AI in Demand: How Expertise Shapes its (Early) Impact on Workers

Eduard Storm, Myrielle Gonschor and Marc Justin Schmidt
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Eduard Storm: Institute for Advanced Studies (IHS) and RWI – Leibniz Institute for Economic Research
Myrielle Gonschor: Kienbaum Consultants
Marc Justin Schmidt: TU Dortmund, RTG 2484

No 61, IHS Working Paper Series from Institute for Advanced Studies

Abstract: We study how artificial intelligence (AI) affects workers’ earnings and employment stability, combining German job vacancy data with administrative records from 2017–2023. Identification comes from changes in workers’ exposure to local AI skill demand over time, instrumented with national demand trends. We find no meaningful displacement or productivity effects on average, but notable skill heterogeneity: expert workers with deep domain knowledge gain while non-experts often lose, with returns shaped by occupational task structures. We also document AI-driven reinstatement effects toward analytic and interactive tasks that raise earnings. Overall, our results imply distributional concerns but also job-augmenting potential of early AI technologies.

Keywords: AI; Online Job Vacancies; Skill Demand; Worker-level Analysis; Employment; Earnings; Expertise (search for similar items in EconPapers)
JEL-codes: D22 J23 J24 J31 O33 (search for similar items in EconPapers)
Pages: 46 pages
Date: 2025-11
New Economics Papers: this item is included in nep-ain, nep-bec, nep-eur, nep-lma and nep-tid
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https://irihs.ihs.ac.at/id/eprint/7345 First version, 2025 (application/pdf)

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