IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v224y2026ics0040162525005499.html

Divergent Paths: Unpacking the role of skill-biased and routine-biased technological change on urban income inequality in China

Author

Listed:
  • Xing, Zuge
  • He, Canfei
  • Pan, Yuxin

Abstract

Biased technological changes have reshaped urban labor markets, significantly affecting income distribution. Despite the recognition of skill-biased technological change (SBTC) and routine-biased technological change (RBTC) frameworks as key to explaining income inequality, it remains unclear whether all types of biased technological changes widen urban income inequality (UII). This paper uses machine learning methods on Chinese census data from 2000 to 2015 to build a large sample urban labor income dataset and analyzes the effects of SBTC, non-routine cognitive RBTC, and non-routine manual RBTC on UII. The results reveal that SBTC and non-routine cognitive RBTC exacerbate UII in China, while non-routine manual RBTC can reduce it and weaken the adverse distributional effects of SBTC. The influence of these technological changes varies markedly across cities with different human capital, foreign direct investment, and economic complexity. The findings of this study contribute to a deeper understanding of the interplay between technological progress and labor market income distribution and offer insights for policymakers in developing countries to formulate targeted labor skill training and employment diversification strategies.

Suggested Citation

  • Xing, Zuge & He, Canfei & Pan, Yuxin, 2026. "Divergent Paths: Unpacking the role of skill-biased and routine-biased technological change on urban income inequality in China," Technological Forecasting and Social Change, Elsevier, vol. 224(C).
  • Handle: RePEc:eee:tefoso:v:224:y:2026:i:c:s0040162525005499
    DOI: 10.1016/j.techfore.2025.124518
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162525005499
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.techfore.2025.124518?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. Benjamin Moll & Lukasz Rachel & Pascual Restrepo, 2022. "Uneven Growth: Automation's Impact on Income and Wealth Inequality," Econometrica, Econometric Society, vol. 90(6), pages 2645-2683, November.
    2. Andrea Gabrielli & Matthieu Cristelli & Dario Mazzilli & Andrea Tacchella & Andrea Zaccaria & Luciano Pietronero, 2017. "Why we like the ECI+ algorithm," Papers 1708.01161, arXiv.org.
    3. Arthur Lewbel, 2012. "Using Heteroscedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 67-80.
    4. Lu, Yi & Tao, Zhigang & Zhu, Lianming, 2017. "Identifying FDI spillovers," Journal of International Economics, Elsevier, vol. 107(C), pages 75-90.
    5. Dirk Te Velde & Oliver Morrissey, 2004. "Foreign Direct Investment, Skills And Wage Inequality In East Asia," Journal of the Asia Pacific Economy, Taylor & Francis Journals, vol. 9(3), pages 348-369.
    6. Edeh, Jude & Prévot, Frédéric, 2024. "Beyond funding: The moderating role of firms' R&D human capital on government support and venture capital for regional innovation in China," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    7. Antonelli, Cristiano & Gehringer, Agnieszka, 2017. "Technological change, rent and income inequalities: A Schumpeterian approach," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 85-98.
    8. Zhang, Zhuo, 2023. "The impact of the artificial intelligence industry on the number and structure of employments in the digital economy environment," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    9. Niu, Meng & Wang, Zhenguo & Zhang, Yabin, 2022. "How information and communication technology drives (routine and non-routine) jobs: Structural path and decomposition analysis for China," Telecommunications Policy, Elsevier, vol. 46(1).
    10. Duan, Wenqi & Li, Chen, 2023. "Be alert to dangers: Collapse and avoidance strategies of platform ecosystems," Journal of Business Research, Elsevier, vol. 162(C).
    11. Xiao Ling & Zhangwei Luo & Yanchao Feng & Xun Liu & Yue Gao, 2023. "How does digital transformation relieve the employment pressure in China? Empirical evidence from the national smart city pilot policy," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 10(1), pages 1-17, December.
    12. Cesar A. Hidalgo & Ricardo Hausmann, 2009. "The Building Blocks of Economic Complexity," Papers 0909.3890, arXiv.org.
    13. Xu, Aiting & Dai, Yujie & Hu, Zhiyuan & Qiu, Keyang, 2025. "Can green finance policy promote inclusive green growth?- Based on the quasi-natural experiment of China's green finance reform and innovation pilot zone," International Review of Economics & Finance, Elsevier, vol. 100(C).
    14. Baltagi, Badi H. & Rich, Daniel P., 2005. "Skill-biased technical change in US manufacturing: a general index approach," Journal of Econometrics, Elsevier, vol. 126(2), pages 549-570, June.
