance of the explanatory variable. In Table 3.3, where we look at actual data, we see the same thing; t-statistics on the spending deficit variable (G) are smaller in absolute value than that on the tax deficit variable (T ) in four of the six decades surveyed. 1990–2000 was also the only period in which the variation in the spending deficit variable over the decade was larger than the variation in the tax deficit variable. Variation was measured as the ratio of the variable’s average yearly change for the decade, relative to its standard deviation. This clearly shows the importance of variation in a data series. Without variation, a variable may appear to have a statistically insignificant relationship with another variable, when in fact the insignificance may be due only to lack of movement in the variable during the sample period. This is a common problem with policy controllable variables like tax cuts or spending increases, where cuts may occur in one period, but not another because of policy decisions. Hence, samples from one period may show a specific explanatory variable having a significant relationship to a dependent variable, but not samples from another period.

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Lack of variation in an explanatory variable can cause it to be found insignificantly related to the dependent variable (the correlation between a constant and a variable is 0). Lack of variation in the spending deficit appears to be what happened in the 1980–1990 decade that caused the spending deficit to typically have lower significance levels in that decade; the average yearly variation in government spending growth from its decade average growth, given by its standard deviation for the decade, was only a third of average yearly growth. Variation of government spending from its decade average for every other decade during the 1960–2010 period was twice as large or more, as shown in Table 3.11 (and less than the variation for all other variables in the consumption function, except population growth). Further, as noted earlier, four of the six t-statistics on the spending deficit variables are smaller in absolute value than that on the tax deficit variable. Only one is larger (1990–2000 decade). This is also the only decade in which the standard deviation in yearly change in government spending during the decade was larger (relative to the average yearly change in government spending for the decade) than variation in the tax deficit variable. This clearly shows the importance of variation in data used in analyses. Without variation, a variable may appear insignificant (implying not related to the dependent variable) when in fact the insignificance is due to lack of movement in the variable during the sample period. In virtually all tests in this study, the statistical significance of the government spending crowd out variable is less than it is for tax cut crowd out variable, sometimes reducing it to statistical insignificance. The lower level of year-by-year variation may account for this. This can be illustrated using the formula for the standard error of the regression coefficient in a simple regression y = ƒ(x) given in Nau (2018):  √  s/ n SEb(slope) = (where s =



Standard Deviation X

 (1/n − 2) ∗ Σe2 , e2 is error variance and n is sample size)

The larger the standard deviation of the explanatory variable (X ) data, ceteris paribus, the smaller the standard error, and therefore the larger the coefficient’s t-statistic. Put another way, even where the underlying economic relationship between a dependent and an explanatory variable

2.0 1.8 1.6

6.7 7.0 8.9 5.0

+.24(3.6) +.09(0.7)

$ −17.8 billion

$ +132.9 −.10(0.6) billion $ −184.7 +.27(3.5) billion $ − 27.8 billion +.42(3.1)

1.7

1.6

0.5

0.2

0.6

1.8

−1.6

1.8

Money multipa

3.7

1.9

2.9

3.3

3.2

4.2

Money multipb

1.05

.61

.69

.32

.80

.67

GSD / GAv

1.35

12.33

.59

.98

2.73

.64

TSD / TAv

spending and total revenues used in calculating the deficit is taken from the Economic Report of the President 2012, and 2006, as described elsewhere in this study

a Taken from Table 10.3 in Chapter 10 (M1 multiplier), which used the coefficients in Table 10.2 model b Calculated as M1/(Tr + A) c Standard consumption model using 1-variable definition of unmodified deficit, without a loanable funds control variable. Data on total government

5.6

1.6

$ + 6.9 billion

4.2

+.51(2.7)

$ + 6.6 billion

M2Velocity

1960–1970 Av.: 1970–1980 Av.: 1980–1990 Av.: 1990–2000 Av.: 2000–2010 Av.: 1960–1980 Average:

M1Velocity

( T − G)c deficit Coef.( t)

Loanable funds growth net of the govt. deficit during the decade

Trends in crowd out significance and movement in other variables

Period

Table 3.11

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Table 3.12 Ratio of standard deviation/average yearly change in standard consumption function variables Variable C (Y − T ) (T − G) T G (S + FB) PR DJAV POPYOUNG/OLD POPTOTAL M2AV−2TO−4 CONBOR

1960–1970

1970–1980

1980–1990

1990–2000

2000–2010

0.4 0.6 −6.1 1.3 0.7 2.3 5.3 1.1 0.9 0.1 0.5 −2.5

0.6 0.6 −5.9 3.1 0.8 4.1 24.8 26.1 57.0 0.1 1.0 −32.4

0.5 0.8 −2.3 1.0 0.3 8.2 −256.5 1.5 −0.5 0.1 1.2 329.9

0.5 0.5 2.3 0.6 0.7 1.2 42.1 1.0 −1.43 0.1 2.1 2.9

0.5 0.5 −1.9 −7.7 0.6 −11.8 −4.5 3.2 15.5 0.2 0.4 −3.5

is real, you must have sufficient variation in the explanatory variable to show it had a statistically significant effect on the dependent variable. An alternative formulation can be derived from this equation. It more commonly used in economics texts, e.g., Hill et al. (2011), to define the standard error of the explanatory variable in simple regressions: SEbslope = σˆ 2 /Σ(x − x)2 In Table 3.12, we show levels of variation each decade in each of our consumption model’s explanatory variables. In looking at the decadeby-decade averages, the low level of variation in the (G) data probably accounts for the lower statistical significance levels routinely found compared to those on (T ). 3.2.5

Left-Out Variables

Some explanatory variables that do affect the dependent variable may not have been included in the model being tested. But the not-included variable that does affect the dependent variable may be correlated with an explanatory variable that is included. If the omitted variable is negatively related to the dependent variable, and positively correlated with an included variable in the model that has a positive effect on the dependent variable, the coefficient will reflect only the small net effect of the

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two contradictory influences and more likely to be insignificant. If the omitted variable is then added to the model, its negative effect is more likely to be captured by its own coefficient, not that of the explanatory variable already in the model with which it is correlated. Several potentially “left-out” variables were tested that might possibly have affected the accuracy of crowd out estimates in some models tested. These included the M1 and M2 velocities of money, and the extent to which M1 grew when FR open market operations increased FR purchases of treasuries and agency securities. As shown in Table 3.11, there was no correlation found between variation in these variables (by decade) and the deficit when included in the standard consumption equation. Hence, there was no reason to think the coefficient on the deficit variable was either over- or understated because of a failure to control for these variables in the model. 3.2.6

Multicollinearity

Levels of multicollinearity vary between explanatory variables from period to period. For this reason alone, period-to-period results may differ. The more highly intercorrelated the two explanatory variables are, the lower their levels of statistical significance tend to be (Fox 1965; Pyndyck and Rubinfeld 1998). A complication with regression estimates from the standard model using only 1980–1990 data is that though the model explained 99% of the variance in consumption during that period, not a single variable was found statistically significant at the 5% level or higher. This suggests that the lack of a statistically significant crowd out effect during this period was due to some larger problem with the model, not lack of crowd out effects. In the 1980–1990 decade, the multicollinearity problem was substantial. There were 8 intercorrelations among the explanatory variables that were equal to or exceeded (.59). This is extremely high compared to typical correlations in other periods in this paper’s models. High levels of multicollinearity occur in other decades as well. For either the 1970–1980 or 1980–1990 decades, there were seven intercorrelations among the explanatory variables equal to or above (0.60). This is likely to be a contributing source (or the only source) of the low t-statistic on the crowd out variable in Table 3.3 for the 1970–1980 decade (t = 1.4). Also insignificant was the t-statistic for the crowd out variable for the subsequent 1980–1990 decade (t = 0.1). Though multicollinearity

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is likely a significant factor here, other evidence discussed earlier suggests the lack of significance in the 1980–1990 decade stems principally from the low ratio of variation of (G) to average decade growth (33% for government spending). 3.2.7

Insufficient Sample Size

There are nine explanatory variables in the standard consumption model, and in some Table 3.3 tests, only ten years of data were used to estimate coefficients. Many variables, including the deficit variable, appear insignificant when testing a model with a 10 observation sample, but become significant when the same model is tested on 20 or 30 observation samples. For example, small sample size, can account for some of the insignificance found in the decade-sized samples used in Table 3.13 increasing sample size to 20 or 30 years, shows significant crowd out effects in one of the three samples. This problem is resolved in this study by requiring samples of data to include at least twenty observations. The other two samples contained the 1980s and 1990s decades data. The 1980s was characterized by lack of substantial fluctuation in the Table 3.13 Single variable deficit significance in standard consumption model (multi-decade samples)

Period 1960–1980 Average: 1970–1990 Average 1980–2000 Average 1990–2010 Average 1960–1990 Average: 1970–2000 Average 1980–2010 Average

Deficit ( T − G) Coef.( t-statistic)a

Deficit ( T − G) Coef.( t-statistic)b

+.42(3.1)

+.45(3.4)

+.16(2.9)

+.23(2.4)

+.02(0.2)

+.01(0.1)

+.25(3.2)

+.39(3.0)

+.22(3.6)

+.29(3.4)

+.09(1.5)

+.13(0.1)

+.28(4.8)

+.37(4.7)

a Standard Consumption Model w/o LF as stand-alone variable or

deficit modifier b Standard Consumption Model with LF as stand-alone variable

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government spending variable and the 1990s by the “crowd in” effect resulting from deficit reduction during the decade. As explained earlier, either of these can cause crowd out effects to appear statistically insignificant when that decade alone is tested. But if one of those decades data is combined with enough additional data from other decades, the insignificance may disappear simply because of the increased degrees of freedom. In some cases, we address this problem by ignoring findings from samples dominated by data when determining the percent of samples with significant crowd out results from these decades. 3.2.8

Spurious Results Indicating Insignificance

Finally, we note that it is possible that findings of crowd out insignificance are spurious. We have attempted to eliminate this problem by testing all models in six to eighteen different, though often overlapping time periods. Initial findings which could not be replicated in most other periods tested were rejected as spurious.

3.3 How Should a Change in Loanable Funds Be Distributed to Tax and Spending Deficits As we show below in Chapters 13–14, there is strong statistical evidence that “crowd out” undermines the stimulus effects of Keynesian-type tax cut and government spending deficits, and was found to have done so in most periods sampled, going back to 1960. There is also evidence to show that growth in the loanable funds pool during periods when deficits occur can offset some or all of the impact of the crowd out (Chapters 16–20). The loanable funds pool may grow endogenously due to changing economic conditions and their effect on saving or if there are changes in the level of foreign borrowing. Loanable funds may also grow for exogenous reasons as a result of FR open market efforts to buy bonds and thereby increase excess reserves in banks. This section describes how these increases in loanable funds are modeled and added to consumption and investment equations. Shown below is a model of how changes in loanable funds (S + FB) could be combined with tax cut deficits (T ) and spending cut deficits (G) to show the reduction in crowd out effects that occurs when the total pool of loanable funds increases. Before modification by changes in loanable funds, we define the magnitude of crowd out as equal to deficit

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size, i.e., (T ) or (G). Also shown is the revised magnitude of crowd out expected after modification of these deficit variables by changes in loanable funds. Let β 1 and β 2 show how the increase in loanable funds might be distributed between tax and spending deficits. Then, the current period change in the deficit (T − G), as modified by a current period change in in loanable funds (S + LF), may be shown as: (T − G) + (S + LF) = (T − G) + β1 (S + LF) + β2 (S + LF) = (T + β1 (S + LF) )−(G − β2 (S + LF)) (3.3) It is impossible from the models used in this study to tell empirically estimate how any increase in loanable funds is divided between these two deficits, but this does not appear to matter. In Table 3.14, we estimate the consumption model given in Eq. 18.1 below, except that it is only estimated using 1990–2000 data. The model was then estimated five times, each using a different division of the increase in (S + FB). Results are shown below. Results indicate crowd out effect coefficients on deficit variables modified by (S + FB) do not change with changes in how changes in loanable funds are allocated to the deficit variables when used to offset crowd out effects. As a matter of convenience, throughout this book, we define β 1 = β 2 = 1.00. Table 3.14 Coefficients of modified T , G variables, and stand-alone (S + LF) when (S + LF) distribution varies Test #

Modified T Coef. ( t-statistic)

Modified G Coef. ( t-statistic)

(S + LF) Coef. ( t-statistic)

Distribution to ( T), ( G)

1.

1.18 (3.7)

.50 (6.5)

−0.46 (−1.4)*

2.

1.18 (3.7)

.50 (6.5)

−1.13 (−1.4)*

3.

1.18 (3.7)

.50 (6.5)

−0.80 (−1.4)*

4.

1.18 (3.7)

.50 (6.5)

−2.05 (−1.4)*

5.

1.18 (3.7)

.50 (6.5)

−1.23 (−1.4)*

0*(S + FB), 0*(S + FB) 1.0*(S + FB), 1.0*(S + FB) 0.5*(S + FB), 0.5*(S + FB) 0.33*(S + FB),.33*(S + FB) 0.2*(S + FB), 0.4*(S + FB)

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References Eckstein, O. (1983). The DRI Model of the U.S. Economy. New York: McGrawHill Book Company. Economic Report of the President. (2012). Washington: Government Printing Office. Economic Report of the President. (2018). Washington, DC: Government Publications Office. Fox, C. (1965). Intermediate Economic Statistics. New York: Wiley. Goldberger, A. S. (1961, December). Stepwise Least Squares: Residual Analysis and Specification Error. Journal of the American Statistical Association, LVI , 998–1000. Griffiths, W., Hill, R., & Lim, G. (2008). Principles of Econometrics. Hoboken: Palgrave Macmillan Publishing. Hill, R., Griffiths, W., & Lim, G. (2011). Principles of Econometrics. Hoboken: Wiley. Heim, J. J. (2017a). An Econometric Model of the U.S. Economy. Hoboken: Palgrave Macmillan. Heim, J. J. (2017b). Crowding Out Fiscal Stimulus. Hoboken: Palgrave Macmillan. Johnston, D. (1963). Econometric Methods. New York: McGraw-Hill. Nau, R. (2018). Statistical Forecasting: Notes on Regression and Time Series Analysis. Duke University, Fuqua School of Business. Available at https://people. duke.edu/~rnau/411home.htm. Pindyck, R., & Rubinfeld, D. (1998). Econometric Models and Econometric Forecasts (4th ed.). Boston: Irwin McGraw-Hill. Tinbergen, J. (1939). Statistical Testing of Business Cycle Theories, Volume 2. Business Cycles in the United States of America 1919–1932. Geneva: League of Nations. Triola, M. (2011). Elementary Statistics. New York: Pearson.

PART II

Theory of Crowd Out and Accommodative Monetary Policy

CHAPTER 4

Theory of Crowd Out and Accommodative Monetary Policy

Section 4.1 below presents, in non-mathematical form, a theory of how, and under what conditions, an increase in Federal Reserve purchases of government securities can work to stimulate the economy. Section 4.2 provides a more mathematical exposition of how government fiscal policy can be used to stimulate the economy, how it can create a crowd out problem, and how increases in the pool of loanable funds, including FR security purchases, can offset the crowd out effects of government deficits, allowing the fiscal stimulus to work.

4.1 How, and Under What Conditions, Can Federal Reserve Purchases of Government Securities Stimulate the Economy 4.1.1

Overview

When the government attempts to stimulate the economy by increasing government spending or by cutting taxes, it creates or increases the budget deficit, which increases aggregate demand. If the deficit is avoided by offsetting changes in the other component of the budge (T or G), it creates offsetting decreases in aggregate demand, causing the stimulus to be ineffective. Hence, the fiscal actions must create a deficit (or reduce a surplus) if anything more than (at best) a marginally small balanced-budget multiplier stimulus effect is to occur. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. J. Heim, Why Fiscal Stimulus Programs Fail, Volume 1, https://doi.org/10.1007/978-3-030-65675-1_4

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To finance these deficits, governments borrow money from the pool of personal, business, and public savings constituting national savings, and/or borrow from foreign countries (from their national savings). This pool of savings and borrowings is the “loanable funds” pool. When government borrows from this pool to finance a deficit, it reduces how much of the pool is available to consumers and businesses to borrow to finance part of their purchases of goods and services. This is a real problem, since in good times or bad, consumers and businesses always borrow money to finance some of their spending. This is why, historically, unborrowed loanable funds (excess reserves) in banks have only been about 2% of total reserves at each year’s end (see Chapter 8), an amount more likely explained as a precautionary savings action by banks than as an indicator that demand for loans is chronically less than supply. For decades, it has been argued that this “crowding out” of private borrowers by government borrowing can cancel out the stimulus effects of government spending and tax cut programs, but that this crowding out effect could be avoided if monetary policy by the Federal Reserve (FR) is “accommodative,” i.e., if the FR increases the pool of loanable funds available to private borrowers enough to replace the loss of pool funds to private borrowers caused by government borrowing. This can be done by having the FR purchases outstanding government securities held by banks, thereby increasing their loanable reserves to levels previously available before the government borrowing began. This solution has been part of standard economic theory for decades. In examining crowd out and accommodative monetary policy, this book has two objectives, one scientific and one institutional and policy oriented in nature. Scientifically, we want to (again) determine if the “crowd out” problem really exists. To do this, we will add a variable (the deficit) to widely accepted (“standard”) models of what variables drive consumption and investment spending. If adding the deficit variable increases the model’s accuracy in explaining consumer or investment spending, and shows through the sign on the coefficient on the deficit variable that declining spending is associated with deficits, we will conclude deficits, cause crowd out. Then, if the deficit (crowd out) is found associated with reduced consumer and business spending, further tests will be undertaken to determine if increasing the pool of loanable funds, via “accommodating” monetary policy, can provide consumers and businesses with funds to

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offset lost borrowing opportunities due to crowd out. From a modeling perspective, if redefining the “crowd out” variable as the deficit reduced by any FR securities purchases creates a modified deficit variable that is even better at explaining variation in consumption and investment than the deficit alone, we will conclude accommodate policy works. This is done in Chapters 10–30 below. This study also looks at the actual institutional and managerial processes the FR uses to implement “accommodative” policy. We examine exactly from whom the FR purchases government securities in response to new or increases in old deficits, and to what extent. We determine if the amount purchased was large enough to create enough additional loanable funds to offset the deficit. Also examined is whether it was an effective process, i.e., one likely, by increasing loanable reserves, to result in increased borrowing by those who wish to purchase real goods and services that raise the GDP and lower unemployment. If the sellers of securities to the Fed are investment banks and brokerage houses, which is the norm, this study looks at whether the proceeds received by such sellers are typically lent out to increase purchases of real goods and services like houses and cars that raise the GDP and lower unemployment, or just used by the sellers to purchase other securities, raising the securities markets. This is done in Chapters 7−9 below. 4.1.2

Detailed Analysis of the Crowd Out and Accommodative Monetary Policy Processes

Accommodative Federal Reserve Purchases from Depository Institutions When the U.S. Federal Reserve (FR) wishes to “accommodate” stimulative fiscal policy, i.e., help ensure it will be effective, it increases the pool of loanable funds (excess or non-required bank reserves). It uses open market purchases of U.S. Treasury, Agency, or mortgage-backed bonds from banks to do this. If the bonds are bought from depository banks, the security sellers would be paid by increasing the banks reserves with the Federal Reserve. 1. Banks, like commercial and savings banks, would be selling bonds to the FR because the demand for bank loans is greater than their supply of loanable funds. They sell bonds from their loan portfolios

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to get more money to lend for mortgage, car, furniture, etc. Such loans are used to increase demand for real goods and services, which stimulate economic growth. 2. Such banks might also sell bonds to the Fed in times of economic decline for precautionary reasons, to provide themselves with additional liquidity in the event that loan repayment failures reach a high enough level to leave the daily inflow of loan repayments lower than what’s needed help pay daily withdrawals by depositors. 3. It could also be that having sold bonds to the Fed, the bank decides economic conditions warrant increased prudence in lending, so the revenue received from selling the bonds just sits as excess reserves in the bank’s account. However, this would seem unusual: Why would the bank have sold the interest-paying bonds in the first place if it thought prospects for lending the resultant reserves were dismal? Federal Reserve Purchases from Non-depository Institutions By comparison, when the Fed purchases bonds from non-depository (i.e., investment) banks and non-bank bond sellers, like brokerage houses, the sellers are paid (electronically by Fed Wire) from the Federal Reserve’s own accounts, unless the seller has a bank account at the Fed, in which case the Fed could pay by increasing its reserves. Most purchases of such bonds by the FR are from the FR’s primary dealers, which are mostly investment banks and securities dealers and brokerage houses. Until the 1999 repeal of Glass-Steagall, they were not allowed to undertake the type of wholesale and retail banking that commercial and savings banks do that stimulates the real economy, e.g., make car and house loans. Even today, the amount of retail loaning they do is miniscule compared to their traditional business of selling securities to get money to buy other securities. If investment banks are selling bonds to the Fed from their own portfolio, principally it would be for portfolio balancing, i.e., to obtain money to buy alternative securities that currently appear to be a more profitable investment than government securities. Their sales of securities to the Fed do not affect the GDP in any direct way, if at all. If they were selling bonds to the Fed for a client (in their capacity as bond brokers/traders/portfolio managers), often it would have been because the client saw alternative securities that appear to be a more profitable investment, i.e., because the client was portfolio balancing. This does not affect the GDP directly, if at all.

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For example, the investment bank may be asked to liquidate bonds owned by a mutual fund client. The client most likely is liquidating the bonds to obtain funds to buy other bonds, stocks or mutual funds, with no real impact on the GDP. The mutual fund might also be liquidating to obtain funds to redeem shares the mutual funds own clients wish to cash in so they can buy a new car or house, but fund trading activity for this purpose is far less common. When the investment bank or securities dealer is paid by the Fed, it deposits the money in its own bank, generating bank reserves, but typically, will draw them down quickly to buy the desired replacement security. The seller of the replacement security, most likely another security dealer, then deposits the payment in their bank. More likely than not, this dealer also sold the security to raise funds to buy yet another security that looks more profitable. This buying and selling by one dealer to and from another continues in a somewhat endless loop. There is a chain reaction of many financial transactions that follow the Fed’s sale, keeping the financial markets active, but few if any transactions that affect the real economy, so GDP and unemployment are not much affected. Payment for the initial purchase of treasury or agency bonds by the Fed was deposited in the dealer’s bank, creating new reserves. From then on, these reserves just move from bank to bank each day as dealers buy securities from one another. Money multiplier effects of fractional reserve banking could expand the amount of reserves created and lent out. Some portion of what’s lent out may affect the real GDP. But if the turnover of funds from trader to trader is rapid, which we would expect in normal times (generally, the only reason a trader is selling a security is to get funds to buy another), there is little opportunity for this money expansion effect lending to substantially affect the real economy. The rapid movement from trader to trader is likely to mean the reserves are little more than overnight reserves allowing for little more than overnight lending. The increase in reserves stemming from FR security purchases may increase demand deposits, increasing (M1). But the additional M1 in investment bank’s bank accounts may mainly increase demand for stocks and bonds, increasing demand in those markets (we test for this in Chapter 12). It also raises security prices, since there is a change in money demand for those securities in what is typically an auction market, but no change in supply (we show in Chapter 12 that this happened with the increase in reserves generated by the QE program). Yet, for the most part, demand for real goods and services (i.e., those counted in the GDP) is

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not likely to be much affected. If demand for real GDP does not change, there is no incentive to change supply; hence, the unemployment rate is not likely to change either. The attempt to use monetary expansion to stimulate the real economy will have failed if the Fed’s purchases of securities from investment bankers and brokerages just increase demand for securities, not real goods and services. There may be some subsequent positive effects on the real GDP because of rising stock and bond markets, but they are likely to have only a more marginal effect on total aggregate demand: 1. More brokers and traders may be hired, or their salaries increase due to the stock and bond market boom. This increases value added in the finance industry, a component of GDP. 2. The rising stock and bond market may also increase GDP by creating a wealth effect, which stimulates consumer spending. Traditional estimates are the effect which is about 2.5% of the growth in stock and bond market value, and it may be subject to a lag (Heim 2017). 3. There may also be some increase in business investment due to a “Tobin’s q” effect from rising bond prices. Based on the analysis above, it may be more accurate to say that attempts to stimulate the economy through open market operations principally stimulate financial markets, but may also have a positive, though modest effect on the real economy. If bond market conditions are declining, broker/dealers may rush to sell treasuries (and other securities) to the FR before the markets drop further. Sellers may keep the proceeds in liquid form, perhaps in their own commercial bank checking accounts, not spending it in anticipation of market prices falling further and presenting even better buying opportunities in the future. The excess reserves portion could, of course, be lent out by the commercial bank to borrowers desiring to buy autos, houses, machinery, etc., raising the GDP. This could raise the GDP without significantly changing the securities markets. But if the economy is bad enough for securities markets to be in decline, it is likely the decline in large enough to markedly decrease overall demand for commercial and personal loans to buy things in the real GDP. When market conditions are bad, even if payments to security dealers for selling securities to the FR are deposited in the dealers’ demand

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deposit accounts, creating lendable reserves, and it is lent out to “real goods and services” borrowers, the positive effects on demand for real goods and services may not be sustainable once the securities market decline ends. When a consensus is reached that the market has bottomed out, traders’ instincts will be to buy additional securities “at the bottom,” paying by drawing down demand deposits. Any loans to goods and services buyers made from the reserves created by dealer deposits into their accounts will have to be recalled, or new money flowing into the bank at that time that would normally be used to make additional loans, will have to be used to fund withdrawals from investment bank/trader accounts to buy new securities. If so, these withdrawals will increase demand for securities, raising security prices, but the real economy’s growth will be slowed or remain flat. Theoretically, it could even decline.

4.2 A Formal Model of the Effects of Fiscal Stimulus Programs, Their Crowd Out Effects, and Accommodative Monetary Policy In typical models of GDP determination, the determinants of consumption, investment, government spending, and the trade balance are hypothesized, and the four equations expressing these determinants, additively combined into one GDP determination model. A simplified version of such a model might look like this: C = c0 + c1 (Y − T)−c2 (Int)

(4.1)

I = i0 − i1 (Int)

(4.2)

G, (X − M) = assumed exogenously determined In such a model, GDP determination is given as GDP = C + I + G + (X − M) = c0 + c1 (Y − T)−(c2 + i1 ) (Int) + i0 + G + (X − M)

(4.3)

Or, consolidating (Y = GDP) terms, in more policy-usable form,   GDP = (1/1 − c1 ) c0 + i0 − c1 (T) + G−(c2 + i1 ) (Int) + (X − M)

(4.4)

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In such models, negative changes in (T) or positive changes in (G), ceteris paribus, stimulate the economy (GDP). But to be stimulative, cutting taxes or raising government spending has to be done holding the other constant, i.e., by incurring a deficit, increasing an existing deficit, or reducing an existing surplus. But how exactly does one finance a deficit? There are three choices: 1. Borrow funds from the pool of loanable funds (national savings + foreign borrowing) that would otherwise be borrowed and spent by consumers and businesses, thereby reducing how much they can borrow and spend. This is called crowd out of private spending. 2. Print more money to finance the increased government spending or tax cut. That way, the deficit does not have to be financed by reducing the pool of loanable funds available for private borrowing. This is commonly referred to as Modern Monetary Policy (MMP). Think of this, conceptually, as one way in which accommodative monetary policy could theoretically work. There is a second, and more traditional, form of accommodative monetary policy. It is not intended to finance a deficit but to compensate for the negative offsetting effects on the economy that occur when deficits occur. This is done by Federal Reserve (FR) actions to replenish the loanable funds pool for losses in funds available for private borrowing due to the deficit, i.e., crowd out. This type of accommodative monetary policy (or at least the theory of it) has been around as part of monetary theory, since at least the 1930s. 1. The actual process for implementing the second type of accommodative policy is complicated. The FR purchases securities from banks that will use the proceeds to make loans to consumers and businesses who wish to buy real goods and services (or so the theory assumes). Some security purchases might be from individuals (the FR also assumes) will use the proceeds to buy real goods and services. 2. If the proceeds received from such security sales to the FR are deposited into the seller bank’s demand deposit account, or the selling individual’s demand deposit accounts, the M1 money supply

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increases. The sale of securities to the FR may also increases the seller’s income, depending on the capital gains realized. Any income not spent (definitionally) increases the pool of national savings. 3. The FR may pay for such security purchases from banks by simply crediting the selling banks’ reserves with the FR. Such funds would then be drawn down as the banks lend these reserves out to those wishing to borrow funds to purchase goods and services. Typically, such loans are deposited in the borrower’s demand deposit account, increasing the money supply. Payment in the form of currency has the same effect, as does the payment by check from the banks own demand deposit account. Should the FR purchase securities? Traditional monetary theory, e.g., Friedman and Swartz (1963) argues the FR reserve should grow the money supply at the expected rate of growth of the economy; more would be inflationary, less deflationary. Crowd out theory is a slight extension of this; it argues the expected rate of growth will be higher with fiscal stimulus than without, but only if monetary policy is accommodative, i.e., grows enough to cover reduction in private borrowing possible because of the deficit. Absent this, we get a “Volcker Effect”; failure of the money supply to grow at the economy’s expected growth rate stifles growth, creating deflationary (or reducing inflationary) effects. 4.2.1

Crowd Out Effects of Deficit Financing

We can show the negative effects of the crowd out problem on private borrowing by modifying the stimulus model shown in Eqs. 4.1–4.4 above by adding a variable to both the consumption and investment equations to show the effects of the deficit on consumer and investment spending. In the equations below, deficit values of total government revenue minus total government spending (T – G) have a negative sign, surpluses a positive sign. The marginal effect on consumption of an increase in (T – G), (a decrease in the deficit), c3 , is assumed positive. C = c0 + c1 (Y − T)−c2 (Int) + c3 (T − G)

(4.5)

I = i0 − i1 (Int) + i2 (T − G)

(4.6)

G (X – M) = assumed exogenously determined

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In such a model, GDP determination is given as the sum of the determinants of production of consumer and investment goods, plus goods produced for government use and for export (net of imports). GDP = C + I + G + (X − M) = c0 + c1 (Y − T)−(c2 + i1 ) (Int) + i0 + G + (X − M) + (c3 + i2 )(T − G) = c0 + c1 (Y) + (−c1 + c3 + i2 )(T)−(c2 + i1 ) (Int) + i0 + (1 − c3 − i2 )(G) + (X − M) (4.7) Or in more policy-usable form,  GDP = (1/1 − c1 )

c0 + i0 + (−c1 + c3 + i2 )(T) + (1 − c3 − i2 ) (G)−(c2 + i1 )(Int) + (X − M)

 (4.8)

Comparing Eqs. 33.4 and 33.8, we see that the pre-multiplier stimulative effect of tax cuts (−c1 T) and spending deficits (1.0)(G) is reduced by the marginal effect on consumption and investment (c3 + i2 ) of reduced funds available for private borrowing. The reduced stimulus is +(−c1 + c3 + i2) (T) for tax cut deficits and (1−c3 −i2 )(G) for deficits caused by spending increases. 4.2.2

How Accommodating Monetary Policy Offsets Crowd Out Effects

If we modify Eqs. 4.5−4.8 above slightly, we can show how implementation of traditional accommodative monetary policy can offset crowd out. Traditional accommodative monetary policy involves expanding the pool of loanable funds available to private borrowers to the level previously available to them, before government borrowing to finance the deficit. The modification to our simple model above involves adding a variable, namely consumer of business borrowing (Borc or BorI ), to the variables already in those models determining the level of consumer or business demand for goods and services. There is extensive evidence, showing that consumer borrowing and business borrowing account for variation in consumer and investment

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spending that would otherwise be unexplained (e.g., Heim (2017), Eqs. 4.4.TR, 5.4.TR). Further, U.S. bank excess loanable reserves at year’s end have only been 1–5% of total reserves during the nearly 50 year period 1960–2007. This near-zero level of excess reserves was found in recessions as well as normal times (see Chapter 8.2 below, Table 14???). Only with the advent of quantitative easing (QE) was that pattern broken, and excess reserves jumped to 96% of total reserves. This suggests that during 1960–2007, the demand for loans chronically has nearly equal to the supply of loanable funds. In reality, the small level of excess reserves suggests it was probably kept for precautionary reasons, not because banks had run out of customers to lend to. This suggests that increasing the pool of loanable funds will increase borrowing, which, in turn, will increase consumer and investment spending, thereby stimulating the economy. Chapter 31 below shows extensive statistical evidence indicating that increases in the loanable funds pool very consistently increase business borrowing, and also consumer borrowing, though not as consistently. Repeating the crowd out model given in Eqs. 4.5–4.8 with this modification for borrowing, we have: C = c0 + c1 (Y − T)−c2 (Int) + c3 (T − G) + c4 (Borc )

(4.9)

I = i0 − i1 (Int) + i2 (T − G) + i3 (BorI )

(4.10)

G (X−M) = assumed exogenously determined In such a model, GDP determination is given as GDP; = C + I + G + (X − M) = c0 + c1 (Y − T)−(c2 + i1 )(Int) + i0 + G + (X − M) + (c3 + i2 )(T − G) + c4 (Borc ) + i3 (BorI ) = c0 + c1 (Y) + (−c1 + c3 + i2 )(T)−(c2 + i1 )(Int) + i0 + (1 − c3 − i2 )G + (X − M) + c4 (Borc ) + i3 (BorI )

(4.11)

Or in more policy-usable form, GDP = (1/1 − c1 )c0 + i0 + [−c1 + c3 + i2 (T) + (1 − c3 − i2 )G −(c2 + i1 )(Int) + (X − M)] + c4 (Borc ) + i3 (BorI )

(4.12)

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The model suggests that both fiscal and monetary policy can expand the economy. For fiscal policy, the expansion is due to the increased demand resulting from tax cuts and increased government spending, but only if crowd out effects are offset by increases in the loanable funds pool. For monetary policy, expansion of the economy can occur through open market operations that expand the loanable funds pool when deficits occur, allowing private borrowing and spending to remain at previous levels. The model also implies that even if there is no deficit, monetary stimulus, through open market purchases of securities by the FR, can have a positive effect on the economy. While monetary policy can offset a deficit’s crowd out effects, in the theory presented above, it does not require a deficit to have a positive effect on the economy. That said, the demand for loanable funds is not insatiable, as we learned with QE, when excess reserves, which had varied only from 1 to 5% of total reserves in the 47 years preceding QE’s start in 2008, rose to 93–95% in the 2008–2010 period. So there is some upper limit to the effectiveness of monetary policy, and it depends and it is set at the maximum amount people are willing to borrow. Increasing the loanable funds pool beyond this just leads to the “pushing on a string” problem. Determining just where that limit is an empirical question beyond the scope of this book, although data in Chapter 15 can give us an idea. However, where there is evidence that where prior levels of private borrowing were once higher, but are now crowded out by a deficit, increasing the loanable funds pool by enough to restore the old borrowing level should offset the negative effect of crowd out, e.g., Chapters 21 and 22. The loanable funds pool can increase due to endogenous factor fluctuations in the economy as well as the FR’s exogenous actions. A booming economy, via increased borrowing, will increase the money multiplier and therefore total lendable reserves. Via this channel, as well as by any increases in savings due to increasing incomes, the pool of loanable funds can be increased. Absent a deficit, any increase in the pool of loanable funds can be applied to additional borrowing and spending, growing the economy (provided the economy is not already at full capacity). With a deficit, part or all of any increase in the pool is drained off just to maintain the same amount of private borrowing and spending as in prior periods. The diversion of part of the growth in loanable fund to restoring private borrowing to its old levels would leave less (or none) available to finance

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new economic growth. While true, diversion of this years loanable funds growth to offsetting crowd out allows the government’s fiscal stimulus program to work. This too will increase economic growth. Hence, both deficits and non-deficit uses of increases in the loanable funds pool can stimulate economic growth, and it is beyond the scope of this book to try to determine which is better. The net effect on the growth rate of one choice versus another will depend on the relative stimulus effect of diverting part of the loanable funds growth from private borrowing (and spending) to public borrowing (and spending). The increase in government demand for goods and services financed by the deficit will provide new stimulus to the economy. If it provides as much stimulus as the same growth in loanable funds would provide if left available for private borrowing, there may be no net effect on the overall long-term growth rate. One may have a larger effect on growth than the other, but that is a difficult question to answer theoretically to everyone’s satisfaction. It is more a question for empirical testing to resolve. And the type of deficit can make a difference. If all tax cuts were saved, there would be no decline in the part of the loanable funds pool available for borrowing (but no net stimulus effect from the tax cut either). The choice of whether to use increases in the loanable funds pool to finance increased public versus private sector demand will change the composition of the economic growth. If allocated to public demand, i.e., used to finance deficits, there will be less private borrowing to buy cars, houses and furniture in the long run, and more borrowing to finance public health, transfer programs, and infrastructure building. Hence, we conclude that using a fairly standard theoretical model of GDP determination shows that: 1. Government deficit spending programs can stimulate the economy. 2. Crowd out effects of deficit financing are real and can reduce or eliminate the stimulus effects of deficits. 3. To offset any crowd out effects, open market operations by the FR (accommodative monetary policy) can increase the pool of loanable funds available to private borrowers to the extent needed to offset crowd out. This allows the full stimulus effects of deficit-financed fiscal policy to be felt in the economy, raising the GDP and lowering unemployment.

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4. Whether there is a deficit or not, when consumer and business borrowing is constrained by inadequate supplies of loanable funds, monetarily expanding, the loanable funds pool policy can stimulate the economy. However, there are limits; the demand for loanable funds is not insatiable. Using monetary policy to expand the pool beyond the limits of what people want to borrow, largely determined by the general level of the economy, is just “pushing on a string, and will not bring the desired benefit.” Endogenous (growing economy) increases in the loanable funds pool due to an improving economy can also offset deficit’s crowd out effects. But without the deficit, it would increase total private borrowing (i.e., spending), stimulating the economy. The question is, if used to offset a deficit’s crowd out effects on private borrowing instead of increasing private borrowing, what will happen? Will the lost stimulus effect to the economy that would have been received by increased private borrowing be offset by the deficit’s own positive effect on economic growth? This is largely an empirical question. The multiplier effect of families buying more cars or houses may be greater (or less) than the multiplier effect of government spending on roads and bridges or transfer payments. We take no position on it here. 4.2.3

Different Crowd Out Effects of Tax Cut and Spending Deficits

The crowd out effects of government spending and tax cut deficits may be different. Our empirical tests often showed differing crowd out effects for tax cut and spending deficits (e.g., part of tax cuts is likely to be saved, reducing crowd out effects), so most crowd out tests in this book test separately for the crowd out effects of tax and spending deficits. There is past evidence of other kinds of differential effects as well. In Heim (2017a, b), crowd out effects of tax cut deficits reduced consumption more than increased spending deficits, and vice versa for investment spending deficits reduced investment more than tax deficits. Distributional effects seemed to explain the different effects of tax and spending deficits. Tax cuts tend to be skewed toward the upper end of the income distribution, and recipients have higher marginal propensities to save. Hence, they consume less per dollar of tax cut and return more to the loanable funds pool than do the typical recipients of spending deficits,

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who are skewed further down the income distribution and spend more on consumption and are less able to save. In addition, as we show in Chapter 31 below, increases in the loanable funds pool tend to be channeled to business borrowers, not consumer borrowers. Hence, consumer borrowing and spending is likely to increase little just because the pool of loanable funds has increased. increases in loanable funds, and business borrowing and spending more, since if saving from the tax cut is large, it offsets crowd out, and leave much or all of the increase in loanable funds available to finance increases in investment spending. To capture these different effects, we can make an additional modification to the crowd out variables in our consumption and investment equations. To do so, we replace c3 (T – G) in our consumption equation with c3a (T) – c3b (G). We also replace i2 (T – G) in our investment equation with i2a (T) – i2b (G). Repeating the crowd out model given in Eqs. 33.9–33.12 with this modification for differential crowd out effects of spending and tax cut deficits, we have: C = c0 + c1 (Y − T)−c2 (Int) + c3a (T) − c3b (G) + c4 (Borc ) I = i0 − i1 (Int) + i2a (T)−i2b (G) + i3 (BorI )

(4.13) (4.14)

G (X − M ) = assumed exogenously determined In such a model, GDP determination is given as GDP =C + I + G + (X − M) =c0 + c1 (Y − T) − (c2 + i1 )(Int) + i0 + G + (X − M) + (c3a + i2a )(T) − (c3b + i2b )(G) + c4 (Borc ) + i3 (BorI ) =c0 + c1 (Y) + (−c1 + c3a + i2a )(T) − (c2 + i1 ) (Int) + i0 + (1 − c3b − i2b )(G) + (X − M) + c4 (Borc ) + i3 (BorI ) (4.15) Or in more policy-usable form, showing the multiplier:   GDP = (1/1 − c1 )[c0 + i0 + (−c1 + c3a + i2a )(T) + 1 − c3b − i2b (G) −(c2 + i1 )(Int) + (X − M)] + c4 (Borc ) + i3 (BorI )

(4.16)

Equation 33.16 shows clearly the negative effect of crowd out on consumption and investment, but allows for the coefficients expressing

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the crowd out effects of tax cut and spending deficits to be different. With crowd out, the pre-multiplier stimulative effect on GDP per dollar of tax cut is reduced from −c1 to +(−c1 +c3a + i2a ), and the stimulative effect per dollar of spending increase is reduced from (1) to (1−c3b −i2b ). Alternative Ways of Modeling Crowd Out Effects In Chapter 31 below, extensive statistical evidence is presented indicating that increases in the loanable funds pool (LF) are associated very consistently with increases in business borrowing, but only associated with increases in consumer borrowing as well in some time periods sampled, not in others. This may be because priority in channeling loanable funds pool funds goes to business lending. Depending on business needs, this leaves differing parts of any increase in loanable funds available for consumer borrowing in some periods, but not in others. Hence, the finding of significant relationships between increases in loanable funds and consumer borrowing in some periods, but not in others. This hypothesis is consistent with our findings in other chapters that a given sample period’s change in loanable funds is more likely to reduce investment crowd out than consumer crowd out. In Chapter 21 data below, changes in loanable funds eliminate investment crowd out in virtually all periods sampled, but for consumption, only in about half the same periods, most often time periods that included QE program years, when increases in loanable funds were far greater than businesses wished to borrow. Increases in the loanable funds pool (S + FB), if large enough, should allow increases in private borrowing and spending that fully offset the negative effects of crowd out—that is, should prevent deficit-induced declines in consumption or investment. Then, we can model the effects of changes in the pool of loanable funds on consumption in two ways. First, in the manner shown in Eqs. 4.13–4.16 above where consumer borrowing (Borc ) is shown as a determinant of consumption. As discussed earlier, the chronically small level of excess reserves in banks during the years since 1960 (QE period excepted) suggests demand for loans typically exceeds supply. Therefore, to a degree of approximation, increases in the pool can normally be expected to increase borrowing. And because people borrow mostly to allow additional spending, increases in the pool increase spending. The model of crowd out and its effects shown below assumes that the level of consumer borrowing is directly influenced by a change in loanable funds available. Empirical evidence in Chapter 31 supports this. The effect

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of a change in loanable funds (LF) is shown as a separate stand-alone variable; this amounts to the assumption that we are showing its effect on borrowing separately, and leaving the consumer borrowing variable in the model to account for the other factors that influence consumer borrowing. The coefficient on this (LF) variable represents its marginal effect on consumption. In consumption models, a change in the LF pool may have two separate and contradictory effects: 1. It may have the positive effect of offsetting crowd out. 2. It may also have a negative effect since at any given income level (and our statistical models, when tested, hold disposable income constant when estimating crowd out and loanable funds effects), any increase in savings out of personal income must (definitionally) come at the expense of consumption, i.e., income = consumption + savings in which case, the coefficient on the stand-alone (LF) variable, where LF = (S + FB), will represent the net of the two effects. The coefficient may be positive or negative, depending on which of the two influences is the larger. It may be zero if they are the same. Assume they are the same. Then, we might rewrite Eq. 4.13 as 4.17 below, where for ease of exposition we are assuming no other factor affects consumer borrowing except changes in LF C = c0 + c1 (Y − T)−c2 (Int) + c3a (T)−c3b (G) + c4 (LF) (assume for now c4 = 0)

(4.17)

Alternatively, we may show the positive and negative effects separately, by including (LF) directly as a variable modifying the deficit variables, as well as include it as a stand alone. In this case, the coefficient on the stand alone will pick up the negative effect on consumption of the decline in mpc necessary to allow an increase in saving (i.e., LF). The coefficients on the now-modified deficit variables will show the positive effects on consumption of crowd out reduction, as shown below. 1. We can show the positive effect by adding any rise in (LF), a positive number, to the tax cut’s gross crowd out effect, i.e., the cut in (T ), which is a negative number, since ceteris paribus, a decline in

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(T ) is a deficit-creating or increasing change. The whole change in loanable funds can be used to offset whichever type of deficit occurs: the tax cut deficit or the spending deficit (we showed earlier in this chapter that doing this or dividing it into two parts yields the same statistical results). For tax cut induced deficits, this gives a modified crowd out effect of (T + LF), e.g. (−100 + 50 = −50). For deficits due to increased spending, we can subtract any increase in loanable funds (LF) from the spending deficit’s gross crowd out effect (G) to get (G − LF). Since spending increases are measured as a positive number, subtraction of LF is an appropriate way to reduce the gross effect for any increase in available loanable funds. For years in which both tax cuts and spending increases occur, the offsetting effects of LF would be prorated, but as notes, statistical tests earlier in this chapter indicated this is not necessary. 2. We also continue to include the same variable (LF) as a separate, stand-alone variable to capture the negative effects on consumption. Then, for consistency with Eq. 4.17, our alternative model must be C = c0 + c1 (Y − T )−c2 (Int) + c3a (T + LF) −c3b (G − LF) + (?)(LF) = c0 + c1 (Y − T )−c2 (Int) + c3a (T )−c3b (G) + (c3a + c3b )(LF)−(c3a + c3b )(LF)

(4.18)

The only way for this alternative model to achieve consistency with Eq. 4.17 assumed net LF effect of zero and maintain the same coefficients on all the other variables in the 4.17 model is for the coefficient on the stand alone to equal the sum of coefficients on the LF modifiers of (T ) and (G), but with the opposite sign. Consistently, throughout this book, our tests show this result for the coefficient on the stand-alone variable, when the deficit variables are also modified. However, we find that negative effect on consumption is larger than the positive effect, and as a result, the coefficient on the stand-alone (LF) variable has a net negative sign. That is, increases in (LF) help reduce crowd out effects, but the decline in the mpc necessary to generate the increased (LF) is even larger, leaving a net negative effect of changes in the (LF) pool on consumption (ceteris paribus). In both model alternatives,

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1. The model with both a stand-alone (LF) variable, and modified deficit variables (T + LF) and (G−LF), and 2. The model with unmodified (T ) and (G), but including a standalone (LF) variable, are both estimated and used in almost all tests throughout this book. This allows for comparison of estimates of the model showing separate positive and negative effects of changes in the loanable funds pool on crowd out (model 1) with estimates of just the net effect for the crowd out variables (model 2). In all actual testing, the consumption function tested includes many more variables than the simple expository model above. See Chapter 21 for the regression results for each variable used in the more sophisticated models. Regression results for variables in the more detailed version of the 4.17 model given in 4.18 (deficit variables modified) are identical to those used in 4.17, with one exception. The exception is the coefficient on the (LF) variable. In the 4.17 model, the single (LF) variable coefficient has a net value equal to that shown by adding the positive and negative effect coefficients on the (LF) variable shown in model 4.18. The parameter estimates for the (LF-modified) deficit variables remain the same in both models because by modifying (T ) and (G) we are not changing the linear way in which these crowd out variables affect consumption. Only the values of the variables multiplied against these parameter estimates change when they are modified: (T) becomes (T + LF), and (G) becomes (G − LF). However, there is a non-acceptable way of modeling the effects of changes in loanable funds on consumption. It would be to just add or subtract LF changes to deficit variables, i.e. (T + LF) and (G − LF), but not include a stand–alone (LF) variable in the model. Suppose the net effect of the two separate loanable funds effects on consumption was zero. If we add a variable which has no net effect on consumption (LF) to two variables (T) and (G) which do have precise crowd out effects, we have changed the values of these quantitative measures of crowd out. We now have imprecisely defined measures of crowd out and are asking the regression to tell us whether these imprecise measures of crowd out effects have the same effect as the more precise measures. Generally, in hundreds of statistical tests in this book, the result is the estimated effect of the crowd out variables declines markedly, and often becomes statistically insignificant. This is exactly the result econometricians expect when confronting

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the “errors in variables” problem, where only a partially inaccurate data set is available for estimation (Johnston 1963). Though illustrated above using consumption Eqs. 4.17 and 4.18, we can repeat the same two separate, but equivalent tests of loanable funds effects on investment, using the standard investment model, and obtain the same results: when moving from just using a stand-alone variable to represent loanable funds to using the stand-alone plus loanable fundsmodified deficit variables, all coefficients in the two models stay the same except for the coefficient on the stand-alone (LF) variable. See, for example, Chapter 21, Eqs. 21.3 and 21.4. Consumption Models With and Without a Stand-alone (LF) Variable With consumption, a change in LF has two separate effects on consumption, a separate positive effect in reducing crowd out and negative reduced mpc effect from shifting income from spending to saving. Combining the two effects by just using (LF) as a modifier of the deficit variables, and dropping the stand-alone (LF) variable from the model, just distorts the real effect of (LF) on crowd out alone. Let LF effects on consumption be given as C = c1 (T + LF)−c2 (G−LF)−c3 (LF)

(4.19)

The combined effect of two (LF) forces on consumption, a crowd out force and a mpc changing force, when combined in a model showing only crowd out variables, but no stand-alone (LF) variable, gives what appears to be crowd out effects defined as c1 (T + (1–c3 )LF) for tax deficits and –c2 (G–(1–c3 )LF) for spending deficits. In a consumption model with deficit variables modified this way, and without the stand alone, we would expect a priori that empirical tests will show reduced coefficients (and significance levels) on the crowd out effects variables compared to consumption models using the (LF) variable as both a deficit offset and separately, as a stand-alone variable to capture mpc effects. For example, assume the real crowd out effect of a tax cut is c1 (T + LF), e.g., c1 (–100 + 50) = c1 (–50), and the larger absolute value of the variable (T + (1–c3 )LF) (e.g., –100 + (1–.5)*50) = –75 is used to estimate c1 . Because the effect of LF on the crowd out crowd out variables is now misstated, we would expect lower levels of statistical significance, and lower R2 s as well. This is exactly what we get when testing this type of consumption model (see Chapter 21, Tables 21.1 and 21.2) Investment Models With and Without Stand-alone (LF) Variables

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However, with investment models, any increase in loanable funds has just one effect, and it is positive: it increases investment. Therefore, increases do offset declines in investment due to crowd out. Unlike consumption, they do not have a second, negative effect on investment of (in some sense) lowering the marginal propensity to invest. Hence, we do not need a model showing two separate effects. Consider the investment model given in Eq. 4.14 above, repeated here (assuming LF = borrowing): I = i0 − i1 (Int) + i2a (T)−i2b (G) + i3 (LF)

(4.20)

If there is no increase in (LF) when deficits occur, financing the deficit from the LF pool reduces the money available from the pool for private investment (I); the magnitude of the drop is the amount of the deficit. If there is an increase in the pool, every dollar can be used to offset the deficit-caused reduction in pool funds available for investment. And this can be done without causing a decline in investment for other reasons, while the marginal propensity to consume may decline when increased savings increase the LF pool, but the marginal propensity to invest does not. Given that, we can also reasonably hypothesize our theory of how crowd out and changes in the loanable funds pool affect investment as one without a stand-alone (LF) variable: I = i0 −i1 (Int) + i2a (T + LF)−i2b (G − LF) + i3 (0 ∗ LF)

(4.21)

If we left the stand-alone (LF) variable in the investment model when estimating its parameters, we would be, in effect, dividing one positive influence into two parts, and assigning part of the influence to the modified deficit variables and the rest to the stand-alone (LF) variable. This would likely understate the stand-alone variable’s effect. This is exactly what we find in testing. See Chapter 21, Tables 21.4 and 21.5. With investment, there is no a priori reason for preference of one model over the other. Using the model that includes the stand-alone (LF) variable plus the LF-modified deficit variables I = i1 (T + LF) − i2 (G − LF) + i3 (LF)

(4.22)

The available loanable funds offset effect is equals (1 + i3 )(LF) for tax deficit years and −(1 + i3 )(LF) for spending deficit years. For a year in

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which deficits are partially (βi ) caused by tax cuts and partially (βj ) by spending increases, the modified crowd out expressions become i1 (T + (1 + i3 βi )LF) for tax deficit part and i2 (G−(1 + i3 βi )LF) for the spending deficit part, where (βi + βj ) = 1.00. Since the only effect a change in loanable funds has on investment is to offset crowd out effects (even to the point of creating what appears to be “crowd in” effects if large enough), so an alternative statement of how effective changes in (LF) are in offsetting deficits is     (4.23) I = i1 (T + (1 + i3 βi )LF) − i2 G − 1 + i3 βj LF The crowd out variable in 4.23 more clearly shows the magnitudes of true crowd out effects of deficits, net of any change in LF, on investment. Findings for both types of models are shown in Chapter 21, Tables 3. 4.2.4

Declining Deficits Create “Crowd in” Effects

When we experience periods of (LF) growth and declining deficits during the same period, the signs on our crowd out variables (T + LF) or (G − LF) may change, indicating a net “crowd in” effect on consumption or investment, i.e., a net positive effect. A slight rewriting of model 4.17 will illustrate the “crowd in” effect. Assume the value of government spending deficits have been larger than changes in LF levels in the past, leaving a net negative effect on consumption. But suppose this year, the spending deficit falls to zero (G = 0). Then C = c0 + c1 (Y − T)−c2 (Int) + c3a (T + LF) − c3b (G − LF) − c4 (LF) (4.24) Or in first differences of the data (where G = 0) C = c1 (Y − T)−c2 (Int) + c3a (T + LF) − c3b (G − LF) − c4 (LF) = c1 (Y − T)−c2 (Int) + c3a (T + LF) + c3b (LF) − c4 (LF) (4.25) The value of the loanable funds modified government deficit variable −c3b (0 − LF) = + c3b (LF) becomes positive. The regression

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also no longer finds a negative relationship between this variable and consumption, but a positive one, and gives it a coefficient with a positive sign. We find this result later in this study when empirically estimating crowd out effects for decades like the 1990s. The 1990s was characterized by falling deficits. With a constant (LF) pool, a falling government deficit definitionally means more money becomes available for private borrowing and spending. Hence, the positive “crowd in” relationship between consumption and (G − LF) we find in the 1990s data (Chapter 21). A decline in deficits due to an increase in government revenue (T > 0) will also have a positive effect on consumption. If (LF) increases in the same period, the growth in C will be even larger. Typically, in empirical testing, we see this in the data as a larger than usual positive coefficient on the tax variable. If crowd out effects for both types of deficit are the same, the coefficient on (T − G) is the same as on the separate deficit variables. Using this single-variable formulation of the deficit, we can see even more clearly, the effect that results if the deficit has been larger than the level of loanable funds, but now falls to less than that level,   C = c0 + c1 (Y − T)−c2 (Int) + c3 (T − G) + LF − (c3 ) (LF) (4.26) Or in first differences of the data,   C = c1 (Y − T)−c2 (Int) + c3  (T − G) + LF − (c3 )  (LF) (4.27) And where  (T – G) = 0 C = c1 (Y − T)−c2 (Int) + c3 [LF] − (c3 )  (LF)

(4.28)

The same periods of “crowd in” effects were also found when testing the investment model. 4.2.5

Should We Use Accommodate Monetary Policy to Offset Crowd Out?

The empirical findings of this study, presented further below, support the theory crowd out effects can be offset by same-period increases in loanable funds. To say we “can” is science, but just because it is possible to do something (offset crowd out) doesn’t mean we should. That is a public

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policy question. This section deals with the question: “should we?”. Are there negative effects that offset the positive effects? Assume there is an increase in loanable funds. Without deficits, growth in the loanable funds pool increases the total amount of funds available to finance new private consumer and investment spending, which increases the GDP. The availability of loanable funds is important. The close to zero (2% average) as to banks’ remaining loanable funds at year’s end suggests that typically, demand for loanable funds may exceed supply, as it did consistently from 1960 to 2007. If deficits occur, financing them requires the government to borrow money from the pool of loanable funds they hadn’t needed before. That reduces funds traditionally available for private borrowing and spending, which lowers the consumption and investment parts of GDP compared to prior periods. Using any same-period growth in loanable funds to offset this decline in the part of the pool available for private borrowing restores some or all of the old level of private spending out of borrowed funds, leaving GDP unchanged. However, using it to preserve prior levels of private spending means it is not used to finance increased levels of private spending means (as it would be if there were no deficit), so no growth in private sector spending results. Hence, no increase in GDP due to increased private spending. But, using these funds to offset the deficit eliminates the crowd out problem, allowing the deficit-financed increase in government spending or tax cut to stimulate the economy, increasing the GDP. While growth in GDP due to increased private spending may not occur if the government spending option is selected, growth in public spending will increase the GDP. For tax cuts, the “public” stimulus does come through the tax cut’s effect on private spending. The increase in loanable funds may offset the deficit, but the increased cash in consumer and business hands from the tax cut increases private spending increasing the GDP. Hence, in periods when the loanable funds pool grows, if no deficits occur in the same period, increased private borrowing and spending creates growth in GDP. In periods when the loanable funds pool grows, but deficits occur in the same period, the increased public borrowing (to finance the deficit) and resultant government spending out of the borrowing create growth in GDP. If the deficit is a result of tax cuts, the growth results from increased private spending. To summarize, when the loanable funds pool grows, or are anticipated to grow, three public policy options are available:

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1. Choose to not deficit and leave any increase in the pool of loanable funds to private parties to borrower and spend. Private borrowers will use it to buy more private goods (e.g., cars, housing, machinery, and factories). 2. Deficit and channel the increase in loanable funds to public borrowers for use in cutting taxes. The cut in taxes will also increase private spending. 3. Deficit and leave the increase to government borrowers to finance more spending on public goods (e.g., more policemen, social workers, schools, roads, and bridges). Public preferences depend on the type of growth people wish to see: Would they rather have growth in public goods and services, or private goods and services? Public preferences would also be shaped by relative growth rates of all parts of the GDP ultimately resulting from choosing one of the three choices above. Those decisions are larger, global policy decisions, and beyond the scope of this study. In this study, our objective is to undertake further scientific analysis of crowd out, and the extent to which loanable funds (“accommodative monetary theory”) offset it.

References Friedman, M., & Swartz, A. (1963). A Monetary History of the United States, 1867–1960. Princeton: Princeton University Press. Heim, J. J. (2017a). An Econometric Model of the U.S. Economy. Hoboken: Palgrave Macmillan. Heim, J. J. (2017b). Crowding Out Fiscal Stimulus. Hoboken: Palgrave Macmillan. Johnston, J. (1963). Econometric Methods. New York: McGraw-Hill.

CHAPTER 5

A Simplified Balance Sheet View of How Open Market Operations to Stimulate the Economy, When Dominated by Primary Dealers, Actually Stimulate Securities Markets, not the Real Economy

The relationship of bank reserves to Fed purchases of securities in the open market is well established in theory. Based on the theory, purchases of government securities by the Fed are assumed to increase reserves in the banking system (loanable funds) by an identical amount, as suggested by the quote below from the Federal Reserve: …Although Federal Reserve purchases of Treasury securities do not involve printing money, the increase in the Federal Reserve’s holdings of Treasury securities is matched by a corresponding increase in reserve balances held by the banking system. The banking system must hold the quantity of reserve balances that the Federal Reserve creates…. (Source Federal Reserve Board [2018])

The quotation above does not necessarily mean the Fed pays for its bonds by directly crediting some bank’s own reserves; they could be paying a securities dealer or investment bank by check. The dealer (primary or otherwise) typically would then deposit the check in its own bank, which when returned to the Fed would be accepted by the Fed in exchange for © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. J. Heim, Why Fiscal Stimulus Programs Fail, Volume 1, https://doi.org/10.1007/978-3-030-65675-1_5

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an increase in the bank’s own reserves by an equivalent amount. The Fed would be paying for the securities purchased from the dealer by eventually, but not immediately, having the result of increasing the dealer’s bank’s own reserves. Of course, if the dealer cashed the check in at its bank in exchange for currency, this would reduce the bank’s vault cash reserves by as much as returning the check to the Fed will increase them. In this case, there would not be an increase in reserves resulting from a Fed purchase of securities in the open market.

5.1 When the FR Goes into the Open Market and Buys $1000 in Treasuries (T) from a Dealer/Broker (Usually a “Primary Dealer”), The Dealer May Be Paid by Check Drawn on the FR (FRck) (If Dealer Is Paid Electronically by Fed Transfer of Funds to Dealer’s Bank, Skip Steps 5.1–3 and Go to Step #5.4)

5.2 Dealer #1Deposits FR Check in Dealer’s Own Bank

5.3 Bond Dealer’s Bank Cashes in the FRck at the Fed. Assume Required Reserve Ratio (RR) = 10% and Let Excess Reserves = (ER)

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5.4 Bond Dealers Make Their Money Buying and Selling Bonds. Bond Dealers (Generally) Would Have no Incentive to Sell Treasuries to the FR, Except to Obtain the Funds Needed to Buy Another Security (Alt Sec) Expected to Pay a Higher Return. This Is Bought from Dealer #2 by Dealer #1 and Paid for with DD (Step Involving Check Payment, and Conversion to Reserves not Shown)

Bond Dealer #2 (Generally) Only Sold Alt Sec to the First Dealer Because Dealer #2 Needed the Liquidity to Buy Another Security (Alt Sec2) that Looked More Promising, Which Dealer #2 then Bought from Bond Dealer #3 Using the Proceeds of the Sale of Alt Sec to Dealer #1 to Finance the Purchase of Alt Sec2. the Cycle Continues in Perpetuity Until no Other Dealers Wish to Sell Securities at This Time. Results for Dealers #2 and #3 and #4 Are Shown Below (with Some Check & Reserves Movement Intermediate Steps Missing)

5.5

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5.6 The Final Result Is Shown Below, After All Intermediate Steps Above Are Cancelled Out, and Assuming Bond Dealer #4 Cannot or Does not Want to Find Any Other Dealer/Broker with Desirable Securities to Buy

Of course, Bond dealer #4 could have sold the security to dealer 3 because dealer #4 needed money to buy real goods and services to enhance the dealer’s own business or personal interests, e.g., built new offices or buy a new car, in which case the real GDP would be positively affected by the Fed’s purchase of securities in the open market (unless the supply of GDP was already at its maximum, given available resources). Otherwise, the GDP is not directly affected by this bond trading process (though rising bond prices could have a wealth effect on consumption, or a Tobin’s q effect on investment that eventually would). Though proceeds can be used to purchase real goods and services, most sales of securities by primary dealers (i.e., the investment banks and brokerages that dominate the “primary dealer” category) are to raise funds to buy other securities, either as broker/dealer, underwriter (to finance a new issue for some corporation), or to finance loans to companies involved in takeovers or mergers so they can gain control of the other firm by buying up its securities. Major activities related to buying and selling securities in the secondary market undertaken by investment banks include: • Proprietary Trading: Buying and selling securities or commodities for their own account. At its peak before the 2008 crash, “Goldman got two-thirds of its profits from such kind of trading” (Viswanathan 2017). • Prime Brokerage: When big institutions and Hedge Funds trade securities or commodities in the market they go through a prime broker like Goldman Sachs. • Private Wealth Management: Assisting wealthy individuals buy and sell securities and commodities.

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• Market Making: Ensuring clients they will always make a market in certain securities clients can buy or sell securities or commodities in. These functions include the traditional underwriting and merger/acquisition support functions performed by investment banks. Clearly, to the extent an investment bank like Goldman Sachs has securities to sell to the Federal Reserve, they are not generally securities bought or sold for individuals who want to cash in investments in order to increase their purchases of goods and services in the real economy (thereby stimulating it). They are more typically selling securities to the Fed that other investors wish to cash in simply to raise the funds necessary to buy other securities (no direct stimulus effect, except possibly wealth or q effect, as noted before). Commercial bank-type lending by investment banks to finance factories, office buildings, housing, etc.—things that increase GDP and reduce unemployment—was prohibited before Glass-Steagall repeal in 1999. Even after repeal, investment banks were slow to enter this area. Goldman Sachs did not enter retail banking until 2018, and it is a very small part of its business at this time. Hence, the principal effect of open market operations is to increase the quantity of securities on the Fed’s balance sheet, which reduces the total supply of securities available in the market and increases number of dollars available to chase them by the amount the Fed pays for these securities. This puts upward pressure on security prices, and usually increases the M1 money supply. However, typically, the real economy appears largely unaffected, as noted by former Federal Reserve Board Member Kevin Warsh (2016). As noted earlier, rising prices in securities markets positively affect wealth, which increases consumer spending and may also create a Tobin’s q effect leading to more investment. Both would increase the GDP and reduce unemployment. How much of an increase relative to the securities market increase resulting from the monetary stimulus is an empirical question, which we will test later in this paper (Chapter 13). Some estimates suggest consumer spending out of an increase in wealth in Europe is only about 4–7% (Sousa 2009) and in Australia 2–6% (Tang 2006). Pending our own empirical tests later in this paper, it appears that the effect of stimulative monetary policy involving open market purchases of government securities on the GDP is small at best relative to securities market effects; that is, most of it does not stimulate the real economy at

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all, though this was the Fed’s goal. Instead, it stimulates the stock and bond markets. There is empirical evidence for this: both the business and professional press agree that Fed monetary stimulus, like QE, causes the stock and bond markets to rise, but there is disagreement over whether the real economy was helped to any significant extent (Chapter 13). Previous work (Heim 2017a, b) indicates that during recessions, the supply of loanable funds drops faster than the demand for them, indicating that the “crowd out” problems associated with deficit-financed fiscal stimulus programs continue in recessions. The fact that the supply of loanable funds drops faster than the demand during recessions (Heim 2017b) suggests that additional funds may be needed just to maintain previous levels of spending out of loanable funds (e.g., for car purchases, every year a certain number have to be replaced, and new cars are usually financed, not bought out of current income). The additional reserves created by the Fed’s purchase of bonds in recessions could meet this need. It could also explain in part the belief of some economists that increased security purchases by the Fed in recessions doesn’t work to stimulate the economy; that is, they are simply replacing lost loanable funds, not increasing them. Other reasons for the decline in borrowing during recessions include the decline in creditworthiness of borrowers, and the declining willingness of banks to lend for prudential reasons, and the decline in the number of consumers or businesses interested in borrowing. Note#1: Money and banking students are taught that in a simple model of the money expansion process, open market purchases of government securities by the FR results in an equal increase in bank reserves. But this result assumes none of the reserves are converted to currency, i.e., withdrawn from banks by customers drawing down demand deposits, thereby reducing reserves without any change in FR holdings of government securities. There are also a number of other more minor factors that can affect the level of reserves relative to FR holdings of government securities. In 2017, the largest of these was a category of reserves called “Reverse Repurchase Agreements.” These are simply a separate category of reserves held by banks that are only temporarily available: they are created by the FR by purchasing bonds from owners willing to repurchase the securities at a set later date. FR Release H.4.1 for 12/28/17 shows ($4,231.1 billion) in total FR owned U.S. Treasury and Agency securities; reserves held

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by banks ($2,201.4 billion); currency in circulation ($1,612.1 billion); and Reverse Repos ($355.0 billion) totaled to approximately the same amount: $4,168.5 billion. Other more minor factors, affecting either the asset or liabilities side of this balance sheet, detailed in FR Release H.4.1, account for the rest. Hence, the large difference between government securities owned by the FR and total bank reserves is not a discrepancy, but just neglects to include some of the factors involved. (Purchases from foreign dealers who deposit the proceeds in foreign banks could be one factor explaining the difference.) Note #2: Ideally, at this point, we would like to disaggregate the sources of the government and agency bonds dealer/brokers sold to the Federal Reserve. We would like to divide the securities into categories: those bought from individuals, those bought from mutual funds, and those bought from other securities dealers. Unfortunately, the data is not readily available in that form from either the published financial reports of primary dealers or the NY Fed.

References Federal Reserve Board. (2018). Current FAQs: Informing the Public About the Federal Reserve. Washington, DC: Board of Governors of the Federal Reserve System. Available at https://www.federalreserve.gov/faqs/money_ 12853.htm. Heim, J. J. (2017a). An Econometric Model of the U.S. Economy. Hoboken: Palgrave Macmillan. Heim, J. J. (2017b). Crowding Out Fiscal Stimulus. Hoboken: Palgrave Macmillan. Sousa, R. (2009, May). Wealth Effects on Consumption: Evidence from the Euro Area (European Central Bank Working Paper # 1050). Tang, K. (2006). The Wealth Effect of Housing on Aggregate Consumption. Applied Economic Letters, 13(3), 189–193. Viswanathan, B. (2017). What Does Goldman Sachs Do? Quora blog. Available at https://www.quora.com/What-does-Glodman-Sachs-do. Warsh, K. (2016). The Federal Reserve Needs New Thinking. Wall Street Journal, 8/24/2016. Available at http://wsj.com/articles/the-federal-res erve-needs-new-thinking-1472076212.

CHAPTER 6

A Money Multiplier Approach to How Open Market Operations Stimulate Securities Markets and the Real Economy

Borrowing actually changes by a multiple of the amount that the loanable funds pool changes, and the multiple can be derived from standard total reserves/required reserves multiplier formulas.

6.1

Simple Money Multiplier

When an increase in (depository) bank reserves occurs, assuming all reserves except required reserves are lent out, and all loans when taken out are redeposited in other banks, we can use the simple money multiplier formula:    Demand Deposits = 1/required reserve ratio  Reserves (6.1) to determine how much the demand deposits portion of the money supply will increase as a result (Mishkin 2009). If you think of an increase in reserves as a change in loanable funds, you can see that through the money multiplier, total reserves (total loanable funds) grow more than just the initial amount. For example, let a $430.4 billion increase in loanable funds occur due to an increase in national savings and foreign borrowing, as occurred on average for each year between 2007 and 2011. Assume these savings were deposited in demand deposit accounts, increasing demand deposits and reserves by $430.4 billion, and increasing bank reserves by the same © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. J. Heim, Why Fiscal Stimulus Programs Fail, Volume 1, https://doi.org/10.1007/978-3-030-65675-1_6

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amount. Let the required reserve ratio be (0.10). Using the simple money multiplier, this would lead to an increase in demand deposits (and total reserves) of 4304.0. Since this simple money multiplier assumes that all non-required reserves are lent out and spent (i.e., eventually redeposited), this means that the increase in saving would increase loans (“borrowing”) by a much larger amount: $3873.6, or nine times the initial increase in the loanable funds pool ( total demand deposits =  total reserves = 4304.0 −  required reserves = $430.4). A decline in loanable funds pool of the same amount, as occurred in 2008–2011 period compared to the earlier 2004–2007 period, would lead to a decline in borrowing of nine times the decline in the loanable funds pool because of contraction of the pool. Though certainly not identical to the drop in total borrowing experienced on average annually during the 2008–2011 period ($2943.9) shown in Table 8.5 above, it does help us conceptually understand the relationship between national income and product accounts (NIPA) data on changes in saving and investment each year and the role these NIPA changes play in determining what changes in total borrowing will occur. In short, because of the money (and reserves) multiplier process, the initial change in saving (loanable funds) is magnified into a many times larger change in loanable funds (and in time of persistent high demand for loans, the level of borrowing).

6.2

A More Sophisticated Money Multiplier

The data for 2004–2011, as shown in Table 8.5 above, indicates that contrary to the assumptions underlying the simple money multiplier, banks kept large amounts of excess reserves. Table 8.5 also indicates borrowers often did not spend (and see redeposited) large portions of what they borrowed. Instead, borrowers used all or part of their loans to increase their cash balances, or if they did spend all of their loan, only part was redeposited, and the rest kept by the vender as cash. There is a more sophisticated money multiplier which takes these factors into account when examining the effect of a change in the loanable funds pool on the money supply and the level of borrowing (Mishkin 2009). The formula is given in two forms below:

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1. The effect of a change in the loanable funds pool (bank reserves) on the total M1 money supply, or M1 = ((1 + C/D)/(RR/D + ER/D + C/D)) ∗ MB

(6.2)

2. On the demand deposit portion (D) alone. D = (1/(RR/D + ER/D + C/D)) ∗ MB

(6.3)

where M1 = M1 money supply C = Currency in circulation D = Demand Deposits RR = Required reserves rr = Required reserve ratio = RR/TR ER = Excess reserves = TR − RR MB = Total Reserves (TR) + Currency in circulation (C ). For consistency with our simple money multiplier analysis, we will examine the effect of a change in loanable funds (reserves) on (2), the demand deposit portion of M1. As shown in Table 10.1 above, the values of the variables in the money multiplier formula for 2011 were (in billions). rr = .087 TR = $1598.7 RR = 96.5 ER = 1502.2 C = 1067C D = 1106.9 With this data, the sophisticated money multiplier formula shows total demand deposits equal to $1106.9. The actual change during the years 2007–2011 in the loanable funds pool was $30.4 billion. There are a number of different scenarios that could result if we add $430.4 billion to system reserves by Fed purchases of securities. 1. $430.4 in reserves is added to the system; simple money multiplier holds (all loans spent and fully redeposited). The components of the sophisticated money multiplier formula would be revised to their 2011 levels plus the $430.4 billion addition to system reserves:

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rr = .087 TR = $1598.7 + 430.4 = $2029.1 RR = 96.5 + .087*(430.4) = 134.0 ER = 1502.2 + (430.4 − .087*430.4) = 1502.2 + (430.4 − 37.45) 1895.15 C = 1067C D = 1106.9 + (1/.087)(430.4) = 1106.9 + (11.49)(430.4) = 6052.2 MB = 2665.7 + 430.4 = 3096.1 Here, we conclude that the increase in demand deposits (D) would be $4945.30 and the increase in borrowing (loans) would be the same if the sales to the Fed were by a commercial bank. They could loan out the whole $430.4 initially received since they would have no required reserve to keep out of it. A decline of reserves of $430.4B (billion) from these numbers would lead to an identical drop in borrowing. This drop is considerably higher than what actually occurred, and in part reflects our assumption that all excess reserves were loaned out (Note If the increase in reserves came from an increase in the loanable funds pool due to • a growth of savings by the population deposited in demand deposit accounts, or • because the Fed purchased the securities from investment banks and brokerages that then deposited the proceeds in their own demand deposit accounts, then part of the initial increase in reserves of $430.4 would have to be retained as required reserves (11.49*430.4 − 430.4 =) 4514.9. This is unlike the situation when the Fed increases savings by buying securities from a depository bank. This increases bank reserves. Since the increase was not a result of increased deposits, there is no reserve requirement; all $430.4 can be lent out.) 2. If we assume that 8.7% is kept as a required reserve, but an additional 16.3% is intentionally kept in the bank as desired excess reserves, this means that only 75% of the reserves added to each bank as a result of each iteration of the money multiplier process get loaned out.

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Assume all purchases of securities are from investment banks or brokerages which deposit payment in their demand deposit accounts at commercial banks. Hence, the first loan out of the proceeds of the $430.4 security purchase by the Fed (e.g., $322.8 after required and desired excess reserve percentages are withheld) is loaned out, the rest kept as required and desired excess reserves, our values for variables in the sophisticated money multiplier become the following: rr = .087 = required reserve ratio on new reserves er = desired ER ratio on new reserves = .163 C = 1067 D = 1106.9 + [1/(.087 + .163)]*(430.4) = 1106.9 + [4]*430.4 = 1106.9 +1721.6 = 2828.50 RR = 96.5 + .087*(1721.6) = 96.5 + 149.78 = 246.28 ER = 1502.2 + .163*(1721.6) = 1502.2 + 280.62 = 1.782.82 TR = $1598.7 + 430.4 = $2029.1 MB = 2665.7 + 430.4 = 3096.1 So the increase in demand deposits that stems from lending out only 75% of the $430.4 increase in total reserves at each iteration of the money multiplier process becomes ($2828.50 − 1106.90 =) 1721.60, and the increase in loans (borrowing) would be (1721.60 − $430.4 required desired excess reserves = $1291.2). A decline in reserves of $430.4 from these levels would lead to identical declines in demand deposits and loans. The decline in loans in this example is much less than the actual decline experienced (2943.9). 3. If we look at these results as an example of how the raising of lending standards by banks after the sub-prime crisis led to an increase in excess reserves, and look at the $430.4 decline in the loanable funds pool used in the illustrations above, we can see, at least in concept, how these factors could lead to a decline in borrowing as shown in scenario #2 above. 4. One additional factor that could affect borrowing is any reduction that occurred because borrowers became more prudent in their borrowing. We could look at this as reducing below the ¾ level the amount lent out of the original $430.4. We could model this by increasing above the (.163) level the ratio of excess reserves expected out of new reserves.

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5. Table 7.6 above indicates that in Q4 of 2014, 37% of the permanent treasury, agency, and GSE securities bought by the Fed were bought from foreign sellers. For scenario #5, assume Fed purchases of securities of $430.4 billion in the open market, but that 1/4 of the purchases are made from foreign investment banks and brokers, who deposit the Fed checks received in payment in their own countries, and assume the money is not reinvested in the U.S. This increases the domestic loanable funds pool by ¾ of $430.4 billion (= $322.8B), and the following changes in money multiplier variables: rr = .087 er = 0 D = 1106.9 + (1/.087)(322.8) = 1106.9 + 11.49*322.8 = 1106.9 + 3710.34 = 4817.24 RR = 96.5 + (.087*3710.34) = 96.5 + 322.8 = 419.30 ER = 1502.2 + (0.0*3710.34) = 1502.2 C = 1067 MB = 2665.7 + 322.8 = 2988.50 The results increase in demand deposits and loans is substantially greater than the result obtained in scenario #2 above, which assumed that all of the sellers of bonds to the Fed deposit their money in U.S. banks, but ¼ of the initial increase in reserves is kept as either required or desired excess reserves. Here, the increase in loans is [(1/.087)*322.8]–322.8 = (11.49*322.8)−322.8 = $3709.0−322.8 = $3386.2. 6. Assume same scenario as in (5) above, but that of 322.8 deposited in U.S. banks (.163)*322.8 + is held back as desired excess reserves. Then, the results would be: rr = .087 er = .163 D = 1106.9 + (1/(.087 + .163))*(322.8) = 1106.9 + 4*322.8 = 1106.9 + 1291.20 = 2398.1 RR = 96.5 + (.087*1291.20) = 96.5 + 112.33 = 208.83 ER = 1502.2 + (.163*1291.20) = 1502.2 + 210.47 = 1712.67 C = 1067 MB = 2665.7 + 322.8 = 2988.50

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Here, the increase in loans is [(1/(.087 + .163))*322.8] − 322.8 = (4*322.8) − 322.8= 1291.20 − 322.8 = $968.40 7. In this example, we wish to look at the effects of a recession on the loanable funds pool (a decline in savings of $430.8 billion in the 2007–2011 average level of loanable funds from its earlier 2004– 2007 average level). The Fed attempted to offset the economic decline by buying $1864.5 billion in securities in the open market during 2007–2011 in an attempt to increase the funds banks had available to lend, assuming the lending would occur, and the lentout funds would be used to buy goods and services, simulating the economy. To simplify, we assume the economic growth of the 2004–2007 period had generated $430.8 billion in additional savings over 2003 levels, all of which had been initially deposited in demand deposit accounts at depository banks. Further, assume that $250 billion of $430.8 billion growth in the loanable funds pool had been taken out as cash, rather than kept in demand deposits. Assume a decline in the economy, equal in size to its 2004–2007 expansion, leading to a drop in the loanable funds pool equal to its increase in 2004– 2007, takes place in one year. Also assume that the velocity of money turnover is five per year, reflecting actual experience during the period following the decline when the quantitative easing program took place (Heim 2018). Assume the required reserve ratio was (.087) of demand deposits. We can show the 2004–2007 expansion of the loanable funds pool, via an expansion of demand deposits in “Bank #1.” These are assumed to be partially lent out, partially converted to cash. Results are shown by showing the changes in the balance sheets for the five banks (money multiplier = 5) involved in the money expansion process. This is shown in Table 6.1. Variables in this table are defined as: R = Total Reserves RR = Required Reserves ER = Excess Reserves D = Demand Deposit FRN = Federal Reserve Notes (Can be Currency in circulation or part of bank reserves).

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Table 6.1 Money expansion resulting from a $430.8 billion increase in national saving Bank #1 A | L +430.8 R | +430.8 D -250.0 FRN | -250.0 D +180.8 R |+180.8 D (=+15.73 RR | +165.07 ER ) | -165.07 ER | +165.07 Loan | ___________ |_______

Bank #2 A | L +165.07 R |+165.07 D (=+14.36 RR | +150.71 ER | -150.71 ER | +150.71 Loan | | | __________|__________

Bank #3 A +150.71 R (=13.11 RR +137.60 ER ) -137.60 ER +137.60 Loan

Bank #4 A | L +137.60 R | +137.60 D (= +11.97 RR | +125.63 ER )| -125.63 ER | +125.63 Loan| -165.07 ER | |

Bank #5 A | L +125.63 R |+125.63 D (=+10.93 RR | +114.70 ER | -114.70 ER | +114.70 Loan | | |

Bank #6 A +114.70 R (=9.98 RR +104.72 ER )

| L |+150.71DD | | | | | | ___________|_____________

| L |+114.70DD | | | | | |

Growth in demand deposits (through the sixth turnover of money) is $$876.51. The increase in loans is the increase in demand deposits minus $180.8 = $695.71. The increase in required reserves is $76.88, and the growth in excess reserves is $104.72 (through the sixth iteration).

References Heim, J. J. (2018). Dynamics In A Large Scale Macroeconomic Model (Unpublished manuscript). SUNY Albany Department of Economics. Mishkin, F. (2009). The Economics of Money, Banking and Financial Markets. New York: Addison Wesley.

