  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 Eloundou et
al. (2023)'s methodology. We then aggregate occupation exposure to the industry
level to obtain industry exposure scores. The results indicate a positive
correlation between occupation exposure and wage levels/experience premiums,
suggesting higher-paying and experience-intensive jobs may face greater
displacement risks from LLM-powered software. The industry exposure scores
align with expert assessments and economic intuitions. We also develop an
economic growth model incorporating industry exposure to quantify the
productivity-employment trade-off from AI adoption. Overall, this study
provides an analytical basis for understanding the labor market impacts of
increasingly capable AI systems in China. Key innovations include the
occupation-level exposure analysis, industry aggregation approach, and economic
modeling incorporating AI adoption and labor market effects. The findings will
inform policymakers and businesses on strategies for maximizing the benefits of
AI while mitigating adverse disruption risks.
