Artificial Intelligence Development and Worker Employment Quality: Evidence from China
Yunting Wu, Xiliang Feng · Sustainability · 16 Oct 2026
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Combining China Family Panel Studies data for 2016-2022 with provincial AI development measures, this paper finds a positive association between regional AI development and workers' employment quality, mainly through industrial upgrading and more intelligent ways of working. The authors note the regional result does not prove individual-level effects, and report larger associations for men, workers outside the state system and workers in eastern China.
Abstract
With the continuous development of new technologies, these new technologies have a profound impact on the labor market. This paper uses micro-individual data from the China Family Panel Studies (CFPS) from 2016 to 2022 and provincial-level macro-data on artificial intelligence (AI), employing a two-way fixed-effects model to systematically examine the actual impact and underlying mechanisms of AI development on the employment quality of workers. The study shows a significant positive correlation between the level of regional artificial intelligence development and the employment quality of local workers. However, this overall effect at the regional level cannot directly prove the individual-level effect of enterprises or employees using artificial intelligence. Mechanism testing further confirms that the effect of the regional AI development on improving the employment quality of workers is mainly achieved through optimizing the industrial structure and promoting the intelligentization of workers’ working mode. Heterogeneity analysis indicates that the regional artificial intelligence has a greater positive impact on employment quality for male workers, outside-system workers, and workers in the eastern region. Therefore, to achieve higher-quality employment for workers, it is necessary to promote the deep integration of AI with the real economy, build a multi-party collaborative education mechanism involving government, enterprises, and universities, and implement policies to promote digital inclusion and differentiated support.
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