PART III

The Effectiveness of Accommodating Monetary Policy Mechanics

CHAPTER 7

The Role of Primary Dealers in Federal Reserve Efforts to Change the Money Supply

The role of primary dealers in the Federal Reserve (FR) efforts to stimulate the economy through open market securities purchases is described below by the treasury and the FR: …Primary dealers are banks and securities broker-dealers that trade in U.S. government securities with the Federal Reserve Bank of New York (FRBNY)…the FRBNY Open Market Desk engages in trades in order to implement monetary policy. The purchase of Government securities in the secondary market by the Open Market Desk adds to reserves in the banking system… The primary dealer system was established by the FRBNY in 1960 and began with 18 primary dealers. In 1988, the number of dealers grew to a peak of 46. As of November 2010 there were 22 primary dealers…. (U.S. Treasury 2018) Primary dealers - banks and broker-dealers that trade in U.S. Treasuries with the New York Fed—are the largest group of buyers at auction. These financial institutions are active in buying and selling U.S. government securities. Other auction participants include investment funds, pensions and retirement funds, insurance companies, foreign accounts, non-profit organizations, and others. Only the designated primary dealers are required to bid a specified amount in every Treasury auction. … Competitive bids are usually submitted by large financial institutions for their own accounts or on behalf of customers…. The detailed list of accepted and rejected competitive bids is not released to the public, but the total amount of

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bids received and total amounts accepted are made available. (NY Federal Reserve Bank 2018) (Notice the absence of commercial and savings banks, on the list of primary dealers. Unlike many of the institutions cited above, these are the institutions whose main business it is to provide money to those who want to buy new goods and services, thereby stimulating the economy.)

7.1

Primary Dealers Dominate Auctions

For decades, the FR has relied heavily on primary dealers to buy and sell government securities, as noted by the FR itself in the quote below: …Auction market participants submit bids through a communications system called TAAPSLink® . Institutions other than primary dealers15 (including depository institutions, other dealers, and institutional investors) use an Internet version called TAAPSLink v1. Primary dealers—which submit the largest volume of bids in almost every auction—use an alternative version called TAAPSLink v2. Retail investors with TreasuryDirect accounts submit bids by mail, telephone, and Internet applications that ultimately reach TAAPS through TAAPSLink v1. (NY Federal Reserve 2005)

Domination of Federal Reserve trading in the open market with primary dealers, rather than the general market, is not a recent phenomenon. SOMA (System Open Market Account) Records for 2006 indicate a similar dominance: During 2006, the value of permanent holdings…(of treasury securities)… in the SOMA portfolio increased by $34.2 billion…The expansion was achieved by $44.7 billion of outright purchases, mostly in the secondary market from primary dealers…. (NY Federal Reserve 2007)

7.2 What Type of Bank Does the Federal Reserve Purchase Securities from: Investment or Depository? There are various monetary policy instruments that can be used to stimulate the economy. The most important, and most commonly used, of these instruments are as follows:

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(1) Increasing bank reserves, which commonly leads to increases in the money supply, though not necessarily to the same extent (depending on money multiplier effects and proportions of new reserves in non-monetary instruments), and (2) Decreasing the short-term interest rate over which the Fed has direct control: the Federal Funds rate. Since doing the first is commonly thought to be required to do the second, both instruments are likely to be adopted simultaneously. This is not always the case, however. It is not impossible that some changes in the Federal funds rate (without changes in reserves) just reflect ex post facto action by the Fed to ensure Fed target rates for the rate remain in synchronized with the actual federal funds rates being negotiated between banks at a given time. Attempts to raise the money supply and cut rates are undertaken by buying up government securities or government agency securities in the open market. To reduce the money supply and increase the federal funds rate, the Open Market Desk at the New York reverses this process, selling securities and thereby reducing bank reserves and thereby, the money supply. The Federal Reserve does make public a complete list of approved “primary dealers” to whom it sells securities to and from whom it buys securities. The total number of dealers involved has varied from 20 to 46 in recent decades. Since the start of the dealer system in 1960 until recently, the dealers have included varying mixes of commercial and savings banks as well as investment banks and securities brokerage houses. Historically, the Federal Reserve has not revealed the names of the firms from whom it has bought or sold bonds (counterparties) in a particular period. In 2010, the Dodd Frank Act required that the Fed, for the first time, published the names of its counterparties in these security purchases and sales, and also the amounts purchased or sold by each firm. The Fed now publishes this data, starting with the third and fourth quarters of 2010, and extending through the first quarter of 2016 (as of this writing). The quarterly data is published after approximately a two-year lag. Presented below is a large sample of the data from 2010 to 2016 to provide a comprehensive picture of the type of firms the Fed most commonly deals with, and the dozen or so most commonly used dealers. The sample includes the results for the first quarter 2016, the full years

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2014 and 2012, and the last two quarters of 2010. The transactions described were permanent purchases of U.S. government securities. Other transactions, involving temporary purchases (“repos”), and purchases of U.S. government agency bonds were also undertaken, but are not included in these lists. These purchases were all part of the “quantitative easing” program started in 2008 designed to stimulate the economy. They were part of the Bush administration/Federal Reserve efforts to rescue the nation’s big banks and investment houses from the threat of collapse. After 2008, QE was also a response to an economy which was responding sluggishly at best, to the $800 billion Obama stimulus program of 2009, particularly the unemployment rate. These FR purchases were intended to stimulate the real economy, that is, raise GDP and lower unemployment, by providing banks with additional reserves (“loanable funds” to lend out (strengthening financial institutions was another objective). Yet surprisingly, none of the purchases were made from depository banks, i.e., the very commercial and savings banks whose lending is largely restricted to making loans that can be used to grow the GDP, and in the process reduce unemployment, e.g., loans to build houses, factories or office buildings, or loans to purchase machinery, cars, or furniture. Instead, based on the data sampled, it seems the quantitative easing purchases were all made from investment banks or securities brokerage houses, which typically sell securities for the purpose of raising funds to purchase other securities. After all, buying and selling securities is the business they are in. Such purchases are not counted in the GDP. The list of dealers from whom securities were actually bought in the period sampled (Q1: 2016, Q1–4: 2014, 2012, and Q3–4: 2010) is relatively small. The complete list is given in Table 7.1. Though not all of these firms sold treasuries to the Fed in all periods surveyed, many did. Note that there is not a single commercial or savings bank on this list. Table 7.2 lists the total amount of treasury securities permanently purchased by the Federal Reserve from the dealers noted above as part of the quantitative easing program during the periods surveyed between Q3: 2010 and Q1: 2016. A survey of primary dealers in 1960, 1966, 1978, 1988, 1998, and 2010 shows the proportion of primary dealers which were commercial or savings banks was markedly lower in recent decades than in 1960. Even in 1960, commercial or other depository banks were still a minority of all

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Table 7.1 Firms from which permanent treasury security purchases were made as part of the quantitative easing program (Q1: 2016; Q1–4: 2014, 2012; and Q3–4: 2010) Seller/Seller’s agent

Type of firm

Barclays Capital BMO Capital Markets BNP Paribus Securities Dawia Capital Markets Deutsche Bank Securities USA Goldman Sachs & Co.

Brokerage Services Investment Bank Brokerage Services Investment Bank Investment Bank Investment bank; No commercial banking until 2018 Investment Bank Securities Brokerage Services Securities Brokerage Services & Smith Incorporated Investment Banking Firm Investment Banking Firm Security Brokerage Firm. The company provides asset-backed loans including mortgage, auto, and manufactured housing loans. It also offers trading and investment banking services Investment Banking Investment Banking and Brokerage Services Securities Brokerage and Trading Services Investment, Corporate Banking, Trade Related Capital Market Services Investment Banking, Real Estate Investing Institutional Brokerage, Portfolio Management, Capital Raising Securities Brokerage Services. It trades mortgage-backed securities, structured financial securities, and equities Investment Banking, Sales and Trading of Equities Investment Banking Investment Banking Investment Bank Securities broker Investment Bank

HSBC Securities (USA) J.P. Morgan Securities Merrill Lynch, Pierce, Fenner Morgan Stanley & Co. LLC RBC Capital Markets RBS Securities

SG Americas Securities, LLC TD Securities (USA) LLC UBS Securities Bank of Nova Scotia, N.Y. Agency Cantor Fitzgerald & Co. Citigroup Global Markets Inc. Nomura Securities International, Inc.

Jefferies LLC Society Generale, N.Y. Branch Credit Suisse Securities (USA) Mizuho Securities USA Inc. Cabrera Securities S G Americas Securities, LLC

(continued)

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Table 7.1 (continued) Seller/Seller’s agent

Type of firm

G.X. Clarke Loop Capital Markets Mischler Group RBC Capital Services Banc of America Securities

Securities Broker Investment Bank Investment Bank/Brokerage Investment Bank Investment Bank

Sources Federal Reserve Bank of New York. Open Market Operations: Transaction Data. Available at www.newyorkfed.org/markets/omo_transaction_data.html; Firm description from Bloomberg Company Overview Reports. www.bloomberg.com/research/stocks/private or other internet sources of public information, usually the firm’s homepage

Table 7.2 Permanent treasury security purchases as part of the quantitative easing program (selected periods Q3: 2010–Q1: 2016) 1 January 2016–31 March 2016 1 October 2014–31 December 2014 1 July 2014–30 September 2014 1 April 2014–30 June 2014 1 January 2014–31 March 2014 1 October 2012–31 December 2012 1 July 2012–30 September 2012 1 April 2012–30 June 2012 1 January 2012–31 March 2012 1 October 2010–31 December 2010 1 June 2010–30 September 2010

$0.319 Billion in U.S. Treasury Bonds Purchased by FR $10.578 Billion in U.S. Treasury Bonds Purchased by FR $52.046 Billion in U.S. Treasury Bonds Purchased by FR $81.287 Billion in U.S. Treasury Bonds Purchased by FR $108.373 Billion in U.S. Treasury Bonds Purchased by FR $272.151 Billion in U.S. Treasury Bonds Purchased by FR $275.037 Billion in U.S. Treasury Bonds Purchased by FR $292.859 Billion in U.S. Treasury Bonds Purchased by FR. $294.968 Billion in U.S. Treasury Bonds Purchased by FR $226.003 Billion in U.S. Treasury Bonds Purchased by FR $ 40.781 Billion in U.S. Treasury Bonds Purchased by FR

primary dealers, but at least were some of the institutions used. In 1988, the number of commercial or savings banks included tapered off, even as the total number of primary dealers rose, and in the 1990s plummeted

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to two, and in 2010 had dropped to zero. Results for the years surveyed were as follows: 2010: 1998: 1988: 1978: 1966: 1960:

0 2 6 7 8 4

of of of of of of

22 35 45 36 20 18

were were were were were were

commercial commercial commercial commercial commercial commercial

or or or or or or

savings savings savings savings savings savings

banks banks banks banks banks banks.

Buying from depository institutions would seem preferred if GDP stimulus was the goal of the QE program. However, this assumes the depository institutions were holding as many government securities as the Fed would have liked to purchase during QE. Unfortunately, that was not the case, as shown further below. Still, to the extent possible, it would seem that giving priority to purchases from commercial and savings banks would have better stimulated the economy. As we showed in the literature review section of this book, the business press was virtually unanimous in stating the main beneficiary of purchases under the QE program was the stock and bond markets, not the real economy (Chapter 2). This sounds much like saying the main beneficiaries of FR purchases were investment banks and brokerages, not commercial and savings banks.

7.3 The Failure of Federal Reserve Securities Purchases During “QE” to Reduce Depository Institutions Holdings of Government Securities, Which Would Have Increased Their Loanable Funds The QE program markedly increased the Federal Reserve’s holdings of these securities, from 497 billion in 2008 to 4,231 billion in 2017, an increase of $3,734 billion. For comparison, Table 7.3 shows total depository institution holdings of treasury, agency, and GSE securities during the period 1990–2017. The evidence suggests the full $3,734 billion QE program could not have been undertaken by only purchasing securities from depository institutions. Table 7.4 shows that during the 2009–2015 period when QE was most active, depository institutions only held from 38 to 64% of the

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Table 7.3 Depository institution holdings of treasury, agency and GSE securities

Year

Treasury securities

Agency and GSE securities

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

163.5 218.5 271.8 291 266.2 216.6 188.1 178.3 133.1 134.5 94.3 66.5 95.6 105.4 84.1 78.4 76.8 78.2 59.1 140.8 231.2 183.7 259.6 230.6 437 458.5 555.6 507

97.9 113 122.6 139.3 454.9 475.9 511.5 573.7 656.9 693.4 705.5 795.9 929.7 1036.5 1100.6 1094.2 1138.4 1024.6 1128.8 1295.9 1378.5 1504.3 1686 1741.9 1769.9 1916.6 2066.0 2214.0

Source Federal Reserve Board. Consolidated Balance Sheet for Commercial & Savings Banks and Credit Unions (Q4 data for year cited Available at https://www.federalreserve.gov/releases/efa/efaproject-consolidated-balance-sheet.htm)

amount of $3,734 billion in securities purchased by the Fed. In 2016 and 2017, the percentages rose to 70 and 72% of QE program size, still below the Fed’s aggressive purchase of securities during the QE years. However, large portions of the FR’s purchases could have been. Even if they had, the Fed would have had no choice but to rely on brokers and investment banks to some extent when undertaking the QE program. But purchasing as much as possible of the desired securities

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133

Year

Total holdings of treasury, agency and GSE securities

2009 2010 2011 2012 2013 2014 2015 2016 2017

1436.7 1609.7 1688 1945.6 1972.5 2206.9 2375.1 2621.6 2721

Source Table 7.3

from depository institutions would have provided the FR with better guarantees that such bank lending as took place out of the proceeds was directed toward stimulating the real economy. There is no such guarantee when the Fed purchases securities from investment banks and brokerage houses, where the proceeds are mostly spent to buy other securities, an action which does not in any direct way affect the GDP. However, to the extent the Fed did buy securities from depository institutions, there is evidence the increase in loanable funds that resulted was difficult to lend out, i.e., that banks faced a “pushing on a string” problem, and for lack of borrowers, turned around and purchased other government securities to obtain some return on their resources. The evidence for this is that net holdings of securities by depository institutions did not decline at all during this period of huge FR security purchases. In fact, they increased during the QE program’s active years. Depository institutions were not selling securities to the Fed (and thereby enhancing loanable reserves); they were competing with the Fed in trying to buy up securities because they were wary of making traditional loans due to depressed economic conditions, but wished to make some money on their loanable funds. In some cases, they were selling securities to the FR, but just using the proceeds to purchase other, more desirable, government securities. The increases in depository institution holdings of government securities were very large during QE: holdings of treasury securities increased from 59.1 to 507 billion, an increase of 758%, and depository institution holdings of agency and GSE securities increased from 1,128.8 to 2,214

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billion, an increase of 96%. Clearly, this was contradicting the intent of the Fed’s QE program. While it is true, bank loanable reserves increased markedly during the QE period, but it was not because there was any net conversion by depository institutions of government securities assets into loanable reserves by selling the securities to the Federal Reserve. The sizeable purchases of bad bank loans by the FR during QE better explain how bank loanable reserves became so large (but inadequate loan demand and fear of lending explains why they were not lent out). In short, the traditional way we think of FR open market purchases of securities stimulating the economy, by securities purchases from banks which increase their loanable funds, simply did not work at all during the QE period. The huge growth of excess reserves during this period indicates little of these lendable reserves obtained by selling government securities to the FR, or obtained by selling bad loans to the FR, were loaned out and spent. These results indicate a “pushing on a string” problem that affects FR efforts to use monetary policy to stimulate the economy in periods of economic decline (Chapter 8). As to the larger question of whether the whole $3,734 billion QE program could have been undertaken by only purchasing securities from depository institutions, the answer clearly appears to be no, as explained earlier. Hence, the Fed would have had no choice but to rely on brokers and investment banks to some extent when undertaking the QE program, even if they had purchased all securities held by depository institutions before entertaining purchases from brokers and investment banks. But purchasing as much as possible from depository institutions before resorting on investment banks and brokerage houses would have provided the Fed with a better guarantee that bank lending out of the proceeds would be directed toward stimulating the real economy. There is no such guarantee when the Fed purchases securities from investment banks and brokerage houses.

7.4 Primary Dealers and the Business They Are in: Selected Years 1960–2014 Listed below in Table 7.5 is a list of the Fed’s primary dealers in select years between 1960 and 2014, and the business they describe themselves as being in on company homepages, or by the FR, or in a few cases, elsewhere.

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Table 7.5 Primary dealers and the business they are in: selected years 1960– 2014 Primary Dealers 2014 (22 Investment Banks and Brokerages, 0 Commercial or Savings Banks ) Bank of Nova Scotia, NY Agency Investment, Corporate Banking, Trade Related, Capital Market Services Barclays Capital Inc. Brokerage Services BMO Capital Markets Corp. Investment Bank BNP Paribas Securities Corp. Brokerage Services Cantor Fitzgerald & Co. Investment Banking, Real Estate Investing Citigroup Global Markets Inc. Institutional Brokerage, Portfolio Management, Capital Raising Credit Suisse Securities (USA) LLC Investment Banking Daiwa Capital Markets America Inc. Investment Banking Deutsche Bank Securities Inc. Investment Banking Goldman, Sachs & Co. Investment Banking HSBC Securities (USA) Inc. Investment Banking Jefferies LLC Investment Banking, Sales and Trading Of Equities J.P. Morgan Securities LLC Securities Brokerage Services Merrill Lynch, Pierce, Fenner & Smith Securities Brokerage Services Mizuho Securities USA Inc. Investment Banking Morgan Stanley & Co. LLC Investment Banking Nomura Securities International Securities Brokerage Services RBC Capital Markets, LLC Investment Banking RBS Securities Inc. Security Brokerage, Investment Banking SG Americas Securities, LLC Investment Bank TD Securities (USA) LLC Investment Banking and Brokerage Services UBS Securities Securities Brokerage and Trading Services Primary dealers 1998 (33 Investment Banks and Brokerages, 2 Commercial or Savings Banks ) ABN Amro Incorporated Commercial Banking Aubrey G. Lanston & Co., Inc. Brokerage Firm Barclays Capital Inc. Investment, Corporate and Personal Bank Bear, Stearns & Co., Inc. Investment Banking, Securities Trading, and Brokerage firm BT Alex. Brown Incorporated Investment Banking

(continued)

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Table 7.5 (continued) Chase Securities, Inc. CIBC Oppenheimer Corp. Credit Suisse First Boston Co. Daiwa Securities America Inc. Deutsche Bank Securities Inc. DLJ Securities Corporation Dresdner Kleinwort Benson Nor First Chicago Capital Markets Fuji Securities Inc. Goldman, Sachs & Co. Greenwich Capital Markets, Inc. HSBC Securities, Inc. J.P. Morgan Securities, Inc. Lehman Brothers Inc. Merrill Lynch Government Sec. Inc. Morgan Stanley & Co. Incorporated Nations Banc Montgomery Securities Nesbitt Burns Securities Inc. Nomura Securities International, Inc. Paine Webber Incorporated Paribas Corporation Prudential Securities Inc. Salomon Smith Barney Inc. The Nikko Securities Co. Int’l. Warburg Dillon Read LLC Zions First National Bank Dean Witter Reynolds Inc. Eastbridge Capital Inc. Sanwa Securities (USA) Co. Citicorp Securities, Inc.

Investment Banking, Security Brokerage Investment Banking, Security Brokerage Investment Banking Investment Bank Investment Bank Brokerage Firm Investment Bank Purchase, Sale and Brokerage of Securities Brokerage Services Investment Bank Investment Banking Investment Bank Securities Brokerage Services Investment Bank Underwriting, Brokerage Investment Banking Investment Bank, Brokerage Services Investment Bank Securities Brokerage Services Stock Brokerage, Asset Management Brokerage Investment Management Services Investment Banking Institutional Brokerage Investment Bank Commercial and Personal Banking Institutional Brokerage Services Investment Management Securities Subsidiary of Sanwa Bank Securities Brokerage Services

Primary Dealers 1988 (40 Investment Banks and Brokerages, 5 Commercial or Savings Banks ) Discount Corporation of N.Y. Securities Trading Continental Bank, National Assoc. Commercial Banking Security Pacific National Bank Commercial Banking Carroll Mcentee & Mcginley Inc. Investment Banking Irving Securities, Inc. N.A. J.P. Morgan Securities, Inc. Securities Brokerage Services

(continued)

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Table 7.5 (continued) Chase Securities, Inc. Citibank Bankers Trust County Natwest Gov. Sec., Inc. Chemical Bank Bear, Stearns & Co., Inc. First Chicago Salomon Brothers Inc. Goldman, Sachs & Co. Kidder, Peabody & Co., Inc. Dean Witter Reynolds Inc. Merrill Lynch Government Sec. Inc. Nomura Securities International, Inc. Harris Government Securities The First Boston Corporation Bank of America NT & SA Sanwa-Bgk Securities Co., L.P. Thomson Mckinnon Securities Inc. Dlj Securities Corporation Aubrey G. Lanston & Co., Inc. Morgan Stanley & Co., Inc. Dillon, Read & Co., Inc. The Nikko Securities Co. Int’l. Drexel Burnham Lambert Daiwa Securities America Inc. Yamaichi Int’l (America), Inc. Lloyds Gov’t Securities, Inc. Crt Government Securities, Inc. Kleinwort Benson Gov’t Sec. Inc. Greenwich Capital Markets, Inc. Westpac Pollock Gov’t Securities Inc. Primary Dealers 1988 Paine Webber Incorporated Smith Barney, Harris Upham & Co., Inc. Shearson Lehman Manufacturers S.G. Warburg & Co., Inc. L.F. Rothschild & Co., Inc.

Investment Banking, Brokerage Commercial Banking Commercial Banking Investment Banking, Brokerage Commercial Banking Investment Banking, Securities Trading Commercial Banking Investment Banking, Underwriting Investment Banking Investment Banking Institutional Brokerage Services Underwriting, Brokerage Services Securities Brokerage N.A. Investment Banking Commercial Banking Securities Sales Brokerage Services Institutional Brokerage Services Brokerage Services Investment Banking Investment Banking Investment Banking Investment Banking Investment Bank Investment Banking Brokerage Services Brokerage Services Investment Banking Investment Banking Securities Trading

Stock Brokerage, Asset Management Asset Management Investment Banking, Brokerage Services Securities Trading Investment Bank Securities Trading

(continued)

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Table 7.5 (continued) Prudential-Bache Midland-Montagu Gov. Sec., Inc.

Brokerage and Investment Banking Securities Trading

Primary Dealers 1978 (28 Investment Banks and Brokerages, 1 mutual Fund, 7 Commercial or Savings Banks ) Aubrey G. Lanston & Co., Inc. Brokerage Services Bank of America Nt & Sa Commercial Banking Bankers Trust Commercial Banking Becker Investment Bank Blyth Eastman Dillon Capital Markets Investment Bank Carroll Mcentee & Mcginley Inc. Government Securities Trading Chase Manhattan Gov’t Securities Securities Trading Chemical Commercial Banking Citibank Commercial Banking Continental Illinois Commercial Banking Dean Witter Reynolds Inc. Brokerage Services Discount Corporation of New York Securities Trading Dlj Securities Corporation Institutional Brokerage Services Drexel Burnham Lambert Investment Banking First Chicago Commercial Banking First Interstate Commercial and Community Banking First Pennco Sec. Inc. Securities Trading Goldman, Sachs & Co. Investment Banking Harris Trust Trust Management Hutton Brokerage Services Irving Securities, Inc. Securities Trading J.P. Morgan Securities, Inc. Brokerage Services Lehman Bros. Investment Bank Merrill Lynch Government Sec. Inc. Underwriting, Brokerage Services Primary Dealers 1978 Midland-Montagu Gov. Sec. Inc. Morgan Stanley & Co. Incorporated Northern Trust Nuveen Gov’t Sec. Inc. Paine Webber Inc. Prudential-Bache Salomon Brothers Inc.

Securities Trading Investment Banking Trust Management, Securities Lending, Brokerage Services Mutual Fund Brokerage, Asset Management Brokerage and Investment Banking Investment Bank

(continued)

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Table 7.5 (continued) Second District Securities Co., Inc. Securities Groups The First Boston Corporation Wm. E. Pollock Gov’t Sec., Inc . Morgan Stanley & Co. Incorporated

N.A. Brokerage Firm; Buys and Sells Securities Investment Banking Securities Trading Investment Banking

Primary Dealers 1966 (12 Investment Banks and Brokerages, 8 Commercial or Savings Banks ) Primary dealer1 Type of firm Bankers Trust Company, New York Commercial Banking Chemical Bank New York Trust Co. Commercial and Retail Banking Continental Illinois National Bank and Trust Company of Chicago Commercial2 The First National Bank of Chicago Commercial and Retail Banking2 First National City Bank, New York Commercial and Retail Banking2 Harris Trust and Savings Bank, Chicago Commercial and Retail Banking Morgan Guaranty Trust Company of NY Commercial United California Bank, Los Angeles Commercial and Retail Banking Non-bank primary lenders Blyth & Co., Inc. Securities Trading Briggs, Schaedle & Co., Inc. Securities Trading Discount Corporation of New York Securities Trading The First Boston Corporation Investment Bank Aubrey G. Lanston & Co., Inc. Brokerage; Underwriter for Treasury Securities Primary and Non-Primary Dealers 1966 Merrill Lynch, Pierce, Fenner & Smith New York Hanseatic Corporation Wm. E. Pollock & Co., Inc. Chas E. Quincey & Co. D. W. Rich And Company, Incorporated Salomon Brothers & Hutzler Second District Securities Co., Inc.

Securities Brokerage Services Bond Trading Company N.A. Stock Brokerage Services N.A. Investment Bank Brokerage Firm—Buys & Sells Securities on Behalf of Clients

(continued)

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Table 7.5 (continued) Source Bernard, N. (1967, March 31). Views of the U.S. Government Securities Dealers. Staff Study Prepared for The Board of Governors, U.S. Federal Reserve; Wikipedia; Source for various of the now-defunct companies in table above was Google search for old webpages, etc., unless source otherwise identified Primary Dealers 1960 (14 Investment Banks and Brokerages, 4 Commercial or Savings Banks ) Aubrey G. Lanston & Co., Inc. ….Brokerage Services Bankers Trust Commercial Banking Bartow Leeds & Co. Bond Dealers C.F. Childs & Co., Inc. N.A. Chemical Bank Commercial Banking Continental Illinois Commercial Banking D.W. Rich & Co., Inc. Bond Dealer Discount Corporation of New York Securities Trading Drexel Burnham Lambert Investment Banking First Chicago Commercial Banking Irving Securities, Inc. Securities Trading J.P. Morgan Securities, Inc. Brokerage Services Malon S. Andrus Inc. N.A. Merrill Lynch Government Sec., Inc. Underwriting, Brokerage Services Salomon Brothers Inc. Investment Bank Securities Groups Brokerage Services; Buying and Selling Securities The First Boston Corporation Investment Banking Wm. E. Pollock Gov’t Sec., Inc. Securities Trading Source NY Federal Reserve. Available at www.newyorkfed.org/markets/primarydealers

7.5 Loss of Efficiency When Using Investment Banks and Brokerages to Implement Accommodative Monetary Policy As shown in the list of primary dealers above, the Federal Reserve, historically, has relied on purchasing securities from investment banks and brokerages. Such institutions sell securities to the Fed (or anybody else) mainly to obtain funds to buy other securities. After all, securities trading is what they do for a living. This helps inflate bond market prices, helping Wall Street, but does little to increase the demand for real goods and services necessary to raise GDP and lower unemployment, which is what is needed if Federal Reserve actions are to help Main Street. That said, some small portion of their sales to the FR may be on behalf of customers

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who need cash to buy cars and houses. Those purchases by the FR would raise the GDP, stimulating the economy. Because of this, testing should show an increase in loanable funds resulting from an increase in FR security purchases will have a smaller marginal effect on consumption and investment than an increase in loanable funds due to growth in the economic conditions driven (endogenous) portion of the loanable funds pool. And this is exactly what we see in Chapter 17 below. For consumption, in 6 of 6 periods tested, the estimated marginal effect of an increase in loanable funds is 94% lower for FR securities purchases than for increases in the endogenous part of the loanable funds pool (0.33 vs. 0.02). For investment, the marginal effect of an increase in loanable funds is 80% lower for FR purchases compared to endogenous growth in 5 of 6 periods tested (0.20 vs. 0.04). See Chapter 17 text accompanying Tables 17.5 and 17.6 for more details. The Federal Reserve’s purchases of securities would more likely stimulate the GDP and reduce unemployment if its purchases of securities were restricted to purchases from U.S. commercial and savings banks. It is these banks, not investment banks and brokerages, that are in the business of directly lending money to consumers and businesses that want to buy, cars, houses, machinery, and other goods and services, the very actions which will raise GDP and reduce unemployment. FR open market operations to accommodate fiscal stimulus programs, both because they have typically only been a small fraction of the size they need to be and because of the reliance on investment banks and brokerages, have not reduced the “crowd out” associated with stimulative fiscal policy much at all. The failure of accommodative monetary policy to accommodate appears responsible for the failure of fiscal stimulus programs to stimulate the economy. This failure has often been viewed as being a failure of Keynesian fiscal policy, but the evidence strongly suggests was mainly caused by a failure of FR accommodative monetary policy to fully accommodate.

7.6 Primary Dealers Who Are Domestic Vs. Foreign Corporations Foreign banks and brokerages, or those with close connections to them, constitute a substantial portion of those who sell securities to the Fed. Table 7.6 shows the proportion of open market purchases of securities

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Table 7.6 Q4: 2014 purchases of US securities by the Federal Reserve: $10,517.5 million Fed payment for securities $259.9 137.1 745.6 2,614.2 542.9 218.6 55.4 1,116.6 892.2 $6,582.5 363.8 473.7 236.2 295.6 57.4 285.0 546.2 867.9 388.0 197.2 106.1 117.9 $3,935.0 Firms

Counterparty (payee)

Headquartered: Domestic (D) or Foreign (F)a

Bank of Nova Scotia, NY D Agency Cantor Fitzgerald & Co. D Citigroup Global Markets D Inc. Goldman, Sachs & Co. D HSBC Securities (USA) D Inc. Jefferies LLC D J.P. Morgan Securities D LLC Merrill Lynch, Pierce, D Fenner & Smith Morgan Stanley & Co. D LLC U.S. Treasury Securities Purchased from Domestic Firms Barclays Capital Inc. F BMO Capital Markets F Corp. BNP Paribas Securities F Corp. Credit Suisse Securities F (USA) LLC Daiwa Capital Markets F America Inc. Deutsche Bank Securities F Inc. Nomura Securities F International RBC Capital Markets, F LLC RBS Securities Inc. F SG Americas Securities, F LLC TD Securities (USA) LLC F UBS Securities F Purchased from Firms Foreign Owned or Closely Related to Foreign Owned

a Firms were defined as “foreign” if they self identified as being a non-U.S. firm, were wholly owned

subsidiaries of foreign firms, or were closely related to foreign firms

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by the Fed were from foreign and U.S. investment banks and brokerages during the 4th quarter of 2014 (37.4%). While we can’t be sure from this evidence what proportion of foreign sellers deposit their FR checks in banks in foreign countries, evidence presented below indicates some do. This provides one explanation of the discrepancy between the increase in Fed holdings of U.S. Treasury, Agency, and GSEs during the QE years and the smaller increase in total reserves and currency in circulation in banks under its supervision in the U.S. Evidence that much of the FR’s security purchases were from foreign bank branches outside the U.S. Confirmation of FR financial transactions with foreign banks is provided by Tooze (2018), who notes: In 2006, European banks generated a third of America’s riskiest privately issued mortgage-backed securities. By 2007, two-thirds of commercial paper issued was sponsored by a European financial entity… The Fed acted aggressively and also in highly ingenious ways, becoming a guarantor of last resort to the battered balance sheets of American but also European banks. About half the liquidity support the Fed provided during the crisis went to European banks… Tooze (2018), cited by Zakaria (2018). (emphasis added)

And …(The crash of the financial system) had spectacular practical consequences in the crisis of 2008. A system characterized by massive corporate interlock faced meltdown. But it lacked an equivalent transnational stabilizing agency. De facto it was therefore the central bank of the United States, the Fed that emerged as the lender of last resort for the entire global banking system…. Tooze (5 July 2018)

And ….the European financial system it not been for massive lender of 2008 and 2009, more than half provided to large banks went to (22 July 2018)

would not have survived the crisis had last resort activity by the Fed. Between of the trillions of dollars in liquidity it banks that were not American….Tooze

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Buying bonds from (or lending money to) foreign banks did mean that a sizeable part of the loanable funds increase resulting from FR actions occurred in foreign countries, not in the U.S. This diminishes the impact any given level of FR security purchases on the U.S. economy compared to what would prevail if security purchases were only from domestic security sellers. Table 7.7 shows FR purchases of treasury and agency securities in the 1960s to be almost identical to growth in the monetary base. In the 1970s, 1980s, and 1990s, the monetary base increased more than FR securities purchases, suggesting a net inflow of foreign purchasers of U.S. securities, the proceeds which were deposited in U.S. depository institutions. In the 2001–2010 and 2011–2017 periods, FR purchases noticeably exceed growth in the monetary base, suggesting substantial portions of the Fed’s purchases of treasuries and agency bonds were from foreign banks and brokerages, with deposits accruing in the foreign countries, not the U.S. In the 2001–2010 period, FR purchases were $234.7 billion greater than the growth in the U.S. monetary base, and in 2011– 2017, FR purchases were $364.7 greater than the increase in the U.S. monetary base. It is of course possible that some funds initially deposited in foreign banks would ultimately be lent out to borrowers in the U.S., but conceptually unlikely that 100% would be; exactly how much was is an empirical question difficult to answer due to fungibility issues. Hence, we conclude Fed purchases from foreign sellers would probably not increase the pool of loanable funds available to U.S. citizens as much as restricting FR purchases to U.S. sellers. This may be part of the reason Table 7.7 FR purchases of treasury and agency securities and growth of the monetary base (billions)

Period

Growth in FR securities purchases

Growth in monetary base

$33.4 58.5 104.6 239.3 1618.7 1615.5

$32.2 84.2 155.7 279.8 1384.4 1250.8

1961–1970 1971–1980 1981–1990 1991–2000 2001–2010 2011–2017 Source See Table 7.3

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why accommodating monetary policy does not seem to accommodate. This issue is the subject of Chapter 9 below.

References Bernard, N. (1967, March 31). Views of the U.S. Government Securities Dealers. Staff Study Prepared for The Board of Governors, U.S. Federal Reserve. Bloomberg Company Overview Reports. www.bloomberg.com/research/sto cks/private. Federal Reserve Board. Consolidated Balance Sheet for Commercial & Savings Banks and Credit Unions (Q4 data for year cited Available at https://www. federalreserve.gov/releases/efa/efa-project-consolidated-balance-sheet.htm). NY Federal Reserve. (2005, February). The Treasury Auction Process: Objectives, Structure, and Recent Adaptations. Current Issues in Economics and Finance, 11(2). Authors: Garbade, K. and Ingber, J. NY Federal Reserve. (2007, February). Domestic Open Market Operations During 2006 (A Report Prepared for the Federal Open Market Committee by the Markets Group of the Federal Reserve Bank of New York), p. 21. Available at www.newyorkfed.org/medialibrary/media/markets/omo/omo2006.pdf. NY Federal Reserve Bank. Open Market Operations: Transaction Data. (2018). Available at www.newyorkfed.org/markets/omo_transaction_data.html. NY Federal Reserve Bank. Primary Dealers 1960. www.newyorkfed.org/mar kets/primarydealers. NY Federal Reserve. (2018). “Treasury Auctions” New York: Federal Reserve Bank of New York 2018 (Auctions by U.S. Treasury, Not FR to Sell Bonds to Finance Deficits). Available at https://www.newyorkfed.org/aboutthefed/ fedpoint/fed41.html. U.S. Treasury. (2018). Who/What Are Primary Dealers? U. S Department of the Treasury Resource Center. Available at https://www.treasury.gov/resource-cen ter/data-chart-center/quarterly-refunding/Pages/primary-dealers.aspx. Zakaria, F. (2018). Looking Back at the Economic Crash of 2008. New York Times, December 8. Available at https://www.nytimes.com/2018/08/ 10/books/review/adam-tooze-crashed.html?rref=collection%2Fsectioncollec tion%2Fbooks&action=click&contentCollection=books®ion=rank&mod ule=package&version=highlights&contentPlacement=6&pgtype=sectionfront.

CHAPTER 8

The Failure of Accommodative Monetary Policy Before Quantitative Easing (QE) and Its Success After; the “Pushing on a String Problem”

Excess reserves are a measure of how adequately the available supply of loanable funds is meeting the demand for loans by consumers and businesses. The excess reserves U.S. depository institutions in recessions and non-recession periods are shown in Table 8.1. Levels of excess reserves in recessions and non-recession periods are compared before and after the “Great Recession” era which we define as starting with the financial system’s troubles in 2008.

8.1

Effectiveness of Accommodative Monetary Policy 1960–2007

Limitations in the supply of loanable funds, not a lack of demand for them, seem to determine the level of business and consumer borrowing. Table 8.1 indicates that prior to the great recession, whether in recessions or non-recession periods, excess reserves were very small, averaging only 2.1–2.2% of total reserves. This strongly suggests that in recessions as well as non-recession periods, there is no shortage of credit worthy borrowers (relative to the available pool of loanable funds, which of course declines in recessions). Therefore, reduction of the pool of loanable funds available for private use by consumers and businesses due to government © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. J. Heim, Why Fiscal Stimulus Programs Fail, Volume 1, https://doi.org/10.1007/978-3-030-65675-1_8

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Table 8.1 Excess reserves in U.S. depository institutions during recessions and non-recessionary periods (billions) Before the great recession era: recession years between 1959 and 2007

Before the great recession era non-recession years between 1959 and 2007 (average)

Recession years

Excess reserves

Total reserves

Excess reserves

Total reserves

1960

$0.74 billion

$19.26 billion

$0.89 billion (Av.)

$41.08 billion (Av.)

1961 1974 1980 1981 1982 1990 2001

0.58 20.13 0.26 36.86 0.51 40.66 0.32 41.93 0.50 41.86 1.67 59.12 1.64 41.05 $0.77 billion $37.61 billion (Av.) (Av.) 2.1% = (excess/total)

2.2% = (excess/total)

Great recession era recession years between 2008 and 2017

Great recession era non-recession years between 2008 and 2017

Year

Excess reserves

Total reserves

Excess reserves

Total reserves

2008

$767.32 billion

$820.86 billion

$1,910.47 billion (Av.)

$2,042.54 billion (Av.)

2009

$1,075.20 1,140.45 $921.26 billion $980.65 billion (Av.) (Av.) 93.9% = excess/total

93.5% = excess\total (Av.)

Data: Taken from Table 10.1

borrowing from the same pool is likely to cause a crowd out problem in recessions as well as non-recession periods. A more extensive study of crowd out effects in recessions and non-recession periods (Heim 2016) also concluded the same thing.

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This is a key finding for public policy to reduce the crowd out problem. This finding suggests the same policies are needed in recessions to combat crowd out as are needed in normal economic times. In the past, some economists have argued crowd out can’t be a problem in recessions, because private demand for loanable funds falls off, leaving funds to finance government deficits without reducing the amounts private consumers and businesses wish to borrow. Hence, the stimulus effects of deficits will not be offset by crowd out. In recessions, it is argued, crowd out should not be considered as a reason why deficit finance won’t stimulate the economy. This study shows that while borrowing may drop off, the supply of loans falls off as fast or faster. This leaves any additional borrowing by government during recessions is likely to crowd out private borrowing as in normal times. Bank holdings of securities during recessions were also virtually unchanged from non-recession periods. Banks can only hold reserves, loan them out in traditional fashion to consumers and businesses, or loan them to the government, i.e., buy government securities with them. Excess reserves as a percent of total reserves could stay the same in recessions, even if the bank is unable to find credit worthy customers, if the bank buys government securities with the excess reserves. The constancy of securities holdings in recession and non-recession periods suggests the demand for loans relative to the supply is about the same in both. Hence, the argument that excess reserves were low during even recession years in the 1960–2007 period was because banks used excess reserves to increase their holdings of treasury and agency securities, because they could not or would not loan out to their usual customers, does not seem to be supported by the facts. The better explanation seems to be that the demand for loans from credit worthy borrowers equaled or exceeded the loanable funds available 1960–2007, including the additional funds made available by the FR’s purchase of treasury and agency securities. This comports with (Heim 2007, 2016), which found that crowd out is a problem even during recessions because the pool of loanable funds tends to fall as fast or faster than the fall in loan demand in the U.S. During the same period, FR real holdings of treasury and agency securities during non-recession years averaged 5.3% of real GDP, but only averaged 4.8% in recession years. One would think that had the FR been trying to implement an accommodative monetary policy, the FR’s

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holdings of government and agency securities would have been larger in recession periods. Clearly, the main answer, at least for recession years through 2007, to the question: Why doesn’t FR “Accommodative” monetary policy actually Accommodate?

is that the FR didn’t try very hard to accommodate fiscal deficits. And there is substantial additional evidence to support this conclusion. Below is Table 8.2 which shows the size of the deficit in every year 1960– 2010, and the extent to which the Federal Reserve purchased enough securities to offset the deficit’s crowd out effects. Note that with the exception of the 1990s, a declining deficit period, the Fed only bought securities equal to 44% of the average deficit between 1960 and 1990, and only 23% between 2001 and 2007. Again we see that the main answer, at least for recession years through 2007, to the question: Why doesn’t FR “Accommodative” monetary policy actually Accommodate?

is that the FR didn’t try to fully accommodate fiscal deficits. The 1990s is an exception, and there was no crowd out problem for the Fed to accommodate; in all but two of the 10 years 1991–2000, the deficit was declining. Hence, no accommodating Fed securities purchases were needed to offset crowd out. Purchases must have been for some reason other than accommodating the crowd out effects of deficits. In addition to just not buying enough securities to offset crowd out recall, it was noted in Chapter 7 that there were two other reasons why Federal Reserve accommodative monetary policy was unsuccessful at offsetting the crowd out problem caused by deficits that accommodating fiscal stimulus programs use of the wrong types of banks as primary dealers and use of foreign banks as primary dealers. Hence, in total, we can say there were three reasons why Fed attempts to accommodate fiscal stimulus programs by increasing the loanable funds pool did not work: (1) In order to accommodate, i.e., offset the crowd out problem caused by deficits, Fed securities purchases must be at least as large

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Table 8.2 Real yearly changes in the deficit (T − G) and FR security purchases (Tr + A) (billions of 2005 dollars) Year

( T − G)

1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990

46.9 −56.5 −28.4 26.3 −27.8 15.8 −17.7 −89.2 57.1 80.6 −120.4 −37.8 78.5 55.3 −53.4 −221.5 90.2 62.2 58.3 14.4 −116.1 15.7 −170.4 −66.6 43.3 −34.2 −45.8 62.3 39.8 10.0 −96.2

(Tr + A) −0.3 7.8 6.0 14.4 12.2 16.7 10.0 18.7 8.3 7.3 7.7 16.8 −5.2 19.1 −9.4 −0.1 5.6 6.4 2.1 −6.1 −16.5 −6.3 −1.1 15.6 4.0 21.6 24.2 23.7 10.5 −24.6 −1.3

Year

( T − G)

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

−68.1 −104.3 58.4 115.6 22.0 95.5 133.0 128.0 53.3 91.6 −255.2 −395.4 −102.0 47.4 155.9 99.3 −75.5 −379.0 −539.5 −23.6

(Tr + A) 30.2 28.0 37.8 30.6 6.2 6.1 37.1 18.9 22.1 26.2 31.2 74.7 25.2 33.3 2.4 10.1 −57.6 −239.1 1226.4 277.3

as the deficit. Chapter 17, Tables 17.5 and 17.6, suggests that because of the lower marginal effect on crowd out of Fed security purchases (caused by factors #2 and 3 below), Fed purchases probably have to be several times the deficit in size to be effective in fully offsetting the crowd out effects deficits create. (2) The Fed mainly purchases securities from investment banks and brokerages, other than commercial and savings banks, who are more likely to make loans for things that will replace lending lost

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to consumers and businesses because of its diversion to finance the deficit. (3) Not all Fed purchases are U.S. banks; during a sample period in 2014, about 49% were made from foreign firms, increasing the chances that part of the Fed’s attempts to accommodate get used to increase the loanable funds available in foreign countries, not the U.S. Is there any evidence there was a “pushing on a string” problem during this period that might have made it pointless for the FR to do more? From 1960 to 2007, the data indicates FR expansion of the monetary base (and money supply) during recession years, to the extent it occurred at all, certainly did not result in a “pushing on a string” problem. Table 8.1 shows that up until the great recession era, slightly less excess reserves were to be found in depository institutions in recession years than during years without recessions. In recessions, the loanable funds pool appears to have declined slightly faster than consumer, business and government’s desire to borrow. Given the constancy of bank security holdings, if loan demand had been falling faster than supply, we would see a build up of excess reserves in recessions. These results suggest that in throughout the 1960–2007 period, the level of borrowing was constrained by limitations on the supply of loanable funds, not by loan demand. Increases in both the monetary base and money supply could have been substantially greater than they were without running into the “pushing on a string” problem. In short, it appears FR monetary policy could have been more stimulative to the economy than it was during recessions occurring in the 1959–2007 era, and probably during non-recessions as well since there were deficits then, too, that we not matched by increases in FR security purchases designed to increase the loanable funds pool by the same amount. We conclude that a crowd out problem will occur unless FR monetary policy is at least as expansive as the fiscal policy (i.e., the deficit) during the same period. Below, we will compare the size of monetary stimulus during recession years with the size of deficit-financed fiscal stimuli occurring at the same time. We will find that the attempts to accommodate fiscal policy were painfully inadequate throughout the 1960–2007 period. Many economists have argued that historically, they have seen little or no evidence that the fiscal stimulus programs worked. This appears to be due more to the inadequacy of accommodate monetary policy than to any flaw

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in fiscal stimulus theory itself. Statistical tests throughout this study show that tax cuts and government spending increases would positively affect consumption and investment were it not for the crowd out problem.

8.2

Effectiveness of Accommodative Monetary Policy 2008–Present

During the great recession era (2008–2017), we have a different story. Total depository institution reserves grew hugely during this period. The growth was from $43.46 billion in 2007 to $820.88 billion in 2008 alone (18.9 times the 2007 value), and to $1,140.45 billion in 2009. By 2017, they had grown to $2,665.94 billion. Clearly, the FR had enacted a very stimulative monetary policy, one which more than offset government deficits. But over 93% of the increased reserves remained unlent, i.e., remained in depository banks or at the FR as excess reserves. Clearly, during the 2008–2017 period, FR attempts to stimulate the economy through securities purchases ran into a “pushing on a string” problem of the type Keynesians have argued make it a poor policy choice for stimulating the economy during recessions (at least in amounts greater than needed to accommodate stimulative fiscal policy undertaken at the same time). In Table 8.3, we look at key statistics for 1959–2017 relating to monetary and fiscal policy during years in which recessions took place in at least two quarters of the year. We examine deficit growth and FR success in accommodating these deficits by making its securities purchases and the money supply grow in amounts equal to the deficit. We will also look at monetary base growth in those recession years. Clearly, FR purchases of treasury and agency securities increased in highly inadequate amounts (and sometimes actually decreased) compared to annual increases in the deficit in most recession years prior to the great recession era. With the exception of 1981, accommodative FR policy appears to have been virtually nonexistent, either by choice, or due to lack of understanding of its critical importance for the success of stimulative fiscal policy. In 2008, accommodative monetary policy turned from at least nominally supportive to negative. The government deficit increased by $412.83 billion, but FR securities purchases declined by $243.7 billion.

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Table 8.3 Levels of accommodating monetary policy compared to fiscal deficits in recession years during 1959–2017 (nominal values) Year

Yearly change in government deficita

Accommodation in deficit years; change in FR holdings of securities

1960 1961 1974 1980 1981 1982 1990 2001 2008 2009

$8.5 billion −10.7 −16.7 −56.0 7.6 −95.1 −70.3 −232.4 −412.8 −592.6

$0.4 billion 0.7 4.4 3.5 8.8 7.7 8.1 40.1 −243.7 1,349.0

Increase in M1 billions % growth

Increase monetary base in billions % growth

$0.7 billion 4.5 11.3 26.7 28.2 38.1 31.8 94.6 228.7 90.6

$0.5 billion 1.0% 1.8 3.4% 9.0 8.4% 8.7 5.2% 9.6 5.4% 11.0 5.9% 22.5 7.0% 50.7 8.1% 833.4 96.3% 365.7 21.5%

0.5% 3.2% 4.3% 7.0% 6.9% 8.7% 4.0% 8.7% 16.6% 5.7%

a Deficit = total government revenues-total government spending on goods, services and transfers Years of deficit increases have negative signs; deficit reductions have positive signs

In recession year 2009, the level of FR purchases was positive. It was more than twice as great in volume than what was needed to cover the bank liquidity problem caused by crowd out. Recall from Table 8.1 that excess reserves were very small before 2008, even during recession years. In the pre-great recession years, banks earned nothing on reserves from the FR, so they had little incentive to keep them. This suggests that if banks had more reserves to lend out, they would have lent them out, simply to maximize profits by earning interest. Hence, the failure of FR monetary policy to be significantly accommodative during this period probably stifled much of the economic recovery that would have naturally stemmed from the deficit-financed fiscal stimulus programs that were undertaken. In fact, it would seem that, intentionally or not, monetary policy fought attempts by fiscal policy to be stimulative during 1959–2007. This failure to adopt a more accommodative monetary policy may explain the limited M1 growth occurring in these recession years. In 8 of the 10 recession years (excepting 1981 and 1960), increases in M1 were not large enough to fully accommodate the growth in the nominal

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deficit, i.e., solve the crowd out problem. The gap between the increase in M1 needed and what actually occurred was large. For growth in the monetary base, the story is much the same. Fed action was inadequate to offset the nominal deficit in 7 of the 10 recession years (all except 1960, 1981, and 2008). The actual anti-stimulative sale of securities by the FR during 2008 seems incredulous to an outside observer, given that were many signs we were approaching a recession year. But there is evidence the FR was trying desperately to keep the federal funds rate from completely collapsing during this period, which would have indicated the FR had lost control of interest rates. The FR may have sold the bonds in an effort to push the rate on the street back up to the FOMC target. Note the following report by the FR on open market operations during 2008: …After September 15, the magnitude of liquidity added to the system through various programs exceeded the Federal Reserve’s ability to offset with draining operations. And from the point shortly afterwards when it began to pay interest on reserves up to the December FOMC meeting, the Federal Reserve adopted an entirely different framework and set of operating procedures to implement monetary policy. Under this new framework, it relied primarily on the level of interest paid on excess reserves to influence market rates, while largely accepting a generally very high and variable level of excess reserves. But despite the efforts described above to improve its control over rates by successively narrowing the spread between the rate paid on excess reserves and the fed funds target, the fed funds rate traded at levels significantly below-target. (Federal Reserve Board, January 2009, p. 6—emphasis added)

Note also that there is some indication here that the FR also understood the policy of paying interest rates on reserves, started in October 2008, was one factor causing unprecedented growth in excess reserves in the system in 2008. Table 8.4 presents results for the same variables as in Table 26.2, but in real, not nominal, terms. In Table 8.4, we see that in 9 of the 10 recession years, the fiscal deficit was growing. In eight of the nine, accommodating monetary policy, as measured by FR purchases of securities in the open market, was either smaller than the deficit (the amount needed to offset crowd out) or negative. For recessions before the great recession, the low level of securities purchases strongly suggests there was no, or virtually no attempt by the

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Table 8.4 Levels of real accommodating monetary policy compared to fiscal deficits in recession years during 1959–2017 Year

Change in government deficit (real)a

Accommodation in deficit years change in FR holdings of securities

1960 $45.8 billion $0.07 billion 1961 −57.6 0.13 1974 −54.54 1.35 1980 −117.23 1.67 1981 14.47 4.60 1982 −171.50 4.27 1990 −97.33 5.86 2001 −256.33 36.37 (Great recession era starts 2008) 2008 −389.14 −364.66 2009 −540.61 1,477.16

Real increase in M1 billions % growth

Real monetary base billions % growth

NA 15.8 billion −40.7 −17.2 −19.7 20.7 1.5 77.1

NA 2.0% −4.6% −2.0% −2.4% 2.4% 0.1% 5.9%

$NA −0.2 billion 1.5 −2.6 −13.7 −2.0 −0.8 −36.8

NA 0.1% 0.4% −0.7% −4.1% −0.6% −0.2% −5.4%

182.8 69.7

12.4% 4.5%

−6.9 753.8

−0.9% 48.6%

Deficit = total government revenues-total government spending; a Years of deficit increases have negative signs; deficit reductions have positive signs. GDP Chain Deflator Used (2005 = 100). ERP (2010, 2018: T.B3)

FR to implement a monetary policy to accommodate stimulative fiscal deficits during those years. Only in the one of the two recession years of the “great recession” era (2009) do we see clear evidence of efforts to use monetary policy to accommodate the crowd out effects of government deficits. Instead of showing accommodating growth, the real M1 money supply actually decreased in two of these recession years when the deficit grew. In the other six years where M1 did grow, M1 growth averaged only 24% of deficit growth. That is, the FR actions accommodated a maximum of only 24% of the deficit’s crowd out effect (a maximum because borrowing can be “chunky”: You may need to borrow X dollars to be able to buy a car, but if only part of that is available because the rest has been lent out to finance the deficit, you borrow none, thereby reducing demand for goods and services by more than the stimulus effect of the deficit increased demand). One could argue that the FR has more control over the monetary base than the M1 money supply, and it is true that the fractional reserve system allows endogenous growth and contraction in M1 in response to

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economic conditions. But Table 8.4 shows that in the nine recession years in which the deficit increased, the monetary base actually declined in seven of them, and in the eighth, only increased a very small percentage of the increase in the deficit. Only in one recession year (2009) did the monetary base increase more than the deficit.

8.3 Does “Pushing on a String” During QE Apply to M1 as Well as Total Loanable Funds? Table 8.5 shows the average nominal values of FR treasury and agency security holdings, total bank reserves, excess reserves, M1, and the monetary base. After the start of QE, as average FR securities holdings increased, the monetary base increased by almost the same amount, indicating the success of the FR in increasing loanable funds. But as the increases in excess reserves show, of the $2426.66 billion increase in FR security holdings resulting from open market purchases, excess reserves increased $1721.01 billion; only $705.65 billion on average was lent out, providing an “accommodative monetary policy” offset to crowd out during the period. The rest ($1721.01) remained in banks in the form of excess reserves, which goes to prove a point: while accommodative monetary policy after QE started can (and did) offset the effects of crowd out, the effectiveness of this policy depends on the general state of the economy and the demand for loans. Clearly, that was far less here than the increase in loanable funds provided by the FR. The “pushing on a string” problem also affected growth in M1 during the QE period. The money multiplier (M1/Total Reserves) fell from an average 29.2 in the decade before QE to 1.4 during the first decade of QE. Hence, while this study strongly supports notion that increased Table 8.5 FR security holdings, reserves, M1 and monetary base; average values before and during QE (nominal values, billions) Average

FR securities

Monetary base

M1

Money multiplier

Total reserves

Excess reserves

1998–2007 2007–2017

$627.17 3053.83

$732.16 3040.28

$1250.92 2540.15

29.2 1.4

$42.86 1836.16

$1.65 1722.66

Source Table 10.1

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FR security purchases can offset the crowd out effects of deficits, it finds no evidence for the belief of some extremist Modern Monetary Theory (MMF) advocates that most economic problems can be solved by endless increases in FR open market security purchases. The findings above indicate any increase in loanable funds is helpful only to the extent that it fills a previous gap between the supply and demand for loanable funds. The increase in loanable funds eliminates the gap. But any larger increase simply will not be borrowed; it will just accumulate in banks as excess reserves. How successful was the QE period increase in loanable funds in offsetting crowd out? Recall that Chapter 4 provides a theory of how increases in loanable funds can offset crowd out. The theory indicates that the sign on the coefficient of the variable representing government spending deficits should normally be negative, indicating that an increase in government spending that causes a deficit should have a negative effect on consumption or investment (because of crowd out). It also hypothesizes that if increases in the loanable funds pool are greater than same-period spending deficits, this coefficient should turn from negative to positive. Chapter 4 also provides evidence from regressions on simulated data that this change in sign from negative to positive actually occurs when loanable funds grow at a faster rate than deficits. This change in sign on the government spending variable from negative to positive is what we find in one of the key models tested when we add the early QE years (2008–2010) to periods tested. Specifically: Table 18.11 —Results: Coefficient on government spending variable in investment models changes from to positive for 1996 and 2000–2009 samples; stays negative but turns from significant to insignificant in all remaining period tested that added QE years to the sample: 1960–2008 and 1960, 1970, 1980–2009 samples. Coefficient on the government spending variable for all 12 samples before the QE period showed a negative sign, statistically significant sign on this variable. Test Method: Government spending deficit variable modified by increases in total loanable funds; no stand-alone control variable used. This model used in Table 18.11 is the best (i.e., most theory consistent) model tested in this book that provides separate estimates of the effects of tax cut and spending deficits.

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These tests contribute significant evidence that the huge increases in securities purchases during the early QE period did eliminate the crowd out problem in those years for investment, a theory-consistent empirical result. By comparison, Table 18.1 indicates that for consumption, the sign on the government spending variable remained negative when the 2008– 2010 years were added to the sample. This is not surprising in light of evidence introduced later in Chapter 18 indicating increases in the pool of loanable funds generally are channeled into business borrowing, not consumer borrowing. So these results should be taken as indicating the best evidence generally supports, though not without exception, the hypothesis that the large loanable funds increases of the QE era more than offset QE, proving a net increase in borrowing power available to consumers and businesses, which they then used, stimulating the economy.

8.4

Conclusions

In recessions prior to the great recession, “pushing on a string” problems did not occur. “Pushing on a string” was not a reason why accommodative monetary policy was of questionable effectiveness. The major reason was that monetary policy has only been accommodative to a small extent of what was needed to offset the crowd out effects of deficit financing. Prior to 2008, in years in which the consolidated government deficit increased, the increase in FR securities purchases and the monetary base was generally much smaller. Thanks to the large increase in FR securities purchases after 2008 through the QE program, crowd out effects of deficits were not only fully offset in 2009, but more than offset. As we demonstrated above, this caused the net effect of loanable funds-modified government spending deficits to swing from having a negative effect on investment to having a positive effect. Before QE, we typically only see negative signs on the coefficient showing the marginal effect of spending deficits on investment. This clearly indicates that if increases in loanable funds are large enough, crowd out can be not only fully offset, but more than fully offset. Being offset means that fiscal stimulus programs can work. Being more than “fully offset” means that in addition to the fiscal stimulus, there is an additional stimulus effect felt stemming from the remaining increase in loanable funds: it allows even further consumer and business borrowing.

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That said there is evidence in 2009 that the increase in loanable funds, more than twice the size of the deficit, was too large relative to the needs of the economy for borrowed funds, and that much of it was left in banks unborrowed. During the great recession/financial crisis era started in 2008, “pushing on a string” definitely limited the ability of accommodative monetary policy via open market purchase of securities to stimulate the economy. Much of the increase in loanable funds stayed located in banks as excess reserves, about 94% of it. Much of this was due to insufficient demand for the huge increase in lendable funds that occurred. While the historically low levels of excess reserves prior to 2008 (about 2.2%) suggest that historically there has been excess demand for loans, this excess demand was relatively small compared to the increase in loanable funds promulgated by the Fed in 2009 and 2010 in our samples. But lack of demand for loans was not the only reason excess reserves were high after 2007. We note that the FR decision in 2008 to pay interest on bank reserves appears to be another factor contributing to the buildup of excess reserves in banks, instead of being lent out. And this was at a time when the economy could have used the stimulus effect of lending the most. This was contrary to pre-great recession era recession behavior, where typically bank excess reserves stayed at or below their very small non-recession levels. The other factor causing the buildup of excess reserves in banks was increasing loanable funds to levels in excess of what the economy could absorb (i.e., in excess of what business and consumers wished to borrow). Experience with bad loans stemming from the housing crisis caused a tightening of loan standards which also had some effect on the supply of loans. Finally, we note that the bizarre anti-stimulus selling off of treasury bonds by the FR in the 2008 recession year appears to have been because of the desire to achieve another (and conflicting) traditional FR objective: keep the actual federal funds rate as near the higher FOMC-authorized target rate as possible. FOMC policy was of limited success in setting federal funds rate policy. In any event, it amounted to “pulling (not pushing) on a string,” which, as the Volcker years taught us, definitely can have a depressing effect on the economy. Private market supply and demand for bank reserves forces may be too large for all but the largest interventions to control. Though beyond the scope of this study to pursue, it may be that typically FR efforts to set the federal funds rate a “followership” exercise, not a leadership exercise. They may just be intended to bring the target rate to where the business cycle is already taking it.

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References Economic Report of the President. (2018, 2010). Washington, DC: Government Publications Office. Heim, J. J. (2007). How Much Does the Prime Interest Rate Affect U.S. Investment? Journal of the Academy of Business and Economics, VII(1). Heim, J. J. (2016). Do Government Stimulus Programs Have Different Effects in Recessions, or by Type of Tax or Spending Program? Empirical Economics, 51(4). NY Federal Reserve. (2009). Domestic Open Market Operations During 2008 (A Report Prepared for the Federal Open Market Committee by the Markets Group of the Federal Reserve Bank of New York). January 2009, p. 6. Available at https://www.newyorkfed.org/medialibrary/media/mar kets/omo/omo2008.pdf.

CHAPTER 9

The Failure of U.S. Loanable Funds to Grow as Much as Federal Reserve Securities Purchases During QE: The Role of Foreign Banks

During QE, the $3.3 trillion in FR securities purchases was only matched by a 2.4 trillion increase in loanable reserves and cash in the U.S. banking system. The most likely reason is that the FR purchased large quantities of securities from foreign banks during this period, increasing loanable funds in those countries, but not the U.S. In Sect. 9.1, we provide a typical textbook explanation of why an increase in FR securities purchases is supposed to result in an equal increase in U.S. loanable funds or an equal increase in loanable funds and cash. In Sect. 9.2, we show the textbook model does not well portray reality, and how FR purchases from foreign banks can keep the resulting level of U.S. loanable funds from growing as much as the increase in FR security purchases. This, of course, results in a lesser stimulus effect to the U.S. economy than would be obtained if all increases in FR purchases were from U.S. banks.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. J. Heim, Why Fiscal Stimulus Programs Fail, Volume 1, https://doi.org/10.1007/978-3-030-65675-1_9

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9.1 The Textbook Equivalence of Increases in FR Securities Purchases and Increases in Loanable Reserves The FR definition of reserves in the monetary base is given as: …The monetary base is defined as the sum of currency in circulation and reserve balances (deposits held by banks and other depository institutions in their accounts at the Federal Reserve). Data on monetary aggregates are reported in the Federal Reserve’s H.3 statistical release. … (Source Federal Reserve Board 2018)

We are used to thinking of the effects of Fed security purchases as increasing bank reserves, i.e., loanable funds, by exactly the same amount as the security purchases themselves. In the typical classroom presentation of how this works, we see something like this: 1. The Fed buys $1000 in securities from a seller, paying with a check drawn on the Federal Reserve (FRck). (Some sellers may have the Fed electronically increase their bank balances, which reduces the number of steps below, but yields the same results.) Bond Seller A |L -1000 Treas.| +1000 FRck |

Federal Reserve A | L . +1000 T |+1000 FRck

2. Seller Deposits FR Check (FRck) in Seller’s Own Bank. Bond Seller A | L -1000 FRck | +1000 DD |

Federal Reserve A | L | |

Bond Seller’s Bank A | L +1000 FRck |+1000 DD |

3. Bond Seller’s Bank Cashes in the FRck at the Fed. Fed Credits Bank’s Reserves with $1000, directly increasing the bank’s loanable funds by the excess reserves portion of the increased reserves. Bond Seller A | L | |

Federal Reserve A | L |-1000 FRck |+1000 Res

Bond Seller’s Bank A | L -1000 FRck| +1000 Res |

Should the bond seller choose to deposit only $500 of the FRck in their checking account and withdraw the other $500 from the bank in cash, the pool of loanable funds would increase by $500 (minus any

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required reserves) and the currency component of the money supply would increase by $500. Between the two, the total amount of money available to buy additional goods would be the same as in Step 3 above, as shown in Steps 2A and 2B below: 2A. Seller Deposits FR Check (FRck) in Seller’s Own Bank. Bond Seller A | L -1000 FRck | +500 DD | +500 Cash |

Federal Reserve A | L | |

Bond Seller’s Bank | A | L +1000 FRck |+500 DD -500 Cash |

2B. Bond Seller’s Bank Cashes the FRck at the Fed. Fed Credits Bank’s Reserves with $500, directly increasing the bank’s loanable funds by the excess reserves portion of the increased reserves, and sends the bank $500 in Federal Reserve notes to replenish its cash if so requested (as is assumed here). A

Bond Seller | L | |

Federal Reserve A | L |-1000 FRck |+500 FR Notes |+500 Res

Bond Seller’s Bank A | L -1000 FR ck | +500 Cash | +500 Res |

Examination of the QE period 2008–2017 in Table 10.1.A in Chapter 10 below does not show this at all. It shows that between 2008 and 2017, the Fed purchased $3784.2 million in U.S. securities, but that total reserves during the same period only increased $1489 million and currency increase and additional $728.4 million, or a total of $2217 considerably less than the $3784.2 million Fed ownership of securities increased. Clearly, the stylized facts shown in Graphs 1–2B above show that this is an oversimplified way of thinking about the relationship between Fed securities purchases and their effect on bank reserves.

9.2 The Effects of Fed Purchases of Securities from Foreign Dealer/Brokers Table 10.1.A in the next chapter indicates Fed securities purchases typically exceed the growth of bank reserves and cash. This is inconsistent with the analysis above, in which the proceeds of all security sales by dealer/brokers end up as bank reserves or currency. How can this be? The answer seems to lie in the fact that total reserves reported by the Fed in Release H.3 reflects only deposits made in U.S. owned banks. But

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some sellers of securities to the Fed are foreign banks or securities brokers. Where do such sellers deposit their Federal Reserve checks received in payment? Some may deposit them in U.S. banks, but some may deposit them in banks in their home country. If we count the reserves of both when counting total reserves, the scenario portrayed in Graphs 1–2B above should hold. Without counting any portion of the Feds securities purchases that are deposited in banks not under the Feds control, i.e., foreign banks, excepting domestic branches or subsidiaries supervised by the Fed, securities purchases will seem to exceed changes in total (domestic) reserves and currency in circulation. This is shown in Step 1B below: 1B. The Fed buys $1000 in securities from a foreign seller, paying with a check drawn on the Fed (FRck). Foreign Bond Seller A |L -1000 Treas.| +1000 FRck |

Federal Reserve A | L . +1000 T |+1000 FRck

1C. Seller Deposits FR Check (FRck) in Seller’s Own (Foreign) Bank branch, outside the U.S. Foreign Bond Seller A | L -1000 FRck | +1000 DD |

Federal Reserve A | L | |

Foreign Bank Location Outside U.S. A | L +1000 FRck |+1000 DD |

Domestic banks A | L 0 | 0

When the FRck is converted to reserves, they are the foreign banks’ reserves. Domestic reserves shown in Fed Release H.3 would remain unchanged even though Fed securities purchases had increased. There is evidence that is consistent with this explanation. Foreign banks and brokerages, or those with close connections to them, constitute a substantial portion of those who sell securities to the Fed. Table 7.6 shows that the proportion of open market purchases of securities by the Fed that were from foreign and U.S. investment banks and brokerages during the 4th quarter of 2014 was 37.4%.

9.3 Trends Since 1960 in M1, Excess Reserves and Currency in Circulation Table 9.1 indicates that though today’s levels of the M1/GDP ratio seem

9

THE FAILURE OF U.S. LOANABLE FUNDS TO GROW …

Table 9.1 M1 trends, as a percent of GDP

167

Years

M1 (% of GDP)

1960–1972 1973–1980 1981–1995 1996–2007 2008–2011 2012–2017

20–27 15–19 14–16 10–14 11–14 15–19

high, levels in the 1960–1972 period were much larger with no adverse effects to the economy, and the same could be said of the extremely low M1/GDP period 1996–2007. Both the period of the highest ratio of M1 to GDP to (1960–1972) and the period with the lowest (1996–2007) were generally boom periods for the U.S. economy. Two periods of lackluster performance (1973– 1980) and (2012–2017) had about the same ratios of M1 to GDP, and one severe slump period (2008–2011) had a relatively low M1/GDP ratio. Overall, we conclude there is no particular pattern of either positive or negative association of M1 with GDP. We should keep in mind that we are looking at the trends in M1 and GDP without controlling any of the myriad of other variables that can affect GDP. In a more controlled environment (Heim 2017a), we do see a significant positive relationship between GDP and M1, though it is not as large as might like (see Chapter 11): a change in M1 is found to have a positive effect on GDP equal to about 20% of the size of the M1 change and another 20% two years later. Table 9.2 shows the trends in excess reserves, currency in circulation and M1 in both levels and as a percentage of GDP during 1955–2017. In the following sections, the results of analyzing the trends in these variables as a percent of GDP are summarized. Currency in circulation, as a percent of GDP, varied only between 5 and 6% from 1960 to 2008, despite widely varying economic conditions. It has varied from 7 to 9% since then, with the 8–9% numbers characterizing the 2014–2018 part of the QE program period where unusually large portions of the increase in FR purchases were taken in cash rather than additional reserves. GDP growth averaged 2.35% during this period. During 2009–2013, GDP growth averaged only 1.04%. Though there is a positive correlation of currency in circulation between 2009 and 2017

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Table 9.2 Excess reserves, currency in circulation and M1 Year

Nominal excess reserves (= Total-Req’d)

1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989

NA NA NA NA 0.506 0.744 0.584 0.572 0.491 0.406 0.424 0.339 0.375 0.426 0.286 0.248 0.182 0.284 0.304 0.258 0.266 0.273 0.19 0.232 0.442 0.514 0.319 0.501 0.561 0.835 1.064 1.173 1.02 1.062 0.942

Nominal currency in circulation NA NA NA NA 32.843 33.082 34.013 35.339 37.661 39.757 42.264 44.634 47.047 50.683 53.658 57.079 61.127 66.063 71.669 78.957 85.905 93.647 102.78 113.373 123.977 136.021 144.399 155.476 170.189 181.752 195.229 209.133 227.246 244.445 256.765

Nominal M1

133.3 134.6 133.5 138.8 140 140.7 145.2 147.8 153.3 160.3 167.8 172 183.3 197.4 203.9 214.4 228.3 249.2 262.9 274.2 287.1 306.2 330.9 357.3 381.8 408.5 436.7 474.8 521.4 551.6 619.8 724.7 750.2 786.7 792.9

Excess Currency in reserves as a circulation % of GDP as a % of GDP NA NA NA NA NA 0.0014 0.0011 0.0010 0.0008 0.0006 0.0006 0.0004 0.0004 0.0005 0.0003 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0001 0.0001 0.0001 0.0002 0.0002 0.0001 0.0002 0.0002 0.0002 0.0003 0.0003 0.0002 0.0002 0.0002

NA NA NA NA NA 0.0627843 0.0623792 0.0603119 0.0609374 0.0598284 0.0586972 0.0565912 0.0564435 0.0556344 0.0544334 0.0549007 0.0541739 0.0532963 0.0517826 0.0526045 0.0523873 0.0512583 0.0505669 0.0493695 0.0483316 0.0487175 0.0460917 0.0477041 0.0480638 0.0461537 0.0462074 0.0468013 0.0479388 0.0478845 0.0467957

M1 as a % of GDP

NA NA NA NA NA 0.267026 0.266294 0.252245 0.248047 0.241228 0.233044 0.218078 0.21991 0.216685 0.206847 0.206218 0.202331 0.201042 0.189952 0.182684 0.175082 0.167601 0.1628 0.15559 0.148842 0.146309 0.139393 0.145681 0.147251 0.140072 0.146696 0.162178 0.158259 0.154107 0.144507

(continued)

9

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169

Table 9.2 (continued) Year

Nominal excess reserves (= Total-Req’d)

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

1.665 0.99 1.155 1.07 1.171 1.291 1.419 1.687 1.513 1.294 1.326 1.644 2.008 1.047 1.909 1.9 1.863 1.785 767.318 1075.2 1006.636 1502.206 1458.75 2416.218 2523.93 2330.46 1925.062 2120.554

Nominal currency in circulation

Nominal M1

282.927 304.467 330.486 362.447 399.006 419.715 444.34 475.35 510.467 599.885 584.252 632.276 678.31 716.376 753.494 784.718 811.145 822.287 878.323 924.426 979.663 1067.014 1158.52 1232.202 1327.755 1416.036 1500.558 1606.695

824.7 897 1024.9 1129.6 1150.7 1127.5 1081.3 1072.3 1095 1122.2 1088.6 1183.2 1220.2 1306.3 1376.3 1375 1367.6 1374.8 1603.5 1694.1 1837.5 2164.6 2461.1 2663.8 2940.1 3094.5 3341.9 3600.4

Excess Currency in reserves as a circulation % of GDP as a % of GDP 0.0003 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0001 0.0001 0.0002 0.0002 0.0001 0.0002 0.0002 0.0001 0.0001 0.0534 0.0762 0.0673 0.0968 0.0903 0.1450 0.1462 0.1321 0.1064 0.1131

0.0487266 0.0507667 0.0520607 0.0543171 0.056271 0.0565605 0.0566423 0.0570005 0.0580035 0.0640822 0.0586589 0.0614133 0.0636854 0.0642713 0.0634821 0.06209 0.0605502 0.0585125 0.0611567 0.0655232 0.0654653 0.068761 0.0717103 0.0739643 0.0769136 0.0802773 0.0829357 0.0856922

M1 as a % of GDP

0.142032 0.149565 0.16145 0.169284 0.162281 0.151941 0.137839 0.128582 0.124423 0.119878 0.109295 0.114925 0.114563 0.117198 0.115954 0.108795 0.102088 0.097828 0.11165 0.120078 0.12279 0.139492 0.152338 0.159898 0.170313 0.175432 0.184706 0.192025

and M1 as a % of GDP, there was no such discernable pattern 1960– 2008. Hence, we conclude there is little systematic relation between the M1/GDP ratio and GDP growth. However, no other variables that affect GDP were controlled for when looking at the relationship of currency in circulation to GDP. Excess reserves, as a percent of GDP, ranged from 1/100 of 1% of GDP to 1.4/100 from 1960 through 2007 and from 1970 to 2007 variation from year to year was infinitesimal, varying only between 1/100 of

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Table 9.3 Additional excess reserves held during recessions (billions)

Period

(Billions) ( t-stat)

1956–1970 1956–1980 1956–1990 1956–2000 1956–2007 2005–2011 1956–2010

$0.30 (2.6) $0.14 (2.1) $0.18 (1.7) $0.27 (4.0) $0.22 (2.8) $528.26 (0.1) $40.10 (0.6)

1% and 2/100 of 1% of GDP, despite widely varying economic conditions, including recessions. In recessions, excess reserves were greater, but only by the small amounts. Note results in Table 9.3 obtained by regressing the nominal value of excess reserves (in billions) on a constant, the GDP as a control variable, and a variable valued a 1 during years with two or more quarters in recession, and zero otherwise. This is consistent with a finding in Heim (2017b) that even in recession there are more people that wish to borrow than there are funds available to borrow. The proof of this is the data above showing virtually no difference in excess reserves in recession and non-recession periods (up until the 2008–2011 period). This means that even in bad times, banks find ways of lending virtually all of their (reduced levels of) loanable funds. Hence, crowd out can be a problem in recessions as well as in normal economic times. However, as Table 9.2 indicates, excess reserves varied during the 2008–2012 QE period from 5 to 9% of GDP, a historically unprecedented increase, at least back to 1960. During the QE period 2013–2017, excess reserves as a percent of GDP rose even further between 11\ and 15% with 2013–2015 the highest, and the percentage then tapering off after that as the QE program tapered off. Table 9.4 shows that between 1960 and 2007, excess reserves varied only between 1 and 5% of total reserves, mostly between 1 and 2%, regardless whether the economy was experiencing normal conditions or in recession. However, excess reserves increased to 93–95% of total during the 2008–2015 period, and in 2016–2017, had only fallen slightly to 92%. Simply put, very few of the new loanable reserves created by the QE program were being lent out. Undoubtedly, in part, this might be because lending standards for housing toughened in response to the sub-prime

9

THE FAILURE OF U.S. LOANABLE FUNDS TO GROW …

Table 9.4 Excess reserves/total reserves ratio

171

Year

Ratio of excess/total reserves

1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998

0.03 0.04 0.03 0.03 0.02 0.02 0.02 0.01 0.01 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.03 0.02 0.02 0.02 0.02 0.02 0.03 0.04 0.03

(continued)

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J. J. HEIM

Table 9.4 (continued)

Year

Ratio of excess/total reserves

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

0.03 0.03 0.04 0.05 0.02 0.04 0.04 0.04 0.04 0.93 0.94 0.93 0.94 0.93 0.95 0.95 0.94 0.92 0.92

crises. But corporate borrowing also dropped radically, as did consumer credit borrowing, in the 2004–2011 period, as shown in Tables 9.5 and 9.6. Comparing the same two 4-year periods, excess reserves had increased from an average of only $1.9 billion during 2004–2007 to an astounding $1087.8 billion during 2008–2011, an increase of 1085.9 billion in the two averages. Hence, the decline in borrowing was nearly three times as large as the increase in loanable funds (excess reserves). We cannot say that the sole cause of the drop in borrowing was because economic conditions caused the pool of loanable funds dropped to drop that much. If there had been no change in other banking practices, including lending practices, excess reserves would have remained at nearzero ($1.9 billion) levels, as they had been historically, regardless of the business cycle. However, excess reserves rose to $1085.9 billion. The fact that reserves that in other recessionary periods would have been lent out, but were not, suggests adoption of more stringent lending policies and more cautious borrowing practices among businesses and consumers. It also suggests there is some truth to the theory that excessive increases in

9

THE FAILURE OF U.S. LOANABLE FUNDS TO GROW …

173

Table 9.5 Credit market borrowing 2004–2011 all sectors, by instrument (billions of dollars) Item

2004

2005

2006

2007

2008

2009

2010

2011

Total 3181.4 3551.9 4056.6 4512.5 2564.3 −539.8 573.7 928.6 Open market 106.2 245.1 317.1 −169.4 −189.0 461.9 −79.9 88.2 paper Treasury 362.5 307.3 183.7 237.5 1239.0 1443.7 1579.6 1006.8 securities Agency and 115.2 80.0 327.9 905.3 768.9 −59.9 −46.2 −20.8 GSE—backed Sec. Municipal 203.7 198.1 170.0 235.5 92.4 155.3 99.7 −52.8 securities Corporate 842.0 863.4 1235.4 1240.3 −233.8 −36.8 −100.5 120.9 and foreign bonds Depository 54.5 169.0 151.7 332.1 689.9 −749.7 −96.5 132.5 institution loans Other loans 123.4 156.9 163.9 528.1 99.6 −428.0 −228.8 12.5 and advances Mortgages 1256.7 1431.8 1391.7 1391.7 77.2 −286.7 −523.1 −328.5 Consumer 117.2 100.4 116.2 115.2 20.1 −115.9 −30.5 86.2 credit

Table 9.6 Average yearly declines in borrowing average annual borrowing in 2004–2007 compared to 2008–2011 (billions of dollars)

Sector

Decline in borrowing (−)/increase in borrowing (+) ($)

Treasury securities Open market paper Agency and GSE securities Municipal securities Corporate and foreign bonds Depository institutions n.e.c. Other loans and advances Mortgages Consumer credit Total

+1059.5 −329.3 −196.6 −128.1 −1107.9 −182.8 −379.3 −1550.9 −128.5 −2943.9

Source Table 8.5

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J. J. HEIM

reserves can have a “pushing on a string” effect because the increase in reserves is greater than the unmet demand for loans. One might expect the decline in borrowing because banks choose to be more selective in making loans during periods surrounding recessions, when uncertainty is greater. This would result in growth in excess reserves in such periods. But examination of the data presented earlier on this topic clearly indicates no significant increase in reserves in such periods, so this theory does not provide a good explanation of the decline in borrowing. The huge increase in excess reserves in the 2008–2011 period more likely reflects the “pushing on a string” problem and (in part) the welldocumented increase in stringency in banks mortgage lending policy following the 2007 subprime lending crisis. In fact, Table 9.6 shows an average decline in yearly mortgage lending from 2004–2007 to 2008– 2011 of $1550.9 billion. This adds up almost precisely to the decline in the loanable funds pool average of $430.4 billion (ERP 2013, T. B.32) due to deteriorating economic conditions, plus $1086 decline due to more stringent lending practices in the post-sub-prime crisis era. Together they total a $1516.4 decline, compared to the $1550.9 actual drop in mortgage lending. However, this may be coincidental, since in addition to the decline in mortgage borrowing, there was also a decline in other borrowing of $1393.0. (2943.9 total borrowing − 1550.9 mortgage borrowing).

References Federal Reserve Board. (2018). What Is the Money Supply? Is It Important? Available at https://www.federalreserve.gov/faqs/money_12845.htm. Heim, J. J. (2017a). An Econometric Model of the U.S. Economy. Hoboken: Palgrave Macmillan. Heim, J. J. (2017b). Crowding Out Fiscal Stimulus. Hoboken: Palgrave Macmillan.

PART IV

Increases in M1—Effects on Stock and Bond Markets and the GDP

CHAPTER 10

Effect of FR Purchases of Government Securities on M1

As we see in the Table 10.1, there is a significant correlation between Federal Reserve purchases of treasury bonds in the open market and the growth of M1, but not much correlation between growth of the M1 money supply and growth of the GDP.

