IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i7p2938-d1368624.html
   My bibliography  Save this article

Sustainable Development of the Rural Labor Market in China from the Perspective of Occupation Structure Transformation

Author

Listed:
  • Zhiyuan Ma

    (Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    United Nations Environment Programme-International Ecosystem Management Partnership (UNEP-IEMP), Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Yunli Bai

    (Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    United Nations Environment Programme-International Ecosystem Management Partnership (UNEP-IEMP), Beijing 100101, China)

  • Linxiu Zhang

    (Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    United Nations Environment Programme-International Ecosystem Management Partnership (UNEP-IEMP), Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract

This study analyzes the structural transformations of the occupations of all off-farm rural laborers in China over the period 2007–2022. The changes in the rural labor market are mainly reflected in the decrease in the share of routine manual laborers from 66.59 percent to 52.77 percent, and the increases in the shares of non-routine cognitive and non-working laborers by 4.48 and 10.73 percentage points from 2007 to 2022, respectively. By adopting decomposition analysis, which improves the definition of occupational classification based on information on sub-sectors in industries and job contents using a dataset with a nationally representative sample covering 2000 rural households, the results show that both composition effect and propensity effect play important roles in the decrease in routine manual occupations; the composition effect dominates the changes in the non-routine cognitive occupation category, while the propensity effect is the main driver of the increasing trend in the non-working group. The economic model further illustrates the results of decomposition analysis. These findings imply that the government should further improve education in rural areas and pay greater attention to female and low-education-attainment groups among rural laborers. This study provides a reference for policies aimed at promoting the sustainable development of the rural labor market.

Suggested Citation

  • Zhiyuan Ma & Yunli Bai & Linxiu Zhang, 2024. "Sustainable Development of the Rural Labor Market in China from the Perspective of Occupation Structure Transformation," Sustainability, MDPI, vol. 16(7), pages 1-22, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:7:p:2938-:d:1368624
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/7/2938/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/7/2938/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Oaxaca, Ronald, 1973. "Male-Female Wage Differentials in Urban Labor Markets," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 14(3), pages 693-709, October.
    2. 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.
    3. Fana, Marta & Giangregorio, Luca, 2024. "The role of tasks, contractual arrangements, and job composition in explaining the dynamics of wage inequality: Evidence from France," Labour Economics, Elsevier, vol. 87(C).
    4. 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.
    5. 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.
    6. Jaison R. Abel & Richard Deitz, 2012. "Job polarization and rising inequality in the nation and the New York-northern New Jersey region," Current Issues in Economics and Finance, Federal Reserve Bank of New York, vol. 18(Oct).
    7. Linxiu Zhang & Yongqing Dong & Chengfang Liu & Yunli Bai, 2018. "Off-farm employment over the past four decades in rural China," China Agricultural Economic Review, Emerald Group Publishing Limited, vol. 10(2), pages 190-214, May.
    8. Wu, Kai & Wan, Shijia, 2023. "Job stability and household financial vulnerability: Evidence from field surveys in China," Finance Research Letters, Elsevier, vol. 58(PC).
    9. Linxiu Zhang & Yongqing Dong & Chengfang Liu & Yunli Bai, 2018. "Off-farm employment over the past four decades in rural China," China Agricultural Economic Review, Emerald Group Publishing Limited, vol. 10(2), pages 190-214, May.
    10. Fang Cai, 2010. "Demographic transition, demographic dividend, and Lewis turning point in China," China Economic Journal, Taylor & Francis Journals, vol. 3(2), pages 107-119.
    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. Naticchioni, Paolo & Ragusa, Giuseppe & Massari, Riccardo, 2014. "Unconditional and Conditional Wage Polarization in Europe," IZA Discussion Papers 8465, Institute of Labor Economics (IZA).
    2. Koomen, Miriam & Backes-Gellner, Uschi, 2022. "Occupational tasks and wage inequality in West Germany: A decomposition analysis," Labour Economics, Elsevier, vol. 79(C).
    3. Cortes, Guido Matias & Jaimovich, Nir & Siu, Henry E., 2017. "Disappearing routine jobs: Who, how, and why?," Journal of Monetary Economics, Elsevier, vol. 91(C), pages 69-87.
    4. Maximilian Longmuir & Carsten Schröde & Matteo Targa, 2020. "De-Routinization of Jobs and Polarization of Earnings: Evidence from 35 Countries," Working Papers 1397, Economic Research Forum, revised 20 Jun 2020.
    5. Cortes, Guido Matias & Jaimovich, Nir & Nekarda, Christopher J. & Siu, Henry E., 2020. "The dynamics of disappearing routine jobs: A flows approach," Labour Economics, Elsevier, vol. 65(C).
    6. Rosalia Castellano & Gaetano Musella & Gennaro Punzo, 2019. "Exploring changes in the employment structure and wage inequality in Western Europe using the unconditional quantile regression," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 46(2), pages 249-304, May.
    7. Cristian Bonavida, 2022. "Lo que hacemos con lo que sabemos. Brechas de género en habilidades y tareas en América Latina," Asociación Argentina de Economía Política: Working Papers 4542, Asociación Argentina de Economía Política.
    8. Tommaso AGASISTI & Geraint JOHNES & Marco PACCAGNELLA, 2021. "Tasks, occupations and wages in OECD countries," International Labour Review, International Labour Organization, vol. 160(1), pages 85-112, March.
    9. Kudoh, Noritaka & Miyamoto, Hiroaki, 2025. "Robots, AI, and unemployment," Journal of Economic Dynamics and Control, Elsevier, vol. 174(C).
    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. 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.
    12. Georg Graetz & Guy Michaels, 2017. "Is Modern Technology Responsible for Jobless Recoveries?," American Economic Review, American Economic Association, vol. 107(5), pages 168-173, May.
    13. Dirk Antonczyk & Thomas DeLeire & Bernd Fitzenberger, 2018. "Polarization and Rising Wage Inequality: Comparing the U.S. and Germany," Econometrics, MDPI, vol. 6(2), pages 1-33, April.
    14. David J. Deming, 2017. "The Growing Importance of Social Skills in the Labor Market," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(4), pages 1593-1640.
    15. Maya Eden & Paul Gaggl, 2018. "On the Welfare Implications of Automation," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 29, pages 15-43, July.
    16. Alex Chernoff & Casey Warman, 2023. "COVID-19 and implications for automation," Applied Economics, Taylor & Francis Journals, vol. 55(17), pages 1939-1957, April.
    17. Bárány, Zsófia L. & Siegel, Christian, 2020. "Biased technological change and employment reallocation," Labour Economics, Elsevier, vol. 67(C).
    18. 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).
    19. Wojciech Hardy & Roma Keister & Piotr Lewandowski, 2016. "Technology or Upskilling? Trends in the Task Composition of Jobs in Central and Eastern Europe," HKUST IEMS Working Paper Series 2016-40, HKUST Institute for Emerging Market Studies, revised Dec 2016.
    20. Davide Consoli & Francesco Vona & Francesco Rentocchini, 2016. "That was then, this is now: skills and routinization in the 2000s," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 25(5), pages 847-866.

    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:gam:jsusta:v:16:y:2024:i:7:p:2938-:d:1368624. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.