Artificial Intelligence Development and Worker Employment Quality: Evidence from China

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.

Authors

Institutions

Publication Details

Journal
Sustainability
Published
2026-10-16
DOI
https://doi.org/10.3390/su182010528
Primary Topic
Labor market dynamics and wage inequality
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Artificial Intelligence Development and Worker Employment Quality: Evidence from China

Xiliang Feng, Yunting Wu
Sustainability
Labor market dynamics and wage inequality
article

Artificial Intelligence Development and Worker Employment Quality: Evidence from China

Xiliang Feng, Yunting Wu
article en

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.

SustainabilityVol. 18(20)
Capital University of Economics and Business (CN)
Capital University of Economics and Business
Decent work and economic growth, Reduced inequalities
Openalex Percentile: Top 8%
Labor market dynamics and wage inequality
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.