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Same Storm, Different Boats: Generative AI and the Age Gradient in Hiring

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Abstract

We show that the age composition of employment within Swedish employers shifts after the arrival of generative AI, with no corresponding reduction in aggregate labour demand. Using 4.6 million job advertisements from Sweden’s largest recruitment platform, we find that the broad decline in postings since 2022 aligns with monetary tightening rather than AI, exploiting Sweden’s seven-month gap between the Riksbank’s first rate hike and the launch of ChatGPT as a timing test. We then use full-population employer– employee register data and an employer-level difference-in-differences design to estimate how AI exposure affects employment composition across six age groups. An event study documents an accelerating decline in employment of 22–25-year-olds in high-AI-exposure occupations, reaching 5.5 per cent by early 2025 relative to less exposed occupations within the same employers, while employment of workers over 50 rose by 1.3 per cent. The widening age gradient suggests that generative AI reshapes hiring composition rather than aggregate demand, with the adjustment burden falling disproportionately on entry-level workers.

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  • Lodefalk, Magnus & Löthman, Lydia & Koch, Michael & Engberg, Erik, 2026. "Same Storm, Different Boats: Generative AI and the Age Gradient in Hiring," Working Papers 2026:2, Örebro University, School of Business.
  • Handle: RePEc:hhs:oruesi:2026_002
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    References listed on IDEAS

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    1. Zens, Gregor & Böck, Maximilian & Zörner, Thomas O., 2020. "The heterogeneous impact of monetary policy on the US labor market," Journal of Economic Dynamics and Control, Elsevier, vol. 119(C).
    2. Axel Cronert, 2022. "When the paper tiger bites: Evidence of compliance with unenforced regulation among employers in Sweden," Regulation & Governance, John Wiley & Sons, vol. 16(4), pages 1141-1159, October.
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    5. Anders Humlum & Emilie Vestergaard, 2025. "Still Waters, Rapid Currents: Early Labor Market Transformation under Generative AI," NBER Working Papers 33777, National Bureau of Economic Research, Inc.
    6. Jonathan Roth, 2022. "Pretest with Caution: Event-Study Estimates after Testing for Parallel Trends," American Economic Review: Insights, American Economic Association, vol. 4(3), pages 305-322, September.
    7. Jiafeng Chen & Jonathan Roth, 2024. "Logs with Zeros? Some Problems and Solutions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 139(2), pages 891-936.
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    Cited by:

    1. Øystein Hernæs & Andreas Ravndal Kostøl, 2026. "Has AI Widened Employment Gaps? Tracking Early-Career Employment by Occupational Exposure in Norway," RFBerlin Discussion Paper Series 26179, ROCKWOOL Foundation Berlin (RFBerlin).
    2. Tom Coupé, 2026. "Is This Time Different? 35 Years of European Expectations about Technology and Jobs," Working Papers in Economics 26/07, University of Canterbury, Department of Economics and Finance.
    3. Hernaes, Øystein & Kostøl, Andreas, 2026. "Has AI Widened Employment Gaps? Tracking Early-Career Employment by Occupational Exposure in Norway," IZA Discussion Papers 18767, IZA Network @ LISER.

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    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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