The Macroeconomics of Automation: Data, Theory, and Policy Analysis

Nir Jaimovich, Itay Saporta-Eksten, Yaniv Yedid-Levi and Henry Siu
Authors registered in the RePEc Author Service: Itay Saporta Eksten

No 14362, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: During the last four decades, the U.S. has experienced a fall in the employment in middle-wage, "routine-task-intensive," occupations. We analyze the characteristics of those who used to be employed in such occupations and show that this type of individual is nowadays more likely to be out of the labor force or working in low-paying occupations. Based on these findings, we develop a quantitative, general equilibrium model, with heterogeneous agents, labor force participation, occupational choice, and investment in physical and automation capital. We first use the model to evaluate the distributional consequences of automation. We find heterogeneity in its impact across different occupations, leading to a significant polarization in welfare. We then use this framework as a laboratory to evaluate various public policies such as retraining, and explicitly redistributive policies that transfer resources from those who benefit from automation to those who bear the brunt of its costs. We assess the tradeoffs between the aggregate impact and welfare distributional consequences of such policies.

Keywords: Polarization; Automation; Routine employment; Labor force participation; Universal basic income; Unemployment insurance; Retraining (search for similar items in EconPapers)
Date: 2020-01
New Economics Papers: this item is included in nep-ias
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Citations: View citations in EconPapers (16)

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Related works:
Journal Article: The macroeconomics of automation: Data, theory, and policy analysis (2021)
Working Paper: The Macroeconomics of Automation: Data, Theory, and Policy Analysis (2020)
Working Paper: The Macroeconomics of Automation: Data, Theory, and Policy Analysis (2020)
Working Paper: The macroeconomics of automation: data, theory, and policy analysis (2020)
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