10.1

Relationship of M1 Growth to Growth in Securities Purchased by the Fed

To estimate the effect on M1 of growth in FR securities purchases, we regressed M1 on securities purchases. Results are shown in Graphs 10.1 and 10.2, and Eqs. 10.1 and 10.2. Data was taken from Table 10.1. Using 1955–2016 Nominal Data, in Levels:   M1 = 462.88 + .67 FRTreas. + Agency Sec (t=)

(10.1)

R = .85 2

(18.5)

DW = 0.2

(10.1)

  M1 = 1.80 FRTreas. + Agency Sec (t=)

(15.6)

R = .55 2

DW = 0.2

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. J. Heim, Why Fiscal Stimulus Programs Fail, Volume 1, https://doi.org/10.1007/978-3-030-65675-1_10

(10.2)

177

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J. J. HEIM

Table 10.1 Historical data: nominal treasuries held by FRB, nominal M1 and real GDP (billions)

Year

Table S.61.a or H.4.1 nominal value: securities held by FR U.S. treasuries

Table S.61.a or H.4.1 U.S. agency

Total, treasury and agency securities held by FR

ERP 2010, 2012; FRED for 2013–2016 nominal M1

1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984

24.4 24.6 23.7 26.3 26.6 27 28.7 30.5 33.6 36.5 40.5 43.7 49 52.9 57.2 62.1 69 69.8 78.5 80.1 86.7 93.3 100.9 109.5 116.3 119.3 127.7 135.6 150.6 159.2

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.5 1.3 1.9 4.7 6.1 6.8 8 7.9 8.2 8.7 9.1 8.9 8.6 8.4

24.4 24.6 23.7 26.3 26.6 27 28.7 30.5 33.6 36.5 40.5 43.7 49 52.9 57.2 62.1 69.5 71.1 80.4 84.8 92.8 100.1 108.9 117.4 124.5 128 136.8 144.5 159.2 167.6

133.3 134.6 133.5 138.8 140 140.7 145.2 147.8 153.3 160.3 167.8 172 183.3 197.4 203.9 214.4 228.3 249.2 262.9 274.2 287.1 306.2 330.9 357.3 381.8 408.5 436.7 474.8 521.4 551.6

Real GDP

2549.2 2596.3 2633.6 2603.3 2763.51 2832.27 2898.17 3072.4 3206.7 3392.3 3610.1 3845.3 3942.5 4133.4 4261.8 4269.9 4413.3 4647.7 4917 4889.9 4879.5 5141.3 5377.7 5677.6 5855 5839 5987.2 5870.9 6136.2 6577.1

Release H.3, T.2 Reserves of depository inst., total reserves

Release H.3, T.2 Reserves of depository inst., required reserves

NA NA NA NA 18.956 19.262 20.131 20.054 20.702 21.596 22.694 23.785 25.291 27.192 28.053 29.246 31.345 31.415 35.108 36.861 34.99 35.237 36.486 41.678 44.02 40.66 41.925 41.855 38.894 40.693

NA NA NA NA 18.5 18.5 19.5 19.5 20.2 21.2 22.3 23.4 24.9 26.8 27.8 29 31.2 31.1 34.8 36.6 34.7 35 36.3 41.4 43.6 40.1 41.6 41.4 38.3 39.9

(continued)

10

EFFECT OF FR PURCHASES OF GOVERNMENT SECURITIES ON M1

179

Table 10.1 (continued)

Year

Table S.61.a or H.4.1 nominal value: securities held by FR U.S. treasuries

Table S.61.a or H.4.1 U.S. agency

Total, treasury and agency securities held by FR

ERP 2010, 2012; FRED for 2013–2016 nominal M1

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

177.8 197.6 218.9 233.7 226.8 235.1 266.5 295 332 364.5 378.2 390.9 430.7 452.1 478 511.7 551.7 629.4 666.7 717.8 744.2 778.9 740.6 476 776.6 1021.5 1663.4

8.2 7.8 7.6 7.6 6.5 6.3 6 5.4 4.6 3.6 2.6 2.2 0.7 0.3 0.2 0.1 0 0 0 0 0 0 0 20.9 1069.3 1148.9 952.2

186 205.4 226.5 241.3 233.3 241.4 272.5 300.4 336.6 368.1 380.8 393.1 431.4 452.4 478.2 511.8 551.7 629.4 666.7 717.8 744.2 778.9 740.6 496.9 1845.9 2170.4 2615.6

619.8 724.7 750.2 786.7 792.9 824.7 897 1024.90 1129.60 1150.70 1127.50 1081.30 1072.30 1095.00 1122.20 1088.60 1183.20 1220.20 1306.30 1376.30 1375.00 1367.60 1374.80 1603.50 1694.10 1837.50 2164.60

Real GDP

Release H.3, T.2 Reserves of depository inst., total reserves

Release H.3, T.2 Reserves of depository inst., required reserves

6849.3 7086.5 7313.3 7613.9 7885.9 8033.9 8015.1 8287.1 8523.4 8870.7 9093.7 9433.9 9854.3 10,283.5 10,779.8 11,226 11,347.2 11,553 11,840.7 12,263.8 12,638.4 12,976.2 13,228.9 13,228.8 12,880.6 13,481.4 13,687.8

48.122 59.369 62.129 63.678 62.732 59.122 55.545 56.578 62.847 61.359 57.896 51.176 47.921 45.208 41.651 38.371 41.051 40.271 42.953 46.847 45.383 43.282 43.463 820.876 1140.45 1078.001 1598.716

47.1 58.2 61.1 62.6 61.8 57.5 54.6 55.4 61.8 60.2 56.6 49.8 46.2 43.7 40.4 37 39.4 38.3 41.9 44.9 43.5 41.4 41.7 53.6 65.3 71.4 96.5

(continued)

180

J. J. HEIM

Table 10.1 (continued)

Year

Table S.61.a or H.4.1 nominal value: securities held by FR U.S. treasuries

Table S.61.a or H.4.1 U.S. agency

Total, treasury and agency securities held by FR

ERP 2010, 2012; FRED for 2013–2016 nominal M1

2012 2013 2014 2015 2016 2017

1666.1 2208.8 2461.4 2461.6 2463.6 2454.20

1018.1 1554.3 1788.4 1786.3 1769.8 1776.9

2684.2 3763.1 4249.8 4247.9 4233.4 4231.1

2461.10 2663.80 2940.10 3094.50 3341.90 3600.40

Year

Excess Reserves Release H.3, ( = Total − T.2 Monetary Req’d) Base, Currency in Circulation

1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975

NA NA NA NA 0.5 0.7 0.6 0.6 0.5 0.4 0.4 0.3 0.4 0.4 0.3 0.2 0.2 0.3 0.3 0.3 0.3

NA NA NA NA 32.8 33.1 34.0 35.3 37.7 39.8 42.3 44.6 47.0 50.7 53.7 57.1 61.1 66.1 71.7 79.0 85.9

Real GDP

Release H.3, T.2 Reserves of depository inst., total reserves

Release H.3, T.2 Reserves of depository inst., required reserves

14,001.8 14,210.4 14,465.1 14,622.9 14,809.6 15,075.3

1570.384 2541.019 2665.937 2481.187 2095.286 2309.823

111.6 124.8 142 150.7 170.2 189.3

(M1-Cur in Circ) = Demand Dep + trav. ck = D NA NA NA NA 107.2 107.6 111.2 112.5 115.6 120.5 125.5 127.4 136.3 146.7 150.2 157.3 167.2 183.1 191.2 195.2 201.2

Col. G&J = Monetary Base (MB)

NA NA NA NA 51.8 52.3 54.1 55.4 58.4 61.4 65.0 68.4 72.3 77.9 81.7 86.3 92.5 97.5 106.8 115.8 120.9

Soph. M Multiplier: M = ((1 + C/ D)/(Rd + ER/ D + C/ D)) * MB NA NA NA NA 140 140.7 145.2 147.8 153.3 160.3 167.8 172 183.3 197.4 203.9 214.4 228.3 249.2 262.9 274.2 287.1

(continued)

10

EFFECT OF FR PURCHASES OF GOVERNMENT SECURITIES ON M1

181

Table 10.1 (continued) Year

1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Excess Reserves Release H.3, ( = Total − T.2 Monetary Req’d) Base, Currency in Circulation

0.3 0.2 0.2 0.4 0.5 0.3 0.5 0.6 0.8 1.1 1.2 1.0 1.1 0.9 1.7 1.0 1.2 1.1 1.2 1.3 1.4 1.7 1.5 1.3 1.3 1.6 2.0 1.0 1.9 1.9 1.9 1.8 767.3 1075.2 1006.6 1502.2 1458.8

93.6 102.8 113.4 124.0 136.0 144.4 155.5 170.2 181.8 195.2 209.1 227.2 244.4 256.8 282.9 304.5 330.5 362.4 399.0 419.7 444.3 475.4 510.5 599.9 584.3 632.3 678.3 716.4 753.5 784.7 811.1 822.3 878.3 924.4 979.7 1067.0 1158.5

(M1-Cur in Circ) = Demand Dep + trav. ck = D 212.6 228.1 243.9 257.8 272.5 292.3 319.3 351.2 369.8 424.6 515.6 523.0 542.3 536.1 541.8 592.5 694.4 767.2 751.7 707.8 637.0 597.0 584.5 522.3 504.3 550.9 541.9 589.9 622.8 590.3 556.5 552.5 725.2 769.7 857.8 1097.6 1302.6

Col. G&J = Monetary Base (MB)

128.9 139.3 155.1 168.0 176.7 186.3 197.3 209.1 222.4 243.4 268.5 289.4 308.1 319.5 342.0 360.0 387.1 425.3 460.4 477.6 495.5 523.3 555.7 641.5 622.6 673.3 718.6 759.3 800.3 830.1 854.4 865.8 1699.2 2064.9 2057.7 2665.7 2728.9

Soph. M Multiplier: M = ((1 + C/ D)/(Rd + ER/ D + C/ D)) * MB 306.2 330.9 357.3 381.8 408.5 436.7 474.8 521.4 551.6 619.8 724.7 750.2 786.7 792.9 824.7 897 1024.9 1129.6 1150.7 1127.5 1081.3 1072.3 1095 1122.2 1088.6 1183.2 1220.2 1306.3 1376.3 1375 1367.6 1374.8 1603.5 1694.1 1837.5 2164.6 2461.1

(continued)

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Table 10.1 (continued) Year

Excess Reserves Release H.3, ( = Total − T.2 Monetary Req’d) Base, Currency in Circulation

2013 2014 2015 2016 2017

2416.2 2523.9 2330.5 1925.1 2120.6

(M1-Cur in Circ) = Demand Dep + trav. ck = D

1232.2 1327.8 1416.0 1500.6 1606.7

Col. G&J = Monetary Base (MB)

1431.6 1612.3 1678.5 1841.3 1993.7

Soph. M Multiplier: M = ((1 + C/ D)/(Rd + ER/ D + C/ D)) * MB

3773.2 3993.7 3897.2 3595.8 3916.5

2663.8 2940.1 3094.5 3341.9 3600.4

Source ERP 2010 and 2012 for 1960–2012 data; FRED 10/17/17 for 2013–2016 data Real GDP is in chained 2005 dollars. Source: Federal Reserve Holdings of Treasury Securities (Table S.61.a or Table H.4.1) (Source Z.1 Financial Accounts of the United States. Historical Annual Tables 1955–1964, 64–74 and 74–84. Washington: Federal Reserve Board. Available at https:// www.federalreserve.gov/releases/z1/current/annuals/a1955-1964.pdf (use same address with 1965– 1975 or 1975–1985) Total and Required Reserves, and Currency in Circulation taken from FRB Release H.3, Historic Data; M1 Money Supply (Billions of Nominal $) taken from Economic Report of the President 2018, T. B26; 2012, T.B70 $4,000B $3,000B $2,000B $1,000B $1,200B

$0B

$800B $400B $0B $-400B $-800B 55

60

65

70

75

80

85

90

95

00

05

10

Fed. Res. Securities Purchases & M1 Residual

Graph 10.1

Actual

Fitted

M1 regressed on FR securities purchases 1955–2016

15

10

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EFFECT OF FR PURCHASES OF GOVERNMENT SECURITIES ON M1

4,000 3,000 2,000 1,200 1,000 800 0 400 0 -400 55

60

65

70

75

80

85

90

95

00

05

10

15

Fed. Res. Securities Purchases & M1 Residual

Actual

Fitted

Graph 10.2 M1 regressed on FR securities purchases 1955–2016 (W/O constant term)

Leaving out the “Great Recession” and aftermath as possibly being unique. We have: Using 1955–2007 Nominal Data, in Levels:   M1 = 180.54 + 1.85 FRTreas. + Agency Sec (t=)

(6.4)

R = .91 2

(21.8)

DW = 0.1

(10.3)

  M1 = 2.26 FRTreas. + Agency Sec (t=)

(28.84)

R = .83 2

DW = 0.1

(10.4)

Either way, there is definitely a strong positive correlation between the money supply and FR purchases of securities, as theory would lead us to expect.

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However, there is a serious autocorrelation problem in those equations (note their Durbin–Watson statistic), which distorts the real relationship. With 1st and 2nd order autocorrelation controls added, the regressions become: Using 1955–2016 Nominal Data in Levels:   M1 = 1928.25 − .07 FRTreas. + Agency Sec + AR(1, 2) (t=)

(1.0)

R = .99 2

(−1.8)

DW = 2.3

(10.5)

  M1 = −0.06 FRTreas. + Agency Sec + AR(1, 2) (t=)

(−1.8)

R 2 = .99

DW = 2.2

(10.6)

Using 1955–2007 Nominal Data in Levels:   M1 = 458.14 + 0.79 FRTreas. + Agency Sec + AR(1, 2) (t=)

(1.6)

R = .99 2

(3.8)

DW = 2.0

(10.7)

  M1 = 0.74 FRTreas. + Agency Sec + AR(1, 2) (t=)

(3.7)

R = .99 2

DW = 2.0

(10.8)

In the regressions above, there is decided difference when the “Great Recession” and aftermath data are included. Here, the huge increase of securities purchases after 2007 was negatively associated with the total quantity of money outstanding, turning the historic positive relationship negative. When the 2008–2016 data is included in the Graphs 10.1 and 10.2, both the M1 and securities purchases variables are in levels. In levels, this data appears to show a negative, though not significant correlation between changes in the level of government securities purchases and the level of M1. To determine if the relationship was the same before the Financial Crisis/Great Recession/QE period, data after 2007 were dropped and the regression rerun. Dropping those years showed a strengthened relationship between Fed securities purchases and the level of M1, and restored the positive relationship. This is best explained by

10

185

EFFECT OF FR PURCHASES OF GOVERNMENT SECURITIES ON M1

the increase in excess reserves and currency holdings associated with the increases in security purchases after 2007, i.e., during the QE period. Both factors caused major reductions in the money multiplier, and hence, the level of M1’s correlation with Fed security purchases. The two graphs immediately (Graphs 10.3 and 10.4), reflecting Eqs. 10.9 and 10.10, regress yearly changes in M1 on yearly changes in total current and previous year purchases of government (treasury and agency) securities. Current year values alone were similarly significant, but did not explain quite as much variance. Graph 10.3 is for the 1955–2007 period, and the Graph 10.4 is for the full 1955–2017 period examined. AR(1) autocorrelation corrections were included to resolve autocorrelation problems.   M1 = 0.61 FRTreas. + Agency Sec  0,−1 + AR(1) (t=)

(6.3)

R = .30 2

DW = 2.1

(10.9) 150 100 50 0

120 80

-50

40 0 -40 -80 1960

1965

1970

1975 Residual

Graph 10.3

1980

1985 Actual

1990

1995

2000

2005

Fitted

M1 regressed on FR purchased securities securities 1955–2007

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400 300 200 100

300

0

200

-100

100 0 -100 -200 60

65

70

75

80

Residual

Graph 10.4

85

90 Actual

95

00

05

10

15

Fitted

M1 regressed on FR purchased 1955–2017

  M1 = −0.06 FRTreas. + Agency Sec  0,−1 + AR(1) (t=)

(−3.7)

R = .55 2

DW = 2.4

(10.10)

Here again, for the 1955–2007 period, there is a systematic positive relationship between government securities purchases by the Fed and increases in the money supply, and the regression coefficient indicates the relationship is .61:1.00: a dollar increase in FR purchase of securities is associated with a sixty-one cent increase in the M1 supply. If we use only the current year variant of the FR securities purchase variable, the coefficient remains .61, close to what was obtained in the regression of levels discussed earlier, but the t-statistic drops to 2.8 and the R 2 drops to .19. Here again we also see that the relationship between M1 growth and securities purchase growth after 2007 was so different that adding the 2008–2017 years to the regression gives a negative coefficient (−0.06) that is statistically significant and increases explanatory power relative to the 1955–2007 sample. We assume the negative sign on

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EFFECT OF FR PURCHASES OF GOVERNMENT SECURITIES ON M1

187

this expanded sample is due to the large increases in securities purchased during the quantitative easing period and soon thereafter onset of the 2009 recession. Other econometric tests: We tested the 1955–2007 sample for stationarity and endogeneity problems. Both the M1 and FR purchases variables were nonstationary, but were cointegrated, so no detrending was required. The dependent variable M1 and the explanatory variable (FR purchases) were found endogenously related, and the FR purchases variable was replaced with a Wald-strong instrument, which itself was not endogenously related to M1 (Sargan test). Results were similar to those presented above. The coefficient on the FR purchases variable was 1.12, and its t-statistic was 2.3 and R 2 = .16. We conclude that there is a statistically significant positive relationship between increases in nominal FR purchases of government securities and increases in nominal M1, except in periods of massive increases in M1 during periods of economic decline. There, the portion of M1 that fluctuates with economic conditions causes a decline in M1 larger than the increase in M1 exogenously generated by the FR’s security purchases. This results in a negative sign on the relationship in regression tests. The magnitude of the regression coefficient describing the relationship varies considerably from test to test, because nothing else that effects M1 is controlled for; hence, in different samples, or in slightly different models, we get different estimates of the magnitude of the relationship. In the following sections of this chapter, we correct for that by adding other explanatory variables.

10.2 More Sophisticated Models of the Relationship Between FR Securities Purchases and M1 The simple models in Sect. 10.1 above include no variables that affect the level of M1 besides FR purchases and sales of government securities. Results indicated a range of roughly 0.6:1 to 1.2:1 effects of a change in FR securities purchases and the related change in M1 for the 1960–2007 period. In the best single variable models, with stationarity and endogeneity controlled, FR securities purchases only explains 30–55% of the variation in M1. This section tests to see if these findings continue to hold when controls are added for other factors that affect M1.

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Among other factors which may affect the level of M1 are the GDP, recessions, the government deficit, the level of national saving, the unemployment rate, and the inflation rate. The economic grounds for considering them determinants of M1 are as follows: 1. The GDP, unemployment rate, and recession status all affect the desire of consumers and businesses to borrow, thereby affecting the money multiplier rate, and therefore, the amount of money expansion resulting from any FR purchases or sales of government securities. 2. Crowd out effects of deficits can dampen the stimulus effect deficits have on the GDP, also affecting the money multiplier. 3. Inflationary periods could increase borrowing unduly and therefore increase the money multiplier, as people buy now to avoid price hikes later. 4. Finally, an increase in national saving could reduce spending, and therefore the need for money, affecting the money multiplier. Hence, there is some basis in standard economic theory for assuming that these factors too may affect the level of M1, and hence should be controlled for when testing for the unique effects of FR securities purchases. The accounting model (Chapter 6) of how central bank security purchases change the money supply indicates its initial effect is to increase bank reserves by the amount of government securities purchased. This exogenous increase in reserves increases loanable funds in the system by the same amount. Once lent out by increasing borrowers’ demand deposit accounts, the money supply is increased. Through the fractional reserve process, further endogenous increases may follow, based on how much economic conditions stimulate further lending. Table 10.2 shows the effect of changes in FR purchases of government securities on M1 in five different, but overlapping time periods, allowing us a way to distinguish possibly spurious relationships from underlying systematic relationships between M1 and FR securities purchases (and the control variables in the model). Following the method used in Heim (2017), initial results were obtained using the full 1960–2010 data set. Four additional time period samples were tested using the same model to ensure model results were replicable and not just spurious effects in

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189

Table 10.2 Relationship of real FR purchases of securities to real M1 growth in five time periods controlling for other determinants of M1 growth (2005 = 100) Explanatory variable

Coef.( t-stat.)

Coef.( t-stat.)

Coef.( t-stat.)

Coef.( t-stat.)

1960–2010

1970–2010

1960–2007

1960–2000

Δ FR purchases Of T&A. −.07(1.1) sec.  Real .06(2.0) GDP  Deficit −.37(4.6)  Natl. .01(0.1) savings  Unem. −22.93(1.5) rate  Infl. rate −3.89(0.6)  AR(1) .35(2.2) R2 .37 DW stat. 1.8

Coef. ( t-stat.) 1970–2000

−.07((1.0)

.90(1.9)

1.26(3.2)

1.36(3.0)

.06(1.6)

.01(0.1)

.03(0.9)

.03(0.7)

−.40(4.1) .01(0.1)

−.07(0.5) −.25(1.8)

−.23(2.0) −.29(3.0)

−.38(2.3) −.30(2.4)

−27.57(1.4)

−28.38(1.6)

−35.97(3.7)

−51.25(3.9)

−3.85(0.5) .37(2.0) .39 1.7

−7.28(0.9) .31(1.9) .50 2.0

−7.71(1.6) .37(1.7) .63 1.6

−7.57(1.4) .34(1.0) .69 1.6

the one period tested. The ability to replicate initial results is required of empirical work that wishes to be considered good science. Hausman endogeneity test indicated none of the explanatory variables in the model were endogenous with the dependent variable (M1), so the models in Table 10.2 were estimated using ordinary least squares (OLS). All variables were ADF stationary except the combined treasury and agency securities variable. However, it was DF cointegrated with the dependent variable (M1), which itself was nonstationary, and did not have to be detrended. Table 10.2 shows a statistically significant positive relationship of FR purchases and M1 growth for the three periods tested including data from some or all of the years 1960–2007, with an average coefficient (t-statistic) = 1.17 (2.7). This is nearly identical to our earlier controlled results in Sect. 10.1. Only for the two test periods including 2008–2010 QE period data does the sign on the FR purchases/M1 relationship become negative and the relationship becomes statistically insignificant. This reflects the fact

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that banks were unable to find borrowers for most of the large increase in loanable funds that occurred during this period. The low money multiplier effect of FR securities purchases (1.17) may be because we are controlling for other endogenous factors which affect the level of the M1 money supply. However, the best of the uncontrolled tests in Sect. 10.1, which controlled for both endogeneity and stationarity, estimated the multiplier effect at 1.12, nearly the same. So adding control variables to the analysis is unlikely to be the reason controlled experiments found the money multiplier so low. However, there is one other possible explanation. As noted in Chapter 7, in at least some time periods, the proportion of securities bought from foreign dealers, by the FR when trying to stimulate the economy was quite large. In the first quarter of 2014, it was 37% of total FR purchases. If payments to foreign firms are deposited in foreign banks, rather than U.S. banks, loanable reserves in the U.S. (the figures used in our analyses) will grow less than the amount of FR securities purchases. For the portion deposited in U.S. banks, the money multiplier conceivably may be high, but the multiplier seen when comparing changes in total FR security purchases to changes in M1, it may be much lower. There may be, in short, a “leakage” problem, similar to the deflationary effect of a trade deficit found in Heim (2017). Regression coefficients on the FR securities purchase variable are this study’s money multiplier estimates. Table 10.3 shows these money multiplier effects were noticeably higher before 1990 than after. There was a larger use by the FR before 1990 of depository banks as primary dealers, Table 10.3 Money multiplier estimates from Table 10.2 model

Time period sampled 1960–1970 1960–1980 1970–1980 1970–1990 1980–1990 1987–1996 1997–2007a 1990–2000 1990–2010 2000–2010 a Dot.com and housing boom period

Money multiplier 1.83 0.49 −1.57 1.78 1.76 2.62 2.38 0.52 −0.06 0.23

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EFFECT OF FR PURCHASES OF GOVERNMENT SECURITIES ON M1

191

and a shift afterword to greater use of investment banks and brokerages. This may have had the effect of reducing loans to consumers and businesses wishing to purchase real goods and services (but probably increased the money in securities markets chasing securities, raising their price but not affecting the GDP). This may also have been a factor in the determining the low money multiplier effect. Other, more endogenous, factors having a significant or marginally significant relationship to M1 in a majority of the periods sampled in Table 10.2 include deficits (negatively related and significant in four of five samples), national savings, and the unemployment rate (negatively related and significant in three of five samples). Adding a constant term did not significantly change any of the model results, nor did results for the FR securities purchase variable when these models were recalculated using nominal values for the securities variable and the M1 variable. Much of the growth in M1 is endogenous and driven by changes in the business cycle (Eq. 10.11). Notice that in Table 10.2, for the two samples including 2008–2010 data (the start of the QE era), there is a statistically significant correlation between real GDP and real M1, but no significant correlation in periods ending before 2007 or earlier. Only when the unprecedentedly large FR purchases of securities 2008–2011 took place under Bernanke’s QE program do we see a positive, statistically significant correlation between M1 and the determinant GDP, suggesting the direction of causation runs from increases in GDP to increases in M1. This is consistent with evidence presented in Chapter 11 below.

10.3 Tests of the Relationship Between M1 and Excess Reserves and FR Securities Purchases In this section, we wish to further test M1 and it is the relationship to two possible determinants of M1: • the availability of excess reserves (as an indicator of loanable funds availability), and • the extent of FR securities purchases (in an attempt to increase the pool of loanable funds).

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Equation 10.7 below shows coefficients estimating the relationship of M1 between 1960 and 2010 to free reserves (excess reserves) in the U.S. banking system and FR securities purchases. To show the results are replicable, Table 10.4 shows estimates of the same relationship in six different though overlapping sample periods. Since the fractional reserve system allows a change in FR securities purchases to result in different levels of change in M1 at different stages of the business cycle, we present different findings of the effect of a change in these variables in years in which 1. The National Bureau of Economic Research (NBER) has indicated two or more quarters of the year were in recession, and 2. Years in which we were in recession for one quarter or less. The non-recession excess reserves and the recession period FR purchases variables were nonstationary (ADF test) but cointegrated (DF test) with the dependent variable, so no detrending was necessary. The recession FR securities purchases variable was found endogenous (Hausman test) with the dependent variable (M1) and was replaced by a strong instrument which was not endogenously related to M1 (Wald and Sargan tests). Newey–West standard errors are used, and tests are in first differences. Equation 10.11 shows results using the whole 1960–2010 data set. M1 = .34 (ExRESR ) + 9.61 (ExRESNR ) TT (t =)

(10.3)

(1.6)

+ .09 (Tr + A)R + .46 (Tr + A)NR (1.9)

R = .47 2

DW = 1.9

(1.9)

SEE = 41.40

(10.11)

Table 10.4 results indicate growth of excess reserves was positively and significantly related to M1 growth in recessions when 2007–2010 data is included in the sample (i.e., the 2009 recession), but not significant in recessions in the 1960–2000 period samples. In all non-recession period samples, there was a positive, but only weakly significant statistical relationship of excess reserves to M1. Results for the growth in FR security purchases were similar: during recessions, samples including the 2007–2010 data were positive and statistically significant, and generally only marginally significant in nonrecession periods.

(R) (NR) (R) (NR) (R) (NR) (R) (NR) (R) (NR) (R) (NR) (R) (NR)

.34 9.61 .72 17.75 .9 12.44 −7.42 23.90 11.63 43.85 −16.12 18.96 −1.66 21.0

Coef.

Excess reserves

10.3 1.6 3.98 1.9 1.8 17.18 −0.6 1.8 0.8 2.6 −1.3 1.5 (3.1) (4.4)

( t-stat.) .09 .46 1.02 .09 1.03 .10 −.51 1.05 −.54 1.43 .37 .50 .24 .61

Coef.

FR securities purchases

1.9 1.9 2.2 0.3 2.3 0.3 −0.8 1.8 −0.7 2.1 0.7 1.6 (0.9) (1.3)

( t-stat.)

All equations contained an AR(1) control. For n = 20, t = 2→p = .06; For n = 30, t = 2→p = .04 Note Adding a separate variable to control for real GDP did not substantially alter the above results. In general, variables significant above stayed significant; those not significant stayed not significant

Average 6 periods:

1960–1980

1960–1990

1960–2000

1960–2007

1960–2008

1960–2010

Period sampled

Table 10.4 Changes in real M1 associated with same period changes in real excess reserves and two year average changes in real FR securities purchases

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Hence, we conclude that FR purchases and excess reserves when tested separately have about the same relationship with M1, namely both seem most consistently related to M1 growth during recessions. Since there is always the possibility, the two variables are measuring the same thing, and next, we will test them together in the same model. Typically, if one a determinant of the dependent variable, and the other largely just a less significant correlate, the stronger of the two variables will show as statistically significant, and the weaker insignificant. Table 10.5 repeats the same analysis, but without separate recession and non-recession estimates of excess reserves and FR security purchases. Three separate model results are obtained for the same six time periods. The first two are for tests of M1 on excess reserves or FR purchases alone, without the other variable in the equation. The third contains both. The real GDP is used as a control variable in all three models. Table 10.5 tests clearly show that the FR purchases variable is more systematically related to M1 purchases than excess reserves. In periods before QE, FR purchases are strongly and significantly related to M1, and excess reserves simply are not. Only in QE period tests did we find both variables significantly and positively correlated with M1 growth. We conclude that excess reserves is probably not a determinant of M1, but that, as shown in earlier tests, FR securities purchases is. Both the stand-alone FR purchases model and the joint Excess Reserves and FR purchases model show a statistically significant relationship between real FR purchases and real M1. On average, in the four samples that preceded the 2008–2010 quantitative easing (QE) program, an increase in FR security purchases was associated with an increase in M1 that was 83% as large. This is reasonably close to our 1.17 finding in earlier tests with more controls. When the data set is expanded to also cover the 3 years of quantitative easing, a much smaller growth in M1 (7%) is seen per dollar of FR purchases. This is consistent with the large growth in excess reserves during those years, indicating difficulty of translating the increase in reserves to an increase in the money supply, via lending, into the hands of the public. (Data for the 2008 sample indicate mixed results, probably reflecting implementation problems in the first year of the quantitative easing program.) It is also consistent with the hypothesis that the endogenous effects of the business cycle on the money multiplier, and therefore M1, during the recession-related 2008– 2011 period offset most of what would have otherwise been closer to the

.23 .26 2.78 7.33 46.22 −10.16

Coef.

Excess res. only

2.9 0.0 0.2 0.4 1.5 −0.2

( t-stat.)

Explanatory variable(s)

.09 −.12 .60 .46 .88 .65

Coef.

FR sec. pur. only

2.3 −0.5 2.7 1.0 2.8 4.3

( t-stat.) .22 .49 8.57 4.23 63.48 17.65

Coef.

Excess res. (&)

3.0 4.6 0.9 0.5 2.0 0.8

( t-stat.)

.06 .55 .62 .86 1.32 .82

Coef.

Sec. purchases

2.8 2.3 2.5 2.3 4.0 4.3

( t-stat.)

All equations contained an AR(1) control. For n = 20, t = 2→p = .06; For n = 30, t = 2→p = .04 Note In the stand-alone Excess Reserves model, adding a separate variable to control for real GDP did not substantially alter the above results. Variables significant above stayed significant; those not significant stayed not significant. In the stand-alone FR purchases model, results when adding a GDP variable were the same, with one exception: in the 1960–1980 sample it reduced the t-statistic on FR purchases from 4.3 to 1.8. In the two-variable model that included both excess reserves and FR purchases as explanatory variables, results, in terms of statistical significance were essentially the same with or without the GDP variable, except for the FR purchases variable in the 1960–1980 sample, which again became insignificant (t = 1.8) when the GDP variable was added

1960–2010 1960–2008 1960–2007 1960–2000 1960–1990 1960–1980

Period sampled

Table 10.5 Changes in real M1 associated with same period changes in real excess reserves and two year average changes in real FR securities purchases

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J. J. HEIM

historic .83:1 growth in M1 for each dollars’ growth in FR purchases of securities.

10.4 Relationship of Growth in M1 to Growth in the Monetary Base When individuals, investment banks, or brokerages deposit the proceeds of securities sales to the FR in their banks, bank total reserves rise by the amount of the deposit. If the deposit is to the seller’s checking account, M1 rises by the same amount as total reserves. If part of the FR payment deposited is at the same time withdrawn as cash, it reduces the increase in bank reserves by a like amount, but increases currency in circulation so that the total change in the monetary base is the same as if the total amount had been deposited in the bank. That said, this analysis only shows the iteration of the money multiplier process in the same year as bank reserves are changed. It is at least theoretically possible that the M1 supply will grow to some multiple of the monetary base in years beyond that. Actual ratios of M1 to the monetary base are shown in Table 10.6. From 1960 to 2007, the relationship between the monetary base and M1 remained close to 1:1, as one would expect. The large decline in the ratio of M1 to the monetary base from 2008 to 2010 most likely is just reflecting the huge increase in reserves resulting from large purchases of securities by the FR, or lending by the Fed to banks. Banks found themselves were unwilling or unable to lend out these reserves during the 2008–2010 period. They were largely kept by banks in the form of excess reserves. FR purchases grew by $1430 billion during 2008–2010; M1 grew by $463 billion and excess reserves grew by $1005 billion in Table 10.6 Ratio of changes in real M1 to changes in real monetary base

Period

M1/MBASEa

1960–2007 1960–2000 1970–2000 1960–2010 1970–2010 a Coefficient of regression of M1 on monetary base

1.12 .94 .90 .26 .26

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197

nominal terms. The increase in excess reserves and M1 is nearly identical to the increase FR purchases. Table 10.7 takes the same five explanatory variables used in Table 10.2 to explain movement in M1 and tests to see if they also explain changes in the monetary base. No explanatory variables were found endogenous (Hausman Test), so no 2 SLS instruments needed. All variables were tested for stationarity (ADF test). Only the treasury and agency security variable was found nonstationarity. The variable was cointegrated with the dependent variable. No detrending was needed. FR purchases of securities were found positively and significantly associated with the monetary base in all periods tested 1960–2007, as was the case with M1. Only in the 2008–2010 period, when theFed was purchasing unusually large amounts of securities does the relation turn negative and insignificant. Note from Table 10.1 that the grow in the monetary base from 2008 to 2010 ($1191 billion) that results from increased FR security purchases is only 83% of the size of the FR purchases ($1430 billion) during the same period. This is likely the result of a “pushing on a string” effect in those years of part of the proceeds being Table 10.7 Relationship of FR purchases of securities to monetary base growth in five time periods controlling for other determinants of monetary base growth Explanatory variable

Coef.( t-stat.)

Coef.( t-stat.)

Coef.( t-stat.)

Coef.( t-stat.)

Coef.( t-stat.)

1960–2010

1970–2010

1960–2007

1960–2000

1970–2000

−.19(1.1)

.56(2.9)

.58(3.5)

.61(2.7)

.03(0.3)

.03(2.2)

.03(3.2)

.03(2.8)

−.95(4.7) .31(0.9)

.02(0.4) −.04(0.9)

.05(1.5) −.05(1.7)

.05(1.0) −.06(1.5)

−39.00(0.9)

−3.08(0.6)

−.85(0.2)

−.72(0.2)

.95(0.0) .29(0.9) .43 1.8

.35(0.2) .49(3.1) .54 2.2

.77(0.4) −.83(3.6) .56 2.1

1.19(0.6) −.87(3.3) .59 2.2

Δ FR purchases Of Tr&A. −.17(1.2) sec.  Real .03(0.3) GDP  Deficit −.87(4.6)  Natl. .28(1.0) savings  Unem. −32.67(0.9) rate  Infl. rate −2.13(0.1)  AR(1) .29(1.0) R2 .41 DW stat. 1.8

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J. J. HEIM

used to increase currency holdings rather than all of it added to bank accounts. An alternative possible explanation of the insignificance during these years is, as noted earlier, some of the money paid by the FR for securities may not be deposited in U.S. banks. These not-deposited funds may be money paid to foreign investment banks and brokerage houses, and deposited by them in their home country banks. Hence, it does not add to the U.S. monetary base and therefore limits the amount of increased lending in the U.S. that can occur from the FR security purchases. In a sample quarter (2014:Q4), 37.4% of the securities purchased by the Fed were purchased from foreign firms (see Table 7.6). We do not have data on how much of the proceeds received by foreign primary dealers were deposited in banks outside the U.S. For real GDP, the relationship is like that of FR security purchases: for any period before 2008, there is a statistically significant positive relationship with the monetary base. One other variable, the government deficit was found significantly, but negatively, related to the monetary base (the bigger the deficit, the bigger the base). This was only found in samples that included the 2008–2010 data, and reflects the FRs efforts to combat the deficit by unprecedently large increases security purchases. Time periods tested up through 2007 showed no significant relationship with the movements of the monetary base.

10.5 The Most Theory Consistent Model of M1’s Determinants The most theory consistent model we tested defines the determinants of M1 as total bank reserves net of FR purchases (TR − FR), FR purchases, and total loanable funds (LF) and includes GDP to control for the effects of fluctuations in the economy on M1. Results using real variables for all variables in the model are shown in Eq. 10.12 below; results using nominal values (except for GDP) are shown in Eq. 10.13 below. Both models include an autocorrelation control and have neither stationarity or endogeneity issues. Real Variables Model M1 = 3.71(TR − FR) + 4.51 FR − .17 (LF) (t=)

(4.2)

(4.6)

+ .05 GDP + .28 AR(1) (1.7)

(3.7)

(−2.9)

10

EFFECT OF FR PURCHASES OF GOVERNMENT SECURITIES ON M1

R 2 = .59

199

(10.12)

Nominal Variables Model M1 = 5.75(TR − FR) + 6.67 FR − .16 (LF) (t=)

(4.3)

(5.1)

(−3.7)

+ .05 GDP + .40 AR(1) (1.9)

R = .55 2

(5.2)

(10.13)

Results indicate exogenous increases in reserves attributable to FR securities purchases are slightly more likely to increase M1 than endogenous increases in reserves, but that both result in increases in M1 several times as large as the increase in reserves, due to the money multiplier effect. Note that total LF, instead of also being positively related to M1 growth, is negatively related. It is not clear why, but may reflect the fact that in ceteris paribus models that hold GDP (~income) constant while estimating the effects of LF; increases in LF (which is mostly comprised of national savings) will be associated with downward changes in spending, which may be accompanied by declines in the demand for money. Adding the QE years 2008–2010 to the sample reduces markedly the coefficients, and statistical significance levels of the endogenous total reserves (TR − FR) and FR purchases variables and markedly reduces R 2 s in both models, as expected due to the “pushing on a string” problem that developed during the QE period.

10.6 Summary of Results of Tests of Relationship of Changes in FR Purchases to Changes in M1 This chapter results and conclusions are summarized in the Table 10.8. Conclusion: All 11 simple models showed a significant relationship between nominal M1 and FR securities purchases, or a change in nominal M1 and a change in securities purchases. The best of the 11 models (Eq. 10.11) indicated that M1 increases a little more than a dollar ($1.12) for every dollar increase in FR purchases. That would include any money multiplier expansion of M1 resulting in the year of the FR purchases. While nice to see increases in FR securities purchases systematically result in an increase in M1, it is disturbing that the money multiplier is only 1.12, far below the 5–9 range more typical during the same period.

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J. J. HEIM

Table 10.8 This chapter summary table effects of changes in FR security purchases on M1 Model

Source

β(t)

R2 in (2) time periods 1955–2016

Simple tests —one Nom M1L, C = ƒ(TR + A)L Nom M1L = ƒ(Tr + A)L Nom M1L, C, AR = ƒ(Tr + A)L Nom M1L, AR = ƒ(Tr + A)L Nom M1AR = ƒ(Tr + A) Nom M1L, C = ƒ(Tr + A)L Nom M1L = ƒ(Tr + A)L Nom M1L, C, AR = ƒ(Tr + A)L Nom M1L, AR = ƒ(Tr + A)L Nom M1C, AR = ƒ(Tr + A) Real M1C, AR = ƒ(TR − FR, FR, LF) Nom M1C, AR = ƒ(TR − FR, FR, LF) Nom M1AR, En = ƒ (GDP)

explanatory variable Eq. 10.1 .67(18.4)

1955–2007

Periods significant

85



1/1

Eq. 10.1

.80(15.6)

55



1/1

Eq. 10.5

−.07(−1.8)

99



1/1

Eq. 10.6

−.06(−1.8)

99



1/1

Eq. 10.10

−.06(−3.7)

55



1/1

Eq. 10.3

1.85(21.8)



91

1/1

Eq. 10.4

2.26(28.84)



83

1/1

Eq. 10.7

.79(3.8)



99

1/1

Eq. 10.8

.74(3.7)



99

1/1

Eq. 10.9

.61(6.3)



30

1/1

Eq. 10.12

3.70(4.3), 4.51(5.1)



59

1/1

Eq. 10.13

3.70(4.3), 4.51(5.1)



55

1/1

Eq. 10.11

1.12(2.3)



16

1/1

L = Data in Levels; C = constant term Included; AR = Autoregressive Control Included; CV = Other Control Variables Included (Unemployment & Inflation Rates, Level of National Savings, Deficit & FR Security Purchases, or as noted in the model above); En = Endogeneity Instrument Used

10

EFFECT OF FR PURCHASES OF GOVERNMENT SECURITIES ON M1

201

Tests of Non-theoretical Models with 5 Control Variables All models test for the effects on M1 of Federal Reserve securities purchases (TR + A) controlling for the effect of the GDP, the government deficit, national savings, inflation, and unemployment.

Source 1960–2010

β( t)

39 – – – – 43 – – – .26

– – – – .41 – – – – .26

1.12





54









50





1960–2007

.94



56









63







1960–2000

90

59









69









1970–2000

1/1

1/1

1/1

1/1

0/1 (Incl.3QEyr)

0/1 (Incl.3QEyr)

1/1

1/1

1/1

0/1 (Incl.3QEyr)

0/1 (Incl.3QEyr)

Periods significant

Autoregressive Control Included; CV = Other Control Variables Included (Unemployment and Inflation Rates, Level of National Savings, Deficit and FR Security Purchases) MB = Monetary Base



.37

1970–2010

R2 in (5) time periods

FR sec.

Tests with 5 control variables Real M1 = ƒ T.10.2 −.07(−1.1) (FR, CV) Real M1 = ƒ T.10.2 −.07(−1.0) (FR, CV) Real M1 = ƒ T.10.2 .90(1.9) (FR, CV) Real M1 = ƒ T.10.2 1.26(3.2) (FR, CV) Real M1 = ƒ T.10.2 1.36(3.0) (FR, CV) Real MB = ƒ T.10.7 −.17(−1.2) (FR, CV) Real MB = ƒ T.10.7 −.19(−1.1) (FR, CV) Real MB = ƒ T.10.7 .56(2.9) (FR, CV) Real MB = ƒ T.10.7 58(3.5) (FR, CV) Real MB = ƒ T.10.7 61(2.7) (FR, CV) Real M1 = ƒ T.10.6 – (–) (MB)Coef.

Model

202 J. J. HEIM

10

EFFECT OF FR PURCHASES OF GOVERNMENT SECURITIES ON M1

203

For the three 1960–2007 period tests, these tests also show a positive and significant relationship averaging a $1.17 increase per dollar increase in FR purchases. This is nearly identical to our best finding ($1.12) testing the simple M1 = ƒ(FR purchases) models. However, when the QE period years of 2008–2010 are added to the samples, the relationship becomes insignificant, reflecting the difficulty banks had getting the huge increases in bank reserves resulting from FR security purchases lent out and into circulation, resulting in large levels of excess reserves. Results for the relationship of changes in M1 to changes in the monetary base (MB) were similar. Except in QE year samples, a dollar increase in MB was related on average to $0.97 increase in M1. Tests of the relationship of growth in the monetary base (MB) to growth in M1 showed the same results: significant before QE, insignificant after. Tests of Relationship of M1 to Excess Reserves and FR Purchases

Source

1960–2010

1960–2008

1960–2007

1960–2000

a Table 10.5 models include GDP control variable in addition to ER, FR variable(s)

Real M1 = ƒ (ER) or M1 = ƒ (FR)—separate, one explanatory variable model for recession and years Recess. Years: ER T.10.4 .34(10.3) .72(4.0) .09(1.8) −7.42(0.6) Non Rec. Yrs: ER T.10.4 9.61(1.6) 17.75(1.9) 12.44(17.2) 23.90(1.8) Recess. Years: FR T.10.4 .09(1.9) 1.02(2.2) 1.03(2.3) −.51(0.8) Non Rec. Yrs: FR T.10.4 .46(1.9) .09 (0.3) .10(0.3) 1.05(1.8) Real M1 = ƒ (ER, GDP) or M1 = ƒ (FR, GDP)—two explanatory models All Yearsa : ER T.10.5 .23(2.9) .26(0.0) 2.78(0.2) 7.33(0.4) All Yearsa : FR T.10.5 .09(2.3) −.12(0.5) .60(2.7) .46(1.0) Real M1 = ƒ (ER, FR, GDP)—three explanatory variable models All Yearsa : ER T.10.5 .22(3.0) .49(4.6) 8.57(0.9) 4.23(0.5) FR T.10.5 .06(2.8) .55(2.3) .62(2.5) .86(2.3)

Model

−16.12(1.3) 8.96(1.5) .37(0.7) .50(1.6) −10.16(0.2) .65(4.3) 17.65(0.8) .82(4.3)

46.22(1.5) .88(2.8) 63.48(2.0) 1.32(4.0)

1960–1980

11.63(0.8) 43.85(2.6) .54(0.7) 1.43(2.1)

non-recession

1960–1990

204 J. J. HEIM

10

EFFECT OF FR PURCHASES OF GOVERNMENT SECURITIES ON M1

205

Conclusions: For all years taken together, without regard to whether they were recession or non-recession years, the two explanatory variable models indicated that growth in either ER or FR purchases is significantly and positively related to growth in M1 in all 6 of 6 periods tested (with one ER exception). On average, M1 increases $0.71 for every dollar increase in FR purchases in this model. The less than one coefficient ($0.71) may occur because some of the increase in FR purchases is stored in bank savings or CD accounts rather than demand deposit accounts or currency. Part of the effect may be that FR payments to foreign banks that are the security sellers may be deposited in non-U.S. banks. Hence, even if deposited in (foreign) demand deposit accounts, it would not show as an increase in U.S. M1. Results are more mixed for the Table 10.4 models which tests ER and FR in the same model, but without a GDP control variable to control for other economic forces which could also affect the level of M1. Excess reserves were significantly related to M1 in 3 of 6 non-recession periods and 4 of 6 recession periods. FR security purchases were significantly related to M1 in 3 of 6 recession and non-recession periods. The absence of any other controls in the model for other factors makes these results considerably less reliable than we obtained in Table 10.5, where we tested all data combined without regard to whether it was recession or non-recession year data, and we also included a variable to control for fluctuation in the economy which could also affect M1. There, we found both FR significantly and positively related to M1 in all 6 periods tested, and ER was positively and significantly related in 5 of 6 tests.

References Economic Report of the President. (2012, 2018). Tables B26, T.B70. Washington, DC: Government Publications Office. Economic Report of the President. (2010, 2012). Various Tables. Washington, DC: Government Publications Office. Federal Reserve Board. Historical Annual Tables 1955–1964, 64–74 and 74–84. Available at https://www.federalreserve.gov/releases/z1/current/annuals/ a1955-1964.pdf. Federal Reserve Board. Real GDP is in Chained 2005 Dollars. Source: Federal Reserve Holdings of Treasury Securities (Table S.61.a or Table H.4.1). Federal Reserve Board. Total and Required Reserves, and Currency in Circulation. Taken from FRB Release H.3, Historic Data. Washington, DC. Heim, J. J. (2017). An Econometric Model of the U.S. Economy. Hoboken: Palgrave Macmillan.

CHAPTER 11

Effect of Increases in Loanable Funds or M1 on the GDP

In this chapter, we test whether changes in monetary policy involving FR purchases of securities, by changing the money supply, translate into changes in the real economy. Extensive empirical testing in Chapter 10 established that changes in M1 were positive, and in the range of ($0.61 to $1.36) per dollar of real value FR securities purchases from 1960 to 2007. However, we also found this relationship falls to near zero when sample periods are expanded to include the quantitative easing (QE) years, 2008–2010. Increases in loanable funds stemming from the FR’s purchases (far) exceeded the amounts that well-qualified borrowers wished to borrow.

11.1

Simple Tests

In this chapter, we examine whether changes in M1 do systematically lead to changes in the real GDP (mindful of the fact that not all increases in M1 in a given period are driven by increases in FR purchases in the same period—changes in M1 may be endogenously related to changes in the business cycle). The results of regressing GDP on M1 are shown in the graphs below. The first two (Graphs 11.1 and 11.2) show the regression of the level of real GDP on the change in nominal M1 and are the same, except the right hand graph contains a constant term. Autocorrelation is uncontrolled in © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. J. Heim, Why Fiscal Stimulus Programs Fail, Volume 1, https://doi.org/10.1007/978-3-030-65675-1_11

207

208

J. J. HEIM $20,000B $15,000B $10,000B $5,000B $0B

$20,000B

$-5,000B

$15,000B

$-10,000B

$10,000B $5,000B $0B $-5,000B $-10,000B 65

70

75

80

85

90

95

00

05

10

15

Real GDP Regress on Real M1 w/o Constant Residual

Graph 11.1

Actual

Fitted

GDP regressed on M1 1961–2017 $16,000B $12,000B $8,000B

$8,000B

$4,000B

$4,000B

$0B

$0B $-4,000B $-8,000B 65

70

75

80

85

90

95

00

05

10

Real GDP Regress on Real M1 Residual

Graph 11.2

Actual

Fitted

GDP regressed on M1 (W/Constant) 1961–2017

15

11

EFFECT OF INCREASES IN LOANABLE FUNDS OR M1 ON THE GDP

209

these graphs. The model is specified this way because much economic theory argues that it is the inflationary impact of a change in M1 that affects the GDP, not the level of M1. GDP = 64.74 M1 (t=)

(7.3)

R 2 = −1.66

GDP = 6666.52 + 28.42 M1 (2.0)

(5.5)

R 2 = .42

DW = .39 DW = .35

(11.1) (11.2)

GDP= 0.22 M1 + 1.72 AR(1) − .72 AR(2) (t=)

(0.8)

R = − .99 2

DW = 2.32

(11.3)

GDP= 8968.77 + 0.22 M1 + 1.72AR(1) − .72 AR(2) (1.6)

R 2 = .99

(0.8)

DW = 2.38

(11.4)

Equations 11.1 and 11.2 show a relationship between GDP and M1, but the relationship is highly autocorrelated. Adding an autocorrelation control variable changes the results radically: there is now no relationship between changes in M1 and the GDP. The left hand Graph 11.1 suggests that the yearly growth rate of M1, i.e., M1, and the level of GDP, i.e., “GDP” have been extremely uncorrelated since the late 1980s. This was when the FR purchases of securities shifted significantly away from commercial banks and toward investment banks and brokerages (though even in the best days, commercial banks were always a minority of the primary dealers). This in turn supports the hypothesis that buying securities from firms like commercial and savings banks is more likely to stimulate the real economy, while the proceeds obtained by investment banks and brokerages from sale of securities to the Fed are more likely to be used to just purchase more securities. That is because, unlike investment banks, commercial and savings banks use the increase in their loanable funds resulting from FR securities purchases to make loans that increase demand for real economic goods, rather than just buying other securities. Results with autocorrelation controls added also indicate no statistically significant relationship between growth in the nominal M1 money supply (M1) and (GDP), with regression coefficients near zero (0.22) and statistical significance levels of only (t = 0.8). Some economic theory,

210

J. J. HEIM

of course, says there should be. Though not shown, lagging the effect of a change in M1 (M1) does not change the result: the change in M1 is still statistically insignificant at about the same level, and R 2 drops noticeably. (In both cases, the R 2 is due to the autocorrelation control variables, not M1.) When we regress the yearly change in GDP on the change in the growth rate of M1, i.e., (M1) (Graph 11.3), or regress the growth rate of GDP on a constant and the change in the growth rate of M1 in the same year (Graph 11.4), we again find a virtually zero correlation between the two variables. In the Graph 11.3, there was a substantial autocorrelation problem, so the model was reestimated with a first-order autocorrelation control (AR(1)). That model is given as Eq. 11.7. The right side model, with a Durbin–Watson statistic of 1.6, did not need autocorrelation controls added. Again, reestimating the model with M1 600 400 200 600

0

400

-200

200

-400

0 -200 -400 1965

1970

1975

1980 Residual

Graph 11.3

1985

1990 Actual

1995 Fitted

GDP regressed on M1 1961–2017

2000

2005

2010

11

EFFECT OF INCREASES IN LOANABLE FUNDS OR M1 ON THE GDP

211 $800B $600B $400B $200B $0B $-200B

$400B

$-400B

$200B $0B $-200B $-400B $-600B 65

70

75

80

85

90

95

00

05

10

15

Change GDP Regressed on Double Change M1 – with Constant Residual

Graph 11.4

Actual

Fitted

GDP regressed on M1 (W/Constant) 1961–2017

lagged one year did not change the results. GDP = .29M1(−0) R 2 = − 1.9; DW = 0.6 (t=)

(0.0)

(11.5)

GDP = 214.72 + .01M1(−1) R 2 = .00; DW = 1.6

(11.6)

GDP = .22M1(−0) + .71 AR(1) R 2 = −0.4;

(11.7)

(10.1)

(0.0)

(0.8)

(5.8) DW=2.4

Regressing real GDP on nominal M1 (rather than M1) also does not change the results: the relationship between the two variables remains statistically insignificant. Additional testing was done and results are shown in Table 10.2, which provides information on the relationship of the real M1 to the real GDP in five different sample periods. Notice that in Table 10.2, there was no statistically significant correlation between real GDP and real M1 in any of the three periods sampled between 1960 and 2007; hence, we

212

J. J. HEIM

do not repeat those tests here. Only when the unprecedentedly large FR purchases of securities 2008–2010 took place do we see a positive, statistically significant correlation, and even here, the direction of causation is not clear. Conclusions Regarding Simple Tests In models with autocorrelation controls, there appears to be no relationship whatsoever between growth of M1 and growth of GDP in the 1960–2010 period, except for possibly the 2008–2010 period of massive purchases of securities by the Fed. Most of the increase in reserves created by the monetary stimulus of 2008 just stayed parked in bank demand deposit accounts and in increased currency in circulation (“pocket cash”). This suggests some major “pushing on a string” effect of these security purchases by the Fed during the “great recession” era.

11.2

More Sophisticated Tests of the Effects of FR Security Purchases on Real GDP

The simple models above generally found no systematic relationship between M1 and GDP. But we cannot infer from that that there was no relationship underlying that between M1 and FR security purchases (one of many factors affecting the level of M1). To determine if a relationship between FR purchases and GDP exists, we directly tested the relationship of FR securities purchases to GDP. In the tests, three major factors thought to affect the real GDP, but which might be correlated with FR purchases, were controlled for, and therefore, if not controlled for, they might affect our estimate of FR purchases’ effect (the “omitted variables” problem). The three variables controlled for regressions of GDP on FR purchases were: recession (RECES) periods, the level of inflation (INFL), and growth in M1 for reasons other than increases in FR security purchases (M1 − (Tr + A)). The relationship of inflation to GDP was found to be positive and statistically significantly related to GDP when the average of the past two year’s inflation was used, an expected Phillips curve result. For the same reason, the total of FR purchases in the preceding two years was used (average values worked as well, but with twice as large a coefficient). As we have indicated in the methodology section (Chapter 4), when theory suggests a relationship between a dependent variable and an explanatory variable, we include this potential explanatory variable in the regression model tested. If theory proves no guidance as to what level of lag to

11

213

EFFECT OF INCREASES IN LOANABLE FUNDS OR M1 ON THE GDP

use, our decision rule is to use the lag level most strongly affirming the theorized relationship. The third control variable included is a variable representing the level of real M1 minus FR purchases (M1 − (Tr + A)) to determine if there were separate effects for this endogenous portion of M1and FR purchases (Tr + A) itself. We used the same controls for other influences we used in Chapter 13 for testing the effects of FR purchases on the stock, bond, and mortgage markets. Certain variables were nonstationarity, but they were cointegrated with the dependent variable, so no detrending was required. The dummy variable used to denote recession years was endogenously related to the dependent variable (GDPreal ) and was replaced by a Wald-strong instrument, which itself was non-endogenous (Sargan test). Results for 1960–2009 period are shown in Eq. 11.8. (1960–2010 was not shown because it was the only period showing FR purchases not related to GDP). A wide variety of sample periods were tested to ensure initial results could be replicated. They are shown in Table 11.1. The three control variables were statistically significant in most samples. A first-order autocorrelation control was also required to keep the Durbin–Watson serial correlation coefficient close to the no autocorrelation level of 2.0.   GDPreal= 215.26 − 195.769(RECES) − 29.81 INFLav−1,−2 (t=)

(9.2)

(−2.7)

(−2.3)

+ .98(Tr + A)av−1−2 − .36(M1 − (Tr + A)av−1−2 ) (2.3)

(−1.2)

+ AR(1) R = 68.0% 2

D.W. = 2.0

MSE = 95.92

(11.8)

Some macroeconomic theories assert that accommodative monetary policy should, by providing additional loanable funds to offset crowd out, allow stimulative fiscal policy to have a positive stimulus effect on the GDP. Demand-driven, Keynesian-type models particularly emphasize this (Mankiw 2010; Mishkin 2007). However, theory is not clear as to whether it is the nominal or real changes in FR purchases (or the money supply) that capture best the extent to which the monetary policy offsets crowd out. Table 11.1 tests a number of options given in columns numbered: 1. FR purchases (Tr + A) is nominal; M1 is real in the expression (M1real −(Tr + A)nom

Coef.

Time periods tested in Heim (2017) 1960–2010 M1a −.26 (−0.6) −.21 1960–2010 FR −.05 (−0.1) .00 1960–2009 M1a −.36 (−1.2) −.34 1960–2009 FR .98 (2.3) 1.01 1960–2000 M1a −.52 (−1.4) −.45 1960–2000 FR 2.19 (2.0) 1.62 1970–2000 M1a −.55 (−1.2) −.49 1970–2000 FR 1.89 (1.2) 1.17 1970–2009 M1a −.36 (−1.1) −.31 1970–2009 FR .93 (2.0) .94 FR significant in (3 of 4) (2 of 4) Other time periods tested that included the 1960s 1960–2008 M1a −.38 (−1.3) −.39 1960–2008 FRa 1.39 (2.3) 1.28 1960–2007 M1a −.48 (−1.5) −.47

( t-stat.)

Coef.

Sampled

(0.1) (0.5) (−0.8) (2.5) (−1.4) (2.4) (−1.3) (1.5) (−1.0) (1.9)

(−1.0) (2.6) (−1.4)

−.40 1.50 −.64 (−1.2) (1.7) (−1.4)

( t-stat.)

.08 .27 −.32 1.08 −.66 2.39 −.71 2.13 −.41 .93 (3 of 4)

Coef.

FR Purchasesnom

M1nom − (Tr + A)nom

(3)

(−0. 5) (0.0) (−1.1) (2.2) (−1.1) (1.2) (−1.1) (0.7) (−0.9) (1.8)

( t-stat.)

FR Purchasesreal

M1real − (Tr + A)real

M1real − (Tr + A)bnom

FR Purchasesnom

(2)

(1)

Period

Option#

−.40 −1.90 −.64

.09 .19 −.80 1.40 −.66 3.04 −.72 2.85 −.41 1.34 (4 of 4)

Coef.

(−1.1) (3.5) (−1.4)

(0.1) (1.4) (−0.8) (4.3) (−1.4) (3.4) (−1.3) (2.4) (−1.0) (3.9)

( t-stat.)

FR Purchasesnom

M1nom +(Tr + A)nom

(4)

Table 11.1 Marginal effects of changes in FR Purchases and (M1 − FR Purchases) on Real GDP

214 J. J. HEIM

1.28 (−2.0) 1.18 −.003 (−0.0) .19 3.19 (2.4) 2.21 .82 (0.9) .46 5.19 (1.7) 1.55 (3 of 4) (0 of 4) tested that started in 1970 or later −.55 (−1.2) −.49 1.89 (1.2) 1.17 −.46 (−1.3) −.44 1.09 (1.4) .81 −.58 (−1.7) −.58 .79 (1.6) .86

Coef.

1960–2007 FR 1960–1990 M1a 1960–1990 FR 1960–1980 M1a 1960–1980 FR FR significant in Other time periods 1970–2000 M1a 1970–2000 FR 1970–2007 M1a 1970–2007 FR 1980–2009 M1a 1980–2009 FR

( t-stat.)

Coef.

Sampled (−2.2) (−0.1) (2.5) (0.8) (0.2)

(−1.3) (1.5) (−1.5) (1.5) (−1.8) (1.2)

−.71 2.13 −.72 1.10 −.73 .66

(−1.1) (0.7) (−1.1) (0.8) (−1.7) (1.9)

( t-stat.)

1.36 −.05 3.20 4.25 .68 (3 of 4)

Coef.

FR Purchasesnom

M1nom − (Tr + A)nom

(3)

(−1.5) (0.4) (1.4) (0.5) (0.9)

( t-stat.)

FR Purchasesreal

M1real − (Tr + A)real

M1real − (Tr + A)bnom

FR Purchasesnom

(2)

(1)

Period

Option#

−.72 2.85 −.72 1.82 −.73 1.38

1.99 −.05 3.25 4.25 −3.57 (3 of 4)

Coef.

(continued)

(−1.3) (2.4) (−1.5) (2.8) (−1.8) (4.0)

(3.6) (−0.1) (2.0) (0.8) (−0.4)

( t-stat.)

FR Purchasesnom

M1nom +(Tr + A)nom

(4) 11 EFFECT OF INCREASES IN LOANABLE FUNDS OR M1 ON THE GDP

215

−.61 .74 −.64 1.50 (0 of 5) (−1.6) (0.8) (−1.4) (0.8)

−.64 .76 −.71 .66 (1 of 5)

Coef.

For n = 20, t = 2-> p = .06; For n = 30, t = 2->p=.04 a Defined as M1 real − FR Purchases b AR(1) Autocorrelation control used. In all models

1980–2007 FR 1980–2000 M1a 1980–2000 FR FR significant in

1980–2007 M1a

( t-stat.)

Coef.

Sampled (−1.7) (0.8) (−1.5) (0.4)

( t-stat.)

FR Purchasesreal

M1real − (Tr + A)real

M1real − (Tr + A)bnom

FR Purchasesnom

(2)

(1)

Period

Option#

Table 11.1 (continued)

−.95 .54 −.97 1.08 (0 of 5)

Coef. (−1.9) (0.6) (−1.6) (0.6)

( t-stat.)

FR Purchasesnom

M1nom − (Tr + A)nom

(3)

−.95 1.49 −.98 2.06 (4 of 5)

Coef.

(−1.9) (1.9) (−1.6) (1.2)

( t-stat.)

FR Purchasesnom

M1nom +(Tr + A)nom

(4)

216 J. J. HEIM

11

EFFECT OF INCREASES IN LOANABLE FUNDS OR M1 ON THE GDP

217

2. FR purchases (Tr + A) is real; M1 is real in the expression (M1real −(Tr + A)real 3. FR purchases (Tr + A) is nominal; M1 is nominal M1 in the expression (M1nom −(Tr + A)nom 4. FR purchases (Tr + A) is nominal; M1 is nominal in the expression (M1nom + (Tr + A) 11.2.1

Summary of Table 11.1 Findings

In Heim (2017), four different time periods were tested; the initial results were obtained by testing the full data set 1960–2010. Attempts were made to replicate these initial results in tests of three other, but overlapping time periods 1960–2000, 1970–2000, and 1970–2010. Because there were only a maximum of 50 observations, testing totally separate samples (1960–1984 and 1985–2010) would have limited the number of replication tests to 1, and both the initial test and the replication test would still have only marginally adequate numbers of observations, leaving open the possibility that variables could be found non-significant simply because of the limited number of degrees of freedom in the two data sets. The findings for samples which included the 2010 year were markedly different from all others. To avoid distortions of the long term, 50-year average effect by the inclusion of one “outlier” year’s data, our initial test was done with 1960–2009 data. Once initial results were obtained, attempts were made to duplicate the results in three other time periods: 1960–2000, 1970–2000, and 1970–2009, the same replication periods used in Heim (2017). Tests of all four nominal and real combinations of M1 − (TR + A) and (Tr + A) in (just) these four time periods (16 tests total) indicated that 12 found changes in the (TR + A) variable positively related to changes in GDP at statistically significant levels. The only one of the four ways of specifying the FR purchases variable in the tests that didn’t prove statistically significant was option #2, which used real (Tr + A) rather than nominal (Tr + A). Real (Tr + A) was not found significantly related to real GDP in any of the four time periods tested. By comparison, using nominal (Tr + A) alone (option 1), using it as well as the variable nominal (M1 − (Tr + A)) (option 3), or using it with the separate variable nominal (M1) (option 4), all led to findings of a positive statistically significant

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relationship between increases in nominal FR purchases and increases in GDP. Ray Fair (2004) also found nominal values of some variables than real values. He found nominal interest rates better explained some real economic variables than real rates. Fair’s and this study’s findings suggest more work is needed unraveling the reasons why some variables are better measured in nominal, not real terms, but that investigation will be too large to be included in the scope of this study. The results above present evidence from 4 sample periods that FR open market purchases policy may affect the GDP, but we only controlled for two other variables (inflation and unemployment) which can influence real GDP when testing for FR and endogenous M1 effects. A large number of the real determinants of GDP, including those you would find in a typical Keynesian IS curve, were not controlled for. Some of them may be sufficiently correlated with recessions or inflation, to influence the coefficients and statistical significance of (M1 − Tr − A) and (Tr + A). Hence, with these limited control models, we can’t be certain of our findings. This may explain our unusual finding of real GDP being more strongly associated with nominal than real values of some variables. Generally, we find real values of some variables to be better associated with real values of another (and possibly the same for nominal on nominal), but this is not the first time this type result has been observed. In four additional time periods tested starting in 1960 and ending in 1980, 1990, 2007, and 2008, 9 of 16 tests found FR securities purchases significantly related to the GDP. However, in 20 other tests starting in either 1970 or 1980, and including data for various periods up through 2009, only 5 out of 20 were statistically significant. Four of those were nominal values. Hence, overall it is not clear how strongly FR purchases are related to the GDP. When samples included the 2010 data, and tested for the whole 1960– 2010 period, results for the effects of FR purchases were noticeably different: FR purchases were found to have a statistically insignificant impact of GDP. This was probably because banks were keeping the increases in loanable funds (excess reserves) rather than lending them out, either because they raised borrowing standards, or because they panicked about the country’s economic situation and worried about what the future might bring, people were afraid to borrow.

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EFFECT OF INCREASES IN LOANABLE FUNDS OR M1 ON THE GDP

219

In almost all tests, the effects of changes in M1 on GDP in Table 11.1 had a negative sign and were statistically insignificant. The lack of significance was also found in Heim (2017) in 15 of the 17 categories of consumption and investment tested using the 1960–2010 50 year sample period, and in tests of GDP as a whole. Consumption and investment account for roughly 85% of GDP over the 50-year period. In Table 11.1, for 44 of 52 tests, the M1 variable was not found to have a statistically significant relationship to GDP. In the other eight, it was only found marginally significant (t-statistics between 1.6 and 1.9), but with a negative relationship between M1 and GDP. In fact, in 45 of the 52 tests, the M1 variable had a negative sign characterizing its relationship with GDP. Hence, to the extent we can consider this model credible given its shortage of control variables, it seems to be saying it is not clear FR securities purchases or M1 are associated with increased GDP. To the extent M1has any effect at all, it seems to be negative. This may simply mean the FR is more likely to purchases securities during downswings in the economy in an attempt halt or reverse the decline.

The finding that there was a positive relationship between growth in real GDP and growth in the (nominal) value of FR purchases may underlie an other study’s findings regarding M1’s effect on housing investment and spending on consumer services. Heim (2017) found that changes in the real M1 money supply, while not generally related to most types of investment and consumption, were positively related to changes in real residential housing investment in the year of the M1 increase, and to real increases in spending on consumer services two years later. No relationship to larger aggregates, such as real GDP, total consumption, or total investment, was found. Findings of this study in Chapter 10, Table 10.2, also indicated a positive, statistically significant relationship of real FR securities purchases and real M1. This might, based on Heim (2017), imply a positive effect of changes in FR purchases, by affecting the M1 money supply, on housing demand and consumer services demand two years later. Without further testing, we can’t be sure that it is the variation in M1 caused by variation in FR securities purchases, or the variation in other factors that endogenously change M1 as the economy changes, that is the cause of variation in real M1 related to changes in these two markets.

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J. J. HEIM

Other tests, reported further below, do indicate both real and nominal FR purchases, when separated out of M1 and tested separately, fail to show a statistically significant, positive relationship to residential housing investment except when the QE years 2009 and 2010, when FR purchases were unusually high. The two-period lagged values of real consumer spending on services were found significantly related to real FR purchases in some periods, but not others. However, a significant, positive relationship of both nominal Lagged FR purchases and nominal lagged (M1 − FR purchases) to consumer services spending was found for all periods tested.

11.3 Testing Housing and Consumer Services Demand for Sensitivity to FR Securities Purchases We retested the residential investment and consumer services spending models to determine if real FR securities purchases (Tr + A) and/or real M1 net of FR purchases (M1 − (Tr + A)) has statistically significant impacts on housing or consumer services demand. We are using the assumption M1 increases on a dollar-for-dollar basis when (Tr + A) increases, but realize this is just a rough approximation. The models retested here are the same models found in Heim (2017), Eqs. 4.12TR and 5.11TR except for a modification of how loanable funds growth was used to offset crowd out effects of tax cut deficits. This study’s results are shown in Eqs. 11.9 and 11.10 for the 1960–2010 sample period. Both the FR purchases and revised M1 variables were found statistically significant in the housing investment model and in the consumer services models for this time period, but significance levels varied when we attempted to replicate these results in others. IRes = .04(Y − T ) + .06(TT ) − .08(G T&I ) − .17DJAV−1−2 (t =)

(2.1)

(−2.3)

(2.5)

(3.0)

− 6.65PRAV(0,−1) + 1.61XRAV(−2−4)+ .14CB2 (−1.9)

(2.0)

(5.8)

+ .38(Tr + A)− + .39(M1 − (Tr + A)) (3.1)

(3.0)

− .001(HouseP/Inc)Real (−5.4)

R = 87.0% 2

D.W. = 2.2MSE = 20.82

(11.9)

11

221

EFFECT OF INCREASES IN LOANABLE FUNDS OR M1 ON THE GDP

  CSer = .22(Y − TT ) + .26(TT −S ) + .13 G T&I−S − 8.21PR (t=)

(3.6)

(7.1)

(−2.9)

(2.1)



+ .12DJ−2 + .51(Tr + A)−2 + .13 M1−2 − (Tr + A)−2 (0.6)

(3.1)



(1.8)

+ .06(M2 − M1)−2 + .012POP (1.0)

R 2 = 77.9%

11.3.1

(1.9)

D.W. = 2.0MSE = 30.44

(11.10)

Housing Investment Effects

The real FR purchases variable was found highly significant in the real housing investment equation for the sample periods 1960–2009 and 1960–2010 data, but statistically insignificant for samples including data for only 1960 to 1980, 1990, 2000, 2007, and 2008. This suggests changes in FR purchases must be huge to impact the real economy, likely because of leakage problems discussed in Chapter 7 (use of investment banks and foreign banks). The revised real M1 variable (M1 − (Tr + A)) was found highly significant in the housing investment equation for all sample periods. This suggests that with the exception of the QE years, it was mostly the endogenous component of M1 growth that stimulated the housing market, not the FR purchases component. The same results for both variables were obtained using nominal, not real, values of the data. This may not necessarily contradict our earlier model’s results, which were shown in Table 11.1. In Table 11.1, results indicated nominal FR purchases were positively related to changes in GDP, but the endogenous part of M1, i.e., (M1 − Tr − A), was not. Here, we find that growth in the endogenous part of M1 is related to a relatively small part of GDP: housing in all sample periods, a result that just gets swamped by the insignificance of the variable generally as a determinant of most of GDP. FR purchases were significantly related to housing in samples containing 2009 and 2010 data only, but insignificant in all other periods. Again, the insignificance of FR purchases before 2008 is swamped by its significant relationship to the larger other parts of GDP. The effects of both endogenous M1 growth and FR purchases on this relatively small part of GDP (housing) because of this “swamping effect” in the total GDP comparison show results which were the opposite, but not contradictory.

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J. J. HEIM

11.3.2

Lagged Consumer Services Spending Effects

When the FR purchases variable and the endogenous M1 variable are measured in nominal terms in the two-year lagged consumer services demand model, the FR purchases variable was significant or marginally statistically significant in all seven periods tested; the endogenous M1 variable was also significant in six of the seven (except 1960–2007). Here again, as in Table 11.1, we see evidence of demand reacting more to nominal than real changes. When the FR purchases variable and the endogenous M1 variable are measured in real terms in two-year lagged consumer services models, the FR purchases variable was found positively and significantly related in the 1960–2007, 2008, 2009, and 2010 samples (mostly samples including the huge QE increases in FR purchases plus one sample including the fairly large increase in response to the 2001 recession). Results for the three samples before that, covering the 1960–2000 period, were not significant. The endogenous M1 variable was significant in three of the time periods tested: 1960–1990, 1960–2000, and 2010. And insignificant in the other four samples. Since the total real M1 variable, not broken into these two separate parts, was significant in a number different time periods in the Heim (2017) study, these results suggest that real FR purchases combined with real endogenous changes in M1 have a statistically significant effect on spending on consumer services after a two-year lag, but that neither component alone consistently has a significant effect. 11.3.3

Total Consumer and Investment Spending Effects

When retested on total consumption, following the model given in (Heim 2017, Eq. 4.1.T.TR), results were similar to those obtained for consumer services alone, no doubt because consumer services constitute so large a portion of total consumption. When nominal values of two-period lagged FR purchases and M1 − FR purchases were used, the nominal FR purchases variable was significant in 6 of the 7 test periods (only the 1960–1990 result was insignificant). The endogenous M1 variable was insignificant in all tests. When real values of FR purchases and the revised M1 variable were used in models of total consumption, FR purchases were significant in only the three QE period samples (1960–2008, 2009, and 2010) and marginally significant in 1960–2007, but insignificant in the earlier three

11

EFFECT OF INCREASES IN LOANABLE FUNDS OR M1 ON THE GDP

223

tests (1960–1980, 1990, and 2000). The lagged revised M1 statistic (M1 − FR purchases) was again insignificant in all periods. In Heim (2017), where all samples showed total real M1 insignificant, only the current value of M1 was tested. When tested here in lagged form, it also shows insignificant. The unexpected strength of nominal vs. real values of FR purchases on consumption is similar to Ray Fair’s large-scale econometric model testing, which found nominal values of interest rates more related to consumption and investment spending (Heim 2017 found real interest rates explained consumption better than nominal rates, but not investment). As noted earlier, the stronger relationship of these nominal variables to other real dependent variables needs to be examined, but is beyond the scope of this study, which is already very large. We conclude that nominal changes in FR purchases, after a two-year lag, seem generally to have a positive effect on total consumption, but when measured in real terms, only the huge FR purchases of the QE years had an effect, endogenous growth had none. Results for two-year lagged real endogenous M2 (M2 exclusive of the FR purchases component) were consistently found to have no significant effect on current year or lagged consumer spending. The same was true of nominal M1 exclusive of FR purchases. To provide an example of the total consumption model tested, results obtained tested using 1960–2010 sample period are shown in Eq. 11.11. C T = .43(Y − TT ) + .35(TT ) + 20(G T&I ) − 9.80P R (t = )

(5.9)

(5.8)

(1.8)

(−3.9)

+ .45 D J −2 + .006P O P + .57I CC −1 + 25.44M2AV (1.0)

(2.3)

(2.0)

(2.1)

+ .16 CB2 + .70 (Tr + A−2 )+ .03 (M1 − (Tr + A))−2 (5.1)

R = 92.6% 2

(5.5)

(0.5)

D.W. = 2.1MSE = 31.64

(11.11)

We also retested the Heim (2017) model of total investment to see if real and nominal FR purchases and endogenous M2 were systematically related to total investment in different time periods. Real FR purchases were only significant in 1 of the 7 periods tested: 1960–2008. Real endogenous M1 was not significant in any of the seven periods. When the two real components were combined into total real M1, only the 1960–1980 sample was significant. We conclude that

224

J. J. HEIM

changes in real M1 or its two components have generally not had a positive impact on total investment. Results for this model for the 1960–2010 sample are shown in Eq. 11.12. As determinants of total investment, nominal FR purchases were statistically insignificant in six of seven samples (1960–1990 was the exception). Nominal modified M1 was also statistically insignificant in six of seven samples (1960–1990 was the exception). IT = + .22(ACC) + .18(TT ) − .11(G T&I ) − 6.32PR−2 (t=)

(3.2)

(5.9)

(−1.4)

(−3.7)

+ .23DJAV + 3.25XRAV + .62DEP (3.1)(3.1)

(2.3)

(2.0)

− .03(Tr + A) + .03(M2 − (Tr + A)) (−0.3)

R = 95.5% 2

(0.4)

W. = 1.8 MSE = 25.74

11.3.4

(11.12)

Full GDP Effects

Finally, we also examined the full GDP determination (“IS”) curve model given in Heim (2017) Eq. 7.2.1. Results for the 1960–2010 sample period are given in Eq. 11.13. Findings indicated that real FR purchases, as a stand-alone variable, were positively and significantly related to GDP only in the 1960–1990 and 2000 samples, but not in the four more recent periods sampled, covering data from 1960 to various parts of the period 2001–2010 (1960–1980 could not be tested due to small sample size given this large model). However, when we tested the two parts combined, i.e., real M1 as a single variable, we obtained different results. In the 1960–2009, or 1960–2010 samples, the coefficient on this variable was statistically significant and indicated a rise in real FR purchases (or real M1) increased the GDP by 21 cents. (The same coefficient found in the residential housing model.) YT = .33(TT ) + .05(G T&I ) − .00 DJ−0 + .72 DJ−2 (t=)

(4.0)

(0.2)

(−0.0)

(2.7)

− 3.43 XRAV − 262.92POP16 + 028POP + 1.39ICC−1 (−0.9−)

(0.9−)

(5.8)

(3.3)

+ 42.64 M2AV + .51(ACC) + 2.58DEP + 3.31CAP−1 (2.8)

(11.3)

(5.6)

(1.1)

11

EFFECT OF INCREASES IN LOANABLE FUNDS OR M1 ON THE GDP

225

−3.15PR − 2 + .27PROF − 0 + .06(CB2 + (IB(−1)) (−0.7)

(2.8)

(0.9)

− .78(X − M) + .04(Tr + A) + .11(M1 − (Tr + A)) (−3.4)

(0.3)

(1.2)

+ .87 AR(1) − .89 AR(2) (−4.6)

(5.1)

R = 96.8%

D.W. = 2.0 MSE = 36.86

2

(11.13)

And the same model except FR purchases and M1 revised is consolidated into one variable: real M1 YT = .38(TT ) − .03(G T&I ) − .03DJ−0 + .45DJ−2 (t=)

(3.8)

(−0.2)

(−0.2)

(1.8)

− 2.58XRAV − 211.27POP16 + .026POP + 1.36ICC−1 (−0.6−)

(4.6)

(−0.7−)

(3.8)

+ 36.35M2AV + .53(ACC) + 3.10DEP + 4.91CAP−1 (2.1)

(16.4)

(5.6)





(1.7)

− 3.53PR−2 + .25PROF−0 + .02 CB2  IB(−1) (−0.8)

(2.9)



(0.4)

− .82  (X − M) + .21  (M1) + .73 AR(1) − .94 AR(2) (−3.5)

R = 96.6% 2

(2.6)

(4.5)

D.W. = 1.9 MSE = 37.45

(5.0−)

(11.14)

When testing the two-year lagged values of FR purchases and endogenous M1, only the lagged real FR variable proved statistically significant, and mainly in the QE samples including 2008, 2008, and 2009, or 2008 + 2009 + 2010 data, and in the 1960–1990 sample, but not in the 1960–2000 or 1960–2007 samples. The coefficient on this variable was statistically significant and indicated a rise in real FR purchases (or real M1) increased the real GDP by 75–80 cents. Nominal values of FR purchases were not significant. In general, we prefer to construct IS models arithmetically from the coefficients on the previously estimated consumption and investment equations to avoid two types of distortions of coefficients common to models with large numbers of variables and representing combinations of other equations (like the consumption and investment equations): (1)

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J. J. HEIM

multicollinearity and (2) adding together the coefficients of variables that are spurious in one of the equations being combined and truly related to the dependent variable in another (e.g., depreciation).

11.4

Summary of Results and Conclusions

Conclusion: 0 of 7 simple models show any significant systematic relationship between GDP and M1’s growth rate or level. Technically, 2 of the 7 have significant findings but not for substantive reasons; they occur as the result of autocorrelation between the two variables. They became insignificant when a first-order autocorrelation control was included in the model (Table 11.2). 1 of 4 models with a number of control variables, taken from Chapter 10, shows a significant relationship between real GDP and real total M1. The one positive result was for the time period that included huge increases in M1 in 2008–2010 related to the QE program. Tests of Models of M1’s Two Parts on GDP in Models With Only Two Control Variables All models test for the effects of Federal Reserve securities purchases (TR + A) and either M1 − (Tr + A) or M1 + (TR + A) on GDP controlling for just two factors, inflation and unemployment (Table 11.1).

