IDEAS home Printed from https://ideas.repec.org/a/taf/rcjaxx/v3y2015i4p320-347.html
   My bibliography  Save this article

Government decentralisation and corporate fraud: Evidence from listed state-owned enterprises in China

Author

Listed:
  • Hang Liu
  • Xiaorong Li

Abstract

This paper examines the economic consequences of government decentralisation from the perspective of corporate fraud. Theoretically, government decentralisation reduces the political costs of state intervention and hence decreases the probability of state-owned enterprises (SOEs) to engage in fraud. It also aggravates the agency costs (the costs of managerial self-dealing), thereby increasing the probability of SOEs to commit fraud. Using pyramidal layers as a proxy of government decentralisation for SOEs, empirical results show that government decentralisation significantly lowers the probability of SOEs to commit fraud. Further categorisation of types of fraud shows that government decentralisation primarily deters disclosure-related fraud and market transaction-related fraud. Finally, the effect of decentralisation on corporate fraud is more pronounced for SOEs in which government intervention is more likely.

Suggested Citation

  • Hang Liu & Xiaorong Li, 2015. "Government decentralisation and corporate fraud: Evidence from listed state-owned enterprises in China," China Journal of Accounting Studies, Taylor & Francis Journals, vol. 3(4), pages 320-347, October.
    • Handle: RePEc:taf:rcjaxx:v:3:y:2015:i:4:p:320-347
    DOI: 10.1080/21697213.2015.1100090
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/21697213.2015.1100090
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/21697213.2015.1100090?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Md Jahidur Rahman & Hongtao Zhu, 2023. "Predicting accounting fraud using imbalanced ensemble learning classifiers – evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(3), pages 3455-3486, September.
    2. Faiz Zamzami & Fuad Rakhman, 2023. "Determinants of Local Government Financial Performance in Indonesia," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 12, September.

    More about this item

    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:taf:rcjaxx:v:3:y:2015:i:4:p:320-347. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/rcja .

    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.