    15. Wang, Shuo & Wang, Yuzhang & Li, Chengyou, 2024. "AI-driven capital-skill complementarity: Implications for skill premiums and labor mobility," Finance Research Letters, Elsevier, vol. 68(C).
    16. Pandya, Sonal S., 2010. "Labor Markets and the Demand for Foreign Direct Investment," International Organization, Cambridge University Press, vol. 64(3), pages 389-409, July.
    17. Gao, Da & Li, Ge & Yu, Jiyu, 2022. "Does digitization improve green total factor energy efficiency? Evidence from Chinese 213 cities," Energy, Elsevier, vol. 247(C).
    18. Nathan Nunn & Nancy Qian, 2014. "US Food Aid and Civil Conflict," American Economic Review, American Economic Association, vol. 104(6), pages 1630-1666, June.
    19. David H. Autor, 2019. "Work of the Past, Work of the Future," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 1-32, May.
    20. Behar, Alberto, 2025. "The elasticity of substitution between skilled and unskilled labor in developing countries: A directed technical change perspective," Journal of Development Economics, Elsevier, vol. 174(C).
    21. Saleh Albeaik & Mary Kaltenberg & Mansour Alsaleh & Cesar A. Hidalgo, 2017. "Improving the Economic Complexity Index," Papers 1707.05826, arXiv.org, revised Jul 2017.
    22. David H. Autor & David Dorn, 2013. "The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market," American Economic Review, American Economic Association, vol. 103(5), pages 1553-1597, August.
    23. Federico S. Mandelman & Andrei Zlate, 2022. "Offshoring, Automation, Low-Skilled Immigration, and Labor Market Polarization," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(1), pages 355-389, January.
    24. Ge, Peng & Sun, Wenkai & Zhao, Zhong, 2021. "Employment structure in China from 1990 to 2015," Journal of Economic Behavior & Organization, Elsevier, vol. 185(C), pages 168-190.
    25. 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.
    26. Stephen Machin & John Van Reenen, 1998. "Technology and Changes in Skill Structure: Evidence from Seven OECD Countries," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 113(4), pages 1215-1244.
    27. 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.
    28. Qiu, Leiju & Zhong, Shunbin & Sun, Baowen, 2021. "Blessing or curse? The effect of broadband Internet on China’s inter-city income inequality," Economic Analysis and Policy, Elsevier, vol. 72(C), pages 626-650.
    29. Sébastien Breau & Dieter F. Kogler & Kenyon C. Bolton, 2014. "On the Relationship between Innovation and Wage Inequality: New Evidence from Canadian Cities," Economic Geography, Taylor & Francis Journals, vol. 90(4), pages 351-373, October.
    30. Daron Acemoglu, 2002. "Technical Change, Inequality, and the Labor Market," Journal of Economic Literature, American Economic Association, vol. 40(1), pages 7-72, March.
    31. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    32. Fu, Yuming & Gabriel, Stuart A., 2012. "Labor migration, human capital agglomeration and regional development in China," Regional Science and Urban Economics, Elsevier, vol. 42(3), pages 473-484.
    33. Eli Bekman & John Bound & Stephen Machin, 1998. "Implications of Skill-Biased Technological Change: International Evidence," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 113(4), pages 1245-1279.
    34. Pierre-Alexandre Balland & David Rigby, 2017. "The Geography of Complex Knowledge," Economic Geography, Taylor & Francis Journals, vol. 93(1), pages 1-23, January.
    35. Belton M. Fleisher & William H. McGuire & Yaqin Su & Min Qiang Zhao, 2024. "Polarization of employment and wages in China," Economics of Transition and Institutional Change, John Wiley & Sons, vol. 32(1), pages 49-71, January.
    36. Acheampong, Alex O. & Opoku, Eric Evans Osei & Amankwaa, Afua & Dzator, Janet, 2024. "Energy poverty and gender equality in education: Unpacking the transmission channels," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    37. Lo Turco, Alessia & Maggioni, Daniela, 2022. "The knowledge and skill content of production complexity," Research Policy, Elsevier, vol. 51(8).
    38. David H. Autor & Lawrence F. Katz & Melissa S. Kearney, 2006. "The Polarization of the U.S. Labor Market," American Economic Review, American Economic Association, vol. 96(2), pages 189-194, May.
    39. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
    40. Li, Yue & Li, Guofu & Xu, Anfeng & Yao, Fengmin, 2025. "Research on the collaborative mechanism of a data trading market based on a four-party evolutionary game in the context of digital intelligence," Socio-Economic Planning Sciences, Elsevier, vol. 100(C).
    41. Jan Eeckhout & Christoph Hedtrich & Roberto Pinheiro, 2026. "IT and Urban Polarization," American Economic Journal: Macroeconomics, American Economic Association, vol. 18(1), pages 223-259, January.
