"Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets
Jin Liu, Xingchen Xu, Xi Nan, Yongjun Li, Yong Tan
TL;DR
This study analyzes how a powerful general-purpose AI, exemplified by ChatGPT, reshapes online labor markets using a large panel from a leading freelance platform. Employing a submarketized Difference-in-Differences design, augmented with Difference-in-Differences-in-Differences and Interrupted Time Series analyses, the authors document a substantial demand displacement, a comparatively smaller decline in supply, and a surge in competition post-ChatGPT. They also show a notable skill-transition effect, with incumbents shifting toward programming, and reveal heterogeneity: higher-skilled freelancers drive the transition while lower-skilled workers gain less. The findings illuminate the dual impact of general-purpose AI—displacing some tasks while enabling cross-occupation upskilling—offering practical guidance for policymakers, platform operators, and workers on training, market design, and strategic adaptation.
Abstract
Large Language Model (LLM)-based generative AI systems, such as ChatGPT, demonstrate zero-shot learning capabilities across a wide range of downstream tasks. Owing to their general-purpose nature and potential to augment or even automate job functions, these systems are poised to reshape labor market dynamics. However, predicting their precise impact \textit{a priori} is challenging, given AI's simultaneous effects on both demand and supply, as well as the strategic responses of market participants. Leveraging an extensive dataset from a leading online labor platform, we document a pronounced displacement effect and an overall contraction in submarkets where required skills closely align with core LLM functionalities. Although demand and supply both decline, the reduction in supply is comparatively smaller, thereby intensifying competition among freelancers. Notably, further analysis shows that this heightened competition is especially pronounced in programming-intensive submarkets. This pattern is attributed to skill-transition effects: by lowering the human-capital barrier to programming, ChatGPT enables incumbent freelancers to enter programming tasks. Moreover, these transitions are not homogeneous, with high-skilled freelancers contributing disproportionately to the shift. Our findings illuminate the multifaceted impacts of general-purpose AI on labor markets, highlighting not only the displacement of certain occupations but also the inducement of skill transitions within the labor supply. These insights offer practical implications for policymakers, platform operators, and workers.