AI and Jobs: Evidence from Online Vacancies

By Daron Acemoglu, P. Restrepo. 2020

160 citations recorded by the source corpus.

We study the impact of AI on labor markets, using establishment level data on vacancies with detailed occupational 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. AI-exposed establishments are differentially eliminating 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 even as they expand AI hiring. However, we find no discernible impact of AI exposure on employment or wages 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.

Subfields: Economics and Econometrics, Sociology and Political Science, Economics & Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Sociology and Political Science

In Economics & Operations Research

Citations in the corpus

GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models (2023, background): “Their far-reaching consequences, which unfold over decades, are diﬃcult to anticipate, particularly in relation to labor demand (Bessen, 2018; Korinek and Stiglitz, 2018; Acemoglu et al., 2020; Benzell et al., 2021).”
New Frontiers: The Origins and Content of New Work, 1940–2018 (2022, background): “1On job polarization, see Autor et al. (2003), Autor et al. (2006), Goos and Manning (2007), Goos et al.”
Artificial intelligence, firm growth, and product innovation (2024, methodology): “Following Autor et al. (2020), we use the Herfindahl-Hirschman Index (HHI) to measure industry concentration.”
Digital technologies, innovation, and skills: emerging trajectories and challenges (2021, background): “, they claim that automation helps increase the focal firm’s market share (Acemoglu et al., 2020), and the impact on employment is negative at the industry level.”
Exposure of occupations to technologies of the fourth industrial revolution (2021, methodology): “, 2020; Webb, 2019) which have been used for economic analyses (Mann & Püttmann, 2017; Acemoglu et al., 2020).”
Tasks, Automation, and the Rise in US Wage Inequality (2021, background): “is in line with several studies that document a decline in routine jobs following automation, including Acemoglu and Restrepo (2020) and Humlum (2020).”
Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses (2021, methodology): “We believe our methodology has the potential to be used in a variety of applications within the field of strategy and, as we describe below, can be used to construct AI exposure at the firm level as well (see, e.g., Acemoglu, Autor, Hazell, & Restrepo, 2020).”