IDEAS home Printed from https://ideas.repec.org/p/kan/wpaper/202504.html

Impact of Artificial Intelligence on Occupational Income Inequality in China

Author

Listed:
  • Jing Yuan

    (School of Statistics, Shandong Technology and Business University, Yantai, Shandong 264005, China)

  • Mengjie Han

    (School of Statistics, Shandong Technology and Business University, Yantai, Shandong 264005, China)

  • Jinxin Cao

    (School of Statistics, Shandong Technology and Business University, Yantai, Shandong 264005, China)

  • Zongwu Cai

    (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA)

Abstract

Using data from the China Family Panel Studies, this paper empirically examines the impact of artificial intelligence (AI) on occupational income inequality by employing the Pareto coefficient to measure the degree of within-occupation income inequality in China. The results show that income inequality across occupations has markedly increased in recent years. Provinces with a relatively high level of occupational income inequality are concentrated primarily in the eastern and central regions in China, whereas the level is notably lower in the western region. Also, AI significantly widens occupational income gaps. Further, mediation analysis reveals that AI significantly aggravates occupational income inequality through two channels: industrial sophistication and technological innovation. Regional heterogeneity analysis indicates significant regional disparities in this effect. The impact is significant and strongest in Western China, significant but moderate in Eastern China, and statistically insignificant in Central China. Going forward, China should improve the regulatory and adjustment mechanisms for occupational earnings, establish a systematic occupational income monitoring system, and deepen the reform of the income distribution system. These measures will help narrow occupational income gaps driven by the skill premium and advance the achievement of common prosperity.

Suggested Citation

  • Jing Yuan & Mengjie Han & Jinxin Cao & Zongwu Cai, 2025. "Impact of Artificial Intelligence on Occupational Income Inequality in China," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202504, University of Kansas, Department of Economics, revised Mar 2026.
    • Handle: RePEc:kan:wpaper:202504
    as

