This paper develops a task-adjusted, country-specific measure of workers’ exposure to artificial intelligence (AI) across 103 countries, covering approximately 86% of global employment. Building on the AI Occupational Exposure Index by Felten et al. (2021), we map AI-related abilities to worker-level tasks using survey data from PIAAC, STEP, and CULS. Using a regression-based approach, we then predict occupational AI exposure in countries lacking survey data. Our findings show that accounting for within-occupation task differences significantly amplifies the development gradient in AI exposure. About 47% of cross-country variation is explained by differences in task content, particularly among high-skilled occupations. We attribute these differences primarily to cross-country differences in ICT use intensity, followed by human capital and globalisation-related firm characteristics. We also document rising AI exposure over the past decade, driven largely by changes in task composition. Our results highlight the central role of digital infrastructure and skill use in shaping global AI exposure.
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, and Omar Arias for useful comments. The usual disclaimers apply. All errors are our own.
