AI in demand: How expertise shapes its (early) impact on workers

Eduard Storm, Myrielle Gonschor and Marc Justin Schmidt

No 1185, Ruhr Economic Papers from RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen

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)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:rwirep:333893

DOI: 10.4419/96973370

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