Replication data for: Where's Waldo? Searching for the Hidden Variable of "Real" Corruption

Corruption is a commonly studied phenomenon, not only in political science, but across the social sciences. However, the difficulty of measuring corruption presents a significant barrier to this research program. In this paper, we use a well-specified model for explaining corruption presented by Campante, Chor, and Doh (2009) as a starting point for evaluating different measures of corruption. Testing aggregated corruption indices, corruption perception measures, and official records of corruption through non-parametric, parametric, and simulation methods, we find that different measures of corruption produce different results and lead to different substantive conclusions. Furthermore, by treating bias as measurement error, we propose a method to obtain a common denominator across different measures of corruption, which can be used as an instrumental variable to isolate the ``non-perception component" of corruption measures from the ``perception component." By running a bivariate regression on these two components, we contribute to the theoretical debates of the field by shedding light on the relationship between corruption and common covariates such as stabilty and democracy.