    42. Feng, Shuaizhang & Xia, Xiaoyu, 2022. "Heterogeneous firm responses to increases in high-skilled workers: Evidence from China's college enrollment expansion," China Economic Review, Elsevier, vol. 73(C).
    43. Guido Matias Cortes, 2016. "Where Have the Middle-Wage Workers Gone? A Study of Polarization Using Panel Data," Journal of Labor Economics, University of Chicago Press, vol. 34(1), pages 63-105.
    44. Zhou, Xiaoxiao & Cai, Ziming & Tan, Kim Hua & Zhang, Linling & Du, Juntao & Song, Malin, 2021. "Technological innovation and structural change for economic development in China as an emerging market," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    45. 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.
    46. Castro Silva, Hugo & Lima, Francisco, 2017. "Technology, employment and skills: A look into job duration," Research Policy, Elsevier, vol. 46(8), pages 1519-1530.
    47. Maarten Goos & Alan Manning & Anna Salomons, 2014. "Explaining Job Polarization: Routine-Biased Technological Change and Offshoring," American Economic Review, American Economic Association, vol. 104(8), pages 2509-2526, August.
    48. Baltagi, Badi H. & Liu, Long, 2025. "Testing for spatial lag dependence and homoskedasticity in a random effects panel data model," Economics Letters, Elsevier, vol. 254(C).
    49. Farla, Kristine & de Crombrugghe, Denis & Verspagen, Bart, 2016. "Institutions, Foreign Direct Investment, and Domestic Investment: Crowding Out or Crowding In?," World Development, Elsevier, vol. 88(C), pages 1-9.
    50. Zifeng Chen & Anthony Gar-On Yeh, 2019. "Accessibility Inequality and Income Disparity in Urban China: A Case Study of Guangzhou," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 109(1), pages 121-141, January.
    51. James Feigenbaum & Daniel P Gross, 2024. "Answering the Call of Automation: How the Labor Market Adjusted to Mechanizing Telephone Operation," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 139(3), pages 1879-1939.
    52. Silvia Vannutelli & Sergio Scicchitano & Marco Biagetti, 2022. "Routine-biased technological change and wage inequality: do workers’ perceptions matter?," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 12(3), pages 409-450, September.
    53. Danyelle Branco & Bladimir Carrillo & Wilman Iglesias, 2025. "Routine-Biased Technological Change and Endogenous Skill Investments," American Economic Journal: Economic Policy, American Economic Association, vol. 17(3), pages 236-280, August.
    54. Liao, Junmin, 2020. "The rise of the service sector in China," China Economic Review, Elsevier, vol. 59(C).
    55. Daron Acemoglu & Pascual Restrepo, 2022. "Tasks, Automation, and the Rise in U.S. Wage Inequality," Econometrica, Econometric Society, vol. 90(5), pages 1973-2016, September.
    56. Qian, Cheng & Zhu, Chun & Huang, Duen-Huang & Zhang, Shangfeng, 2023. "Examining the influence mechanism of artificial intelligence development on labor income share through numerical simulations," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    57. 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.
    58. Antonelli, Cristiano & Scellato, Giuseppe, 2019. "Wage inequality and directed technological change: Implications for income distribution," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 59-65.
    59. Lee, Jong-Wha & Wie, Dainn, 2015. "Technological Change, Skill Demand, and Wage Inequality: Evidence from Indonesia," World Development, Elsevier, vol. 67(C), pages 238-250.
    60. Tomas Havranek & Zuzana Irsova & Lubica Laslopova & Olesia Zeynalova, 2024. "Publication and Attenuation Biases in Measuring Skill Substitution," The Review of Economics and Statistics, MIT Press, vol. 106(5), pages 1187-1200, September.
    61. Daron Acemoglu, 1998. "Why Do New Technologies Complement Skills? Directed Technical Change and Wage Inequality," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 113(4), pages 1055-1089.
    62. Wang, Jun & Hu, Yong & Zhang, Zhiming, 2021. "Skill-biased technological change and labor market polarization in China," Economic Modelling, Elsevier, vol. 100(C).
    63. David H. Autor & Lawrence F. Katz & Alan B. Krueger, 1998. "Computing Inequality: Have Computers Changed the Labor Market?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 113(4), pages 1169-1213.
    64. Acemoglu, Daron & Autor, David, 2011. "Skills, Tasks and Technologies: Implications for Employment and Earnings," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 12, pages 1043-1171, Elsevier.
    65. Akinori Tomohara & Kazuhiko Yokota, 2011. "Foreign direct investment and wage inequality: is skill upgrading the culprit?," Applied Economics Letters, Taylor & Francis Journals, vol. 18(8), pages 773-781.