Simple tests Nom GDPL = ƒ(M1) Nom GDPL,C = ƒ(M1) Nom GDPL = ƒ(M1)AR Nom GDPL = ƒ(M1)AR Nom GDP = ƒ(M1) Nom GDP = ƒ(M1−1 ) Nom GDP = ƒ(M1) Real M1 = ƒ (GDP)CV 1/1 (Incl.3QEyr) Real M1 = ƒ (GDP)CV Real M1 = ƒ (GDP)CV Real M1 = ƒ (GDP)CV

Model

64.74(7.3) 28.42(5.5) .22(0.8) .22(0.8) .29(0.0) .01(0.0) .22(0.8) .06(2.0)

.01(0.1) .03(0.7) .03(0.9)

Equation 11.2

Equation 11.3

Equation 11.4

Equation 11.5

Equation 11.6

Equation 11.7

T.10.2

T.10.2

T.10.2

T.10.2

β( t)

Equation 11.1

Source











39



−.04 .37





−02 00









1960–2007

99

99

42

−02

1960–2010

R2 in (4) time periods

Table 11.2 Chapter 11 summary table: Effects of changes in M1 on GDP



69

















1970–2000

63





















1960–2000

0/1

0/1

0/1

0/1

0/1

0/1

0/1

0/1

1/1

1/1

Periods significant

11 EFFECT OF INCREASES IN LOANABLE FUNDS OR M1 ON THE GDP

227

M1real − (Tr + A)nom FR Purchasesnom M1real − (Tr + A)real FR Purchasesnom M1nom − (Tr + A)nom FR Purchasesnom M1nom + (Tr + A)nom FR Purchasesnom

Sample period

NS S NS S NS NS NS S

2009

2010

NS NS NS NS NS NS NS NS

1960–

1960– NS S NS S NS S NS S

1960– 2000 NS NS NS NS NS NS NS S

2000

1970– NS S NS S NS S NS S

1970– 2009 NS S NS S NS S NS S

1960– 2008 NS S NS NS NS S NS S

2007

1960– NS S NS NS NS S NS S

1960– 1990 NS S NS NS NS NS NS NS

1980

1960–

Significance of (FR) or M1 Changes in M1 in (13) Time Periods

NS NS NS NS NS NS NS S

1970– 1990 NS NS NS NS NS NS NS S

1970– 2007 S S S S S NS S S…S

1980– 2009 S NS S NS S NS S NS

1980– 2007 NS NS NS NS S NS S

2000

1980–

Total 2/13 7/13 2/13 5/13 3/13 5/13 3/13 11/13

significant

228 J. J. HEIM

11

EFFECT OF INCREASES IN LOANABLE FUNDS OR M1 ON THE GDP

229

Conclusions: Results with all four models indicate that FR purchases are more often significantly related to changes in the GDP than are changes in the non-FR purchases part of M1 (models 1–3), or M1 plus FR purchases. This may mean endogenous changes in M1 simply do not often cause GDP to change (or may result from, not cause, GDP change). Generally, even the frequency with which changes in GDP seem related to changes in M1 is not impressive. The first three models explicitly test whether variation in M1 is caused by either variation in FR purchases or variation in endogenous M1. The results for both the first three models were dismal; considerably less than half the periods tested showed any significant effect. For the 4th model, results show increases in FR purchases almost always significantly associated with increases in GDP. But, the real causes of these difficult to explain results may be that our models may be bad. They test for the effect of FR purchases and M1 while controlling for any effect of inflation or unemployment. Wouldn’t changes in FR purchases or M1, if they did affect GDP, usually also change either the unemployment and/or the inflation rates? If so, by controlling for these variables, we are essentially already controlling for most changes in the GDP when we test for whether FR purchases or M1 cause changes in GDP. If this is the case, we should generally find the two test variables insignificantly related to GDP. Tests of Housing and Consumer Services Demand, Consumption, Investment and GDP With Many Control Variables All models test for the effects on GDP of Federal Reserve securities purchases (TR + A) and either M1 − (Tr + A) or M1 + (TR + A), while controlling for a large number of other variables found to influence housing, or consumer services purchases as well as total consumption, total investment, and the GDP. Seven periods were tested including part or all of the period between 1960 and 2010.

1960–2010

Residential housing investment M1real − (Tr + S A)real FR Purchasesreal S M1nom − (Tr + S A)nom FR Purchasesnom S

Sample period

S NS S NS

S S S

1960–2008

S

1960–2009

Significance in (7) time periods

NS

NS S

S

1960–1907

NS

NS S

S

1960–2000

NS

NS S

S

1960–1990

NS

NS S

S

1960–1980

2/7

2/7 7/7

7/7

Total significant

230 J. J. HEIM

11

EFFECT OF INCREASES IN LOANABLE FUNDS OR M1 ON THE GDP

231

Conclude: Residential housing investment is significantly related to changes in M1, but mostly just endogenous (business cycle) changes. Real and nominal changes yield same results. 2-Lagged Consumer Services Purchases M1real − (Tr + A)real FR Purchasesreal

M1nom − (Tr + A)nom FR Purchasesnom

S S

NS S

S S

NS S

S S

NS S

S S

S NS

NS S

S S

NS NS

S S

S NS

3/7 4/7

S S

6/7 7/7

Conclude: Nominal increases in FR purchases most significantly related to lagged consumer services purchases. Total Consumption M1real − (Tr + A)real−2 FR Purchasesreal−2

NS S

M1nom − (Tr + A)nom−2 FR Purchasesnom−2

NS S

NS S

NS NS

NS S

NS S

NS S

NS S

NS NS

NS NS

NS NS

0/7 4/7

NS S

NS S

NS S

0/7 6/7

Conclude: Nominal increases in FR purchases lagged two years most significantly related to total consumer purchases. Total Investment M1real − (Tr + A)real FR Purchasesreal

M1nom − (Tr + A)nom FR Purchasesnom

NS NS

NS NS

NS NS

NS S

NS S

NS NS

NS NS

NS NS

NS NS

NS NS

NS NS

0/7 1/7

NS NS

NS NS

NS NS

0/7 1/7

Conclude: Neither nominal or real increases in FR purchases, or endogenous increases in M1 are significantly related to total investment in most cases.

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J. J. HEIM

Total GDP M1real − (Tr + A)real FR Purchasesreal Total M1real (Alone)

– NS S

– NS S

– NS NS

– NS NS

– S NS

– S NS

– – –

0/6 2/6 2/6

Conclude: Effect of M1 or FR purchases on GDP most often insignificant. The total M1 finding fits with past tests: significant during at least some QE years, but not otherwise. The common finding of insignificance for FR purchases is puzzling, in light of earlier findings showing FR purchases significant in same 6 of 7 tests of total consumption and investment (which constitute about 85% of the GDP, and 7 of 7 periods for lagged consumer services). The insignificance suggests this GDP finding is a result of statistical problem (multicollinearity), not reasonably interpreted as meaning there is no relationship between M1 and GDP. (Nominal GDP tests were not available) Hence, overall for Chapter 11 tests, we find housing investment significantly related to increases in both FR purchases and endogenous M1, real or nominal. Consumer services growth was also significantly related to both FR purchases and endogenous M1 growth, but only the nominal values of M1 growth. Total consumption and investment were systematically related to growth in the nominal values of FR purchases, but not to real values, and not at all to either real or nominal values of the endogenous part of M1. We only obtain these significant findings when testing full-blown structural models; models that fully control for all the other variables commonly thought to affect consumption and investment. Single variable test of the relationship of M1 to GDP almost always find the relationships statistically insignificant. This is also true of models that contain a few control variables, but ones are not clearly related to some well-accepted theory of consumption, investment, or the GDP’s determinants.

References Fair, R. (2004). Estimating How the Macroeconomy Works. Cambridge, MA: Harvard University Press. Heim, J. J. (2017). An Econometric Model of the U.S. Economy. Hoboken: Palgrave Macmillan. Mankiw, N. G. (2010). Macroeconomics (7th ed., Chapter 11). New York. Worth Publishers. Mishkin, F. (2007). The Economics of Money, Banking and Financial Markets. New York: Addison Wesley.

CHAPTER 12

Effect of FR Security Purchases and M1 on Stock, Bond, and Mortgage Markets

Literature reviewed in Chapter 2.B.1 and 2.B.2 indicates there is a broad consensus that the aggressive FR securities purchases program since 2008, referred to as the quantitative easing program (QE), led to increases in stock and bond prices. The extent, type, and quality of statistical modeling involved in these studies varied considerably. This chapter, through extensive statistical testing, evaluates whether this assertion is supported by the data for the 1960–2010 period using more comprehensive models of the relationship. It is not easy to test the hypothesis that FR open market purchases have affected the stock and bond markets. There is no generally accepted theory of what drives those markets. Hence, there is no general set of other variables to control for, which could also be simultaneously affecting those markets, when testing for the effects of FR security purchases. The variables used as controls for influences other than M1 on the stock and credit market tests below were selected heuristically; they seem reasonable and within the bounds of mainstream economic thinking, but it is certainly possible that others should be included as well. Three variables included as controls in this chapter’s models (GDP, inflation, and unemployment) are included as a way of controlling for factors that might simultaneously be affecting the stock and bond markets at the same time out test variable FR purchases varies. And we control for the overall level

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. J. Heim, Why Fiscal Stimulus Programs Fail, Volume 1, https://doi.org/10.1007/978-3-030-65675-1_12

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of the endogenous part M1 for the same reason. Most fluctuation in M1 is endogenous and determined by fluctuations in the economy, which effects the size of the money multiplier. Our interest in this chapter is to determine how much changes in FR security purchases affect the stock and bond markets.

12.1 Effect of FR Open Market Operations on the Stock Market For stock market prices, the model tested is whether year-to-year variation in nominal FR purchases of treasury and agency securities (Tr + A)N is related to variation in the NYSE composite average. The effect of FR purchases is tested while controlling for changes in the nominal M1 money supply (M1), the real GDP (GDPreal ), recessions, and inflation. Estimates of the model obtained from the full 1960–2009 data set available are given in Eq. 12.1. The model was tested in first differences and had no stationarity problems. M1 was found endogenously related to the dependent variable and was replaced by a Wald-strong instrument. The Sargan test was applied to ensure the instrument was not also endogenous. A first-order autocorrelation control was needed to eliminate autocorrelation effects on parameter estimates. Newey–West standard errors were used. NYSE = .95(Tr + A)N(av−1−2) − .55(M1)N + 08GDPreal (t=)

(−2.7)

(7.3)

(2.7)

− 5.14(INFL) + .99(RECES) + .30AR(1) (−2.0)

R = 66.8% 2

D.W. = 1.9

(0.1)

(2.9)

MSE = 32.67

(12.1)

Clearly, the nominal value of FR purchases had a highly statistically significant positive relationship with the NYSE composite index over the 50 years sampled. Results suggest that ceteris paribus, a $100 billion purchase of securities by the FR over the past two completed years was associated with a 95 point increase in the current year NYSE composite index. The significant results represent the effects of the very large FR debt securities purchases (QE) during those two years but not for the four samples testing the earlier 1960–2007 periods. For the two years, FR purchases had a significant positive effect on the stock market (2008– 2009); the relationship f FR purchases to M1 growth was negative.

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This reflected the tendency of the market to drop during those difficult economic years, even though M1 was growing. Much the same thing happened in the depression, when FR security purchases increased markedly, but the total M1 money supply still declined, see Table 12.1. (In 1960–2000 samples, the sign on total M1 was positive, but it was not statistically significantly related to the stock market index.) Table 12.1 clearly shows a highly systematic positive relationship between FR purchases and the stock market during the QE period, but not during earlier periods sampled, back to 1960. There was one exception, where the relationship was significant but negative. This we ignore as likely just a spurious aberration. These results provide substantial support for those who argue FR accommodative monetary policy during the QE period benefited the stock market. Other lag levels were tested beside the average of one- and two-year lags on the M1 and FR purchases variables. None explained as much variation in the data as the one- and two-year average lag used above. The results were also robust to significant model changes. When the recession and inflation variables were dropped from the model, and it was reestimated, results again indicated FR purchases had a statistically significant positive relationship to stock market prices in samples that included the QE years, but not otherwise. Nor did results change when two more Table 12.1 Marginal effects of FR purchases on the NYSE composite index

Period Key Samples 1960–2009a 1970–2009a 1970–2000a 1960–2000a Additional Samples 1960–2008 1960–2007 1960–1990 1960–1980 1970–2007 1980–2000

Sample Coefficient

Regression ( t-statistic)

.95 .90 −.15 −.00

(7.3) (6.2) (−0.3) (−0.0)

1.23 .79 .24 −1.30 .72 −.24

(2.1) (1.1) (0.9) (−2.1) (1.0) (−0.3)

a Sample periods tested in (Heim 2017a, b), except 2010 used in

first two samples instead of 2009

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variables (10-year treasury yields and lagged inflation) were added to the model above. The time - period, lag level, and model change tests provide the reader with a level of confidence in this book’s results they would otherwise not have if just one model, with one set of lags, for one time period had been tested. These robustness tests also provide a way of determining if our results can be replicated. The ability to replicate is a requirement of good science.

During 2008 and 2009, the NYSE composite index declined about 2200 points; FR purchases of treasury, agency, and mortgage-backed securities were $2,343 billion. Using the .95 marginal effect figure cited above, this suggests, FR purchases had an upward effect on the market of 2,226 points at the same time it was in this overall decline. This suggests that without these FR purchases, the market would have declined and additional 4,426 points during this period. Hence, the evidence suggests the QE program had a major effect in slowing the downward trajectory of the stock market during this period. Alternative Model Yielding Same Results The tests above used (Tr + A)Av(−1,−2 ) and M1total as hypothesized monetary determinants of stock prices. But a slightly different model provides a more consistent, less ambiguous way of dividing the exogenous and endogenous parts of the M1 for separate analysis might be more useful. It may be a less ambiguous way of examining in the same model the separate effects of both FR purchases and (M1total − (Tr + A)), the endogenous component of the money supply. Test results in detail are shown for the 1960–2009 period in Eq. 12.2 and for the monetary variables for 11 test periods in Table 12.2. NYSE = .41(Tr + A)N(av−1−2) − .54(M1 − (Tr + Aav−1−2 ))N (t =)

(−3.7)

(1.5)

+ 08GDPreal − 5.11(INFL) + .58(RECES) + .30AR(1) (2.4)

R = 66.8% 2

D.W. = 1.9

(−1.7)

MSE = 32.64

(0.0)

(2.0)

(12.2)

Notice that the sum of the two coefficients for (Tr + A) in each test are identical to the coefficient on (Tr + A) in Table 12.1 for the same period; for example, for the 1960–2009 period tested, the regression coefficients

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Table 12.2 Marginal effects of FR purchases on the NYSE composite index, controlling for endogenous changes to M1

Sample Period Key Samples 1960–2009a 1970–2009a 1970–2000a 1960–2000a Additional Samples 1960–2008 1960–2007 1960–1990 1960–1980 1970–2007 1980–2000

(Tr + A)

M1–(Tr + A)

Regression

Regression

Coefficient

( t-statistic)

Coefficient

( t-statistic)

.41 .38 −.03 +.11

(1.5) (1.3) (−0.1) (0.2)

−.54 −.51 .12 .09

(−3.7) (−3.4) (0.8) (0.5)

+.69 +.62 .47 −.72 .58 −.18

(1.3) (1.1) (1.5) (−0.9) (0.9) (−0.2)

−.54 −.16 +.23 .58 −.14 +.07

(3.6) (−0.8) (2.4) (1.5) (−0.7) (0.4)

a Sample periods tested in (Heim 2017a, b), except 2010 used in first two samples instead of 2009

on the two monetary variables in Table 12.2 were:   .41(Tr + A)N(av−1−2) − .54 M1 − (Tr + A) N = .41(Tr + A)N(av−1−2) − .54(M1)N + .54(Tr + A)N = .95(Tr + A)N(av−1−2) − .54(M1)N Hence, with this model, as well as the Eq. 12.1 model, we get the same results: only when the huge levels of expansion of FR security purchases during the 2008–2009 “QE” years are included in the sample do we find FR securities purchases have a significant positive relationship with stock market levels.

12.2 Effect of FR Open Market Operations on Bond and Mortgage Markets The effects of FR security purchases on interest rates for mortgages, 10year treasury bonds, and 30-year treasury bonds are shown in Eqs. 12.2, 12.3, and 12.4 below. Estimates are obtained from models containing the test variable FR purchases (Tr + A), as well as control variables for

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current period M1−(Tr + A), the NYSE Composite Index, GDP, a recession period dummy, and inflation. These variables are included to control for the separate effects of the business cycle on bond yields. A possible effect of the stock market index variable is related to portfolio shifting. It is commonly argued that a good stock market pulls money out of the bond market in search for the stock markets increasing returns. Hence, if there is such an effect of consequence, we should see a positive sign on the stock market index variables, indicating bond prices are declining (interest rates rising) in the rush to get out of the bond market and into the stock market. This study finds this theoretically expected positive sign on the stock market index variable, but in all samples found such portfolio shifting inconsequential in explaining interest rates, i.e., statistically insignificant. Tests indicated the mortgage interest rate and M1 were nonstationary but cointegrated. The mortgage market interest rate variable was also found endogenously related to the NYSE composite stock market index. The stock index variable was replaced with a Wald-strong instrument, which was not endogenous (Sargan test). Newey–West standard errors were used. To deal with autocorrelation and reduce multicollinearity, all variables were run in first differences except the recession year dummy variable. Durbin–Watson statistics indicated autocorrelation was within acceptable limits. Various lag levels for the M1 and FR purchases variable were tried, but current values explained the most variance and are used for that reason. The results indicate both FR purchases and endogenous changes in M1 changes are associated with a rise in mortgage prices, and in four of the five other time periods tested (see Table 12.3). If we revise our way of calculating the portion of M1 not attributable to current year FR purchases to reflect the money multiplier effect on any increase in FR purchases, the coefficient on the net M1 variable remains the same, as does its significance level, model R 2 , and the coefficients/t-statistics of all other variables. Money multipliers ranged from five to nine during 1960– 2010. Using a money multiplier of five, i.e., (M1−5(Tr + A)) raises the coefficient on the FR purchases variable from −.043 to −.21; changing the money multiplier to nine raises the coefficient to −.39. Subtracting two variables from the model, the recession and lagged inflation variables left the sign and statistical significance of the M1 and FR purchases

12

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Table 12.3 Marginal effects of changes in endogenous M1 and FR securities purchases on bond and mortgage market yields and stock market prices 30 year treasuryb

Period

10 year treasury

Sampled

Coef. ( t-stat.)

1960–2010 1960–2010 1960–2008 1960–2008 1960–2007 1960–2007 1960–2000 1960–2000 1960–1990 1960–1990 1960–1980 1960–1980

M1a FR M1a FR M1a FR M1a FR M1a FR M1a FR

−.02 −.09 −.02 −.09 −.02 −.09 −.02 −.10 −.04 −.16 −.04 .11

(−2.6) (−2.6) (−2.5) (−2.4) (−2.4) (−2.4) (−2.5) (−2.2) (−3.0) (−4.1) (−2.2) (1.5)

Mortgagec

NYSE index

Coef.

( t-stat.)

Coef.

( t-stat.)

Coef.

( tstat.)

−.01 −.03 −.01 −.04 −.01 . − .04 −.01 −.04 −.04 −.15 .05 .56

(−2.1) (−2.1) (−2.0) (−2.1) (−1.4) (−1.7) (−1.0) (−1.4) (−3.1) (−2.2) (1.8) (2.1)

−.01 −.04 −.01 −.05 −.01 −.06 −.01 −.06 −.01 −.08 .03 .12

(−4.4) (−4.5) (−5.6) (−5.6) (−4.1) (−5.2) (−3.4) (−4.2) (−1.0) (−3.8) (−1.7) (1.6)

−.55 .95 −.54 1.23 −.17 .79 .11 −.00 .23 .24 .58 −1.31

(−2.7) (7.3) (−2.6) (−2.1) (−1.3) (1.1) (0.7) (−0.0) (3.7) (0.9) (1.9) (2.0)

Except for the stock market model, no equations required an AR(1) control. p for n = 20, t = 2→= .06; For n = 30, t = 2→p = .04 a M1 is abbreviation for M1−TR + A for all credit market tests; M1 alone was only used for stock market b Starting year in samples tested was 1977 c Starting year for samples tested was 1964

variables the same. Multiple levels of lags on the FR purchases and net M1 variables were tried, but current period values explained the most variance, so they were used. Mort = + .97(RECES) + .001(DJAV) + .18(INFL−1 ) + .0003GDP (t=)

(2.9)

(0.4)





(2.6)

(0.7)

− .008 M1 − (Tr + A)av−2,−3 − .043(Tr + A)av−2,−3 (−4.5)

(−4.4)

R = 54.2% 2

D.W. = 1.8

M SE = 0.58

(12.3)

Tests indicated the 10-year treasury bond yield and M1 were nonstationary but cointegrated. The FR purchases explanatory variable was found endogenously related to the 10-year yield and replaced by a strong instrument. Newey–West standard errors were used. To deal with autocorrelation, all variables were run in first differences except the recession

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year dummy variable. Durbin–Watson statistics indicated autocorrelation was within acceptable limits. Various lag levels for the M1 and FR purchases variable were tried, but current values explained the most variance and are used for that reason. The results, which indicate 10year treasury bond prices, are positively related to changes in both FR purchases and the endogenous portion of M1. Results were replicated in four of five other time periods (see Table 12.3). If we revise our way of calculating the portion of M1 not attributable to current year FR purchases (endogenous M1) to reflect the money multiplier effect on any increase in FR purchases, the coefficient on the net M1 variable remains the same, as does its significance level, model R 2 , and the coefficients/tstatistics of all other variables. Using a money multiplier of five, i.e., (M1−5(Tr + A)) changes the coefficient on the FR purchases variable from −.043 to −.21; changing the money multiplier to nine raises the coefficient to −.39. Subtracting two variables from the model, the recession and lagged inflation variables left the sign and statistical significance of the M1 and FR purchases variables the same. Adding and subtracting variables can be an important way of seeing how reliable parameter estimates on important variables really are. It is a test for multicollinearity effects. If they are large, it means small changes in the model are likely to yield results on important variable significantly different than on the earlier model. However, the variables to add and subtract must be selected with care. Pulling out variables that are themselves major explanatory variables is likely to distort remaining variable parameter estimates, and major variables tend to move in unison with others. Removing them results in the regression trying to assign its variance to variables remaining in the equation, thereby distorting their values compared to the better specified model. Ideally, the variables added or subtracted to those have little effect on the dependent variable, making their multicollinearity effects within a well-specified equation most likely the only substantial effect of adding or subtracting them. Treas10 = .91(RECES) − .009(DJAV) + .18(INFL(−1)) + .002GDP (t=)

(1.7)

(−1.8)

(2.6)

(2.0)

− .02(M1 − (Tr + A)) − .09(Tr + A) (−2.8)

R = 39.6% 2

D.W. = 1.7

(−2.6)

MSE = 0.84

(12.4)

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Tests indicated the 30-year treasury bond yield and M1 were nonstationary but cointegrated. No endogeneity was found, so OLS was used. 30-year bond data was only available from the Economic Report of the President since 1977, so the sample periods tested and shown in Eq. 12.5 are limited to those covering periods between 1977 and 2010. Ordinary standard errors were used. To deal with autocorrelation, all variables were run in first differences except the recession year dummy variable. Durbin– Watson statistics indicated autocorrelation was within acceptable limits. In four of six periods tested, FR purchases pushed down interest rates (raised bond prices). For endogenous M1, it was three of six periods (see Table 13.2). Hence, there seems to be some positive tendency of M1 and FR purchases to be positively related to 30-year bond prices, but it is as not reliable as the effect was on the 10-year treasury bond. Lagged values of M1 and loanable funds were tested, but none explained as much variance as current period values of endogenous M1 and FR purchases. As was the case with the 10-year treasury tests, subtracting from M1 an estimated money multiplier effect time FR purchases leaves all statistics in the model unchanged, except for the coefficient on the stand-alone FR purchases variable, which increases fivefold when a multiplier of five is used and ninefold when a multiplier of nine is used. Subtracting two variables (current period inflation and lagged stock market average) from the model does not change the results for the net M1 and FR purchases variables, nor did adding two (10-year treasury lagged two years and inflation lagged one year). Treas30 = .63(RECES) − .003(DJAV(−1)) + .15(INFL) + .001GDP (t=)

(1.3)

(−1.6)





(1.6)

(1.6)

− .01 M1 − (Tr + A)av−2,−3 − .03(Tr + A)av−2,−3 (−2.1)

(−2.1)

R 2 = 10.0%

D.W. = 1.8

MSE = 1.34

(12.5)

Clearly, tests of the full 1960–2010 data set, which yielded the results in Eqs. 12.1–12.4, indicate that there is a statistically significant positive relationship between FR securities purchases and the stock and bond markets. Attempts to replicate the results were made in in five other, but overlapping time periods. Replication efforts indicated that only during the QE period were stock prices significantly affected by FR purchases. By comparison, both the 10-year treasury and the mortgage interest rates were systematically and negatively related to FR purchases in all

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periods tested, and hence, positively related to their prices. The 30-year treasury bond prices were found related to the level of FR purchases in some samples, but not others. For the bond and mortgage market tests, results for the M1 money supply (net of FR purchases) were the same as the FR purchases results: generally, where one was significant, both were significant; where one was insignificant, both were. There was only one exception to this rule. Results of replication efforts are shown in Table 12.3. Stock market results are taken from the previous section (12.1). Overall, the sample results indicate that increasing the money supply is associated with increased asset values in mortgage and 10-year treasury markets, and in the stock market (but only) during the housing crisis in 2007 and QE in 2008–2009. Our findings support the conclusions of others cited in the literature review that FR purchases of securities have a consistently positive impact on credit market prices in most periods and also on stock market prices during the QE period.

12.3 Do FR Open Market Operations also Affect GDP Results above indicate FR open market operations to purchase government securities helped Wall Street. In the next section, we compare this result for Wall Street to how they affected “Main Street,” i.e., the real economy. This is done by presenting a brief summary of earlier finds on how these FR policies, and changes in the money supply in general, affected the GDP or its components. Our earlier tests (Chapter 10) found that during normal economic times, nominal FR security purchases were associated with M1 increases of about the same size. In recessions, there was no statistically significant relationship of such purchases to M1 growth. We assume this was because of the “pushing on a string” problem limiting the effectiveness of attempts at monetary expansion during recessions. Some, but not all, of the prior professional literature surveyed had the same findings. In the simplest models in Chapter 11, no relationship was found between M1 and GDP, except during the QE era. In the more sophisticated models tested in Chapter 11, where we controlled for inflation, depression periods, and changes in M1 (minus FR purchases), we did find nominal, but not real, changes in FR purchases of treasury and agency securities positively related to GDP.

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The most sophisticated tests in Chapter 11 used models from Heim (2017a) that found current year changes in parts of investment (Housing) were related to current period changes in M1, and changes in consumer spending on services (two years after the M1 change) related to changes in the M1 money supply. When M1 was divided into two parts, FR purchases and (M1−FR purchases), both parts were statistically significant and positively related to current year housing and two-year lagged consumer services. When total consumption was tested using the same sophisticated models, FR purchases were found systematically positively related to consumption. The rest of M1 was not found significantly related to total consumption spending. Neither part of M1 was found significantly related to total investment. The stock and credit market results discussed above provide an empirical basis for the somewhat commonly held opinions (in the business press ) that accommodative monetary policy (FR security purchases) during the QE period and in earlier periods helped increase prices in the bond and stock markets, i.e., “Wall Street.” But where the business press found they did not help “Main Street,” this study found they did via their effect on housing demand, consumer services demand, and total consumer spending. Effect of Stock and Bond markets on the GDP Changes in FR security purchases and M1 generally are systematically related to the federal funds and prime interest rates. There is empirical evidence changes in the FF and prime interest rates change bond rates, not vice versa (Heim 2009). Changes in interest rates affect the GDP though their impact on consumer and business spending. We also know from Heim (2017a) that stock market prices, as a measure of wealth, affect consumption, and as an imperfect proxy for Tobin’s “q,” also affect investment. We inserted a bond market variable into the standard stock market model tested earlier, to see if bond market changes affected the GDP indirectly, through their effect on stock prices. The theory was that perhaps increasing bond market prices caused an increase in stock market prices on the theory that general economic forces pushing up prices in one asset market would push them up in all (contagion): i.e., Stock Prices = f (Bond Prices, . . . + ..standard model variables . . .) The same model can also be used to test the alternative hypothesis that changes in bond prices cause portfolio shifting, i.e., rising bond prices

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divert money from the stock to bond markets, creating a negative relationship between prices in the two markets. The relationship between the NYSE composite index and the 10- and 30-year treasury bond rates, both current and lagged, was tested in six different time periods. No relationships were found that support either the contagion or portfolio shifting hypotheses. The investment and the consumer spending models in Heim (2017a) indicate the stock market positively affects them. However, in this study, our best models, in 11 periods sampled, only found a positive relationship between FR purchases and stock market levels in two samples, the huge securities purchases of the 2008–2010 period.

12.4

Summary of Findings and Conclusions

Models testing the effect of FR security purchases on the stock market (NYSE composite index) control for the GDP, inflation, and unemployment to control for variables that might simultaneously affect the stock and bond markets at the same time FR purchases vary. And we control for the overall level of M1 for the same reason. Most fluctuation in M1 is endogenous and determined by fluctuations in the economy, which effect the size of the money multiplier. But our interest in this chapter is to determine how much changes in FR security purchases actually affect the stock and bond markets. Tables 12.1 and 12.2 clearly show a highly systematic positive relationship between FR purchases and the stock market during the QE period, but not during earlier periods sampled, back to 1960. (There was one exception, where the relationship was significant but negative. This we considered mostly likely just a spurious aberration.) These results provide strong support for those who argue FR accommodative monetary policy during the QE period benefited the stock market. Credit Market Effects of a change in M1 were also tested. Estimates were obtained from models containing the current period FR purchases (Tr + A), as well as control variables for current period endogenous M1, i.e., (M1 − (Tr + A)), the NYSE composite index, GDP, a recession period dummy, and inflation. These variables are included to control for the separate effects of the business cycle on bond yields. A possible effect of the stock market index variable is related to portfolio shifting or contagion.

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Overall, the sample results show most tests indicate increasing the money supply is associated with increased asset values in mortgage and 10-year treasury markets. Our findings support the conclusions of others cited in the literature review that FR purchases of securities have a consistently positive impact on credit market prices. Therefore, we conclude FR purchases almost always are beneficial to 10-year treasury bond owners and mortgage lenders in terms of the capital value of their assets, and sometimes to 30-year treasury bond holders. FR open market operations seemed to help Wall Street. Effect of Stock and Bond markets on the GDP Changes in the FR security purchases change the federal funds rate, which changes the prime interest rate. Their changes change bond rates in the same direction, not vice versa (Heim 2009). Changes in bond market prices related to changes in their interest rates can affect the GDP though their wealth impact on consumer spending and their “Tobin’s q” effect on business investment spending. Hence, rising stock and bond prices resulting from initial FR security purchases do seem to have a positive impact on GDP.

References Heim, J. J. (2009, February). Which Interest Rate Seems Most Related to Business Investment. Journal of the American Society of Business and Behavioral Sciences, 5(1). Heim, J. J. (2017a). An Econometric Model of the U.S. Economy. Hoboken: Palgrave Macmillan. Heim, J. J. (2017b). Crowding Out Fiscal Stimulus. Hoboken: Palgrave Macmillan.

PART V

Does Crowd Out Really Occur?

CHAPTER 13

Does Crowd Out Really Occur? Initial Empirical Evidence: One Time Period

Heim (2017a) contains examples of “standard” economic models, based on an extensive survey of the last 50 years consumption and investment literature. “Standard” models are comprised of variables commonly held by most economists to be determinants of consumption or investment, based on the findings of these past studies. They are the models used as a starting point in this book’s analysis of “crowd out’s” effects on consumption and investment, and to what extent changes in loanable funds can offset these effects. Heim (2017a) found that many of the variables that were significant in earlier studies were tested in models of dubious plausibility. Therefore, variables found significant in this prior review of previous studies were retested before inclusion in Heim (2017a). The first step in this process was retesting in preliminary, or “skeleton,” models controlling for only two other variables commonly considered by economists to be the most important determinants of consumption or investment. For consumption, the two other variables were a variant of disposable income and interest rates. For investment models, the two other variables were a Samuelson accelerator variable and an interest rate variable. All variables found significant when tested in these simple models were then included in one comprehensive consumption or investment model and tested as a group to see which continued to be found statistically © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. J. Heim, Why Fiscal Stimulus Programs Fail, Volume 1, https://doi.org/10.1007/978-3-030-65675-1_13

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significant, controlling for the other variables in the model. Variables that survived this initial statistical testing, i.e., were statistically significant, were taken to constitute the “initial” model findings of what variables constituted determinants of consumption and investment. These initial models were included in Heim (2017a), but only to indicate initial, not final results. Good science requires replication. Therefore, variables found to be statistically significant in the initial models were then subjected to a rigorous process of verification. That process included retesting initial results in 3 additional time periods. Only variables also found statistically significant in at least two of the three additional tests, as well as the initial model, were kept in the model. These time period robust models were then retested to see if specific variable results remained statistically significant when other variables included in the model were varied. The time period robust models were retested with two additional variables added, and then retested again with two of the variables in the initial model subtracted. Only so long as their estimated marginal effects in the initial test stayed within 30% of their initial test values, and the variable remained statistically significant were the initial results considered sufficiently scientifically replicable to be acceptable as the “standard” model in Heim (2017a). Many variables, significant in initial models, could not meet these replication standards and were excluded from the final, “standard” model. Good science requires the ability to replicate initial results in different time periods and in different (but reasonable) models. It also requires the fortitude to throw away variables that don’t, no matter how attractive they otherwise may seem. Other branches of science would not accept less. Why, 350 years after Newton and the scientific revolution, should economists? One variable entering the standard models of consumption and investment is the government deficit. In demand-driven theories of GDP determination, including Keynesian theory, government budget deficits are commonly expected to stimulate the economy by increasing demand. However, some studies have found these deficits have “crowd out” effects which, whatever the deficit’s stimulus effect, simultaneously force a reduction in consumer and investment spending. This “crowd out” is thought to occur because financing the deficit involves borrowing funds from banks. That borrowing reduces the banks’ loanable funds available to private consumers and businesses, cause a decline in private spending

13

DOES CROWD OUT REALLY OCCUR? …

251

(which in part depends on borrowing) offsetting the government stimulus. Two of the studies that found this crowd out effect was a pervasive and offsetting effect of incurring deficits were (Heim 2017a, b). Those studies involved hundreds of empirical tests of U.S. consumption and investment spanning the 1960–2010 period, or subsamples thereof. In those studies, the deficit variable(s) used, i.e., the single variable (T −G) or separately (T ) and (G), were added to “standard” consumption and investment models of the type described above. When these “standard” models were retested, it was found the deficit variables had a negative and statistically significant impact on consumption and investment, and their addition markedly increased the amount of variance the model explained. In consumption it was 26% and in investment 28%, as shown below. Hence, we conclude the crowd out problem associated with deficits is a real problem negatively affecting consumption and investment tests in Heim 2017a, b) show completely offset the stimulus effects of deficits. In other words, the statistical studies say Keynesian—type fiscal stimulus programs don’t work unless the crowd out effect can somehow be offset.

13.1

Consumption

This study’s Baseline (BL) Standard Consumption Model with no Deficit Variables Included, nor any loanable funds variables included (estimated using 1960–2010 data). (2SLS—strong instrument For (Y −T )) CD = .54(Y − TT ) + 2.70PR + .27DJ−2 − .714.38POP16/65 (t=)

(0.6)

(7.2)

(3.0)

(1.6)

+ .013POP − 1.58M2AV + .13CB2 (3.2)

R = 60.3% 2

(−0.1)

Adj. R = 56.5% 2

(2.0)

D.W. = 1.7

MSE = 43.98 (13.1A)

(Same as Eq. 18.1AA) In Eq. 13.1B below, the deficit variable (T −G), divided into two separate variables (T ) and (G) to pick up any differences in effects on consumption of tax cut deficits vs. increased spending deficits), is added to the model, and yields adds an astounding 43.6% (26.3 percentage points) to the amount of variation in consumption explained by the model. Adding the deficit variables also improves parameter estimates of other variables

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in the model: it changes the sign on the interest rate variable to the theoretically expected negative sign and strengthens the statistical significance of all but one variable in the model. In estimating crowd out effects, the consumption function tested controls for the state of the economy by controlling for after-tax income, defined as GDP net of taxes. Results are shown in Eq. 13.2A, which is identical to Eq. 13.1A except for the addition of the deficit variables: CD = .31(Y − TT ) + .32(TT ) − .16(G T&I ) − 7.14PR + .49DJ−2 (t=)

(6.6)

(6.5)

(1.9)

(−3.1)

(4.5)

− .459.68POP16/65 + .017POP + 36.27M2AV + .09CB2 (2.4)

4.0

R 2 = 86.6% Adj. R 2 = 83.9% D.W. = 2.1

(3.8)

3.9

MSE = 26.17 (13.2A)

Rerunning the model using only the one-variable form of the deficit (T −G) yields a coefficient of +.28 (t = 6.2) on the deficit variable and the R 2 and the coefficients and t-statistics on the other variables remain essentially the same as in Eq. 13.2A above. Why do deficits account for this much variance, i.e., explain so much of the variation in consumption that no other variable in the model can explain? There are two theories: 1. One theory is that deficits have crowd out effects as described above. Money borrowed from banks to fund government deficits reduces the amount of money banks have left to lend to private consumers and businesses. And the historically low level of excess reserves in banks 1960–2007, about 2%, suggests that historically, private borrowing (and spending out of it) has been constrained by the limited supply available (and foreign borrowing often is called upon to fill the gap. Reducing what’s available for private borrowing forces a reduction in consumption and investment spending, some of which is done with borrowed money each year. If this theory is true, attempts to stimulate the economy by deficit-financed increases in government spending or tax cuts may fail. They will fail completely if the reductions in private spending (“crowd out”) caused by the deficits equals or exceeds the stimulus effects to consumer and business spending expected from the deficits. Evidence shown in Heim (2017b) indicates crowd out effects of deficits historically have been slightly larger than stimulus

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effects, leading these stimulus programs to have a slight negative effect on the economy. (This book introduces evidence that this was not the case after 2007, thanks to the large offsetting increase in loanable funds banks had due to Federal Reserve Chair Bernanke’s quantitative easing (QE) program.) 2. Another theory is that declining economic conditions automatically cause deficits to increase. It is argued that simultaneously, but not causally, the same declining economic conditions that cause deficits to rise cause consumption and investment to decrease. Hence, the simultaneous rise in deficits and decline in private spending are highly correlated, but not causally related as implied by crowd out theory. The alternative theory argues it is correlational, not causal, and that it is the underlying decline in the economy that causes both. 3. But Heim (2017b) exhaustively controlled for the state of the economy to ensure state-of-the-economy effects did not affect estimates of crowd out’s effects. In over 200 tests of different consumption and investment models, even controlling for the state of the economy, that study still found pervasive evidence of crowd out, and that its effects on consumption and investment are large enough to fully offset the stimulus effects of Keynesian deficits.

13.2

Investment

The crowd out effect of deficits is also related to investment spending. Equation 13.1 presents the “standard” investment model used later in Chapters 17 and 18, except the deficit variables and the loanable funds modifier variable (S + FB) have been removed. The full 1960–2010 data sample is tested in the investment equations below. Below is a standard investment model without crowd out variables and without a loanable funds variable: (2SLS strong instrument for the accelerator variable is used). No variable controlling for economic conditions (GDP) is included. (In Chapter 20, one is included as Eq. 20.3B.) ID = + .41(ACC) + .007POP − 1.25PR−2 (t=)

(7.4)

(2.1)

(−0.3)

+ 6.95XRAV + 11.00CAP−1 (2.1)

R = 69.4% 2

2 RAdj

(Same as Eq. 17.3C)

(2.8)

= 66.7%

D.W. = 1.4

MSE = 47.38

(13.1)

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Adding the deficit variables to the model provides a way to estimate any negative effect on investment deficits may have (Eq. 13.2). Adding the deficit variables increases the amount of variance explained by 19.6 percentage points (28%) and increases the statistical significance of several other variables. Adding crowd out variables markedly increases our ability to explain what drives the level of investment. As was the case with consumption, the existence of a crowd out effect of deficits on investment seems undeniable. Baseline Model with Deficit and GDP Control Variable, but No Loanable Funds Variables (1960–2010) ID = + .27(ACC) + .33TT − 33G T&I + .012POP − 4.95PR−2 (t=1)

(2.6)

(6.4)

(2.8)

(−3.9)

(−2.5)

+ 6.68XRAV + 2.43CAP−1 − .02GDP (3.5)

R = 89.0% 2

(1.8)

Adj. R = 85.9% 2

(−0.2)

D.W. = 1.9

MSE = 29.87 (13.2)

(Same as Eq. 18.4A)

13.3

Conclusion

A major determinant of both consumption and investment is the crowd out of private spending caused by financing government deficits, which completely eliminates the stimulus effects of deficit-financed fiscal stimulus programs.

References Heim, J. J. (2017a). An Econometric Model of the U.S. Economy. Hoboken: Palgrave Macmillan. Heim, J. J. (2017b). Crowding Out Fiscal Stimulus. Hoboken: Palgrave Macmillan.

CHAPTER 14

Does Crowd Out Really Occur? Empirical Evidence: Replication in Many Time Periods

In the last chapter, we mentioned two recent major statistical studies seem to show beyond any reasonable doubt that government financing of fiscal stimulus programs by borrowing from the pool of loanable funds (deficit financing) is associated with reductions in consumer and investment spending. The assumed reason is that this is because in good times and bad, consumers and businesses wish to borrow money, and loaning to the government to finance the deficit leaves less available for them to borrow. This reduces consumer and investment spending.

14.1

The Heim (2017b) Study

In the first of these studies, Heim (2017b) undertook 228 tests of consumption, investment, and the GDP. These were full structural models of the determinants of consumption and investment. Each model included variables testing for the effects of government deficits, controlling for other variables commonly thought to be determinants of them. Heim (2017b, Table 5.7) found the following average effects of deficits (T − G) on 16 of the best consumption models: C = .45av (T − G) (t=)

(6.2av )

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. J. Heim, Why Fiscal Stimulus Programs Fail, Volume 1, https://doi.org/10.1007/978-3-030-65675-1_14

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To ensure the results of the initial sample were not spurious, four of the key models from Table 5.7 were tested in four different, though somewhat overlapping time periods. Statistically significant negative relationships of crowd out to consumption or investment were found in fourteen of sixteen tests (Table 5.8). The relationship of deficits to investment was also found to be negative and statistically significant. Averages for different subsets of 23 tests are given below (Heim 2017b, Table 6.8). I = .(30 − 35)av (T − G)

(t=)

(3.4−5.6)av

Four of the initial models were each tested in four different time periods and all sixteen tests showed deficits had a statistically significant negative relationship with investment (Heim 2017b, Table 6.10). Heim 2017b also tested the effects of deficits tax cut caused by tax cuts, and deficits caused by spending increases separately. Both types of deficits were found negatively related to consumer and investment spending. Results for their effects on consumer spending are shown below and are taken from Heim 2017b, Table 7.1:   C = .59(T ) − .28(G) average, 5 models (t=)

(12.0av )

(−3.8)

  C = .59av (T ) − .28av (G) average, 17 models (t=)

(12.0av )

(−3.8)

Clearly, this study showed overwhelming evidence of deficits having “crowd out” effects on private spending. The models tested were nearly identical to what we describe in this book as “standard” consumption, investment, and GDP models.

14.2

The Heim (2017a) Study

In Heim (2017b), only total investment and total consumption were tested. In a later study (Heim2017a), the effect of government deficits on the three component of consumption (durables, nondurables, and services) as well as consumer imports and domestically produced consumer goods were also studied. Similarly, the three component of investment (plant and equipment, housing, and inventories) as well

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257

as total investment imports and total domestically produced investment goods were also separately studied. All models were tested for a range of econometric problems including stationarity, endogeneity, and heteroskedasticity, and corrected for these problems if found (as was also the case in the 2017b study). Results for the 1960–2010 sample period are reported below only if initial test results were found replicable in at least two of three additional, but overlapping time periods tested. Equation Numbers cited are the equations in Heim (2017a) from which the results were taken (NS = not significant). CT = .57(T ) − .38(G)

Total Consumption

(t=)

Domestic Consumption

CM = .25(T ) − .18(G)

(4.2.TR)

(t=)

(7.4)

(−5.4)

(t=)

(5.9)

(−5.4)

CND = .18(T ) − .12(G)

Nondurables

(t=)

Services

(−4.7)

(4.11.TR) (4.13)

IT = .30(T ) − .32(G)

(5.2.TR)

(t=)

(8.5)

(t=)

(2.7)

(−5.4)

(−4.4)

ID = .27(T ) − .30(G)

(5.4.TR)

IM = .05(T ) − .(NS)(G)

(5.6.TR)

IP&E = .14(T )(T ) − .14(G)

(5.10.TR)

Domestic Investment Imports Investment Plant and Equipment

(7.2)

(4.9)

CSer = .45(T ) − .25(G)

Total Investment

(t=)

(t=)

Residential Const. Inventory

(4.4.TR)

(−4.5)

6.5

CDur = .24(T ) − .14(G)

Durables

(4.1T.TR)

CD = .34(T ) − .23(G) (t=)

Imports Consumption

(−7.9)

11.0

(2.9)

(−3.8)

(2.0)

(t=)

(NS)

(2.0)

(NS)

IRes = .21(T ) − .21(G) (t=)

(5.6)

(−7.5)

IInv = .NS(T ) − .NS(G) (t=)

(NS)

(NS)

(5.11.TR) (5.13.TR)

Clearly, this study, like (2017b), shows that government deficits negatively affect almost all of the components of consumption and investment.

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14.3

Crowd Out Findings in This Study

The key models in this book are given in Chapters 17, 18, and 25 below. They exhaustively test models of (domestically produced) consumption and (domestically produced) investment goods to determine if crowd out is a problem affecting them. Results are shown below. Later in the same chapters, the question of whether increases in total loanable funds (Chapters 17, 18) or changes in just the portion of loanable funds increases caused by Federal Reserve (FR) purchases of securities (Chapter 25) can offset crowd out is examined. The relative suitability of total loanable funds compared to only the endogenous or exogenous parts of it is the main topic dealt with in much of the rest of this study. But that’s for later chapters. In Chapter 13 above and this chapter’s tests, we just deal with whether crowd out is a problem affecting consumption or investment (Table 14.1, results taken from Table 17.3). The large majority of these tests show a significant negative effect on consumption and investment of crowd out. Some do not and can lack of significance can occur for a variety of reasons: the nonsignificant finding can be spurious, or the period measured may have shown no growth in the deficit. Alternatively, the deficit may have been growing but not consumption or investment (or they may have been growing half the period and declining in the other half). In Chapter 18 the same 18 periods are retested using the same models, except this time using separate variables to account for the effects of deficits caused by tax cuts and deficits caused by increases in government spending. For consumption, 16 of 18 periods sampled showed tax cut deficits having a significant negative effect on consumption, but only 9 of 18 spending deficits (Table 18.2). The mixed spending deficit results appear to be the result of econometric, not substantive problems and will be discussed in detail in the paragraph below. For investment, 11 of 18 tax cut deficits had a significant negative effect, as did 17 of 18 government spending deficits. When testing the deficit as two separate variables, one of the major reasons one or the other sometimes show as insignificant (even when generally they are found significant) has to do with the fact that in such models, the spending variable can be increasing, even though the deficit is declining, so there is no or negative crowd out. We refer to such periods as “crowd in” periods. For consumption, for example, this occurs in 6 of the

14

Table 14.1 Unmodified effects of deficits (crowd out) consumption and investment

DOES CROWD OUT REALLY OCCUR? …

259

Time Period

(Table 17.1)* Consumption Deficit ( T − G) Coef. ( t-stat.)

(Table 17.3)* Investment Deficit ( T − G) Coef. ( t-stat.)

1960–1980 1960–1990 1960–2000 1960–2007 1960–2008 1960–2010 1970–1990 1970–1900 1970–2007 1970–2010 1980–2000 1980–2010 1975–2004 1980–2004 1985–2004 1985–2005 1996–2010 2000–2010

.48 .31 .22 .37 .36 .38 .23 .13 .36 .37 .01 .37 .27 .29 .28 .29 .69 .42

.01 .14 .21 .15 .23 .23 .13 .22 .16 .24 .20 .22 .12 .06 .18 .18 .25 .24

(1.8) (4.4) (2.6) (5.9) (5.6) (6.3) (2.4) (1.1) (4.3) (5.2) (0.1) (4.8) (2.7) (2.7) (1.2) (1.0) (3.7) (4.4)

(0.1) (1.8) (2.1) (2.1) (2.6) (2.7) (1.1) (1.8) (1.9) (2.6) (1.2) (2.0) (1.2) (0.6) (1.6) (1.5) (1.7) (1.1)

*Models shown include a variable controlling for the size of the loanable funds pool when measuring crowd out. When not controlled for, 16 of 18 consumption tests show statistically significant crowd out; for investment 17 of 18. show significant crowd out

9 time periods where we found the crowd out effects of spending deficits to be statistically insignificant. These six time periods were periods in which from 1/3 to 1/2 of all the data observations included were for the 1990s “crowd in” period, the rest from “crowd out” periods. Both the “crowd in” and “crowd out” periods, when tested separately were statistically significant (but with opposite signs), but when combined, offset each other’s effects, leaving generally very small magnitude “net” coefficients and statistical insignificance. This is discussed in detail in Chapter 18, where some of the samples’ results were heavily influenced by the “crowd in” of the 1990s that occurred because deficits during that decade were declining. If crowd out theory is correct, this “crowd in” should have led to positive increases in consumption, and it did.

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References Heim, J. J. (2017a). An Econometric Model of the U.S. Economy. Hoboken: Palgrave Macmillan. Heim, J. J. (2017b). Crowding Out Fiscal Stimulus. Hoboken: Palgrave Macmillan.

PART VI

Increases in Total Loanable Funds (S+FB)—Do They Reduce Crowd Out?

CHAPTER 15

Initial Tests of Whether Crowd Out Can Be Offset by Increases in Loanable Funds

The evidence in Chapters 13 and 14 taken from Heim (2017a, b) and some from Chapters 17 and 18 below convincingly suggests deficits “crowd out” private spending by reducing the pool of loanable funds available for private borrowing, thereby offsetting the stimulus effects of deficit-financed government fiscal policy initiatives. But, the overall size of the pool of loanable funds is policy controllable. Policy actions could be taken to accommodate the fiscal stimulus program by increasing the pool of loanable funds exogenously. This would offset the loss in loanable funds in the existing pool available to private borrowers. Allowing more Keynesian methods of stimulating the economy to work without creating offsetting losses in private spending. The loanable funds pool can also grow endogenously. This occurs mainly in response to growth in the economy which increases incomes. The increased incomes result in increased savings, the largest component in the pool of loanable funds. Unfortunately, this comes during upswings in the economy, but the need to implement fiscal stimulus programs is needed most in economic downswings when the pool if anything, is declining. The question is not whether accommodative monetary policy exogenously, or improvements in the economy endogenously, can increase the pool of loanable funds. For investment, anyway, and possibly consumption, that is shown possible in Chapters 17 and 18. The real question is, have increases in the past occurred, and if so have they occurred in © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. J. Heim, Why Fiscal Stimulus Programs Fail, Volume 1, https://doi.org/10.1007/978-3-030-65675-1_15

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large enough quantities to offset the negative effects of crowd out, and done so consistently across multiple time periods? Much of the rest of this book is devoted to establishing the science necessary to provide reliable information as to whether they have or haven’t. If increases in loanable funds do offset crowd out, we can get more accurate estimates of actual crowd out effects by modeling the crowd out effect as the deficit minus the effect of any same-period growth in the pool of loanable funds. One way to test this is to simply add a variable to consumption and investment equations below to pick up an additional variance in those equations that adding the change in the loanable funds pool accounts for. A change in the pool is defined as:   Loanable Funds Pool = (S + FB) =  National Savings + Foreign Borrowing .

15.1 Methodology for Testing Increases in Loanable Funds as an Offset to Consumption Crowd Out This is done below for consumption by adding a total loanable funds variable (S + FB) to the consumption standard model in Chapter 18, Eq. 18.1A. All consumption and investment models in this section are taken from Chapter 18, and have been adjusted, as necessary for stationarity, endogeneity, and heteroskedasticity issues. The model below was estimated using one sample: the full 1960–2010 data set, see Chapter 18 for more details. First we present the same model except that no loanable funds variable is included. CD = .31(Y − TT ) + .32(TT ) − .16(G T&I ) − 7.14PR + .49DJ−2 (t=)

(6.4)

(6.6)

(−1.9)

(−3.1)

(4.5)

− .459.68 POP16/65 + .017POP + 36.27M2AV + .09CB2 (4.0)

(2.4)

R = 86.6% 2

Adj.R = 83.9% 2

D.W. = 2.1

(3.8)

(3.9)

MSE = 26.17 (18.1A)

15

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INITIAL TESTS OF WHETHER CROWD OUT CAN BE OFFSET …

Then we add the stand-alone loanable funds variable (S + FB) to the model:         CD = .38 Y − TT + .43 TT − .24 G T&I − .14 ST + FB − 6.09PR (t=)

(8.0)

(6.7)

(−2.8)

(−4.1)

(−2.8)

+ .40DJ−2 − 398.48POP16/65 + .016POP + 33.67M2AV + .10CB2 (5.0)

R 2 = 88.3%

(−1.9)

Adj.R 2 = 85.8%

(3.7)

(3.5)

D.W. = 1.9 MSE = 24.68

(4.5)

(18.2)

Adding the stand-alone loanable funds variable strengthens the explanatory power of the model, adding 1.7% to explained variance, and strengthening the estimated magnitude and statistical significance of the deficit variables. The small increase suggests that past increases in loanable funds have offset some, but not much, crowd out. Most likely this is because the increases in loanable funds were too small, or because large parts of the increases were used to buy securities, or used to buy foreign goods and services, neither of which raises U.S. consumption or GDP, as discussed in other sections of this study. The model results indicate there is a net negative effect of a growth in the pool of loanable funds on consumption. This is because of the necessity of lowering the marginal propensity to consume (mpc) if the marginal propensity to save (mps) is increased to increase savings (loanable funds). This occurs in ceteris paribus models, like regression models, that hold disposable income and other variables in the model constant when estimating loanable funds effects. Regression methods use the methods of multivariate calculus used to estimate regression coefficients, i.e., partial derivatives that calculate effects of one variable on another holding all other variables constant. Because of this second effect in consumption models, the coefficient on the loanable funds variable above represents the net effect of a positive effect in reducing crowd out, and this negative effect. We find this to be an issue in estimating consumption effects, but not investment effects. An increase in investment due to an increase in loanable funds does not require a decrease in other investment to fund it.

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Results indicate there is a net negative effect on consumption. The negative coefficient (−.14) on the loanable funds variable in Eq. 18.2 is the net marginal effect of increasing savings (and therefore the pool of loanable funds) on consumption. Separating the Positive and Negative Effects of an Increase in Loanable Funds on Consumption To separate the negative and positive effects on consumption of an increase in loanable funds, we redefine the crowd out effect. In initial models with no loanable funds variable, it was just the value of the deficit. Now the crowd out effect will be the value of the deficit reduced by any same-period growth in the pool of loanable funds. This is done by 1. adding the change in loanable funds to any changes in taxes. Tax cuts, ceteris paribus, create deficits since government spending is held constant in a ceteris paribus model when estimating their marginal effect. Reductions in taxes (the tax cut deficit) have a negative sign in the data used and are therefore offset by an increase in loanable funds). Hence, the loanable funds modified crowd out effect of a tax cut deficit will be given by Tax Cut Crowd Out = T +(S + FB), and 2. subtracting the change in loanable funds from any increases in spending (which have a positive sign). Increases in spending, ceteris paribus, create a spending deficit. Hence, the loanable funds modified crowd out effect of a government spending increase deficit will be given by Spending Increase Crowd Out = G−(S + FB). These loanable funds modified deficit variables will represent the positive effect on consumption of a change in loanable funds. They are modeled to have the same marginal effect on consumption as does an increase in the deficit (except with the opposite sign), and hence, share the same coefficient. This reflects the assumption that a dollar’s worth of crowd out is caused by a dollar’s worth of deficit and can be offset by a dollar’s increase in loanable funds. The new consumption model also continues to include a separate, stand-alone (S + FB) variable to measure the negative effect of savings growth on consumption. If the modeling is correct,

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INITIAL TESTS OF WHETHER CROWD OUT CAN BE OFFSET …

267

the sum of the positive effects on the modified deficit variables minus the negative effects shown on the alone variable should precisely equal the net effects shown on the stand-alone (S + FB) variable in Eq. 18.2 above (−.14). They do. Results are presented in Eq. 18.1A taken from Chapter 18 below.   CD = .38(Y − TT ) + .43(T + LFm ) − .24 G − LFT&I(m) (t=)

(8.0)

(6.7)

(−2.8)

− .81(ST + FB) − 6.09PR + .40DJ−2 − 398.48POP16/65 (−2.8)

(−4.1)

(5.0)

(−1.9)

+ .016POP + 33.67M2AV + .10CB2 (3.7)

R = 88.3% 2

(3.5)

Adj.R = 85.8% 2

(4.5)

D.W. = 1.9

MSE = 24.68 (18.1A)

Note the positive effects of the modification are now given by reducing the magnitude of the estimated crowd out effect from their premodification level given in Eq. 18.2: .43(T ) − .24(G) (from Eq. 18.2 above) to their now lower modified levels given in Eq. 18.1A. .43(T + (S + FB)) − .24(G − (S + FB)) (from Eq. 18.1A) Hence, a negative change in (T ), a tax cut, is reduced in magnitude by a positive change in (S + FB), and a positive change in (G), an increase in government spending, is reduced by a negative change in (S + FB). Assuming the underlying relationship between deficits and consumption is linear over the range of the change, coefficients should remain unchanged in testing, and they do, as shown above. Notice the magnitude in absolute terms of the negative stand-alone (S + FB) effect increases markedly from −.14(S + FB) to −.81(S + FB). This is the only variable that changes its Eq. 18.2 values and statistical significance levels when adding the modifiers to (T ) and (G) in Eq. 18.1A.

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The sizable change in the stand-alone (S + FB) variable’s coefficient occurs because Eq. 18.1A separates the two effects that are netted out in Eq. 18.2. Combine the separate loanable funds effects into one standalone variable, as shown in Eq. 18.2, and they precisely equal the net effect shown in that equation, i.e., .43(T + (S + FB)) and − .24(G − (S + FB)) − .81(S + FB) (from Eq. 18.1A) = .43(T ) − .24(G) + (+.43 + .24 − .81) (S + FB) = .43(T ) − .24(G) − .14(S + FB) (from Eq. 18.2) Hence, when adding a modifier to a model that has both positive and negative effects, it is important to model the two effects separately to clearly understand the underlying economic structure, and properly understand the net effect. Both individual effects may be statistically significant, but their net effect insignificant if the separate effects are close enough in value, except for the value’s sign, to almost cancel each other out. Separating the effects by using (S + FB) twice in the same equation does not change the regression coefficients or statistical significance of the measured crowd out effect. It just means the (previously determined) marginal effect of crowd out on consumption, is now, typically, multiplied by a smaller magnitude variable estimating crowd out effect than before. The modified definitions of the crowd out variable are now measured as T +(S + FB) or G−(S + FB). And the higher R 2 (when the loanable funds variable is added to the model in either gross or net fashion) show that this is a more correct way of estimating crowd out effects than using the deficit variables alone. In theory, we generally expect significance levels of the deficit variables stays the same or increase since the modified crowd out variables are more accurately representing the magnitude of crowd out’s effect on consumption, but there are a number of conditions, discussed elsewhere (Chapter 18) where this might not hold, e.g., if we mix “crowd out” and “crowd in” periods together in one sample.

15

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15.2 Taxes: Another Variable That Has Both Positive and Negative Effects on Consumption The loanable funds variable is not the only one in economic theory to have offsetting positive and negative effects. The government receipts variable (shorthanded as “taxes” in this study) is another variable that has both positive and negative effects on consumption. Using gross income (Y ), and not disposable income (Y −T ), in a consumption function, while also including deficit variables (T ) and (G) to measure crowd out, leads to only net effects of tax changes being measured in a regression model. Failure to separate the positive Keynesian stimulus effects of tax cuts, given when using disposable income (Y −T ) as an explanatory variable, and the negative effects of tax cuts on consumption due to crowd out, when (T ) is used as a stand-alone tax variable, can lead to regression coefficients on that variable that reflect the net effect of both effects, thereby obfuscating the separate influences of both effects. To see this, reestimate Eq. 18.2 making one change: instead of disposable income (Y −T ), use gross income (Y ) as the income variable. Then the coefficient on the tax variable becomes the sum of the two effects given on Eq. 18.2: The positive effect on consumption of tax cuts given by .38(Y −T ), plus the negative effect of a tax cut given by the stand-alone crowd out variable +.43(T ), i.e., −.38(T ) + .43(T ) = +.05T These results are shown in Eq. 15.1 below. Notice absolutely nothing else in the equation changes, except the coefficient and statistical significance of the stand-alone variable (T ). CD = .38(Y ) + .05(TT ) − .24(G T&I ) − .14(ST + FB) (t=)

(8.0)

(0.7)

(−2.8)

(−4.1)

− 6.09PR + .40DJ−2 − 398.48POP16/65 + .016POP (−2.8)

(5.0)

(−1.9)

(3.7)

+ 33.67M2AV + .10CB2 (4.5)

(3.5)

R = 88.3% 2

Adj.R = 85.8% 2

D.W. = 1.9

MSE = 24.68

(15.1)

Clearly, the estimates of the effect of a change in taxes are misleading in Eq. 15.1. It obscures the two separate effects of a tax cut: the (Keynesian) stimulus effect given by −.38(T T ) in the disposable income variable,

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and the Keynesian deficit’s crowd out effect given by the separate variable +.43(T T ). This error was made by Gale and Orszag (2004) in their consumption model. They used net national product as the income variable. When this was used alone, accompanied by a stand-alone tax variable, the sign on the stand-alone tax variable in their model was negative, suggesting that a cut in taxes would have a positive effect on consumption. To retest this model, Heim (2017a) replaced NNP with disposable NNP = (NNP−T ) and the sign on the stand-alone tax variable turned positive (since it now only represents crowd out effects). Combine the coefficients on the two (T ) components, and you get a small net negative value, as obtained in Gale and Orszag’s model. This is the same result shown found using this study’s data above, differing only in its estimate of the net effect, which may be accounted for by the difference in sample periods used in the two studies. This is not a unique outcome. For any stand-alone variable in a model, if you also modify other variables in the model by adding or subtracting the stand-alone variable from them, the sum of the effects on these adjusted-value variables, plus the stand alone, will be exactly the same as the net value obtained when a model containing only the stand-alone version of the variable is tested. The reader may wish to test this with their own models.

15.3 Methodology for Testing Increases in Loanable Funds as an Offset to Investment Crowd Out The standard investment model with deficit (crowd out) variables (T , G) but without any (S + FB) loanable funds variable is given in Eq. 15.2 below: ID = .23(ACC) + .33(TT ) − .36(G T&I ) + .010POP (t=)

(6.1)

(3.9)

(2.9)

(−4.7)

− 4.22PR−2 + 6.77XRAV + 1.59CAP−1 (8.8)

(−2.1)

R = 88.5% 2

Adj.R = 85.9% 2

(0.9)

D.W. = 1.8

MSE = 29.63

(15.2)

Equation 15.3 below shows the standard investment model with two crowd out (deficit) variables (T ) and (G), and also a stand-alone loanable funds variable (S + FB). It does not include any modification of the

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deficit variables by changes in loanable funds. The estimation performed used the full 1960–2010 data set: ID = .18(ACC) + .21(TT ) − .23(G T&I ) + .16(S + FB) (t=)

(1.8)

(5.6)

(−2.6)

(2.1)

+ .007POP − 3.24PR−2 + 5.54XRAV + 1.00CAP−1 (2.7)

R = 90.4% 2

(−1.6)

Adj.R 2 = 87.5%

(2.8)

D.W. = 1.9

(0.7)

MSE = 27.49

(15.3)

Adding a stand-alone variable (S + FB) to measure the effect of changes in the loanable funds pool (which can offset crowd out) increases explained variance by (1.9) percentage points and the added variable is statistically significant. As was the case with consumption, the small increase in explanatory power suggests increases in loanable funds have helped reduce the effects of crowd out, but only to a relatively minor extent. This was probably because the increase was too small to fully offset crowd out, or a sizeable part of the increase was spent of existing securities or used to buy foreign goods and services. It also shows that the net effect of any increase in loanable funds on investment is positive, unlike the effect on consumption. This is to be expected. Increases in savings and foreign borrowing (loanable funds) both theoretically and empirically (see ERP 2013, Table 32) are associated with identical increases in investment in the standard (S + FB = I ) formulation of the national income accounts. In the empirical data presented in the Flow of Funds accounts, or the annual Economic Report of the President, they are also equal (except for statistical discrepancy in both cases). Below is the standard investment model with 2 variable Crowd out (T ) and (G), with stand-alone loanable funds variable, and modification of the deficit variables by changes in loanable funds to give loanable funds— modified definitions of the real crowd out effects of deficits: T +(S + FB) and G−(S + FB). (estimated using 1960–2010 data).     ID = .18(ACC) + .21 TT(m) − .23 G T&I(m) − .28(S + FB) (t=)

(5.6)

(1.8)

(−2.6)

(1.1)

+ .007POP − 3.24PR−2 + 5.54XRAV + 1.00CAP−1 (2.7)

R = 90.4% 2

(−1.6)

Adj.R = 87.5% 2

(2.7)

D.W. = 1.9

(0.7)

MSE = 27.50

(15.4)

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Here, the two deficit variables (T ), (G) are modified to show reductions in deficits by any same-period growth of loanable funds (LF), where (LF) = (S + FB). The coefficients on the modified deficit and stand-alone variables give the sum of the (S + FB) offset effects. When these are added to the coefficient on the stand-alone (S + FB) variable, the net effect is seen to be .16(S + FB), exactly the result obtained in Eq. 15.3 when (S + FB) is only used as a stand-alone variable in the model. As was the case with consumption, the values of all other variables except the stand alone (S + FB) remained unchanged, as does R 2 . Note, however, that when loanable funds are also included as modifiers to the deficit variables, the stand-alone loanable funds variable becomes statistically insignificant. This means that unlike consumption, it has no separate, contradictory second effect on investment; its only effect is its effect on crowd out. Hence, unlike consumption, we can use an investment model that only includes (S + FB) as a modifier of the deficit. This reduction adequately describes how the crowd out effect of deficits is reduced by same-period growth in loanable funds. In investment models, we can delete the stand-alone (S + FB) variable as unnecessary. Also, our general rule is to drop statistically insignificant variables from the model, unless there are compelling theoretical arguments for keeping it. Dropping the variable gives the following model, shown as Eq. 15.5 below: ID = .17(ACC) + .14(TT ) − .11(G T&I ) + .004POP (t=)

(4.3)

(1.2)

(−2.6)

(1.8)

− 2.68PR−2 + 4.92XRAV + 1.30CAP−1 (2.7)

(−1.4)

R = 89.7% 2

Adj.R = 87.1% 2

(1.0)

D.W. = 2.0

MSE = 28.05

(15.5)

In this model, the net effect on investment of adding a dollar to the loanable funds pool is (.14 + .11)/2 = +.125, compared to the +.16 estimate in Eq. 15.3. Some difference is to be expected. Because regression coefficients and standard errors are in part determined by the level of multicollinearity between all the explanatory variables, dropping or adding a variable to a model usually has some effect on these statistics, A decline in R 2 also occurs, but it is not large (90.4% vs. 89.7%). Though insignificant in Eq. 15.4, the loanable funds variable’s t-statistic (1.1) is still high enough to result in some loss of R 2 , and reduce the explanatory power of some variables a bit. Hence, it should probably be left in. More

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generally, if the stand alone is statistically significant, it means the increase in loanable funds is more than large enough to eliminate all crowd out effects; the left over part is actually creating a crowd in effect. And should be included in the investment model. As we will discuss later in the text, comparing models without deficit modification to models with it allows comparison of the gross crowd out effects of deficits to their crowd out effects net of any offsetting effects of loanable funds growth. For investment, this often can be done with models that do not include a stand-alone loanable funds variable.

15.4

Conclusions

The conclusion of this chapter is that there is a solid scientific basis for believing increases in loanable funds can be used to offset crowd out; we see the science when we see that adding them increases the explanatory power of the model. However, historically, increases in loanable funds, particularly FR increases, have not been large enough to offset crowd out effects as we noted in Chapters 8 and 9; the QE period after 2007 is an exception. Other reasons why increases in loanable funds by the FR may not have offset crowd out’s effects on the real economy are that they may have been used to purchase existing securities or foreign goods and services, neither of which increases the U.S. GDP in any direct way in anything like the magnitude needed to fully offset crowd out.

References Economic Report of the President. (2013). Washington, DC: Government Publications Office. Gale, W. G., & Orszag, P. R. (2004). Deficits, National Saving, and Interest Rates. Brookings Papers on Economic Activity, 2004(2), 101–187. Heim, J. J. (2017a). An Econometric Model of the U.S. Economy. Hoboken: Palgrave Macmillan. Heim, J. J. (2017b). Crowding Out Fiscal Stimulus. Hoboken: Palgrave Macmillan.

CHAPTER 16

Which Models Best Explain How Changes in Loanable Funds Offset Crowd Out?

As we established in Chapters 13 and 14, to evaluate the stimulus effects of a government deficit, we must include any crowd out effects that occur from financing the deficit. Financing the deficit reduces the portion of the pool of loanable funds available for borrowing by consumers and businesses, thereby reducing their spending which counters the deficit’s stimulus effects, eliminating them. We also showed in Chapter 15 that deficit-sized estimates of the crowd out effect may overstate its negative effects. Growth in the pool of loanable funds during the same period would increase the funds available to private borrowers that could offset the losses to private borrowers due to crowd out. Hence, at least theoretically, any deficit-sized estimate of the crowd out effect could be offset by any same-period change in the size of the loanable funds pool. This of course assumes the growth is made available for borrowing by the same consumers and business adversely affected by crowd out or to other private borrowers. To be clear, the evidence presented in earlier studies (Heim 2017a, b) and elsewhere in this study clearly indicates crowd out does occur when deficits occur, regardless of what’s happening with the loanable funds pool. Growth in the loanable funds pool does not prevent it from happening; it just offsets part or all of its negative effects. In this chapter, we further empirically test this hypothesis to determine if growth in the pool in deficit years really is associated with reduced crowd out effects © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. J. Heim, Why Fiscal Stimulus Programs Fail, Volume 1, https://doi.org/10.1007/978-3-030-65675-1_16

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in a wider range of sample periods than the single periods tested in Chapters 13 and 15 above. The definition of the pool of loanable funds (LF) used in this study is taken from Economic Report of the President, 2013, Table B32. The pool is defined as the combination of national savings (S) available and borrowings from foreign sources (FB) used in any period to finance investment in any year. It is the definition used in the Federal Reserve’s Flow of Funds Accounts in defining the savings/investment identity, and in the annual Economic Report of the President. Growth in loanable funds can occur several ways: (1) FR open market operations can increase bank reserves (“loanable funds”) when deficits occur. This is monetary policy designed to “accommodate” fiscal policy actions to stimulate the economy by increasing the privately available portion of the loanable funds pool to pre-deficit levels. This will restore private borrowing to predeficit levels, unless economic conditions have declined since then. This would be an exogenous increase in the pool of loanable funds. However, if FR open market purchases of securities are done for another reason, e.g., the desire to stimulate the economy beyond previous levels by lowering interest rates, this requires lowering rates enough to attract additional borrowing, not just restore old levels. If the increase in bank reserves is only equal to the crowd out problem, it will only guarantee that current levels of borrowing will continue. It will not provide any additional net stimulus to the economy by allowing private borrowing to increase. However, many economists would argue that the deficit’s stimulus effects, now unfettered by crowd outside effects, should. This is the key point to be empirically tested in this chapter. With no deficit, any increase in the pool resulting from FR open market operations could, if demand for loans were sufficiently large, finance new private borrowing and spending, also resulting economic growth. In short, FR open market operations can be used to accommodate fiscal policy stimulus programs, or to directly stimulate the economy as a monetary policy stimulus program. (2) Endogenous growth in the savings portion of the loanable funds pool can occur when the economy is growing, which means incomes are growing, and people and businesses are saving part of

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their increased income. This increase in saving can also offset the loss of privately available loanable funds due to crowd out, allowing the stimulus effects of deficits to be felt unfettered. But using the new loanable funds to offset crowd out effects of a deficit implies there is a public policy choice to be made regarding deficits: A. If there were no deficit, the growth in loanable funds could finance growth in new private borrowing to buy new cars, houses, or machinery, not just restore its old levels, bringing about growth in the economy. B. If there is a decision to deficit, ensure that the stimulus effects of deficits are not wiped out by crowd out. This allows the direction of growth in the economy to be set by public policy (tax cuts to increase private spending) spending increases on transfer payments, environmental or health programs or infrastructure spending to increase public goods and services. (3) Growth in the loanable funds pool due to increased foreign borrowing. Effects are the same as for growth in the national savings component: it could finance additional new loans over current levels to private citizens or companies. Or it could finance additional loans to the government to finance deficits. In the U. S., we do both. It becomes largely the province of public policy decision making to decide which choice is most appropriate at a particular time. However, there is the question of whether deficit-financed stimulus, with enough loanable funds growth to offset crowd out, or no-deficit private stimulus of the same growth in loanable funds leads to the most growth. That is a choice that should be informed by empirical estimation of the effects of both choices, as well as public preferences, but is beyond the scope of this study. This chapter looks at the more limited issue of whether increasing the loanable funds pool can reduce the crowd out problem, allowing the stimulus effects of fiscal policy to work, and to what extent. In examining this question will also examine whether growth in the loanable funds pool, when there are no deficits, does result in additional economic growth.

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16.1

Effects on the Consumption Function

Consider the effects of changes in the loanable funds pool on sales of consumer goods produced in the U.S. (C D ). As noted in earlier chapters, the accuracy of estimates depends heavily on testing full structural models, i.e., there cannot be any possible “left out variables” problem. Models used in this study depend heavily on decisions made I Heim (2017a) as to what variables needed to be include in consumption and investment models to avoid the crowd out variables problem Heim (2017a) surveyed a large number of past studies to see which variables were commonly hypothesized to be determinants of consumption. All determinants identified were included as controls in preliminary regression testing the effects of crowd out, modified by changes in loanable funds on consumption. Those controls found significant in the initial test were then tested in three additional different, but overlapping time periods. They were discarded if they were not statistically significant in at least three of the total of four periods sampled, since this indicated that they were probably only spuriously, not systematically, related to consumption. Those that proved time period robust were then tested for model specification robustness. This was done by adding to, and in subsequent tests, subtracting two variables from the time period robust model. Any variable in the time period robust model whose regression coefficient (marginal effect) varied by more than 30% from its values in the time series robust model was also deleted from the model, on the grounds estimated effects of the variable were unreliable, i.e., the multicollinearity between explanatory variables was so bad that coefficient values could not be taken as reasonable approximation of true marginal effects. The variables that survived these time period and model specification robustness tests were included in this study in the following model referred to as the “standard” consumption model. In that model, current period values have no time period subscript; where subscripts are used, they indicate the number of year’s lags with which the variable is used. Sources are from the statistical appendix B of the Economic Report of the President, 2012 (ERP 2012), as indicated below. To ensure the data set included data back to 1960, earlier ERP year tables were also used. Dependent Variable—Consumption Model CD = Total consumption   − Total imports − Capital goods, Industrial supplies, and materials

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(Tables B2, 104) Explanatory Variables—Consumption Model (Y −T ) = Disposable income (B2, B83) (T −G) = the consolidated deficit for all U.S. governmental entities taken collectively (B83) T = Deficits generated by tax or other revenue cuts (our initial measure of crowd out caused by tax cuts) (B83) G = Deficits generated by total government spending on goods, services, and transfers (our initial measure of crowd out caused by spending deficits) (B83) S = Gross U.S. saving = personal + corporate + depreciation + government (B32) FB = Foreign Borrowing (B32) PR = the Prime interest rate (B73) DJ−2 = Wealth measure; NYSE composite average lagged two years (B95) POP20/65 = Ratio of those 20−24 to those 65 or older in the population (B34) POP = U.S. population (B34) M2−2−4 = M2 money supply (or M2−M1 component): a measure of recent year (liquid) saving history (B69) C B = Consumer borrowing (FR Flow of Funds Accounts:  Consumer Debt) All these variables were found statistically significant and robust to different time periods tested and robust to addition or subtraction of certain other variables in the equation. A “modified” crowd out effect variable was calculated, representing the initial tax cut or spending deficit (T ) or (G), and any growth in loanable funds (S + FB) used to reduce it. This model was then tested for its effects on consumption. The modified crowd out variables, i.e., either (T + (S + LF)) or G−(S + LF), used were derived as follows: (The Deficit) + (Loanable funds) = (T − G) + (S + FB) = T + β1 (S + LF) or G − β2 (S + LF) Precise allocations of part of (S + FB) were not available, so we tested different allocations to see which fit the data best. Tests indicated weights

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assigned to β 1 and β 2 (unexpectedly) did not affect the results, so typically in testing below, both weights were each taken to be 1.00, since that was the easiest formulation to use, and any other choice would have been equally arbitrary and not changed the results. Let us call the model above, exclusive of the loanable funds variable (S + FB), the standard consumption model. The standard model in Eq. 16.1 below was estimated using 1960–2010 data. Just prior to Eq. 16.1, we also include the standard model (Eq. 18.1A.A) before adding the crowd out variables (T ) and (G) to measure crowd out effects. Adding the crowd out variable hugely increases explanatory power, clearly indicating that crowd out, unless offset, is clearly a major problem causing reductions in consumer and investment spending. CD = .54(Y − TT ) + 2.70PR + .27DJ−2 − .714.38POP16/65 (t=)

(0.6)

(7.2)

(3.0)

(1.6)

+ .013POP − 1.58M2AV + .13CB2 (3.2)

R = 60.3% 2

(−0.1)

D.W. = 1.7

(2.0)

MSE = 43.98

(21.1AA)

CD = .32(Y − TT ) + .31(TT ) − .16(G T&I ) − 7.17PR + .50DJ−2 (t=)

(6.6)

(−2.0)

(6.6)

(−3.2)

(4.5)

− .462.21POP16/65 + .016POP + 35.87M2AV + .09CB2 (−2.5)

R 2 = 86.7%

2 RAdj = 84.1%

(3.7)

D.W. = 2.1

(3.8)

MSE = 26.04

(3.9)

(16.1)

Notice that we use changes in total taxes (T T ) to represent tax deficits. This is because, as explained in Chapter 15, the effect of a change in taxes on consumption is estimated holding government spending (G T&I ), and the other variables in Eq. 16.1 constant, i.e., ceteris paribus. Since the coefficient measures the marginal effect on consumption of a tax change holding government spending constant, it is a measure of the effect of a tax-induced change in the government deficit on consumption The same is true for government spending. The effect of a change in government spending on consumption is estimated holding taxes and other variables in Eq. 16.1 constant. All variables in the model tested in Eq. 16.1 and in models tested in Table 16.1 models below were found stationary or cointegrated their dependent variable. All models except model #2 in Table 16.1 were tested using OLS since no explanatory variables were found endogenously

.14(2.2) – .04(−0.4)

.08(2.6) R 2 = .81 R 2 Adj = .77

.32(6.1) – .16(−3.4)

.09(3.9) R 2 = .87 R 2 Adj = .84

TDef : GDef : Sav: FB: CB :

Model 2

Model 1

Variable .44(6.8) – .25(−3.3) – .87(6.7) – .73(4.0) .10(4.3) R 2 = .89 R 2 Adj = .86

Model 3 .44(6.8) – .25(−3.3) – .19(–4.1) – .04(–0.4) .10(4.3) R 2 = .89 R 2 Adj = .86

Model 4

.05(1.9) R 2 = .85 R 2 Adj = .82

.30(5.3) −.12(1.5)

Model 5

Table 16.1 Effects of different loanable funds offset models on crowd out in consumption models

.43(6.8) – .25(–3.3) – .28(–6.2) – .14(–1.2) .10(4.3) R 2 = .89 R 2 Adj = .86

Model 6

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related to the dependent variable. In model #2, both modified crowd out variables were found endogenously related to consumption, the dependent variable. They were replaced by a Wald-strong instrument, were not found endogenously related to the dependent variable (Sargan test), and were estimated using 2SLS,). Newey–West standard errors were used to avoid heteroskedasticity problems. The models were estimated in first differences in the data to help reduce multicollinearity and stationarity problems. All models used in the rest of this book were required to pass the same tests before use, unless noted otherwise. In Table 16.1, we will also show results of testing variations of this standard model to see which produced the best estimates of crowd out effects of deficits and loanable funds: (1) Using the deficit to measure crowd out, unmodified by offsetting effects of changes in loanable funds. Testing indicates that both tax (T ) and spending (G) deficits create highly statistically significant crowd out problems, with t-statistics of (6.1) and (−3.4), respectively. (2) The model is then modified by subtracting any growth in loanable funds (S + FB) from initial measures of tax cut deficit crowd out (T ) and spending deficit crowd out (G). The new “modified” crowd out measures become T + (S + FB) and G−(S + FB). With this model, R 2 does drop noticeably from 86 to 81%. It also eliminates the statistical significance of the hypothesized modified crowd out effect associated with spending deficits, and lowers the significance level of tax cut deficits, though leaving them highly significant. The lowered R 2 suggests we have modified a reasonably accurate definition of crowd out effects by a modifier whose growth just does not affect consumer crowd out. Modifying known crowd out relationships given by (T ) and (G) alone, by a variable which has no effect on them, introduces an “error in variables” problem into our variables measuring crowd out, explaining the notable drop in the model’s ability to explain variation in consumption. There was some evidence, that growth in loanable funds tends to be channelled toward financing business borrowing, not consumer borrowing, which would be consistent with the observed effect. Alternatively, it may just mean changes in loanable funds have two, contradictory effect on consumption, leaving the net effect at or near zero. This also would reduce the statistical significance levels

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of the deficit variables and R 2 . Model (3) below attempts to correct for this possible problem. (3) Repeating step (2), but adding two separate stand-alone variables for savings (S) and foreign borrowing (FB). The theoretical basis for doing this is that, given constant income, increased saving, whatever its crowd out benefits, can only occur by reducing the marginal propensity to consume (mpc). Therefore, this mpc effect should be tested separately from our modification of the deficit variables, which is designed to capture just the positive effect of increases in loanable funds in reducing crowd out. Allowing for separately testing the two competing effects will avoid conflating them with test results obtained when testing for the net effect of these two influences by including only as separate stand alone (S) and (FB) variables, as we do in Model 4. If our theory in Model 3 is correct, we should get a negative sign on the coefficient for the stand-alone (S) variable. Test results for Model 3 indicate that the modified tax and spending crowd out effect variables retain the same coefficients and significance levels as before. The separate savings variable (S) is found to have the expected, negatively significant effect. The FB variable was also negatively significant. (4) The standard model with only the stand-alone separate variables (S) and (FB) included to represent the effects of (S, FB), in a “black box”/St Louis equation sort of way. No modifications of the deficit variables (T ) and (G) were included. These crowd out variables (T ) and (G) remained fully statistically significant, as did the stand-alone national savings variable (negatively), but the FB variable became insignificant. This suggests the FB variable has no significant “net” effect on consumption. (5) We then changed the hypothesized channel through which increases in loanable funds mitigate the crowd out problem: (a) from modification of the tax and spending deficit variables T + (S + FB), and G−(S + FB), to (b) modifying the size of the consumer borrowing variable (C B ) to (C B ) + (S + FB), but no separately entered stand-alone loanable funds variables. In tests, this reduces the R 2 , magnitude, and statistical significance of the consumer borrowing variable, suggesting this

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is either not the channel through which loanable funds offsets crowd out, or more likely, that the level of borrowing already incorporates the effects of changes in loanable funds that occurred during the year, and we again are just introducing “errors in variables.” Finally, (6) We redo step (5), except we add (S) and (FB) as stand-alone variables as well as include them in the consumer borrowing variable. Here the coefficient and significance of the consumer borrowing variable increase, as does the model’s R 2 to the levels found in models 3 and 4, where the modifier was used as a stand alone only, or coupled with modified deficit variable effects. This indicates we can model the way the effect of loanable funds is felt in reducing crowd out in three different ways modifying the deficit variables and adding a stand alone, only adding a stand alone, or modifying the consumer borrowing variable while including a stand alone. All three raise the R 2 by 2 percentage points over unmodified deficit baseline model, and all three have the same deficit variable and loanable funds coefficients. The estimates of crowd out marginal effects are actually larger when national saving and foreign borrowing are controlled for by inclusion as a separate variable. One possible explanation is that reducing the crowd out variables (T ) and (G) magnitude by the amount of (S + FB), does not change the basic relationship between crowd out and consumption, given by the crowd out variable’s coefficient. It just increases the magnitude of the coefficient on the crowd out variables to reflect the fact that the magnitude of the variables themselves has been reduced by the modifier. Foreign borrowing is probably related to the business cycle; the worse the economy, the less is available in the domestic part of the loanable funds pool, and the more foreign borrowing is relied on, which may be why the sign on the stand-alone foreign borrowing variable is negative. As we noted in Chapter 15, relative to the pool of loanable funds, borrowing in the U.S. remains strong even in recessions, as indicated by the small historic levels of excess bank reserves in recessions. Heim (2017b) noted that during the early 1980s recession period, the domestic part of the pool of loanable funds dropped far faster than did the demand for loans,

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and was accompanied by an increase in foreign borrowing was used to accommodate some of this borrowing need. Only when the loanable funds variables are included as stand-alone variables does the standard model’s explanatory power increase. Since there are two separate effects, one positive, one negative, that accompany a change in the loanable funds pool, either both ways have to be shown separately as we do in the model (#3) by including both deficit modifiers and stand alone (S) and (FB) variables, or only as a stand-alone variable (model #4), whose regression coefficient registers the net effect of the two forces. By comparing our findings, for models 3 and 4, we can compare the two separate effects and their net effect. From doing so it is clear that changes in loanable funds can mitigate the negative crowd out effects of deficits on consumption (if they couldn’t, the coefficients on the stand-alone variables in models #3 and #4 would be the same). However, if the increase in loanable funds results from reduced consumption (rather than income growth, or foreign borrowing growth that is not a substitute for declining domestic savings), the net effect on consumption is to reduce it. It may increase investment, as we show below.

