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Artificial Intelligence and the Future of Work

Paper Session

Saturday, Jan. 4, 2025 2:30 PM - 4:30 PM (PST)

Hilton San Francisco Union Square, Franciscan A
Hosted By: American Economic Association
  • Chair: Kathrin Ellieroth, Colby College

New Technologies and Jobs in Europe

Stefania Albanesi
,
University of Miami
Antonio Dias da Silva
,
European Central Bank
Juan Francisco Jimeno
,
Bank of Spain
Ana Lamo
,
European Central Bank
Alena Wabitsch
,
University of Oxford

Abstract

We examine the link between labour market developments and new technologies such as artificial intelligence (AI) and software in 16 European countries over the period 2011- 2019. Using data for occupations at the 3-digit level in Europe, we find that on average employment shares have increased in occupations more exposed to AI. This is particularly the case for occupations with a relatively higher proportion of younger and skilled workers. This evidence is in line with the Skill Biased Technological Change theory. While there exists heterogeneity across countries, only very few countries show a decline in employment shares of occupations more exposed to AI-enabled automation. Country heterogeneity for this result seems to be linked to the pace of technology diffusion and education, but also to the level of product market regulation (competition) and employment protection laws. In contrast to the findings for employment, we find little evidence for a relationship between wages and potential exposures to new technologies.

GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models

Daniel Rock
,
University of Pennsylvania
Tyna Eloundou
,
Open AI
Sam Manning
,
Open AI
Pamela Mishkin
,
Open AI

Abstract

We investigate the potential implications of Generative Pre-trained Transformer (GPT) models and
related 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

Carlo Pizzinelli
,
International Monetary Fund
Augustus Panton
,
International Monetary Fund
Marina Tavares
,
International Monetary Fund
Mauro Cazzaniga
,
Getulio Vargas Foundation
Longji Li
,
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 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

Philippe Aghion
,
College of France and London School of Economics
Xavier Jaravel
,
London School of Economics
Simon Bunel
,
Bank of France
Alexandra Roulet
,
Insead

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