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

Autor/inn/en	Pizzinelli, Carlo; Panton, Augustus J.; Tavares, Ms. Marina Mendes; Cazzaniga, Mauro; Li, Longji
Institution	Internationaler Währungsfonds
Titel	Labor Market Exposure to AI: Cross-country Differences and Distributional Implications. Gefälligkeitsübersetzung: Arbeitsmarktexposition gegenüber KI: Unterschiede zwischen den Ländern und Auswirkungen auf die Verteilung.
Quelle	Washington, DC (2023), 58 S. PDF als Volltext kostenfreie Datei (1); PDF als Volltext kostenfreie Datei (2) Link als defekt meldenVerfügbarkeit
Reihe	IMF working papers / International Monetary Fund. 2023,216
Sprache	englisch
Dokumenttyp	online; Monographie
Schlagwörter	Künstliche Intelligenz; Automatisierung; Industriestaat; Beschäftigungseffekt; Schwellenland; Verteilungseffekt; Berufsgruppe; Qualifikation; Internationaler Vergleich; Arbeitspapier; Auswirkung; Einflussfaktor; Geschlechtsspezifik; Brasilien; Großbritannien; Indien; Kolumbien; Südafrika (Staat); USA
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." The study refers to the period 2018-2022. (Author's abstract, IAB-Doku)..
Erfasst von	Institut für Arbeitsmarkt- und Berufsforschung, Nürnberg
Update	2024/1