Avani Goenka

INTRODUCTION

Artificial intelligence (AI) has been emerging as a major technological force with the potential to transform labor markets across various industries and occupations. As AI’s ability to perform routine tasks increases, concerns regarding how technological innovation may affect labor demand and employment across different economies have grown. Past empirical research on large exogenous economic shocks provides useful frameworks for studying their macroeconomic effects. For example, Autor, Dorn, and Hanson (2013) show that U.S. regions more exposed to Chinese imports experienced larger job losses and labor market disruptions. Their findings demonstrate how differences in economic structure can cause global shocks to have heterogeneous labor-market effects.
Our empirical study applies a similar framework to the global expansion of artificial intelligence since 2018, and especially to the expansion of large language models after 2022. These developments represent major technological shifts influencing labor demand across occupations and sectors. This leads to our research question: How does exposure to artificial intelligence affect unemployment across countries, and do countries with higher AI exposure experience differential labor-market outcomes following the diffusion of AI technologies after 2018 and the expansion of large language models after 2022?
To answer this question, we adopt an interaction framework inspired by Rajan and Zingales (1998) and use cross-country differences in occupational composition to measure structural exposure to AI. If AI affects labor demand, countries whose employment structures are more concentrated in AI-exposed occupations should experience greater unemployment changes.