IDEAS home Printed from https://ideas.repec.org/a/eee/reveco/v92y2024icp678-689.html
   My bibliography  Save this article

The impact of big data tax collection and management on inefficient investment of enterprises — A quasi-natural experiment based on the golden tax project III

Author

Listed:
  • Guo, Yixuan
  • Wang, Jian
  • Wang, Heng
  • Zhang, Fan

Abstract

The Golden Tax Project is an important innovation in China's tax collection and management. Taking Golden Tax Project III as a quasi-natural experiment through DID model, we study the impact of big data tax administration on the enterprises' inefficient investment. The results indicate that big data tax administration can effectively reduce the enterprises' inefficient investment, particularly in areas with high information asymmetry and fierce regional tax competition. Digital tax administration exerts a disincentive effect by improving information transparency and mitigating agency conflicts. Therefore, it is recommended to improve the informatization level of tax administration platforms and strengthen data integration capabilities.

Suggested Citation

  • Guo, Yixuan & Wang, Jian & Wang, Heng & Zhang, Fan, 2024. "The impact of big data tax collection and management on inefficient investment of enterprises — A quasi-natural experiment based on the golden tax project III," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 678-689.
  • Handle: RePEc:eee:reveco:v:92:y:2024:i:c:p:678-689
    DOI: 10.1016/j.iref.2024.02.012
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1059056024000832
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.iref.2024.02.012?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.

    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:92:y:2024:i:c:p:678-689. 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.