人工智能、资本-技能互补与企业全要素生产率  

Artificial Intelligence, Capital-Skill Complementarity and Enterprise Total Factor Productivity

作　　者：李健斌 周浩 Li Jianbin;Zhou Hao(Institute of Industrial Economics,Jinan University;Industrial Big Data Application and Economic Decision Research Lab,Jinan University)

机构地区：[1]暨南大学产业经济研究院,510632 [2]暨南大学产业大数据应用与经济决策研究实验室,510632

出　　处：《经济评论》2025年第1期20-36,共17页Economic Review

摘　　要：作为新一代通用目的技术,人工智能在赋能企业高质量发展中发挥重要作用。本文利用2010—2020年沪深A股制造业上市企业数据,考察了人工智能技术对企业全要素生产率的影响。研究发现,人工智能技术显著提高了企业全要素生产率。机制检验表明,人工智能技术显著增加了企业固定资产和无形资产投资,并引致高技能劳动力占比提升,进而实现资本-技能互补以促进全要素生产率的提高。异质性检验表明,对于高技术、数字化应用场景丰富的行业,人工智能技术促进企业全要素生产率提升的作用更强;对于人工智能技术的细分领域,相较于符号系统和机器人,机器学习技术的生产率促进效应更明显。本文有助于为推进人工智能赋能企业高质量发展提供参考价值。As a new generation of general-purpose technology, artificial intelligence(AI) plays an important role in empowering the high-quality development of enterprises. In this article, we examine the impact of AI technology on enterprise total factor productivity(TFP) using Shanghai and Shenzhen's A-share manufacturing listed companies as a sample from 2010 to 2020. The study revealed that AI technology significantly enhances enterprise TFP. The mechanism test shows that AI technology increases enterprises' investment in fixed assets and intangible capital, and leads to an increase in the proportion of high-skilled labor, which in turn achieves capital-skill complementarity to promote TFP. The heterogeneity test indicates that AI technology has a stronger effect on enhancing TFP for enterprises in industries with high-tech and digital application scenarios. Moreover, the productivity effect of AI technology is heterogeneous among different subdivided technology fields. Machine learning technology has a more obvious productivity-promoting effect compared to symbolic systems and robotics. Our findings provide suggestions for AI-enabled high-quality development of enterprises.

关 键 词：人工智能 全要素生产率 资本-技能互补 专利 

分 类 号：F49[经济管理—产业经济] F273.1