@TechReport{iza:izadps:dp18235,
            	author={Lewandowski, Piotr and Madoń, Karol and Park, Albert},
            	title={Workers’ Exposure to AI Across Development Stages},
            	year={2025},
	month={Oct},
            	institution={Institute of Labor Economics (IZA)},
            	address={Bonn},
            	type={IZA Discussion Paper},
            	number={18235},
            	url={https://www.iza.org/publications/dp18235},
	abstract={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.},
	keywords={AI;occupations;job tasks;technology;skills},
            }