Corruption and Social Interaction: Evidence from China
Bin Dong and
Benno Torgler
CREMA Working Paper Series from Center for Research in Economics, Management and the Arts (CREMA)
Abstract:
We explore theoretically and empirically whether social interaction, including local and global interaction, influences the incidence of corruption. We first present an interaction-based model on corruption that predicts that the level of corruption is positively associated with social interaction. Then we empirically verify the theoretical prediction using within-country evidence at the province-level in China during 1998 to 2007. Panel data evidence clearly indicates that social interaction has a statistically significantly positive effect on the corruption rate in China. Our findings, therefore, underscore the relevance of social interaction in understanding corruption.
Keywords: Awards; Signals; Status; Anonymity; Globalization (search for similar items in EconPapers)
JEL-codes: D64 D72 J24 K42 O17 (search for similar items in EconPapers)
Date: 2010-11
New Economics Papers: this item is included in nep-dev, nep-law, nep-soc and nep-tra
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://www.crema-research.ch/papers/2010-22.pdf Full Text (application/pdf)
https://www.crema-research.ch/abstracts/2010-22.htm Abstract (text/html)
Related works:
Journal Article: Corruption and social interaction: Evidence from China (2012) 
Working Paper: Corruption and Social Interaction: Evidence from China (2011) 
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:cra:wpaper:2010-22
Access Statistics for this paper
More papers in CREMA Working Paper Series from Center for Research in Economics, Management and the Arts (CREMA) Contact information at EDIRC.
Bibliographic data for series maintained by Anna-Lea Werlen ( this e-mail address is bad, please contact ).