JOURNAL ARTICLE

The Evergrande Story: A Look into Chinese Corporate Law and the Redefinition of Privately Owned Enterprises and Their Relationship to State-Owned Enterprises in the Chinese Economy.

  • Published In: Chinese Journal of Comparative Law, 2024, v. 12. P. 1 1 of 3

  • Database: Legal Source 2 of 3

  • Authored By: Chow, Yun Kei 3 of 3

Abstract

This article examines the Chinese corporate governance landscape through a detailed case study of China Evergrande Group, a privately owned enterprise (POE) that defaulted on its debt in December 2021, triggering a prolonged real estate crisis. It highlights the significant role of State-owned enterprises (SOEs) and various State-owned Assets Supervision and Administration Commissions (SASACs) in mitigating Evergrande's financial distress by acquiring assets, completing unfinished projects, and providing financial support, despite the Chinese government's official stance against bailing out private firms. The study argues that the traditional distinction between SOEs and POEs in China is blurred, as POEs like Evergrande operate with SOE-like protections and are deeply intertwined with State interests, which influences corporate governance, regulatory frameworks, and market dynamics. The article further discusses the implications of this interdependence for future reforms, suggesting that the Chinese State is likely to increase its control over enterprises, using crises like Evergrande's as opportunities to reinforce SOE dominance and limit POE autonomy.

Additional Information

  • Source:Chinese Journal of Comparative Law. 2024/01, Vol. 12, p1
  • Document Type:Article
  • Subject Area:Economics
  • Publication Date:2024
  • ISSN:20504802
  • DOI:10.1093/cjcl/cxae005
  • Accession Number:182370172
  • Copyright Statement:Copyright of Chinese Journal of Comparative Law is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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