Workers' exposure to artificial intelligence across development stages

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 data from the Programme for the International Assessment of Adult Competencies (PIAAC) 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 United States (US) 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 information and communications technology intensity and human capital. Highincome 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.