IDEAS home Printed from https://ideas.repec.org/a/eee/streco/v74y2025icp158-174.html

Suppress or let go? The time-varying roles of automation towards labor market

Author

Listed:
  • Zhang, Jingting
  • Shi, Zhiru

Abstract

It has been widely concerned that automation advancements may have imbalanced effects on the labor market, with the excessive unemployment of low-skill workers. To investigate the issue, we design and construct a general equilibrium model, embedded with different types of capital and labor, and a robot tax. The research suggests that it is effective to implement a robot tax to reasonably regulate the development of automation. However, further research shows a negative robot tax or an automation subsidy may be necessary in the long run, especially when considering the low birth rates and aging population. Empirical evidence proves the crowding out effect of automation on the employment of low- and middle-skilled labor, as well as the promotion effect of aging population on automation.

Suggested Citation

  • Zhang, Jingting & Shi, Zhiru, 2025. "Suppress or let go? The time-varying roles of automation towards labor market," Structural Change and Economic Dynamics, Elsevier, vol. 74(C), pages 158-174.
    • Handle: RePEc:eee:streco:v:74:y:2025:i:c:p:158-174
    DOI: 10.1016/j.strueco.2025.03.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0954349X25000402
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.strueco.2025.03.007?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    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. Jurkat Anne & Klump Rainer & Schneider Florian, 2022. "Tracking the Rise of Robots: The IFR Database," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 242(5-6), pages 669-689, December.
    3. Zhou, Yixiao & Tyers, Rod, 2019. "Automation and inequality in China," China Economic Review, Elsevier, vol. 58(C).
    4. Joseph Zeira, 1998. "Workers, Machines, and Economic Growth," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 113(4), pages 1091-1117.
    5. Stähler, Nikolai, 2021. "The Impact of Aging and Automation on the Macroeconomy and Inequality," Journal of Macroeconomics, Elsevier, vol. 67(C).
    6. Nathalie Chusseau & Michel Dumont & Joël Hellier, 2008. "Explaining Rising Inequality: Skill‐Biased Technical Change And North–South Trade," Journal of Economic Surveys, Wiley Blackwell, vol. 22(3), pages 409-457, July.
    7. Zhu, Jun & Zhang, Jingting & Feng, Yiqing, 2022. "Hard budget constraints and artificial intelligence technology," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    8. Tan, Youchao & Liu, Xiumei & Sun, Hanwen & Zeng, Cheng(Colin), 2022. "Population ageing, labour market rigidity and corporate innovation: Evidence from China," Research Policy, Elsevier, vol. 51(2).
    9. Pi, Jiancai & Zhang, Pengqing, 2018. "Skill-biased technological change and wage inequality in developing countries," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 347-362.
    10. Jongwanich, Juthathip & Kohpaiboon, Archanun & Obashi, Ayako, 2022. "Technological advancement, import penetration and labour markets: Evidence from Thailand," World Development, Elsevier, vol. 151(C).
    11. J. A. Mirrlees, 1971. "An Exploration in the Theory of Optimum Income Taxation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 38(2), pages 175-208.
    12. Brent Neiman, 2014. "The Global Decline of the Labor Share," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 129(1), pages 61-103.
    13. 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.
    14. Battisti, Michele & Gatto, Massimo Del & Parmeter, Christopher F., 2022. "Skill-biased technical change and labor market inefficiency," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    15. Prettner, Klaus & Strulik, Holger, 2020. "Innovation, automation, and inequality: Policy challenges in the race against the machine," Journal of Monetary Economics, Elsevier, vol. 116(C), pages 249-265.
    16. Jun Zhu & Jingting Zhang & Wenhong Yan & Yiqing Feng, 2024. "The Contrasting Impacts of Chinese Local Government Debt on Firm’s Total Factor Productivity: Crowding-Out vs Crowding-In," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 60(15), pages 3512-3537, December.
    17. Krenz, Astrid & Prettner, Klaus & Strulik, Holger, 2021. "Robots, reshoring, and the lot of low-skilled workers," European Economic Review, Elsevier, vol. 136(C).
    18. Yunus Aksoy & Henrique S. Basso & Ron P. Smith & Tobias Grasl, 2019. "Demographic Structure and Macroeconomic Trends," American Economic Journal: Macroeconomics, American Economic Association, vol. 11(1), pages 193-222, January.
    19. Mao, Fengfu & Hou, Yuqiao & Wang, Rong & Wang, Zongshun, 2023. "Can industrial intelligence break the carbon curse of natural resources in the context of Post-Covid-19 period? Fresh evidence from China," Resources Policy, Elsevier, vol. 86(PA).
