Tasks, Automation, and the Rise in US Wage Inequality

By Daron Acemoglu, P. Restrepo. 2021

433 citations recorded by the source corpus.

We document that between 50% and 70% of changes in the U.S. wage structure over the last four decades are accounted for by relative wage declines of worker groups specialized in routine tasks in industries experiencing rapid automation. We develop a conceptual framework where tasks across industries are allocated to different types of labor and capital. Automation technologies expand the set of tasks performed by capital, displacing certain worker groups from jobs for which they have comparative advantage. This framework yields a simple equation linking wage changes of a demographic group to the task displacement it experiences. We report robust evidence in favor of this relationship and show that regression models incorporating task displacement explain much of the changes in education wage differentials between 1980 and 2016. The negative relationship between wage changes and task displacement is unaffected when we control for changes in market power, deunionization, and other forms of capital deepening and technology unrelated to automation. We also propose a methodology for evaluating the full general equilibrium effects of automation, which incorporate induced changes in industry composition and ripple effects due to task reallocation across different groups. Our quantitative evaluation explains how major changes in wage inequality can go hand‐in‐hand with modest productivity gains.

Subfields: Sociology and Political Science, Natural Language Processing

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Citations in the corpus

Evaluating the Social Impact of Generative AI Systems in Systems and Society (2023, background): “[14] shows how technological advancements have historically increased earning inequality between education, sex, race, and age groups.”
GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models (2023, background): “Several studies have shown that automation technologies have resulted in wage inequality in the US, driven by relative wage declines for workers specializing in routine tasks (Autor et al., 2006; Van Reenen, 2011; Acemoglu and Restrepo, 2022b).”
The Labor Market Impacts of Technological Change: From Unbridled Enthusiasm to Qualified Optimism to Vast Uncertainty (2022, methodology): “Acemoglu and Restrepo (2021) estimate that 50% to 70% of the increase in earnings inequality between education, sex, race, and age groups during 1980 through 2016 — and the entirety of the fall in real wages of men without high school — are due to the adverse effects of automation on worker groups…”
Technology, Vintage-Specific Human Capital, and Labor Displacement: Evidence from Linking Patents with Occupations∗ (2022, background): “…Kearney, 2006; Goos and Manning, 2007; Autor and Dorn, 2013); or the substitution between workers and capital (Krusell et al., 2000; Hornstein, Krusell, and Violante, 2005, 2007; Karabarbounis and Neiman, 2013; Acemoglu and Restrepo, 2021; Caunedo, Jaume, and Keller, 2021; Hemous and Olsen, 2021).”
Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models (2023, background): “Recent works by Agrawal, Gans, and Goldfarb (2019), Webb (2019), Acemoglu et al. (2022), Acemoglu and Restrepo (2022), Babina et al. (2022), Noy and Zhang (2023) have examined the extent of job exposure and vulnerability to AI-related technology as well as the consequences for employment and…”