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Does artificial intelligence promote common prosperity within enterprises? —Evidence from Chinese-listed companies in the service industry

Author

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  • Chen, Kaiming
  • Chen, Xiaoqian
  • Wang, Zhan-ao
  • Zvarych, Roman

Abstract

As the largest industry that absorbs labor from different levels of employment and provides different levels of labor remuneration, the service industry has faced severe challenges from the wave of artificial intelligence replacement. This study examines whether artificial intelligence promotes shared prosperity among service industry enterprises based on microdata of listed companies in the Chinese service industry from 2008 to 2022. The main research results indicate that the application of artificial intelligence in the service industry significantly reduces enterprises' labor income share. The main mechanisms of action include the employment structure effect of squeezing out low-educated and frontline workers and the wage-productivity effect that improves labor productivity but benefits capital income, leading to an unequal income distribution. Research has found that improving the integration of regional labor markets and workers' bargaining power can help alleviate the negative effects of artificial intelligence applications on the share of labor income in the service industry. This study helps developing countries, such as China, manage the impact of the widespread application of artificial intelligence on the labor market in the service industry and provides important policy insights for achieving common prosperity.

Suggested Citation

  • Chen, Kaiming & Chen, Xiaoqian & Wang, Zhan-ao & Zvarych, Roman, 2024. "Does artificial intelligence promote common prosperity within enterprises? —Evidence from Chinese-listed companies in the service industry," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    • Handle: RePEc:eee:tefoso:v:200:y:2024:i:c:s004016252300865x
    DOI: 10.1016/j.techfore.2023.123180
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    1. Daron Acemoglu & Pascual Restrepo, 2018. "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment," American Economic Review, American Economic Association, vol. 108(6), pages 1488-1542, June.
    2. David Autor & David Dorn & Lawrence F. Katz & Christina Patterson & John Van Reenen, 2017. "Concentrating on the Fall of the Labor Share," American Economic Review, American Economic Association, vol. 107(5), pages 180-185, May.
    3. Acemoglu, Daron & Koster, Hans & Ozgen, Ceren, 2023. "Robots and Workers: Evidence from the Netherlands," CEPR Discussion Papers 17993, Centre for Economic Policy Research.
    4. Jung, Jin Hwa & Lim, Dong-Geon, 2020. "Industrial robots, employment growth, and labor cost: A simultaneous equation analysis," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    5. Ayhan, Fatih & Elal, Onuray, 2023. "The IMPACTS of technological change on employment: Evidence from OECD countries with panel data analysis," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    6. Erik Brynjolfsson & Daniel Rock & Chad Syverson, 2018. "Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 23-57, National Bureau of Economic Research, Inc.
    7. Jeffrey D. Sachs & Laurence J. Kotlikoff, 2012. "Smart Machines and Long-Term Misery," NBER Working Papers 18629, National Bureau of Economic Research, Inc.
    8. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The Skill Content of Recent Technological Change: An Empirical Exploration," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(4), pages 1279-1333.
    9. David Autor & Anna Salomons, 2018. "Is Automation Labor Share–Displacing? Productivity Growth, Employment, and the Labor Share," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 49(1 (Spring), pages 1-87.
    10. Paul Gaggl & Greg C. Wright, 2017. "A Short-Run View of What Computers Do: Evidence from a UK Tax Incentive," American Economic Journal: Applied Economics, American Economic Association, vol. 9(3), pages 262-294, July.
    11. Daron Acemoglu & Pascual Restrepo, 2018. "Low-Skill and High-Skill Automation," Journal of Human Capital, University of Chicago Press, vol. 12(2), pages 204-232.
    12. repec:bin:bpeajo:v:49:y:2019:i:2018-01:p:1-87 is not listed on IDEAS
    13. Philippe Aghion & Benjamin F. Jones & Charles I. Jones, 2018. "Artificial Intelligence and Economic Growth," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 237-282, National Bureau of Economic Research, Inc.
    14. Aksoy, Cevat Giray & Özcan, Berkay & Philipp, Julia, 2021. "Robots and the gender pay gap in Europe," European Economic Review, Elsevier, vol. 134(C).
    15. Seth G. Benzell & Laurence J. Kotlikoff & Guillermo LaGarda & Jeffrey D. Sachs, 2015. "Robots Are Us: Some Economics of Human Replacement," NBER Working Papers 20941, National Bureau of Economic Research, Inc.
    16. Anton Korinek & Joseph E. Stiglitz, 2018. "Artificial Intelligence and Its Implications for Income Distribution and Unemployment," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 349-390, National Bureau of Economic Research, Inc.
    17. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    Full references (including those not matched with items on IDEAS)

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