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Labour Market Exposure to AI: Cross-Country Differences and Distributional Implications

Pizzinelli

2023IMF Working Paper53 citations

Exposure / measurement

AI (General)GenderAugmentation vs. substitutionGeneral automation

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.

Summary

Pizzinelli et al. extend the Felten et al. (2021) AI occupational exposure measure by adding a complementarity adjustment based on O*NET work contexts, then apply both measures to labor force microdata from six countries (US, UK, Brazil, Colombia, India, South Africa) to study cross-country differences and within-country distributional patterns of AI exposure.

Main Finding

Advanced economies face higher AI exposure than emerging markets due to greater employment in professional/managerial occupations, but after adjusting for complementarity potential, cross-country differences are substantially reduced; within countries, women and college-educated workers face higher exposure, and high-income workers are more likely in high-exposure, high-complementarity occupations while displacement risk is more evenly distributed.

Primary Datasets

ACS

Labor outcomes

O*NET

Other

US American Community Survey (2019); UK Labour Force Survey (2022); Brazil Pesquisa Nacional por Amostra de Domicílios Contínua (2022); Colombia Gran Encuesta Integrada de Hogares (2022); India Periodic Labour Force Survey (2018-19); South Africa Labor Market Dynamics Survey (2019); O*NET work contexts and job zones

Secondary Datasets

Felten et al. (2021)

AI-focused

Felten et al. (2021) AI Occupational Exposure Index; O*NET abilities data; Cortes et al. (2020) routine task intensity classification

Key Methods: Descriptive cross-tabulation of AI exposure measures across occupations, countries, demographic groups, and earnings distribution using labor force survey microdata
Sample Period: 2018-2022
Geographic Coverage: International
Sample Size: Worker-level microdata from labor force surveys in 6 countries; exact sample sizes not reported but nationally representative samples of employed population aged 16-64
Level of Analysis: Individual, Occupation, Country
Occupation Classification: ISCO-08 (4-digit level for most countries, 3-digit for India); US SOC 2010 for O*NET linkage
Industry Classification: None

Notes

IMF Working Paper 2023/216 [Claude classification]: First paper to systematically compare AI exposure between advanced economies and emerging markets at granular occupational level; proposes novel complementarity adjustment to standard exposure measures based on O*NET work contexts; purely descriptive analysis with no causal identification [Claude classification]: First paper to systematically compare AI exposure between advanced economies and emerging markets at granular occupational level; proposes novel complementarity adjustment to standard exposure measures based on O*NET work contexts; purely descriptive analysis with no causal identification [Claude classification]: First paper to systematically compare AI exposure between advanced economies and emerging markets at granular occupational level; proposes novel complementarity adjustment to standard exposure measures based on O*NET work contexts; purely descriptive analysis with no causal identification [Claude classification]: First paper to systematically compare AI exposure between advanced economies and emerging markets at granular occupational level; proposes novel complementarity adjustment to standard exposure measures based on O*NET work contexts; purely descriptive analysis with no causal identification [Claude classification]: First paper to systematically compare AI exposure between advanced economies and emerging markets at granular occupational level; proposes novel complementarity adjustment to standard exposure measures based on O*NET work contexts; purely descriptive analysis with no causal identification [Claude classification]: First paper to systematically compare AI exposure between advanced economies and emerging markets at granular occupational level; proposes novel complementarity adjustment to standard exposure measures based on O*NET work contexts; purely descriptive analysis with no causal identification [Claude classification]: First paper to systematically compare AI exposure between advanced economies and emerging markets at granular occupational level; proposes novel complementarity adjustment to standard exposure measures based on O*NET work contexts; purely descriptive analysis with no causal identification [Claude classification]: First paper to systematically compare AI exposure between advanced economies and emerging markets at granular occupational level; proposes novel complementarity adjustment to standard exposure measures based on O*NET work contexts; purely descriptive analysis with no causal identification [Claude classification]: First paper to systematically compare AI exposure between advanced economies and emerging markets at granular occupational level; proposes novel complementarity adjustment to standard exposure measures based on O*NET work contexts; purely descriptive analysis with no causal identification [Claude classification]: First paper to systematically compare AI exposure between advanced economies and emerging markets at granular occupational level; proposes novel complementarity adjustment to standard exposure measures based on O*NET work contexts; purely descriptive analysis with no causal identification [Claude classification]: First paper to systematically compare AI exposure between advanced economies and emerging markets at granular occupational level; proposes novel complementarity adjustment to standard exposure measures based on O*NET work contexts; purely descriptive analysis with no causal identification