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Workers’ exposure to AI across development

Author

Listed:
  • Piotr Lewandowski
  • Karol MadoÅ„
  • Albert Park

Abstract

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. We then predict occupational AI exposure in countries lacking survey data using a regression-based approach. 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.

Suggested Citation

  • Piotr Lewandowski & Karol MadoÅ„ & Albert Park, 2025. "Workers’ exposure to AI across development," IBS Working Papers 02/2025, Instytut Badan Strukturalnych.
    • Handle: RePEc:ibt:wpaper:wp022025
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    References listed on IDEAS

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    1. Edward W. Felten & Manav Raj & Robert Seamans, 2018. "A Method to Link Advances in Artificial Intelligence to Occupational Abilities," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 54-57, May.
    2. Gordon H. Hanson, 2017. "What Do We Really Know about Offshoring? Industries and Countries in Global Production Sharing," Development Working Papers 416, Centro Studi Luca d'Agliano, University of Milano.
    3. Mariarosaria Comunale & Andrea Manera, 2024. "The Economic Impacts and the Regulation of AI: A Review of the Academic Literature and Policy Actions," IMF Working Papers 2024/065, International Monetary Fund.
    4. Acemoglu, Daron & Autor, David, 2011. "Skills, Tasks and Technologies: Implications for Employment and Earnings," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 12, pages 1043-1171, Elsevier.
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    Cited by:

    1. Gmyrek, Pawel, & Viollaz, Mariana, & Winkler, Hernan,, 2026. "Disruption without dividend? how the digital divide and task differences split GenAI’s global impact," ILO Working Papers 995694369002676, International Labour Organization.

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    More about this item

    Keywords

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    JEL classification:

    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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