Workers’ Exposure to AI Across Development Stages

Piotr Lewandowski (), Karol Madon and Albert Park ()
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Piotr Lewandowski: Institute for Structural Research (IBS)
Albert Park: Hong Kong University of Science & Technology

No 18235, IZA Discussion Papers from Institute of Labor Economics (IZA)

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 low-income 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: AI; occupations; job tasks; technology; skills (search for similar items in EconPapers)
JEL-codes: J21 J23 J24 (search for similar items in EconPapers)
Date: 2025-10
New Economics Papers: this item is included in nep-ain, nep-ict, nep-ifn, nep-lma and nep-tid
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