Large Language Models at Work in China's Labor Market

TL;DR

This paper investigates how large language models influence China's labor market, showing they may disproportionately affect higher-wage and experience-rich jobs, with implications differing from traditional automation theories.

Contribution

It introduces a novel empirical analysis of LLM exposure across occupations and industries in China, and proposes a new AI learning theory based on entropy and information theory.

Findings

Higher occupational exposure correlates with higher wages and experience premiums.

Occupational and industrial exposure scores align with expert assessments.

The impact of LLMs deviates from the routinization hypothesis.

Abstract

This paper explores the potential impacts of large language models (LLMs) on the Chinese labor market. We analyze occupational exposure to LLM capabilities by incorporating human expertise and LLM classifications, following the methodology of Eloundou et al. (2023). The results indicate a positive correlation between occupational exposure and both wage levels and experience premiums at the occupation level. This suggests that higher-paying and experience-intensive jobs may face greater exposure risks from LLM-powered software. We then aggregate occupational exposure at the industry level to obtain industrial exposure scores. Both occupational and industrial exposure scores align with expert assessments. Our empirical analysis also demonstrates a distinct impact of LLMs, which deviates from the routinization hypothesis. We present a stylized theoretical framework to better understand this deviation from previous digital technologies. By incorporating entropy-based information theory into the task-based framework, we propose an AI learning theory that reveals a different pattern of LLM impacts compared to the routinization hypothesis.