Artificial Intelligence and the Future of Work
Paper Session
Saturday, Jan. 4, 2025 2:30 PM - 4:30 PM (PST)
- Chair: Kathrin Ellieroth, Colby College
GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
Abstract
We investigate the potential implications of Generative Pre-trained Transformer (GPT) models andrelated technologies on the U.S. labor market. Using a new rubric, we assess occupations based on their
correspondence with GPT capabilities, incorporating both human expertise and classifications from GPT-4.
Our findings indicate that approximately 80% of the U.S. workforce could have at least 10% of their work
tasks affected by the introduction of GPTs, while around 19% of workers may see at least 50% of their
tasks impacted. The influence spans all wage levels, with higher-income jobs potentially facing greater
exposure. Notably, the impact is not limited to industries with higher recent productivity growth. We
conclude that Generative Pre-trained Transformers exhibit characteristics of general-purpose technologies
(GPTs), suggesting that these models could have notable economic, social, and policy implications
Labor Market Exposure to AI: Cross-Country Differences and Distributional Implications
Abstract
This paper examines the impact of Artificial Intelligence (AI) on labor markets in both AdvancedEconomies (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 variation 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.
How Different Uses of AI Shape Labor Demand: Evidence from France
Abstract
This paper leverages a new dataset to document the effects of AI adoption on employment in France. Using event studies as well as the quasi-random allocation of AI subsidies, we estimate the impact of AI adoption on firm-level employment dynamics, documenting heterogeneity by workers' socio-demographic characteristics and for different uses of AI (cloud, machine learning, administrative processes, etc.).Discussant(s)
Alexander Copestake
,
International Monetary Fund
Alessandra Bonfiglioli
,
University of Bergamo
Hyejin Park
,
University of Montreal
Zara Contractor
,
Middlebury College
JEL Classifications
- J2 - Demand and Supply of Labor
- O3 - Innovation; Research and Development; Technological Change; Intellectual Property Rights