Artificial Intelligence Policy and Wage Gaps: Evidence from China
DOI:
https://doi.org/10.62177/apemr.v3i5.1737Keywords:
Artificial Intelligence Policy, Wage Gap, Innovation Technology Benefit Bias, Industrial Structure, Technology DiffusionAbstract
A new round of technological revolution centered on artificial intelligence (AI) is reshaping the global labor market. Using panel data for 280 Chinese cities from 2011 to 2023, this study treats the establishment of National New-Generation Artificial Intelligence Innovation and Development Pilot Zones (AIIDPZs) as a quasi-natural experiment and employs a staggered difference-in-differences model to examine the impact of AI policy on wage gaps. The results show that the AIIDPZs significantly widen wage gaps within cities. Mechanism tests point to three channels: innovation technology benefit bias, industrial restructuring, and technology diffusion. The effects are stronger in cities in the eastern region, cities with well-developed digital infrastructure, and cities with lower human capital. These findings support incorporating the effects of income distribution into policy evaluation, strengthening skills training and mechanisms for sharing innovation gains, tailoring support to local conditions, and coordinating AI policy with employment policy.
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Luo, S., Lei, W., & Hou, P. (2024). Impact of artificial intelligence technology innovation on total factor productivity: An empirical study based on provincial panel data in China. National Accounting Review, 6(2), 172–194. DOI: https://doi.org/10.3934/NAR.2024008
Zhang, Y., Chang, Y., Zhang, X., et al. (2025). Artificial intelligence and enterprise technological innovation: Evidence from China. Managerial and Decision Economics, 46(7), 4056–4069. DOI: https://doi.org/10.1002/mde.70002
Autor, D. H., Levy, F., & Murnane, R. J. (2003). The skill content of recent technological change: An empirical exploration. The Quarterly Journal of Economics, 118(4), 1279–1333. DOI: https://doi.org/10.1162/003355303322552801
Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3–30. DOI: https://doi.org/10.1257/jep.33.2.3
Acemoglu, D., Autor, D., Hazell, J., & Restrepo, P. (2022). Artificial intelligence and jobs: Evidence from online vacancies. Journal of Labor Economics, 40(S1), S293–S340. DOI: https://doi.org/10.1086/718327
Autor, D. H., Katz, L. F., & Krueger, A. B. (1998). Computing inequality: Have computers changed the labor market? The Quarterly Journal of Economics, 113(4), 1169–1213. DOI: https://doi.org/10.1162/003355398555874
Liu, H. Y., Huang, X., & Zhu, Q. (2024). Artificial intelligence, skill training, and wage gap among workers. Journal of Central University of Finance & Economics, (11), 103–116.
Lü, Y., Qiao, X. R., & Kong, X. (2026). Research progress on the impact of artificial intelligence on the evolution of employment structure. Economic Perspectives, (5), 203–224.
Wei, X. H., Zeng, C. Y., Chen, J. W., et al. (2025). Robots, aging workforce, and wage gap. Economic Research Journal, 60(6), 38–56.
Zhu, Q., & Liu, H. Y. (2020). The income distribution effect of artificial intelligence technology: Frontier progress and literature review. Chinese Journal of Population Science, (2), 111–125, 128.
Zhang, X., & Lao, Z. B. (2026). How can artificial intelligence narrow the intra-firm pay gap? A quasi-natural experiment based on the National New Generation AI Innovation Pilot Zones. Journal of Technology Economics, 45(3), 98–112.
Brynjolfsson, E., Li, D., & Raymond, L. (2025). Generative AI at work. The Quarterly Journal of Economics, 140(2), 889–942. DOI: https://doi.org/10.1093/qje/qjae044
Wang, X. H., & Xi, H. N. (2025). Digital economy, skill premium, and labor wage gap. Economic Review, (2), 21–37.
Li, C., Zhang, J., Zhong, C. B., et al. (2026). New-generation artificial intelligence and total factor productivity of service-sector enterprises. Journal of Yunnan University of Finance and Economics, 42(4), 59–71.
Yu, D. S., Yu, J. J., & Chen, M. Y. (2026). AI empowerment and new quality productivity: Evidence from enterprises’ green total factor productivity. China Soft Science, (2), 137–148. DOI: https://doi.org/10.3724/1005-0566.20260212
He, X. G., & Guo, X. B. (2026). AI technological innovation and corporate total factor productivity. Research on Economics and Management, 47(3), 144–160.
Alekseeva, L., Azar, J., Giné, M., Samila, S., & Taska, B. (2021). The demand for AI skills in the labor market. Labour Economics, 71, 102002. DOI: https://doi.org/10.1016/j.labeco.2021.102002
Bone, M., González Ehlinger, E., & Stephany, F. (2025). Skills or degree? The rise of skill-based hiring for AI and green jobs. Technological Forecasting and Social Change, 214, 124042. DOI: https://doi.org/10.1016/j.techfore.2025.124042
Acemoglu, D., & Restrepo, P. (2022). Tasks, automation, and the rise in U.S. wage inequality. Econometrica, 90(5), 1973–2016. DOI: https://doi.org/10.3982/ECTA19815
Gans, J. S., & Stern, S. (2003). The product market and the market for “ideas”: Commercialization strategies for technology entrepreneurs. Research Policy, 32(2), 333–350. DOI: https://doi.org/10.1016/S0048-7333(02)00103-8
Schulte, P. (2021). Skill bias magnified: Identifying the role of international technology diffusion. Journal of International Economics, 129, 103442. DOI: https://doi.org/10.1016/j.jinteco.2021.103442
Li, T. L. (2026). Integration of the digital economy and the real economy and inter-industrial wage inequality: A theoretical analysis. Economic Theory and Business Management, 46(2), 121–136.
Chen, L., & Wu, H. J. (2015). Research status and potential problems of the difference-in-differences method in China. Journal of Quantitative & Technological Economics, 32(7), 133–148.
Dong, Z. Q., Tan, Y. S., & Zhao, H. (2022). Does artificial intelligence technology induce industry reverse-gradient transfer? Evidence from industries and interprovincial characteristics. Academic Monthly, 54(8), 55–66.
Gan, C. H., Zheng, R. G., & Yu, D. F. (2011). An empirical study of the effects of industrial structure on economic growth and fluctuations in China. Economic Research Journal, 46(5), 4–16, 31.
Zhang, L., Lu, D. F., & Wei, Z. Y. (2023). Can the digital economy drive the development of China’s technology market? An empirical analysis based on provincial panel data. Science & Technology Progress and Policy, 40(22), 12–21.
Zhao, T., Zhang, Z., & Liang, S. K. (2020). Digital economy, entrepreneurship, and high-quality economic development: Empirical evidence from urban China. Journal of Management World, 36(10), 65–76.
Ning, L., Wang, F., & Li, J. (2016). Urban innovation, regional externalities of foreign direct investment and industrial agglomeration: Evidence from Chinese cities. Research Policy, 45(4), 830–843. DOI: https://doi.org/10.1016/j.respol.2016.01.014
Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200–230. DOI: https://doi.org/10.1016/j.jeconom.2020.12.001
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Copyright (c) 2026 Jinghai Li, Jingshan Huang, Huanhuan Yang

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
DATE
Accepted: 2026-09-28
Published: 2026-10-14








