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

Magnus Lodefalk (), Lydia Löthman (), Michael Koch () and Erik Engberg ()
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Magnus Lodefalk: Örebro University School of Business, Postal: Örebro University, School of Business, SE - 701 82 ÖREBRO, Sweden, https://www.oru.se/english/employee/magnus_lodefalk
Lydia Löthman: Örebro University School of Business, Postal: Örebro University, School of Business, SE - 701 82 ÖREBRO, Sweden, https://www.oru.se/english/employee/lydia_lothman
Michael Koch: Aarhus University, Postal: Aarhus University, Universitetsbyen 51, 1814, 261, 8000 Aarhus C, Denmark, https://pure.au.dk/portal/en/persons/mkoch%40econ.au.dk
Erik Engberg: Örebro University School of Business, Postal: Örebro University, School of Business, SE - 701 82 ÖREBRO, Sweden, https://www.oru.se/english/employee/erik_engberg

No 2026:2, Working Papers from Örebro University, School of Business

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.

Keywords: Generative artificial intelligence; Job postings; Labour demand; Employment composition; Monetary policy (search for similar items in EconPapers)
JEL-codes: J23 J24 O33 (search for similar items in EconPapers)
Pages: 44 pages
Date: 2026-03-16
New Economics Papers: this item is included in nep-ain, nep-eur, nep-hrm and nep-lma
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