IDEAS home Printed from https://ideas.repec.org/a/eee/ecmode/v153y2025ics0264999325003037.html

Does digital finance spatial correlation drive income convergence?

Author

Listed:
  • Fang, Xia
  • Zhang, Yun
  • Lv, Shuqing
  • Tan, Longxin

Abstract

Digital finance significantly boosts household incomes and reduces regional disparities. Although digital finance can narrow income inequality, the spatial impact of digital finance on income convergence remains inadequately examined. This study evaluates whether and how the spatial correlation with central digital finance regions promotes income convergence, by employing a modified gravity model and data from Zhejiang Province in China. The empirical evidence shows that digital finance spatial correlation can contribute to income growth. Notably, low-income regions derive a stronger growth impetus by enhancing spatial correlation with central digital finance regions, narrowing the income inequality with high-income regions. Specifically, capital flow, technological innovation, and business creation are three effective mechanisms. Heterogeneity analysis demonstrates that the convergence effect is particularly significant for regions with mountains and without high-speed rail (HSR) stations, underscoring the spatial penetration capabilities of digital finance. Governments should encourage more cross-regional digital finance cooperation to achieve balanced regional development.

Suggested Citation

  • Fang, Xia & Zhang, Yun & Lv, Shuqing & Tan, Longxin, 2025. "Does digital finance spatial correlation drive income convergence?," Economic Modelling, Elsevier, vol. 153(C).
    • Handle: RePEc:eee:ecmode:v:153:y:2025:i:c:s0264999325003037
    DOI: 10.1016/j.econmod.2025.107308
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0264999325003037
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econmod.2025.107308?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.

    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:eee:ecmode:v:153:y:2025:i:c:s0264999325003037. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30411 .

    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.