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Panel study finds AI hiring expansion selectively concentrates labor demand into digitally complementary occupations in Chinese cities, highlighting growing within-city job divergence.
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.
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Sun et al. (2026) studied this question.
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