16.2

Effects on the Investment Function

U.S. sales of domestically produced investment goods (I D ) are defined as total U.S. investment minus investment in imported machinery, industrial supplies, and materials. Demand for these products is also affected by changes in crowd out and any changes in the loanable funds pool that offsets crowd out. To obtain the most accurate possible estimates of their effects on investment, we must control for the effects of the other variables that affect investment. As noted earlier, Heim (2017a) reviewed a large number of past studies to see which variables were most commonly hypothesized to be determinants of investment and determined which of those were found to be consistent determinants of investment in different models and time periods. In this study, we include all of those variables as control variable to ensure, as best is possible, that we do not have a “left out variables” problem that could be improperly skewing our estimates of “left in” variables effects. The variables that survived this robustness testing were included in the following model, which we define as the “standard” investment model. In the model, current period values have no time period subscript; where

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subscripts are used, they indicate the number of year’s lags with which the variable is used: Data were taken from Tables in statistical appendix B in The Economic Report of the President 2012 (ERP 2012). Dependent Variable—Investment ID = =Total investment −imported capital goods and imported industrial supplies and materials

(B2, B104) Explanatory Variables—Investment (Y ) = The change in current year GDP (the accelerator) (B2) (T −G) = the consolidated deficit for all U.S. governmental entities taken collectively (B83) T = Deficits generated by tax cuts (our measure of crowd out caused by tax cuts) (B83) G = Deficits generated by government spending (our measure of crowd out caused by spending deficits) (B83) S = Gross U.S. saving = personal + corporate + depreciation + government (B32) FB = Foreign Borrowing (B32) PR−2 = the Prime interest rate, lagged two periods (B73) CapUtil = Level of capacity utilization (B54) Prof = Level of Profits (B28) All these variables were found statistically significant and robust to different time periods tested and robust to addition or subtraction of certain other variables in the equation. A loanable funds modified crowd out variable, i.e., either T + (S + LF) or G−(S + LF), are obtained by adding loanable funds as an offset to the tax variable or subtracting it from the spending deficit, just as was done earlier when testing consumption models. The modified crowd out variables used were derived as follows: (T − G) + (Loanable funds) = (T − G) + (S + FB) = T + β1 (S + LF) or G − β2 (S + LF) Precise data was not available to show how much of each increase in the loanable funds pool was channeled into consumer versus investment borrowing. To find the combination that best explained investment,

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287

several weighting schemes were tested. Tests indicated weights assigned to β 1 and β 2 (unexpectedly) did not affect the results, so typically in testing below, both weights were each taken to be 1.00, since that was the easiest formulation to use. Let us call the model above (exclusive of the national saving and foreign borrowing variables) the standard investment model. Estimates of the model’s parameters are given in Eq. 16.2 below, estimated from 1960–2010 annual data. The model is slightly different from that in Heim (2017a) in that no loanable funds modification to the (T ) and (G) variables has yet been made. Variables in this and similar models tested in Table 16.2 were found stationary or cointegrated. No variables in the models tested were found endogenously related to the dependent variable, so OLS was used for model estimation. Newey–West standard errors were used to avoid heteroskedasticity problems. The models were estimated in first differences in the data to help reduce multicollinearity and stationarity problems. We will, in this chapter, examine and test the question of whether deficits have related crowd out effects, and whether changes in loanable funds can offset these crowd out effects. In Table 16.2, we will examine crowd out effects in the standard model several ways: (1) Using only the deficit variables (T ) and (G) to measure crowd out, unmodified by loanable funds variables which may lessen the crowd out effects of deficits. Tests of this model indicate that both tax and spending deficits create highly statistically significant investment crowd out problems. Table 16.2 Results of different investment models of the effects of loanable funds on crowd out Variable

Model 1

Model 2

Model 3

Model 4

TDef : GDef : Sav: FB: :

.32(3.8) −.36(−4.6)

.14(2.2) −.11 (−1.5)

R 2 = .89 R 2 Adj = .87

R 2 = .90 R 2 Adj = .88

.22(1.9) −.23(−2.9) −.32(−1.4) −.24(−0.9) R 2 = .90 R 2 Adj = .89

.22(1.9) −.23(−2.9) .13(1.4) .21(1.8) R 2 = .90 R 2 Adj = .89

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(2) The deficit model in test 1 is changed by directly modifying the deficit variables (T ) and (G) by adding same-period changes in loanable funds (S + FB) directly to (T ) and subtracting it from spending deficit crowd out, i.e., the crowd out variables now become T +(S + FB) and G−(S + FB). In the consumption section above it was explained why the different signs on the loanable fund variable in the modified deficit variables were appropriate. With model #2, the modified tax cut deficit variable remains statistically significant, but significance levels decline. The spending deficits crowd out variable becomes statistically insignificant. R 2 increases marginally from 89 to 90%. More explanatory power and reduced statistical significance of crowd out variables suggests that increases in loanable funds do help offset crowd out effects, but the declining significance levels of the deficit variables suggests total loanable funds is at best an imperfect proxy for however much of the total goes into increased investment lending. (3) Repeating test #2, but adding two separate stand-alone variables (S) and (FB) to the model. As was the case with consumption, this effect should be tested separately to determine if there are two separate, and possibly conflicting, effects of a change in saving on investment. That said, a strong a priori argument can also be made that unlike consumption, a separate stand-alone variable is not needed. This is because increases in the savings pool definitionally result in increased investment, due to the savings = investment equality thought to hold in macroeconomics. For model #3, testing indicated that the (S + FB) modified crowd out effect variables remain statistically significant for both deficit variables, but the magnitude of their crowd out effect declines, though is still substantial. Both the separate savings variable (S) and the separate (FB) variable are statistically insignificant, indicating any second effect of these variables is not significantly different from zero. This suggests no stand-alone variables are needed. R 2 was the same as in model #2, so no explanatory power is lost by eliminating them. (4) For model #4, (S) and (FB) only included in the standard model as separate, stand-alone variables. The deficit variables (T ) and (G) were not modified by (S + FB). As stand-alone variables only, they would show the net effect of any two or more separate influences (S) and (FB) might have.

16

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The magnitude and statistical significance of the tax and spending crowd out variables, i.e., (T ) and (G), without the modifier remained exactly the same as in (3) where they were modified. The sign on both the stand-alone saving variable and the (FB) variable changed to positive, but only the (FB) variable was statistically significant. R 2 was the same as in model #2, so no explanatory power is lost by eliminating them, but with equal R 2 s, it is hard to say one model expresses the data better than the other (except on an “Occam’s Razor” basis). Above Eq. 16.2, we first show how well investment is explained by the standard model without crowd out variables (Eq. 17.3C). Note that it explains noticeably less variance than does Eq. 16.2 which includes the crowd out variables. Clearly investment crowd out, unless somehow offset, is also a real problem likely to reduce investment spending. ID = + .48(ACC) + .008POP + .76PR−2 + 7.37XRAV + 14.08CAP−1 (2.5)

(10.6)

(t=)

R 2 = 69.4%

(2.2)

(0.2)

(4.3)

D.W. = 1.6 MSE = 47.87

(Same as Eq. 17.3C) Baseline, No Modification Model: (S + FB) Not Used to Modify Deficit Variables, or as Two Stand Alones, (S) and (FB) ID = + .23(ACC) + .33(TT ) − .36(G T&I ) + 1.59CAP−1 (t=)

(6.19)

(−4.6)

(3.8)

(0.9)

− 4.22PR−2 + 6.77XRAV + .011POP (−2.1)

R = 88.6% 2

2 RAdj

(3.8)

= .87.0

(2.9)

D.W. = 1.8

MSE = 29.62

(16.2)

These models were compared using just one sample; its results should only be taken seriously if they can be replicated in other time periods. In Chapter 18, we shall retest these models in 90 different consumption and 18 different investment time periods, though the periods are partially overlapping. If we can replicate the results in other time periods, it will provide assurance our finding here are not spurious, but rather accurately represent how the U.S. economy responds to government deficits and crowd out and how changes in the pool of loanable funds affect can reduce the negative effects of those reactions.

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Chapter 14 also tests the (S + FB) variable as a modifier to the deficit variables (T ) and (G), with no stand alone (S) or (FB) modifiers, using the 1960−2010 sample, and arrived at the same conclusions: the ability to explain variation in consumption went down, the ability to explain variation in investment went up.

References Economic Report of the President. (2012, 2013). Washington, DC: Government Publications Office. Heim, J. J. (2017a). An Econometric Model of the U.S. Economy. Hoboken: Palgrave Macmillan. Heim, J. J. (2017b). Crowding Out Fiscal Stimulus. Hoboken: Palgrave Macmillan.

CHAPTER 17

Do Loanable Funds Modify the Crowd Out Effects of the One-Variable Deficit (T − G)?

In this chapter, we test to see if modifying the one-variable formulation of the deficit (T − G) by any changes in the total loanable funds pool, i.e., national savings plus foreign borrowing (S + FB), provides a better definition than the deficit alone of how much crowd out is actually created by deficits. The modified definition of crowd out is defined as (T − G) + (S + FB). We also separately add loanable funds (S + FB) to the model as a stand-alone variable. This is done to test the hypothesis that, for consumption anyway, an increase in the loanable funds pool has two separate and contradictory effects: it reduces crowd out, which increases consumption, a positive effect. But if disposable income is held constant, which is in the standard consumption model tested, increases in saving can only occur by reducing consumption, a negative effect; ceteris paribus, an increase in savings definitionally means a reduction in consumption.

17.1 Consumption Results When also Including (S + FB) as a Separate Variable In this section, we examine crowd out effects of both loanable funds— modified deficits and unmodified deficits. Crowd out is initially defined as the magnitude of the government deficit (T − G). The crowd out variable is then modified by any same-period change (S + FB) and redefined as the modified crowd out variable (T − G) + (S + FB). In Eq. 17.1AA, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. J. Heim, Why Fiscal Stimulus Programs Fail, Volume 1, https://doi.org/10.1007/978-3-030-65675-1_17

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a baseline standard consumption model without a deficit variable and without any loanable funds modifier is defined. In Eq. 17.1 below, the results are presented for the same consumption model adding the (T − G) deficit variable alone to define crowd out effects. Tests are based on the 1960–2010 data sample. Equation 17.2 presents results for exactly the same model, except the crowd out variable (T − G) is modified by the level of loanable funds (S + FB). In both cases, we have added a separate stand-alone control variable to the regression, also equal to the value of total loanable funds (S + FB). This is done to insure our modified crowd out variable coefficients only pick up the positive effect of crowd out reduction, and not the net of that effect plus the second negative effect resulting from the declining mpc necessary to raise mps and giving us the increase in loanable funds we are testing. The deficit variables, both modified by (S + FB) and unmodified, were found to be stationary (ADF test) as were the other variables in the consumption model except the dependent variable, consumption, and the government spending variable, both of which were detrended. No explanatory variables were found endogenous with the dependent variable (Hausman test). Newey–West standard errors were used to protect against heteroskedasticity, and variables are tested in first differences to reduce nonstationarity and multicollinearity. For comparison, the standard consumption model for the same time period (1960–2010) taken from (Heim 2017, Eq. 4.4TR) is also shown. It differs from this study’s Eqs. 17.1 and 17.2, in that the deficit is divided into two variables (T , G), and coefficients for each type of deficit effect are estimated separately. There are two additional ways Heim (2017, Eq. 4.4TR) is different than the models 17.1 and 17.2 below: 1. There is no stand-alone control variable (S + FB) in Heim (2017), and 2. The modifier of T and G for changes in the loanable funds pool is an improved version of the one used in Heim (2017). Standard Consumption Model from Heim (2017) CD = 0.29(Y − TT ) + 0.34(TT ) − 0.23(GT&I ) (t =)

(6.2)

(6.5)

(−4.5)

− 5.44PR + 0.48DJ−2 − 0.515.07POP16/65 (−2.1)

(5.1)

(3.2)

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DO LOANABLE FUNDS MODIFY THE CROWD OUT EFFECTS …

293

+ 0.020POP + 38.00M2AV + 0.09CB2 (6.0)

R = 87.8%

(3.7)

(4.9)

D.W. = 2.2

2

MSE = 24.88

(4.4TR)

This study’s Baseline (BL) Standard Consumption Model with no Deficit Variables Included, nor any loanable funds variables included (1960–2010 data). (2SLS strong instrument used for (Y −T ) used to eliminate endogeneity problem) CD = 0.54(Y − TT ) + 2.70PR + 0.27DJ−2 − 0.714.38POP16/65 (t =)

(0.6)

(7.2)

(3.0)

(1.6)

+ 0.013POP − 1.58M2AV + 0.13CB2 (3.2)

R = 60.3% 2

(2.0)

(−0.1)

Adj. R = 57.3%(est.) 2

D.W. = 1.7

MSE = 43.98 (17.1AA)

Standard 1960–2010 Consumption Model with Deficit Variable (T − G), i.e., “Crowd out” Added, But Before Adding Loanable Funds Variable (otherwise same as Model 20.1 below) (OLS no endogenous variables) CD = 0.29(Y − TT ) + 0.28(T − G) − 7.30PR + 0.49DJ−2 (t=)

(5.9)

(−3.1)

(6.3)

(4.8)

− 0.579.55POP16/65 + 0.021POP + 43.55M2AV + 0.10CB2 (−2.9)

R 2 = 85.9%

Adj. R 2 = 83.6%

(5.9)

D.W. = 2.1

(4.7)

(4.1)

MSE = 26.47 (17.1A)

The baseline equation before adding the deficit variable explains only 60.3% of the variation in consumption data over the period 1960–2010. When the deficit variable is added to the same model to account for the negative crowd out effects of deficits on consumption, explained variance rises to 85.9%, a 42% increase. To ensure this finding was not just a spurious, abnormal result caused by highly unusual conditions in the initial period sampled, we retested the same models in 17 additional time periods, shown in Table 17.1A. In every case, exactly the same result was obtained: adding the crowd out variable increased explained variance markedly, an average of 17.4% points for the 18 samples. Clearly, the crowd out effect of deficits has been a consistent problem throughout the past 50 years, both in recessions and in good times. The

a AR(1) required

20 baseline T.17.1A (w/Def)

Model

From Model# 20 baseline Eq. 17.1AA (w/oDef)

86

72

60

86

90

86

1960 –2000

89

43

1960 –1990

2 2 (Av. R = 88.8%); (Av. R Adj = 82.8%)

86

72

1960 –2007

(Av. R = 71.4%)

2

1960 –2008

1960 –2010

88

77

1960 –1980

93

91

1970 –1990

92

91

1970 –2000

85

68

1970 –2007

88

55

1970 –2010

93

86

1980 –2000

86

37

1980 –2010

86

63

1975 –2004

86

74

1980 –2004

83

67

1985 –2004

83

65

1985 –2005

99

83

1996 –2009

9/11 (T −G )

(T −G )

NA

a

100 14/18

NA NA

Test ratio T−G Signif. 95 NA

2000 –2010

Table 17.1A Growth in explained variance when adding unmodified crowd out to a standard model

294 J. J. HEIM

17

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DO LOANABLE FUNDS MODIFY THE CROWD OUT EFFECTS …

question we now wish to ask is: Will increases in loanable funds offset the crowd out effects of deficits? Standard Model from (17.1A) with Stand Alone Loanable Funds Variable (S + FB) Added CD = 0.36(Y − TT ) + 0.38(T − G) − 0.13(S + FB) − 6.37PR (t =)

(7.3)

(6.3)

(−6.1)

(−2.8)

+ 0.40DJ−2 − 0.533.54POP16/65 + 0.021POP (5.3)

(5.4)

(−2.5)

+ 42.35M2AV + 0.11CB2 (4.6)

(4.7)

R = 87.4% Adj. R = 85.0% 2

2

D.W. = 1.8

MSE = 25.29

(17.1)

Standard 1960–2010 Consumption Model from (20.1) with 1 Variable Crowd out, but after (T − G) Modified by the Loanable Funds Pool (S + FB) Variable Added. CD = 0.36(Y − TT ) + 0.38(T − G)m − 0.51(S + FB) (t=)

(7.3)

(−4.3)

(6.3)

− 6.37PR + 0.40DJ−2 − 0.533.54POP16/65 (−2.8)

(5.3)

(−2.5)

+ 0.021POP + 42.35M2AV + 0.11CB2 (5.4)

(4.7)

R = 87.4% Adj. R = 85.0% 2

2

(4.6)

D.W. = 1.8

MSE = 25.29

(17.2)

As expected from the modeling theory for consumption models developed in Chapters 15 and 16, increasing the loanable funds pool while holding disposable income constant has two contradictory effects on consumption. Equation 20.2 shows that After Total Loanable Funds (S + FB) Added to the Deficit variable, Showing (+0.38) Effect in Reducing the Crowd Out Variable’s impact, But this is More than Offset by a (−0.51) Effect of the Stand Alone (S + FB) Variable, Leaving a Net (−0.13) Effect (Using 1961–2010 Data). Equation 17.1 also shows the net of these two effects (−0.13). We know the increase in loanable funds is helping offset crowd out, because our estimate of its positive effect (a one-to-one reduction in the deficit’s crowd out effect, is consistent with the net effect obtained in a separate test), but the net effect test leaves R 2 unchanged, again indicating the two tests are saying the same thing. We conclude that any increase in the pool of loanable funds that comes by raising national savings by diverting money from consumption will

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have a net negative effect on consumption (even though it reduces crowd out) due to this redirecting effect. Note in unmodified model, Eq. 17.1, the coefficient (t -statistic) on the deficit (crowd out) variable is = 0.38 (t = 6.3), signifying a large, highly statistically significant, crowd out effect. When the model is retested in Eq. 17.2 with the crowd out variable modified to show deficit values reduced by any same-period growth in loanable funds, this coefficient and t-statistic remains unchanged. This is also true for the coefficients and t-statistics on all other variables in the model except the stand-alone variable. In Eq. 17.1, the unmodified deficit model, the coefficient on the stand-alone loanable funds variable (S + FB) is −0.13. This proves to be identical to the sum of the coefficient on the (S + FB) part of the modified deficit (T − G) + (S + FB), which is =0.38, and the coefficient on the stand-alone (S + FB) variable, which is −0.51. In short, the two models are equivalent, each showing the loanable funds variable to have the same total effect on consumption. In Eq. 17.1, the positive effect of the change in the loanable funds pool on crowd out is real, but was intentionally not used to modify the crowd out variable directly, and hence, our crowd out variable did not show it. The stand-alone variable had to show the net of the change in consumption due to shifting spending from consumption to saving (−0.51), and the +0.38 effect had in reducing the deficit. This left a net (−0.13) effect on consumption of the change in the loanable funds pool. Hence, if estimates of net effect are desired, it is actually unnecessary to modify the crowd out variable to account for the change in loanable funds, as long as a stand-alone version of the total loanable funds variable is included in the model. This equivalence, we might add, is only true if exactly the same variable is used as a modifier of the deficit (T −G) as is used as a standalone variable. Also, as we will show with investment further below, if loanable funds only have one effect, or more than one effect, but both in the same direction, the stand-alone variable “competes” with the modified deficit variable to explain the same variance, often leaving both statistically insignificant, when including just one of the two would leave it significant. Coefficients and significance levels of the crowd out variable in both unmodified (T −G) and loanable funds-modified form (T −G) + (S + FB) are presented in Table 17.1 for the time period 1960–2010. To ensure the results were replicable, and not just spurious, the model was also separately tested in 17 other different time periods, though some of the periods partially overlap. The results, presented in Table 17.1, indicate

17

DO LOANABLE FUNDS MODIFY THE CROWD OUT EFFECTS …

297

Table 17.1 Crowd out effects on consumption, with and without offsetting changes in loanable funds Variable ( T−G)

1960–1980

1960–1990

w/o

with

w/o

with

0.48a

0.48a

0.31a

0.31a

1960–2000 w/o

with

1960–2007 w/o

with

1960–2008 w/o

with

1960–2010 w/o

Coef: t-stat (1.8) (1.8) (4.4) (4.4) 0.88 0.88 0.91 0.91 R2 0.76 0.76 0.86 0.86 Adj. R 2 a AR(1) required

0.22 0.22 0.37 0.37 0.36 0.36 0.38 (2.6) (2.6) (5.9) (5.9) (5.1) (5.1) (6.3) 0.90 0.90 0.87 0.87 0.87 0.87 0.87 0.87 0.87 0.84 0.84 0.84 0.84 0.85

Variable ( T−G)

1970–1990

1970–2007

Coef: t-stat R2 Adj. R 2

0.23 0.23 0.13 0.13 0.36 0.36 0.37 0.37 0.01 0.01 0.37 (2.4) (2.4) (1.1) (1.1) (4.3) (4.3) (5.2) (5.2) (0.1) (0.1) (4.8) 0.94 0.94 0.92 0.92 0.87 0.87 0.88 0.88 0.93 0.93 0.89 0.90 0.90 0.89 0.89 0.83 0.83 0.85 0.85 0.89 0.89 0.80

Variable ( T−G)

1975–2004

Coef: t-stat R2 Adj. R 2

0.27 0.27 0.29 0.29 0.28a 0.28 0.29a 0.29 0.69 0.69 0.42b (2.7) (2.7) (2.7) (2.7) (1.2) (1.2) (1.0) (1.0) (3.7) (3.7) (4.4) 0.85 0.85 0.85 0.85 0.83 0.83 0.83 0.83 0.95 0.95 0.99 0.80 0.80 0.78 0.78 0.65 0.65 0.65 0.65 0.88 0.88 0.97

w/o

w/o

with

with

1970–2000 w/o

with

1980–2004 w/o

with

w/o

with

1985–2004 w/o

with

1970–2010 w/o

with

1985–2005 w/o

with

1980–2000 w/o

with

1996–2010 w/o

with

with 0.38 (6.3) 0.87 0.85

1980–2010 w/o

with 0.37 (4.8) 0.89 0.80

2000–2010 w/o

with 0.42b (4.4) 0.99 0.97

a AR(1) required; b unresolvable autocorrelation in the model

that in 14 of 18 time periods, the unmodified crowd out variable had a statistically significant negative effect on consumption (+β1 (T − G)). The 4 periods tested for which it was insignificant were periods where 1990s data, which shows “crowd in” due to declining deficits, was a large part of the sample tested and tended to cancel out significant crowd out effects found in other periods included in the sample. After modification, the same 14 of 18 showed significant crowd out effects. In Table 17.1, 14 of the 18 time periods sampled, the crowd out variable (T − G) had a marginally or fully statistically significant negative effect on consumption, both before and after being modified by any change in the size of the loanable funds pool.

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For 4 of the tests, no crowd out problem was found, even before the (T , G) modifiers were added. As noted in previous chapters, nonsignificant results can occur in an occasional sample even when there is an underlying relationship between variables that is systematic enough to be statistically significant, for two reasons: • If during a certain period, one does not move. You can’t show correlation between two variables when one does not move and the other does. • If statistical problems arise in one sample not found in others, e.g., multicollinearity, or simply the selection of an “outlier” sample from the distribution of possible samples that can be drawn. • When part of the time period sampled, consumption was rising, but the deficit falling (1990s), and in the other part of the period sampled, both consumption and the deficit were both rising (1980s). Hence, the relationship between the two variables would have been statistically significant in each decade, if each decade was tested alone, with large magnitude coefficients, one with an apparent positive relationship to consumption and one with a negative relationship to consumption. However, when combined, the coefficient (a weighted average of adding positive and negative values) is reduced to a net coefficient value near zero and with each observation representing a large magnitude plus or minus from the weighted mean value (the coefficient), the standard error (which like any standard deviation measures the extent to which individual values are unlike their average value) becomes very large and the result becomes statistically insignificant.

17.2 Consumption Results When not Including (S + FB) as a Separate Variable Table 17.2 shows results of testing the Table 17.1 model, except without a separate stand-alone loanable funds variable. The stand-alone loanable funds variable is deleted to test the hypothesis that increases in the loanable funds pool only have one effect on consumption; they increase it by modifying (reducing) the crowd out effect of deficits on borrowing by private borrowers. Therefore, below we test the hypothesis it may be just duplicative to include (S + FB) in the same equation both as part

w/o 0.09 (1.3) 0.92 0.90

1970–1990

w/o 0.16 (2.2) 0.93 0.90

Variable ( T−G)

a AR(1) required

Coef: t-stat R2 Adj. R 2

Variable ( T−G)a

0.13 (3.8) 0.85 0.79

0.24 (3.0) 0.86 0.78

w/o

w/o

0.25 (3.6) 0.86 0.79

1980–2004

1975–2004 with

with 0.05 (1.2) 0.92 0.87

1970–2000

0.09 (1.9) 0.85 0.76

0.41a (2.8) 0.88 0.77

Coef: t-stat R2 Adj. R 2 a AR(1) required

Coef: t-stat R2 Adj. R 2

w/o

w/o 0.21a (2.3) 0.89 0.84

1960–1990 with

1960–1980

Variable ( T−G)

0.12 (2.6) 0.86 0.77

with

with 0.03 (0.9) 0.92 0.89

0.09 (1.9) 0.85 0.79

with

0.12 (2.3) 0.82 0.66 (2.6) 0.83 0.68

with

0.25a

with 0.10 (3.5) 0.81 0.76

0.07 (2.2) 0.89 0.86

with

w/o

1985–2004

w/o 0.25 (4.9) 0.85 0.82

1970–2007

0.16 (2.4) 0.90 0.87

w/o

1960–2000

(3.3) 0.83 0.68

0.28a

w/o

1985–2005

w/o 0.27 (6.2) 0.88 0.83

1970–2010

0.27 (4.9) 0.86 0.83

w/o

1960–2007

0.14 (3.0) 0.82 0.67

with

with 0.12 (4.4) 0.81 0.77

0.11 (3.1) 0.81 0.78

with

(24.3) 0.99 0.80

0.53a

w/o

1996–2010

w/o 0.02 (0.2) 0.93 0.89

1980–2000

0.27 (5.0) 0.86 0.84

w/o

1960–2008

(1.7) 0.97 0.67

0.24a

with

with 0.01 (0.2) 0.93 0.89

0.12 (3.2) 0.81 0.78

with

(1.8) 100 0.96

0.11a

w/o

2000–2010

w/o 0.28 (5.3) 0.85 0.81

1980–2010

0.28 (6.3) 0.86 0.84

w/o

1960–2010

0.06a (1.2) 0.97 0.89

with

with 0.12 (3.6) 0.79 0.73

0.12 (4.2) 0.81 0.78

with

Table 17.2 Here crowd out effects on consumption, with and without offsetting changes in loanable funds (no standalone S + FB)

17 DO LOANABLE FUNDS MODIFY THE CROWD OUT EFFECTS …

299

300

J. J. HEIM

of the crowd out variables modifying their deficit effects and then again as a stand-alone variable. With this hypothesis, two separate effects on consumption of a change in loanable funds do not need to be separately represented. Conclusions regarding Table 17.2. In Table 17.2, 14 of 18 tests show statistically significant crowd out when estimated with or without the modifier (S + FB) being attached to the deficit variable (T − G). This we take as indicating conclusively that there is a crowd out problem associated with government deficits. With the (S + FB) modifier added to the deficit variable, all 14 had smaller coefficients, significance levels, and R 2 s, meaning the modified crowd out variable explained crowd out’s real relationship with consumption less well than the deficit variable alone. The reduced significance levels are because we are making very large modifications to the deficit, which we know has a highly significant negative relationship to consumption with a variable that has a previously determined near-zero net effect (−0.13 in Eq. 17.1) on consumption. Hence, we are distorting a highly statistically significant relationship between the deficit alone and consumption by reducing the deficit’s magnitude each year by (S + FB), whose net effect on consumption of near zero leaves it essentially a random variable deduction from the deficit. This is likely to reduce the variable’s statistical significance and the equation’s overall ability to explain variance (R 2 ). That is what did happen. That is why the model that includes the stand-alone variable, in Eq. 17.1 and Table 17.1, is to be preferred; it allows separate estimation of the substantial positive and negative effects on consumption of an increase in loanable funds, ceteris paribus. Conclusions Regarding Consumption The consumption function with a stand-alone loanable funds variable is the preferred model. It explains more variance (1.6% points on average for 18 periods tested) than the same model without any loanable funds variable. It also explained the data better than the same model with only a deficit-modifying (S + FB) variable, but no stand-alone (S + FB) variable. In those models, R 2 actually declined relative to the no (S + FB) base line model by an average of 2.7% points in the 18 periods tested. The poor showing appears to be the result of changes in loanable funds having negative as well as positive effects on consumption which tend to cancel each other out, and which need to be shown separately in the

17

DO LOANABLE FUNDS MODIFY THE CROWD OUT EFFECTS …

301

model, i.e., by modifying the deficit variable and by also including it as a separate variable in the consumption model.

17.3 Investment Results When also Including (S + FB) as a Separate Variable In Table 17.3, results are presented for 18 tests comparing unmodified (T −G) crowd out and modified (T −G) + (S + FB) crowd out variable coefficients and significance levels. The same model is tested in all 18 tests; only the time period tested differs between models. The 17 follow-up tests are done to ensure initial results are replicable, indicating an enduring underlying economic relationship, not just an idiosyncratic finding peculiar to the initial sample. Test samples generally contain 20 or 30 observations taken from different parts of a 50-year sample of data, 1960–2010. The samples all cover different, but sometimes overlapping, periods of time, e.g., 1970–2000 and 1960–1990. If the underlying relationship is stable over the 50 years, and we are controlling adequately for all the other variables that can affect consumption, we should get the same fit of the model from decade to decade, as was obtained in Heim (2017) for essentially the same consumption and investment models when comparing the model’s fit in different decades of the same 50-year period. (And the author hopes readers will allow an editorial opinion offered at this point: this mechanical consistency of underlying economic relations between variable is what makes economics, at least macroeconomics, so much more like real science than the other social sciences. It is also why well-designed structural models with reasonably complete sets of control variable for all variables that affect the dependent variable in any equation, tend to explain the economy’s behavior in decades beyond the same period a well as the do the decades within the sample period. And better that VAR or DSGE models. See out-of-sample comparisons of the three types done in Heim (2017). It is more accurately thought of as an engineering science than as the type of (non)science one typically thinks of when one thinks of “social science.”) Now back to this chapter analysis of investment functions and their replicability in multiple time periods: Below, we compare this study’s results to the crowd out effect found in a recent study that also used the “standard model.” There a loanable funds modifier to deficit size was used when defining crowd out effects, but no additional, separate, stand-alone loanable funds variable

302

J. J. HEIM

was included (Heim 2017, Eq. 5.4TR). This model’s results are provided to show continuity with the prior literature when examining this paper’s “standard model” given in Eqs. 17.3 and 17.4. The models used in Eqs. 17.3 and 17.4 use the same time period robust determinants, modified only by the change from a two-variable definition of the deficit to the one-variable definition (T − G) used here. Standard, Time Period Robust, Investment Model, with Modified Deficit Variables from Heim (2017) ID = + 0.26(ACC) + 0.27(TT ) − 0.30(G T&I ) + 0.011POP (t=)

(8.7)

(2.9)

(5.7)

(−3.8)

− 4.72PR−2 + 6.81XRAV + 2.55CAP−1 (2.9)

(−2.7)

R = 83.3%

D.W. = 2.0

2

(1.7)

MSE = 28.25

(5.4TR)

This Study’s Baseline Model has No Deficit Variables or Loanable Funds Variables Included, but GDP Variable is Included to Control for the State of the Economy: ID = + 0.47(ACC) − 0.00POP − 0.85PR−2 (t=)

(0.0)

(4.0)

(−0.3)

+ 5.21XRAV + 10.39CAP−1 + 0.10GDP (2.0)

R = 76.1% 2

(−1.3)

(29)

Adj. R = 73.2% 2

D.W. = 2.1

MSE = 43.06 (17.3B)

Same as Eq. 18.10C below. Standard investment model without deficit and loanable funds variables: (from Chapter 13; a 2SLS strong instrument for Accelerator used), but no GDP variable included: ID = + 0.48(ACC) + 0.008POP + 0.76PR−2 (t=)

(2.5)

(10.6)

(0.2)

+ 7.37XRAV + 14.08CAP−1 (2.2)

R = 69.4% 2

(4.3)

Adj. R = 66.7 2

D.W. = 1.6

MSE = 47.87

(17.3C)

This Study’s Standard “Baseline” Investment Model with 1 Variable Deficit Variable (T − G), before Adding Deficit Modifiers (Using

17

303

DO LOANABLE FUNDS MODIFY THE CROWD OUT EFFECTS …

1961–2009 data) ID = + 0.26(ACC) + 0.32(TT − G T&I ) + 0.011POP (t=)

(6.5)

(8.3)

(5.5)

− 4.51PR−2 + 8.86XRAV + 2.66CAP−1 (3.4)

(−2.4)

R = 88.7% 2

Adj. R = 87.4 2

(1.6)

D.W. = 1.9

MSE = 29.00

(17.3A)

The baseline equation before adding the deficit variable explains only 69.4% of the variation in consumption data over the period 1960–2010. When the deficit variable is added to the same model to account for the negative crowd out effects of deficits on consumption, explained variance rises to 88.7%, a 28% increase. To ensure this finding was not an anomaly, the same models were retested in 17 additional time periods, shown in Table 17.2A. In every case, exactly the same result was obtained: adding the crowd out variable increased explained variance markedly, an average of 17.4% points for the 18 samples, verifying that the crowd out problem caused by deficits is real. The crowd out effect of deficits has been a consistent problem throughout the past 50 years, both in recessions and in good times. The question we now wish to ask is: Will increases in loanable funds offset the crowd out effects of deficits? Standard Investment Model with 1 Variable Crowd out (T − G), After Adding Separate Accommodating FR purchases (Using 1961–2009 data) ID = + 0.22(ACC) + 0.23(TT − G T&I ) + 0.13(S + FB) (t=)

(5.5)

(2.7)

(1.7)

+ 0.008POP − 3.72PR−2 + 7.93XRAV + 2.03CAP−1 (4.0)

R = 90.3% 2

(−2.0)

Adj. R = 89.0% 2

(2.7)

D.W. = 1.9

(1.2)

MSE = 27.38

(17.3)

Adding the separate loanable funds variable increases explained variance 1.6% points and indicates increases in the loanable funds pool have a positive ($0.13) effect on investment for every dollar increase in the pool. Hence, we again have proof deficits reduce investment ($0.23 per dollar of deficit) and that same-period increases in loanable funds can partially or fully offset that depending on the actual magnitude of the loanable funds increase. (Notice adding the deficit variables to the model increased R 2 much more than adding the loanable funds variable to the model afterword.

From Model#

69

1960 –2010

67

1960 –2008

2

66

1960 –2007

76

2

70

71

a AR(1) required

2

87

80

(Av. R = 87.3%); (Av. R Adj = 83.8)

2

(Includes GDP control variable) (Av. R = 79.8%) 20 baseline Eq. 20.5 89 86 83 (w/Def)

20 baseline T.20.3B (w/o Def)

63

1960 –2000

(Does not include GDP control variable) (Av. R = 68.3%)

20 baseline T.20.3C (w/o Def)

Model

85

78

65

1960 –1990

92

91

72

1960 –1980 −61

87

82

1970 –1990

89

81

56

1970 –2000

83

71

65

1970 –2007

89

76

72

1970 –2010

88

81

69

1980 –2000

89

80

77

1980 –2010 −64

84

80

1975 –2004

82

81

71

1980 –2004

83

75

63

1985 –2004

83

75

57

1985 –2005

Table 17.2A Growth in explained variance when adding crowd out to a standard model

96

93

92

1996 –2009

a

NA

(T − G ) 10/11 (T − G )

97 17/18

a

NA

NA

NA

95 NA

NA

NA

91 NA

2000 Test ratio –2010 T−G Signif.

304 J. J. HEIM

17

305

DO LOANABLE FUNDS MODIFY THE CROWD OUT EFFECTS …

This is because the deficit and loanable funds variable are highly multicollinear (r = 0.83). To a large extent, before adding the loanable funds variable, the coefficients on the deficit variables represented the effect of the deficit net of offsetting “left out” variable (loanable funds) effects. Adding the loanable funds variable only picked up effects of the variable not perfectly correlated with movements in the deficit. Hence, the increase in R 2 , though real, is relatively small.) Next, we consider the Standard Investment Model with 1 Variable Crowd out (T − G), adjusted for accommodating FR purchases (Using 1961–2009 data) ID = + 0.22(ACC) + 0.23(TT − G T&I )m − 0.10(S + FB) (t=)

(5.5)

(−0.6)

(2.7)

+ 0.008POP − 3.72PR−2 + 7.93XRAV + 2.03CAP−1 (4.0)

R = 90.3% 2

(−2.0)

(2.7)

Adj. R 2 = 89.0%

D.W. = 1.9

(1.2)

MSE = 27.38

(17.4)

Note in Eq. 17.3 the coefficient (t-statistic) on the deficit (crowd out) variable is 0.23 (t = 2.7), signifying a highly significant, large magnitude crowd out effect. In Eq. 17.4, with the loanable funds, modified crowd out variable has the same coefficient and t-statistic results for the crowd out variable (and all other variables except one) are the same, as is R 2 as obtained in the model with only a stand-alone (S + FB) variable (Eq. 17.3). The only difference is the coefficient and t-statistic on the stand-alone (S + FB) variable. These have changed for reasons discussed earlier in analysis of the consumption function changes when the (S + FB) variable was added, where we had the same result. In Table 17.3, we repeat the tests undertaken in Eqs. 17.3–17.4, for 18 different, though sometimes overlapping, time periods to show the robustness over time of our results. Eleven of the 18 periods sampled showed crowd out had a statistically significant negative effect on consumption before and after adjusting for hypothesized offsetting changes in the loanable funds pool due to changes in savings and foreign borrowing levels. Recall that for consumption, using the same loanable funds modifier (S + FB), 14 out of 18 tests of the same periods showed statistically significant crowd out effects before and after the crowd out variable was modified. No statistically significant crowd out problem was found in the other seven investment periods tested, before or after the (T , G) modifiers were

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Table 17.3 Effects of crowd out on investment, with and without loanable funds modification of the deficit; stand alone variable included Variable ( T−G)

1961–1980

Coef: t-stat R2 Adj. R 2

0.01 0.01 0.14 0.14 0.21 0.21 0.15 0.15 0.23 0.33 0.23 (0.1) (0.1) (1.8) (1.8) (2.1) (2.1) (2.1) (2.1) (2.7) (2.7) (2.7) 0.96 0.96 0.91 0.91 0.91 0.91 0.87 0.87 0.88 0.88 0.90 0.91 0.91 0.88 0.88 0.89 0.89 0.85 0.85 0.86 0.86 0.89

Variable ( T−G)

1970–1990

Coef: t-stat R2 Adj. R 2

0.13 0.13 0.22 0.22 0.16 0.16 0.24 0.24 0.20 0.20 0.22 (1.1) (1.1) (1.8) (1.8) (1.9) (1.9) (2.6) (2.6) (1.2) (1.2) (2.0) 0.90 0.90 0.91 0.91 0.87 0.87 0.91 0.91 0.90 0.90 0.90 0.85 0.85 0.88 0.88 0.85 0.85 0.88 0.88 0.85 0.85 0.88

Variable ( T−G)

1975–2004

Coef: t-stat R2 Adj. R 2

0.12 0.12 0.07 0.07 0.18 0.18 0.18 0.18 0.25 0.25 0.24 (1.2) (1.2) (0.6) (0.6) (1.6) (1.6) (1.5) (1.5) (1.7) (1.7) (1.1) 0.88 0.88 0.87 0.87 0.84 0.84 0.87 0.87 0.97 0.97 0.96 0.84 0.84 0.83 0.83 0.78 0.78 0.78 0.78 0.94 0.94 0.88

w/o

w/o

w/o

with

with

with

1961–1990 w/o

with

1970–2000 w/o

with

1980–2004 w/o

with

1961–2000 w/o

with

1970–2007 w/o

with

1985–2004 w/o

with

1961–2007 w/o

with

1970–2009 w/o

with

1985–2005 w/o

with

1961–2008 w/o

with

1980–2000 w/o

with

1996–2009 w/o

with

1961–2009 w/o

with 0.23 (2.7) 0.90 0.89

1980–2009 w/o

with 0.22 (2.0) 0.90 0.88

2000–2009 w/o

with 0.24 (1.1) 0.96 0.88

added. As noted earlier, non-significant results can occur in an occasional test even when there is an underlying significant relationship between variables. In this case, there were 7 of the 18 test periods in which “crowd in” data was mixed with “crowd out” period data in amounts between 33 and 50% of the total. As shown in Chapter 18, in this model, crowd in and crowd out effects have different signs and this causes mixed samples to look insignificant even though both parts, when tested, separately, are significant. Deleting these seven samples, we find 9 of the 11 remaining deficits showing statistically significant negative crowd out effects, before and after modification.

17

DO LOANABLE FUNDS MODIFY THE CROWD OUT EFFECTS …

307

17.4 Investment Results When not Including (S + FB) as a Separate Variable In theory, changes in loanable funds have two possible effects on investment, both positive: 1. The change can offset crowd out, or 2. If the change is larger than needed to offset crowd out, it may increase total business borrowing and spending on investment. Both effects can be picked up by just modifying the deficit variable. In years, in which the growth in loanable funds is less than the growth in the deficit, the modified variable still has a remaining negative magnitude, indicating crowd out has a negative effect. In years when the growth is in excess of the growth in the deficit, the coefficient on the modified deficit variable becomes positive indicating “crowd in”, i.e., in a world where S = I showing the change in S in loanable funds had a positive effect on investment. In either case, the modification in the deficit is associated with what is really happening to investment. Unlike consumption, it is not clear the separate stand-alone (S + FB) variable is needed; there is no theory suggesting additional effects that have to be measured separately. With our earlier consumption example, modifying the deficit by a number which had little or no net effect on consumption reduced its ability to show its positive deficit’s crowd out effects crowd out. Therefore, a way of separating its two offsetting effects had to be found. Adding a stand alone to pick up the negative effects did that. In Table 17.4, we repeat these tests for 18 different, though sometimes overlapping, time periods to show the robustness over time of our results. Table 17.4 shows that in every period, even with accommodation by growth in loanable funds, there has been a net negative effect on investment that is at least marginally statistically significant. This clearly indicates that the no stand-alone (S + FB) variable model is needed. The results of the model generally show increased R 2 and significance levels for the deficit variable when we define the amount of the deficit that causes a crowd out problem to be the deficit minus any growth in the loanable funds pool. Hence, the crowd out problem can be reduced by policies that increase the size of the privately available portion of the loanable funds pool. Since the pool can grow for endogenous (business cycle

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J. J. HEIM

Table 17.4 Effects on investment of crowd out, with and without same-period changes in loanable funds Variable ( T−G)

1961–1980

Coef: t-stat R2 Adj. R 2

0.33 0.23 0.38 0.24 0.41 0.23 030 (3.7) (6.1) (4.0) (6.7) (7.8) (10.1) (3.9) 0.92 0.95 0.85 0.90 0.87 0.90 0.83 0.90 0.93 0.82 0.88 0.86 0.89 0.81

Variable ( T−G)

1970–1990

1970–2000

1970–2007

1970–2009

1980–2000

1980–2009

w/o

with

w/o

with

w/o

with

w/o

with

w/o

with

w/o

with

Coef: t-stat R2 Adj. R 2

0.43 (4.6) 0.87 0.82

0.25 (6.5) 0.90. 0.87

0.43 (9.5) 0.89 0.86

0.23 (10.0) 0.91 0.89

0.30 (3.6) 0.83 0.81

0.17 (5.3) 0.87 0.85

0.33 (5.0) 0.89 0.88

0.18 (6.8) 0.91 0.89

0.45 (7.2) 0.88 0.84

0.22 (7.1) 0.90 0.86

0.31 (3.7) 0.89 0.87

0.17 (5.0) 0.90 0.89

Variable ( T−G)

1975–2004

Coef: t-stat R2 Adj. R 2

0.29 0.18 0.25 0.16 0.24 0.14 0.25 0.14 0.29 0.15 0.28 (3.1) (4.1) (2.4) (3.2) (3.0) (3.4) (3.2) (3.5) (1.7) (2.5) (1.5) 0.84 0.87 0.82 0.86 0.83 0.84 0.83 0.84 0.96 0.97 0.94 0.81 0.85 0.78 0.83 0.78 0.79 0.78 0.80 0.94 0.94 0.89

w/o

w/o

with

with

1961–1990 w/o

with

1980–2004 w/o

with

1961–2000 w/o

with

1985–2004 w/o

with

1961–2007 w/o

with

w/o

with

1961–2009 w/o

0.17 0.23 0.19 0.33 (5.8) (2.6) (6.0) (5.5) 0.87 0.86 0.88 0.89 0.86 0.84 0.87 0.87

1985–2005 w/o

1961–2008

with

1996–2009 w/o

with

with 0.18 (7.8) 0.91 0.88

2000–2009 w/o

with 0.16 (2.1) 0.96 0.90

related) and exogenous (FR securities purchases related) reasons, these findings mean that crowd out is a real problem for investment, but that there is clear evidence increases in the loanable funds pool can offset it, and, as shown in later chapters, that FR accommodative monetary policy can increase the size of the pool. Below, we compare this study’s results to the crowd out effect found in testing in a recent study using the standard model (Heim 2017), where a total saving and foreign borrowing definition of the loanable funds modifier to deficit size was used, but where no separate control variable loanable funds were used (Heim 2017, Eq. 5.4TR). The Heim (2017) model results are compared with a version of this paper’s “standard model” given below in Eqs. 17.5 and 17.6. It is the same standard model, modified only by a changed definition of how the (S + FB) modifier was used to offset government deficits. Here, unlike Eqs. 17.3 and 17.4, there is no addition of a stand-alone loanable funds variable.

17

309

DO LOANABLE FUNDS MODIFY THE CROWD OUT EFFECTS …

Standard Time Period Robust Investment Model from Heim (2017). (Using 1960–2010 data) ID = + 0.26(ACC) + 0.27(TT ) − 0.30(G T&I ) (t=)

(8.7)

(2.9)

(−3.8)

+ 0.011POP − 4.72PR−2 + 6.81XRAV + 2.55CAP−1 (5.7)

R = 83.3%

(−2.7)

D.W. = 2.0

2

(2.9)

(1.7)

MSE = 28.25

(5.4TR)

This Study’s “Baseline” Standard Investment Model, Including 1 Variable Crowd out (T − G), before modification by (S + FB) (Using 1961–2009 data—2SLS Used) ID = + 0.26(ACC) + 0.33(TT − G T&I ) + 0.011POP (t=)

(6.5)

(8.3)

(5.5)

− 4.51PR−2 + 8.86XRAV + 2.66CAP−1 (3.4)

(−2.4)

R = 88.7% 2

Adj. R = 87.4% 2

(1.6)

D.W. = 1.9

MSE = 29.19

(17.5)

Standard Investment Model with 1 Variable Crowd out (T − G) modified by (S + FB). No Separate Stand Alone (S + FB) Variable Used. (Using 1961–2009 data) ID = + 0.22(ACC) + 0.18 (TT − G T&I )m + 0.007POP (t=)

(6.1)

(7.8)

(5.0)

− 3.39PR−2 + 7.40XRAV + 2.05CAP−1 (2.7)

(1.9)

R = 90.5% 2

Adj. R = 89.3% 2

(1.2)

D.W. = 1.9

MSE = 26.90

(17.6)

Note in Eq. 17.5 the coefficient (t-statistic) on the deficit (crowd out) variable is 0.33 (t = 5.5), signifying a highly significant, large magnitude crowd out effect. Adding the modifier in Eq. 20.6 increases R 2 by 1.8% points, reduces the measured magnitude of the crowd out effect downward to (T − G) − (S + FB), and also reduces its estimated marginal effect to ($0.18 per dollar of modified deficit). Statistical significance is improved. The modified version given in Eq. 17.6 appears to be the better model. Seventeen of 18 periods sampled showed crowd out had a statistically significant negative effect on investment before adjusting for offsetting changes in the loanable funds pool. After adjustment, all 18 showed

310

J. J. HEIM

significant crowd out effects. More important, when the deficit variable was modified by the change in loanable funds, the “crowd out effect” became even more statistically significant and explained more of the variance in the data. This supports the notion that the unmodified deficit variable (T − G) is the “errors in variables” version of crowd out’s true effect on investment, and that the modified version (T − G) + (S + FB) more accurately captures the real effect of deficit-induced crowd out on investment. Finally, we note the no stand-alone version of the investment model explains slightly more of the variance in investment than does the version with a stand-alone variable (90.5% vs. 90.3%). The virtually identical R 2 s provide some basis for saying the model with and the model without the stand-alone variable provide only marginally different results. Conclusions Regarding the Effects of Changes in the Total Loanable Funds Pool on Investment Both model with and the model without a stand-alone (S + FB) variable show that deficits have a statistically significant negative effect on investment, and that the effect can be partially or fully offset by increases in the pool of loanable funds, provided they are large enough. The model that modifies the deficit variable, but does not include the stand-alone variable (S + FB) appears to provide a marginally better description of how the deficit and changes in loanable funds affect the crowd out problem.

17.5 Comparing the Effects of Exogenous (FR Purchases Induced) and Endogenous (Economic Driven Change Induced) Loanable Funds Growth 17.5.1

Effects on Consumption

Previous sections indicate crowd out effects can be modified by growth in loanable funds. In this section, we alter slightly the standard model (Eq. 17.2) used earlier in this to see if increases in loanable funds from endogenous sources (changes in the business cycle or mpc) reduce crowd out as much as changes from exogenous sources (FR securities purchases). The model we will test is the same as Eq. 17.2 above except that we are dividing the modified deficit variable used previously (T −G) + (S + FB), into two separate variables, the deficit plus

17

DO LOANABLE FUNDS MODIFY THE CROWD OUT EFFECTS …

311

1. Total loanable funds minus the part of it generated by FR purchases: (T −G) + (S + FB – Tr − A), and 2. The deficit plus only FR purchases: (T −G) + (Tr + A). The new model is shown in Eq. 17.7 below. Note that total (S + FB) is kept as a stand-alone variable. CD (t=)

= 0.31(Y − TT ) + 0.19((T − G) + (S + FB) − (Tr + A)) (5.1)

(7.3)

+ 0.17((T − G) + (Tr + A)) − 0.37(S + FB) (3.8)

(−2.8)

− 4.84PR + 0.43DJ−2 − 0.571.32POP16/65 (−2.0)

(5.5)

(3.1)

+ 0.023POP + 44.96M2AV + 0.11CB2 (5.6)

R = 88.0% 2

(3.1)

(5.7)

Adj. R = 85.4% 2

D.W. = 2.0

MSE = 24.98

(17.7)

New model test results are shown for six different time periods in Table 17.5. R2 results were 1 point higher for 4 of the 6 models, 1 the same and 1 lower, compared to Table 17.1, where only total loanable funds were tested, not its two separate parts. Results indicate endogenous growth in loanable funds positively and significantly related to consumption in all six periods tested, and hence, it is a reliable offset to at least some crowd out. However, five of the six Table 17.5 Endogenous and exogenous changes in loanable funds: effects on consumption crowd out Variable

1960–1980

1960–1990

1960–2000

Endogenous: (T −G) + (S + FB – Tr − A) 0.73 0.20 0.20 (2.1) (1.6) (2.4) Exogenous: (T −G) + (Tr − A) −0.31 0.09 0.05 (−0.9) (0.7) (0.5) R2 0.87 0.88 0.90 0.76 0.84 0.88 Adj. R 2

1960–2007

1960–2008

1960–2010

0.34 (4.9)

0.31 (5.6)

0.19 (7.3)

0.01 (0.1) 0.88 0.85

0.09 (1.3) 0.88 0.85

0.17 (3.8) 0.88 0.85

312

J. J. HEIM

tests indicate exogenous growth (FR securities purchases) was not significantly related to consumption spending. For these periods, FR intentional (or unintentional) attempts at accommodative monetary policy appear to have had no significant impact on consumption. The only exception (which provides some proof accommodative policy, if it involves large enough purchases of securities, can work) was during the QE period 2008–2010, when the FR purchased historically unprecedented quantities of securities. Since 2009 was the only year in the study when FR purchases exceeded the year’s deficit, this means that the net effect of 0.17((T − G) + (Tr − A)) was positive as well as statistically significant. When adding only 2008 to the 1960–2007 sample, the significance level of FR purchases increases markedly, by not enough to change the effect to statistically significant. When the whole 2008–2010 period was added to the 1960–2007 period data, FR purchases becomes a highly significant effect, turning the crowd out variables effect on consumption from negative to positive (i.e., “crowd in”). Hence, on average over the 50 years, Eq. 17.6 above shows the virtual wash between the positive effects on reducing crowd out, given by the coefficients on the modified deficit variables, and the negative effects on the mpc. But, in the QE period, Bernanke’s flooding of the system with liquidity not only offset crowd out effects on consumer borrowing, but increased net loanable funds available to consumers which we see in the form of increased consumer spending associated with the increased liquidity. 17.5.2

Effects on Investment

Similarly, we can test to see if increases in loanable funds from endogenous sources (changes in the business cycle, or the mpc) work better to reduce investment crowd out problems than FR securities purchases (Tr + A). The model we will test is the same that given in Eq. 17.6 above except that in this new model, we are dividing the modified deficit variable used earlier in this chapter (T −G) + (S + FB), into two separate variables, equal to the deficit plus: 1. Total loanable funds minus the part of it generated by FR purchases: (T −G) + (S + FB – Tr − A), and 2. The deficit plus only FR purchases: (T − G) + (Tr + A).

17

313

DO LOANABLE FUNDS MODIFY THE CROWD OUT EFFECTS …

The model is shown in Eq. 20.8 below. Note that this model is the investment model without a stand-alone loanable funds variable: ID = + 0.23(ACC) + 0.11((TT − G T&I ) + (S + FB − Tr − A)) (t=)

(6.4)

(7.1)

+ 0.14((TT − G T&I ) + ( Tr + A)) + 0.008POP (5.8)

(4.2)

− 3.20PR−2 + 7.78XRAV + 2.40CAP−1 (2.9)

(−1.7)

R = 90.7% 2

Adj. R = 89.4% 2

(1.5)

D.W. = 1.8

MSE = 26.85

(17.8)

Test results are shown for six different time periods in Table 17.6. Results in five of six cases indicate endogenous growth in the loanable funds pool left the modified crowd out variable negatively and significantly related to increased investment spending. The sixth was almost significant. The findings indicate FR purchases did not significantly offset some or all of the effects of crowd out in 3 of the 4 1960–2007 tests, i.e., in the periods tested prior to QE. (In one, there was marginal significance, but with the wrong sign, which we interpret as a spurious result.) They appear to have had no significant impact on investment during those three tests (i.e., no effect controlling for the effect of the bulk of (S + FB)— the endogenous part). However, when the 2008–2010 years representing the large quantities of FR securities purchases during the QE period are added to the sample, FR purchases did seem related to increases in investment. This suggests that FR accommodative monetary policy, though it Table 17.6 Endogenous and exogenous changes in loanable funds: effects on investment crowd out Variable

1961–1980

1961–1990

1961–2000

Endogenous: (T − G) + (S + FB – Tr − A) 0.27 0.33 0.24 (1.7) (6.2) (3.3) Exogenous: (T − G) + (Tr − A) 0.07 −0.18 −0.03 (0.4) (−1.6) (−0.2) R2 0.94 0.92 0.91

1961–2007

1961–2008

1961–2009

0.15 (2.3)

0.07 (1.5)

0.11 (7.1)

0.03 (0.3) 0.86

0.20 (4.9) 0.89

0.14 (4.2) 0.91

*Correlation between endogenous and exogenous components is r = (−0.10)

314

J. J. HEIM

didn’t seem to work in most of the period studied (1960–2007) when FR purchases only a small fraction of deficit size, does have a positive effect on investment, as it did on consumption, if implemented in large enough quantities. Declining economic conditions lead to declining tax revenue and also declining investment but such economic conditions are associated with an increased government spending; that is, taxes are positively correlated with investment trends, and spending is negatively correlated. To be sure, our significant findings in Table 17.6 above truly reflected crowd out effects, and not just economic conditions, the same model was reestimated, with the addition of a variable to clearly control for economic conditions (the GDP). The GDP variable was endogenous with investment, so it was replaced with a Wald-strong instrument, which was not endogenous (Sargan test). The model was retested including current period real GDP as a control for changes in economic conditions. Results are shown in Table 17.7 and were generally the same as with the model without the GDP control. With one exception, where Table 17.6 model showed significant results, so did Table 20.7 model with additional control variable. The same was also true for instances of nonsignificance. However, the model with the GDP explained markedly less of the variance in investment in most periods. Hence, we conclude this is a less satisfactory model than Table 21.6 model. Table 17.7 Endogenous and exogenous changes in loanable funds: effects on crowd out (GDP control added to Table 17.6 model) Variable

1961–1980

1961–1990

1961–2000

Endogenous: (T −G) + (S + FB – Tr − A) 0.27 0.34 0.26 (1.5) (5.6) (3.2) Exogenous: (T − G) + (Tr − A) −0.15 −0.17 −0.01 (−0.5) (−1.4) (−0.1) R2 0.94 0.91 0.90 0.91 0.88 0.88 Adj. R 2

1961–2007

1961–2008

1961–2009

0.17 (2.2)

0.11 (1.9)

0.14 (7.1)

0.06 (0.4) 0.80 0.76

0.21 (4.8) 0.83 0.80

0.16 (4.1) 0.86 0.84

17

DO LOANABLE FUNDS MODIFY THE CROWD OUT EFFECTS …

17.6

315

Conclusions

Results for effects of deficits and the ability of the total pool of loanable funds to offset crowd out are presented in the two Tables 17.8 and 17.9: 1. Baseline standard model (no deficit or loanable funds variables included): Average R 2 = 71.4% 2. When deficit variables only added to standard model, R 2 increases to 88.8%, an increase of 24%, clearly indicating consumption cannot be explained without allowing for significant crowd out effects. Even adjusted R 2 increases to 82.8%. 3. When the stand-alone loanable funds modifier (S + FB) is added to the standard model with deficits, R 2 increases to 89.1 compared to the 88.8% for the standard deficit model, indicating loanable funds are not an important explanatory variable (though not indicating its net effect is negative if the change in loanable funds stems from a declining mpc). Adjusted R 2 decreases to 82.8%, indicating adding the loanable funds variable to the consumption model does not reduce crowd out. When adding a (S + FB) modifier to the deficit, without including the stand-alone (S + FB) variable, explained variation in consumption dropped to 86.1. For 18 tests, average R 2 dropped the unmodified (i.e., baseline) average of 88.8–86.1%, 2.7% points, in the modified model. By comparison, R 2 also dropped from 88.7 to 85.2 in the comparable twodeficit variable model in Chapter 18, which also used total loanable funds for a deficit modifier, but not as a stand alone. In short, this chapter and Chapter 18 models in the next chapter, both of which used total loanable funds as a crowd out offset, and were otherwise identical except for the use of one- vs. two-variable deficits, had about the same consumption results, as expected. 1. Baseline standard model (no deficit or loanable funds variables included): Average R 2 = 79.8% 2. When deficit variables only added to baseline standard model, R 2 increases to 87.3%, an increase of 9.4%, indicating investment cannot be explained without allowing for significant crowd out effects. 3. When the stand-alone loanable funds modifier is added to standard model with deficits, R 2 grows to 90.1% (Adj. R 2 = 83.8) compared

72

1960 –2008

86 2

86

72

1960 –2007

90

86

1960 –2000

87 2

87

90

87 2

87

90

86 2

86

90

81

2

81

89

(Av. R = 84.8); (Av. R Adj = 79.3%)

2

81

(Av. R = 88.1%); (Av. R Adj = 81.2%)

2

86

(Av. R = 89.1%); (Av. R Adj = 82.8%)

2

87

(Av. R = 89.1%); (Av. R Adj = 82.8%)

2

87

(Av. R = 87.3%); (Av. R Adj = 83.8%)

2

86

(Av. R = 71.4%)

2

60

1960 –2010

85

89

91

91

89

43

1960 –1990

85

88

88

88

88

77

1960 –1980

92

93

94

94

93

91

1970 –1990

92

92

92

92

92

91

1970 –2000

81

85

87

87

85

68

1970 –2007

81

88

88

88

88

55

1970 –2010

93

93

93

93

93

86

1980 –2000

79

85

89

89

86

37

1980 –2010

85

86

85

85

86

63

1975 –2004

86

86

85

85

86

74

1980 –2004

a 7 samples containing 1/3–1/2 of all observations from “Crowd In” years Removed, leaving 11 of 18

17 modified T.17.2 (wo/s-a)

17 unmodif. T.17.2 (wo/s-a)

17 modified T.17.1 (w/s-a)

17 unmodif. T.17.1 (w/s-a)

17 baseline T.17.2 (w/Def)

17 T.18.1A baseline A (w/oDef)

From Table #

82

83

83

83

83

67

1985 –2004

82

83

83

83

83

65

1985 –2005

Table 17.8 Total loanable funds deficit modifier, W/WO separate (S + FB) control variable

97

99

95

95

99

83

1996 –1909

(T − G )

a

NA

NA

(T −G )

(T − G )

(T − G )

(T − G ) 9/11 (T − G )a

97 14/18

9/11 (T − G )a

100 16/18

9/11 (T − G)a

99 14/18

9/11 (T −G )a

99 14/18

9/11 (T − G )a

100 14/18

NA

95 NA

2000 Test ratio –2010 T G

316 J. J. HEIM

From Table#

2

69

67 2

66

R (18 time periods) 1960 1960 1960 –2010 –2008 –2007 63

1960 –2000

T.17.3

T.17.4

T.17.4

17 modified (w/s-a)

17 unmodif. (wo/s-a)

17 Modified (wo/s-a)

86

83

87

85

88 2

87

91

91

88 2

87

91

91

96

86 2

83

87

88 2

87

90

(Av. R = 89.7%) (Av. R Adj = 87.1%)

2

91

(Av. R = 87.2) (Av. R Adj = 84.2%)

2

89

90

85

95

92

(Av. R = 90.0%) (Av. R Adj = 86.2% for 18 samples w/o GDP)

2

90

96

92

91

90

87

90

90

87

82

1970 –1990

72 −61

1960 –1980

(Av. R = 90.0%); Av. R Adj = 86.2% for 18 samples)

2

90

(Av. R Adj = 83.6% for 18 samples)

2

(Av. R = 87.3% for 18 samples; 89.0% for first 6)

2

89

78

65

1960 –1990

91

89

91

91

89

81

56

1970 –2000

87

83

87

87

83

71

65

1970 –2007

91

89

91

91

89

76

72

1970 –2009

90

88

90

90

88

81

69

1980 –2000

90

89

90

90

89

80

87

84

88

88

84

80

1975 –2004

77 −64

1980 –2010

86

82

87

87

82

81

71

1980 –2004

a 7 samples containing 1/3–1/2 of all observations from “Crowd In” years Removed, leaving 10 of 1

T.17.3

17 unmodif. (w/s-a)

17 baseline T.17.3 (w/Def) (No GDP control variable)

(Includes GDP control variable) (Av. R = 79.8%)

2

(Does not include GDP control variable) (Av. R = 68.3%) 17 baseline T.17.3B 76 70 71 80 (w/o Def)

17 baseline T.17.3C (w/o Def)

Model

84

83

84

84

83

75

63

1985 –2004

Table 17.9 Total loanable funds modifier, W/WO separate (S + FB) control variable

84

83

87

87

83

75

57

1985 –2005

97

96

97

97

96

93

92

1996 –2010

a

a

96

97

96

96

a

10/11 (T − G )

18/18 (T −G )

10/11 (T −G )a

17/18 (T − G )

9/11 (T −G )a

11/18 (T − G )

9/11 (T − G )a

11/18 (T − G )

10/11 (T − G )

NA

NA 97 17/18

NA

95 NA

NA

NA NA

Sigif./Total Test ratio T G

91 NA

2000 –2010

17 DO LOANABLE FUNDS MODIFY THE CROWD OUT EFFECTS …

317

318

J. J. HEIM

to the standard deficit model without a loanable funds modifier (87.3%). This indicates that adding the total loanable funds gives a model that explains noticeably more of investment’s variation than a model without it. The same total loanable funds model is used in Chapter 18, except with a two-variable deficit, and explains even more (91.2%) of the variance. Clearly, crowd out can be offset by growth in the pool of loanable funds. 4. When the total loanable funds modifier is added as a modifier of the deficit only, but not included as a stand-alone variable, R 2 increases on average in the 18 periods tested, from its average of 87.3% before adding the endogenous loanable funds deficit modifier to an average of 89.7% after, or 2.4% points higher than before any loanable funds variable were added to the deficit variable in the model. This increase was less than when the loanable funds variable was added as both a stand alone and a deficit modifier (90.1%), though the difference is small enough to be spurious. Hence, the result does not seem large significant enough to imply our general conclusion that stand alones are not needed in investment models is incorrect. Next, the results of consumption testing with total loanable funds endogenous and exogenous components tested separately are summarized. The consumption function with a stand-alone loanable funds variable is the preferred model. It explains more variance (1.6% points on average for 18 periods tested) than the same model without any loanable funds variable. It also explained the data better than the same model with a no standalone (S + FB) variable, but with a deficit-modifying (S + FB) variable. In models without the stand alone, R 2 actually declined even relative to the no (S + FB) base line model by an average of 2.7% points in the 18 periods tested. The poor showing appears to be the result of changes in loanable funds having negative as well as positive effects on consumption which tend to cancel each other out, and which need to be shown separately in the model, i.e., by modifying the deficit variable and by also including it as a separate variable in the consumption model. Results When Testing the Effects of Endogenous and Exogenous Loanable Funds Growth Separately are given in the two Tables 17.10

72

1960 –2008

2

86

86

(Av. R = 71.4%)

2

60

1960 –2010

2

2

90

90

86

1960 –2000

91

89

43

1960 –1990

87

2

87

90

91

88

90 2

88

(Av. R = 88.2%) (Adj. Av. R = 83.8%)

2

87

88

94

94

93

91

1970 –1990

88 –

88

88

88

77

1960 –1980

Endogenous (EN) and Exogenous (EX) modifiers model:

(Av. R = 89.1%) (R for leftmost 6 = 88.3%)

2

87

(Av. R = 89.1%); (Av. R for leftmost 6 = 88.3%)

87

86

72

1960 –2007



92

92

92

91

1970 –2000



87

87

85

68

1970 –2007



88

88

88

55

1970 –2010



93

93

93

86

1980 –2000



89

89

86

37

1980 –2010



85

85

86

63

1975 –2004



85

85

86

74

1980 –2004

a 7 samples containing 1/3–1/2 of all observations from “Crowd In” years Removed, leaving 11 of 18 b Significant only in sample including QE years 2008–2010

17 modified T.17.5 (w/s-a)

17 modified T.17.1 (w/s-a)

(Av. R = 88.8%) Total loanable funds 17 unmodif. T.17.1 87 87 (w/s-a)

17 Eq. 17.1 baseline A (w/Def)

Model

From Table# 17 Eq. 17.1 baseline AA (w/oDef)



83

83

83

67

1985 –2004



83

83

83

65

1985 –2005



95

95

99

83

1996 –2009



a

99

99

(T − G )

(EN)

6/6

1/6

6/6 1/6b

(EX)

9/11 (T − G )a

11/18 (T − G )

9/11 (T − G )a

11/18 (T − G )

9/11 (T − G)

100 14/18

NA NA

NA

95 NA

2000 Test ratio –2010 T G

Table 17.10 Separate (S + FB − TR − A) an (Tr + A) deficit modifiers, with stand alone (S + FB) control variable

17 DO LOANABLE FUNDS MODIFY THE CROWD OUT EFFECTS …

319

320

J. J. HEIM

and 17.11 immediately below: For the Periods Tested: 1. Baseline standard model (no deficit or loanable funds variables included): Average R 2 = 71.4%. 2. When deficit variables only added to standard model, average R 2 increases to 88.8%, an increase of 24%, clearly indicating consumption cannot be explained without allowing for significant negative crowd out effects of deficits. 3. When the total loanable funds variable is added as a stand-alone variable, average R 2 increases slightly to 89.1%. 4. When two separate deficit modifiers are used, endogenous and exogenous loanable funds (S + FB – Tr − A) and (Tr + A), while also including the stand-alone total loanable funds variable (S + FB), i.e., added to the standard model with deficits, R2 decreases from 89.1 using to total loanable funds variable to 88.2 using the two separate components. 5. The endogenous-modified deficit variable was found significant in all six periods tested. However, the exogenous loanable funds modified deficit was only statistically significant during the QE period when the level of FR security purchases was huge. Prior to 2008, FR purchases were typically small (1/8 – ¼) compared to deficit size. Modifying the deficit with them merely created an error in variables problem that reduced the crowd out variable to statistical insignificance. 1. Baseline standard model (no deficit or LF variables or GDP Included): Average R 2 = 68.3% 2. When deficit variables were added to baseline standard model, R 2 increases to 87.4%, an increase of 28%, clearly indicating investment cannot be explained without allowing for significant crowd out effects. 3. When the total loanable funds variable is added as a deficit modifier, but not as a stand-alone variable, average R 2 increases from 87.4 to 89.7% (Adj. R 2 increases from 84.2 to 87.1%), indicating increases in LF can offset crowd out. 4. When the two separate deficit modifiers, endogenous and exogenous loanable funds (S + FB – Tr − A) and (Tr + A), replace the total LF modifier, with no stand-alone loanable funds variable, R 2 rises slightly from 90.2% for the 6 samples tested to 90.5%. Like the total

From Table#

2

69

67

66

R (18 time periods) 1960 1960 1960 –2010 –2008 –2007

76

70

T17.4

2

87

80

2

85

78

65

1960 –1990

2

86

83 2

87

2

88

87

90 2

90

85

95

92

−87

−61

90

87

82

1970 –1990

a

2

2

2

2

2

89

91 –









87

83

83

71

65

1970 –2007





91

89

89

76

72

1970 –2009





90

88

88

81

69

1980 –2000





90

89

89

80

77

1980 –2010





−84

−64

87

84

80

1975 –2004





86

82

82

81

71

1980 –2004

a 7 samples containing 1/3–1/2 of all observations from “Crowd In” years Removed, leaving 11 of 18 b 2 of 3 significant periods included 2008 or 2008–2010 QE period years when FR purchases were huge

(Av. R = 90.5%) (Adj. Av. R = 84.5%)

(Av. R = 90.5%) (Adj. Av. R = 87.7%) Endogenous (EN) and Exogenous (EX) modifiers model (GDP control variable added) 17 modified T.17.7 94 92 91 86 89 91 – (wo/s-a)

86

91

89

89

81

56

1970 –2000

(18 sample Av. R = 89.7%);(Left 6 sample Av. R = 90.2%) 10/11 (T − G )

91

(18 sample Av. R = 87.4%) (Adj. R = 84.2)

89

92

91

72

1960 –1980

(Leftmost 6 sample Av. R =0 87.0%; 6/6 (6/6) significant)

(18 sample Adj. R = 87.1%) Endogenous (EN) and Exogenous (EX) modifiers model 17 modified T.17.6 94 92 91 (wo/s-a)

17 modified (wo/s-a)

Total loanable funds 17 unmodif. T.17.4 (wo/s-a)

83

71

63

1960 –2000

(18 sample Av. R = 87.4%) (Adj. R = 84.2%)

2

(Includes GDP control variable) (79.8% Av.) 17 baseline T.17.3 89 86 (w/Def)

17 baseline T.17.3B (w/o Def)

(Does not include GDP control variable) (68.3% Av.)

17 baseline T.17.3C (w/o Def)

Model





84

83

83

75

63

1985 –2004





84

83

83

75

57

1985 –2005





97

96

96

93

92

1996 –2010





(T − G )

NA

a

NA

NA

a

NA

(T − G )

(EN)

(EX)

(T − G )

6/6 2/6b

5/6 3/6b

96 18/18

10/11 (T − G )a

94 17/18

10/11 (T − G )

94 17/18

NA

95 NA

NA

91 NA

2000 Test ratio –2010 T G

Sigif./Total

Table 17.11 Separate (S + FB − TR − A) an (Tr + A) deficit modifiers, no stand alone (S + FB) control variable 17 DO LOANABLE FUNDS MODIFY THE CROWD OUT EFFECTS …

321

322

J. J. HEIM

loanable funds modifier, it adds to the investment model’s explanatory power a noticeable improvement, and again indicating loanable funds increases can offset crowd out. The finding is consistent with the Chapter 18 model without a stand-alone (S + FB) variable, whose R 2 increased to 90.7% when the deficit modifier (S + FB) was added. Chapter 18 tests the same model except using a two-variable formulation of the deficit. 5. Changes in the endogenous part of total loanable funds appears to have been the main offset to crowd out in the past, being significant in 5 of 6 or 6 of 6 tests, depending on whether the model contained a GDP control variable. Increases in the exogenous part only were significant in 2 of tests, those including data from the early QE years in the sample.

Reference Heim, J. J. (2017). An Econometric Model of the U.S. Economy. Hoboken: Palgrave Macmillan.

CHAPTER 18

Do Loanable Funds Modify the Crowd Out Effects of the Two-Variable Deficit (T), (G)?

In Sects. 18.1 and 18.2, we test the standard consumption and investment models the same way as in Chapter 17, with one change: here we use two separate deficit variables, one for tax deficit effects (T ) and one for government spending deficit effects (G). In Chapter 17, only one variable (T − G) was used to represent the government deficit. The two-variable deficit model is used because the multiplier effects of tax cut and spending deficits are known to differ in standard multiplier analysis, raising questions about whether the crowd out effects may also differ. Separating the deficit into its two parts allows us to test whether tax cut and spending deficits have different crowd out effects either before or after modification changes in the loanable funds pool (S + FB).

