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.

该研究探讨了大型语言模型（LLMs）对中国劳动力市场的潜在影响，通过人工专业知识和 LLM 分类的分析，我们研究了 LLM 能力对职业暴露的影响。结果显示职业暴露与工资水平 / 经验溢价呈正相关，暗示高薪和经验密集型工作可能面临更大的被 LLM 软件取代的风险。该研究在理解中国日益强大的 AI 系统对劳动力市场的影响方面提供了分析基础，创新之处在于职业层面的暴露分析、行业聚合方法以及纳入 AI 采用和劳动力市场效应的经济模型。该研究结果将为决策者和企业提供关于最大化 AI 效益和减轻负面影响风险的策略建议。