Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/333674 
Erscheinungsjahr: 
2025
Schriftenreihe/Nr.: 
IZA Discussion Papers No. 18235
Verlag: 
Institute of Labor Economics (IZA), Bonn
Zusammenfassung: 
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.
Schlagwörter: 
job tasks
occupations
AI
technology
skills
JEL: 
J21
J23
J24
Dokumentart: 
Working Paper

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