    20. Deng, Liuchun & Fujio, Minako & Lin, Xin & Ota, Rui, 2023. "Labor shortage and early robotization in Japan," Economics Letters, Elsevier, vol. 233(C).
    21. Leibrecht, Markus & Scharler, Johann & Zhoufu, Yan, 2023. "Automation and unemployment: Does collective bargaining moderate their association?," Structural Change and Economic Dynamics, Elsevier, vol. 67(C), pages 264-276.
    22. Bronwyn McCredie & Kerrie Sadiq & Larelle Chapple, 2019. "Navigating the fourth industrial revolution: Taxing automation for fiscal sustainability," Australian Journal of Management, Australian School of Business, vol. 44(4), pages 648-664, November.
    23. Foster-McGregor, Neil & Nomaler, Önder & Verspagen, Bart, 2021. "Job Automation Risk, Economic Structure and Trade: a European Perspective," Research Policy, Elsevier, vol. 50(7).
    24. Fabiano Compagnucci & Andrea Gentili & Enzo Valentini & Mauro Gallegati, 2019. "Robotization and labour dislocation in the manufacturing sectors of OECD countries: a panel VAR approach," Applied Economics, Taylor & Francis Journals, vol. 51(57), pages 6127-6138, December.
    25. Zhang, Pengqing, 2019. "Automation, wage inequality and implications of a robot tax," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 500-509.
    26. Uwe Thuemmel, 2023. "Optimal Taxation of Robots," Journal of the European Economic Association, European Economic Association, vol. 21(3), pages 1154-1190.
    27. David Hémous & Morten Olsen, 2022. "The Rise of the Machines: Automation, Horizontal Innovation, and Income Inequality," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(1), pages 179-223, January.
    28. 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.
    29. David Card & John E. DiNardo, 2002. "Skill-Biased Technological Change and Rising Wage Inequality: Some Problems and Puzzles," Journal of Labor Economics, University of Chicago Press, vol. 20(4), pages 733-783, October.
    30. Frey, Carl Benedikt & Osborne, Michael A., 2017. "The future of employment: How susceptible are jobs to computerisation?," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 254-280.
    31. Daron Acemoglu & Fabrizio Zilibotti, 2001. "Productivity Differences," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 116(2), pages 563-606.
    32. Gasteiger, Emanuel & Prettner, Klaus, 2022. "Automation, Stagnation, And The Implications Of A Robot Tax," Macroeconomic Dynamics, Cambridge University Press, vol. 26(1), pages 218-249, January.
    33. Arnaud Costinot & Iván Werning, 2023. "Robots, Trade, and Luddism: A Sufficient Statistic Approach to Optimal Technology Regulation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(5), pages 2261-2291.
    34. Max Risch, 2024. "Does Taxing Business Owners Affect Employees? Evidence From A Change in the Top Marginal Tax Rate," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 139(1), pages 637-692.
    35. 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.
    36. Daron Acemoglu & Pascual Restrepo, 2017. "Secular Stagnation? The Effect of Aging on Economic Growth in the Age of Automation," American Economic Review, American Economic Association, vol. 107(5), pages 174-179, May.
    37. Andreas Irmen, 2021. "Automation, growth, and factor shares in the era of population aging," Journal of Economic Growth, Springer, vol. 26(4), pages 415-453, December.
    38. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, July.
    39. Zhu, Jun & Zhu, Man & Yang, Zhiwei, 2024. "The financialization of local government debt in China and its risk transmission to commercial banks," Economic Modelling, Elsevier, vol. 133(C).
    40. Costantini, Valeria & Sforna, Giorgia, 2020. "A dynamic CGE model for jointly accounting ageing population, automation and environmental tax reform. European Union as a case study," Economic Modelling, Elsevier, vol. 87(C), pages 280-306.
    41. Joao Guerreiro & Sergio Rebelo & Pedro Teles, 2022. "Should Robots Be Taxed?," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 89(1), pages 279-311.
    42. Joel Mokyr & Chris Vickers & Nicolas L. Ziebarth, 2015. "The History of Technological Anxiety and the Future of Economic Growth: Is This Time Different?," Journal of Economic Perspectives, American Economic Association, vol. 29(3), pages 31-50, Summer.
    43. Fierro, Luca Eduardo & Caiani, Alessandro & Russo, Alberto, 2022. "Automation, Job Polarisation, and Structural Change," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 499-535.
    44. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The skill content of recent technological change: an empirical exploration," Proceedings, Federal Reserve Bank of San Francisco, issue nov.
    45. Daron Acemoglu & Pascual Restrepo, 2022. "Demographics and Automation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 89(1), pages 1-44.
