| 盛丹,杨卓凡.工业机器人使用与新职业就业[J].数量经济技术经济研究,2026,(7):159-184 | | 工业机器人使用与新职业就业 | | Industrial Robot Application and Employment in Emerging Occupations | | | | DOI： | | 中文关键词: 工业机器人 新职业 创新补偿 消费升级 任务重构 | | 英文关键词: Industrial Robot Adoption Emerging Occupations Innovation Compensation Consumption Upgrading Task Restructuring | | 基金项目: | | | 中文摘要: | | 工业机器人的广泛使用对中国就业格局产生了深刻影响，推动了新型职业的形成和发展，促进了中国的新职业就业。本文基于《中华人民共和国职业分类大典》、人口普查和国际机器人协会（IFR）的工业机器人数据，使用自然语言处理与生成式AI的复合方法，测度了中国不同职业的新职业强度和城市的新职业指数，并考察了工业机器人使用对中国城市新职业就业的影响及其作用的微观机制。实证结果显示，工业机器人使用能够提高中国城市层面总体新职业就业水平，同时对科技工业类、生活消费类和专业服务类新职业就业水平都有提升作用。机制分析发现，工业机器人使用对城市新职业就业的促进作用主要通过生产端创新补偿、需求端收入—消费升级、组织端任务重构等机制实现。此外，工业机器人使用主要促进低学历、农业户口劳动者和第三产业占比、产业多样性较高城市劳动者的新职业就业。进一步研究表明，城市新职业就业水平提升促进了城市总体就业水平和平均工资水平的增长，同时也会提升劳动者的主观收入满意度和生活满意度。 | | 英文摘要: | | The widespread application of industrial robots has profoundly reshaped China’s employment landscape, driving the creation and development of novel occupations and promoting new occupational employment. Previous studies focused on the “complementary effect” of industrial robots in absorbing labor through the creation of new tasks; however, they failed to highlight the key carrying role of new occupations in new task creation, neglecting the fundamental restructuring of labor factor combinations induced by technological change. Furthermore, there is the lack of a systematic analysis of the internal heterogeneity and classification dimensions of new occupations, as well as of the theoretical mechanisms through which industrial robots promote new occupational employment. Employing data from the Occupational Classification Dictionary of the People’s Republic of China, population censuses, and the International Federation of Robotics (IFR), this study adopts a hybrid approach combining natural language processing (NLP) and generative AI to measure the new occupational intensity across different professions and the new occupational index at the city level. Moreover, it examines the impact of industrial robot application on new occupational employment in Chinese cities, exploring the underlying micro-level mechanisms.
More in detail,first, based on the occupational definitions and the task descriptions in the Occupational Classification Dictionary, we adopteda hybrid methodology combining the Continuous Bag-of-Words (CBOW) model and AI to measure the textual similarity of occupational descriptions across different periods, identifying new occupations. We found that new occupations are widely distributed across various sectors, including the technology industry, lifestyle consumption, and professional services, exhibiting distinct task and skill characteristics, as well as demographic and urban heterogeneity. Our baseline regression results indicate that industrial robots significantly promote both the aggregate and sector-specific new occupational indices at the city level. This finding remains consistent even after addressing potential endogeneity issues and various robustness checks.
Second, we examined the mechanisms underlying the impact of industrial robots on the promotion of new occupational employment. Our analysis reveals that in the technology industry, industrial robots foster new occupational employment through innovation incentives and the expansion of high-tech sectors. In the lifestyle consumption sector, they do so by raising income levels and driving the demand for high-end and differentiated consumption. Finally, in professional services, they promote new occupational employment by facilitating intra-firm process reengineering, increasing coordination complexity at the industry level, and upgrading social operational models.
Third, the results of our heterogeneity analysis indicate that the positive impact of industrial robots on new occupational employment is more pronounced among those workerswith lower educational attainment and rural hukou (household registration), as well as in cities with a high share of the tertiary industry and greater industrial diversity. The occupational newness index and the AI substitution rate are highly complementary, suggesting that AI technology will drive complementary new occupational employment in the long run. Moreover, new occupational employment significantly stimulates urban employment growth and raises average wages, increasing the probability that individuals will transition to new occupations, enhancing their wage levels, and improving subjective satisfaction. This indicates that industrial robots generate positive welfare effects.
These findings have significant and insightful policy implications to achieve high-quality employment. To fully leverage the value of new occupations in stabilizing employment and achieving high-quality job creation in the new era, governments should adopt a multifaceted approach. First, policymakers should formulate coordinated technology-employment policies for the present and the future, capitalizing on new technologies, such as industrial robots and next-generation AI, to actively cultivate new growth poles for employment. Second, based on the “skill-biased” nature of technological change, it is imperative to expand the reform of the vocational skills training systems and build a lifelong system for the cultivation of human capital, adapted to the evolution of new occupations. Third, governments should optimize the spatial layout of urban industries and extend the integration of advanced manufacturing and modern services, so as to strengthen the industrial ecological foundation that nurtures new occupations. Finally, it is crucial to improve the institutional guarantees and welfare systems for new employment forms, weave a dense social safety net, and facilitate the transformation of technological dividends into inclusive growth.
In conclusion, this study contributes to the existing literature by providing micro-level evidence and expanding the research on the “complementary effect” of industrial robots in labor absorption. By improving the accuracy in the identification of new occupations, it systematically demonstrates the promoting effect of industrial robots on new occupational employment and its underlying mechanisms. Our findings underscore the importance of fully leveraging the enhancement of employment quality by new occupations, and have significant implications for the design of targeted vocational training systems, urban industrial policies, and social security systems aligned with technological advancement. | | 查看全文 相关附件： 下载数据代码附录 |
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