Workers' exposure to AI across development stages

Piotr Lewandowski, Karol Madoń and Albert Park

No 1183, Ruhr Economic Papers from RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen

Abstract: This paper develops a task-adjusted, country-specific measure of workers' exposure to Artificial Intelligence (AI) across 108 countries. Building on Felten et al. (2021), we adapt the Artificial Intelligence Occupational Exposure (AIOE) index to worker-level PIAAC data and extend it globally using comparable surveys and regression-based predictions, covering about 89% of global employment. Accounting for country-specific task structures reveals substantial cross-country heterogeneity: workers in low-income countries exhibit AI exposure levels roughly 0.8 U.S. standard deviations below those in high-income countries, largely due to differences in within-occupation task content. Regression decompositions attribute most cross-country variation to ICT intensity and human capital. High-income countries employ the majority of workers in highly AI-exposed occupations, while lowincome countries concentrate in less exposed ones. Using two PIAAC cycles, we document rising AI exposure in high-income countries, driven by shifts in within-occupation tasks rather than employment structure.

Keywords: job tasks; occupations; AI; technology; skills (search for similar items in EconPapers)
JEL-codes: J21 J23 J24 (search for similar items in EconPapers)
Date: 2025
New Economics Papers: this item is included in nep-ain, nep-ict and nep-lma
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:rwirep:331883

DOI: 10.4419/96973368

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