    46. Wang, Jun & Hu, Yong & Zhang, Zhiming, 2021. "Skill-biased technological change and labor market polarization in China," Economic Modelling, Elsevier, vol. 100(C).
    47. 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.
    48. Stiglitz, Joseph E., 1982. "Self-selection and Pareto efficient taxation," Journal of Public Economics, Elsevier, vol. 17(2), pages 213-240, March.
    49. Schmidpeter, Bernhard & Winter-Ebmer, Rudolf, 2021. "Automation, unemployment, and the role of labor market training," European Economic Review, Elsevier, vol. 137(C).
    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. Daniele Angelini, 2023. "Aging Population and Technology Adoption," Working Paper Series of the Department of Economics, University of Konstanz 2023-01, Department of Economics, University of Konstanz.
    2. Aisa, Rosa & Cabeza, Josefina & Martin, Jorge, 2023. "Automation and aging: The impact on older workers in the workforce," The Journal of the Economics of Ageing, Elsevier, vol. 26(C).
    3. Jasmine Mondolo, 2022. "The composite link between technological change and employment: A survey of the literature," Journal of Economic Surveys, Wiley Blackwell, vol. 36(4), pages 1027-1068, September.
    4. Bloom, David E. & Prettner, Klaus & Saadaoui, Jamel & Veruete, Mario, 2025. "Artificial intelligence and the skill premium," Finance Research Letters, Elsevier, vol. 81(C).
    5. Ikeshita, Kenichiro, 2025. "Effects of automation and human investment on skill premium," Innovation and Green Development, Elsevier, vol. 4(2).
    6. Arthur Jacobs & Freddy Heylen, 2021. "Demographic change, secular stagnation and inequality: automation as a blessing?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 21/1030, Ghent University, Faculty of Economics and Business Administration.
    7. Peralta, Catarina & Gil, Pedro Mazeda, 2025. "Automation, education, and population: Dynamic effects in an OLG growth and fertility model," Journal of Economic Behavior & Organization, Elsevier, vol. 234(C).
    8. Stähler, Nikolai, 2021. "The Impact of Aging and Automation on the Macroeconomy and Inequality," Journal of Macroeconomics, Elsevier, vol. 67(C).
    9. 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).
    10. Oscar Afonso & Rosa Forte, 2023. "How powerful are fiscal and monetary policies in a directed technical change model with humans and robots?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 3008-3032, July.
    11. Gasteiger, Emanuel & Prettner, Klaus, 2022. "Automation, Stagnation, And The Implications Of A Robot Tax," Macroeconomic Dynamics, Cambridge University Press, vol. 26(1), pages 218-249, January.
    12. Abeliansky, Ana Lucia & Prettner, Klaus, 2023. "Automation and population growth: Theory and cross-country evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 208(C), pages 345-358.
    13. Hideki Nakamura & Joseph Zeira, 2024. "Automation and unemployment: help is on the way," Journal of Economic Growth, Springer, vol. 29(2), pages 215-250, June.
    14. Burkhard Heer & Andreas Irmen & Bernd Süssmuth, 2023. "Explaining the decline in the US labor share: taxation and automation," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 30(6), pages 1481-1528, December.
    15. Ana L. Abeliansky & Klaus Prettner & Roman Stoellinger, 2023. "Infection Risk at Work, Automatability, and Employment," Department of Economics Working Papers wuwp352, Vienna University of Economics and Business, Department of Economics.
    16. Du, Junhong & He, Jiajia & Yang, Jing & Chen, Xiaohong, 2024. "How industrial robots affect labor income share in task model: Evidence from Chinese A-share listed companies," Technological Forecasting and Social Change, Elsevier, vol. 208(C).
    17. Arntz, Melanie & Gregory, Terry & Zierahn-Weilage, Ulrich, 2019. "Digitalization and the Future of Work: Macroeconomic Consequences," IZA Discussion Papers 12428, Institute of Labor Economics (IZA).
    18. Liu, Yunxin & Cao, Yuqiang & Lu, Meiting & Shan, Yaowen & Xu, Jiangang, 2024. "Automating efficiency: The impact of industrial robots on labor investment in China," Economic Modelling, Elsevier, vol. 140(C).
    19. Jurkat, Anne & Klump, Rainer & Schneider, Florian, 2023. "Robots and Wages: A Meta-Analysis," EconStor Preprints 274156, ZBW - Leibniz Information Centre for Economics.
    20. Wang, Linhui & Cao, Zhanglu & Dong, Zhiqing, 2023. "Are artificial intelligence dividends evenly distributed between profits and wages? Evidence from the private enterprise survey data in China," Structural Change and Economic Dynamics, Elsevier, vol. 66(C), pages 342-356.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:eee:streco:v:74:y:2025:i:c:p:158-174. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/525148 .

    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.