企业人工智能技术水平与劳动力需求结构变化: 基于大语言模型的新方法和新发现  

The Level of AI Technology in Enterprises and Changes in Labor Demand Structure:New Methods and Findings Based on Large Language Models

作　　者：陈澍 韩清 张伯超 Chen Shu;Han Qing;Zhang Bochao(Institute of Economics,Shanghai Academy of Social Sciences,Shanghai 200235,China)

机构地区：[1]上海社会科学院经济研究所,上海200235

出　　处：《技术经济》2025年第1期1-13,共13页Journal of Technology Economics

基　　金：国家社会科学基金一般项目“数据要素赋能培育新质生产力的理论机理与路径研究”(24BJL087)。

摘　　要：基于任务偏型技术进步范式分析人工智能对劳动力市场的影响已成为共识,但现有工作类型分类方法仍存在细致度和准确率不足的问题。为解决这一不足,本文通过优化Chinese-BERT-wwm大模型,将2013—2019年上市企业的招聘信息区分为非常规型和常规型工作,测试集分类准确率近93%。同时,利用GLM4大模型根据岗位名称和职责描述,将其匹配到《中华人民共和国职业分类大典(2022年版)》的小类标准职业名称以识别数字职业,分析人工智能技术对劳动力需求结构的影响。实证结果表明,企业人工智能技术水平提高显著增加了对非常规岗位的需求,减少了对常规岗位的需求,且这一效应在非国有企业、高科技行业和制造业中尤为显著。进一步分析发现,非常规岗位需求的上升主要源于非常规认知型岗位需求的增长。机制分析显示,企业人工智能技术提升通过生产率效应和创造数字职业等新岗位促进非常规岗位需求,同时通过替代效应减少常规岗位需求。研究成果拓展了大语言模型在经济学文本分析中的应用。The impact of artificial intelligence(AI)on the labor market,based on the Routine-Biased Technological Change paradigm,is widely acknowledged.However,existing job classification methods lack detail and accuracy.To address this limitation,the Chinese-BERT-wwm model was optimized to classify recruitment data from listed companies between 2013 and 2019 into routine and non-routine jobs,achieving a test set accuracy of accuracy of nearly 93%.Additionally,the GLM4 model was used to match job titles and descriptions to the“Chinese Occupational Classification(2022 Edition)”to identify digital occupations and analyze the impact of AI technology on labor demand structure.Empirical results show that higher AI technology levels significantly increase demand for non-routine jobs and reduce demand for routine jobs,with pronounced effects in non-state-owned enterprises,high-tech industries,and manufacturing.Further analysis reveals that the increased demand for non-routine jobs is primarily driven by growth in non-routine cognitive positions.Mechanism analysis shows that AI adoption increases non-routine job demand through productivity effects and the creation of new digital occupations,while reducing routine job demand through substitution effects.It expands the application of large language models in economic text analysis.

关 键 词：人工智能 任务偏向 非常规工作 常规工作 大语言模型 

分 类 号：F272.92[经济管理—企业管理] TP18[经济管理—国民经济]