Measuring Corruption: A Critical Analysis of the Existing Datasets and Their Suitability for Diachronic Transnational Research

Any researcher on corruption has faced at some point the dataset dilemma. How can one assess the incidence of a phenomenon on corruption levels if we cannot determine how much corruption is there in the first place? The problem compounds when the research has a transnational or comparative element. How can one assess how different corruption levels are in different jurisdictions if we cannot be sure if the measurements are comparable? It becomes critical when the research has a diachronic component, and tries to incorporate changes over time, as the stability and consistency of datasets become essential. This article reviews the literature on the topic from the last fifteen years and evaluates all the main options available today for researchers and policy designers in terms of validity and reliability, explaining first the particularities of these two concepts in the context of measurements of the prevalence of corruption. It pays particular attention to the limitations of the different datasets and determines the validity and reliability of the oft-used Corruption Perceptions Index post-2012 (CPI) and the Control of Corruption (CoC) indicator for the whole data series. This conclusion partially vindicates those researchers and policy designers who have used these datasets in the past, especially when compared to all the other options, while firmly warning users about the kind of conclusions that can be extracted from them.