IDEAS home Printed from https://ideas.repec.org/a/eee/reveco/v102y2025ics1059056025004939.html
   My bibliography  Save this article

Can digital transformation be a “powerful tool” to curb corporate tax avoidance? Mechanism analysis and policy implications based on Chinese data

Author

Listed:
  • Sun, Xiaoyan
  • Han, Jie
  • Işık, Cem

Abstract

In the economic downturn, enterprises often adopt tax avoidance to relieve operational pressure and increase profits, yet this undermines national tax revenues and economic balance. This paper explores whether China's promotion of corporate digital transformation can mitigate tax avoidance and balance macro tax revenues with micro corporate performance. Using data of Chinese listed firms from 2012 to 2022, the study finds that digital transformation significantly restrains tax avoidance and benefits the macro tax environment. The internal mechanism shows that downside risk, management emotional tone, and political cost act as key mediators. Digital transformation reduces tax avoidance impulses by cutting downside risk and political costs, though it may also strengthen management emotional tone, spurring more aggressive tax avoidance tendencies. Additionally, digital transformation notably reduces tax avoidance across enterprises, especially for small and medium enterprises and non-state holding enterprises. Overall, the research offers practical guidance for the government and enriches the theoretical understanding of digital transformation and tax avoidance.

Suggested Citation

  • Sun, Xiaoyan & Han, Jie & Işık, Cem, 2025. "Can digital transformation be a “powerful tool” to curb corporate tax avoidance? Mechanism analysis and policy implications based on Chinese data," International Review of Economics & Finance, Elsevier, vol. 102(C).
  • Handle: RePEc:eee:reveco:v:102:y:2025:i:c:s1059056025004939
    DOI: 10.1016/j.iref.2025.104330
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1059056025004939
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.iref.2025.104330?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:reveco:v:102:y:2025:i:c:s1059056025004939. 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/620165 .

    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.