18.1 Testing the Two-Variable Deficit Consumption Model The “standard” model of consumption’s determinants reflecting the views of many economists on what variables are determinants of consumption is taken from Heim (2017a, Eq. 4.4.TR). This time period robust, standard

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. J. Heim, Why Fiscal Stimulus Programs Fail, Volume 1, https://doi.org/10.1007/978-3-030-65675-1_18

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consumption model with deficit variables offset by (S + FB) is shown below: CD = .29(Y − TT ) + .34(TT ) − .23(G T&I ) − 5.44PR (t=)

(6.2)

(6.5)

(−2.1)

(−4.5)

+ .48DJ−2 − .515.07POP16/65 + .020POP (5.1)

(3.2)

(6.0)

+ 38.00M2AV + .09CB2 (4.9)

R = 87.8% 2

D.W.= 2.2

(3.7)

MSE = 24.88

(4.4.TR)

Including this model as a reference model is a way of showing what variables others have found in the past to be consumption’s determinants, and hence, variables that need to be controlled for in our studies. The accuracy of the coefficients and levels of statistical significance on this study’s crowd out and loanable funds variables, or any other variable, depend greatly on how effectively we control for the fluctuations of other variables that can affect consumption. Any determinant of consumption not controlled for by inclusion in the model can cause findings for the crowd out and loanable funds variables to be distorted if our variables of interest are at all correlated. This is the “left out” variables problem described by Goldberger (1961). With the left out variables problem, coefficients on our variables of interest are likely to reflect not only their own effect on the dependent variable, but the effect of same-period changes in some determinant of consumption “left out” of the model when tested. The Heim 2017a study used an exhaustive process of multiperiod testing to develop a reasonably definitive list of all key variables found related consumption in prior studies. They are included here in all models tested as control variables to avoid the “left out variables” problem. Using this “standard” model in all tests, and just adding crowd out or loanable funds variables to them, also simplifies showing how the models tested in this study are just a logical extension of the past findings, and hence consistent with the past canon of consumption function research. The methodology of this study for testing crowd out and loanable funds effects is not likely to look so unusual that it is difficult to know how reliable the models are. Equations 18.1 and 18.2 are built on the model above. They show test results for the initial sample tested for models extending consumption theory to better account for the modifying effects on crowd out

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of changes that occur in loanable funds at the same time. These two models include all variables in the standard model noted above, but add an additional stand-alone variable measuring the total pool of loanable funds: total U.S. national savings + foreign borrowing, or (S + FB). These models are identical with those tested in Chapter 16; except in Chapter 16, national savings (S) and foreign borrowing (FB) were added to the standard model as two separate variables, not one, as is done here. Chapter 16 also only tested the model in one time period; here, we attempt to replicate our initial results in 17 additional time periods. Methodologically, the approach used to test is based on the following: 1. We first establish a baseline model in which the deficit variables are tested without inclusion of any additional variables to account for same-period changes in loanable funds. We then add a single loanable funds variable to control for the effect of fluctuations in the loanable funds pool on consumption. Results of the baseline model without loanable funds are then compared with results from the model with loanable funds added. We evaluate results by noting if adding the loanable funds variable to the model increases the amount of variation in consumption explained. 2. The coefficient on the loanable funds variable will provide an estimate of the marginal effect of a change in loanable funds on consumption. 3. There is reason to believe that the coefficient on this single, standalone loanable funds variable will represent the net of two effects: a positive effect of reducing crowd out and a negative effect because increasing loanable funds by increasing the level of savings in the economy while holding disposable income constant, which requires a reduction in the marginal propensity to consume, a by-product of the regression’s ceteris paribus method of testing, which holds disposable income constant. The models tested here are ceteris paribus models where disposable income (and all other variables in the model) is held constant when measuring the effects on consumption of an increase in loanable funds. With income constant, growth in loanable funds can only occur by increasing the marginal propensity to save by decreasing the marginal propensity to consume, i.e., consumption must decline. We need some way to account for this in negative effect in the model

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when testing whether an increase in loanable funds also reduces crowd out, and not get the two effects confused. Hence, the first model tested (Eq. 18.1), which only adds one variable to the model, the stand-alone loanable funds variable, must measure both the two separate effects of loanable funds changes on consumption. Since it has to measure two effects, the coefficient on this variable will measure the net impact on consumption of the two effects. In a second model tested (Eq. 18.2), this loanable funds variable is included as a stand alone, and also included a second time as modifier of the deficit variables’ values. The deficit variables, when entered unmodified in the model, indicate an assumption that there is a one-to-one correspondence between the size of deficits and crowd out. If the crowd out effect is modified by changes in loanable funds, subtracting loanable funds changes from the deficit should give us a modified crowd out variable that may better represents the actual crowd out effect that occurs in a period in which a deficit occurs. The second model thereby gives us two ways to measure the two contradictory effects of increases in loanable funds separately in the model. 1. The crowd out reducing effect will be given by the coefficient on the modified deficit variable. 2. The mpc-reducing effect, the second effect, will be captured by the coefficient on the stand-alone loanable funds variable, and should have a negative sign if we are correct in our assumption regarding what it measures. If we have designed the 2nd model correctly, the sum of the two effects should be exactly equal to effect found when testing just the net effect of the variables in the 1st model, i.e., including just a stand-alone (S + FB) variable, with no deficit variable modifiers. The sum of the two effects shown in the 1st model does turn out to be identical to the sum of the two effects we see in the second model, as shown below. The unmodified deficit model, Eq. 18.1, defines crowd out as equal to the size of the deficit. That is, it measures crowd out before reducing the deficit by the amount of any same-period growth in loanable funds that might offset its effects. This we refer to as the deficit model without

18

DO LOANABLE FUNDS MODIFY THE CROWD OUT …

327

modification or (“w/o”). Equation 18.2 measures crowd out effects after the deficit as reduced by changes in loanable funds that might offset crowd out. This is the deficit model after modification of the deficit by changes in loanable funds. Both models were estimated using the same 1960–2010 data set. All consumption models tested below have been tested for stationarity and endogeneity problems. All variables are either stationary or cointegrated with their dependent variables. No endogeneity problems were found (Hausman test) with any of the consumption or investment models. Newey–West standard errors were used to avoid heteroskedasticity problems, and the model was estimated in first differences of the data to help reduce nonstationarity and multicollinearity. Equation 18.1AA presents this study’s baseline (BL) standard consumption model with no deficit variables or loanable funds variables included (1960–2010 data) (2SLS) Two Stage Least Squares (2SLS) since Y − T end CD = .54(Y − TT ) + 2.70PR + .27DJ−2 (t=)

(0.6)

(7.2)

(3.0)

− .714.38POP16/65 + .013POP (3.2)

(1.6)

− 1.58M2AV + .13CB2 (2.0)

(−0.1)

R = 60.3% 2

  Adj.Av,R 2 = 56.5%

D.W.= 1.7

MSE = 43.98 (18.1AA)

We also include a baseline (BL) standard consumption model with crowd out (i.e., deficit) variables (Eq. 18.1A) to provide a measure of the full crowd out effects of deficits that can occur if no offsetting change in loanable funds occurs. CD = .31(Y − TT ) + .32(TT ) −.16(G T&I ) (t=)

(6.4)

(6.6)

(−1.9)

− 7.14PR + .49DJ−2 − .459.68POP16/65 (−3.1)

(4.5)

(2.4)

+ .017POP + 36.27M2AV + .09CB2 (4.0)

R = 86.6 2

(3.8)

Adj.R = 83.9% 2

(3.9)

D.W = 2.1

MSE = 26.17

(18.1A)

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Notice the baseline equation before adding the deficit variable explains only 60.3% of the variation in consumption data over the period 1960– 2010. When the deficit variable is added to the same model to account for the negative crowd out effects of deficits, explained variance rises to 86.6%, a 26.3%-point increase. This evidence indicates crowd out is a real problem having negative effects on consumption. To ensure this finding was not an anomaly, we retested the same models in 17 additional time periods, shown in Table 18.1D. In every period tested adding the crowd out variable markedly increased explained variance. The increase averaged 17.4 percentage points for the 18 samples. There seems to have been no sample period between 1960 and 2010 when crowd out was not a problem. In Eq. 18.1, a (stand-alone) loanable funds variable is added to the baseline deficit model above to determine if changes in loanable increase the model’s ability to explain consumption behavior. The model was estimated using OLS because no endogeneity issues were uncovered. No stationarity issues were found except those resolved by cointegration. Heteroskedasticity issues were resolved using Newey–West standard errors, and first differencing of the data was used to reduce multicollinearity and serial correlation issues. CD = 38(Y − TT ) + .43(TT ) − .24(G T&I ) (t=)

(8.0)

(6.7)

(−2.8)

− .14(ST + FB) − 6.09PR + 40DJ−2 (−2.8)

(−4.1)

(5.0)

− 398.48POP16/65 + .016POP (3.7)

(−1.9)

+ 33.67M2AV + .10CB2 (4.5)

(3.5)

R = 88.3% 2

Adj.R = 85.8% 2

D.W.= 1.9

MSE = 24.68

(18.1)

Results indicate changes in loanable funds themselves have a significant effect on consumption which adds 1.7 percentage point to explained variance. Adding the loanable funds control variable strengthened the deficit variables’ statistical significance levels. We interpret the coefficient on the loanable funds variable as indicating the net effect on consumption of two separate effects: the positive effect of reducing crowd out and the negative effect of reducing the marginal propensity to consume (mpc).

T21.1AA

T21.1A

21 Baseline (wo/Def)

21 Baseline (w/Def)

87

72

87

72

(Av. R 2 = 89.4% “w/o”); (Adj. R 2 Av= 90.6% “w”)

87

(Av. R 2 = 71.4%)

60

1960—2010 1960—2008 1960—2007 1960–2000

91

86

1960–1990

89

43

1960—1980

91

77

1970–1990

93

91

1970–2000

92

91

1970–2007

86

68

88

55

1970—2010

1980—2000

94

86

1980–2010

85

37

1975– 2004

88

63

88

74

86

67

87

65

92

83

(T)

Test Ratio

NA

6/18

5/5

5/5aa

10/11 5/11a

99 15/18

NA

NA

(G)

Signif./Total

95 NA

1980—2004 1985—2004 1985—2005 1996—2009 2000—2010

*With 7 samples mixing large amounts of “crowd out” and “crowd in” periods data excluded. Mixing periods of statistically significant crowd out effects with periods of statistically significant crowd in effects leaves the effects cancelling each other out, leaving a nonsignificant statistic for the effects of deficits, for technical, not substantive reasons (see Sect. 18.1.1) ** With 7 samples mixing crowd out and crowd in periods data excluded, and 1980s data also excluded because of lack of significant variation in government spending. Lack of variation also leads to insignificant estimates of spending deficit crowd out effects for technical, not substantive reasons: you can’t find a correlation between a variable that is fluctuating and one that is not, even if there is an underlying substantive relationship. (See explanation below)

From Model#

Model

Table 18.1D Growth in explained variance when adding crowd out to a standard model 18 DO LOANABLE FUNDS MODIFY THE CROWD OUT …

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To obtain separate estimates for these two effects, we reestimate Eq. 18.1 by modifying the deficit variables by any change in loanable funds that may have occurred in the same period as the deficit, while continuing to include the loanable funds variable as a stand-alone variable in the model. Results are presented in Eq. 18.2: CD = .38(Y − TT ) + .43(TT )m − .24(G T&I )m (t=)

(8.0)

(6.7)

(−2.8)

− .81(ST + FB) − 6.09PR + .40DJ−2 (−2.8)

(−5.6)

(5.0)

− 398.48 POP16/65 + .016POP (3.7)

(−1.9)

+ 33.67 M2AV + 10 CB2 (4.5)

(3.5)

R = 88.3% 2

Adj.R = 85.8% 2

D.W.= 1.9

MSE = 24.68

(18.2)

Note that after modification of the crowd out effect by adding (S + FB) to the tax deficit variable (T ), and subtracting it from the spending deficit variable (G), results for all variables except the stand alone (S + FB) variable remain the same, as does R 2 . Also note the separate estimates for the three crowd out effects in Eq. 18.2, two of which measure the positive effects of a change in loanable funds on consumption βi (T + (S + FB)) and − βj (G − (S + FB)), and one of which measures the negative effect on the mpc of increasing mps: − βk (S + FB). This equals (+.43) − (−.24) + (−.81) = (−.14) in Eq. 18.2, which is precisely equal to the net effect given in Eq. 18.1. All the positive and negative effects in Eq. 18.2 are statistically significant. The identical result in both equations for the net effect is proof there are two separate and contradictory effects on consumption of increasing the loanable funds pool. The fact that the coefficients (marginal effects) of deficits in both models stay the same reflects the fact that changing the magnitude of the estimated crowd out effect does not (and should not) affect the magnitude of the deficit variable’s estimated parameter, if loanable funds offset deficit caused crowd out on a dollar-for-dollar basis. The marginal effect of a change in crowd out should stay the same, regardless of whether the change is a dollar change from a large number or smaller number (assuming the relationship is linear). Table 18.1B presents baseline model coefficients (with deficit variables added). Stationarity issues with the consumption and governments

R2 .91 .89 .91 .87 .87 .87

G β(t)

−.21(−1.6) −.10(−1.7) −.03(−0.5) −.08(−1.5) −.08(−1.6) −.16(−1.9)

T β(t)

.53(3.9) .28(3.2) .23(2.8) .33(5.6) .33(5.6) .32(6.6)

Sample Period

1960–1980 1960–1990 1960–2000 1960–2007 1960–2008 1960–2010

1970–1990 1970–2000 1970–2007 1970–2009 1980–2000 1980–2010

Period .11(1.4) .10(1.3) .32(3.9) .33(4.6) .09(1.3) .31(4.5)

T β(t) −.25(−2.1) −.07(−0.7) −.05(−0.5) −.08(−1.1) .11(0.7) −.20(−1.4)

G β(t) .93 .92 .86 .88 .94 .85

R2

1975–2004 1980–2004 1985–2004 1985–2005 1996–2010 2000–2010

Period

.35 .34 .33 .34 .39 .26

(3.8) (4.0) (4.2) (5.0) (2.2) (4.8)

T β(t)

Table 18.1B Base line model with only deficit variables added: estimates of consumption crowd out

.03(0.3) .06(0.4) .25(1.3) .26(1.4) −.82(−1.7) −.05(−0.2)

G β(t)

.88 .88 .86 .87 .92 .99

R2

18 DO LOANABLE FUNDS MODIFY THE CROWD OUT …

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spending variables were resolved by detrending; there were no endogeneity issues. Surprisingly, in only 6 of the 18 test periods, spending deficit variables were found statistically significant. Two problems appear to account for this: 1. Samples that contain large portions (1/3 − 1/2) of their observations from 1990s “crowd in” data with the crowd out data characteristic of other decades typically will have those effects cancel each other out and yield insignificance. This problem is examined in detail in Sect. 18.1.1 of this chapter. Eliminating those samples reduces the number of significant spending crowd out findings from 6 of 18 to 5 of 11 remaining samples. 2. The second problem was the lack of variation in the government spending in the 1980s when the standard deviation on spending during the decade was only 30% of the average level of spending. For the other four decades in our data set, the standard deviation was between 64 and 229%. Without significant variation there can’t be statistically significant correlations. Five of the remaining 11 samples had this problem. Removing them from the 11 remaining samples (after the 1990s samples are deleted) leaves five remaining samples, all 5 showing statistically significant negative spending deficit effects. (No such data variation problem arose for tax deficits, whose decade standard deviations as a percent of decade average values varied from 61 to 766%. For the seven 1990s problem samples, the typical effect was to reduce the average coefficients and significance levels from 31.2(4.1) for the unaffected 11 samples to 26.6(3.4) for the affected seven samples, as calculated from Table 18.1B data, but in 5 of 7 cases, leave the tax deficit variable still statistically significant. Hence, tax deficits showed significant crowd out in almost all of the 18 samples). 3. There is another reason why for consumption, tax deficits are found to have significant crowd out effects more often than spending deficits. In the baseline model for consumption, we found only 6 of 18 samples showed significant crowd out, but that two technical problems accounted for the 12 insignificant findings, namely the 1990s crowd in period problem and the limited amount of variation in the government spending variable in the 1980s. For investment (as we show in Table 18.10), using the same government spending variable we do not see these insignificant findings

18

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333

in most samples. One possible reason why is that government spending is a more important determinant of investment spending than consumer spending. Heim (2017a, Tables 4.4.1 and 5.4.1) shows that with very similar models for the 1960–2010 period, variation in the government spending variable accounts for much more of the variation in investment than does government spending in the consumption model. Depending on whether the “first out” or “first in” method of stepwise regression is used to determine the government spending variable’s contribution to total variance explained by the model, government spending explains 2–11 times as much variation in investment model than in the consumption model. It explains from 11–22% of the total variation in investment compared to 2–5.6% for consumption. This implies that even small fluctuations we found in government spending lead to significant moves in investment and be easier for the regression to distinguish from movement in other variables. This may explain our much more frequent findings of government spending being significantly related to investment than we found with consumption, despite the 1990s and variation problems with the data. With the tax deficit variable, the situation is just the opposite. The tax deficit variable explains 2.4–14 times as much variation in consumption as the government spending variable (12 vs. 5% using “1st out” stepwise regression; 28 v 2% using “1st In” stepwise regression). Tax deficits hurt consumption, i.e., cause more variation in consumption, much more than spending deficits, and hence, are more likely to show statistically significant crowd out effects. We theorize that the reason for this is that financing tax cut deficits reduces the money available for consumers to borrow, and the tax cuts for the most part go to those who save and invest the money, not spend it on consumer goods. The increased savings that take place is shifted our of consumption toward investment. Hence, the strong crowd out effect of tax cut deficits. Financing spending deficits, though it also reduces funds available to consumers to borrow and spend, are more likely to be channeled to those at the lower end of the income spectrum, who are most likely to replace the lost borrowing power with increased spending out of their new spending deficit generated income. As a result, in consumption models, we find few periods in which tax deficits are insignificant, but many in which spending deficits are insignificant.

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For investment, just the opposite is true, and most likely for the same reasons. Tax cuts are most often saved and invested offsetting crowd out effects on investment. Spending deficits also cause crowd out, but recipients are more likely to be consumers than businesses; hence, they do not provide an offset to investment crowd out, and spending deficits are found to create statistically significant crowd out effects for investment more often than tax cut deficits (see analysis of Table 18.10 results). Table 18.1B presents estimates of R 2 and deficit variables’ statistical significance for 18 time periods for the baseline consumption model with deficits, but before inclusion of any loanable funds variable. These are presented for comparison with the same models including loanable funds variables during the same 18 periods, which are presented in Table 18.1. For comparison with the initial sample results, Tables 18.1 presents crowd out and loanable funds test results for the loanable funds models used in Eqs. 18.1. and 18.2. Results are shown for the initial tests using the whole 1960–2010 test period and also show results of attempts to replicate these results in 17 shorter time periods between 1960 and 2010. For each time period given in Table 18.1, two sets of statistics are presented: one in which includes the separate, stand-alone loanable funds variable, but no modification of the deficit variables ([T, G] by changes in loanable funds “w/o”), and one in which includes the separate loanable funds variable and the deficit variables are modified by same-period changes in loanable funds (“with”). The modified deficit crowd out variables are T + (S + FB) and G − (S + FB). The separate loanable funds variable is defined as (S + FB). Table 18.1 does contain three t-statistics for each deficit variable’s coefficient, each based on a different type of standard error: Newey–West, White, and ordinary least squares errors. In this study, normal practice is to deal with heteroskedasticity by using Newey–West errors; the others are presented for comparison and to show that only rarely does the choice of error affects decisions about whether a variable is statistically significant or not. A summary of Table 18.1 findings is presented below at the end of the table. Effects on R2 : Adding the stand-alone loanable funds variable to the baseline model with deficit increases R 2 in 10 of 18 periods tested, with no change in R 2 for the other 8 periods. The average increase for the 18 periods was small, 1.3

.72 (5.3) (4.9) (5.0)

−.28 (−2.8) (2.1) (−2.1)

−.36 (−3.0)

−.28 (−2.8) (1.5) (2.1)

−.47 (−2.6) (2.0) (2.5) .94 .88

1970–1990 w/o with

.18 (1.7) (1.7) (1.7)

.72 (5.3) (4.9) (5.0)

.43 (1.7) (1.7) (1.7)

−.36 (−3.0)

T Def : NW tstat White t Ordin. t

G Def : NW tsta White t Ordin. t

ST + FB NW tstat White t Ordin. T R2 Adj.R 2

Variable

T Def : NW tstat White t Ordin. T

G Def NW tstat

−1.46 (−4.2) (−3.7) (−3.7) .94 .88

1960–1980 w/o with

Variable

−.65 (−2.5) (−2.3) (−2.8) .90 .85

−.16 (−2.2) (−1.6) (−1.5)

.36 (2.7) (2.9) (3.9)

−.11 (−0.8)

.14 (1.2) (1.0) (1.2)

−.48 (−1.8) (−1.7) (−2.1) .91 .88

−.09 (−0.9) (−0.8) (−0.8)

.29. (2.6) (2.5) (3.2)

−.14 (−1.5)

.44 (4.2) (4.4) (5.1)

−.14 (−1.5)

.44 (4.2) (4.4) (5.1)

1970–2007 w/o with

−.10 (−1.2) (−1.4) (−1.2) .91 .88

−.09 (−0.9) (−0.8) (−0.8)

.29 (2.6) (2.5) (3.2)

1960–2000 w/o with

−.74 (−4.7) (−4.5) (−4.1) .89 .86

−.17 (−2.4) (−1.9) (−1.6)

.44 (5.7) (5.4) (6.3)

−.17 (−2.0)

.41 (4.6) (5.2) (5.5)

−.17 (−2.0)

.41 (4.6) (5.2) (5.5)

1970–2009 w/o with

−.13 (−3.6) (−2.8) (−2.4) .89 .86

−.17 (−2.4) (1.9) (−1.6)

.44 (5.7) (5.4) (6.3)

1960–2007 w/o with

−.69 (−4.7) (−4.7) (−4.0 .89 .86

−.16 (−2.5) (−1.9) (−1.5)

.42 (5.5) (5.5) (6.5)

.11 (0.7)

.07 (0.8) (0.6) (0.5)

.11 (0.7)

.07 (0.8) (0.6) (0.5)

1980–2000 w/o with

−.11 (−3.4) (−2.8) (−2.1) .89 .86

−.16 (−2.4) (−1.9) (−1.5)

.42 (5.5) (5.5) (6.5)

1960–2008 w/o with

(−5.6) (−5.7) (−4.9) (−4.9) .88 .86

−.24 (−2.8) (−2.4) −.82

.43 (6.7) (6.9) (6.8)

−.28 (−1.8)

.40 (4.7) (5.1) (4.5)

(continued)

−.28 (−1.8)

.40 (4.7) (5.1) (4.5)

1980–2010 w/o with

(−4.1) (−3.1) (−2.7) (−2.7) .88 .86

−.24 (−2.8) (−2.4) −.14

.43 (6.7) (6.9) (6.8)

1960–2010 w/o with

DO LOANABLE FUNDS MODIFY THE CROWD OUT …

−.11 (−0.8)

.14 (1.2) (1.0) (1.2)

1970–2000 w/o with

−.13 (−1.3) (−1.3) (−1.5) .90 .85

−.16 (−2.2) (−1.6) (−1.5)

.36 (2.7) (2.9) (3.9)

1960–1990 w/o with

Table 18.1 Comparing Robustness Over Time of Effects on Consumption of Crowd out, With and Without Compensating Loanable Funds (Separate Stand-Alone S + FB Variable Included)

18

335

1975–2004 w/o with

.35 (3.2) (3.0) (3.2)

+.02 (0.2) (0.1) (0.1)

−.02 (−0.3) (−0.2) (−0.2) .88 .82

Variable

T Def NW tsta White t Ordin. T

G Def : NW tstat White t Ordin. t

ST + FBNW tstat White t Ordin. t R2 Adj.R 2

−.36 (−1.7) (−1.4) (−1.1) .88 .82

+.02 (0.2) (0.1) (0.1)

.35 (3.2) (3.0) (3.2)

−1.46 (−2.6) (−2.5) (−2.6) .95 .90

−.13 (−1.2) (−1.4) (−1.5) .95 .90

ST + FBNW tstat White t Ordin. t R2 Adj.R 2

(−2.9) (−2.3)

(−2.9) (−2.3)

White t Ordin. t

Table 18.1 (continued)

−.32 (−1.0) (−0.9) (−1.2) .92 .89

(−0.7) (−0.8)

−.05 (−0.6) (−0.5) (−0.4) .88 .81

.02 (0.2) (0.1) (0.1)

.38 (3.3) (2.8) (3.0)

−.42 (−1.6) (−1.3) (−1.1) .88 .81

.02 (0.2) (.0.1) (0.1)

.38 (3.3) (2.8) (3.0)

1980–2004 w/o with

−.06 (−0.6) (−.06) (−0.8) .92 .89

(−0.7) (−0.8) −.71 (−4.6) (−3.9) (−3.3) .88 .84

(−1.1) (−1.0)

−.07 (−0.4) (−0.4) (−0.4) .86 .74

.19 (1.0) (0.7) (0.6)

.39 (2.4) (2.1) (2.2)

−.27 (−0.7) (−0.5) (−0.5) .86 .74

+ .19 (1.0) (0.7) (0.6)

.39 (2.4) (2.1) (2.2)

1985–2004 w/o with

−.14 (−3.0) (−2.5) (2.0) .88 .84

(−1.1) (−1.0) −.68 (−5.1) (−2.5) (−3.5) .89 .86

(−1.4) (−1.2)

−.07 (−0.4) (−0.4) (−0.4) .87 .76

+.21 (1.1) (0.8) (0.7)

.40 (2.7) (2.2) (2.3)

−.26 (−0.7) (−0.5) (−0.5) .87 .76

+.21 (1.1) (0.8) (0.7)

.40 (2.7) (2.2) (2.3)

1985–2005 w/o with

−.11 (−2.9) (−2.5) (−1.7) .89 .86

(−1.4) (−1.2) .02 (0.2) (0.1) (0.1) .94 .88

(0.5) (0.6)

−1.93 (−3.1) (−3.2) (−4.1) .97 .90

−1.09 (−2.8) (−2.8) (−3.6)

−1.09 (−2.8) (−2.8) (−3.6) −.19 (−3.1) (−2.8) (−2.6) .97 .90

.66 (3.3) (3.7) (4.0)

.66 (3.3) (3.7) (4.0)

1996–2010 w/o with

.01 (0.1) (0.1) (0.1) .94 .88

(0.5) (0.6)

−.81 (−3.9) (−4.5) (−3.5) .87 .82

(−1.8) (−1.8)

−.11 (−1.4) (−1.1) (−1.0) .99 .95

−.56 (−1.3) (−1.0) (−1.0)

.45 (2.4) (2.3) (2.2)

−1.11 (−1.6) (−1.4) (−1.3) .99 .95

−.56 (−1.3) (−1.0) (−1.0)

.45 (2.4) (2.3) (2.2)

2000–2010 w/o with

−.13 (−3.4) (−2.7) (−1.7) .87 .82

(−1.8) (−1.8)

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percentage points. This indicates that changes in loanable funds, ceteris paribus, may not have an effect on consumption, or not a huge one. We conclude the R 2 results are theory consistent: changes in loanable funds should affect levels of consumer spending. Other evidence developed later suggests this minimal effect is due to channeling of new loanable funds away from consumption and toward investment. When the deficit variables were replaced by loanable funds-modified deficit variables in a model with a stand-alone loanable funds variable, R 2 was unchanged in all 18 test periods, which is as it should be: the modified model just breaks into two parts the net effect shown in the unmodified (“w/o”) model. Effects on Crowd Out Variables When the separate loanable funds variable was added to the model, the number of tests showing significant crowd out increased. Tax cut crowd out was significant in 17of 18 tests, but government spending crowd out increased to only 9 of 18 of tests. This does not mean they have no crowd out effect. Six of the 9 spending deficit tests that showed statistically insignificant crowd out effects were for periods that mix substantial periods of statistically significant “crowd out” due to rising deficits with substantial periods of statistically significant “crowd in,” which had substantially declining deficits (namely, the 1990s). Tests combining the two subperiods combine effects that tend to cancel each other out in the regression, leaving a near-zero coefficient on the crowd out variable and leaving it statistically insignificant (see theory Chapter 4 for a discussion and examples of this problem). The tests are insignificant because a large portion of the sample shows crowd in, while the rest shows crowd out. Hence no clear, overall trend emerges. For spending deficits, because the results do not mean that crowd out (or crowd in) is statistically insignificant, these time period samples are best not included in evaluation of the test results. For the remaining 12 samples, the results 9 of 12 show significant crowd out effects of deficits. Tax cut deficits tend to show reduced coefficients and significance levels in the same six periods, though remaining significant in 5 of 6 cases. Removing them leaves 12 of 12 periods showing significant crowd out effects of deficits resulting from tax cuts. Ignoring the samples with this technical problem, we conclude our crowd out effect results overall are theory consistent. The topic is discussed in more detail in Sect. 18.1.1.

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Changes in Loanable Funds Have Positive and Negative Effects on Consumption As noted earlier, changes in loanable funds can be both a positive and a separate negative effect on consumption. The positive effect comes from crowd out effect of additional loanable funds, modeled as a reduction in the deficit by any same-period increase in loanable funds. The negative effect results because absent changes in income, the increases in loanable funds (saving) can only occur by decreasing consumption. In the models used here, disposable income is held constant when estimating the effects of an increase in loanable funds, and for the savings, the main component of (S + FB), to cause (S + FB) to grow, the marginal propensity to consume must decline. Results Generally Invariant to Type of Standard Error Used Results in Table 18.1 are shown using three different types of standard errors: Newey–West, White, and ordinary. This is done to show the results are generally the same regardless of which is used. Generally, in this study, we used Newey–West standard errors because they are useful in addressing both heteroskedasticity and autocorrelation. This choice is discussed in detail in the Sect. 18.1.4 further below. Table 18.1 shows that the type of standard errors used can change tstatistics slightly, but almost never affect the count of how many results were found to be statistically significant. Only in two of 36 tests did one type of standard error produce significant results while the others did not. In 34 of the 36 tests, if one standard error produced a significant t-statistic, they all did; if one produced insignificant results (which does happen), all three types of standard errors did. Combining Samples with Crowd Out and Crowd In Effects We do need an explanation for why nine of the 18 spending deficit tests in Table 18.1 showed no statistically significant crowd out effect, either before or after modification. Also, tax cut coefficients on all six tax cut deficits, though still significant, were also markedly lower than in other periods. The next section of this chapter (18.1.1) addresses that issue. 18.1.1

Mixing Crowd Out and Crowd in Periods May Distort Results

There are a number of reasons why crowd out can be found insignificant in some empirical tests:

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1. There are periods when crowd out may not exist. 2. During some of periods sampled, there may be no fluctuation of government spending or taxes. You can’t correlate something with a constant or near constant and get a statistically significant correlation. 3. Some sample sizes may be too small to generate significant results. In our sampling, the spending deficit crowd out variable has the expected sign, but was insignificant when only 1970s data was sampled. It also had the expected sign but was insignificant when only 1980s data was sampled, but had the right sign and was significant when the two samples were combined! 4. Samples that mix data from statistically significant periods of “crowd out” with data from periods of statistically significant “crowd in” will tend to cancel each other out and leave a nonsignificant result. This is the problem we address in this section. There were some periods, particularly in the 1990s, when loanable funds increased markedly due to a booming economy, which increased funds available for private borrowing. A decline in budget deficits increased privately available loanable funds even further. The increase in privately available loanable funds was so great the U.S. actually had “crowd in,” not “crowd out.” In the 1990s, the average yearly growth in loanable funds net of government saving (or deficits) was $132.9 billion (2005) dollars. The average yearly decline in the deficit was $33.7 billion (in 2005 dollars). A decline in the deficit means more of a (constant sized) loanable funds pool is available for private borrowing and spending. So, the pool available for private borrowing increased noticeably during the 1990s, as did consumption. Government spending also increased during this period (but less than taxes, so the deficit declined). Statistically, the correlation between government spending growth and consumption growth was positive, so testing this decade alone, we find the sign on the (G) variable was positive instead of the negative sign we expect because normally government spending growth increases deficits and causes crowd out. Without a deficit, the whole loanable funds pool would be available for increased private borrowing and spending. There is a similar effect during periods when the deficit is growing, but the pool of loanable funds is growing even faster. The growth in the pool replaces the loanable funds lost due to crowd out and provides some additional new loanable funds to finance new private borrowing. Table 18.2 shows the negative relation-

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Table 18.2 Changes in regression coefficients and t-statistics associated with loanable funds changes Period

00–10 60–80 80–90 60–70 70–80 90–00

Average loanable funds growth net of the govt. deficit during the decade Average: Average: Average: Average: Average: Average:

$ $ $ $ $ $

−184.7 Billion −27.8 Billion −17.8 Billion +6.6 Billion +6.9 Billion +132.9 Billion

( T−G) Deficit Coef.(t)

T Only Deficit Coef.(t)t

G Only Deficit Coef.(t)

+.42(4.2) +.45(3.3) +.02(0.1) +.53(2.4) +.22(1.4) −.09(0.5)

.45(2.4) .72(5.3) .10(0.2) .82(12.0) .22(0.9) 1.18(3.7)

−.56(−1.3)a −.28(−2.8) −.20(−0.2) −.42(−7.7) −.20(−0.8) +.50(6.5)

a Result sensitive to adding or subtracting even one year. Coefficient (t) = −.69 (−4.3) if 1998–1999

data included with the 2000–2010 decade; −.55 (−1.8) if only 1999 data is added

ship between average annual growth in the loanable funds pool and the statistical significance of crowd out on consumption. The more loanable funds grows the less likely crowd out is found to be a statistically significant problem negatively affecting consumption. Periods of large growth in loanable funds and declining deficits can combine to create big increases in privately available loanable funds not needed to offset deficits. This leads to not just restoration of pre-deficit consumption levels, but net increased consumption. That leaves a net “crowd in” effect. This can occur when both government spending and revenue are both increasing, but revenue is growing faster than spending, reducing the deficit. Just a reduction in the deficit alone can increase loanable funds available to private borrowers, leading to increased private spending. If the pool of loanable funds is also growing, it will further increase funds available for private borrowing and spending. In such periods, for taxes, the correlation between increases in taxes and consumer spending will be positive. For government spending (when deficits are declining), there will also be a positive correlation between government spending and consumer spending, which may show as a positive coefficient on the government spending variable in a sample dominated by this effect, like the 1990s. By comparison, when deficits are rising, the correlation is negative; then, positive growth in government spending is associated with negative growth in private spending, even if loanable funds are rising, but their growth is less than the spending deficit.

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The 1990s was a decade of declining deficits and large growth in loanable funds. Tests on that decade alone (Table 18.1.A) show positive, statistically significant coefficients on both the tax and spending deficit variables, consistent with our explanation above. This is a perfectly normal statistical finding, given the behavior of the underlying data during this period. It is not telling us there is no negative effect of spending deficits on private consumption spending. A finding of statistical insignificance in one or both of the deficit variables can occur when you combine statistically significant crowd in periods with data from other periods showing statistically significant crowd out, and to be expected. Here is why: Regression coefficients are but weighted averages of the relationship of changes in taxes (or government spending) to consumption in each of the years in a period sampled, ceteris paribus. If you add a series of observations (like the 1960–1980s) showing a negative effect of increased government spending on consumption (due to rising deficits due to the spending) with a period in which the observed relationship between spending and consumption is positive due to declining deficits prevailing, even though government spending is increasing (like the 1990s), the regression coefficient will be the weighted average of the two, and since it is a net effect, generally small in size. The resulting coefficient may be insignificant. The variable’s standard error (like any standard deviation) merely show the average way any individual year’s data on the relationship of a deficit variable to consumption differs from the mean difference for the whole period. Suppose the average effect (regression coefficient) for the positive years was (+.50), and for negative years, the coefficient was (−.40). Suppose also for each sample, these coefficients were highly statistically significant, i.e., the individual observations in each sample closely matched their averages for the whole period. Then, assuming both samples are weighted the same, when combined the coefficient will be (+.10), and all the individual year’s observations will be considerably bigger or smaller than this average, leading to a large standard error and low t-statistic (statistical insignificance). To illustrate, in Table 18.3, we repeat findings from Table 18.1 for 1960–1980 and 1960–1990 (all crowd out, no crowd in) and 1960–2000 (part crowd out, part crowd in). Notice that as soon as we add the 1990s data in, crowd out results on the government spending variable go from significant to insignificant. This is also true when we add in 1990s data to a 1970–1990 sample. The net effect of combining the two samples is

T Def : t stat G Def : t stat

Variable

.72 (5.3) −.28 (−2.8)

.72 (5.3) −.28 (−2.8)

.36 (2.7) −.16 (−2.2)

.36 (2.7) −.16 (−2.2)

with

w/o

w/o

with

1960–1990

1960–1980

.29 (2.6) −.09 (−0.9)

w/o .29 (2.6) −.09 (−0.9)

with

1960–2000

| | | |

.43 (1.7) −.36 (−3.0)

| w/o .18 (1.7) −.36. (−3.0)

with

| 1970–1990

Table 18.3 Effects of adding “crowd out” and “Crowd In” Periods

.14 (1.2) −.11 (−0.8)

w/o

.14 (1.2) −.11 (−0.8)

with

1970–2000

| | | |

1.18 (+3.7) +.50 (+6.5)

| w/o

with

| 1990–2000

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a smaller coefficient and statistical insignificance even though the 1990s decade data by itself has a highly significant positive government spending coefficient (crowd in years), and the 1960–1980 or 1990, and 1970– 1990 data has a highly significant negative coefficient on the government spending variable (crowd out years). Clearly, the examples above show that simply adding highly significant crowd out and crowd in results from different period samples together can result in sample results indicating deficits appear to have no statistically significant effect on consumption (the null hypothesis), when in fact they do. The alternative hypothesis is that adding the two together caused the insignificance. To show this alternative hypothesis can explain nonsignificant crowd out effects, we construct our own simplified “crowd out” (“Decade X ”) and “crowd in” (“Decade Y ”) data sets, and separately estimate their regression coefficients and significance levels. Separate hypothetical data sets for crowd out decade (X ) and the crowd in decade (Y ) are created. There are 10 observations for each decade. Equation 18.3 shows a simple model of crowd out effects constructed using the underlying data sets. Then, we add the two data sets together and reestimate the model to obtain coefficients and significance levels for the combined model. C = α + β(T −G) + e = α + βT − β G + e

(18.3)

Positive and negative stochastic error terms (e) had to be included in the consumption (C ) variable in order for the model to be stochastic and run properly. To do this, for both the crowd in and crowd out decades, roughly equal valued additions and subtractions from the calculated values for consumption were made. This was done to reflect the usual expectation that their expected value in the population, though not necessarily their actual value in each sample, is zero. Data sets were devised for both decades to show statistically significant crowd out effects in decade (X ) and statistically significant crowd in effects in decade (Y ), before any loanable funds effect was added. The data for decade (X ) and decade (Y ) was separately estimated using OLS, and then, the combined sample of decades (X ) and (Y ) data was estimated, also in OLS. Results are shown in Table 18.4. Using a one-tail test of significance (critical value of t = 1.65), both variables show significant crowd out or crowd in effects when tested in their own decades only. However, when the two samples were combined

Decade ( X) Sample

.88 (6.0) −.71 (−2.1) .86

Variable

(T ) deficit (t-statistic) (G) deficit (t-statistic) R2

.02 (1.6) 1.04 (38.9) .99

Decade ( Y) Sample

(0.1) (.01)

.03 (0.4)

Decades ( X) and ( Y) Combined Sample

Table 18.4 Simulated results of combining statistically significant “Crowd In” and “crowd out” samples

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the deficit variables (T deficit) and (G deficit) both fall to insignificance. They also have smaller coefficients and R 2 s, as was predicted. Note that while crowd in changes the sign on the spending deficit variable, it just reduces the coefficient on the tax cut deficit variable. Hence, based on the evidence provided above, we conclude that either statistically significant crowd out (or crowd in) can be observed in periods sampled, as long as the sample periods do not mix periods of crowd in with periods of crowd out. In samples that combine “crowd out” and “crowd in” periods, the crowd out variables are likely to appear statistically insignificant. This does not mean they are. In the Table 18.1 tests, 9 of the 18 tests show (G) to be statistically insignificant, but 8 of the 9 insignificant periods sampled mix the 1990s crowd in decade data with crowd out data from the 1960–1990 period, and in 6 of the 9, crowd in decade data was a full 1/3 to 1/2 of all the data. In the 9 of 18 tests showing (G) to have a significant crowd out effect, three contained no 1990s data, one contain only four observations from the 1990s decade, and 5 that did contain 1990s data were very large samples of 39–50 observation in which the ten 1990 observations effect was relatively small compared to the preponderance of the data in the sample, which did show crowd out. Hence, in averaging the crowd out coefficients dominated, they were close enough to the weighted average coefficient to leave the deficit variables statistically significant. The 2000–2010 decade shows the deficit variables insignificant for a different reason. At the end of that decade, the quantitative easing (QE) period increase in loanable funds that was so much larger than private demand for loanable funds that 94% of it remained unborrowed. We experienced the age-old “pushing on a string problem” which can hamper the effectiveness of monetary policy. Hence, in the 2000–2010 period, the government deficit, though it rose, when modified by the change in loanable funds was insignificant related to consumer spending. The large loanable funds deductions from deficit values left a modified deficit variable that more closely resembled the QE loanable funds increase than the deficit, and the QE increase for the most part never left the banks, so it couldn’t affect consumption. Remove the QE period years “outliers” from the sample, and the (G) variable again becomes significant. This difference in reconciling different sample results having (legitimately) different signs is not a common problem in economics. Most variables economists study (like income and consumption) show either a consistent positive or negative relationship, not positive in one period,

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Table 18.5 Effects of adding a separate, sand alone loanable funds variable to a crowd out model Variable

Decade ( X) Sample (without)

Decade ( X) Sample (with)

Decade ( Y) Sample (without)

Decade ( Y) Sample (with)

(T ) deficit (t-statistic) (G) deficit (t-statistic) R2

.88 (6.0) −.71 (−2.1) .86

.93 (10.8) 3.18 (3.1) .96

.02 (1.6) 1.04 (38.9) .995

.03 (1.8) 1.12 (8.4) .996

negative in another over large numbers of periods. And spuriously insignificant results are just that—results that appear in one or a few periods, but not often. Adding a Separate, Stand Alone Loanable Funds Variable to a Crowd Out Model The models shown in Table 18.4 show crowd out effects. They do not show the effects of any change in the loanable funds pool that may be occurring at the same time. If a separate, stand-alone variable is added to the simulation model above, and the model is reestimated, results change markedly, and in the predicted way: if the increase in loanable funds is larger than the growing deficit (Model X ), it changes the sign on the crowd out effect of government spending from negative to positive and increases the positive coefficient on the tax variable. For models with growing loanable funds pools, which show the effects of declining deficits, the coefficients on both variables are already positive due to deficit decline alone (Model Y ). Adding the increasing loanable funds variable just increases coefficient value on both the (T ) and (G) variables. See Table 18.5. As predicted, when adding the separate loanable funds variable when its value is larger than the deficit increases coefficients and t-statistics on the crowd out variables and raises the model’s R 2 . Section 18.1.1.1 Conclusions: 1. Table 18.1 showed significant tax cut crowd out in virtually all (16 of 18) samples. However, only half the samples (9 of 18) showed

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statistically significant crowd out related to government spending deficits. This may mean tax cut deficits do cause crowd out problems which can offset some or all of their stimulus effect, but spending deficits do not, at least not predictably. While we cannot rule this explanation out with certainty, points 2– 5 below suggest another cause may be more likely, namely mixing crowd out and crowd in periods in the same sample. There is a theory, and empirical evidence, to support this claim. We could not think of a compelling hypothesis to explain why, if crowd out doesn’t affect consumer spending, we should get a full 9 of 18 tests saying it does. Further, our tests in Table 18.1 test for the existence of crowd out effects controlling for the level of loanable funds, i.e., holding them constant. In Table 18.2, we relax that assumption and estimate the effect of spending crowd out on consumption without controlling for any offsetting growth in the pool of loanable funds pool. When we do that, the percentage of tests in Table 18.2 showing highly significant crowd out effects (t > 2.0) of spending deficits falls to zero. In Table 18.1, controlling for loanable funds, in the same six tests, all six had highly statistically significant crowd out effects. This is consistent with the hypothesis that spending crowd out exists as a real problem, but that increases in the loanable funds pool can offset it. 2. Even though crowd out exists and has a test-verified negative effect on consumption, the data may show no decline in consumption if loanable funds are growing at or near the rate at which the deficit is growing in a given period. Regression results will indicate no statistically significant relationship between consumption and crowd out, but not because crowd out does not exist, but because it is offset by loanable funds pool increases in the same period. 3. If the pool of loanable funds is growing at a faster rate than the deficit, the coefficient and significance level of the tax variable will grow, and the sign on the spending variable change from negative to positive (and its statistical significance may grow). 4. If you combine data from periods when there was statistically significant crowd out with data from periods where there was statistically significant crowd in (i.e., loanable funds growth exceeds deficit growth, as noted in [2]), coefficients on the crowd out variable may be found statistically insignificant.

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5. All the major test periods shown: 1960–1980, 1960–1990, 1970– 1990, and 1990–2000 showed statistically significant crowd out or crowd in. The 2000–2010 period did not, but not for a crowd out related reason. There, QE in the 2008–2010 period caused a massive growth in loanable funds far in excess of consumers’ interests in borrowing. Since it was not borrowed, it was not spent. Hence, the relationship between loanable funds-modified deficits and consumer spending breaks down and it found statistically insignificant. Replacing the 2008–2010 years in the standard model tests of Table 18.1 with 1987–1989 restores the statistically significant crowd out effect. (Adding the 1987–1989 data was necessary because the standard model has so many variables it won’t run with less than 10 data observations.) Can Table 18.1 Results Be Replicated in Other Samples? Table 18.1 results are illustrative and test a significant number of time periods. A more comprehensive list of different, but overlapping periods sampled and their crowd out results are given in Table 18.6. In Table 18.6, coefficients and t-statistics for the loanable funds (S + FB) modified deficit variables and unmodified deficit variables (T , G) and R 2 are the same (because models include the stand-alone [S + FB] control); so they are only presented once. In most cases, each new sample shown includes the prior sample years, plus one additional year’s data. This allows careful examination of how the coefficients and significance levels vary as we slowly add crowd in year data to samples initially dominated by crowd out data. As more and more crowd in data is added to a sample that initially represented only a crowd out period, it reduces crowd out coefficients and significance levels because of the offsetting effects of crowd in and crowd out on consumption (as we discussed above). 90 tests were undertaken. Fifty-one of 90 show fully or marginally statistically significant coefficients on the government spending deficit variable. As we noted earlier in connection with Table 18.1, the model is estimated holding the level of loanable funds constant. This is so we do not confound the effects on consumption of changes in the loanable funds pool, as explained earlier, with the crowd out effects of the deficit variables in the same equation. With Table 18.1, of 18 different spending deficit samples, we found half showed statistically significant crowd out effects

1960–1970 1960–1971 1960–1972 1960–1973 1960–1974 1960–1975 1960–1976 1960–1977 1960–1978 1960–1979 1960–1980 1960–1981 1960–1982 1960–1983 1960–1984 1960–1985 1960–1986 1960–1987

Period Sampled:

Spend. Deficit t-stat (−7.7) (−5.3) (−3.2) (−3.1) (−3.0) (−3.1) (−3.5) (−2.2) (−2.5) (−2.2) (−2.8) (−2.8) (−1.9) (−2.1) (−2.1) (−2.0) (−2.1) (−2.2)

Coef. −.43 −.38 −.38 −.33 −.32 −.31 −.31 −.25 −.26 −.25 −.28 −.27 −.23 −.26 −.23 −.21 −.19 −.18

Tax Deficit t-stat (12.0) (6.6) (5.3) (3.9) (4.1) (7.1) (7.4) (5.0) (5.2) (5.1) (5.3) (5.5) (4.4) (3.9) (4.5) (3.7) (3.4) (3.4)

Coef.

.82 .76 .76 .78 .71 .83 .83 .75 .74 .75 .71 .71 .69 .63 .59 .52 .50 .50

.99 .98 .95 .93 .95 .94 .94 .93 .93 .93 .94 .94 .91 .92 .93 .92 .93 .93

R2

1980–1990 1980–1991 1980–1992 1980–1993 1980–1994 1980–1995 1980–1996 1980–1997 1980–1998 1980–1999 1980–2000 1980–2001 1980–2002 1980–2003 1980–2004 1980–2005 1980–2006 1980–2007

Period Sampled:

.10 .15 .12 .26 .22 .20 .15 .13 .14 .08 .08 .05 .24 .35 .38 .40 .43 .47

Coef. (0.2) (0.5) (0.4) (1.2) (1.1) (1.1) (0.9) (0.9) (1.3) (0.7) (0.8) (0.4) (2.6) (2.6) (3.3) (4.1) (4.1) (4.8)

t-stat

Tax Deficit

Table 18.6 Crowd out variable coefficients, t-statistics and R 2 in different sample periods*

−.20 −.05 .07 −.25 −.15 −.11 −.30 −.02 .08 .09 .11 .12 .19 .02 .02 .04 −.01 −.04

Coef.

.97 .97 .97 .96 .96 .95 .95 .94 .92 .93 .94 .93 .91 .90 .88 .89 .88 .88

R2

(continued)

(0.3) (0.1) (0.1) (−1.0) (−0.8) (−0.8) (−0.7) (−0.1) (0.5) (0.6) (0.7) (0.7) (1.1) (0.2) (0.2) (0.2) (−0.1) (−0.3)

t-stat

Spend. Deficit

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1960–1988 1960–1989 1970–1980 1970–1981 1970–1982 1970–1983 1970–1984 1970–1985 1970–1986 1970–1987 1970–1988 1970–1989 1970–1990 1970–1991 1970–1992 1970–1993 1970–1994

Period Sampled:

Spend. Deficit t-stat (−2.8) (−2.0) (0.8) (0.8) (−4.7) (−2.7) (−7.1) (−5.4) (−5.3) (−4.0) (−4.6) (−3.2) (−3.0) (−0.8) (−1.2) (−1.8) (−1.6)

Coef. −.19 −.14 −.20 −.24 −.33 −.36 −.58 −.57 −.56 −.44 −.38 −.34 −.36 −.19 −.20 −.23 −.22

Tax Deficit t-stat (3.5) (2.9) (0.9) (1.3) (2.4) (2.3) (8.4) (5.2) (6.6) (4.9) (4.7) (1.7) (1.6) (1.3) (1.4) (1.4) (1.5)

Coef.

.51 .37 .22 .25 .31 .25 .50 .36 .34 .33 .33 .20 .18 .18 .18 .18 .18

Table 18.6 (continued)

.93 .91 .99 99 .99 .99 .99 .99 .99 .98 .98 .96 .95 .91 .91 .91 .92

R2

1980–2008 1980–2009 1980–2010 1990–2000 1990–2010 1990–2002 1990–2003 1990–2004 1990–2005 1990–2006 1990–2007 1990–2008 1990–2009 1987–1997 1988–1998 1989–1999 1990–2000

Period Sampled:

.39 .42 .40 1.18 .78 .73 .85 .57 .57 .61 .57 .39 .44 .09 1.46 .65 1.18

Coef. (4.2) (4.7) (4.7) (3.7) (1.7) (7.8) (6.5) (3.0) (3.2) (5.9) (10.8) (4.1) (4.2) (0.1) (2.5) (2.9) (3.7)

t-stat

Tax Deficit

−.05 −.13 −.28 .50 .38 .37 .29 .34 .34 .34 .37 .31 .08 .15 .52 .29 .50

Coef.

(−0.5) (−1.2) (−1.8) (6.5) (7.6) (6.4) (2.2) (1.8) (2.0) (2.0) (2.0) (1.7) (0.4) (0.3) (2.4) (2.1) (6.5)

t-stat

Spend. Deficit

.88 .89 .87 .99 .99 .99 .98 .91 .92 .92 .92 .90 .90 .96 .99 .99 .99

R2

350 J. J. HEIM

Spend. Deficit t-stat (−1.5) (−1.5) (−1.7) (−0.9) (−0.9)

Coef. −.18 −.18 −.19 −.12 −.12

Tax Deficit t-stat (1.2) (1.2) (1.1) (1.3) (1.2)

Coef.

.17 .16 .15 .16 .15

*Same Model used as in Eqs. 18.1 and 18.2 and in Table 18.1

1970–1995 1970–1996 1970–1997 1970–1998 1970–1999

Period Sampled:

.90 .90 .90 .90 .92

R2

1991–2001 1992–2002 1993–2003 1994–2004 1995–2005 1996–2006 1997–2007 1998–2008 1999–2009 1990–2010 2000–2010 1999–2010 1998–2010 1996–2010 1993–2010

Period Sampled:

1.03 −.81 .93 .80 1.55 1.00 .87 .47 −.14 .41 .45 .45 .45 .66 .32

Coef. (1.4) (0.4) (9.0) (5.6) (1.3) (2.1) (4.3) (1.8) (0.2) (3.2) (2.4) (3.3) (3.3) (3.2) (3.3)

t-stat

Tax Deficit

.39 .01 .89 .85 2.95 −.62 .35 −.47 .10 −.23 .56 −.55 −.55 −1.09 −.54

Coef. (1.2) (0.0) (4.9) (2.7) (1.0) (−0.3) (0.4) (−0.5) (0.1) (−0.7) (−1.3) (−1.8) (−1.8) (−2.8) (−2.0)

t-stat

Spend. Deficit

.99 .98 .99 .99 .96 .95 .99 .99 .99 .99 .99 .99 .99 .97 .91

R2

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even after modification of the deficit variables in models including a loanable funds variable. Table 18.6 tests indicated 51 of 90 show significant spending deficit crowd out results. When interpreting Table 18.6, recall that regression coefficients conceptually represent the average way one-variable changes when there is variation in another during a given period of time, holding constant all other variables in the model. Add or subtract some periods of time from the sample and you are likely to change this average, especially with policy variables like tax and spending levels which fluctuate with the economy, but in part are policymaker (exogenously) controlled. Recall that data used is yearly change data (1st differences). Insignificant regression coefficients on the deficit variables can result for substantive reasons (no real relationship). Insignificance can also result for technical reasons even if there is a significant underlying relationship between deficits and consumption. Consider a period of time in which the spending deficit is negatively related to consumption (like the 1960– 1980s), with a large negatively signed coefficient found on the spending deficit variable in testing. Suppose 1990s data was added to the sample. The 1990s was a decade where “crowd in” prevailed, and for that decade, a positive relationship between spending and consumption would typically be found because of the declining deficit, even if government spending were increasing (but not as much as government receipts). In tests of the 1990s only data, this would leave the coefficient on the spending deficit variable positive and it might be large for those years. Combine the two periods, one crowd out (1960–1990) and one crowd n (1990s) and you are likely to get a small magnitude net regression coefficient. It is also likely to be statistically insignificant since significance is determined based on how close individual observations are to the net or average vale. Here, it means comparing the large effects of the crowd out and crowd in years with the small net coefficient. Standard errors are but a measure of the average way individual data points differ from this small whole data set average. Since the coefficients on the deficit variable for the crowd out and crowd in periods were both large, finding the “average” way they differ from the small combined sample average will yield a large standard error and low levels of statistical significance. To elaborate, recall again that regression standard errors, like all standard deviations, measure the average way that individual year’s data relate that year’s changes in the deficit to same-year changes in consumption. Calculating the standard deviation involved finding the average way, these

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individual changes differ from the average way they vary (i.e., the regression coefficient) for the whole data set. The standard errors are likely to be large relative to the combined samples’ small net average (coefficient), so the coefficient is likely to be found statistically insignificant. This would be true even if separately, the two individual samples, the crowd out and crowd in samples, in the composite sample, were each strongly statistically significant when tested alone. We see this in Table 18.6. The samples that include only the 1960s, 1970s, and 1980s data have large positive coefficients on the tax cut deficit variable, indicating negative effects on consumption when the change in taxes is negative (tax cut). For spending deficits, samples from these decades are found to have negative coefficients on the government spending variable, indicating increases in government spending deficits are associated with negative changes in consumption, ceteris paribus. As shown below, samples including all 1960s and 1970s years data, or 1960s through 1980s data provide exactly these deficit variable results—results indicate that both tax cut deficits and spending deficits cause statistically significant crowd out problems. The same is true for the samples containing just 1970s and 1980s data, as shown below (see Table 18.6). 1960–1979 data: .75 (t = 5.1) tax deficit effect and −.25 (t = − 2.2) spending deficit effect 1960–1989 data: .37 (t = 3.9) tax deficit effect and −.14 (t = − 2.0) spending deficit effect However, during the 1990s, the deficit declined in 7 of 10 years, reducing government’s borrowing from the pool of loanable funds, which increased the part of the loanable funds pool available to for private borrowing (“crowd in”). This leads to increased private borrowing, which leads to increased consumer spending. The increase in consumer spending may occur even if government spending is increasing, but increasing less than the increase in government receipts, so that the deficit is declining. If consumption is found to be increasing at the same time, government spending and it can create a statistical finding that increased government spending is related, perhaps significantly, to a positive increase in consumption. In reality, this increase in consumption at the same time government spending is growing is occurring because taxes are growing even faster than government spending, leading to a decline in the deficit.

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It is the decline in the deficit, not the increase in government spending, that is causing the growth in consumption. Examples of data sets from Table 18.6 dominated by 1990s data show this apparent positive result. For example, samples from the following years show this result: 1990–2000 data: 1.18 (t = 3.7) tax deficit effect and +.50 (t = 6.5) spending deficit effect 1989–1999 data: .65 (t = 2.9) tax deficit effect and +.29 (t = 2.1) spending deficit effect 1988–1998 data: 1.46 (t = 2.5) tax deficit effect and +.52 (t = 2.4) spending deficit effect Alternatively, we see when we combine crowd out and crowd in samples (e.g., the 1980s and 1990s data samples), the coefficients on the crowd out variables decline in absolute size and become statistically insignificant, as shown in the following examples from Table 18.6. 1980–1999 data: .08 (t = 0.7) tax deficit effect and +.09 (t = 0.6) spending deficit effect 1980–1997 data: .13 (t = 0.9) tax deficit effect and −.02 (t = − 0.1) spending deficit effect The small coefficients are because the average effect of crowd out on consumption during the 1980s and 1990s is small. This is because there was a large magnitude, statistically significant negative effect of spending increases (deficits) in the 1980s offsetting most of large, statistically significant positive effects in the 1990s. Further, because the large valued positive and negative changes (represented by the coefficients above) in both subsamples are so different from the small composite sample coefficient, the joint 1980s and 1990s samples show statistically insignificant net crowd out effects for both tax cut and spending increase deficits. Hence, in evaluating Table 18.1 results, we should discount results obtained from samples combining relatively even numbers of deficit decrease and deficit increase years, and count only the results for samples strongly dominated by one or the other effect. This would mean our first six samples in Table 18.1, which start with the 1960–1980 period, and the following additional samples: 1960–1990, 1960–2000, 1960–2007, 1960–2008, and 1960–2010.

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This is not a common problem to run into when econometrically testing a variable’s statistical significance. For example, when testing the income–consumption relationship, we never have to worry that we may find a strong positive, statistically significant relationship in one decade and then retest it on another decade and getting a strongly significant negative relationship. Nor do we expect that by adding two statistically significant samples together, we will find no statistically significant relationship. With the income–consumption relationship, or other expected relationships, we expect to find either a significant positive or negative relationship in different samples, or statistical insignificance (but not significant positive and significant negative relationships in different samples). For most variables, theory doesn’t specify the relationship can be positive in one period and negative in another for good economic reason. But in this study, where both crowd out and crowd in can occur for theory-consistent reasons, statistical insignificance can occur when both crowd out and crowd in periods are tested in the same sample. This technical reason for insignificance should not be taken as a finding that no significant crowd out relationship exists. Table 18.6 also shows that we have a similar problem when looking at individual years in the 2000–2010 decade when combining them with 1990s data. To show what was going on in the 2000–2009 decade alone, below we reproduce 11-year data samples in which the end year was a different year in the 2000–2010 period. (We need at least 10 observations for the regression to calculate results when testing our standard consumption model given in Eqs. 18.1 and 18.2.) Note that 5 of the 11 tax deficit coefficients are statistically insignificant, as are 8 of the 11 spending deficit coefficients:

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1999 2009 data:

.14 ( t = 0.2) tax deficit effect and +.10 (t = 0.1) spending deficit effect

1998 2008 data:

.47 (t = 1.8) tax deficit effect and .47 (t = 0.5) spending deficit effect

1997 2007 data:

.87 (t = 4.3) tax deficit effect and +.35 (t = 0.4) spending deficit effect

1996 2006 data: 1.00 (t = 2.1) tax deficit effect and .62 (t = 0.3) spending deficit effect 1995 2005 data: 1.55 (t = 1.3) tax deficit effect and +2.95 (t = 1.0) spending deficit effect ----------------------------------------------------------------------------------------------------------------1994 2004 data:

.80 (t = 5.6) tax deficit effect and +.85 (t = 2.7) spending deficit effect

1993 2003 data:

.93 (t = 9.0) tax deficit effect and +.89 (t = 4.9) spending deficit effect

1992 2002 data:

.81 (t = 0.4) tax deficit effect and +.01 (t = 0.0) spending deficit effect

1991 2001 data: 1.03 (t = 1.4) tax deficit effect and +.39 (t = 1.2) spending deficit effect 1990 2000 data: 1.18 (t = 3.7) tax deficit effect and +.50 (t = 6.5) spending deficit effect

Clearly, as we add a few years from the early 2000s to the 1990s decade data (removing an equal number of early 1990s years to maintain a constant 11 years in the sample), results generally stay significant with the expected “+” sign on the government spending variable we expect in “crowd in” periods. In these samples, the 1990s data still constitutes half or more of the data set. As we add in additional years’ data from the latter half of the 2000s, increasing in eight of the nine 1990s years, government spending becomes insignificant. We are mixing too heavy a dose of crowd out decade data in with the crowd in decade data. The tax variable coefficients also become insignificant in the 1998–2008 and 1999–2009 samples, which are most removed from the 1990s period only data. Overall, when the data used are 1990s data alone, or with only a few observations from another decade, the effects for the 1990s dominate and show large, statistically significant positive coefficients on the spending variable and crowd in. When you mix data from crowd out and crowd in periods together, the effects tend to cancel each other out and you get very small coefficients which are statistically insignificant. In addition, during the 1990s, the total pool of loanable funds available for borrowing increased, making even larger amounts of the loanable

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Table 18.7 Annual Growth (+)/Decline (−) in Deficits 1990–2000 Year

Deficit Growth(+)/Decline(−)

Loanable Funds Growth(+)/Decline(−)

1990 1991 1992 1993 1994 1996 1995 1997 1998 1999 2000

+96.23 +68.10 +104.25 −58.39 −115.58 −22.02 −95.53 −133.03 −127.96 −53.32 −91.60

−78.19 −115.52 +8.47 + 50.86 +189.14 +108.70 +131.32 +202.18 +216.28 +135.78 +184.10

Source ERP 2012 for nominal values, then deflated using the GDP deflator

funds pool available for private borrowing increased consistently in most years, as shown in Table 18.7. Even if deficits had not declined, this would have given us another source of “crowd in” effect besides declining deficits during this decade. Unlike (G), a period of declining deficits does not change the sign on the (T ) variable’s coefficient. A change upward in government revenues (T ) which reduces the deficit also creates a positive change in consumption by increasing the loanable funds pool available for private borrowing. Hence here, the sign on the (T ) variable is already positive, so we observe the added positive effect as an increase in the coefficient on the tax variable. (T ) growing faster than (G) caused the deficit level to decline during the 1990s. This offset the more traditional negative effects of yearly deficits characterizing the 1970s and 1980s data, so no statistically significant impact of crowd out is shown when data for the 1990s is added to data for the 1970s and 1980s. It was not clear why deficits had such a positive effect on consumption in the 2000–2008 period. We disaggregated total government spending into its goods and services component (which enters the GDP), and its transfers spending component (which doesn’t), and retested. The positive sign on the spending deficits in the 2000–2008 period in Table 18.6 is totally associated with changes in transfer spending, which often translates into high levels of consumer spending. The findings suggest there are distributional effects of transfer spending that offset the crowd out

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problem, namely more spending on consumer goods takes place out of transfer payments than is lost to consumer borrowing through crowd out. The declining deficits of the1990s were due in part to the 1990s being the decade of welfare reform and the large 1993 tax increase, as well as the booming economy’s effect on tax collections (Table 18.8). This chapter’s focus is on doing good science. The objective is to determine if the crowd out problem really exists and the extent to which changes in the loanable funds pool can offset it. But doing good science doesn’t necessarily answer important public policy questions related to government deficits. • If there were no deficits to offset, the whole of any increase in the loanable funds pool (including increases due to FR open market operations) could be used to finance additional, new private consumption or investment spending, assuming we were below full employment potential). This would grow the private sector of the economy. • But with deficits, there is crowd out, which requires some or all of the increase in loanable funds to just to maintain pre-deficit levels of private consumer and business spending. Not as much, if any, is left to finance new growth in the economy. That said, the fiscal stimulus programs, whose deficit is funded by the money taken from the loanable funds pool would stimulate an alternative form of economic growth. This type growth would not be available without the deficit. • Hence, both deficiting and not deficiting can lead to economic growth, but of different types. How to resolve the public policy issue of whether to deficit or not comes down to two things: 1. Social preference for future growth to be more private goods and services (cars, houses, entertainment), or public goods and services (roads, bridges, transfer payments) 2. Comparing the economic growth rates likely to result from the deficit and no deficit options. Even small differences in growth rates make significant differences in standards of living over time (Solow 1957), so the right choice is very important.

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Table 18.8 Annual Growth (+)/Decline (−) in Deficits 1960–2000 Year

Deficit Growth(+)/Decline(−)

Loanable Funds Growth(+)/Decline(−)

1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

−46.93 +56.52 +28.37 −26.34 +27.85 −15.82 +17.67 +89.25 −57.09 −80.55 +120.38 +37.82 −78.48 −55.30 +53.44 +221.50 −90.18 −62.23 −58.34 −14.42 +116.13 −15.67 +170.40 +66.63 −43.29 +34.19 +45.80 −62.27 −39.81 −10.00 +96.23 +68.11 +104.26 −58.39 −115.58 −22.03 −95.54 −133.03

−6.14 +5.39 +44.33 +30.70 +28.66 +69.89 +40.41 −13.62 +28.59 +31.94 −74.29 +40.48 +87.77 +101.78 −42.83 −139.22 +117.98 +136.09 +130.42 +11.08 −102.71 +111.18 −103.06 −20.71 +304.12 −26.47 −31.19 +93.58 +72.41 −56.11 −78.20 −115.53 +8.48 +50.87 +189.14 +108.71 +131.32 +202.19

(continued)

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Table 18.8 (continued) Year

Deficit Growth(+)/Decline(−)

Loanable Funds Growth(+)/Decline(−)

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

−127.96 −53.32 −91.60 +255.23 +395.37 +101.98 −47.44 −155.87 −99.28 +75.48 +379.04 +539.51 +23.59

+216.28 +135.78 +184.11 −205.34 −102.92 +13.89 +238.65 +207.19 +255.41 −302.12 −222.40 −571.12 +244.98

Source ERP, 2013, Nominal Values Tables GDP Deflator Table B3 (2005 = 100)

Heteroskedasticity (or Heteroscedasticity) and Autocorrelations In this study, there were many instances of heteroskedasticity and many of autocorrelation. Much autocorrelation was removed by using first differences, not levels, of the data. In some cases, an autocorrelation control, typically AR(1) was also used. After this, in most cases, our Durbin– Watson statistics met or exceeded minimum standards, indicating we had eliminated any first-order autocorrelation problems. Griffiths, Hill, and Lim (2011) recommended using a DW criterion of (−1.3) as a lower limit when determining if autocorrelation was significant. In fact, we typically used (−1.6) as the lowest level of acceptable autocorrelation, and most of the regressions in this study did have Durbin–Watson statistics of −1.6 to +2.5. Newey–West standard errors can give an additional margin of safety from the effects of the autocorrelation problem, as well as heteroskedasticity problems, and were generally used in this study. the estimator is used to try to overcome autocorrelation (also called serial correlation) and heteroskedasticity in the error terms in the models, often for regressions applied to time series data” (Wikipedia, “Newey – West estimator”) Also, from Wooldridge, Introductory Econometrics, 3rd ed. Cptr. 12: Serial correlation and heteroskedasticity in time series regressions “…A very common strategy for considering the possibility of AR(1) errors is the Durbin-Watson test…”…..”…the methodology to compute what are often

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termed heteroskedasticity and autocorrelation – consistent (HAC) standard errors was developed by Newey and West; thus they are often referred to as Newey – West standard errors…

Newey–West and perhaps White standard errors sometimes yield higher t-statistics than ordinary standard errors when applied to a given data set, such as the set used in developing Table 18.1. By separately examining significance levels of variables tested in Table 18.1 above using Newey– West, White, and ordinary standard errors, we can determine if our general choice of Newey–West errors affected findings on whether or not crowd out was a statistically significant problem affecting consumption. We estimated Table 18.1 consumption model t-statistics using White and ordinary standard errors on all 108 coefficients on the deficit variables and the stand-alone (S + FB) variable reported in Table 18.1. The results were as follows: 1. 52 of the 108 t-statistics calculated using Newey–West standard errors were equal to or smaller than t-statistics calculated using ordinary standard errors. 2. 67 of the 108 t-statistics calculated using Huber–White standard errors were equal to or smaller than t-statistics calculated using ordinary standard errors. 3. 73 of 108 Newey–West t-statistics were fully (63) or marginally (10) statistically significant. 4. 67 of 108 Huber–White t-statistics were fully (53) or marginally (14) statistically significant. 5. 63 of 108 Ordinary t-statistics were fully (54) or marginally (9) statistically significant. Overall, there was a marginal tendency for Newey–West t-statistics to be found statistically significant more often. This we believe was because they were better at taking autocorrelation and heteroskedasticity issues into consideration. In general, in this book, we use Newey–West standard errors to calculate t-statistics because of its usefulness in helping address both serial correlation and heteroskedasticity. Huber–White only treats heteroskedasticity. Ordinary standard errors treat neither. We also reestimated standard errors for the investment model results shown in Table 18.11 further below; t-statistics using Newey–West, Huber–White, and ordinary standard errors on all 72 coefficients on

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the deficit variables and the stand-alone (S + FB) variable reported in Table 18.1. The results were as follows: 1. 24 of the 72 t-statistics calculated using Newey–West standard errors were equal to or smaller than t-statistics calculated using ordinary standard errors. 2. 31 of the 72 t-statistics calculated using Huber–White standard errors were equal to or smaller than t-statistics calculated using ordinary standard errors. 3. 57 of 72 Newey–West t-statistics were fully (46) or marginally (11) statistically significant. 4. 57 of 72 Huber–White t-statistics were fully (45) or marginally (12) statistically significant. 5. 53 of 72 Ordinary t-statistics were fully (45) or marginally (8) statistically significant. Overall, there was no difference using the Newey–West and White standard errors in the number of tests found at least marginally significant. There was a slight tendency for both to be found only statistically significant more often than occurred using ordinary standard errors. As before, this we believe was because they were better at taking autocorrelation and heteroskedasticity issues into consideration. White t-statistics were also marginally more likely to be found statistically significant than ordinary t-statistics. We typically use Newey–West standard errors to calculate t-statistics because of its usefulness in helping address both serial correlation and heteroskedasticity. Huber–White only treats heteroskedasticity. Ordinary standard errors treat neither. 18.1.2

Comparing One-Variable and Two-Variable Deficit Results

Chapter 17 examined whether variations in the consolidated government deficit, expressed as one variable (T − G), were related to variation in consumption and investment, controlling for other factors thought to affect consumption. In Table 17.1, we presented results for 18 different, though often overlapping samples of data from the 1960–2010 period. The initial test was on the full 1960–2010 data set and indicated consumption declined $0.38 for every dollar increase in the deficit variable (T − G). The result was highly statistically significant (t = 6.3). In

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an attempt to see if these results could be replicated, 17 other tests of different parts of the original 50 year test period were tested using the same model. Fourteen of the 17 tests did replicate the original replicate the findings in the sense that the regression coefficients had the same sign, had roughly the same magnitudes (with some exceptions), and were statistically significant at the 5% level or better. The four insignificant ones were samples in which 1990s data (with crowd in) was roughly even matched with data from the 1970 and/or eighties or 2000s which showed crowd out effects, as was the case with some of our findings in Table 18.1.A above. Hence, we concluded in Chapter 17 that the government deficit was in fact the systematically associated with crowd out problems. In this first part of this chapter, we tested the exact same models as in Chapter 17 except that we divide the deficit variable (T − G) into the deficit’s two component parts (T ) and (G) and test each, ceteris paribus, i.e., while holding the other deficit variable constant, as well as other determinants of consumption, as was done in Chapter 17. In Table 18.6, the standard consumption model with an added standalone variable representing total loanable funds availability was tested. 90 regressions representing different, by overlapping, sample periods were tested. Results, presented in Table 18.6, typically indicated crowd out was a statistically significant problem, whether caused by tax cut deficits (51 tests) or spending increase deficits (39 tests). Tests also indicated the 1990s was a “crowd in” period. In eight of the 11 years 1990– 2000, taxes grew more than spending, a reduction in the deficit, which increased the pool of loanable funds available for consumer or business borrowing causing a statistically significant “crowd in” situation. When testing combined amounts of the 1990s data with crowd out data from the 1970s, 1980s, or 2000s, the two effects cancelled each other out; regression coefficients declined in size, measuring just a residual effect, and became statistically insignificant (19 spending cases). But this did not mean deficits don’t induce crowd out; it only means that symmetrically, surpluses induce “crowd in.” Combining the two in one data set tends to create an average effect (regression coefficient) near zero, and statistical insignificance, since both types of periods separately tended to have large coefficients, making the average unrepresentative of the individual data components. This is the type of “statistical insignificance” that large standard errors relative to coefficient size are intended to convey. Overall then, we conclude our larger table of 90 tests in Table 18.6 supports our Table 18.1 results specified earlier. Most samples in both

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tables showed some form of crowd out. Both tax cut and spending deficit crowd out were generally supported by our statistical findings, as is the symmetric notion of crowd in when there are budget surpluses, at least when tested in models including a stand-alone variable representing loanable funds. In short, the one- and two-variable deficit models yield consistent results.

18.2 Consumption Models Without Stand-Alone (S + FB) Results presented in Table 18.1 allowed us to determine the effectiveness of loanable funds in offsetting crowd out problems created by government deficit financing. This was done by comparing the amount of variance explained by the model before and after the loanable funds variable was added to the model. The role of loanable funds in offsetting crowd out was judged significant if adding the variable to the model allowed us to better explain variation in consumption. In the model tested, loanable funds were included as a stand-alone variable, as well as also used to modify the deficit variables. The evidence suggested that while the deficit modifiers allow us to determine the crowd out offsetting effects of increase in the loanable funds pool, there was a second result of increasing loanable funds (i.e., the mps). It lowered mpc, which had a separate negative effect in consumption. To separately estimate these of that effect, required the model also include a stand-alone loanable funds variable. To determine if this second stand-alone use of the loanable funds variable is really necessary, i.e., whether the second negative effect of increasing loanable funds is real, we would like to see more direct results of how subtracting increases in loanable funds from the deficit variables affect the coefficients and significance levels of these variables, when no separate stand alone loanable funds variable is also included in the model. Removing the stand alone, only the model’s R 2 , deficit variables’ coefficients, and significance levels would change when the model was: 1. Tested with deficit variables alone measuring crowd out effects, and then

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2. Retested with deficits reduced by any growth in the loanable funds pool during the same period. Table 18.9 retests the same “with” and “without” models as in Table 18.1, but does not include a separate, stand-alone loanable funds (S + FB) control variable in the otherwise standard model. Testing is to determine if modifying the deficit variables by same-period changes in loanable funds provides a better explanation of the of crowd out’s effects on consumption, i.e., improves R 2 —our ability to explain the variation in consumption that occurs from year to year. There were no stationarity problems with either the modified or unmodified variables. The model without the deficit variables being modified had no endogeneity problems, so it was estimated using OLS However, in the modified model, G + (S + FB) was found endogenous with the dependent variable and was replaced by a Wald-strong instrument, which was not endogenous (Sargan test). R2 Effects dropped in all six models after the deficit variable used as the initial estimate of crowd out was replaced by the modified deficit. The loanable funds-modified deficit model did not explain variation in consumption nearly as well as the unmodified deficit model. This implies one of two things:

R2

1. Changes in loanable funds do not reduce consumption crowd out, and just distort the values of variables the deficit that do when used to modify them. 2. The modified deficit hypothesis was modeled inappropriately for testing and gave misleading results. (The same model with a standalone modifier variable raised R 2 in the same test periods.) Crowd Out Variable Effects Crowd out effects of tax cut deficits are statistically significant in all six time periods tested, both in the baseline model and after adjustment for changes in loanable funds. Modifying the deficit reduced coefficients and significance levels on the tax cut deficit variable. For government spending deficits, the spending deficit variable before modification was significant in four of the six periods sampled. After modification, it was insignificant in all six periods tested.

T Def : t-stat G Def : t-stat R2 Adj.R 2

Variable

.50 (3.0) −.19 (−1.3) .90 .82

.24 (2.2) +.06 (0.5) .85 .73

.25 (3.4) −.10 (−1.2) .90 .86

.14 (2.7) .08 (1.3) .87 .82

with

w/o

w/o

with

1960–1990

1960–1980

.23 (2.8) −.03 (−0.5) .90 .88

w/o .11 (1.8) .04 (0.6) .90 .87

with

1960–2000

.32 (5.1) −.13 (−1.1) .87 .84

w/o .16 (2.2) .07 (0.6) .82 .79

with

1960–2007

.31 (5.1) −.13 (−1.2) .87 .84

w/o

.18 (2.5) .19 (0.7) .83 .79

with

1960–2008

.31 (6.3) −21 (−1.9) .88 .85

w/o

.15 (2.3) .02 (0.1) .83 .79

with

1960–2010

Table 18.9 Robustness of effects of crowd out on consumption (No Stand-Alone Loanable Funds Control Variable)

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Overall, the reduced R 2 accompanying deficit modification suggests that the modifying variable (loanable funds) had a zero or near-zero net effect on consumption. Therefore, using it to modify a variable (the deficit) known from baseline model to be significant without modification is something like subtracting a random variable from a significant variable: it reduces the frequency with which it is found significant. Without the stand-alone variable, when we add or subtract (S + FB) from the deficit variables, we are modifying variables we know have a statistically significant crowd out effect on consumption by variables by a large variable that has little or no net effect on consumption because of its competing positive and negative effects. Hence, the best interpretation for why adding the modifier to the deficit caused the variables to become insignificant is because it had an “errors in variables” effect: it modified the values of a statistically significant variable with non-significant values, causing the resultant variable to only partially accurately reflect the underlying crowd out effect that was making consumption change. And by distorting the deficit’s values, it reduced the accuracy of a significant relationship, causing the model to explain less variation in consumption, reducing R 2 . These results are different than those obtained earlier in Table 18.1 where the model tested also included a stand-alone loanable funds variable, and adding the loanable funds modifiers increased R 2 . There, the same six sample periods showed identical coefficients and highly significant t-statistics for the same number of deficit variables, before and after modification, and that is considered the more theory-consistent model because it allows for separate estimation of both the positive and negative effects on consumption of a change in the pool of loanable funds. In short, results obtained from this form of the consumption model seem to be a result of bad modeling, and the earlier form of modeling, whose results are shown in Table 18.1, which included a stand-alone loanable funds model should be preferred. In the next section, more detailed evidence will be presented showing that the results above were the result of bad modeling, and not indicative that we had proven that increases in loanable funds eliminate spending deficit crowd out problems.

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18.3 Crowd Out Effects on Investment Using Stand Alone Loanable Funds Variable We take Eq. 5.4.TR from Heim (2017a) as a “standard” investment model containing variables most economists would agree are determinants of investment, and variables which should be controlled for to avoid the “left out” variables problem when estimating the separate effects of any one variable on investment. The “standard” model is presented to show that the models developed here are built on previous findings regarding investment’s determinants. It is in this way we attempt to add to past findings regarding investment, while showing our findings leave previous findings intact, not replace them. The model from Heim (2017a) is: Standard Investment Model from Heim (2017a) (Using 1960–2010 data) ID = + .26(ACC) + .27(TT ) − .30(G T&I ) (t=)

(8.7)

(2.9)

(−3.8)

+ .011POP − 4.72PR−2 (5.7)

(−2.7)

+ 6.81XRAV + 2.55 CAP−1 (2.9)

R = 83.3% 2

D.W.= 2.0

(1.7)

MSE = 28.25

(5.4.TR)

Equations 18.4 and 18.5 present the same model shown in Heim (2017a), with the addition of a stand-alone loanable funds variable tested using the same 1960–2010 data. Equation 18.4 adds a stand-alone loanable funds variable to the baseline deficit model, but does not include any modification of the deficit variables as a second way to reflect changes in loanable funds (S + FB) in the model. Equation 18.5 includes both. In Heim (2017a), the deficit was also modified by changes in loanable funds during the same period, but not in quite the same way. Nor did it include a stand-alone loanable funds variable. Here, we simply add the change in loanable funds to the effects of any negative change in total taxes resulting from a tax cut (the tax cut deficit) represented by a negative change in (T ). Adding loanable funds thereby reduces the deficit’s estimated crowd out effects. The treatment of spending deficits is different. We subtract the change in loanable funds from any change in the spending deficit (represented by a positive change in (G)). This has the effect of reducing the estimated crowd out effect of deficits caused by increased spending.

18

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All variables were found Augmented Dickey–Fuller (ADF) stationary; No Hausman—endogeneity was found between the dependent and explanatory variables except for the GDP variable which was replaced by a Wald-strong, non-endogenous (Sargan test) instrument. Newey–West standard errors were used to avoid heteroskedasticity and help address autocorrelation problems. Below is a standard investment model with no deficit variables or loanable funds. A 2SLS strong instrument, Sargan tested, was used for the accelerator. No GDP Control Variable Included. ID = + .48(ACC) + .008POP + .76PR−2 (t=)

(2.5)

(10.6)

(0.2)

+ 7.37XRAV + 14.08 CAP−1 (2.2)

R 2 = 69.4%

(4.3)

Adj.R 2 = 66.7%

DW. = 1.6

MSE = 47.87

(17.3C)

(Same as Eq. 17.3C) The model below presents this Study’s Baseline Model, With No Deficit Variables or Loanable Funds Variables Included, but GDP Variable Included to Control for the State of the Economy ID = + .47(ACC) − .00POP − 0.85PR−2 (t=)

(0.0)

(4.0)

(−0.3)

+ 5.21XRAV + 10.39CAP−1 + .10GDP (2.0)

R = 76.1% 2

(−1.3)

(2.9)

Adj.R = 73.2% 2

D.W.= 2.1

MSE = 43.06 (18.10C)

(Same as Eq. 17.3B; and same as Eq. 18.10C below) To the baseline model, we now add deficit variables, but not a loanable funds variable (1960–2010 Sample), without and with a GDP control variable added: ID = +.26(ACC) + .32TT − 33G T&I (t=)

(3.9)

(7.2)

(−3.9)

+ .010POP − 4.91PR−2 (2.9)

(−2.6)

+ 6.53XRAV + 2.21 CAP−1 (4.0)

R = 89.4% 2

(1.5)

Adj.R 2 = 86.0%

D.W.= 1.9

MSE = 29.01 (18.4A1)

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ID = + .27(ACC) + .33TT − 33 G T&I (t=)

(2.6)

(6.4)

(−3.9)

+ .012POP − 4.95PR−2 + 6.68XRAV (2.8)

(3.5)

(−2.5)

+ 2.43CAP−1 − .02GDP (−0.2)

(1.8)

R 2 = 89.0%

Adj.R 2 = 85.9%

D.W.= 1.9

MSE = 29.87 (18.4A)

(old Eq. 18.10A, Table 18.10A) The baseline equation before adding the deficit variable explains only 76.1% of the variation in consumption data over the period 1960–2010. When the deficit variable is added to the same model to account for the negative crowd out effects of deficits on consumption, explained variance rises to 89.0%, a 12.9%-point increase. To ensure this baseline deficit model was not an anomaly, we retested the same models in 17 additional time periods, shown in Table 18.10A. In every case, exactly the same result was obtained: adding the crowd out variable increased explained variance markedly in the 18 samples. There can be little question but that crowd out is a problem negatively affecting investment. The crowd out effect of deficits has been a consistent problem throughout the past 50 years, both in recessions and in good times. The crowd out variable, on average, adds 10.0 percentage points to explained variance in models with a GDP control variable. The next issue to be addressed is whether increases in loanable funds can offset the negative crowd out effects of deficits. To do this, we add a stand-alone loanable funds variable to the deficit model above, but no deficit modifiers are added. This model is given in Eq. 18.4, estimated using 1960–2010 data. ID = + .23(ACC) + .22TT − 16G T&I + .19((S + FB)) (t=)

(1.6)

(5.1)

(−1.9)

2.8

+ .008POP − 4.25PR−2 + 5.25XRAV (2.0)

(2.3)

(−2.1)

+ 1.48 CAP−1 − 04.GDP (0.6)

(1.2)

R = 90.6% 2

Adj.R = 88.7% 2

D.W.= 2.0

MSE = 27.93

(18.4)

Adding the stand-alone loanable funds variable increases explanatory power of the model 1.6%.

