%0 Report
        %A Lewandowski, Piotr
%A Madoń, Karol
%A Park, Albert
        %T Workers’ Exposure to AI Across Development Stages
        %D 2025
        %8 2025 Oct
        %I Institute of Labor Economics (IZA)
        %C Bonn
        %7 IZA Discussion Paper
        %N 18235
        %U https://www.iza.org/publications/dp18235
        %X 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.
        %K AI
%K occupations
%K job tasks
%K technology
%K skills