The macroeconomics of automation: data, theory, and policy analysis

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

No 340, ECON - Working Papers from Department of Economics - University of Zurich

Abstract: The U.S. economy has experienced a significant drop in the fraction of the population employed in middle wage, “routine task-intensive” occupations. Applying machine learning techniques, we identify characteristics of those who used to be employed in such occupations and show they are now less likely to work in routine occupations. Instead, they are either non-participants in the labor force or working at occupations that tend to occupy the bottom of the wage distribution. We then develop a quantitative, heterogeneous agent, general equilibrium model of labor force participation, occupational choice, and capital investment. This allows us to quantify the role of advancement in automation technology in accounting for these labor market changes. We then use this framework as a laboratory to evaluate various public policies aimed at addressing the disappearance of routine employment and its consequent impacts on inequality.

Keywords: Polarization; automation; routine employment; labor force participation; universal basic income; unemployment insurance; retraining (search for similar items in EconPapers)
JEL-codes: E00 E23 E25 E60 J01 J2 (search for similar items in EconPapers)
Date: 2020-01
New Economics Papers: this item is included in nep-big, nep-ias, nep-lab and nep-mac
References: View references in EconPapers View complete reference list from CitEc
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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Persistent link: https://EconPapers.repec.org/RePEc:zur:econwp:340

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