76

89

T17.3B

20 Baseline (w/o Def)

21 Eq.18.10 Baseline A (w/Def)

69

T17.3C

20 Baseline (w/o Def)

86

70

67

84

71

66

1960 —2010 1960 —2008 1960 –2007

From Model#

Model

89

80

63

1960 –2000

87

78

65

1960 –1990

95

91

72

1960 –1980

56

65

72

2

69

81

71

76

90

86

90

89

81

(incl. GDP control var.) (Av. R 2 = 89.8%), (Av. Adj. R 2 = 85.9%)

90

(includes GDP Control Variable) (Av. R 2 = 79.8%)

82

(Does not include GDP Control Variable) (Av. R = 68.3%)

−61

90

80

77

89

80

−64

1970 –1990 1970 –2000 1970 –2007 1970 –2010 1980 –2000 1980 –2010 1975 –2004

89

81

71

89

75

63

89

75

57

1980 –2004 1985 –2004 1985 –2005

Table 18.10A Growth in explained variance when adding crowd out to a standard model

98

93

92

1996 –2009

98

95

91

2000 –2010

8/11

11/18

NA

NA

NA

NA

9/11

16/18

NA*

NA

NA*

NA

(G)

Test Ratio

Signif./Total (T)

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(Notice adding the deficit variables to the model increased R 2 much more than adding the loanable funds variable to the model afterword. This is because the deficit and loanable funds variable are highly multicollinear [r = .83]. To a large extent, before adding the loanable funds variable, the coefficients on the deficit variables represented the effect of the deficit net of offsetting “left out” variable [loanable funds] effects. Adding the loanable funds variable only picked up effects of the variable not perfectly correlated with movements in the deficit. Hence, the increase in R 2 , though real, is relatively small.) Next, while continuing to include the stand-alone variable in the model, the deficit variables are replaced by the modified deficit variables (Eq. 18.5 also estimated using 1960–2010 data). ID = + .23(ACC) + .22TT − 16 G T&I (t=)

(1.6)

(5.1)

(−0.8)

− .19((S + FB)) + .008POP − 4.25PR−2 (2.0)

(−2.8)

(−2.1)

+ 5.25XRAV + 1.48 CAP−1 − .04GDP (2.3)

R = 90.6% 2

(1.2)

Adj.R = 88.7% 2

(0.6)

D.W.= 20

MSE = 27.93

(18.5)

As in prior models that include the separate stand-alone loanable funds variable, Eqs. 18.4 and 18.5 are identical except for whether they contain the modified or unmodified form of the deficit variables. For both models, all the coefficients and t-statistics are the same in the two equations except for the stand-alone loanable funds variable (S + FB). There, the (.19) coefficient in Eq. 18.4 exactly equals the sum of the three (S + FB) coefficients in Eq. 18.5: (+.22) − (−.16) + (−.19) = + (.19) as discussed in the earlier consumption sections of this chapter. Definitions of the variables used in these equations are as follows: in the actual tests, the  sign in the equations above preceding these variables indicates first differences of the data are used, not levels, in estimating the models: ID = (ACC) =

Domestically produced investment goods (total − (imported capital goods + industrial supplies and materials) The accelerator ( U.S. GDP)

18

TT = G T&I = (S + FB) = POP = PR−2 = XRav = CAP−1 = GDP =

DO LOANABLE FUNDS MODIFY THE CROWD OUT …

373

Total consolidated U.S. federal, state and local government revenues Total consolidated U.S. federal, state and local government spending, including transfers Total U.S. loanable funds: National savings plus foreign borrowing U.S. population U.S. prime interest rate lagged two years U.S. real exchange rate average for current and past three years) U.S. capacity utilization lagged one year U.S. GDP

Table 18.10A shows the baseline deficit model values of the deficit variables, their significance levels, and R 2 s before the addition of any loanable funds variables. Results are shown for the 18 time periods tested using one or the other of the two investment models tested in this chapter. For tax deficits, crowd out effects for 11 of 18 are statistically significant, and of the seven insignificant, four are for samples with the 1990s “crowd in” problem discussed in the consumption sections of this chapter. For spending deficits, 17 of 18 were significant. Recall that in the baseline model for consumption, we found only 6 of 18 samples showed significant crowd out from spending deficits, but that two technical problems accounted for the 12 insignificant findings, namely the 1990s crowd in period problem and the limited amount of variation in the government spending variable in the 1980s. Here, using the same government spending variable we do not see these problems leading to statistical insignificance in most samples. One possible reason why is that government spending is a more important determinant of investment spending than consumer spending. Heim (2017a, Tables 4.4.1 and 5.4.1) shows that with very similar models for the 1960–2010 period, variation in the government spending variable accounts for much more of the variation in investment explained by the model than does government spending in the consumption model. Depending on whether the “first out” or “first in” method of stepwise regression is used to determine the government spending variable’s contribution to total variance explained by the model, government spending explains 2–11 times as much variation in investment model as in the consumption model. It explains from 11–22% of the total variation in

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investment compared to 2–5.6% for consumption. This implies that even small fluctuations we found in government spending lead to significant moves in investment and be easier for the regression to distinguish from movement in other variables. This may explain our much more frequent findings of government spending being significantly related to investment than we found with consumption, despite the 1990s and variation problems with the data. With the tax deficit variable, the situation is just the opposite. The tax deficit variable explains 2.4–14 times as much variation in consumption as the government spending variable (12 vs. 5% using “1st out” stepwise regression; 28 v 2% using “1st In” stepwise regression). Tax deficits cause more variation in consumption than spending deficits and hence, are more likely to show statistically significant crowd out effects. We theorize that the reason for this is that financing tax cut deficits reduces the money available for consumers to borrow, but the tax cuts for the most part go to those who save and invest the money, not spend it on consumer goods. Hence, the strong crowd out effect of tax cut deficits. Financing spending deficits, though they also reduce funds available to consumers to borrow and spend are more likely to be channeled to those at the lower end of the income spectrum, who are most likely to replace the lost borrowing power with increased spending out of there new spending deficit generated income. As a result, in consumption models, we find few periods in which tax deficits are insignificant, but many in which spending deficits are insignificant. For investment, just the opposite is true, and most likely for the same reasons. Tax cuts are most often saved and invested offsetting crowd out effects on investment. Spending deficits also cause crowd out, but recipients are more likely to be consumers than businesses; hence, they do not provide an offset to investment crowd out, and spending deficits are found to create statistically significant crowd out effects for investment more often than tax cut deficits (see analysis of Table 18.10 results). Table 18.10 repeats the key crowd out and loanable funds findings in Eqs. 18.4 and 18.5, and for 5 other test periods. All results for the 5 other time periods estimated use exactly the same models as Eqs. 18.4 and 18.5. Only the length and dates of the test period changes, reflecting our desire to see if the initial results can be replicated (the hallmark of good science).

T Def : t-stat G Def t-stat S + FB t-stat R2 Rev.R 2

Variable

−.10 (−1.2) −.12 (−1.3) .57 (2.8) .98 .96

−.10 (−1.2) −.12 (−1.3) .59 (5.4) .98 .96 .09 (0.9) −.21 (−2.4) .30 (3.1) .92 .88

w/o

with

w/o .09 (0.9) −.21 (−2.4) .00 (0.0) .92 .88

with

1960–1990

1960–1980

.20 (1.5) −.19 (−1.9) .27 (2.4) .90 .88

w/o .20 (1.5) −.19 (−1.9) −.12 (−0.4) .90 .88

with

1960–2000

.12 (1.2) −.17 (−1.7) .20 (3.6) .88 .85

w/o .12 (1.2) −.17 (−1.7) −.08 (−0.4) .88 .85

with

1960–2007

.21 (1.5) −.18 (−2.5) .19 (2.8) .88 .86

w/o

.21 (1.5) −.18 (−2.5) −.21 (−0.8) .88 .86

with

1960–2008

.22 (1.6) −.16 (−1.9) .19 (1.9) .91 .89

w/o

.22 (1.6) −.16 (−1.9) −.19 (−0.8) .91 .89

with

1960–2010

Table 18.10 Effects on investment of crowd out, with and without modification by loanable funds (Stand Alone (S + FB) and GDP Variables Included)

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In Table 18.10 for each time period, two sets of statistics are presented. In one set, there is a stand-alone loanable funds variable, but no modification of the deficit variables (T ) and (G) by the loanable funds variable. Columns showing these results of these tests are labeled “w/o.” In the other set of results, labeled “with,” the model includes a stand-alone loanable funds variable (S + FB) and deficit variables modified by the same loanable funds variable. We made these tests to test whether, as was the case for consumption, there is a second, negative, effect of changes in loanable funds that offset their positive effect of reducing crowd out’s effects. R2 Results In all 6 periods sampled, adding a separate stand-alone total loanable funds variable to the model increased the model’s ability to explain variation in investment. The average increase was substantial: 2.9 percentage points. Adding the modified deficit variable to the model with a stand alone and R 2 remains unchanged, suggesting the two ways of modeling the loanable funds effect are equivalent. In five of six cases, the standalone loanable funds variable becomes insignificant. The reason for this appears to be that the (S + FB) variable, when used to modify the deficit variables, fully explains how the loanable funds effect investment (by reducing crowd out), and that in these samples, there is no additional variance caused by (S + FB) left to explain. Crowd Out and Loanable Funds Effects: For the baseline model with deficit variables, 5 of 6 periods sample showed statistically significant crowd out effects of unmodified tax cut deficits, and 6 of 6 unmodified spending deficits showed crowd out effects. Clearly, government deficits are systematically associated with crowding out of private investment spending, a finding consistent other recent studies on this topic (Heim 2017a, b). This again indicates that deficit-financed fiscal stimulus programs will reduce private investment spending unless the crowd out effect is offset in some way. Adding the stand-alone loanable funds variable to the baseline deficit model, the number periods that tax cut deficits showing significant crowd out effects fell from 5 of 6 to 1 of 6. The number of significant crowd out effects for spending deficits fell slightly from 6 of 6 to 5 of 6. Results were unchanged, when (S + FB) was also added as a deficit modifier. The finding that after adding the loanable funds variable, the number of significant crowd out effects for tax cut deficits dropped from 5 of 6

18

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377

to 1 of 6 sample periods suggests that most money received from deficitfinanced tax cuts is saved, and simply offsets any loss of privately available loanable funds loss to finance the deficit, hence no significant crowd out effect. Tax cuts usually favor the upper end of the income stream, where (often) additional disposable income current spending is already at desired levels. Therefore, the tax cut, or large parts of it, is often just saved and invested. Hence, the lack of significant crowd out effect is not surprising. However, there is a second possible explanation. The taxes and loanable funds variables are highly correlated (.74) compared to other variables in this study, and reduced statistical significance is a common consequence of multicollinearity. Hence, the drop may be misleading; it may be for technical, not substantive reasons. Hence, it is not completely clear whether tax cut deficits have no crowd out effects on investment, or whether the test results above are just showing a technical problem. For government spending deficits, 6 of 6 tests showed them associated with statistically significant negative crowd out effects in pre-modification models as well as 5 of 6 post-modification models. Recipients of government spending programs, particularly transfer program recipients, tend to need the money to maintain current levels of consumption. Hence, little of the increased income generated is saved and available as an offset to investment borrowing reduced by crowd out. Hence, our finding that spending deficits are systematically associated with statistically significant crowd out effects.

18.4 Crowd Out Effects in Investment Models Without a Stand Alone Loanable Funds Variable Equations 18.5A and 18.5B rerun the same “with” and “without” deficit modification models as in Table 18.10, but without the separate standalone control variable (S + FB). As before, a GDP variable is added to the standard investment model to control for effects of the business cycle. When modeling consumption, increases in loanable funds were found to have two effects on consumption: (1) increases reduced deficit caused crowd out, increasing consumption, (2) but the same increase in loanable funds, ceteris paribus, also definitionally reduced consumption, because reducing consumption was necessary to increase savings. With investment, increases in loanable funds resulting from increases in savings simply increase the funds available to offset crowd out, or if there are no deficits, for new investment. By dropping the separate loanable

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funds variable from the model, we are testing to see if simply reducing the tax and spending deficit variables by any same-period changes in loanable funds adequately shows the effect of loanable funds changes in reducing investment crowd out. (Of course, if the increase in loanable funds is coming out of existing income, it may reduce consumption in the short run—a typical Solow growth model side effect, but one which generally increases long-term economic growth, including consumption levels.) Repeated below is the baseline unmodified deficit model from Eq. 18.10A and Table 19.10.A and No Stand Alone (S + FB) Variable Included (1960–2010 Sample) ID = + .27(ACC) + .33TT − 33G T&I (t=)

(2.6)

(6.4)

(−3.9)

+ .012POP − 4.95PR−2 + 6.68XRAV (2.8)

(3.5)

(−2.5)

+ 2.43CAP−1 − .02 GDP (−0.2)

(1.8)

R = 89.0% 2

Adj.R = 87.1% 2

D.W.= 1.9

MSE = 29.87 (18.10A)

Results for this model in 18 different time periods are given in Table 18.10B. Below, the same model as 21.10A, except the deficit variables, is modified by changes in (S + FB) (1960–2010 Sample) ID = + .22(ACC) + .18TT(m) − 06G T&I(m) (t=)

(5.0)

(2.0)

(−0.7)

+ .007POP − 4.12PR−2 (2.1)

(−2.2)

+ 4.77XRAV + 1.51 CAP−1 − .05 GDP (2.4)

R 2 = 90.2%

D.W.= 2.0

(1.2)

MSE = 28.20

(−0.7)

(18.10B)

Notice the modified deficit definition of crowd out increases explained variance by 1.2 percentage points, indicating the modified version of the deficit variables better expresses the true magnitude of deficit crowd out effects than just the deficit alone. In this sample, Eqs. 18.10A and 18.10B show that modifying the deficit variables results in a drop in coefficients and significance levels of the deficit variables, which may indicate increases in loanable funds offset crowd out, at least in part. Unlike the comparable consumption model,

R2 .95 .87 .89 .84 .86 .89

G β(t)

−.35(−3.8) −.42(−3.2)

−.40(−5.2) −.36(−3.2) −.33(−2.8) −.33(−3.9)

T β(t)

.13(0.9) .28(2.2)

.32(2.7) .26(2.3) .33(2.7) .33(2.6)

Sample Period

1960–1980 1960–1990

1960–2000 1960–2007 1960–2008 1960–2010

1970–2007 1970–2009 1980–2000 1980–2010

1970–1990 1970–2000

Period

.23(2.0) .32(2.3) .27(1.5) .30(2.0)

.25(1.1) .33(2.5)

T β(t)

−.37(−3.1) −.34(−3.5) −.42(−3.3) −.31(−2.4)

−.55(−2.8) −.45(−4.5)

G β(t)

.86 .90 .89 .85

.90 .90

R2

1980–2004 1985–2005 1996–2010 2000–2010

1975–2004 1980–2004

Period

.09(1.3) .09(1.3) .29(2.6) .01(0.0)

.15(1.8) .09(1.1)

T β(t)

Table 18.10B Base line model with deficit variables added: estimates of investment crowd out

−.41(−4.4) −.42(4.5) .52(1.1) 1.11(1.8)

−.40(−3.7) −.41(−3.7)

G β(t)

.89 .89 .89 .89 .89 .98 .98

R2

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J. J. HEIM

modifying the deficit variables also increases in the variation in investment explained (R 2 increases 1.2%), though not as much as the (2.9%) of the investment model in the previous section that included a stand-alone variable. Because of the increase in R 2 , we can be reasonably certain, the lower coefficient and significance levels (in absolute value) obtained for the deficit variables in this model are indeed better estimates of crowd out effects (i.e., deficits net of loanable funds growth). In this sample, the reason the increase in R 2 is relatively small is probably because there was little or no tax cut crowd out to start with, unlike consumption, where we had both tax cut and spending deficit crowd out. The tax cut crowd out was a factor for consumption because in general, the tax cut money was not spent on consumer goods, so it did not offset consumer crowd out. As we show below, there is also evidence that increases in loanable funds do go to reduce consumer crowd out problems. In Table 18.11, we show the 1960–2010 results shown above and retest the model on 17 additional time periods to determine if the initial results can be duplicated. R2 Effects: Results indicate R 2 was higher with the modification of the deficit variables in 13 of 18 time periods tested, the same in three, and lower in two. For the 18 time periods sampled, R 2 rose an average of 0.78 percentage points higher in models where the deficit variables were modified by total loanable funds (S + FB). This is not much, and the model with a stand alone rose much more: (2.9%). The sign in the stand alone in most cases is negative, suggesting that “forcing” the loanable funds and crowd out effects to share the same coefficient may be overstating the extent to which an increase in loanable funds can offset crowd out. Part of the increase may go into foreign, not U.S. domestic investment, or part may go into investment in stocks and bonds, which generally do not lead in any direct way to an increase in investment. Effects of Choice of Standard Errors: As was the case with consumption, choice of standard errors did not make a difference: what was significant or marginally significant using one type error was significant or marginally significant using all types of errors. The same is true for instances of nonsignificance. We conclude that, when deficits occur, increases in the loanable funds pool have had a small, effect of reducing the decline in investment associated with the crowd out problem, but the evidence using this no

.05 (0.6) (0.7) (0.7)

−.27 (−4.1) (−3.8) (−3.9) .97 .95

−.24 (−2.5) .92 .88

−.35 (−3.8) (−3.0) (−2.9) .95 .91

1970–1990 w/o with

.05 (0.6)

.13 (0.9) (1.2) (1.0)

.25 (1.1)

−.55 (−2.8) .90 .84

T Def : NWt-stat White t Ordin. T

G Def : NWt-stat White t Ordin. t

Variable

T Def : t-stat

G Def : NWt-stat

Adj.R 2

Adj.R 2

1960−1980 w/o with

Variable

−.20 (−3.1) (−2.7) (−2.4) .91 .89

.10 (1.9) (1.9) (1.1)

−.45 (−4.5) .90 .87

.33 (2.5) −.15 (−1.7) .90 .88

.15 (1.4)

1970–2000 w/o with

−.42 (−3.2) (−3.1) (−2.9) .87 .83

.28 (2.2) (2.1) (2.0)

1960–1990 w/o with

−.14 (−2.2) (−2.4) (−2.2) .90 .88

.16 (1.9) (1.9) (1.9)

−.38 (−3.1) .86 .82

.23 (2.0) −.14 (−1.7) .89 .86

.08 (1.1)

1970–2007 w/o with

−.40 (−5.2) (−4.4) (−4.3) .89 .86

.32 (2.6) (2.9) (2.7)

1960–2000 w/o with

−.13. (−1.8) (−1.8) (2.0) .88 .85

.10 (1.7) (1.7) (1.7)

−.34 (−3.5) .90 .88

.32 (2.3)

.−.05 (−0.5) .91 .89

.18 (1.7)

1970–2009 w/o with

−.34 (−3.2) (−3.1) (−3.2) .84 .81

.26 (2.3) (2.4) (3.5)

1960–2007 w/o with

−.07 (−0.7) (−0.8) (−1.0) .87 .85

.17 (1.9) (2.0) (2.9)

−.42 (−3.3) .89 .83

.27 (1.5)

−.18 (−2.0) .90 .85

.05 (0.5)

1980–2000 w/o with

−.33 (−2.8) (−2.8) (−2.9) .86 .83

.33 (2.7) (3.0) (4.8)

1960–2008 w/o with

(−.06) (−0.7) (−0.8) (−1.0) .90 .89

.18 (2.0) (2.1) (3.1)

−.03 (−0.2) .91 .88

.18 (1.5)

(continued)

−.31 (−2.4) .90 .87

.30 (1.6)

1980–2009 w/o with

−.33 (−3.9) (−3.5) (−3.4) .89 .87

.33 (2.6) (2.9) (4.9)

1960–2010 w/o with

Table 18.11 Estimates of investment of crowd out, with and without modification by loanable funds (No Stand Alone (S + FB); GDP variable included)

18 DO LOANABLE FUNDS MODIFY THE CROWD OUT …

381

.06 (0.9)

−.17 (−2.1) .90 .87

.15 (1.8)

−.41 (−3.7) .89 .86

T Def : NWt-stat

G Def : NWt-stat

Adj.R 2

1975–2004 w/o with

Variable

Table 18.11 (continued)

−.41 (−3.7) .89 .84

.09 (1.1) −.20 (−2.5) .90 .86

.00 (0.0)

1980–2004 w/o with

−.42 (−4.5) .89 .83

.09 (1.3) −.18 (−2.1) .86 .78

.01 (0.2)

1985–2004 w/o with

−.42 (−4.5) .89 .83

.09 (1.3) −.18. (−2.2) .86 .79

.01 (0.2)

1985–2005 w/o with

+.52 (+1.1) .98 .95

.29 (2.6) +.20 (5.0) .98 .96

.29 (4.6)

1996–2009 w/o with

+1.11 (+1.8) .98 .93

.01 (0.0)

+.18 (+1.4) .97 .89

.25 (2.5)

2000–2009 w/o with

382 J. J. HEIM

18

DO LOANABLE FUNDS MODIFY THE CROWD OUT …

383

stand-alone model is somewhat ambiguous. The investment model with a stand alone appears to better explain the variation in investment. Crowd Out and Loanable Funds Effects: For the baseline model without deficit modification, results indicated statistically significant levels of tax cut deficit crowd out in 11 of the 18 periods sampled (recall that because of the higher than average propensity of tax cut recipients to save rather than spend, we do not expect as much crowd out as with consumption). After deficit modification, this drops to 8 of 18 in the no stand-alone model. (In the model with a stand alone, it dropped to from 6 of 6 to 1 of 6.) The baseline model showed statistically significant crowd out resulting from spending deficits in 17 of 18 periods tested. After loanable funds modification, these deficits showed significant crowd out effects in 13 of 17 cases. (In the model with a stand alone, modification increased the number significant from 5 of 6 to 6 of 6.) Unlike models with a stand-alone loanable funds variable, we do not necessarily expect that adding the (S + FB) modifier to the deficit will always leave it with the same coefficient and significance levels, and we see that here, but only a few are so affected by modification they cease to show a remaining “net” crowd out effect. However, even those that remain significant typically have reduced coefficients and significance levels, suggesting modification has at least partially offset the crowd out effects of deficits. One hypothesis for why we more often find significant crowd out effects for tax deficits with this model, but not the earlier model containing a separate loanable funds variable, is because of “left out” variable bias. Goldberger (1961) noted, when a explanatory variable that is “left out” of a regression is correlated with a “left in” variable, the left in variable will pick up the variance it explains, and the variance of the left out variable, to the extent the two variables are correlated. We have that situation here. The loanable funds variable is highly positively correlated (+.79) with the tax growth variable, but loanable funds are not controlled for in the model. Hence, every time taxes increase, the regression assigns to them their own (lack of significant) effect on consumption and also the positive effect on investment of any growth in loanable funds. Similarly, when taxes decline, loanable funds, being positively correlated, also decline. Though the decline in taxes does not effect investment, the decline in loanable funds does, and is associated with a decline in investment. Hence, the discrepancy with the nonsignificant finding for tax cut

384

J. J. HEIM

crowd out’s effect on consumption in Table 18.10, where the level of loanable funds was controlled for when calculating the marginal effects on investment of tax cut deficits. General 2SLS Model Tested, Using 1960–2010 Data (Using Total Loanable Funds as Deficit Modifier): ID = + .22(ACC) + .18TT − .06 G T&I (t=)

(2.0)

(4.9)

(−0.7)

+ .008POP − 4.11PR−2 + 4.77XRAV (2.0)

(2.4)

(−2.2)

+ 1.50 CAP−1 − .05. GDP (−0.7)

(1.2)

R = 90.2% 2

Adj.R = 88.5% 2

D.W.= 2.0

MSE = 28.20

(18.5)

Effects of Choice of Standard Errors: As was the case with consumption, choice of standard errors did not make a difference: what was significant or marginally significant using one type error was significant or marginally significant using all types of errors. The same is true for instances of nonsignificance.

18.5

Chapter Summary

The two tables below summarize the findings and conclusions for Chapter 21. *7 samples containing 1/3−1/2 of all observations from “Crowd In” years Removed, leaving 11 of 18 **Unmodified spending deficit shows lower % significant than after modification. Probably an error in variables problem, where the spending deficit w/o modification far overstates actual crowd out effect. Actual effect better shown in the modified models with most spending deficits found significant. The R 2 s clearly indicate that adding the unmodified deficit variables increases explained variance. Also, the 1980s decade show little variation in government spending, a key requirement for showing statistical significance. Eliminating the samples with the 1990s crowd in problem reduces then number of samples but still leaves a lot (6 of 11) with insignificant (G) statistics. But then deleting the 1980s lack of variation problems leaves only five samples, all of which show a significant negative relationship of government spending deficits to consumption. Notice that when the spending deficit variables are modified by changes

18

DO LOANABLE FUNDS MODIFY THE CROWD OUT …

385

in loanable funds, there is enough variation in the modified variable so that the lack of variation problem disappears. (See Table 18.1B detailed discussion of the lack of variation issue.) Tax deficits do not have the limited variation problem, so we see fairly consistently in all periods tested a tendency for tax cut deficits to be sig (Table 18.12). 1. Baseline standard model (no deficit or loanable funds variables included): Average R 2 = 71.4%. 2. When deficit variables only added to standard model, R 2 increases to 89.4% (84.3% Adj.), an increase of 25%, clearly indicating consumption cannot be explained without allowing for significant crowd out effects. 3. When loanable funds modifiers are added to standard model with deficits, either as a stand-alone variable, or as a stand alone and also as a deficit modifier, average R 2 increases from 89.4% (84.3% Adj.) to 90.7% (85.2% Adj.), indicating adding loanable funds increases explanatory power, but does not have a net positive effect on consumption by eliminating crowd out. This is because there is also a negative mpc effect on consumption of raising the level of loanable funds (controlling for income) that needs to be controlled for. The stand-alone loanable fund variable, when used alone, shows the net effect of the two opposing forces. When also used as a deficit modifier in the same model, the coefficient on the modifier clearly shows it can offset crowd out, but the slightly larger negative coefficient on the stand-alone variable indicates its mpc effect, which is larger than the crowd out effect. 4. When the loanable funds modifier is added to the standard model as a modifier of the deficit variables, but without also being included as a stand-alone variable, average R 2 drops an average of 3.5% points (3.9%), indicating the model without a stand-alone loanable funds variable does not explain consumption crowd out as well as the model with it. In all 6 models tested, the model explained less variance after the deficit was modified by the loanable funds variable than before. This is because of the two, separate, competing effects of an increase in loanable funds: the positive loanable funds effect and the negative mpc effect, which are not captured well without a stand-alone variable in the model. 5. In the best consumption model, the model including a standalone loanable funds variable, for 90 periods tested, averaged R 2

From Table#

T18.1AA

T18.1B

T18.1

T18.1

T18.6

T18.9

T18.9

Model

18 Baseline

18 Baseline (w/Def)

18 Unmodif.d (w/s − a )

18 Modified (w/s − a)

18 Modified (w/s − a )

18 Unmodif.( wo/s − a )

Modified (wo/s − a )

83

87

88

88

87

60

1960–2010

83

87

89

89

87

72

1960–2008

82

87

89

89

87

72

1960–2007

90

91

91

91

91

86

87

89

90

90

89

43

1960–2000 1960–1990

86

91

94

94

91

77

1960–1980

95

95

93

91

87

65

92

83

99

95

NA

NA

NA *

NA

G

Test Ratio

Signif./Total

T

92

92 89

88

89

(Av.R 2 = 90.2%) (Av Adj. R 2 = 85.3%)

88

87

87

(Av.R 2 = 85.2% Av.Adj.R 2 = 79.8%)

(Av.R 2 = 88.7%; Av.Adj.R 2 = 85.0%)

(R 2 average = 94%. For 90 time periods tested)

94

94

88

88

88

88

86

86

87

87





97

97





99

99

0/6 0/6* 6/6

4/6* 6/6

4/6

6/6

6/6

52/90 44/50*

46/50

8/11* 62/90

11/11

8/11* 9/18

11/11 16/18

9/18

16/18

5/5**

86

67

1996–2010 2000–2010

5/11*

88

74

1985–2004 1985–2005

(5/5

88

63

1980–2004

(Leftmost 6 Sample Av. R 2 = 88.7% Significant T 6/6, G 4/6)

85

37

1975–2004

6/18*

94

86

1980–2010

15/18

88

(Av. R 2 = 71.4%)

55

1980–2000

16/11

86

68

1970–2009

R2 (18 Time Periods)

1970–2007

(18 Sample Av. R 2 = 89.4%) (Av. Adj. R 2 = 84.3%)

92

91

1970–2000

(T21.1 a subset of 18 time periods from the 90 periods in T21.6)

1970–1990

Table 18.12 Cptr. 18 Consumption Summary Table

386 J. J. HEIM

18

DO LOANABLE FUNDS MODIFY THE CROWD OUT …

387

= 94%. With removal of periods in which “crowd in” characterized 1/3−1/2 of the data, for the remaining 50 periods tested, 46(T ) were found significant, and 44 (G). Our theory (Chapter 2) said that if crowd out could be offset by increases in loanable funds, the crowd out variables (T ) and (G) should remain significant after modification. In almost all cases, they did (Table 18.13). 1. Baseline standard model (no deficit or loanable funds variables included): Average R 2 = 79.8%. 2. When deficit variables only added to standard model, R 2 increases to 89.8%, an increase of 13%, clearly indicating investment cannot be explained without allowing for significant crowd out effects. 3. When loanable funds modifiers are added to standard model with deficits, either as a stand-alone variable, or as a stand alone and also as a deficit modifier, average R 2 increases to 91.8% (89.5 Adj. R 2 ), indicating increases in deficits net of loanable funds changes do better explain the exact extent to which deficits are related to investment crowd out than just the deficit variables alone. 4. In investment models without a stand-alone loanable funds variable, when the deficit variables are modified by the loanable funds variable, R 2 increases in every one of the 18 periods tested compared to the unmodified model, 89.8 to 90.7%. Improved ability to explain investment is what we would expect if increases in loanable funds truly do offset part of the crowd out effects of deficits on investment. Our theory (Chapter 2) said that if crowd out could be offset by increases in loanable funds, the crowd out variables (T ) and (G) should remain significant after modification. In almost all cases, they did, particularly when the mixed crowd out/crowd in period samples were not included. (The model with a stand-alone variable increased R 2 even more, to 91.8%, suggesting the model with the stand alone is the better model. There is no theoretical basis for assuming there is also a “second effect” of a change in loanable, so this is somewhat surprising. The sign on the stand-alone crowd out variable in Table 18.10 is negative for most tests. This indicates that the coefficients on the modified deficit variables overstate their true offset value. They assume a dollar of loanable funds increase offsets a dollar of crowd out. Part of the increase in loanable funds may be spent on foreign investment, or used to buy securities, neither of which directly affect investment. Enough of the increase in loanable funds

From Table# 69

1960–2010 67

1960–2008

R2 (18 Time Periods)

1960–2007

76

70

Eq.18.10 A

a ) T18.10

a ) T18.11

86

84

89

80

63

86

88

88

84

88

88

87

88

(Av. R 2 = 90.7% Adj.Av.R 2 = 87.2% for 6 samples;)

90

(Av. R 2 = 89.8%; Adj.Av.R 2 = 85.9% for 6 samples)

89

91

91

90

89

90

90

91

87

92

92

87

78

65

1960–1990

(Leftmost 6 Sample Av. R 2 = 88.3%; Significant T 6/6, G 6/6)

(18 sample period Av. R 2 = 89.8; Adj. R 2 = 86.3%)

89

71

66

1960–2000

1960–1980

90

82

90

81

56

97

95

98

92

90

90

90

(91.8% Av., Adj. R 2 = 89.5%)

98 (91.8% Av., Adj. R 2 = 89.5%)

95

91

72 −61

89

86

86

71

65

91

90

90

76

72

90

89

89

81

69

1970–1990 1970–2000 1970–2007 1970–2009 1980–2000 1980–2010

91

90

90

80

77 −64

90

89

89

80

1975–2004

90

89

89

81

71

86

89

89

75

63





1980–2004 1985–2004 1985–2005

86

89

89

75

57

*7 samples containing 1/3 – 1/2 of all observations from “Crowd In” years Removed, leaving 11 of 18

(wo/s − a ) T18.11

Modified

wo/s

18 Unmodif.(

(w/s − a ) T18.10

18 Modified

(w/s

18 Unmodif.d

18 Baseline (w/Def)

(includes GDP Control Variable) (Av. R 2 = 79.8% )

17 Baseline T17.3B (w/o Def)

(Does not include GDP Control Variable) (Av. R 2 = 68.3% )

17 Baseline T17.3C (w/o Def)

Model

Table 18.13 Cptr. 18 Investment Summary Table





1996–2010

98

98

98

93

92





2000–2010

97

98

98

NA

1/6

1/6

1/6

1/6

8/11

5/6*

5/6*

5/6

5/6

9/11

11/18 16/18

NA*

NA

NA*

14/18

7/11 7/11*

8/18

8/11 10/11*

11/18 17./18

NA

95 NA

NA

91 NA

Signif./Total Test ra o T G

388 J. J. HEIM

18

DO LOANABLE FUNDS MODIFY THE CROWD OUT …

389

may be used to increase investment, but not enough to explain the effect of loanable funds on investment as well as the model with a standalone variable. Therefore, the sand-alone model seems to provide the preferred explanation. Either model, however, supports the notion that crowd out is reduced by increases in the loanable funds pool, because both models show R 2 increased when loanable funds are added to the model.) We conclude this chapter strongly indicates deficits cause both consumption and investment crowd out problems which reduce or eliminate their effectiveness, and that increases in the loanable funds pool can help offset these effects for investment, allowing deficit-financed fiscal stimulus programs to have the stimulus effects on the economy they were intended to have. For consumption, the loanable funds increase is obtained by reducing mpc controlling for all other factors, and this would have negative effects. But, as is shown in another chapter, increasing loanable funds by FR open market action avoids this problem and allows for consumer crowd out to be offset.

References Economic Report of the President. (2012 [2013]). Washington, DC: Government Publications Office. Goldberger, A. S. (1961). Stepwise Least Squares: Residual Analysis and Specification Error. Journal of the American Statistical Association, LVI (December 1961), 998–1000. Griffiths, W. Hill, R., & Lim, G. (2011 [2003]). Principles of Econometrics. Hoboken: John Wiley and Sons. Heim, J. J. (2017a). An Econometric Model of the U.S. Economy. Hoboken: Palgrave Macmillan. Heim, J. J. (2017b). Crowding Out Fiscal Stimulus. Hoboken: Palgrave Macmillan. Solow, R. (1957, August). Change and the Aggregate Production Function. The Review of Economics and Statistics, 39(3), 312–320. Wooldridge, J. M. (2005). Introductory Econometrics (3rd ed). Cincinnati: Southwestern Publishers. Cptr. 12: Serial correlation and heteroskedasticity in time series regressions.

CHAPTER 19

Does M1 or Total Loanable Funds Better Measure Offset Effects to Crowd Out?

When governments incur new or expanded deficits, they need to borrow money from the loanable funds pool to finance them. This reduces the private sector’s ability to borrow out of this same pool. It “crowds out,” i.e., reduces, private borrowing and spending, offsetting the stimulus effect of deficits. As a first approximation, the reduction can be defined as equal to the size of the deficit, i.e., the amount of the loanable funds pool formerly available for private borrowing, which ceteris paribus, is no longer available for private use because it has been channeled to the government to finance the deficit. But other things are not ceteris paribus (i.e., unchanged), when a deficit occurs or expands. During the same period, the loanable funds pool, defined as total national saving plus foreign borrowing (S + FB), may grow. This growth in the loanable funds pool would offset some or all of the loss of loanable funds available for private borrowing because of the deficit. Growth in the pool during the same period the deficit occurs would reduce or eliminate the need for private spending to decline in order to finance the deficit. In the analyses below we will test the crowd out effects of government deficits on consumption or investment spending in two different ways: 1. using the size of the deficit (T − G) as the definition of the “real” size of “crowd out” in our models, and then © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. J. Heim, Why Fiscal Stimulus Programs Fail, Volume 1, https://doi.org/10.1007/978-3-030-65675-1_19

391

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2. retest exactly the same models, except redefining the crowd out effect as the deficit reduced by any same-period growth in the total loanable funds pool, e.g. (T − G) + (S + FB), or, alternatively by the growth in the M1 portion of loanable funds (T − G) + M1. This is done because it can be argued that growth in M1 is a better measure of what part of any increase in the pool of loanable funds is actually borrowed for use in purchasing consumer or investment goods. Which of the two definitions of crowd out more accurately defines the extent to which the deficit’s crowd out effects can be offset is an empirical question that is examined below. Since the models tested are exactly the same except for the deficit variable used, we expect the model with the modified crowd out variable that most accurately defines the true size of crowd out to explain the most variation in consumption and investment. If we reduce the deficit by the less accurate modifier, we create an “errors in variables” problem which tends to reduce both the coefficient and statistical significance of the deficit variable compared to its unmodified value (Johnston 1963). If no government deficit occurs, any increase in loanable funds would via consumer and business borrowing, to finance new spending, i.e., increase growth in new private spending, rather than crowd out replacement private spending. Growth in the pool allows maintenance of past levels of private spending, despite the diversion of funds to finance the deficit. Growth in the pool allows the stimulus to provide the new growth to the economy, not just offset growth lost due to crowd out. The new growth to the economy may or may not equal the growth generated by the increase in the loanable funds pool if there had been no deficit. Whether one is as large as the other is an empirical question. In Heim (2017), the conclusion was that typically the growth in loanable funds (LF) was not large enough to offset the full crowd out problem. After reducing the deficit (T − G) by the growth in loanable funds (LF) some part of the deficit remained to be financed by reducing the total amount of loanable funds available for private borrowing. The one exception would be the quantitative easing (QE) years, where the increase in loanable funds far exceeded deficit growth. In this chapter, the deficit variable, total government receipts minus total government spending or (T − G), is used to measure gross crowd out. To measure the net or “modified” crowd out effect of a deficit, we

19

DOES M1 OR TOTAL LOANABLE …

393

use the deficit (T − G) reduced by any same-period changes in the loanable funds pool (S + FB). We separately estimate the effects of tax cut (T) and spending (G) induced deficits to determine if they have different gross effects, and then modify each for same-period changes in the size of the loanable funds pool. Hence, when measuring crowd out effects net of any growth in the total pool of loanable funds available, we use the net tax cut deficit figure T + (S + FB) or the net spending deficit figure G − (S + FB). This ensures we only measure the deficit’s effect on the total quantity of loanable funds privately available. Any increase in the loanable funds pool will (at least) partially offset the extent to which the deficit reduces privately available loanable funds. This approach is less commonly used in the literature on crowd out effects than the approaches used earlier in Chapters 11 and later in 35. In Chapter 11, a number of different ways of modeling the hypothesis that changes in loanable funds affect the economy were tried. These included 1. Simple regression models of GDP on M1 showed no discernable effect. (They presume all changes in M1 reflect changes in the pool of loanable funds, whether the change in M1 is exogenous, i.e., driven by FR open market purchases of securities, or endogenous, i.e., driven by expansion or contraction of the money multiplier an accordance with changes in the business cycle). 2. Slightly more sophisticated models, which controlled for inflation and recession when testing the effects of the money supply changes. These tests provided some evidence that increases in the exogenous part of the M1 money supply induced by nominal FR securities purchases do effect GDP positively, but not the endogenously determined fluctuations in M1 related to changing economic conditions. 3. The most sophisticated models tested in Chapter 11 were full structural equations for consumer services and residential housing investment. These models, taken from Heim (2017), indicated M1 lagged two periods had a significant positive effect on consumer services spending; current year M1 had a positive effect on residential housing demand. In Chapter 11, we also attempted to discern if the effect was due to endogenous changes in M1, i.e., M1 − (Tr + A), or exogenous changes driven by FR security purchases (TR + A), on the assumption that every dollar increase in TR + A) by the FR

394

J. J. HEIM

was converted into M1. Findings indicated nominal (but not real) changes in both parts of M1 positively affected consumer services purchases (as well as total consumer spending) after a two-year lag, but neither part of M1 growth had an effect on housing (or total investment), except during the QE years, when the FR purchases component did have a positive effect on housing investment. In Chapter 21, instead of examining how M1 directly affected consumption and investment, we examined how changes in the money supply affected three variables known to be underlying determinants of investment and consumption, and then used the known relationship of these three variables to consumption and investment to determine the effect M1 had on them These three determinants of consumption or investment were the level of consumer borrowing, the inflation rate, and the prime interest rate. These were the only three of the 30 structural equations found in Heim (2017) to be determined in part by the money supply. In this study, we replace M1 with its two determinants, FR purchases, and (M1 − FR purchases) in what otherwise are the same Heim (2017) structural models for these three determinants of consumption or investment. This way, we can test to see whether either or both components of M1, endogenous and exogenous, are useful in reducing crowd out. 1. Consumer Borrowing: Both nominal (TR + A) and M1 − (Tr + A) were positively and significantly related to consumer borrowing 1960–2000, but not after. In real terms, neither was significant in any period tested. 2. Prime Interest Rate: Growth in real total M1 was negative and significantly related to the real prime rate, but only in samples including QE years (liquidity effect). One year lagged real M1 was positively and significantly affected the prime rate (inflation effect). Again, it was only in samples including the QE years, but not otherwise. When total M1 was divided into its exogenous and endogenous parts, neither had a statistically significant effect on the prime rate when tested using a Taylor Rule model. This suggests the inflation and unemployment, not changes in M1, may be driving the prime interest rate. More likely, changes in M1 are causing the changes in inflation.

19

DOES M1 OR TOTAL LOANABLE …

395

3. Inflation: The endogenous part of M1, M1 − (Tr + A), lagged two years, had a positive and significant effect on inflation in almost all tests, as did total M1. The FR purchases part was insignificant in all tests except those including data from the QE years. During the QE years included in this study, it had a positive and significant effect on inflation but not enough to offset other factors holding inflation down. See the inflation equation in Heim (2017). In the first major set of tests in this chapter (Sect. 19.1), we will estimate crowd out effects with and without modifying deficit’s value by M1 or by loanable funds availability.

19.1

Comparing Unmodified, LF Modified, and M1 Modified Deficit Variables

Table 19.1 below, we consider the crowd out effect of unmodified tax cuts (T) and government spending increases (G), both of which, ceteris paribus (i.e., assuming no change in the other), create deficits or reduced surpluses. The table also shows how crowd out effects decline when the deficit measure of crowd out is modified by same-period changes in loanable funds, (S + FB). First, we modify the deficit by the definition of loanable funds (S + FB) given in the Federal Reserve’s Flow of Funds accounts: “savings = investment” is detailed. For an open economy like the U.S., the savings side of the identity means the sum of personal, business, and government savings plus any foreign savings borrowed (S + FB). Hence, our crowd out variables now become T + (S + FB) and G − (S + FB). The sign on the tax deficit modification is positive because a tax cutinduced deficit shows a decline in T, i.e., T has a negative sign; we modify this by adding any change in loanable funds available (S + FB). A government spending deficit shows as a positive increase in (G), i.e., G, so we subtract (S + FB) from it to modify its effect. We also modify the deficit by subtracting any same-period changes in M1 that occur. When proceeds of FR purchases are deposited in banks by the securities’ sellers, it increases loanable bank reserves, and if the seller’s deposit is credited to into the seller’s demand deposit account, it simultaneously increases the M1 money supply. Endogenous increases in M1 may also subsequently occur as bank lending, through the money

396

J. J. HEIM

Table 19.1 Regression estimates of crowd out effects using different definitions of loanable funds crowd out effect per $ of deficit and (t-statistic) shown Def.

Domestically produced consumer goods

type

LF offset

M1 offset

LF + M1 offsets

LF offset

M1 offset

LF + M1 offsets

T G

.21 (4.4) .13(1.8)

.30 (5.3) .11 (1.7)

.23 (4.6) .16 (2.1)

.14 (2.2) −.11 (−1.5)

.24 (2.5) .11 (1.0)

R 2 = .82 R 2 Adj = .78

R 2 = .82 R 2 Adj = .78

R 2 = .81 R 2 Adj = .90

R 2 = .90 R 2 Adj = .88

R 2 = .78 R 2 Adj = .75

.18 (1.9) −.03 (−0.3) R 2 = .87 R 2 Adj = .84

Def.

Domestically produced consumption No offset .32 (6.6) −.16 (2.0) R 2 = .87 R 2 Adj = .84

Domestically produced investment No offset .33(3.9) −.36 (4.7) R 2 = .89 R 2 Adj = .87

type T G

Domestically produced investment goods

t-statistics in parenthesis; See An Econometric Model of the U.S. Economy (Heim 2017, eqs. 4.4.TR and 5.4.TR) for other variables in the models tested. LF = National Savings plus Foreign Borrowing. Adjusted R 2 included where the growth in R 2 by adding a variable might have been spurious. Adj. R 2 shows the growth reduced by an possible spurious component of it; R 2 shows both components

multiplier, increases in response to this increase in loanable funds. The extent to which this occurs fluctuates with the business cycle. In a later chapter, we shall examine the same model, but separate the M1 modification of the deficit into its two component parts: the part due to FR security purchases, and the endogenous part M1 − (Tr + A). Both parts are tested for their effect on consumption and investment spending. In Table 19.1, there are results for four consumption and four investment models shown. The models are identical except for differences in how the deficit (crowd out) variable is modified: net of loanable funds (S + FB), net of M1, net of both, or without any modification at all. Equations 19.1 and 19.2 below present results for the consumption and investment models based on data for 1960–2010. Equations 19.1 and 19.2 show the case where the crowd out variable is defined as (tax deficit + loanable funds), or (spending deficit—loanable funds). The models

19

397

DOES M1 OR TOTAL LOANABLE …

used are identical to the “standard” models given by Eqs. 4.4.TR and 5.4.TR in Heim (2017), except for differences in how the modifier (S + FB) is used to modify the deficit. The “standard” models in (2017) are based on an extensive literature search of studies testing possible determinants of consumption and investment. As always in this study, equations are run in first differences, stationarity and endogeneity issues have been addressed and where required, Waldstrong instruments are used. Newey–West heteroskedasticity corrected standard errors are used. The full 1960–2010 data sample was used in estimating these equations. Equations 19.1 and 19.2 describe the determinants of demand for only U.S. produced consumption and investment goods, consistent with the models in Heim (2017) on which they are based, with the deficit variables modified by (S + FB), i.e. (T)m and (G)m . They are compared to the unmodified versions of the deficit in the equations just above them. This study’s Baseline (BL) Standard Consumption Model with 2 Variable deficit is shown below and repeated from Eq. 18.1A. No loanable funds variable is included. CD = .31(Y − TT ) + .32(TT ) − .16(G T&I ) − 7.14PR (t=)

(6.4)

(6.6)

(−3.1)

(−1.9)

+ .49DJ−2 − .459.68POP16/65 + .017POP (4.5)

4.0

(2.4)

+ 36.27M2AV + .09CB2 (3.9)

(3.8)

R = 86.6% 2

D.W. = 2.1

M S E = 26.17

(18.1A)

And after modification of the deficit variables by loanable funds (LF) CD = .31(Y −TT ) + .21(TT )m + .13(G T&I )m − 4.59PR (t=)

(4.3)

(4.4)

(−1.9)

(+1.8)

+ .51DJ−2 − .397.77POP16/65 + .012POP + 21.71M2AV (3.6)

(2.2)

(3.1)

(1.4)

+ .10CB2 (3.4)

R = 86.6% 2

D.W. = 2.1

MSE = 30.62

(19.1)

Notice R 2 drops significantly (5%-points) when the deficit variables are modified to reduce the deficit’s crowd out effect by the growth in total loanable funds, suggesting adding loanable funds does not reduce crowd

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J. J. HEIM

out, and because it doesn’t, modifying the deficit, which does show crowd out, just creates an errors in variables problem that reduces the statistical significance of the deficit variables and reduces the total amount of variance the model explains. Next, the baseline vs. modified investment equation is examined in the same fashion. Baseline Model With Deficit and GDP Control Variable, but No Loanable Funds Variables (1960–2010). ID = + .27(ACC) + .33TT − 33G T&I + .012POP (t=)

(2.8)

(−3.9)

(2.6)

(6.4)

− 4.95PR−2 + 6.68XRAV + 2.43CAP−1 (3.5)

(−2.5)

(1.8)

− .02GDP (−0.2)

R = 89.0% 2

D.W. = 1.9

MSE = 29.87

(18.4A)

Baseline Model With Deficit (w/o GDP Control Variable,) but No Loanable Funds Variables (1960–2010). ID = + .25(ACC) + .32TT − 34G T&I + .011POP (t=)

(3.9)

(6.8)

(3.0)

(−3.6)

− 4.59PR−2 + 7.99XRAV + 2.52CAP−1 (−2.4)

R = 89.2% 2

D.W. = 1.9

(4.7)

(1.5)

MSE = 29.31

(21.4A)

Modified Deficit Variables (w/o GDP variable) ID = + .20(ACC) + .16(TT )m −. 07(G T&I )m (t=)

(6.5)

(2.3)

(−0.7)

+ . 00POP − 3.89PR−2 + 5.37XRAV (1.0)

(−2.0)

(3.0)

+ 1.30CAP−1 (0.9)

R = 90.8% 2

D.W. = 2.0

MSE = 27.01

(19.2)

For investment, modifying the deficit variables appears to increase explained variance a bit (1.6%), suggesting it may have offset some crowd out, but the modification also reduced deficit variables’ t-statistics which seems inconsistent with a rise in R 2 , unless modifying the deficit does

19

DOES M1 OR TOTAL LOANABLE …

399

reduce crowd out, somewhat, increasing explanatory power, but part of the loanable funds increase has no effect on crowd out (e.g., any part used to buy securities in financial markets as opposed to real goods and services), which would likely lower statistical significance of the crowd out variable. We first test the strength of the crowd out variable when modified only by changes in loanable funds (T + (S + FB), G − (S + FB)). Next, we examine crowd out variable strength when it is modified by M1. Finally, we examine crowd out variable strength when it is modified by both naturally occurring changes in LF, and also by the more endogenous changes in the money supply that may have occurred due to the nature of the fractional reserve system. All of these are then compared to crowd out effects measured by the unmodified deficit alone. In comparing statistical models, we generally determine which is better at describing how the world actually works (empirical reality) by comparing R 2 s and t-statistics, i.e., by comparing how much of the variation in the dependent variable the model explains and how systematically (“significantly”) the variable(s) of interest move with movement in the dependent variable. Only one time period is tested: 1960–2010. As Table 19.1 shows, using standard models, in 5 of 6 tests, the unmodified (T) and (G) deficit models had larger R 2 s than modified deficit variable models reduced by (S + FB) or M1 models. This indicates the unmodified deficit values more accurately represented the level of crowd out felt “on the street,” than did the deficit values modified by either (S + FB) or M1, or both. For the C model, R 2 drops from .87 to .82 after modification by either (S + FB) or (M1). Significance levels for both the (T) and (G) modified deficit variables drop, but remain at least marginally statistically significant. There was no difference in R 2 results when adding either the M1 or (S + FB) modifier in the consumption model. The reason for the declines in explained variance consumption when the deficit variables are reduced (modified) by any increase in loanable funds is because the model is not a good one. In Chapter 18, we showed that changes in loanable funds have two effects, one that can be represented by a modification in the deficit variables, and one that requires that the loanable funds variable also be included as a stand-alone variable. The same chapter also shows a separate stand-alone variable is not necessary for investment models, hence our better reading in the investment model above. Chapter 20, following this chapter, also shows that systematically,

400

J. J. HEIM

over many sample periods, that M1 explains less variance than the total loanable funds variable. This indicates it is a poorer measure of how well a change in loanable funds affects consumption and investment than the change in loanable funds itself. R 2 also dropped for investment when either the M1 or (M1 + S + FB) modifiers were added, indicating they did not explain the effects of deficits on investment as well as did the unmodified deficit values. However, when investment, when modified by just (S + FB), R 2 increased. The modified tax deficit variables remain significant but with a smaller coefficient and significance level. The spending deficit crowd out variable became statistically insignificant. The huge increase in loanable funds in QE years may have helped offset crowd out a bit, but mostly went borrowed and just reduced significance levels on the deficit variables in this sample below what they otherwise would be (error in variables problem). These results suggested growth in the loanable funds pool could offset some or all of crowd out’s negative effects. The higher R 2 even more definitively suggests the loanable funds—modified deficit is the more accurate estimate of the size of crowd out. As explained in Chapter 18 where the same model was tested, the lack of a stand-alone LF variable in consumption models may cause the LF modifier to have to represent the net effect of LF in the model. But the net effect was found to be close to zero, suggesting that in this model, the deficit modifier is acting like a random variable that makes large changes I the deficit value, but has no real effect on consumption. This creates an errors in variables problem that leaves the modified deficit variable insignificant, and causes the drop in R 2 . In our 1960–2010 sample, changes in M1 included the huge increases of the QE years, which are not associated with increases in consumption or investment, and this may explain our findings of non-significance of M1 and its lower R 2 s when modified. However, it is also possible changes in M1 do not as accurately reflect the extent to which crowd out are reduced compared to the loanable funds modifier. In other chapters, we will test M1 in other periods to determine if our findings here were an anomaly.

19.2

Adding a Separate, Stand-Alone M1 Variable to the Model

As straight forward as Table 19.1 crowd out effect results seem to be, they change if we add a separate, stand-alone real M1 money supply variable

19

DOES M1 OR TOTAL LOANABLE …

401

to the model. We might do this to see if real M1 has any independent effect on consumption and investment when testing deficit models with and without modifiers. Some monetary economists argue changes in M1 have a direct effect on the real economy. Results of adding a stand-alone M1 variable to the models shown above in Table 19.1 are shown in Table 19.2, using the same modifiers as in the earlier table. The results indicate that adding the money supply as an additional separate variable does not significantly change the strong negative effect of crowd out on consumption or investment that we found Table 19.2 Regression estimates of crowd out effects using different definitions of loanable funds (Table 19.1 Models With Stand Alone M1 Variable Added) Def. type

Domestically produced consumer goods LF offset

T G M1

M1 offset

.21 (4.2) .13(1.9)

.32 (6.2) −.16 (2.0) −.04 −.48 (−0.5) (4.7) R 2 = .82 R 2 = .87 R 2 Adj = .78

LF + M1 offsets

LF offset

.24 (4.0) .22 (3.2)

.18 (3.2) .01 (0.2)

−.07 (−0.8) R 2 = .80

R 2 Adj = .84

R 2 Adj = .76

Def. type

Domestically produced consumption Consumption no offset 32 (6.2) −.16 (2.0) −.005 (−0.1) R 2 = .87 R 2 Adj = .85

T G M1

Domestically produced investment goods M1 offset

LF + M1 offsets

.31 (7.2) −.30 (3.8) −.20 (−1.9) −.75 (3.6) R 2 = .85 R2 = .89a R 2 Adj = .82 R 2 Adj = .87

.18 (3.2) .01 (0.2) −.37 (−3.4) R 2 = .85 R 2 Adj = .82

Domestically produced investment Investment no offset .31 (3.8) −.31 (−3.6) −.14 (−1.4) R 2 = .89a R 2 Adj = .87

t-statistics in parenthesis; See An Econometric Model of the U.S. Economy (Heim 2017) for other variables in the models tested. LF = National Savings plus Foreign Borrowing a While adding the stand alone m1 variable increases explained variance compared to the other models, evidence in Chapter 18 indicates using total loanable funds, not just the part resulting in the increase in M1 is the better model, explaining 90.8% of the variance and 88.7% of the adjusted explained variance

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J. J. HEIM

in the in Table 19.1 model that had no modifier on the deficit variables. In addition, when added as a stand-alone variable to the M1 modified consumption model and all three modified investment models, M1 has a statistically significant negative impact on consumption and investment, a finding wholly without foundation in economic theory. This may just be just observing a tendency of M1 to grow the most when the real GDP and its components are in decline. Our findings from M1 may be correlative, but not causal (Table 19.2). Hence, while increases in M1 can offset deficit crowd out, not as much as increases in total loanable funds, and, at constant velocity rates, increases in M1 only occur if there is a larger increase in loanable funds. For the consumption and investment models which included a M1 stand-alone variable, but left the deficit variables unmodified, the deficit variables had a statistically significant negative effect, essentially the same as in the unmodified models before M1 was added as a stand alone. The added M1 variable was not found to make a statistically insignificant contribution to the model. Hence, on the face of it, there would seem to be no reason to prefer these models over Table 19.1 models without the stand-alone M1 variable. Because of its statistical insignificance, adding the stand-alone (M1) variable to the unmodified deficit models leaves R 2 the same as it was before: Table 19.2 shows that in the unmodified consumption model with M1 added, R 2 is (.87) and for unmodified investment, R 2 is (.89). The M1 models with a stand-alone M1 variable as well as M1-modified deficit variables give precisely the same results for the deficit variables as the unmodified deficit models with a M1 stand alone. The coefficient and significance levels of the stand-alone M1 variable increase markedly, even though no other variable in the stand model used change at all. Basically this is because we are now asking the stand-alone M1 (which has an insignificant effect on consumption and investment) to do double duty: to represent its own independent effect (if any) and to represent the negative crowd out effect of deficits no longer shown because of the reduced magnitude of the crowd out variables due to subtracting M1 from them. The unmodified deficit variables represented the true crowd out effect, but we reduced their values by M1; the increased (in absolute value) coefficient on the stand-alone M1 captures this change precisely, as shown in

19

DOES M1 OR TOTAL LOANABLE …

403

the equations below derived from coefficients on the unmodified (T ) (G) and M1 variables in Table 19.2. I = .31T –.30G – .14M (Unmodified Table 19.2) =.31(T + M) −.30(G–M) − .75M (Modified Table 19.2) Because the true magnitude of crowd out is given by the unmodified (T ) and (G) variables, subtracting M1 from them reduces the amount of variation in investment they do explain from the higher level they can explain. The amount they reduce it by is +(−.30M − .31M). If there were no stand-alone M1 to which this variation could be assigned, we would just be reducing the model’s R2 . But there is another M1 variable in the model whose coefficient can be modified enough to precisely pick up this variation, so R 2 stays the same. The sum of the regression effects of M1 on investment (+.31 + .30 −.75 = −.14) is the same as the coefficient on the stand-alone M1 variable in the unmodified model. The stand-alone M1 coefficient (−.75) is now highly significant, reflecting the fact that we have now added a part of the highly significant (T ) and (G) variables to it. Models with an M1 stand alone, but which used some other deficit modifier, i.e. (S + FB) or (M1 + S+FB) are even less desirable; they explain even less of the variation than the unmodified M1 stand-alone model, and are rejected for that reason. Even if one argued they were theoretically attractive, the theory they express does not fit the data very well (Tables 19.3 and 19.4). There is no justification for the stand-alone M1 variable in the models above, except a “St. Louis equation” type justification. That type of justification infers from significant regression results linking two variables that there is a theoretical justification for the observed correlation, i.e., some mechanical way they are connected, otherwise why would the observed empirical correlation occur? But the lack of statistical significance in the “stand alone M1 only” models indicates that in this case, there is no empirical correlation that needs explaining. On the other hand, there is some theoretical justification for using it to offset the effects of deficits on crowd out in our crowd out variables, without including it as a stand alone. We could argue increasing the pool of loanable funds (at least the portion directly attributable to increased FR open market securities purchases) increases the amount of the total pool available for borrowing by those wishing to buy consumer or investment goods, loanable funds and, therefore, can reduce deficit effects. This too is an assumption that was tested in a preliminary way

404

J. J. HEIM

Table 19.3  Investment regression results for all variables included in the one and three variable M1 models Explanatory Variable

One M1 model Coefficient ( t-stat)

Three M1 model Coefficient ( t-stat)

 Accelerator  Tax deficit  Spend. deficit.  M1  Capacity util.  Prime int.Real  Exch. rateAV  Pop. size R2 DW

.237(6.2) .309(7.2) −.304(0.1) −.140(−1.4) 1.377(0.6) −5.209(−1.7) 6.838(5.4) .011(2.8) .89 2.0

.237(6.2) .309(7.2) −.304(0.1) −.753 (−6.2) 1.377(0.6) −5.209(−1.7) 6.838(5.4) .011(2.8) .89 2.0

*Dependent variable is sales of domestically produced investment goods from NIPA accounts, defined as total investment spending minus imports of capital goods, supplies and materials. Significance levels: 2.0 = 5%; 2.7 = 1%

Table 19.4  Consumption regression results for all variables included in the one M1 and three M1 models Explanatory Variable

One M1 model Coefficient ( t-stat)

Three M1 model Coefficient ( t-stat)

 Disp. income  Tax deficit  Spend. def.  M1  Prime int.Real  NYSE index-2 Pop. age dist  Pop. size  M2 Av.−2−4 Cons. borrow. R2 DW

.31(6.3) .32(6.2) −.16(1.9) −.01 (0.1) −7.16(3.1) .49(4.3) −462.48(2.2) .017(3.9) 36.46(3.9) .09 (3.8) .87 2.1

.31(6.3) .32(6.2) −.16(1.9) −.48(4.7) −7.16(3.1) .49(4.3) −462.48(2.2) .017(3.9) 36.46(3.9) .09 (3.8) .87 2.1

*Dependent variable is sales of domestically produced consumer goods from NIPA accounts, defined as total consumer spending—spending on (total imports -imports of capital goods, supplies and materials. Model estimated using OLS. Significance levels: 2.0 = 5%; 2.7 = 1%

19

DOES M1 OR TOTAL LOANABLE …

405

in Tables 19.1 and 19.2 above, where one of the deficit modifiers tests was changes in the pool of loanable funds. In general, the LF-modified deficit models in those two tables left the models with less explanatory power than the unmodified models. However, one LF-modified investment model did explain more variance. In Chapters 17 and 18 above we exhaustively tested this type of loanable funds effect on consumption and investment. There is some economic theory that says that crowd out effects can be offset by expansion of the money supply. Hence, using Table 19.1 models, which have no stand-alone M1 variable in the models tested, but do allow for inclusion of M1 as a deficit offsetting factor in the crowd out variables, do seem more theoretically justifiable. Therefore, the results of models specified with M1 only as an offset to the deficit’s value in the crowd out variables should be the models that tell us more accurately whether increasing the money supply when government deficits increase is effective in offsetting crowd out. However, such models decrease explained variance from 86.6 to 81.6% in the consumption equation, and from 89.2% in the three variable to 78.2% in the two-variable investment equation (where the only offset to the deficit in the crowd out variables is M1). This suggests that the deficit variables without any M1 adjustment are better indicator of real crowd out effects. Changes in M1 may not in any systematic, reliable way reduce crowd out (though changes in loanable funds, which sometimes but not always result in changes in M1, may be, as we discuss in Chapters 17 and 18 above).

19.3

A Note on the Relationship of National Savings to M1

We regressed the total savings component (S) of loanable funds (LF) on the M1 money supply, controlling for foreign borrowing (FB). Graph 19.1 shows how well variation in US savings is explained from year to year by yearly variation in M1 and foreign borrowing. U.S. Real Savings = 1528.0 − .46Real M1 − .92Real For. Borr. (−1.7)

(3.5)

(t−statistic)

+ .98 AR(1) (26.5)

R = .95 2

DW = 1.6

(−3.6)

406

J. J. HEIM

Savings = ƒ(M1, FB) - Predicted and Actual

$2,400B $2,000B $1,600B $1,200B

$600B

$800B

$400B

$400B

$200B $0B $-200B $-400B 60

65

70

75

80

85

90

95

00

05

10

Real Savings Regressed on Real M1 & Foreign Borrowing 1959 - 2010 Residual

Graph 19.1

Actual

Fitted

Savings = ƒ(M1, FB)—predicted and actual

In first differences, results are nearly identical. If we regress the full loanable funds variable (S + FB) on M1, we get Loanable Funds (S&FB) = 1507.4 −. 46 Real M1 +. 97 AR(1) (3.6)

(t − statistic)

R = .87 2

(1.7)

(28.1)

DW = 1.6

Clearly, loanable funds which is mainly national savings, and M1 tend to move in the opposite directions over time. Estimated in first differences, which gets rid of autocorrelation, the relationship is negative. A dollar’s increase in M1 is associated with a 53 cent drop in loanable funds. In levels, the result is also negative and about the same (48 cents). The direction of causation is an important consideration here. It may be that as national savings goes down (generally, due to business cycle decline), the FR takes action to increase by increasing bank reserves to offset lost deposits stemming from the decline in saving. To the extent the increased reserves are deposited initially in the bank’s own demand

19

DOES M1 OR TOTAL LOANABLE …

407

deposit account, the M1 money supply would increase. Certainly to the extent these new reserves are lent out, M1 would increase. If the FR tendency is to increase M1 when investment is falling (due to reduced saving causing reductions in the pool of loanable funds), this might explain for the negative relationship of investment and consumer spending and M1 that our earlier statistical tests showed. This explanation seems more likely than that increasing the money supply and foreign borrowing reduce domestic savings. However, it is not impossible that growing access to foreign saving would, at least for a while, decrease the need to save domestically, or that increases in loanable funds due to FR actions might lead to increased borrowing would reduce the need to save. It is also possible that people just decide to save less and spend more by shifting money from savings accounts to demand deposit accounts. Both factors may help explain the decline in personal savings in the U.S. in recent decades.

19.4

Summary of Results and Conclusions

Conclude: The baseline models show that significantly more variance is explained by models containing crowd out variables, than without them, indicating crowd out is a real problem that adversely affects consumer spending (Table 19.5). However, adding LF or M1 to the model as a change in the deficit variables but not as a stand alone did not offset consumption crowd out; if either had, modifying the deficit variables should have increased R 2 beyond its unmodified level. Instead, generally the modifiers lowered it indicating fluctuations in the modifier had no systematic effect on crowd out, and just created an error in variables problem. The one exception was a case where adding the modifier left the amount of variance explained unchanged from its full crowd out (unmodified) level. These findings are consistent with those of models tested in other chapters, which generally show that increases in loanable funds do not seem to be reduce consumption crowd out, but do help offset investment crowd out (Table 19.6). Conclude: The baseline models show that significantly more variance is explained by models containing crowd out variables, indicating crowd out is a real problem that adversely affects investment.

92

91

(5/5

10\11

99 15/18

NA

95 NA

T

Sigif./Total Test ratio

G

*

*

5/5 )

*

5/11

6/18

NA*

NA

*

19 deficit + T19.1 M1 mod.

*

01\01

*Significance levels: 2.0 = 5%; 2.7 = 1%

(wM1/s − a)

*

01\01

01\01

81

19 def.+ LF +M1 mod.

T19.1

01\01

01\01

1/1

1/1

01\01

82

(wM1/s − a)

1/1

01\01

(wM1/s − a)

*

01\01

01\01

82

01\01

T19.1

19 deficit + LF mod.

1/1

01\01

(wM1/s − a)

87

*

01\01

19 deficit unmodified

T19.1

01\01

1/1

01\01

1/1

1/1

01\01

81

82

82

(wo/s − a)

T19.1

T19.1

T19.1

1/1

*

92

83

2000 –2010

01\01

87

65

1996 –2009

19 def. + LF + M1 mod.

86

67

1985 –2005

(wo/s − a)

88

74

1985 –2004

*

88

63

1980–2004

01\01

85

37

1975 –2004

01\01

94

86

1980–2010

01\01

88

55

1980–2000

19 deficit + M1 mod.

1970–2010

(wo/s − a)

86

68

1970–2007

(Av. Adj. R 2 = 84.3%)

93

91

1970–2000

*

91

77

1970–1990

01\01

89

43

1960–1980

01\01

91

86

1960–1990

19 deficit + LF mod.

87

72

1960–2000

01\01

87

87

72

1960–2007

01\01

T19.1

19 deficit unmodified

(Av. R 2 = 89.4%)

87

(Av. R 2 = 71.4%)

1960–2008

01\01

Eq.18.1A

18 Baseline (w/def)

60

1960–2010

(wo/s − a)

T18.1AA

From Table#

18 baseline (wo/def)

Consumption

Model

Table 19.5 Summary table standard consumption model with LF, M1 deficit modifiers

408 J. J. HEIM

Eq.18.10A

T19.1

18 baseline (w/def)

19 deficit unmodified

T19.2

T19.2

T19.2

T19.2

T19.2

T19.1

T19.1

69

1960–2008

70

90

86

71

65

1970–2010

90

76

72

89

81

69

1980–2000

90

1980–2010

80

77 −64

4

80

1975 –200

89

1980–2004

89

81

71

89

1985 –2004

75

63

89

1985 –2005

75

57

98

93

92

1996 –2009

Sigif./Total

T

Test ratio

G

*

01\01

NA

*

98 01\01

NA

*

NA

NA

NA

95 NA

NA

91 NA

2000 –2010

82

89

82

87

87

87

78

90 (Adj.R 2 = 88)

0/1 0/1

01\01 01\01

0/1

0/1 0/1

01\01 01\01

01\01

0/1 0/1

01\01

01\01

0/1 0/1

01\01

01\01

0/1 0/1

01\01

01\01

0/1 0/1

01\01

01\01

0/1 0/1

01\01

01\01

1/1 0/1

01\01 01\01

*

*

*

*

*

*

*

*

*

90

(Av. R 2 = 79.8%)

81

56

1970–2007

01\01

95

82

1970–2000

1/1

87

91

(Av. R 2 = 68.3%)

72 −61

1970–1990

01\01

89

78

65

1960–1980

01\01

84

80

63

1960–1990

(Av. R 2 = 89.8%; Adj. R 2 = 88.3% )

86

71

66

1960–2000

89 (Adj.R 2 = 87)

89

(includes GDP Control Variable)

76

67

1960–2007

*Significance levels: 2.0 = 5%; 2.7 = 1%

(wM1/s − a)

19 def. + LF + M1 mod

(wM1/s − a)

19 Deficit + M1 Mod

(wM1/s − a)

19 deficit + LF mod.

(wM1/s − a)

19 deficit + M1 mod

(wM1/s − a)

19 deficit unmodified

(wM1/s − a)

19 def. + LF + M1 mod

(wo/s − a)

19 deficit + M1 mod

(wo/s − a)

19 deficit + LF Mod

T19.1

T17.3B

18 Baseline (w/def)

(wo/s − a)

T17.3C

Investment 17 baseline (wo/def)

(Does not include GDP Control Variable)

From Table# 1960–2010

Model

R 2 (18 time periods)

Table 19.6 Summary table standard investment model with LF, M1 deficit modifiers

19 DOES M1 OR TOTAL LOANABLE …

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Modifying the deficit variables to account for any changes in loanable funds (LF) that occurred did increase the explanatory power of the investment model slightly, but not the consumption model. Using M1, or both LF&M1, as the modifier, just reduced the model’s explanatory power, or left it unchanged. Hence, we conclude that loanable funds are a better measure of crowd out offset than M1 for the one period 1960–2010 tested here. Our overall conclusion for this chapter is that M1 is not as effective a modifier as Chapter 18 showed total loanable funds to be.

References Heim, J. J. (2017). An Econometric Model of the U.S. Economy. Hoboken: Palgrave Macmillan. Johnston, D. (1963). Econometric Methods. New York: McGraw-Hill.

PART VII

Determining M1 Effects on Crowd Out

CHAPTER 20

Does M1 or Total Loanable Funds More Accurately Define the Extent to Which Crowd Out Can Be Modified?

In this chapter, we test the consumption (Section 32.1) and investment model (Section 32.2) in the same way we did in Chapter 18 except we test the M1 money supply as a modifier, and compare it with other possible deficit modifiers. The models tested or compared will be: 1. total loanable funds (S + FB), 2. the endogenous portion of loanable funds only (S + FB) − (Tr + A), 3. the money supply (M1), and 4. the money supply combined with the exogenously created part of the loanable funds pool (M1 + Tr + A). M1 is tested with the same models we’ve used in recent chapters. The hypothesis is that M1 may be a better indicator than total loanable funds of how much of any increase in total loanable funds actually gets lent out and used to purchases goods and services. We also test M1 and the loanable funds pool (S + FB) as one combined variable to see if M1 might be an additional source of funds, in excess of (S + FB) which might also modify crowd out effects. Finally, we test M1 with the exogenously determined part of total loanable funds to see if It and M1 might even better explain the magnitude of crowd out effects than just M1 alone.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. J. Heim, Why Fiscal Stimulus Programs Fail, Volume 1, https://doi.org/10.1007/978-3-030-65675-1_20

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Testing the Consumption Model

20.1

Below we again test crowd out effects using the model we take as the “standard” consumption model, taken from Heim (2017), Eq. 4.4.TR, which is included here to present an idea of what, in previous studies, the determinants of consumption have been found to be. The standard consumption model from Heim (2017): CD = .29(Y − TT ) + .34(TT ) − .23(G T&I ) − 5.44PR + .48DJ−2 (t=)

(6.2)

(6.5)

(−2.1)

(−4.5)

(5.1)

− .515.07POP16/65 + .020POP + 38.00M2AV + .09CB2 (6.0)

(3.2)

R 2 = 87.8%

D.W. = 2.2

(3.7)

(4.9)

MSE = 24.88

(4.4.TR)

To start, we show this study’s Baseline (BL) Standard Consumption Model with no Deficit Variables Included, nor any loanable funds variables included (1960–2010 data). This model is presented to show clearly how much succeeding models, which add in deficit variables and loanable funds variables, actually increase the explanatory power of the model: CD = .54(Y − TT ) + 2.70PR + .27DJ−2 − .714.38POP16/65 (t=)

(0.6)

(7.2)

(3.0)(3.0)

(1.6)

+ .013POP − 1.58M2AV + .13CB2 (3.2)

R = 60.3%

(−0.1)

D.W. = 1.7

2

(2.0)

MSE = 43.98

(18.1AA)

Next we show this study’s Baseline (BL) Standard Consumption Model with 2 Variable Crowd Out (T and G) Deficit Effects Estimated Separately. This model is Estimated Before Deducting Loanable Funds Changes from (T ) or (G), and before (T + G) is Added as a Stand-Alone Variable: CD = .38(Y − TT ) + .43(TT ) − .24(G T&I ) − .14(ST + FB) (t=)

(8.0)

(6.7)

(−2.8)

(−4.1)

− 6.09PR + .40DJ−2 − 398.48POP16/65 + .016POP (−2.8)

(5.0)

(−1.9)

(3.7)

+ 33.27M2AV + .10CB2 (4.5)

(3.5)

R = 88.3% 2

Adj.R = 86.5% 2

D.W. = 1.9

MSE = 24.68 (18.1A)

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DOES M1 OR TOTAL LOANABLE FUNDS MORE ACCURATELY …

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Next we show Eq. 20.1A, which includes (S + FB) as the stand-alone variable only, Eq. 20.1B which includes it as both a stand alone and as a deficit variable modifier, and Eq. 20.2, which includes M1 with the loanable funds modifier (S + FB + M1) as both a stand alone and as a deficit variable modifier. All consumption and investment models tested below have been tested for stationarity and endogeneity problems. All variables were stationary or cointegrated with their dependent variables. No endogeneity problems were found (Hausman test) with any of the consumption or investment models. The standard consumption model with 2 Variable Crowd out (T , G), with only stand-alone (S + FB) offset changes: CD = .38(Y − TT ) + .43(TT ) − .24(G T&I ) − .14(ST + FB) (t=)

(8.0)

(6.7)

(−2.8)

(−4.1)

− 6.09PR + .40DJ−2 − 398.48POP16/65 + .016POP (−2.8)

(5.0)(5.0)

(3.7)

(−1.9)

+ 33.67M2AV + .10CB2 (4.5)

(3.5)

R = 88.3% 2

Adj.R = 86.5% 2

D.W. = 1.9

MSE = 24.68 (20.1A)

The Standard Consumption Model with 2 Variable Crowd out (T ), (G), after offsetting (S + FB only) changes to (T ) and (G) as well. Stand-alone (S + FB) variable still included in the model: CD = .38(Y − TT ) + .43(TT )m − .24(G T&I )m − .81(ST + FB) (t=)

(8.0)

(6.7)

(−5.6)

(−2.8)

− 6.09PR + .40DJ−2 − 398.48POP16/65 + .016POP (−2.8)

(5.0)

(3.7)

(−1.9)

+ 33.67M2AV + .10CB2 (4.5)

(3.5)

R = 88.3% 2

Adj.R = 86.5% 2

D.W. = 1.8

MSE = 24.89 (20.1B)

Next we wish to test a second model, the Standard Consumption Model with 2 Variable Crowd out (T ), (G), with only stand alone and deficit modifier (S + FB +M1): CD = .36(Y − TT ) + .38(TT )m − .20(G T&I )m (t=)

(7.1)

(6.6)

(−2.4)

− .68(ST + FB + M1) − 6.86PR (−5.4)

(−3.3)

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+ .41DJ−2 − 459.31POP16/65 + .017POP (4.1)

(−2.3)

(4.1)

+ 37.69M2AV + .10CB2 (3.59)

R = 87.7% 2

D.W. = 1.9

(3.6)

MSE = 25.28

(20.2)

As you can see, results are very similar for all variables using either the (S + FB + M1) modifier or only the (S + FB) modifier used in Chapter. 18. Also, crowd out variable t statistics are highly significant in both models. However, the model using the (S + FB + M1) modifier has slightly lower variable significance levels, and R 2 than the (S + FB) model. This suggests that total (S + FB) is a better indicator of the extent to which changes in loanable funds affect consumption than (S + FB) and M1 together. M1 can vary due to fluctuation in the money multiplier even when (S + FB) is constant. In addition, not every dollar lent out stays in the form of M1; some borrowers may deposit their loans in savings or CD accounts. Note that in Equation 20.1B after modification of the crowd out effect by adding (S + FB) to the tax deficit variable (T), and subtracting it from the spending deficit variable (G), results for all variables except the stand-alone (S + FB) variable remain the same, as does R 2 . This result has occurred repeatedly in earlier chapters and is explained there. See, for example, Chapters 16 and 18. In Table 20.1 below, we present results using the standard model with one of four additional variables added. The added variable is either 1. (S + FB) variable (results taken from Chapter 18 and Equation 20.1 models), 2. (S + FB + M1) (results based on Eq. 20.2 model), 3. M1 only, or 4. (M1) + (Tr + A). All results cited in Table 20.1 below for different time periods were estimated using the same models as in Eqs. 20.1A, 20.1B and 20.2 above except for varying the loanable funds variable used. Only the time periods tested changes. For each time period given in Table 20.1, two sets of statistics are presented.

1960–1980 w/o with

1960–1990 w/o with

(S + FB) Modification Only Model from Chapter 18: T Def : .72 .72 .36 .36 t-stat (5.3) (5.3) (2.7) (2.7) G Def : −.27 −.27 −.16 −.16 t-stat (−2.8) (−2.8) (−2.2) (−2.2) ST + FB − −.47 −1.46 −.13 −.65 t-stat (−2.6) (−4.2) (−1.3) (−2.5) R2 .94 .94 .90 .90 Adj.R 2 .88 .88 .85 .85 .91 .89 BL R 2 * (S + FB + M1) Modification Model from Chapter 20) T Def : .60 .60 .32 .32 t-stat (5.0) (5.0) (2.6) (2.6) G Def : −.24 −.24 −.12 −.12 t-stat (−2.3) (−2.3) (−1.8) (−1.8) S + FB + M1 −.06 −.38 −.09 −.37 t-stat (−1.3) (−3.7) (−0.7) (−1.9) R2 .92 .92 .89 .89 .85 .85 .84 .84 Adj.R 2 .91 .89 BL R 2 * (M1 Modification Only Model from Chapter 20) T Def : .53 .53 .29 .29 t-stat (3.6) (3.6) (3.0) (3.0)

Variable

.44 .44 (5.7) (5.7) −.17 −.17. (−2.4) (−2.4) −.13 −.74 (−3.6) (−4.7) .89 .89 .86 .86 .87 .42 .42 (5.8) (5.7) −.14 −.14. (−2.2) (−2.4) −.07 −.45 (−3.0) (−5.5) .89 .89 .86 .86 .87 .33 .33 (5.3) (5.3)

.28 .28. (3.1) (3.1) −.07 −.07 (−1.0) (−1.0) −.12 −.54 (−1.8) (−3.0) .91 .91 .88 .88 .91 .22 .22 (2.6) (2.6)

1960–2007 w/o with

.29 .29 (2.6) (2.6) −.09 −.09 (−0.9) (−0.9) −.10 −.48 (−1.2) (−1.8) .91 .91 .88 .88 .91

1960–2000 w/o with

33 .33 (5.2) (5.2)

37 .37 (5.6) (5.6) −12 −.12 (−2.2) (−2.2) −.09 −.68 (−2.6) (−5.5) .88 .88 .85 .85 .87

.42 .42 (5.5) (5.5) −16 −.16 (−2.4) (−2.4) −.11 −.69 (−3.4) (−4.4) .89 .89 .86 .86 .87

1960–2008 w/o with

DOES M1 OR TOTAL LOANABLE FUNDS MORE ACCURATELY …

(continued)

.32 .32 (6.2) (6.2)

. 38 .38 (6.6) (6.6) −.20 −.20 (−2.4) (−2.4) −.19 −.79 (−4.1) (−6.3) .88 .88 .85 .85 .87 .

.43 .43 (6.7) (6.7) −.24 −.24 (−2.8) (2.8) −.14 −.82 (−4.1) (5.6) .88 .88 .86 .86 .87 .

1960–2010 w/o w.

