Labor Market Exposure to AI: Cross-country Differences and Distributional Implications
Open accessAbstract
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.
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The Pizzinelli et al. (2023) index correlates more weakly with the other indices (Pearson 0.36-0.46, Spearman 0.44-0.52).
“The index ofPizzinelli et al. (2023) correlates more weakly with the others (Pearson 0.36-0.46,Spearman 0.44-0.52)”
From Labor Market Consequences of Generative AI: Early Evidence from Norway (2026) · cites this paper as Pizzinelli et al. (2023)
The paper describes its complementarity-adjusted AI exposure measure and compares AI exposure measures in §2.5 (p. 18) and Appendix A.2 (p. 45). Those passages do not report that its index correlates more weakly with other indices or give the stated Pearson and Spearman ranges; they discuss mostly positive associations for the baseline measure, with one negative exception. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe four occupational AI exposure indices are measured on different scales, ranging from a standardized score with mean near zero and standard deviation near one to a right-skewed usage-weighted share.
“The four indices live on different scales -Felten et al. (2021) delivers a standardized score with mean roughly zero and standard deviation roughly one; Pizzinelli et al. (2023) delivers a positive, bounded score on a wider range;”
From Labor Market Consequences of Generative AI: Early Evidence from Norway (2026) · cites this paper as Pizzinelli et al. (2023)
Section 2.3 (p. 15) says Pizzinelli et al.’s complementarity measure θ is bounded between 0 and 1, and Figure B.7 (p. 58) says AIOE and C-AIOE are normalized to that range for its plots. These passages support the existence of bounded scores, but do not establish that Pizzinelli et al.’s occupational AI exposure index is inherently a positive score on a wider range. They also do not substantiate the paraphrase’s right-skewed, usage-weighted-share description. (LLM, conf=0.82)
Experimental — accuracy not yet measuredPizzinelli et al. (2023) extend this measure to allow for potential complementarities with labor which could dampen displacement effects.
“Pizzinelli, Panton, Mendes Tavares, Cazzaniga, and Li (2023) extend this measure to allow for potential complementarities with labor which could dampen displacement effects.”
From Labor Market Consequences of Generative AI: Early Evidence from Norway (2026) · cites this paper as Pizzinelli et al., 2023
The weaker correlation of the Pizzinelli index reflects its labor-complementarity adjustment, which downweights occupations whose AI-exposed tasks involve high-judgment work.
“reflecting its labor-complementarity adjustment which downweights occupations whose AI-exposed tasks involve high-judgment work.”
From Labor Market Consequences of Generative AI: Early Evidence from Norway (2026) · cites this paper as Pizzinelli et al. (2023)
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References 39 · Cited by 66
Research works printed but not linked: 11
- Acemoglu, D. and P. Restrepo (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives 33 (2), 3-30.
- Acemoglu, D. and P. Restrepo (2022). Tasks, automation, and the rise in us wage inequality. Econometrica 90 (5), 1973-2016.
- Agrawal, A., J. Gans, and A. Goldfarb (2018). Introduction. In A. Agrawal, J. Gans, and A. Goldfarb (Eds.), The economics of artificial intelligence: An agenda , pp. 197-236. University of Chicago Press.
- Autor, D. H. and D. Dorn (2013). The growth of low-skill service jobs and the polarization of the us labor market. American economic review 103 (5), 1553-1597.
- Brynjolfsson, E., T. Mitchell, and D. Rock (2018, May). What can machines learn, and what does it mean for occupations and the economy? AEA Papers and Proceedings 108 , 43-47.
- Brynjolfsson, E., D. Rock, and C. Syverson (2018). Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. In A. Agrawal, J. Gans, and A. Goldfarb (Eds.), The economics of artificial intelligence: An agenda , pp. 23-57. University of Chicago Press.
- Brynjolfsson, E., D. Rock, and C. Syverson (2021). The productivity j-curve: How intangibles complement general purpose technologies. American Economic Journal: Macroeconomics 13 (1), 333-372.
- Felten, E., M. Raj, and R. Seamans (2021). Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses. Strategic Management Journal 42 (12), 2195-2217.
- Goldfarb, A., B. Taska, and F. Teodoridis (2023). Could machine learning be a general purpose technology? a comparison of emerging technologies using data from online job postings. Research Policy 52 (1), 104653.
- Hall, W. J., M. V. Chapman, K. M. Lee, Y. M. Merino, T. W. Thomas, B. K. Payne, E. Eng, S. H. Day, and T. Coyne-Beasley (2015). Implicit racial/ethnic bias among health care professionals and its influence on health care outcomes: a systematic review. American journal of public health 105 (12), e60-e76.
- Lipsey, R. G., K. I. Carlaw, and C. T. Bekar (2005). Economic transformations: general purpose technologies and long-term economic growth . Oup Oxford.
- Authors
- Pizzinelli, C., Panton, A., Mendes Tavares, M., Cazzaniga, M., & Li, L.
- Affiliations
- International Monetary Fund (US)
- Corresponding author
- Pizzinelli, Carlo
- Venue
- IMF Working Papers
- Publisher
- International Monetary Fund (IMF)
- Published
- 2023-10
- Volume
- 2023
- Issue
- 216
- Pages
- 1
- ISSN
- 1018-5941 (ISSN-L)
- OpenAlex
- W4387776334
- Cited by
- 66
- References
- 39
- Citations by year
- 2026: 14 · 2025: 31 · 2024: 21
- FWCI
- 13.45
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- IMF Working Paper (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Labor market dynamics and wage inequality
- Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- artificial intelligence, labor market, AI exposure, labor complementarity, labor substitution, job displacement, occupational exposure, developed countries, emerging markets, gender differences, educational attainment, wage distribution, professional and managerial occupations
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