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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
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    1. 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.
    2. 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.
    3. Lei Wen & Haiwen Zhou, 2023. "The choice of technology in economic development," Australian Economic Papers, Wiley Blackwell, vol. 62(4), pages 747-763, December.
    4. 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.
    5. 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.
    6. Louisa Prause, 2021. "Digital Agriculture and Labor: A Few Challenges for Social Sustainability," Sustainability, MDPI, vol. 13(11), pages 1-14, May.
    7. 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.
    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. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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).
    21. 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.
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