| 何小钢,毛莘娅,郭晓斌.人工智能技术、职业技能重塑与工资效应[J].数量经济技术经济研究,2026,(5):54-78 | | 人工智能技术、职业技能重塑与工资效应 | | Artificial Intelligence Technology, Occupational Skills Reshaping, and Wage Effects | | | | DOI： | | 中文关键词: 人工智能技术 招聘工资 职业技能结构 技能多样性 | | 英文关键词: Artificial Intelligence Technology Recruitment Wages Skill Diversity Human Capital Upgrading | | 基金项目: | | | 中文摘要: | | 人工智能技术正深刻重塑职业技能需求进而影响工资形成机制，探究其具体影响效应对于实现高质量充分就业、改善收入分配格局具有重要意义。本文利用 2015~2023 年中国在线招聘数据和上市公司数据，实证分析企业人工智能技术对招聘岗位工资水平的影响及作用机制。研究发现，人工智能技术主要通过职业技能重塑的结构效应和质量效应提升招聘岗位工资水平。具体而言，人工智能技术一方面会推动职业结构向去常规化和高知化方向演进，另一方面会增加职业内数字技能需求及技能多样性。进一步分析发现，人工智能技术对招聘岗位工资的提升作用在劳动力议价能力更强、规模更大的企业及对工作经验要求更高的岗位更为显著。从职业类型看，这一效应在非常规交互型和常规型职业中尤为突出。人工智能技术在推动整体工资水平上涨的同时，也会拉大不同教育和技能层级劳动者之间的收入差距，但劳动收入份额并未提高。本文为理解人工智能时代“技术—技能—工资”的匹配机制提供了微观证据，对更好地把握中国人力资本结构升级方向以及扎实推进共同富裕具有重要的政策启示。 | | 英文摘要: | | As the core driving force spearheading a new wave of technological revolution and industrial transformation, artificial intelligence (AI) technology is reshaping corporate production processes and work tasks with unprecedented depth, emerging as a pivotal factor influencing employment decisions and income distribution patterns. How to guide AI technology to drive high-quality economic development while promoting high-quality full employment and advancing common prosperity is an urgent and critical issue. The paradigm shift from technological substitution to skill augmentation implies that the mechanisms through which next-generation AI technologies shape corporate human capital requirements and wage levels have become more complex. However, existing research has yet to fully elucidate how these technologies reconfigure enterprises’ labor demand and wage-setting behavior at the micro level.This study examines the impact of next-generation AI technologies on corporate vocational skill requirements and wage decisions for recruitment positions, combining data from Chinese listed companies with online recruitment big data. The findings indicate that AI technology significantly elevates firms’ willingness-to-pay for advertised positions, establishing it as a key determinant of recruitment decisions. Mechanism analysis reveals that AI primarily boosts wages through two channels-optimizing inter-occupational skill structures and upgrading intra-occupational skill quality. First, by shifting occupational structures toward social-interaction roles and promoting knowledge-intensive occupations, AI raises overall recruitment wages. Second, by enhancing the quality of occupational skills, it increases demand for digital competencies within the same occupation and broadens the scope of occupational skills, thereby generating wage premiums within occupations. Heterogeneity analysis reveals that the wage-boosting effect of AI technology is more pronounced in positions where workers possess stronger bargaining power, enterprises are larger, and higher work experience is required, as well as in nonroutine interactive and routine occupations. Further analysis indicates that while AI technology elevates overall recruitment wages, it widens income disparities between workers with varying educational and skill profiles, without a corresponding increase in labor’s income share.Based on these findings, the study proposes the following policy recommendations: First, promote the orderly reconfiguration of corporate job systems to enhance workers’ adaptability to occupational structural shifts. Second, establish a forward-looking human resource development system to strengthen the dynamic alignment between skill supply and industrial demand. Third, implement differentiated industrial and enterprise support policies to facilitate the cross-group sharing of technological dividends. Fourth, refine skill-oriented remuneration incentive mechanisms to mitigate income distribution disparities arising from technological transformation.The main contributions of this study are as follows: First, from a research perspective, it shifts the analysis of AI’s wage effects from labor market equilibrium outcomes to the demand side of corporate employment decisions. By examining the wages firms are willing to pay as advertised in recruitment postings, it effectively isolates the impact of AI on corporate remuneration decisions, free from supply-side confounding factors. This not only provides direct demand-side evidence for the “technology-skills-wages” transmission chain but also offers a forward-looking perspective for anticipating the dynamic effects of AI on labor market structures. Second, regarding identification strategy, this study constructs occupational task indices and job skill demand indicators based on online recruitment data, refining the research level to the firm-job level. By employing a multilevel fixed-effects model, it effectively distinguishes between occupational structure shift effects and intra-occupational wage increase effects, thereby overcoming the limitations of macro data or aggregated firm data in identifying causal mechanisms. Third, in terms of theoretical mechanisms, this study validates both the occupational skill structure effect and the occupational skill quality effect of AI on wages, deepening our understanding of skill-biased technological progress. Unlike existing research that predominantly examines AI’s direct impact on wage levels through substitution or productivity effects, this study shifts the analytical focus to how technology influences wage decisions via indirect channels by restructuring task composition and skill requirements within occupations. This approach unlocks the black box of the mechanisms through which AI influences occupational restructuring and skill adjustments within enterprises. | | 查看全文 |
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