    Download full text from publisher

    File URL: https://kuwpaper.ku.edu/2025Papers/202504.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Daron Acemoglu & Pascual Restrepo, 2019. "Automation and New Tasks: How Technology Displaces and Reinstates Labor," Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 3-30, Spring.
    2. Kleibergen, Frank & Paap, Richard, 2006. "Generalized reduced rank tests using the singular value decomposition," Journal of Econometrics, Elsevier, vol. 133(1), pages 97-126, July.
    3. David H. Autor, 2015. "Why Are There Still So Many Jobs? The History and Future of Workplace Automation," Journal of Economic Perspectives, American Economic Association, vol. 29(3), pages 3-30, Summer.
    4. Lemos, Sara, 2009. "Minimum wage effects in a developing country," Labour Economics, Elsevier, vol. 16(2), pages 224-237, April.
    5. Jing Yuan & Teng Ma & Yinghui Wang & Zongwu Cai, 2025. "Measurement and Decomposition Analysis of Occupational Income Inequality in China," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202502, University of Kansas, Department of Economics, revised Jan 2025.
    6. Philip Armour & Richard V. Burkhauser & Jeff Larrimore, 2016. "Using The Pareto Distribution To Improve Estimates Of Topcoded Earnings," Economic Inquiry, Western Economic Association International, vol. 54(2), pages 1263-1273, April.
    7. Morten Olsen & Joshua Gottlieb & David Hemous & Jeffrey Clemens, 2017. "The Spill-over Effects of Top Income Inequality," 2017 Meeting Papers 332, Society for Economic Dynamics.
    8. Wang, Changlin & Jiao, Du, 2025. "Impact of artificial intelligence on the labor income distribution: Labor substitution or production upgrading?," Finance Research Letters, Elsevier, vol. 73(C).
    9. Thor Berger & Carl Benedikt Frey, 2016. "Structural Transformation in the OECD: Digitalisation, Deindustrialisation and the Future of Work," OECD Social, Employment and Migration Working Papers 193, OECD Publishing.
    10. Jing Yuan & Teng Ma & Yinghui Wang & Zongwu Cai, 2025. "Measurement and Decomposition Analysis of Occupational Income Inequality in China," Stats, MDPI, vol. 8(1), pages 1-26, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tao Chen & Shuwen Pi & Qing Sophie Wang, 2025. "Artificial Intelligence and Corporate Investment Efficiency: Evidence from Chinese Listed Companies," Working Papers in Economics 25/05, University of Canterbury, Department of Economics and Finance.
    2. Qihang Li & Yituan Liu & Wenjie Li & Linman Zheng, 2025. "Will Industrial Robots Terminate Enterprise Innovation?—An Empirical Evidence from China’s Enterprise Robot Penetration," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(2), pages 10074-10103, June.
    3. Gao, Jie & Li, Zhizhuo & Nguyen, Thithuha & Zhang, Wentao, 2025. "Digital transformation and enterprise employment," International Review of Economics & Finance, Elsevier, vol. 99(C).
    4. Caselli, Mauro & Fracasso, Andrea & Scicchitano, Sergio & Traverso, Silvio & Tundis, Enrico, 2025. "What workers and robots do: An activity-based analysis of the impact of robotization on changes in local employment," Research Policy, Elsevier, vol. 54(1).
    5. Sindhwani, Rahul & Pereira, Vijay & Sampat, Brinda & Shankar, Amit & Nigam, Achint & Salwan, Prashant, 2025. "Exploring barriers to social robot adoption: A mixed-method study in the Indian retail sector," Technological Forecasting and Social Change, Elsevier, vol. 212(C).
    6. David Kunst, 2019. "Deskilling among Manufacturing Production Workers," Tinbergen Institute Discussion Papers 19-050/VI, Tinbergen Institute, revised 30 Dec 2020.
    7. Montobbio, Fabio & Staccioli, Jacopo & Virgillito, Maria Enrica & Vivarelli, Marco, 2022. "Robots and the origin of their labour-saving impact," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    8. Zhou, Yuwen & Shi, Xin, 2025. "How does digital technology adoption affect corporate employment? Evidence from China," Economic Modelling, Elsevier, vol. 147(C).
    9. Thomas Gries & Wim Naudé, 2022. "Modelling artificial intelligence in economics," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 56(1), pages 1-13, December.
    10. Huanan Liu & Yan Wang & Zhoufu Yan, 2024. "Artificial Intelligence and Food Processing Firms Productivity: Evidence from China," Sustainability, MDPI, vol. 16(14), pages 1-18, July.
    11. Usabiaga, Carlos & Núñez, Fernando & Arendt, Lukasz & Gałecka-Burdziak, Ewa & Pater, Robert, 2022. "Skill requirements and labour polarisation: An association analysis based on Polish online job offers," Economic Modelling, Elsevier, vol. 115(C).
    12. Ziteng Fan & Jing Ning & Alex Jingwei He, 2026. "Slowing Down or Adapting to Technological Progress? Robot Replacement Risks and Policy Preferences," Regulation & Governance, John Wiley & Sons, vol. 20(1), pages 134-148, January.
    13. Narvaez, Cristian Camilo Moreno, 2022. "Automation and the Labor Market: Evidence from Technological Change in Colombia, 2009-2017," SocArXiv v382q_v1, Center for Open Science.
    14. Dami'an Vergara, 2022. "Minimum Wages and Optimal Redistribution," Papers 2202.00839, arXiv.org, revised Dec 2022.
    15. Wang, Yijing & Xi, Kang, 2026. "Carbon markets, industrial robots, and the labour skill premium," Economic Analysis and Policy, Elsevier, vol. 89(C), pages 986-1004.
    16. Piotr Lewandowski & Wojciech Szymczak, 2024. "Automation, Trade Unions and Involuntary Atypical Employment," IBS Working Papers 02/2024, Instytut Badan Strukturalnych.
    17. Kerstin Hötte & Angelos Theodorakopoulos & Pantelis Koutroumpis, 2024. "Automation and taxation," Oxford Economic Papers, Oxford University Press, vol. 76(4), pages 945-969.
    18. Daron Acemoglu & Pascual Restrepo, 2020. "The wrong kind of AI? Artificial intelligence and the future of labour demand," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 13(1), pages 25-35.
    19. Freire, Clovis, 2025. "Is this time different? Impact of AI in output, employment and inequality across low, middle and high-income countries," Structural Change and Economic Dynamics, Elsevier, vol. 73(C), pages 136-157.
    20. Zhang, Xinchun & Sun, Murong & Liu, Jianxu & Xu, Aijia, 2024. "The nexus between industrial robot and employment in China: The effects of technology substitution and technology creation," Technological Forecasting and Social Change, Elsevier, vol. 202(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • D33 - Microeconomics - - Distribution - - - Factor Income Distribution
    • E25 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Aggregate Factor Income Distribution
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kan:wpaper:202504. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Professor Zongwu Cai (email available below). General contact details of provider: https://edirc.repec.org/data/deuksus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.