What Determines the Return to Bribery? Evidence from Corruption Cases Worldwide

Yan-Leung Cheung (), P. Raghavendra Rau () and Aris Stouraitis ()
Additional contact information
Yan-Leung Cheung: Education University of Hong Kong, Hong Kong, China
P. Raghavendra Rau: University of Cambridge, Cambridge CB2 1TN, United Kingdom
Aris Stouraitis: Hong Kong Baptist University, Hong Kong, China

Management Science, 2021, vol. 67, issue 10, 6235-6265

Abstract: We analyze a hand-collected sample of bribery cases from around the world to describe how the payment of bribes affects shareholder value. The net present value of a bribe conditional on getting caught is close to zero for the median firm in our sample. However, controlling for industry, country, and firm characteristics, a $1 increase in the size of the bribe is associated with an ex ante $6–$9 increase in the value of the firm, suggesting a correlation between the size of bribes and the size of available benefits. Proxies for information disclosure appear significant in explaining these benefits with more disclosure associated with lower benefits. However, this result is driven by democratic countries where bribe-paying firms receive smaller benefits relative to the bribes they pay. Information disclosure is not significant in autocratic countries.

Keywords: corruption; bribes; firm performance; country characteristics (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.2020.3763 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:67:y:2021:i:10:p:6235-6265

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().