    66. Miriam Rinawi & Uschi Backes-Gellner, 2021. "Labour market transitions after layoffs: the role of occupational skills," Oxford Economic Papers, Oxford University Press, vol. 73(1), pages 76-97.
    67. Neil Lee, 2011. "Are Innovative Regions More Unequal? Evidence from Europe," Environment and Planning C, , vol. 29(1), pages 2-23, February.
    68. Gray, Rowena, 2013. "Taking technology to task: The skill content of technological change in early twentieth century United States," Explorations in Economic History, Elsevier, vol. 50(3), pages 351-367.
    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. Tan, Joanne, 2024. "Multidimensional heterogeneity and matching in a frictional labor market — An application to polarization," Labour Economics, Elsevier, vol. 90(C).
    2. 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).
    3. Manuel A. Hidalgo-Pérez & Benedetto Molinari, 2022. "The effect of early automation on the wage distribution with endogenous occupational choices," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 39(3), pages 1055-1082, October.
    4. Falck, Oliver & Heimisch-Roecker, Alexandra & Wiederhold, Simon, 2021. "Returns to ICT skills," Research Policy, Elsevier, vol. 50(7).
    5. Bhalotra, Sonia & Fernandez, Manuel & Wang, Fan, 2022. "The Distribution of the Gender Wage Gap: An Equilibrium Model," CEPR Discussion Papers 17253, Centre for Economic Policy Research.
    6. Cavenaile, Laurent, 2021. "Offshoring, computerization, labor market polarization and top income inequality," Journal of Macroeconomics, Elsevier, vol. 69(C).
    7. Fonseca, Tiago & Lima, Francisco & Pereira, Sonia C., 2018. "Job polarization, technological change and routinization: Evidence for Portugal," Labour Economics, Elsevier, vol. 51(C), pages 317-339.
    8. Lu, Chia-Hui, 2025. "Automation and job polarization," Journal of Macroeconomics, Elsevier, vol. 84(C).
    9. Janssen, Simon & Mohrenweiser, Jens, 2018. "The Shelf Life of Incumbent Workers during Accelerating Technological Change: Evidence from a Training Regulation Reform," IZA Discussion Papers 11312, IZA Network @ LISER.
    10. T. Gries & R. Grundmann & I. Palnau & M. Redlin, 2017. "Innovations, growth and participation in advanced economies - a review of major concepts and findings," International Economics and Economic Policy, Springer, vol. 14(2), pages 293-351, April.
    11. David Kunst, 2019. "Deskilling among Manufacturing Production Workers," Tinbergen Institute Discussion Papers 19-050/VI, Tinbergen Institute, revised 30 Dec 2020.
    12. David Autor & Caroline Chin & Anna Salomons & Bryan Seegmiller, 2024. "New Frontiers: The Origins and Content of New Work, 1940–2018," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 139(3), pages 1399-1465.
    13. 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.
    14. Vuković, Danijela Lazović & Damijan, Jože P., 2025. "Drivers of income inequality in OECD countries: Testing the Milanovic's TOP hypothesis," Structural Change and Economic Dynamics, Elsevier, vol. 74(C), pages 416-440.
    15. Maarten Goos & Melanie Arntz & Ulrich Zierahn & Terry Gregory & Stephanie Carretero Gomez & Ignacio Gonzalez Vazquez & Koen Jonkers, 2019. "The Impact of Technological Innovation on the Future of Work," JRC Working Papers on Labour, Education and Technology 2019-03, Joint Research Centre.
    16. Peng, Fei & Anwar, Sajid & Kang, Lili, 2017. "New technology and old institutions: An empirical analysis of the skill-biased demand for older workers in Europe," Economic Modelling, Elsevier, vol. 64(C), pages 1-19.
    17. Graetz, Georg, 2020. "Technological change and the Swedish labor market," Working Paper Series 2020:19, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    18. Cirillo, Valeria & Evangelista, Rinaldo & Guarascio, Dario & Sostero, Matteo, 2021. "Digitalization, routineness and employment: An exploration on Italian task-based data," Research Policy, Elsevier, vol. 50(7).
    19. Caselli, Mauro & Fracasso, Andrea & Scicchitano, Sergio & Traverso, Silvio & Tundis, Enrico, 2021. "Stop worrying and love the robot: An activity-based approach to assess the impact of robotization on employment dynamics," GLO Discussion Paper Series 802, Global Labor Organization (GLO).
    20. Fonseca, Tiago & Lima, Francisco & Pereira, Sonia C., 2018. "Understanding productivity dynamics: A task taxonomy approach," Research Policy, Elsevier, vol. 47(1), pages 289-304.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • P25 - Political Economy and Comparative Economic Systems - - Socialist and Transition Economies - - - Urban, Rural, and Regional Economics

    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:tefoso:v:224:y:2026:i:c:s0040162525005499. 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.sciencedirect.com/science/journal/00401625 .

    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.