Corruption clubs: empirical evidence from kernel density estimates

Thomas Herzfeld and Christoph Weiss

Applied Economics, 2007, vol. 39, issue 12, 1565-1572

Abstract: A common finding of many analytical models is the existence of multiple equilibria of corruption. Countries characterized by the same economic, social and cultural background do not necessarily experience the same levels of corruption. In this article, we use Kernel Density Estimation techniques to analyse the cross-country distribution of corruption. Particular emphasis will be given to the question whether the distribution of corruption shows more than one peak. We find that most of the estimated densities exhibit twin peaks. We also provide some evidence on the intra-distribution dynamics and the persistence of corruption. We find the group of countries classified within the two 'clubs' to be very stable. Corruption is a highly persistent phenomenon. Substantial changes in the economic, political and cultural environment of countries within the 'corruption club' are required before a significant decline of corruption is to be expected.

Date: 2007
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/00036840500461980 (text/html)
Access to full text is restricted to subscribers.

Related works:
Journal Article: Corruption clubs: empirical evidence from kernel density estimates (2007)
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:taf:applec:v:39:y:2007:i:12:p:1565-1572

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEC20

DOI: 10.1080/00036840500461980

Access Statistics for this article

Applied Economics is currently edited by Anita Phillips

More articles in Applied Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().