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Assessing Early Labor Market Effects of Generative AI - Evidence from Population Data

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  • Kauhanen, Antti
  • Rouvinen, Petri

Abstract

This study examines the short-term impact of generative artificial intelligence (GAI) on employment and wages using data covering all wage earners from Finland. Employing a synthetic difference-in-differences approach, we analyze how the launch of ChatGPT affected occupations with varying levels of exposure to GAI. Our findings reveal that wages increased more in highly GAI-exposed occupations compared to less exposed ones following ChatGPT’s introduction. However, we do not observe statistically significant changes in employment levels between more and less exposed occupations. Additional analyses comparing more- and less-exposed occupations within specific occupational groups yield qualitatively similar results. These findings contrast with some previous studies on online labor markets but align more closely with research using nationally representative data. The positive wage effect observed in AI-exposed occupations could indicate that GAI is primarily enhancing rather than replacing human labor. The lack of significant employment effects might suggest that the impact of GAI on job creation or destruction may take longer to materialize or might be offset by other factors in the labor market.

Suggested Citation

  • Kauhanen, Antti & Rouvinen, Petri, 2024. "Assessing Early Labor Market Effects of Generative AI - Evidence from Population Data," ETLA Working Papers 121, The Research Institute of the Finnish Economy.
    • Handle: RePEc:rif:wpaper:121
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    References listed on IDEAS

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    1. Daron Acemoglu & David Autor & Jonathon Hazell & Pascual Restrepo, 2022. "Artificial Intelligence and Jobs: Evidence from Online Vacancies," Journal of Labor Economics, University of Chicago Press, vol. 40(S1), pages 293-340.
    2. Albanesi, Stefania & Dias Da Silva, António & Jimeno, Juan F. & Lamo, Ana & Wabitsch, Alena, 2023. "New technologies and jobs in Europe," Working Paper Series 2831, European Central Bank.
    3. Dmitry Arkhangelsky & Susan Athey & David A. Hirshberg & Guido W. Imbens & Stefan Wager, 2021. "Synthetic Difference-in-Differences," American Economic Review, American Economic Association, vol. 111(12), pages 4088-4118, December.
    4. Damian Clarke & Daniel Paila~nir & Susan Athey & Guido Imbens, 2023. "Synthetic Difference In Differences Estimation," Papers 2301.11859, arXiv.org, revised Feb 2023.
    5. Edward Felten & Manav Raj & Robert Seamans, 2021. "Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses," Strategic Management Journal, Wiley Blackwell, vol. 42(12), pages 2195-2217, December.
    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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