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.
We thank Du Yang and Jia Peng of the Institute of Population and Labor Economics Chinese Academy of Social Sciences for help with China Urban Labor Survey data. We thank Omar Arias, Maddalena Honorati, participants of TASKSVII conference in Luxembourg, ELMI conference in Warsaw, and SkiLMeeT conference in Sofia for useful comments. This project has received funding from the European Union’s Horizon Europe Research and Innovation Programme under grant agreement No. 101132581 (project SkiLMeeT). The usual disclaimers apply. All errors are ours.
