Corruption estimates rely largely on self-reports of affected individuals and officials. Yet survey respondents are often reticent to tell the truth about sensitive subjects, leading to downward biases in surveybased corruption estimates. This paper develops a method to estimate the prevalence of reticent behavior and reticence-adjusted rates of corruption using survey responses to sensitive questions. A statistical model captures how respondents answer a combination of conventional and randomresponse questions, allowing identification of the effect of reticence. GMM and maximum likelihood estimates are obtained for ten countries. Adjusting for reticence dramatically alters the perceptions of the extent of corruption.

Many panel data sets encountered in macroeconomics, international economics, regional science, and finance are characterized by cross-sectional or “spatial” dependence. Standard techniques that fail to account for this dependence will result in inconsistently estimated standard errors. In this paper we present conditions under which a simple extension of common nonparametric covariance matrix estimation techniques yields standard error estimates that are robust to very general forms of spatial and temporal dependence as the time dimension becomes large. We illustrate the relevance of this approach using Monte Carlo simulations and a number of empirical examples.