Table 20.1 Effects on Standard Consumption Model of an additional separate variable, with and without also adding it as a deficit modifier 20

417

1960–1980 w/o with

1960–1990 w/o with

*BL R 2 = Baseline Model R 2 taken from Table 18.1A

G Def : −.21 −.21 −.11 −.11 t-stat (−1.5) (−1.5) (−2.0) (−2.0) M1 .05 −.69 .13 −.27 t-stat (0.2) (−2.5) (1.5) (−1.9) R2 .91 .91 .89 .89 .84 .84 .84 .84 Adj.R 2 .91 .89 BL R 2 * (M1 + Tr + A) Modification Model from Chapter 20) T Def : .53 .53 .29 .29 t-stat (3.6) (3.6) (3.0) (3.0) G Def : −.21 −.21 −.11 −.11 t-stat (−1.5) (−1.5) (−1.9) (−1.9) M1 + Tr + A .12 −.28 −.07 −.33 t-stat (0.2) (−2.5) (1.5) (−2.0) R2 .91 .91 .89 .89 .84 .84 .86 .86 Adj.R 2 BL R 2 * .91 .89

Variable

Table 20.1 (continued)

−.08 −.08 (−1.4) (−1.4) −.01 −.42 (−0.1) (−4.6) .87 .87 .84 .84 .87 .32 .32 (5.3) (5.3) −.07 −.07 (−1.4) (−1.4) −.07 −.47 (−1.2) (−5.5) .87 .87 .84 .84 .87

.22 .22 (2.7) (2.7) −.03 −.03 (−0.5) (−0.5) −.08 −.47 (−1.2) (−3.0) .91 .91 .88 .88 .91

1960–2007 w/o with

−.03 −.03 (−0.4) (−0.4) −.06 −.31 (−1.0) (−2.8) .91 .91 .88 .88 .91

1960–2000 w/o with

32 .32 (5.1) (5.1) −07 −.07 (−1.4) (−1.4) −.04 −.44 (−1.2) (−5.5) .88 .88 .85 .85 .87

−08 −.08 (−1.5) (−1.5) .00 −.41 (0.0) (−4.7) .87 .87 .85 .85 .87

1960–2008 w/o with

.29 .29 (4.4) (4.3) −.11 −.11 (−1.3) (−1.3) .04 −.70 (−1.5) (−4.7) .87 .87 .84 .84 .87

−.16 −.16 (−1.9) (1.9) −.01 −.48 (−0.1) (4.7) .87 .87 .84 .84 .87

1960–2010 w/o w.

418 J. J. HEIM

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DOES M1 OR TOTAL LOANABLE FUNDS MORE ACCURATELY …

419

1. One in which there is one of the four variables above added as a stand alone, e.g., (S + FB), but no modification of the deficit variables (T ) and (G) by the same modifier, and 2. one in which the regression model in (1) above is reestimated adding the same variable e.g., (S + FB) also as a modifier to the deficit variables., e.g., T + (S + FB) and G − (S + FB). We may summarize Table 20.1 results as follows: (S + FB) Model From Chapter 18: Adding the (S + FB) total loanable funds variable to the baseline deficit model without a loanable funds variable increased R 2 in 5 of the 6 models tested and left it unchanged in one. The average gain was 1.5 percentage points, up from the baseline 88.7 to 90.2% (Adj.R 2n up from baseline 85.2 to 86.5%). For tax cut deficits, 6 of 6 (T ) and 5 of 6 spending deficits (G) show significant crowd out, before and after modification. This is very similar to the baseline model which showed 5 of 6 (T ) significant and 6 of 6 (G) significant. The higher R 2 shows that adding loanable funds improves our ability to explain what variable affect consumption. As explained in Chapter 18, the crowd out variable’s marginal offsetting effect is given by the net of the positive values of the coefficients on the (T ) and (G) variables. (S + FB + M1) Model: Adding the (S + FB + M1) total loanable funds variable to the baseline model increased R 2 in 4 of the 6 models tested but only slightly and left it unchanged in two. The average gain was 0.8 percentage points, less than the 1.5 points average gain obtained using (S + FB) alone. After adding the modifier, Adj. R 2 was 85.7%, below the 86.5 obtained for loanable funds modifier alone. 6 of 6 (T ) and 5 of 6 (G) show significant crowd out, before and after modification. This is very similar to the baseline model which showed 5 of 6 (T ) significant and 6 of 6 (G) significant. The higher R 2 compared to the baseline deficit (but no loanable funds) model shows that adding loanable funds improves our ability to explain what variable affect consumption, but the fact that it is found lower than that found using (S + FB) alone suggests that M1 does not add to explanatory power, and may reduce it marginally. (M1 Only) Model: Adding the (M1) variable to the baseline model left R2 unchanged in all 6 of the 6 models tested unchanged; adding it to the baseline deficit model without loanable funds variable provided no gain in explanatory power. Six of 6 (T ) were found significant, compared to 5 of 6 in the baseline model but only 2 of 6 (G) show at least marginally significant crowd out before and after modification, compared to 6 of 6 in the

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baseline model. The M1 model suggests that in 4 of the 6 time periods, there was no crowd out from government spending deficits. This seems to contradict most of our earlier chapter findings on whether spending deficits cause crowd out, and for no clear reason. If M1 had proven to be a significant determinant of consumption, this would be a big issue, but since, unlike the (S + FB) models, it adds nothing to explained variance, it is not a variable worth further examination. The reduced numbers of samples with significant spending deficits probably reflect the errors in variables” problem. It may also reflect the multicollinearity problem, since changes in M1 and government spending are fairly highly correlated. (M1 + Tr + A): Model: Adding the (M1 + TR + A) total loanable funds variable to the baseline model left R2 unchanged in 5 of the 6 models tested; in one it increased one percentage point. Hence, the average gain was 0.2 percentage points, which is better than the M1 only model above, but not enough o indicate that FR action alone has much effect on consumption. 6 of 6 (T ) significant before and after, compared to 5 of 6 in the baseline model. 1 of 6 (G) is marginally significant, and an additional 3 are close to marginally significant (t = 1.4 or 1.5), compared to 6 of 6 in the baseline model. Again, the results for the spending deficit probably reflect the errors in variables or multicollinearity problems. Conclude (for Consumption) Table 20.1 seems to indicate that changes in (S + FB) better explain what how much of changes in the loanable funds variable can offset crowd out than M1, or combinations of M1 and FR purchases. R 2 was higher for (S + FB) models in all but 3 of the 24 models tested. For those three, it was the same. This suggests M1 is not a good proxy for total loanable funds and that FR purchases have not traditionally been large enough to have much impact on consumption. Table 20.2 retests the same models as in Table 20.1, but without including the separate loanable funds (S + FB), (S + FB + M1), M1, or (M1 + Tr + A) control variables. With these tests, we can see if including them as separate variables, in addition to including them as modifiers to the tax and spending variables makes a difference in our crowd out estimates. There are no stationarity problems with either the modified or unmodified variables. The model without the deficit variables being modified was without endogeneity problems, so it was run in OLS. In the modified model, G − (S + FB) was found endogenous with the dependent variable and was replaced by a Wald-strong instrument, which was not endogenous (Sargan test).

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Table 20.2 Robustness over time of effects on consumption of crowd out, with and without offsetting loanable funds (no stand-alone modifying variables were used) Variable 1960–1980 w/o with (S + FB T Def : t-stat G Def : t-stat

1960–1990 w/o with

1960–2000 w/o with

1960–2007 w/o with

Only) Model from Chapter 18: .50 .24 .25 .14 .23 .11. .32 .16 (3.0) (2.2) (3.4) (2.7) (2.8) (1.8) (5.1) (2.2) −.19 +.06 −.10 .08 −.03 .04 −.13 .07 (−1.3) (−1.2) (−0.5) (−1.1) (0.5) (1.3) (0.6) (0.6) R2 .90 .85 .90 .87 .90 .90 .87 .82 .86 .82 .88 .87 .84 .79 Adj.R 2 .82 .73 (S + FB + M1) Modification Model from Chapter 20) TDef : .51 .22 .24 .15 .20 .13. .31 .24 t-stat (2.9) (2.5) (3.3) (3.0) (2.6) (2.4) (5.1) (3.4) G Def : −.22 .02 −.04 .09 −.03 .11 −.06 .20. t-stat (−1.2) (−0.5) (−0.2) (−0.5) (0.2) (1.2) (1.7) (2.0) R2 .90 .86 .89 .87 .91 .90 .87 .83 .85 .82 .88 .87 .84 .79 Adj.R 2 .82 .74 (M1 Modification Only Model from Chapter 20) T Def : .51 .37 .24 .22 .21 .16 .32 .29 t-stat (2.9) (3.3) (3.3) (3.9) (2.6) (2.6) (5.1) (4.0) G Def : −.22 −.07 −.04 .05 −.03 .20 −.06 .22 t-stat (−1.2) (−0.5) (−0.3) (−0.5) (−0.8) (0.6) (2.3) (2.3) R2 .90 .89 .89 .89 .91 .89 .87 .83 .85 .84 .88 .86 .84 .79 Adj.R 2 .82 .81 (M1 + Tr + A) Modification Model from Chapter 20) T Def : .53 .36 .29 .22 .23 .14 .33 .27 t-stat (3.9) (3.2) (3.2) (3.0) (2.7) (2.4) (5.6) (3.6) G Def : −.21 −.01 −.10 −.01 −.03 .12 −.08 .19 t-stat (−1.6) (−1.7) (−0.5) (−1.6) (−0.1) (−0.2) (2.3) (2.8) R2 .91 .88 .89 .88 .91 .89 .87 .83 Adj.R 2 .85 .80 .85 .84 .88 .87 .84 .81

1960–2008 w/o with

1960–2010 w/o with

.31 .18 (5.1) (2.5) −.13 .19 (−1.2) (0.7) .87 .83 .84 .79

.31 .15 (6.3) (2.3) −21 .02 (−1.9) (0.1) .88 .83 .85 .79

31 .25 (5.1) (3.8) −06 .21 (−0.5) (2.2) .87 .83 .84 .79

.32 .23 (6.3) (4.2) −.14 .16 (−1.2) (1.6) .88 .84 .86 .80

.31 .29 (5.6) (5.1) −.06 .22 (−1.6) (0.5) .87 .83 .84 .80

.32 .32 (6.3) (5.5) −.14 .18 (−1.2) (2.1) .88 .84 .86 .81

. 33 .27 (5.6) (3.7) −08 .20 (−1.6) (2.9) .87 .83 .85 .81

.32 .21 (6.6) (3.0) −.16 .29 (−1.9) (4.9) .87 .82 .84 .78

We may summarize Table 20.2 results as follows: (S + FB) Model From Chapter 18: Adding the (S + FB) total loanable funds variable to the baseline model as a deficit variable modifier reduced, not increased, the baseline deficit model’s ability to explain variance (R 2 )

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in all 6 of the 6 models tested. The average decline was 3.5%. As we noted in Chapter 18, the likely reason for the decline is that increases in loanable funds (S + FB) have two offsetting effects on consumption: they eliminate crowd out, but, ceteris paribus, can only occur (income constant) if there is a cutback in consumption. The net of these two leaves a large number, (S + FB), being subtracted from the deficit variables, which has little if any net significant impact on consumption. Deficits do have a significant relationship, so this creates an error in variables problem reducing R 2 and deficit variables significance levels. Six of 6 (T ) show significant crowd out, before and after modification (but lower significance levels after). 5 of 6 (G) show marginally significant or significant crowd out before modification; but 0 of 6 show significant crowd out effects after. But these declines in significance levels seem more related to expected results of error in variables problems than to success in reducing crowd out. In short, for consumption, the “no-stand alone” loanable funds model seems to be a bad model. Because changes in loanable funds have two, largely offsetting effects on consumption, without modeling them separately distorts the impact of the variables (the deficit) to which they are added. A stand-alone loanable funds variable is also needed to allow both effects to be shown separately. Including the stand alone leaves a theory consistent result for the loanable funds effect (same coefficient as the deficit variable alone, higher R 2 compared to baseline model) and for the stand alone (negative sign on regression coefficient). (S + FB + M1) Model: Adding the (S + FB + M1) variable to the baseline model as a deficit variable modifier reduced, not increased, the baseline model’s ability to explain variance (R 2 ) in all 6 of the 6 models tested. The average decline was 3.3%. The reasons for the decline appear to be the lack of a stand-alone loanable funds variable, and the lack of a significant relationship between changes in consumption and changes in the money supply (see the M1 model discussion below). Six of 6 (T ) show significant crowd out, before to and after modification (but lower significance levels after). By comparison, for spending deficits, 5 of 6 (G) before modification were significant, and 4 of 6 modified (G) were statistically significant, but had positive signs after modification (1960–2000, 2007,2008, and 2011 samples). This suggests the combined loanable funds and M1 modifier caused net crowd in effects to result. We see this effect again further below in the model that uses M1 and only the portion of the changes in loanable funds attributable to FR purchases. Particularly in the last three samples, the economy (including

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endogenous loanable funds) was in decline, but FR purchases and M1 were increasing. (M1) Model: Adding the (M1) variable to the baseline model as a deficit variable modifier reduced, not increased, the baseline model’s ability to explain variance (R 2 ) in all 6 of the 6 models tested. The average decline was 2.7%. Six of 6 tax cut deficits (T ) showed statistically significant crowd out effects before and after modification. For spending deficits, 3 of 6 (G) showed significant crowd out effects before modification, but none were significant after. Since R 2 was markedly lower after modification in 6 of 6 tests, it suggests that adding the modification is resulting in lowed deficit significance levels for both (T ) and (G) because it is creating an “error in variables” problem, i.e., is distorting the true crowd out effect, not because it is really causing increases in consumption large enough to offset crowd out. (In addition, in Table 32.1, the sign on M1 as a stand-alone variable is consistently negative, which is difficult to explain theoretically.) (M1 + Tr + A) Model: Adding the (Tr + A + M1) variable to the baseline model as a deficit modifier reduced, not increased, the baseline model’s ability to explain variance (R 2 ) in all 6 of the 6 models tested. The average decline was 3.2%. Six of 6 tax cut crowd out variables (T ) were significant before and after modification (though with lower significance levels). For spending deficits, 5 of 6 (G) show marginally significant crowd out before modification, with the expected negative sign, and four of six are significant after modification, but have positive signs! Only the two samples ending with 1980 or 1990 data have negative signs. As we explained earlier, this may stem from the rapidly declining economy of the 2007–2010 period, causing major reductions in the endogenous part of the loanable funds pool. The lower R 2 after modification in all 6 periods tested suggests that modifying the deficit variables by (Tr + A + M1) is creating an “error in variables” problem. Overall, the markedly lower R 2 s in the models, compared to the comparable Table 32.1 models, suggest that consumption models with stand-alone modifier variables are the better models. R 2 in Table 32.2 after modification drops even further. That said, there is some evidence suggesting that (S + FB + M1) and (Tr + A + M1) can offset at least spending deficit crowd out (if the increase in FR purchases is huge).

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20.2 Testing the Two---Variable Deficit Investment Model The model we take as the “standard” investment model, i.e., the model containing many variables previous researchers has found to be significant determinants of investment, but excluding any such variables for which findings could not be replicated in at least three of four different time periods and in three different models. This standard model is taken from Heim (2017), Eq. 5.4.TR. 20.2.1

Investment Models with a Stand-Alone Loanable Funds Modifier

For comparison of representative past findings with this study’s work, we include the Standard Investment Model from Heim (2017) (Estimated using 1960–2010 data): ID = + .26(ACC) + .27(TT ) − .30(G T&I ) + .011POP − 4.72PR−2 (t=)

(8.7)

(2.9)

(5.7)

(−3.8)

(−2.7)

+ 6.81XRAV + 2.55CAP−1 (2.9)

R = 83.3% 2

(1.7)

D.W. = 2.0

MSE = 28.25

(5.4.TR)

This Study’s Baseline Investment Model, With No Deficit or Loanable Funds Variables Included, and No GDP Variable Included to Control for the State of the Economy is estimated as: ID = + .41(ACC) + .007POP − 1.25PR−2 (t=)

(7.4)

(2.1)

(−0.3)

+ 6.95XRAV + 11.00CAP−1 (21)

R = 69.4% 2

2 Adj.RAdj

(2.8)

= 66.7%

D.W. = 1.4

MSE = 47.38

(Same as Eq. 17.3C) This Study’s Baseline Investment Model, With No Deficit or Loanable Funds Variables Included, but GDP Variable Included to Control for the

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State of the Economy is estimated as: ID = + .47(ACC) − .00POP − 0.85PR−2 + 5.21XRAV (t=)

(0.0)

(4.0)

(2.0)

(−0.3)

+ 10.39CAP−1 + .10GDP (−1.3)

(2.9)

R = 76.1% 2

D.W. = 2.1 MSE = 43.06

(Same as Eq. 17.3B; and same as Eq. 21.10C below) Baseline Model With Deficit and GDP Control Variable, but No Loanable Funds Variables (1960–2010) ID = + .27(ACC) + .33TT − 33GT&I + .012POP − 4.95PR−2 (t=)

(6.4)

(2.6)

(2.8)

(−3.9)(−3.9)

(−2.5)

+ 6.68XRAV + 2.43CAP−1 − .02GDP (3.5)

R = 89.0%

(−0.2)

(1.8)

D.W. = 1.9

2

MSE = 29.87

(18.4A)

The same model, with the addition only of a control variable for changes in the pool of loanable funds (S + FB), follows in Eq. 20.3A, and with both this stand-alone variable and a (S + FB) deficit modifier, in Eq. 20.3B. Here, we simply add the change in loanable funds to the negative-valued sign on any tax cut, reducing the estimated crowd out effect. We subtract it from any change in the deficit caused by changes in government spending, also reducing the estimated crowd out effect. In Eqs. 20.4A and 20.4B, the same equations are repeated, but this time the variable used to modify (T ), (G) and as a stand alone (S + FB + M1). All variables were found Augmented Dickey–Fuller (ADF) stationary; no Hausman endogeneity was found between the dependent and explanatory variables, and Newey-West standard errors were used to avoid heteroskedasticity. The Standard Investment Model with 2 Variable Crowd out (T , G), before (S + FB), is used to modify their value, but with (S + FB) StandAlone Variable (Using 1960–2010 data): ID = + .18(ACC) + .21TT − 23G T&I + .16(S + FB) + .007POP (t=)

(5.6)

(1.8)

(−2.6)

(2.1)

(2.7)

− 3.24PR−2 + 5.54XRAV + 1.00CAP−1 (−1.6)

R = 90.4% 2

D.W. = 1.9

(2.8)

(0.7)

MSE = 27.49

(20.3A)

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(same as Eq. 18.3) The Standard Investment Model with 2 Variable Crowd out (T , G), Includes the Stand-Alone and Deficit-Modifying Effects of (S + FB) (Using 1960–2010 data): ID = + .18(ACC) + .21TT(m) − 23G T&I(m) − .28(S + FB) (t=)

(5.6)

(1.8)

(−2.6)

(1.1)

+ .007POP − 3.24PR−2 + 5.54XRAV + 1.00CAP−1 (2.7)

R = 90.4% 2

(2.7)

(−1.6)

D.W. = 1.9

(0.7)

MSE = 27.50

(20.3B)

(same as Eq. 18.4) Equations 20.4A and 20.4B repeat models shown immediately above except they use (S + FB + M1) as the variable used to test for loanable funds effects. Standard Investment Model with 2 Variable Crowd out (T , G), before modifying (S + FB + M1) added to deficit variables) (Using 1960–2010 data): ID = + .20(ACC) + .29TT − 33G T&I + .08(S + FB + M1) (t=)

(5.7)(5.7)

(−4.1)

(2.5)

(1.1)

+ .009POP − 3.14PR−2 + 6.08XRAV + 1.38CAP−1 (2.8)

R = 89.1% 2

(3.0)

(−1.4)

D.W. = 1.8

(0.8)

MSE = 29.22

(20.4A)

Standard Investment Model with 2 Variable Crowd out (T , G), adjusted for accommodating (S + FB + M1) (Using 1960–2010 data) ID = + .20(ACC) + .29TT(m) − 33G T&I(m) − .53(S + FB + M1) (t=)

(5.7)

(−4.1)(−4.1)

(2.5)

(−2.6)

+ .009POP − 3.14PR−2 + 6.08XRAV + 1.38CAP−1 (2.8)

R = 89.1% 2

(−1.4)

D.W. = 1.8

(3.0)

MSE = 29.22

(0.8)

(20.4B)

Table 20.3 below presents results for testing these four models in six different, though overlapping, time periods. Results in the six periods are estimated use exactly the same model. Then, Table 20.4 drops the standalone (S + FB + M1) variable and retests the remaining model in the same six time periods.

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Table 20.3 Comparing robustness over time of effects on investment of crowd out, with and without accommodating loanable funds modification Variable

1960–1980 w/o with

1960–1990 w/o with

(S + FB only) Model from Chapter T Def : −.02 −.02 .13 .13 t-stat (−0.4) (1.6) (1.6) (−0.4) G Def : −.12 −.12 −.11 −.11 t-stat (−1.3) (−1.2) (−1.3) (−1.2) S + FB .65 .54 .36 .12 t-stat (9.7) (4.1) (4.2) (0.6)

1960–2000 w/o with

1960–2007 w/o with

1960–2008 w/o with

1960–2010 w/o with

18: .17 .17 .12 .12 .20 .20 .21 .21 (1.8) (1.8) (1.4) (1.4) (1.8) (1.8) (1.8) (1.8)

−.15 −.15 (−1.7) (−1.7) .30 −.02 (3.1) (−0.1) R2 .97 .97 .90 .90 .90 .90 .90 .83 .86 BL R 2 : (S + FB + M1) Model from Chapter 20: T Def : .07 .07 .19 .19 .27 .27 t-stat (1.6) (1.6) (1.4) (1.4) (2.2) (2.2) G Def : −.20 −.20 −.21 −.17 −.31 −.31 t-stat (−1.9) (−1.9) (−3.4) (−1.9) (−1.9) (−3.4) S + FB .41 .14 .23 .17 .20 −.38 + M1 t-stat (8.7) (1.2) (1.4) (0.5) (1.6) (−1.4) R2 .95 .95 .87 .87 .88 .88 .93 .93 .83 .83 .86 .86 Adj.R 2 .90 .83 .86 BL R 2 : (M1 only) Model from Chapter 20: T Def : .29 .29 .40 .40 .42 .42 t-stat (3.2) (3.2) (3.4) (3.4) (5.3) (5.3) G Def : −.30 −.30 −.27 −.27 −.34 −.34 t-stat (−2.1) (−2.2) (−3.3) (−2.1) (−2.2) (−3.3) M1 .26 −.33 −.17.−.85 −.08 −.84 t-stat (1.2) (1.4) (−1.0) (−1.3) (−3.2) (−8.2) R2 .91 .91 .84 .84 .86 .86 .85 .85 .79 .79 .83 .83 Adj.R 2

−.13.−.13. (−1.3) (−1.3) .22 −.02 (3.1) (−0.1) .87 .87 .82

−16 −16 (−1.5) (−1.5) .19 −.17 (2.4) (−0.7) .88 .88 .85

−.23 −.23 (−2.6) (−2.6) .16 −.28 (2.1) (−1.1) .90 .90 .89

.15 .15 (1.7) (1.7) −.24.−.24. (−2.3) (−2.3) .19 −.20

.28 .28 (2.5) (2.5) −27 −27 (−2.4) (−2.4) .10 −.44

.29 .29 (2.5) (2.5) −.33 −.33 (−4.1) (−4.1) .08 −.53

(2.8) (−1.2) .86 .86 .83 .83 .82

(1.5) (−2.2) .87 .87 .84 .84 .85

(1.1) (−2.6) .89 .89 .87 .87 .89

.29 .29 (3.4) (3.4) −.25.−.25. (−2.0) (−2.0) −.09 −.63 (−1.1) (−4.5) .82 .82 .79 .82

.31 .31 (3.8) (3.8) −23 −23 (−1.7) (−1.7) −.14 −.68 (−1.7) (−5.3) .86 .86 .84 .86

.31 .31 (3.8) (3.8) −.31 −.31 (−3.6) (−3.6) −.14 −.76 (−1.8) (−7.9) .89 .89 .87 .89

(continued)

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Table 20.3 (continued) Variable

1960–1980 w/o with

BL R 2 : .90 (M1 +Tr + A) Model T Def : .29 .29 t-stat (3.4) (3.4) G Def : −.30 −.33 t-stat (−2.3) (−2.3) M1 .41 −.22 t-stat (1.9) (−1.1) .92 .92 R2 .85 .85 Adj.R 2 BL R 2 : .90

1960–1990 w/o with

1960–2000 w/o with

1960–2007 w/o with

1960–2008 w/o with

1960–2010 w/o with

.83 from Chapter .40 .40 (3.4) (3.4) −.27 −.27 (−2.2) (−2.2) −.16.−.83 (−1.4) (−3.3) .84 .84 .79 .79 .83

.86 20: .42 .42 (5.3) (5.3) −.34 −.34 (−3.3) (−3.3) −.08 −.84 (−1.1) (−7.9) .86 .86 .83 .83 .86

.82

.85

.89

.29 .29 (3.4) (3.4) −.30.−.30. (−2.0) (−2.3) −.00 −.58 (−0.0) (−3.8) .82 .82 .79 .82 .82

.34 .34 (4.0) (4.0) −31 −.31 (−2.4) (−2.4) .04 −.61 (0.4) (−3.7) .86 .86 .84 .86 .85

.33 .33 (3.7) (3.7) −.36 −.36 (−2.8) (−2.8) −.00 −.69 (−0.1) (−4.8) .89 .89 .87 .89 .89

*BL = Baseline model with no (S + FB), (S + FB + M1), (M1) or (M1 + Tr + A) stand alone or deficit modifying variable

Comparing Eqs. 20.4A to 20.4B, we find that all coefficients and tstatistics, except one, are the same before and after modification. The exception is the stand-alone (S + FB) variable, whose value changes from (+.08 (t = 1.1)) to (−.53 (t = −2.6)) R 2 also remains the same. This is also true when the modifier is (S + FB + M1). It is the result typically achieved when testing a model with a stand-alone variable only, and then retesting it with both the stand alone and using it to modify (T ) and (G). In Table 20.3 we use the standard investment model with deficit variables, adding an additional variable to test for whether the (S + FB), (S + FB + M1), M1, or (M1 + Tr + A) add to the explanatory power of the standard model. In Table 20.3 below, for each time period given, two sets of statistics are presented. In one set, there is a stand-alone variable, either (S + FB), (S + FB + M1), M1, or (m1 + Tr + A) added to the model, but no modification of the crowd out variables (T , G) by the same variable, and one in which there is both the stand-alone variable and modification of the deficit variables’ by the same variable. We may summarize Table 20.3 results as follows: (S + FB) Model From Chapter 18: Adding the (S + FB) total loanable funds variable to the baseline model increased R 2 in 6 of the 6 models tested. The average gain was substantial: 4.5 percentage points.

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Table 20.4 Comparing robustness over time of effects on investment of crowd out, with and without loanable Funds and M1 modification (no stand-alone modifier) Variable 1960–1980 w/o with (S + FB T Def : t-stat G Def : t-stat

1960–1990 w/o with

1960–2000 w/o with

only) Model from Chapter 18: .31 .14 .37 .17 .42 .17 (3.0) (2.7) (3.8) (3.6) (5.4) (3.5) −.29 −.26 −.30 −.16 −.37 −.14 (−1.9) (−2.3) (−4.2) (−3.1) (−2.2) (−2.6) R2 .90 .96 .83 .90 .86 .90 .79 .88 .83 .89 Adj.R 2 .86 .94 (S + FB + M1) Model from Chapter 20: T Def : .31 .12 .37 .12 .42 .13 t-stat (3.0) (2.5) (3.8) (2.4) (5.4) (2.0) G Def : −.29 −.24 −.30 −.15 −.37 −.20 t-stat (−1.9) (−2.4) (−4.2) (−3.0) (−1.6) (−2.3) R2 .90 .95 .83 .87 .86 .87 .80 .83 .83 .84 Adj.R 2 .87 .93 (M1 only) Model from Chapter 20: T Def : .31 .25 .37 .17 .43 .19 t-stat (3.0) (2.8) (3.8) (2.3) (5.4) (1.8) G Def : −.29 −.25 −.30 −.02 −.37 .04 t-stat (−1.9) (−2.4) (−4.2) (−2.0) (−0.2) (−0.5) R2 .90 .90 .83 .77 .86 .69 .80 .71 .83 .64 Adj.R 2 .87 .86 (M1 +Tr + A) Model from Chapter 20: T Def : .31 .26 .37 .15 .43 .18 t-stat (2.9) (3.2) (3.8) (2.1) (5.4) (1.8) GDef : −.29 −.30 −.30 .04 −.37 .07 t-stat (−1.9) (−2.2) (−4.2) (−2.1) (0.4) (0.8) R2 .90 .91 .83 .76 .86 .69 Adj.R 2 .86 .88 .79 .70 .83 .63

1960–2007 w/o with

1960–2008 w/o with

1960–2010 w/o with

.29 .11 (3.4) (2.5) −.29.−.12. (−2.7) (−1.9) .82 .87 .79 .85

.34 .16 (4.0) (2.6) −.30 −.07 (−2.6) (−0.9) .85 .88 .83 .87

.33 .14 (3.9) (2.2) −.36 −.11 (−4.7) (−1.5) .89 .90 .87 .88

.29 .09 (3.4) (1.8) −.29 −15. (−2.7) (−1.8) .82 .85 .79 .83

.34 .18 (4.0) (2.0) −30 −04 (−2.6) (−0.3) .85 .84 .82 .81

.33. 15 (3.9) (1.7) −.36 −.08 (−4.7) (−0.7) .89 .85 .86 .83

.29 .20 (3.4) (2.6) −.29 .06 (−2.6) (−0.6) .82 .71 .79 .67

.34 .25 (4.0) (2.8) −.30 .22 (−2.6) (−1.7) .85 .75 .82 .71.

.33 .23 (3.9) (2.2) −.36 .05 (−4.7) (0.4) .89 .74 .86 .71

.29 .20 (3.4) (2.5) −.29 .06. (−2.7) (0.7) .82 .72 .80 .68

.34 .26 (4.0) (3.1) −30 .06 (−2.6) (0.6) .85 .77 .83 .73

.33 .20 (3.9) (2.2) −.36 .28 (−4.7) (3.3) .89 .75 .87 .72

The sign on the added (S + FB) variable was positive and highly significant in all 6 tests, indicating increases in loanable funds are associated with positive changes in investment. Hence, we conclude, increases in loanable

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funds can effectively offset the negative effects of crowd out caused by deficits. For tax cut deficits, in 6 of 6 periods tested using the baseline deficit model (see Table 32.4 below), which had no (S + FB) explanatory variables, statistically significant crowd out effects of deficits were found. After adding just the stand-alone (S + FB) variable, this dropped to 4 of 6 (T ) (but at lower significance levels) and stayed at 4 of 6 after the standalone model was modified further by modifying the deficit variables. For spending deficits, 6 of 6 showed statistically significant crowd out in the baseline deficit model, but this dropped to only 2 of 6 (G) show significant crowd out after adding the stand-alone loanable funds variable, and stayed at 2 of 6 after further adding the deficit variable modification. Since adding (S + FB) raises R 2 at the same time it reduces some periods’ crowd out effect to insignificance, it may be that (S + FB) can sometimes eliminate crowd out problems caused by spending deficits, but not as reliably as we would like. After being added as a modifier of the deficit variables, the stand alone generally becomes statistically insignificant in 5 of 6 test periods, unlike consumption tests, where it typically became more significant. This strongly suggests that unlike consumption, there is only one effect of a change in loanable funds on investment, and using two variables in the same model to represent its effects simply dilutes them, resulting in a showing of lower levels of significance. Theoretically, the investment model including a stand alone is also questionable; the rationale for a separate second effect on investment resulting from adding a stand-alone (S + FB) variable is not as obvious as it is with consumption. (S + FB + M1) Model: Adding the (S + FB + M1) variable to the baseline model increased R 2 in 5 of the 6 models tested and left it unchanged in one. The average gain was 2.8 percentage points, markedly less than the 4.5% gain using only (S + FB), and suggesting M1 changes are not related to investment changes or are already accounted for in the total loanable funds variable (S + FB). For the modifier variable (S + FB + M1), as a stand alone, it was found significant in 3 of 6 tests. After the deficit variables were also modified, only 2 of 6. This provides a little further evidence that changes in loanable funds only have one effect on investment, and that is through their ability to compensate for crowd out effects. For tax cut deficits, 6 of 6 baseline deficit models showed statistically significant crowd out associated with deficits. This fell to 5 of 6 (T ) in

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both the stand-alone (S + FB + M1) model and the same model with the deficit variables also modified. For spending deficits, the baseline model showed significant crowd out in 6 of 6 periods tested. After adding the stand-alone variable, 6 of 6 (G) still showed significant crowd out, before and after deficit variable modification. This suggests M1 is not effective as an offset to crowd out, at least not in addition to the offset advantages provided by (S + FB) alone. (M1 Only) Model: Adding the (M1) funds variable to the baseline model increased R 2 marginally (1 percentage point) in 3 of the 6 models tested and left it unchanged in three. The average gain was 0.5 percentage points, with the results strongly indicating M1 is not an effective crowd out offset compared to the total loanable funds variable (S + FB). For tax cut deficits, 6 of 6 (T) significant and 6 of 6 (G) show significant crowd out before and after modification, as was the case with the baseline model. This suggests changes in M1 alone do not help offset crowd out or do not help enough to eliminate crowd out as a significant problem. (M1 + Tr + A): Model: Adding the (M1 + Tr + A) variable to the baseline model increased R 2 in 3 of the 6 models tested and left it unchanged in three. The average gain was small: 0.7 percentage points, again, noticeably less than the total loanable funds model. For tax cut deficits, 6 of 6 (T ) significant before and after. 6 of 6 (G) also significant, as was the case with the baseline model. Coefficients and t-statistics are nearly identical to those in the (M1 only) model. Results suggest neither M1 or (Tr + A) is effective in eliminating investment crowd out. 20.2.2

Investment Models Without a Stand-Alone Loanable Funds Modifier

Next, the same investment models without a stand-alone modifier variable were examined. There was evidence this model, which allows for only the deficit variables to show the effect of loanable funds or M1 modification on the crowd out problem. The stand alone is dropped because in theory also there is no easy way to explain why two separate representations are needed.

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All variables were found Augmented Dickey–Fuller (ADF) stationary; no Hausman endogeneity was found between the dependent and explanatory variables, and Newey-West standard errors were used to avoid heteroskedasticity. All results in Table 20.4 below use exactly the same investment models tested in Table 20.3 above, except 20.4 drops the stand-alone variables (S + FB), (S + FB + M1), (M1), and (M1 + Tr + A). Only the dates of the period used to test the models changes. In Table 20.4 below, for each time period given, two sets of statistics are presented. In one set, there is no direct modification of the crowd out variables (T , G) by the same-period change in the modifier used in the model, and one in which there is modification. We may summarize Table 20.4 results as follows: (S + FB) Model From Chapter 18: Adding the (S + FB) total loanable funds variable to the baseline model with deficit increased R 2 in 6 of the 6 models tested. The average gain was substantial: 4.3 percentage points (about the same as for the model with only a stand-alone (S + FB) variable (4.5%). This strongly indicates changes in total loanable funds have a significant impact on investment by offsetting) crowd out. For tax cut deficits, the baseline model indicated significant crowd out in 6 of 6 periods tested. After adding the(S + FB) variable, 6 of 6 (T ) were still showing significant crowd out effects after modification, indicating the negative effects of government deficits on the pool of loanable funds could be offset dollar-for-dollar by increases in loanable funds. For spending deficits, the baseline model showed 6 of 6 periods tested had significant crowd out effects, but only 4 of 6 (G) show significant crowd out after modification (the two tests including early QE years, involving large increases in FR purchases, showed insignificant crowd out effects of spending deficits, suggesting “errors in variables” problems stemming from the huge increase in FR purchases unassociated with increased investment). (S + FB + M1) Model: After modification R 2 increased in 4 cases, and declined in two others. The average gain was 1.3 percentage points, suggesting adding M1 to the (S + FB) modifier is reducing the explanatory power of the model compared to just using (S + FB) alone. Put another way, it suggests increasing M1 has no positive impact, and is just distorting the regression’s ability to see clearly the offsetting effects of increases in loanable funds.

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For tax cut deficits, 6 of 6 showed crowd out before deficit modification, and 6 after, though at lower significance levels. For spending deficits, 6 of 6 showed significant crowd out before modification of the deficit variables, 4 of 6 (G) after modification (the two tests including early QE years, involving large increases in FR purchases, showed insignificant crowd out effects of spending deficits). (M1 Only) Model: R 2 markedly lower after modification in 5 of 6 cases and unchanged in one test. The average decline in R 2 is 9.8%. For tax cut deficits, 6 of 6 (T ) showed significant crowd out effects before and after modification (but with lower significance levels after). For spending deficits, Six of 6 (G) show significant crowd out before, but only 2 of 6 after. The declining R 2 in modified tests indicates that the decline in significance levels of the deficit variables more likely results from an “errors in variables” (or multicollinearity problem) than because M1 has some effect in offsetting crowd out from spending deficits. (M1 + Tr + A): Model: R 2 markedly lower after modification in 5 of 6 cases. The average decline is 9.2%, slightly less than adding M1 alone, indicating there may be a small positive effect of FR purchases in offsetting crowd out, but very small at best For tax cut deficits, 6 of 6 (T ) showed significant crowd out before and after modification. For spending deficits, 6 of 6 (G) show significant crowd out before, but only 2 of 6 after. Results are nearly identical to those for the M1 test alone. This suggests (TR + A) is not adding to the deficit offsetting capabilities of M1. R 2 s for the (S + FB) model were higher in all six periods tested than in the other three modifier models tested, and hence, appears to be the more accurate way of showing the extent to which investment crowd out is offset than by any one of the three M1 variants tested. 20.2.3

Investment Models Without a Stand-Alone Loanable Funds or M1 Modifier, but with a Business Cycle Control Variable

As we noted in an earlier Chapter 31, (S + FB) is positively correlated with movements in the economy and with its investment component. That means rising taxes are positively related to investment, and rising government expenditures negatively related to investment. Hence, our two crowd out variables (T ) and (G) may be found statistically significant, simply because the economy fluctuates, if we do not estimate their effects holding the level of the economy constant. We can test for whether

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our hypothesis that this control is needed is correct, by adding a variable (the GDP) to control for the economy’s fluctuations. GDP was endogenous with the dependent variable and replaced by a Wald-strong, non-endogenous instrument. There were no stationarity issued. Newey West standard errors were used, and the model was estimated in first differences of the data. All results in Table 20.5 below use exactly the same investment models tested in Table 20.4 above, except 20.5 adds a variable (GDP) to control for the state of the economy when estimating crowd out effects. Only the time periods used to test the models changes. In Table 20.5, for each time period given, two sets of statistics are presented. In one set, there is no modification of the crowd out variables (T , G) by the same-period change in the modifier used in the model, and one in which there is modification. General 2SLS Model Tested, Using 1960–2010 Data (Using Total Loanable Funds as Deficit Modifier): ID = + .22(ACC) + .18TT − .06G T&I + .008POP − 4.11PR−2 (t=)

(2.0)

(4.9)

(2.0)

(−0.7)

(−2.2)

+ 4.77XRAV + 1.50CAP−1 − .05GDP (2.4)

R = 90.2% 2

(1.2)

Adj.R = 88.5% 2

(−0.7)

D.W. = 2.0

MSE = 28.20

(18.5)

We may summarize Table 20.5 results as follows: (S + FB) Model From Chapter 18: Adding the (S + FB) total loanable funds variable to the baseline model increased R 2 in 6 of the 6 time periods tested. The average gain was substantial: 2.2 percentage points, indicating changes in total loanable funds had a significant impact on investment. Our presumption is that the investment increase occurred because increases in loanable funds can offset the crowd out effects of deficits. This gain in R 2 was only half the size of the 4.5 point gain observed in the comparable Table 27.4 model, which does not have a variable holding business cycle fluctuations constant when estimating the effects of loanable funds on deficits. The difference indicates that half the crowd out effect of deficits arises from deficit—inducing changes in the business cycle rather than exogenously determined increases in spending or tax cuts. For tax cut deficits, 5 of 6 (T) showed significant crowd out before and after modification (but reduced coefficients and significance levels after). For spending deficits, 6 of 6 (G) show significant crowd out before and

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Table 20.5 Effects on investment of crowd out, with and without loanable funds and M1 modification (no stand-alone modifier, GDP control added) Variable 1960–1980 w/o with (S + FB T Def : t-stat G Def : t-stat

1960–1990 w/o with

1960–2000 w/o with

1960–2007 w/o with

only) Model from Chapter 18: .13 .04 .28 .10 .32 .16 .26 .10 (0.9) (0.6) (2.2) (1.9) (2.6) (1.9) (2.3) (1.7) −.35 −.27 −.42 −.20 −.40 −.14 −.34.−.13. (−3.8) (−3.2) (−5.2) (−3.2) (−4.1) (−3.1) (−2.2) (−1.8) R2 .95 .97 .87 .91 .89 .90 .84 .88 Adj.R 2 .91 .95 .83 .89 .86 .88 .81 .85 Average R 2 = 88.3% unmodified; 90.5% modified Average Adj. R 2 = 85.2% unmodified; 88.7% modified (S + FB + M1) Model from Chapter 20: T Def : .12 .04 .20 .05 −.36 .07 .35 .04 t-stat (0.7) (0.5) (1.1) (0.5) (−2.5) (2.1) (0.7) (0.8) G Def : −.34 −.25 −.45 −.19 −.41 −.22 −.32.−.18. t-stat (−3.8) (−3.1) (−5.2) (−2.5) (−3.4) (−1.9) (−2.7) (−2.1) R2 .95 .96 .88 .88 .88 .89 .77 .87 .84 .83 .85 .86 .74 .84 Adj.R 2 .91 .94 Average R 2 = 85.1% unmodified; 88.5% modified Average Adj. R 2 = 81.8% unmodified; 84.8% modified (M1 only) Model from Chapter 20: T Def : .12 .03 .20 −.07 .36 −.04. .35 .12 t-stat (0.7) (0.2) (1.1) (2.4) (2.1) (1.0) (−0.3) (−0.3) G Def : −.34 −.27 −.45 −.13 −.41 −.14 −.32 −.04 t-stat (−3.8) (−3.1) (−5.2) (−2.5) (−2.9) (−1.4) (−1.6) (−0.3) R2 .95 .94 .88 .82 .88 .82 .77 .77 .84 .81 .85 .76 .74 .74 Adj.R 2 .91 .88 Average R 2 = 85.1% unmodified; 83.7% modified Average Adj. R 2 = 81.8% unmodified; 79.5% modified (M1 + Cr + A) Model from Chapter 20: T Def : .12 .05 .20 −11 .36 −.04 .35 .13 t-stat (0.7) (0.5) (1.1) (2.4) (0.4) (2.1) (1.1) (−0.6)

1960–2008 w/o with

1960–2010 w/o with

.33 .17 (2.7) (1.9) −.33 −.07 (−2.8) (−0.7) .86 .87 .83 .85

.33 .18 (2.6) (2.0) −.33 −.06 (−3.9) (−0.7) .89 .90 .87 .89

.42 .18 (3.0) (1.4)

.42 .18 (3.1) (1.6)

−31 −04 (−2.2) (−0.2) .79 .84 .75 .82*

−.35 −.03 (−3.4) (−0.3) .84 .87 .82 .80

.42 .24 (3.0) (1.6)

.42 .20 (3.1) (1.5)

−.31 .21 (−2.2) (1.0) .79 .75 .75 .77

−.35 .09 (−3.4) (0.6) .84 .81 .82 .81

.42 .23 (3.0) (1.8)

.42 .20 (3.1) (1.3)

(continued)

436

J. J. HEIM

Table 20.5 (continued) Variable 1960–1980 w/o with

1960–1990 w/o with

1960–2000 w/o with

1960–2007 w/o with

−.34 −.30 −.45 −.10 −.41 −.10 −.32 −.02 (−3.8) (−3.1) (−5.2) (−2.5) (−2.8) (−1.0) (−1.2) (−0.2) R2 .95 .94 .88 .82 .88 .82 .77 .77 .84 .82 .85 .77 .74 .75 Adj.R 2 .91 .88 Average R 2 = 85.1% unmodified; 83.8% modified Average Adj. R 2 = 81.8% unmodified; 79.8% modified

G Def : t-stat

1960–2008 w/o with

1960–2010 w/o with

−31 .02 (−2.2) (0.2) .79 .78 .75 .76

−.35 .25 (−3.4) (1.3) .84 .80 .82 .81

4 of 6 after modification. So we interpret the result as consistent with the theory that increases in loanable funds reduce crowd out effects. (Had the reduction in deficit variable coefficients and significance levels resulted in reduced R 2 , we would have concluded adding a modifier just created an error in variable problem, and that that was the cause of the decline in deficit coefficients and significance levels.) (S + FB + M1) Model: Adding the (S + FB + M1) total loanable funds variable to the baseline model increased R 2 in 6 of the 6 models tested, but less than adding (S + FB) alone. The average gain was 1.8 percentage points. This suggests changes in M1 do not positively affect investment, ceteris paribus, and that just adding it to a variable (S + FB) that we do know affects investment just creates an error in variables problem that reduces the observed significance of (S + FB) alone. For tax cut deficits, 4 of 6 (T ) before modification showed significant crowd out, only 1 of 6 (T ) after, for (G), 6 of 6 showed significant crowd out before modification, but only and 4 of 6 (G) after. For spending deficits, results are same as with (S + FB) alone, but with lower coefficients and significance levels. The declines in R 2 and significance levels compared to the (S + FB) only model suggest an error in variables problem, not success in reducing crowd out (M1 Only) Model: Adding the (M1) variable to the baseline model decreased R 2 in 5 of the 6 models tested, and left it unchanged in one. The average loss was substantial: 3.3%. For tax cut deficits, 4 of 6 (T ) time periods tested showed significant crowd out before modification, but only 1 of 6 after modification. For spending deficits, 6 of 6 (G) tests show significant crowd out before, but only 2 of 6 after. Declining significance levels associated with modifying

20

DOES M1 OR TOTAL LOANABLE FUNDS MORE ACCURATELY …

437

the deficit variables also results in declining R 2 . This suggests the M1 modifier was creating an “errors in variables” problem for the deficit variables, not reducing crowd out, i.e., that M1 does not affect investment, and just distorts the values of variables that do when used to modify them. (M1 + Tr + A): Model: Adding the (M1 + Tr + A) variable to the baseline model decreased R 2 in 5 of the 6 models tested, and left it unchanged in one. The average drop in R 2 was substantial: 3.0%. For tax cut deficits, model: 4 of 6 (T) significant before and 1 of 6 (T ) after modification, the same as when using just M1 alone as the modifier. For spending deficits, Six of six (G) show significant crowd out before, but only 1 of 6 after (compared to 2 of 6 using M1 alone). Overall, the results for the M1 only and (M1 + TR + A) model are very similar, indicating adding FR purchases to M1 over the periods studied did not have much of an effect on investment. In Table 20.5, R 2 s for the (S + FB) model were higher in all six periods tested than for the other three modifier models tested, and indicate it more accurately defines which variable best describes the extent to which crowd out effects can be modified.

20.3

Comparing Model Results with (Table 20.5) and Without (Table 20.4) GDP Control

Adding the business cycle control variable generally increased explained variance, hence its addition helps us better understand the correlates of changes in investment. For example, in the models using total loanable funds as the deficit modifier, adding the GDP control variable increased R 2 in 8 of 12 tests, left it unchanged in 3 others. Overall, two general findings stand out when comparing models with and without a GDP control variable: 1. The total loanable funds deficit modifier (S + FB) seemed by far the best at explaining how growth in a loanable funds-related factor can offset the negative effects of crowd out. R 2 s were higher in all tests than for the other three models. The M1 modifier showed no positive relationship to investment. 2. About half of all deficit-induced crowd out of investment spending is due to endogenous factors related to the business cycle. The other

438

J. J. HEIM

half appear related to exogenous actions undertaken by government to either increase spending or reduce government revenues. Table 32.4 shows the combined effects of both. Table 32.5, by controlling for GDP, shows the exogenous changes only. For tax deficits, models without the GDP control variable showed significant crowd out after modification 22 out of 24 possible times (4 models, 6 time periods). With the GDP control variable for the state of the economy, only 8 of 24 showed significant crowd out remaining, i.e., crowd out caused by policy decisions to deficit. For spending deficits, models without the GDP control variable showed significant crowd out after modification 12 out of 24 possible times (4 models, 6 time periods). With the GDP control variable for the state of the economy, only 11 of 24 showed significant remaining crowd out, i.e., crowd out caused by policy decisions to deficit. Finally, we note that consumption and investment results for the four modifiers, when compared against each other, were about the same when Table 20.1 was compared with 20.4 or 5. For consumption using the same four models, R 2 s and t-statistics on (S + FB) models remained virtually identical when M1 was added to the (S + FB) modifier and stand alone, suggesting M1 added nothing to the (S + FB) modifier. When M1 alone was used as modifier, R 2 s and t’s dropped markedly. When (M1 + Tr + A) as added to the baseline model, R 2 only increased in 1 of 6 cases, and then only by one point, but significance levels increased relative to those on the stand-alone (M1) modifier.

20.4

Summary of Chapter 20 Results

The 4 tables and notes below summarize our findings for Chapter 20 consumption and investment models (Table 20.6): For the six periods tested: 1. Baseline standard model (no deficit or loanable funds variables included): Average R 2 = 68.3% 2. Baseline standard model with deficit variables added: average R 2 increases to 88.7%, an increase of 30%, clearly indicating consumption cannot be explained without allowing for significant negative crowd out effects.

From Table#

b

T20.1

T20.1

T18.1

Eq.18.1A

87

72

87

72

91

86

91

91

88

88

89

89

88

89

91

91

89

89

87

87

91

91

(Av. R 2 = 89.4% for 18 samples; 88.7% for 6 used below)

87

(Av. R 2 = 71.4%)

60

88

87

89

90

89

43

93

91

92

91

87 (Av. R 2 = 88.8%; Adj.R 2 = 85.6%)

87 (Av. R 2 = 88.7%; Adj.R 2 = 85.5%)

92 (Av. R 2 = 89.5%; Adj.R 2 = 86.5%)

86

68

1970–1990 1970–2000 1970–2007

94 (Av. R 2 = 90.2%; Adj.R 2 = 86.5%)

91

77

1960–2010 1960–2008 1960–2007 1960–2000 1960–1990 1960–1980 1970–2010

88

55

94

86

1980–2000

85

37

1980–2010

88

63

88

74









86

67

87

65

92

83

NA

G

5/5b ) (5/5

6/6

6/6

2/6

6/6

a

a

1/6

1/6

b

2/6

2/6

a

5/6

5/6

6/6

6/6

5/6

6/6

a

5/6

5/11

a

a

6/18

NAa

NA

10/11

99 15/18

6/6

Test ratio

T 95 NA

1975 –2004 1980–2004 1985 –2004 1985 –2005 1996 –2009 2000 –2010

a 7 samples containing 1/3–1/2 of all observations from “crowd in” years Removed, leaving 11 of 18 b Results for modified and unmodified models were the same for R 2 , coefficients and t-statistics on all variables except the “stand alone” variable hypothesized as an offset. Hence, only one set of deficit variable and R 2 results for each model are shown above

Modifierb

Modifierb 20 Modified (w/s-a) T20.1 (M1) + (Tr + A)

Modifierb 20 Modified (w/s-a) (M1)

Modifier) 20 Modified (w/s-a) (S + FB+ M1)

18 Modified (w/s-a) (S + FB

18 Baseline (w/Def)

18 baseline T18.1AA (wo/Def)

Model

Table 20.6 Chapter 20 Consumption Summary Table 1 (S + FB), (S + FB + M1), M1, and (M1 + FR purchases) deficit modifier and stand-alone models

20 DOES M1 OR TOTAL LOANABLE FUNDS MORE ACCURATELY …

439

440

J. J. HEIM

3. When the stand-alone loanable funds modifier (S + FB) is added to the standard model with deficits, R 2 increases from (88.7%) to (90.2%), an increase of (1.5%). As noted in earlier chapters, this suggests the true effect of crowd out is better stated as the deficit net of any changes in total loanable funds that occur in the same period. 4. Only one of the other three modifiers (S + FB + M1) explains noticeably more variance (R 2 = 89.5) than the standard model with deficit variables (88.7%), and that is less of an increase than obtained using the fourth modifier (S + FB), alone. This suggests that adding M1 to (S + FB) just creates an errors in variables problem. It would appear that the effects of a change in M1 are captured when modeling total loanable funds or that it simply is not a significant determinant of consumption. 5. When using the M1 + FR Purchases modifier (M1 + Tr + A), R 2 increases from (88.7%) to (88.8%) over the standard model with deficit variables but without any crowd out offset variable. Essentially, it does not seem that increases in M1 and FR purchases have been effective in offsetting crowd out in the past. 6. Even less successful was using M1 alone as the offset. Adding this variable left R 2 unchanged at the same level (88.7%) as prevailed in the model before any hypothesized crowd out offsets were added. 7. In addition, both the M1 and M1 plus FR purchases model result in most spending deficits to move from statistically significant 5 of 6 in the (S + FB) and (S + FB + M1) models to statistically insignificant in the M1 and (M1 + TR + A) models, contrary to our theoretical expectations, probably due to multicollinearity or “errors in variables” effects. For this reason also, these models seem inappropriate as explanations of what, if anything, serves to offset the crowd out effects of deficits (Table 20.7). For the six periods tested: 1. Baseline standard model (no deficit or loanable funds variables included): Average R 2 = 68.3%

T18.1AA

Eq.18.1A

18 Baseline (w/Def)

84

89

88

91

88

83

84

87

87

88

82

83

87

87

83

87

72

87

(Av. R 2 = 89.4%)

87

(Av. R 2 = 71.4%)

60

1960–2010 1960–2008

89

91

84

87

83

87

82

87

87

72

1960–2007

83

87

89

91

90

91

90

91

91

86

83

87

88

89

87

89

87

89

89

43

86

67

87

65

92

83

(5/5

6/6

10/11

99 15/18

NA

95 NA

6/6 6/6

6/6 6/6

– –

– –

90 (Av. R = 88.5%; Adj. = 84.8%) 88 (Av. R 2 = 85.8%; Adj. = 81.8%)

87 (Av. R 2 = 88.7% Adj. = 86.2%) 82 (Av. R 2 = 85.5% Adj. = 81.8%)

2

6/6

6/6

88

74

Test ratio

T

6/6

88

63

1985 –2004 1985 –2005 1996 –2009 2000 –2010



85

37

1980–2004



94

86

1975 –2004

86 (Av. R 2 = 87.2%; Adj. = 80.2%)

88

55

1980–2000 1980–2010

90 (Av. R 2 = 89.5%; Adj. = 85.3%)

1970–2010



86

68

1970–2007



92

91

1970–2000

86 86 (Av. R 2 = 85.2%; Adj. = 79.8%)

93

91

1970–1990

91 (Av. R 2 = 88.7%; Adj. = 84.8%)

91

77

1960–2000 1960–1990 1960–1980

a 7 samples containing 1/3–1/2 of all observations from “crowd in” years Removed, leaving 11 of 18 b No samples containing “crowd in” decade (1990–1999) data were removed, so no separate results shown

(M1) Modifier 20 Unmodified T20.2 (wo/s-a) 20 Modified T20.2 (wo/s-a) (M1 + Tr + A) Modifier 20 Unmodified T20.2 (wo/s-a) 20 Modified T20.2 (wo/s-a)

(S + FB Modifier) 18 Unmodified T18.9 (wo/s-a) 18 Modified T18.9 (wo/s-a) (S + FB + M1) Modifier 20 Unmodified T20.2 (wo/s-a) 20 Modified T20.2 (wo/s-a)

From Table#

Model

18 Baseline (wo/Def)

1/6

2/6

4/6

0/6

0/6

4/6

5/5b )

5/11

a

a

6/18

NAa

NA

G

4/6

5/6

Table 20.7 Chapter 20 Consumption Summary Table 2 (S + FB), (S + FB + M1), M1, and (M1 + FR purchases) deficit modifier (no stand-alone offset) models

20 DOES M1 OR TOTAL LOANABLE FUNDS MORE ACCURATELY …

441

442

J. J. HEIM

2. Baseline standard model with deficit variables added: average R 2 increases to 88.7%, an increase of 30%, clearly indicating consumption cannot be explained without allowing for significant negative crowd out effects. 3. When the loanable funds (S + FB) variable is added as a deficit modifier to the standard model with deficits, R 2 decreases from (88.7%) to (85.2%), a decrease of (3.5%). As noted in earlier chapters, this suggests that the better formulation is the one which also has a stand-alone loanable funds variable of the same type, which resulted in an increase in R 2 to 90.2% when (S + B) was added. 4. When the (S + FB + M1) is added as a deficit modifier to the standard model with deficits, R 2 decreases from (88.7%) to (87.2%), a decrease of (1.5%), again indicating the model with a stand alone, which saw an increase in R 2 to 89.5% is the better consumption model (though not as good as (S + FB) model). 5. When using the M1 + FR Purchases modifier (M1 + Tr + A), R 2 decreases from (88.5%) to (85.8%) unmodified version. Again, when a stand-alone variable of the same type was included, R 2 was higher (88.7%), but still not as high as the comparable (S + FB) model. 6. When using M1 alone as the offset, i2 slightly increased using the deficit modifier from 85.5 to 85.8%, but still was 3% below the same model including a stand-alone M1 variable as well. 7. In addition, in all four models, adding the deficit modifier without a stand-alone modifier resulted in a noticeable increase in deficit variables that were significant before modification, becoming statistically insignificant. As we have noted before, theoretically, this should not happen in a good modifier model (Table 20.8). For the six periods tested: 1. Baseline standard model (no deficit or loanable funds variables included): Average R 2 = 77.7% 2. Baseline standard model with deficit variables added: average R 2 increases to 88.3%, an increase of 14%, clearly indicating consumption cannot be explained without allowing for significant negative crowd out effects. 3. When the stand-alone loanable funds modifier (S + FB) is added to the standard model with deficits, R 2 increases from (89.8%) to

67

b

T20.3

T20.3

T18.10

89

80

63

89

89

89

91

86

86

87

88

82

82

86

88

86

86

88

90

84

84

87

92

87

78

65

−61

90

82

90

81

56

92 (Av. R 2 = 88.7%; Adj. = 86.0%)

91 (Av. R 2 = 86.3%; Adj. = 86.2%)

95 (Av. R 2 = 88.7%; Adj. = 85.8%)

86

71

65

1970–2007

98 (Av. R 2 = 91.2%; Adj.R 2 = 87.5%)

95

91

72

1970–2000

90

76

72

89

81

69

90

80

77

−64

89

80









89

81

71

89

75

63

89

75

57

98

93

92

T

G

a

1/6

a

5/6

a

6/6

a

6/6

6/6

6/6

6/6

6/6

6/6

6/6

6/6

6/6

5/6

5/6

5/6

1/6

16/18

9/11

8/11

NAa

NA

NAa

NA

98 11/18

NA

95 NA

NA

91 NA

1970–2010 1980–2000 1980–2010 1975 –2004 1980–2004 1985 –2004 1985 –2005 1996 –2009 2000 –2010

a 7 samples containing 1/3–1/2 of all observations from “crowd in” years Removed, leaving 11 of 18 b Results for modified and unmodified models were the same for R 2 , coefficients and t-statistics on all variables except the “stand alone” variable hypothesized as an offset. Hence only one set of deficit variable and R 2 results for each model are shown above

Modifierb

Modifierb 20 Modified T20.3 (w/s-a) (M1) + (Tr + A)

Modifierb 20 Modified (w/s-a) (M1)

Modifier) 20 Modified (w/s-a) (S + FB + M1)

18 Modified (w/s-a) (S + FB

84

71

66

(Av. R 2 = 89.8% for 18 Samples; Adj. Av. R 2 = 86.3) (88.3% for 6 samples used below. Significant: T = 6/6, G = 6/6)

(includes GDP Control Variable) (Av. R 2 = 79.8%) 18 89 86 Baseline Eq. 18.10A (w/Def)

(Does not include GDP Control Variable) (Av. R 2 = 68.3%) 17 76 70 Baseline T17.3B (w/o Def)

69

1970–1990

T17.3C

1960–1980

Test ratio

1960–1990

17 Baseline (w/o Def)

1960–2000

Sigif./total

1960–2007

From Table#

1960–2010 1960–2008

R 2 (18 time periods)

Model

Table 20.8 Chapter 20 Investment Summary Table 3 (S + FB), (S + FB + M1), M1, and (M1 + FR Purchases) Deficit Modifier and Stand-Alone Models

20 DOES M1 OR TOTAL LOANABLE FUNDS MORE ACCURATELY …

443

444

J. J. HEIM

(91.2%), an increase of (1.5%). As noted in earlier chapters, this suggests the true effect of crowd out is better stated as the deficit net of any changes in total loanable funds that occur in the same period. 4. Only one of the other three modifiers (S + FB + M1) explains more variance (R 2 = 88.7) than the standard model with deficit variables (88.3%), and that is less of an increase than obtained using. (S + FB) alone, suggesting that adding M1 to (S + FB) detracts from the explanatory power of the (S + FB) model, i.e., mostly, it just creates an errors in variables problem. It would appear that the effects of a change in M1 are captured when modeling total loanable funds variable itself. 5. When using the M1 + FR Purchases modifier (M1 + Tr + A), R 2 decreases from (88.3%) to (86.5%) compared to the standard model with deficit variables (but without any crowd out offset variable). Essentially, adding M1 + (Tr + A) detracts from the explanatory power of the (S + FB) model or even the deficit model itself. 6. Even less successful was using M1 alone as the offset. Adding this variable caused R 2 to decline to (85.3%), which is markedly lower than prevailed in the deficit model (88.3%) before any hypothesized crowd out offsets were added. 7. There is one problem complicating the interpretation of the results: deficits vary with the phase of the business cycle, as do consumption and investment; so growing deficits are negatively correlated to private spending, and sometimes it is just the business cycle driving the relationship, not intentional deficit creation as a stimulus measure (Table 20.9). For the six periods tested: 1. Baseline standard model (no deficit or loanable funds variables included): Average R 2 = 77.7% 2. Baseline standard model with deficit variables added: average R 2 increases to 88.3%, an increase of 14%, clearly indicating consumption cannot be explained without allowing for significant negative crowd out effects. 3. When the loanable funds (S + FB) variable is added as a deficit modifier to the standard model with deficits (but no stand-alone

67

89

80

63

86

87

79

84

79

75

79

78

89

90

84

87

84

81

84

80

77

77

77

77

87

77

88

84

82

88

82

88

89

88

90

89

(Av. R 2 = 91.2% for 18 samples; 88.3% for 6 used below)

84

71

66

82

88

82

88

88

88

91

87

87

78

65





95 (Av. R 2 = 85.2%; Adj. = 82.8%)



– –

94 (Av. R = 81.8%; Adj. = 71.6%)

95 (Av. R 2 = 85.2%; Adj. = 82.8%) 94 (Av. R 2 = 82.2%: Adj. = 72.3%)

2



90

80

96 (Av. R 2 = 88.5%; Adj. = 84.5%)

89

81

77

95 (Av. R 2 = 85.2%; Adj. = 82.8%)

90

76

69



86

71

72



90

81

6565

97 (Av. R 2 = 90.5%; Adj. = 84.3%)

90

82

56

1975–2004

95 (Av. R 2 = 88.3%; Adj. = 82.8%)

95

91

72 −61

1980–2010

89

80

−64

89

81

71

89

75

63

89

75

57

98

93

92

T

1/6

4/6

5/6

4/6

1/6

4/6

1/6

4/6

6/6

4/6

6/6

1/6

6/6

2/6

6/6

9/11

8/11

5/6

16/18

NAa

NA

NAa

NA

G

98 11/18

NA

95 NA

NA

91 NA

1980–2004 1985–2004 1985–2005 1996–2009 2000–2010

a 7 samples containing 1/3–1/2 of all observations from “crowd in” years Removed, leaving 11 of 18 b No samples containing “crowd in” decade (1990–1999) data were removed, so no separate results shown

(S + FB Modifier) 18 Unmodifie T18.11 d (wo/s-a) 18 Modified T18.11 (wo/s-a) (S + FB + M1) Modifier 20Unmodif ied (wo/s- T20.4 a) 20 Modified T20.4 (wo/s-a) (M1) Modifier 20 Unmodifie T20.4 d (wo/s-a 20 Modified T20.4 (wo/s-a) (M1 + Tr + A) Modifier 20 Unmodifie T20.4 d (wo/s-a) 20 Modified T20.4 (wo/s-a)

(includes GDP Control Variable) (Av. R 2 = 79.8%) 18 89 86 Baseline Eq.18.10A (w/Def)

(Does not include GDP Control Variable) (Av. R 2 = 68.3%) 17 Baseline T17.3B 76 70 (w/o Def)

69

1980–2000

T17.3C

1970–2010

17 Baseline (w/o Def)

1970–1990 1970–2000 1970–2007

Sigif./total 1960–1980

Test ratio

1960–2007 1960–2000 1960–1990

From Table#

1960–2010 1960–2008

R 2 (18 time periods)

Model

Table 20.9 Chapter 20 Investment Summary Table 4 (S + FB), (S + FB + M1), M1, and (M1 + FR Purchases) Deficit Modifiers; (No Stand Alone, GDP Added) Models

20 DOES M1 OR TOTAL LOANABLE FUNDS MORE ACCURATELY …

445

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variable), R 2 increases from (88.3%) to (90.5%), a decrease of (2.2%). This is a slightly better result than obtained with the same model, but including a stand-alone (S + FB) variable (90.3%). 4. When the (S + FB + M1) is added as a deficit modifier to the standard model with deficits, R 2 increases from (85.2%) to (88.5%), a increase of (3.3%), but still less than the (S + FB) only model. The same model with a stand alone saw an increase in R 2 to 88.7%, a slightly better result (but including a stand alone without theoretical justification). 5. When using the M1 + FR Purchases modifier (M1 + Tr + A), R 2 decreases from (85.1%) in its unmodified version to (82.3%) in the modified version, both less than the 86.3% achieved when also including a stand-alone modifier of the same type, but worse than obtained in either of the two models above using the (S + FB) modifier. 6. When using M1 alone as the offset, R 2 decreased using the deficit modifier from 85.1 to 82.2%, but still was 3% below the same model including a stand-alone M1 variable as well. 7. Essentially, adding one of the two modifiers including total loanable funds (S + FB), increased the model’s explanatory power, indicating changes in (S + FB) can offset crowd out. For the two models with an M1 component in the modifier, but no (S + FB) component, adding the M1-based modifier to the deficit variables reduced the models’ ability to explain variance, i.e., ceteris paribus, changes in M1 do not affect the level of total investment. 8. In addition, the (S + FB) model had the highest number of significant deficit variable(crowd out) effects after modification in the six time periods tested, which was the expected finding. As we have noted before, theoretically, this should happen in a good modifier model.

Reference Heim, J. J. (2017). An Econometric Model of the U.S. Economy. Hoboken: Palgrave Macmillan.

PART VIII

Non-Black Box Models: Structural Mechanisms Through Which Loanable Funds Affects Consumption and Investment

CHAPTER 21

Do Consumer Borrowing, Inflation, and Prime Interest Rate Increase When M1 Is Increased?

Chapter 11.4 above examined the effect of changes in M1 on residential housing investment and consumer services spending. This was done by adding M1 to the existing structural equations for housing investment and consumer services spending in a modified “St. Louis equation” way. The original St. Louis equation was simply GDP = ƒ(M1). A modified St. Louis equation, as defined here, includes a few other control variables in the model when examining the impact of M1 on GDP, consumption or investment, but does not specify the mechanism that connects M1 to GDP’s components. M1 was inserted into the housing and consumer services equations without specifying the structural economic mechanism through which M1 came to affect these variables. In this sense, it is a modified St. Louis equation model. If M1 affects housing and consumer services variables, it is by affecting one or more of their known structural determinants of consumption, e.g., changes in M1 might affect the prime interest rate, which may in turn affect demand for housing or consumer services. In this chapter, we shall test for these underlying mechanisms, i.e., structural relationships which connect M1 to housing investment and consumer services spending. The equations explaining how these determinants are formed should indicate which of them are determined in part by M1. Once these equations are identified, the M1 variable will be replaced by its two © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. J. Heim, Why Fiscal Stimulus Programs Fail, Volume 1, https://doi.org/10.1007/978-3-030-65675-1_21

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components, the exogenously determined part (Tr + A), and the endogenously determined part (M1−(Tr + A)). Then the equations explaining one or more of consumption and investment’s determinants will be retested to see if it was the exogenous FR purchases component, or the endogenous revised M1 component that was most responsible for the effect that M1 had on the equation’s dependent variable. Results of this investigation are presented in this chapter. In the IS equation in Heim (2017), a 56 equation structural econometric model of the U.S. economy, there are only 3 equation which include either current or lagged values of M1 as one of their determinants: 1. the consumer borrowing equation, (Eq. 4.6.TR), 2. the inflation equation (Eq. 11.1.TR), and 3. the prime interest rate determination equation (Eq. 9.2.TR, 10.2). Each is considered in the following three sections, 21.1–21.3 of this paper. Each equation from Heim (2017) has been tested for stationarity, endogeneity, and Newey-West standard error corrections were used to avoid heteroskedasticity errors. Each was estimated in first differences to help avoid serial correlation problems and minimize multicollinearity problems. Any further changes needed to avoid these problems when replacing the single M1 variable with its FR purchases and endogenous M1 components will be discussed as these changes occur in the sections below.

The Consumer Borrowing Equation

21.1

The consumer borrowing equation (Eq. 4.6.TR in Heim 2017) is reproduced below. All variables are given in first differences. CBor(0) = .44Y0 − TTot(0) + .47TDef(0) − 46G Def(0) − 14.62PR0 − 1.37DJ−1 (t=)

(4.2)

(4.2)

(2.0)

(−2.2)

(−3.2)

 + 19.84 XRAV(0) − .07 PerSAV0−9 + (M2 − M1)0−3 (−3.0)

R = .52 2

(3.8)

(−2.7)

(4.6.TR)

We do not have a model showing how M2 is related to growth in M1, but over the 1960–2010 period M2 averaged 4.15 times M1’s average value. We shall use that to estimate the growth in (M2−M1) = (4.15M1−M1)

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or CBor(0) = −.07 (M2 − M1)0−3

(21.1)

CBor(0) = −.07(M2 − M1)0−3    = −.07 M2 −  (Tr + A) + (M1 − (TR + A)) 0−3   = −.07 M20−3 − (Tr + A)0−3 − (M1 − (TR + A))0−3    CBor(0) = −.07  M20−3 − (Tr + A)0−3 − (M1 − (TR + A))0−3 (21.2)

Or CBor(0) = −.07(M2 − M1)0−3 CBor(0) = −.07[( M2 −  M1)]0−3

(21.3)

  CBor(0) = −.07 ( M2)0−3 − ( M1)0−3   = −.07 ( M2)0−3 − (((Tr + A) + (M1 − (TR + A)))0−3   CBor(0) = −.07 ( M2)0−3 − ((Tr + A))0−3 − ((M1 − (TR + A)) 0−3 (21.4)

To test the effects of M1 and its components (Tr + A) and (M1−Tr + A)) on consumer borrowing, we shall use the second formulation of Eq. 21.3 and the formulation of Eq. 21.4. Because of data limitations, the “real” models tested could only be tested on 1960–2007 data. Four nominal M1 samples from within this period were tested: 1960–1980, 1960–90, 1960–2000, and  1960–2007  The relevant variable Sper−(1−9) + (M2 − M1)0−3 used in the Heim 2017 equation describing the determinants of consumer borrowing, when retested for this study, was significant in all four time periods  variable was broken into two parts Sp−1−9  tested. When that and (M2 − M1)0−3 , the personal savings variable was significant in all tests, but the savings component of M2 variable (approximated as 3.15M1) was significant only in the 1960–2007 sample, and marginally significant in the 1960–2000 sample. It was insignificant in the earlier two samples. Hence, we have mixed results as to whether total M1 affects consumer borrowing.

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Results for the model used to test effects of the savings components of real M2, i.e., real (M2−M1), are given in Eq. 21.4a, tested using data for 1960–2007 (data is real and in first differences). CBor(0) = .44Y0 −TTot(0) + .55 TDef(0) − .50 G Def(0) − 15.22 PR0 (2.3)

(3.1)

(t=)

(−2.3)

(−3.0)



− 1.55 DJ−1 + 20.44 XRAV(0) − 20 PerSAV0−9 (−2.9)

(3.7)



(−2.5)

− .07 (M2)0−3 + .03 (M1)0−3 (−1.8)

(0.3)

R = .54

(21.4a)

2

The same model for the same test period, using the two components of real  M1, real (Tr + A) and real  (M1−Tr + A), is given in Eq. 21.4b:   CBor(0) = .48 Y0 − TTot(0) + .57TDef(0) − .53G Def(0) (t=)

(2.4)

(2.3)

(−2.4)

− 16.24PR0 − 1.59DJ−1 + 21.57XRAV(0) (−3.0)

(−2.8)

(3.9)





− .23 PerSAV0−9 −.06(M2)0−3 (−2.1)

(−0.4)

− .12(Tr + A)0−3 + 06(M1 − Tr − A)0−3 (−0.2)

R = .54 2

(0.4)

(21.4b)

Tests of real M1,  (Tr + A) and (M1 − (Tr + A)) as stand-alone variables   When (M2 − M1)0−3 was split into two parts (M2)0−3 and (M1)0−3 and the model retested, both real and nominal M1 (and both parts of M1) were found to have a statistically insignificant effect on consumer borrowing in all 3 samples tested (1960–2007, 1960–2000, 1970–2000). This was somewhat surprising, since the records consistently indicate almost all bank excess reserves were loaned out by year’s end in each year, including recession years, during the 1960–2007 period. The process of lending excess reserves out, commonly (though not always) results in increases in M1, which increases further through the money multiplier process. During the 1960–2007 period, consistently low excess reserve

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levels suggest demand for loans just about equaled supply, and may have exceeded it, if we interpret the low level as a precautionary liquidity cushion maintained by banks. Since some small amount of excess reserves has to be kept on hand at banks for precautionary purposes, banks having small amounts of excess reserves would not necessarily mean demand is less than supply. However, this refers to loan demand from all sources, not just consumers. Chapter 22 results suggest that the effect on consumer borrowing is not statistically significant because most increases in loanable funds are systematically channeled into lending to the business community, not to consumers. Changes in M1 resulting from changes in total bank reserves will vary due to money multiplier fluctuations related to changing economic conditions. So a change in the pool of loanable funds, defined as (S Total + Foreign Borrowing), is not necessarily translated into the same size change in M1 each time the pool increases. That said, the assumption of a reliable positive relationship of M1 to bank reserves is the essence of why “accommodating” monetary policy is thought to work, and the assumption is that most if not all increases in national savings end up initially deposited in bank accounts before further disposition. As we noted earlier, demand for bank loans appears typically equal to or in excess of the supply of loanable funds. Provided this trend continues, it should guarantee that any increase in bank reserves due to FR open market operations should end up increasing borrowing. Disposition of borrowed funds may: 1. increase demand for products in the GDP, though not necessarily consumer products. Or: 2. the increase in bank reserves are not lent out to consumers desiring to buy additional real goods and services, but instead are lent to those desiring to buy securities (or not lent out), then we would expect no change in consumer borrowing, and our statistical finding would be explained. 3. Other evidence presented in earlier chapters supports this notion. An additional factor which could disrupt the FR purchases/M1 relationship would be the situation where proceeds received from selling securities to the FR are deposited in non-M1 parts of M2, such as savings accounts or CDs. This also can disrupt the expected relationship, i.e., (M1) × (the M1 money multiplier).

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By comparison, the nominal M2 variable was significant in all three periods. It had a negative sign, indicating that as the savings components of M2 increased, the need for consumer borrowing decreased. When only the savings component of M2 was tested i.e., (M2 − M1)0−3 results were the same: the sign was negative, and results were significant in three of the four periods tested. However, when tested in real terms, real M2, like real and nominal M1, was found insignificant in all four periods tested.  When (M2 − M1)0−3 was split into three parts (M2)0−3 , (Tr + A)0−3 , and (M1 − (Tr + A))0−3 and the model retested, real and nominal (Tr + A) was statistically insignificant in 2 of the 3 samples. Real and nominal (M1 − Tr + A) was insignificant in all 3 samples. Results for the 1960–2000 sample are shown in Eq. 12.4b for real values of the M1 and (Tr + A) data, and below in Eq. 35.4.c for M1 and (Tr + A) in nominal values. (Models estimated in first differences):   CBor(0) = .64 Y0 − TTot(0) + .82TDef(0) − .71G Def(0) (t=)

(4.7)

(5.2)

(−6.2)

− 19.44PR0 − .81DJ−1 + 14.90XRAV0 (−5.2)



(3.5)

(−0.9)



− 17 PerSAV0−9 − .08(M2)0−3 (−1.7)

(−2.1)

− 1.14(Tr + A)0−3 + .27(M1 − Tr − A)0−3 (−2.3)

R = .63 2

(1.2)

(21.4c)

Conclude: When tested as a stand-alone variable, M1, in either real or nominal form, was insignificant in all tests of consumer borrowing. Also, in all samples tested, when M1 was divided into (Tr + A) and (M1−(Tr + A)), all estimates of (M1−TR + A), both real and nominal, were statistically insignificant. In both nominal and real tests, (Tr + A) was significant (but negatively so) in the 2 of 3 samples. Hence, the effects on consumer borrowing of growth in M1 and endogenous M1 seem to be nil. Another explanation is the connection between the money supply and the loanable funds pool. In Chapter 23, we find that changes in the pool of loanable funds are systematically related to changes in M1, but only explains about 1/3 of the variation.

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The savings component of (nominal, but not real) M2 was found consistently found negatively related to consumer borrowing, as would be expected: when savings goes up, the need for borrowing declines. Business borrowing’s relationship to total M1 was also tested. In 6 of 9 periods tested, covering the period 1960–2005, M1 was found significantly related to business borrowing. Only in the three samples containing all or part of the 2007–2010 data was M1 an insignificant determinant of business borrowing.

21.2

The Inflation Equation

The Phillips curve model (Eq. 11.1.TR) in Heim (2017) provides a model of inflation’s determinants. The model was also reestimated and results are repeated below in Eq. 21.5A. Test results for the 1960–2010 period are shown in both cases and are very similar.     (inf l) = −2.20 UnemAv(0 and −1) + .009 M1Real(−2) (t=)

(−10.0)

(4.7)

  −135.67((M − X )/Y )Real AV(0,−1) + 13.12  ForBor−1 /Inv−1 Real (5.7)

(−2.8)

  −46.46  Gross Sav−1 /Y−1 Real + 2.73 (OPEC73&78 Shock) (11.0)

(−5.1)

+ .52Ar(2) (3.5)

R 2 = .78;

(11.1.TR)

DW = 1.7

And the reestimated model shows nearly identical results:     (infl) = −2.13 UnemAV(0 and −1) + .009 M1Real(−2) (t=)

(−8.8)

(2.9)

  −128.59((M − X )/Y )Real AV(0,−1) + 12.80  ForBor−1 /Inv−1 Real 

(−3.5)



(3.0)

− 44.68  Gross Sav−1 /Y−1 Real + 2.75 (OPEC73&78 Shock) (−2.7) (4.3) + .51Ar(2) (3.7)

R 2 = .76;

DW = 1.6

(21.5A)

In both cases, increases in the real M1 money supply, lagged two years, were found to have a significant positive effect on inflation.

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Replacing the single variable (M1−2 ) with its two component parts (Tr + A)−2 and ((M1−(Tr + A))−2 ) allows use to test to see if it is the FR purchases portion, or the endogenous portion of M1 that are most related to inflation. To illustrate the findings of this model, results from testing the 1960–2010 sample are presented in Eq. 21.5.    (inf l) = −.211 UnemAv(0 and −1) + .011 (Tr + A)Real(−2) (t=)

(−8.2)

(1.5)

+ 009(((M1 − Tr + A)) Real(−2) − 129.75((M − X )/Y )Real AV(0,−1) (3.2)

(−3.1)

+ 13.55 (ForBor−1 /Inv−1 )Real − 44.21 (Gross Sav−1 /Y−1 )Real (3.0)

(−2.6)

+ 2.73 (OPEC73&78 Shock) + 52Ar(2) (11.0)

R = .76.5; 2

(3.5)

DW = 1.6

(21.5)

The seven sample periods used in Chapter 11 covering various parts of the period 1960–2010 were tested. The one-variable M1−2 definition was found to have a statistically significant positive relationship with inflation in five of the seven data samples; it was insignificant only in the two oldest: 1960–1980 and 1960–1990. Breaking M1 into its two component parts, FR purchases and (M1−FR) purchases. The FR purchases variable was only significant in 1 of 7 samples. By comparison, the endogenous M1 component (M1 − FR purchases) was significant in the five most recent periods sampled. It was insignificant only in the two oldest: 1960–1980 and 1960–1990. Conclusion: Overall, the evidence strongly suggests increases in the M1 money supply affect inflation, but that it is the economy-driven endogenous portion of growth that affects inflation, not the FR purchases portion. That said, when through 2012, or through 2012 and 2013, data was added to the sample, the FR purchases variable, lagged two periods, became a highly statistically significant factor, and positively related to inflation. This suggests the QE program’s high level of FR purchases did have a significant positive impact on inflation two years later, ceteris paribus. Since the inflation rate did not actually increase noticeably during 2012 and 2013, other factors in the ceteris paribus condition (like

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growing trade deficits, which are deflationary) must have had an offsetting effect. In fact, we have shown elsewhere that this is exactly what happened. In Chapter 23 below, we test a slightly different formulation. There we look at which of the two parts of the loanable funds pool (S + FB)−(Tr + A), or (TR + A) is most systematically related to the growth in M1. We f