IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-94-6239-689-0_19.html

Does a Public Pension Improve Rural Livelihoods?

In: Proceedings of the 2026 2nd International Conference on Data Mining and Project Management (DMPM 2026)

Author

Listed:
  • Ziyun Wang

    (Hainan Vocational University of Science and Technology, School of Accounting)

  • Zhe Li

    (City University of Macau, Faculty of Finance)

  • Wenze Xiong

    (University of Auckland, School of Business)

Abstract

This paper evaluates the impact of the New Rural Social Pension Insurance (NRSP) pilot on rural income and consumption in China. Using provincial panel data for 2000 to 2021, we estimate a difference-in-differences model with province and year fixed effects as well as province-specific linear trends. In the analysis, any province with a raw pilot start before 2009 is recoded to 2009 in order to align treatment timing with the official launch of the NRSP. Under this policy-consistent timing rule, the preferred specification shows that the NRSP pilot is associated with increases of approximately 39 per cent in rural per capita disposable income and 32 per cent in rural per capita consumption expenditure. Richer fiscal and rural-development controls leave the positive association intact, and short-run coefficients are also statistically significant. Supplementary province-year financial regressions based on county-aggregated deposit and loan balances do not provide statistically precise evidence on the savings or debt-repayment channel. The study therefore offers policy-relevant macro evidence while clarifying the identification limits that remain under a more historically consistent treatment coding.

Suggested Citation

  • Ziyun Wang & Zhe Li & Wenze Xiong, 2026. "Does a Public Pension Improve Rural Livelihoods?," Advances in Economics, Business and Management Research, in: Ljiljana Trajkovic & José Alfredo F. Costa & Zaher Al Aghbari & Nor Azman Ismail & Dariusz Jacek Jak (ed.), Proceedings of the 2026 2nd International Conference on Data Mining and Project Management (DMPM 2026), pages 196-202, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6239-689-0_19
    DOI: 10.2991/978-94-6239-689-0_19
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    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:spr:advbcp:978-94-6239-689-0_19. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: http://www.springer.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.