IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v12y2025i1d10.1057_s41599-024-04326-1.html
   My bibliography  Save this article

The effect of digital economy on rural workforce occupation transformation ability: Evidence from China

Author

Listed:
  • Bin Xiong

    (Guangxi University
    The Key Research Base of Humanities and Social Sciences in Guangxi Colleges and Universities)

  • Qi Sui

    (Guangxi University)

Abstract

Occupation transformation is a frequently discussed topic among scientists, politicians and the general public. Along with the development of science and technology, how to take advantage of the digital economy to improve the transformation ability of rural labor has become the key to rural revitalization. Using the China Labor Dynamics Survey data (CLDS), this research empirically investigates the influence of digital economy involvement on rural labor force occupation transformation ability, drawing on the sustainable livelihoods framework. The findings indicate that engagement in the digital economy can greatly improve the labor force’s occupation transformation ability. Rural workers promote their occupation self-efficacy and subjective well-being by participating in the digital economy, which improves the ability for occupational transformation. Further research revealed that the digital economy has a “spillover effect” on the labor force’s occupation transformation ability. Meanwhile, machine learning analysis revealed that age, house value, and total household income capacity are the primary elements driving heterogeneity in the digital economy’s ability to impact the occupation transformation ability. Overall, this research sheds new light on the long-term development of rural labour force employment in the digital economy era.

