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© 2021 International Monetary Fund
WP/21/187
IMF Working Paper
Fiscal Affairs Department and Institute for Capacity Development
For the Benefit of All: Fiscal Policies and Equity-Efficiency Trade-offs in the Age of Automation
Prepared by Andrew Berg, Lahcen Bounader, Nikolay Gueorguiev, Hiroaki Miyamoto, Kenji Moriyama, Ryota Nakatani, and Luis-Felipe Zanna1
Approved by Vitor Gaspar
July 2021
IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
Abstract
Many studies predict massive job losses and real wage decline as a result of the ongoing widespread automation of production, a trend that may be further aggravated by the COVID-19 crisis. Yet automation is also expected to raise productivity and output. How can we share the gains from automation more widely, for the benefit of all? And what are the attendant equity-efficiency trade-offs? We analyze this issue by considering the effects of fiscal policies that seek to redistribute the gains from automation and address income inequality. We use a dynamic general equilibrium model with monopolistic competition, including a novel specification linking corporate power to automation. While fiscal policy cannot eliminate the classic equity-efficiency trade-offs, it can help improve them, reducing inequality at small or no loss of output. This is particularly so when policy takes advantage of novel, less distortive transmission channels of fiscal policy created by the empirically observed link between corporate market power and automation.
JEL Classification Numbers: E25; H30; O30; O40.
Keywords: Automation; Fiscal Policy; Redistribution; Technological Change; Mark-up.
Authors’ E-Mail Addresses: ABerg@imf.org; LBounader@imf.org; NGueorguiev@imf.org; HMiyamoto@tmu.ac.jp; KMoriyama@imf.org; RNakatani@imf.org; FZanna@imf.org.
Contents
ABSTRACT
I. INTRODUCTION
II. LITERATURE
A. Empirical Studies on the Impact of Automation on Employment and Income Distribution
B. Impact of the COVID-19 Crisis on Employment, Income Distribution, and Market Power
C. Policy Impact to Address the Trade-off of Automation by Quantitative Models
III. THE MODEL AND POLICY PACKAGES
A. The Model
B. Policy Packages
IV. TRANSITION DYNAMICS OF A MODEL ECONOMY WITH POLICY PACKAGES
A. Baseline
B. Tax-and-Redistribute Group of Policy Packages
C. Tax-and-Invest Group
D. Non-Fiscal Measure Group
V. COMPARING WELFARE ACROSS POLICY PACKAGES DURING TRANSITION
A. Income and Inequality
B. Disposable Income of the Two Types of Workers
C. Equally Distributed Equivalent Income (EDEI)
D. Social Welfare Based on Utility Function
VI. ENDOGENOUS MARK-UP—AN ILLUSTRATIVE EXAMPLE
A. Relationship Between Automation and Price Mark-up
B. Impact of Endogenous Mark-up on Production Efficiency: Two Additional Transmission Channels
C. Welfare Implications of the Endogenous Mark-up
VII. SENSITIVITY ANALYSIS
VIII. SUMMARY OF THE RESULTS AND POLICY IMPLICATIONS
REFERENCES
FIGURES
1. Growth Accounting: Baseline
2. Disposable Income Decomposition: Baseline
3. Raising Tax on Capital Income
4. Wealth Tax
5. Consumption Tax
6. Robot Tax
7. Raising Tax on Mark-up
8. Growth Accounting: Lowering Tax on Unskilled Wage
9. Raising Tax on Capital Income for Education Spending
10. Growth Accounting: Rigid Unskilled Wage
11. Exogenous Mark-up: Income and Inequality
12. Exogenous Mark-up: Disposable Incomes
13. Equally Distributed Equivalent Income
14. Social Welfare Function
15. Robot Tax
16. Raising Tax on Mark-up
17. Endogenous Mark-up: Income and Inequality
18. Endogenous Mark-up: Disposable Incomes
19. Equally Distributed Equivalent
20. Social Welfare Function
21. Evolution of Economic Variables in the Sensitivity Analysis
22. Income and Inequality
23. Disposable Incomes
24. Equally Distributed Equivalent Income
25. Social Welfare Function
26. Desirable Policy Packages
Annex
I. Full Description of the Policy Packages
Authors appreciate helpful comments from Vitor Gaspar, Paolo Mauro, Jiro Honda, and other participants in FAD seminars. Paulomi Mehta and Eslem Imamoglu provided excellent research assistance support and Julie Vaselopulos and Melissa Artavia Caravaca provided quality administrative assistance support.