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AI and jobs: evidence from online vacancies

Acemoglu, Daron, Autor, David, Hazell, Joe and Restrepo, Pascual (2021) AI and jobs: evidence from online vacancies. Journal of Labor Economics. ISSN 0734-306X (In Press)

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Abstract

We study the impact of AI on labor markets using establishment-level data on vacancies with detailed occupation and skill information comprising the near-universe of online vacancies in the US from 2010 onwards. We classify establishments as "AI exposed" when their workers engage in tasks that are compatible with current AI capabilities. We document rapid growth in AI related vacancies over 2010-2018 that is not limited to the professional and business services and information technology sectors and is significantly greater in AI-exposed establishments. These AI-exposed establishments also differentially eliminate vacancy postings that list a range of previously- posted skills while simultaneously posting skill requirements that were not previously listed. Establishment-level estimates suggest that AI-exposed establishments are reducing hiring in non-AI positions. However, we find no discernible relationship between AI exposure and employment or wage growth at the occupation or industry level, implying that AI is currently substituting for humans in a subset of tasks but it is not yet having detectable aggregate labor market consequences.

Item Type: Article
Official URL: https://www.journals.uchicago.edu/toc/jole/current
Additional Information: © 2022 The University of Chicago
Divisions: Economics
Subjects: H Social Sciences > HD Industries. Land use. Labor
JEL classification: J - Labor and Demographic Economics > J2 - Time Allocation, Work Behavior, and Employment Determination and Creation; Human Capital; Retirement > J23 - Employment Determination; Job Creation; Demand for Labor; Self-Employment
O - Economic Development, Technological Change, and Growth > O3 - Technological Change; Research and Development > O33 - Technological Change: Choices and Consequences; Diffusion Processes
Date Deposited: 05 Jan 2022 14:21
Last Modified: 06 Jan 2022 08:48
URI: http://eprints.lse.ac.uk/id/eprint/113325

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