Economic Uncertainty and Corruption: Evidence from a Large 
Cross-Country Data Set

Rajeev Goel is Professor of Economics at Illinois State University. He is the author of Economic Models of Technological Change and Global Efforts to Combat Smoking.

Substantial research has considered numerous causes and correlates of 
corruption. Also, there have been many studies of the consequences of 
various forms of uncertainty. However, exploration of the nexus 
between economic uncertainty and corruption appears scarce. After 
providing an intuitive and heuristic linkage between general economic 
uncertainty and corruption, this paper uses a large cross-country data 
set to augment a fairly standard model with simple proxies for 
uncertainty and to investigate how economic uncertainty might affect 
prevalence of corruption. In addition, a quantile-regression framework 
is used to judge how the strength of various covariates may differ 
with the level of corruption. Seven main points emerge from the 
estimates. First, economic uncertainty is associated positively with 
corruption, and the relation seems robust across measures of 
uncertainty and corruption. Second, quantile regression estimates 
indicate considerable parametric heterogeneity across the distribution 
of corruption. Third, GDP per capita has the expected 
corruption-mitigating role. Fourth, increased political rights and 
civil liberties also appear to lower corruption. Fifth, greater 
government consumption is associated with lower corruption. Sixth, 
while the hyperinflation dummy lacks significance in most OLS 
regressions, its significance varies across the distribution of 
corruption. Seventh, neither police force nor government subsidies 
shows significance, but transition economies have more corruption.