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Artificial Intelligence and Jobs: Evidence from US Commuting Zones

Author

Listed:
  • Alessandra Bonfiglioli
  • Rosario Crinò
  • Gino Gancia
  • Ioannis Papadakis

Abstract

We study the effect of Artificial Intelligence (AI) on employment across US commuting zones over the period 2000-2020. A simple model shows that AI can automate jobs or complement workers, and illustrates how to estimate its effect by exploiting variation in a novel measure of local exposure to AI: job growth in AI-related professions built from detailed occupational data. Using a shift-share instrument that combines industry-level AI adoption with local industry employment, we estimate robust negative effects of AI exposure on employment across commuting zones and time. We find that AI’s impact is different from other capital and technologies, and that it works through services more than manufacturing. Moreover, the employment effect is especially negative for low-skill and production workers, while it turns positive for workers at the top of the wage distribution. These results are consistent with the view that AI has contributed to the automation of jobs and to widen inequality.

Suggested Citation

  • Alessandra Bonfiglioli & Rosario Crinò & Gino Gancia & Ioannis Papadakis, 2023. "Artificial Intelligence and Jobs: Evidence from US Commuting Zones," CESifo Working Paper Series 10685, CESifo.
    • Handle: RePEc:ces:ceswps:_10685
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    References listed on IDEAS

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    1. Kirill Borusyak & Peter Hull & Xavier Jaravel, 2022. "Quasi-Experimental Shift-Share Research Designs [Sampling-based vs. Design-based Uncertainty in Regression Analysis]," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 89(1), pages 181-213.
    2. Sotiris Blanas & Gino Gancia & Sang Yoon (Tim) Lee, 2019. "Who is afraid of machines?," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 34(100), pages 627-690.
    3. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    4. Gordon H. Hanson, 2021. "Immigration and Regional Specialization in AI," NBER Working Papers 28671, National Bureau of Economic Research, Inc.
    5. Erik Brynjolfsson & Tom Mitchell & Daniel Rock, 2018. "What Can Machines Learn, and What Does It Mean for Occupations and the Economy?," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 43-47, May.
    6. Rodrigo Adão & Michal Kolesár & Eduardo Morales, 2019. "Shift-Share Designs: Theory and Inference," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 134(4), pages 1949-2010.
    7. Rodrigo Ad~ao & Michal Koles'ar & Eduardo Morales, 2018. "Shift-Share Designs: Theory and Inference," Papers 1806.07928, arXiv.org, revised Aug 2019.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    artificial intelligence; automation; displacement; labor;
    All these keywords.

    JEL classification:

    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • 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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