Labor Market Exposure to AI: Cross-country Differences and Distributional Implications

Carlo Pizzinelli, Augustus Panton, Marina Tavares, Mauro Cazzaniga and Longji Li

No 2023/216, IMF Working Papers from International Monetary Fund

Abstract: This paper examines the impact of Artificial Intelligence (AI) on labor markets in both Advanced Economies (AEs) and Emerging Markets (EMs). We propose an extension to a standard measure of AI exposure, accounting for AI's potential as either a complement or a substitute for labor, where complementarity reflects lower risks of job displacement. We analyze worker-level microdata from 2 AEs (US and UK) and 4 EMs (Brazil, Colombia, India, and South Africa), revealing substantial variations in unadjusted AI exposure across countries. AEs face higher exposure than EMs due to a higher employment share in professional and managerial occupations. However, when accounting for potential complementarity, differences in exposure across countries are more muted. Within countries, common patterns emerge in AEs and EMs. Women and highly educated workers face greater occupational exposure to AI, at both high and low complementarity. Workers in the upper tail of the earnings distribution are more likely to be in occupations with high exposure but also high potential complementarity.

Keywords: Artificial intelligence; Employment; Occupations; Emerging Markets; impact of artificial intelligence; exposure to AI; labor market exposure; employment share; educated worker; Labor markets (search for similar items in EconPapers)
Pages: 58
Date: 2023-10-04
New Economics Papers: this item is included in nep-ain, nep-lam and nep-tid
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