JOURNAL ARTICLE

Corruption, Land, and Housing: The Cleaning and Chilling Effects of China's Anti‐Corruption Campaign.

  • Published In: Review of Development Economics, 2025, v. 29, n. 4. P. 2481 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Shi, Hongyan; Wang, Benlai; Zhu, Bohong 3 of 3

Abstract

This study examines the impact of China's anti‐corruption campaign on the land and housing markets. Leveraging the exogeneity of the central inspection activities, we apply a General Difference‐in‐Differences model to assess the campaign's effect using provincial panel data from 2005 to 2019. Our findings show that the campaign reduced land prices by 668.7 yuan/m2 (29.5% of the average, same below), with residential land prices falling by 880.7 yuan/m2 (21.4%) and commercial land prices by 497.6 yuan/m2 (9.5%), while land transaction areas remained unaffected. We identify two opposing forces: a "cleaning effect," where reduced bribery raises land prices, and a "chilling effect," where local officials restrict private firms' access to land market, thereby suppressing demand and lowering prices. The chilling effect dominates, causing an overall decrease in land prices. This price decline subsequently extends to the housing market, reducing housing prices by 12% (600 yuan/m2). Residential property prices decreased by 15.6% (737.7 yuan/m2), while commercial business property prices dropped by 6.6% (471.6 yuan/m2). The decline in housing prices directly lowers homeownership costs and enhances residents' welfare. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Review of Development Economics. 2025/11, Vol. 29, Issue 4, p2481
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2025
  • ISSN:1363-6669
  • DOI:10.1111/rode.13238
  • Accession Number:188775156
  • Copyright Statement:Copyright of Review of Development Economics is the property of Wiley-Blackwell 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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