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Subsidizing Failing Firms: Evidence from Chinese Restaurants

Published online by Cambridge University Press:  12 September 2023

Yinglu Deng ,
Fangzhou Lu Open the ORCID record for Fangzhou Lu [Opens in a new window] ,
Jiaheng Yu  and
Hao Zheng
Yinglu Deng
Affiliation:
Tsinghua University PBC School of Finance dengyl@pbcsf.tsinghua.edu.cn
Fangzhou Lu*
Affiliation:
University of Hong Kong Faculty of Business and Economics
Jiaheng Yu
Affiliation:
University of Hong Kong Faculty of Business and Economics yujh@hku.hk
Hao Zheng
Affiliation:
University of International Business and Economics School of Insurance and Economics zhenghao@uibe.edu.cn
*
lufz@hku.hk (corresponding author)
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Abstract

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Using data on nearly 20,000 restaurants in China during the COVID-19 outbreak, we find evidence that the government-sponsored rent reduction program reduced debt overhang problems. Rent reductions, which averaged 36,000 RMB per restaurant, increase the open rate of restaurants by 3.7%, revenue by 11,000 RMB, and the number of employees by 0.36. Larger restaurants with higher committed costs benefit more from the rent reduction. The stimulus has a positive spillover effect that boosts the revenue of restaurants in the immediate vicinity of subsidized restaurants. The treatment effect varies with organizational structure in a manner consistent with an information frictions hypothesis.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , First View , pp. 1 - 32
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

Footnotes

We thank an anonymous referee, Manuel Adelino, Sumit Agarwal, Ben Bernanke, Jennifer Carpenter, Hui Chen, Ran Duchin (the editor), Xavier Giroud, Robin Greenwood, Song Ma, Gregor Matvos, Jonathan Parker, Wenlan Qian, David Thesmar, and Owen Zidar for providing invaluable comments. We also thank seminar participants at MIT, HKU, and the 2021 Midwest Finance Association annual meeting, particularly our discussant Dmitri Koustas. We are especially grateful to Beijing Duolaidian Information Technology Co. Ltd (a.k.a. Hualala) for providing us with the data. Hao Zheng acknowledges financial support from the National Natural Science Foundation of China (Grant No. 72204045) and the Fundamental Research Funds for the Central Universities in UIBE (Grant No. 21QD22).

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