AI-Related Hiring Expansion and Within-City Occupational Demand Reallocation in China: Evidence From Listed-Firm Job Postings

Abstract

AI-related hiring demand is unevenly distributed across urban labor markets. Using online job-posting data from Chinese listed firms between 2016 and 2024, this study constructs a city–occupation–year panel and examines whether expanding AI-related hiring demand is associated with differences in the structure of occupational hiring demand within cities. The results show that: (1) higher city-level AI-related hiring demand is significantly associated with greater relative hiring demand for occupations that are more closely related to AI. This finding indicates that AI-related opportunities are not evenly distributed across occupations; instead, hiring demand is selectively concentrated in occupations with stronger links to AI. The result remains robust across a series of robustness checks; (2) AI-related hiring expansion is associated with stronger hiring demand for digitally complementary occupations and a larger share of AI-related vacancies within occupations; and (3) these associations are more pronounced in cities with greater higher-education capacity and more developed innovation ecosystems. This study extends the literature on AI and the structure of urban labor demand and provides recruitment-based evidence relevant to cities seeking to improve talent-development systems, strengthen their innovation environments, and address the growing divergence in occupational demand associated with AI.