Suggested Citation

  • Bin Xiong & Qi Sui, 2025. "The effect of digital economy on rural workforce occupation transformation ability: Evidence from China," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-15, December.
  • Handle: RePEc:pal:palcom:v:12:y:2025:i:1:d:10.1057_s41599-024-04326-1
    DOI: 10.1057/s41599-024-04326-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-024-04326-1
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1057/s41599-024-04326-1?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Victor Chernozhukov & Iván Fernández‐Val & Ye Luo, 2018. "The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages," Econometrica, Econometric Society, vol. 86(6), pages 1911-1938, November.
    2. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    3. Fulian Li & Wuwei Zhang, 2023. "Research on the Effect of Digital Economy on Agricultural Labor Force Employment and Its Relationship Using SEM and fsQCA Methods," Agriculture, MDPI, vol. 13(3), pages 1-17, February.
    4. Xueming Luo & Xianghua Lu & Jing Li, 2019. "When and How to Leverage E-commerce Cart Targeting: The Relative and Moderated Effects of Scarcity and Price Incentives with a Two-Stage Field Experiment and Causal Forest Optimization," Information Systems Research, INFORMS, vol. 30(4), pages 1203-1227, December.
    5. 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," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-17, December.
    6. Joshua D. Merfeld, 2023. "Labor elasticities, market failures, and misallocation: Evidence from Indian agriculture," Agricultural Economics, International Association of Agricultural Economists, vol. 54(5), pages 623-637, September.
    7. Xiumei Wang & Yongjian Huang & Yingying Zhao & Jingxuan Feng, 2023. "Digital Revolution and Employment Choice of Rural Labor Force: Evidence from the Perspective of Digital Skills," Agriculture, MDPI, vol. 13(6), pages 1-19, June.
    8. Nguyen, Trung Thanh & Nguyen, Thanh-Tung & Grote, Ulrike, 2022. "Internet use, natural resource extraction and poverty reduction in rural Thailand," Ecological Economics, Elsevier, vol. 196(C).
    9. Lei Wen & Haiwen Zhou, 2023. "The choice of technology in economic development," Australian Economic Papers, Wiley Blackwell, vol. 62(4), pages 747-763, December.
    10. Barnett, William A. & Hu, Mingzhi & Wang, Xue, 2019. "Does the utilization of information communication technology promote entrepreneurship: Evidence from rural China," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 12-21.
    11. Qiao, Yuxuan & Ao, Xugao, 2024. "Digital transformation and rural labour force occupational mobility," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 42-50.
    12. Min, Shi & Liu, Min & Huang, Jikun, 2020. "Does the application of ICTs facilitate rural economic transformation in China? Empirical evidence from the use of smartphones among farmers," Journal of Asian Economics, Elsevier, vol. 70(C).
    13. Huasheng Zhu & Yawei Chen & Kebi Chen, 2019. "Vitalizing Rural Communities: China’s Rural Entrepreneurial Activities from Perspective of Mixed Embeddedness," Sustainability, MDPI, vol. 11(6), pages 1-21, March.
    14. Georg Duernecker & Berthold Herrendorf, 2022. "Structural Transformation of Occupation Employment," Economica, London School of Economics and Political Science, vol. 89(356), pages 789-814, October.
    15. Higashi, Yudai, 2018. "Spatial spillovers in job matching: Evidence from the Japanese local labor markets," Journal of the Japanese and International Economies, Elsevier, vol. 50(C), pages 1-15.
    16. Louisa Prause, 2021. "Digital Agriculture and Labor: A Few Challenges for Social Sustainability," Sustainability, MDPI, vol. 13(11), pages 1-14, May.
    17. Jordi López-Tamayo & Raul Ramos & Vicente Royuela, 2023. "Wage flexibility and employment resilience in the Spanish labour market over the Great Recession," Regional Studies, Taylor & Francis Journals, vol. 57(12), pages 2443-2456, December.
    18. Liu, Ying & Huang, Jikun & Zikhali, Precious, 2016. "The bittersweet fruits of industrialization in rural China: The cost of environment and the benefit from off-farm employment," China Economic Review, Elsevier, vol. 38(C), pages 1-10.
    19. Fennell, Shailaja & Kaur, Prabhjot & Jhunjhunwala, Ashok & Narayanan, Deapika & Loyola, Charles & Bedi, Jaskiran & Singh, Yaadveer, 2018. "Examining linkages between Smart Villages and Smart Cities: Learning from rural youth accessing the internet in India," Telecommunications Policy, Elsevier, vol. 42(10), pages 810-823.
    20. Jonathan M.V. Davis & Sara B. Heller, 2017. "Using Causal Forests to Predict Treatment Heterogeneity: An Application to Summer Jobs," American Economic Review, American Economic Association, vol. 107(5), pages 546-550, May.
    21. Yihan Guo & Junling Xu & Yuan Zhou, 2022. "Effects of Internet Adoption on Health and Subjective Well-Being of the Internal Migrants in China," IJERPH, MDPI, vol. 19(21), pages 1-15, November.
    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. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
    2. 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.
    3. Ruyi Ge & Zhiqiang (Eric) Zheng & Xuan Tian & Li Liao, 2021. "Human–Robot Interaction: When Investors Adjust the Usage of Robo-Advisors in Peer-to-Peer Lending," Information Systems Research, INFORMS, vol. 32(3), pages 774-785, September.
    4. Anthony Strittmatter, 2018. "What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?," Papers 1812.06533, arXiv.org, revised Dec 2021.
    5. Anya Shchetkina & Ron Berman, 2024. "When Is Heterogeneity Actionable for Personalization?," Papers 2411.16552, arXiv.org.
    6. Lechner, Michael, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," IZA Discussion Papers 12040, Institute of Labor Economics (IZA).
    7. Hayakawa, Kazunobu & Keola, Souknilanh & Silaphet, Korrakoun & Yamanouchi, Kenta, 2022. "Estimating the impacts of international bridges on foreign firm locations: a machine learning approach," IDE Discussion Papers 847, Institute of Developing Economies, Japan External Trade Organization(JETRO).
    8. Sallin, Aurelién, 2021. "Estimating returns to special education: combining machine learning and text analysis to address confounding," Economics Working Paper Series 2109, University of St. Gallen, School of Economics and Political Science.
    9. Naguib, Costanza, 2019. "Estimating the Heterogeneous Impact of the Free Movement of Persons on Relative Wage Mobility," Economics Working Paper Series 1903, University of St. Gallen, School of Economics and Political Science.
    10. J. Michelle Brock & Ralph De Haas, 2023. "Discriminatory Lending: Evidence from Bankers in the Lab," American Economic Journal: Applied Economics, American Economic Association, vol. 15(2), pages 31-68, April.
    11. Michael C. Knaus, 2021. "A double machine learning approach to estimate the effects of musical practice on student’s skills," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 282-300, January.
    12. Michael C Knaus & Michael Lechner & Anthony Strittmatter, 2021. "Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 134-161.
    13. Pons Rotger, Gabriel & Rosholm, Michael, 2020. "The Role of Beliefs in Long Sickness Absence: Experimental Evidence from a Psychological Intervention," IZA Discussion Papers 13582, Institute of Labor Economics (IZA).
    14. Burgess, Simon & Metcalfe, Robert & Sadoff, Sally, 2021. "Understanding the response to financial and non-financial incentives in education: Field experimental evidence using high-stakes assessments," Economics of Education Review, Elsevier, vol. 85(C).
    15. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    16. Diogo G. C. Britto & Paolo Pinotti & Breno Sampaio, 2022. "The Effect of Job Loss and Unemployment Insurance on Crime in Brazil," Econometrica, Econometric Society, vol. 90(4), pages 1393-1423, July.
    17. Cevat Giray Aksoy & Christopher S. Carpenter & Ralph De Haas & Mathias Dolls & Lisa Windsteiger, 2023. "Reducing Sexual Orientation Discrimination: Experimental Evidence from Basic Information Treatments," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 42(1), pages 35-59, January.
    18. Arthur Charpentier & Emmanuel Flachaire & Ewen Gallic, 2023. "Optimal Transport for Counterfactual Estimation: A Method for Causal Inference," Papers 2301.07755, arXiv.org.
    19. Cockx, Bart & Lechner, Michael & Bollens, Joost, 2023. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Labour Economics, Elsevier, vol. 80(C).
    20. Carlana, Michela & La Ferrara, Eliana, 2021. "Apart but Connected: Online Tutoring and Student Outcomes during the COVID-19 Pandemic," CEPR Discussion Papers 15761, C.E.P.R. Discussion Papers.

    More about this item

    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:pal:palcom:v:12:y:2025:i:1:d:10.1057_s41599-024-04326-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.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.