The Bayesian Corruption Index: A New and Improved Method for Aggregating Corruption Perceptions

As most readers of this blog are likely aware, two of the most widely used measures of corruption perceptions—Transparency International’s Corruption Perceptions Index (CPI) and the Worldwide Governance Indicators (WGI) corruption index—are composite indicators that combine perceived corruption ratings from a range of different sources (including private rating agencies, NGOs, international development banks, and surveys of firms and households). The CPI takes a simple average of the available sources for each country; the WGI uses a somewhat fancier “unobserved component model” (UCM) which assumes that each source’s score is a noisy signal of the “true” level of perceived corruption; the UCM differs from a simple average in a few ways, perhaps most notably by giving less weight to “outlier” sources, though in practice the WGI and CPI are highly correlated, and the WGI’s creators report that the results for the WGI turn out not to change very much if one takes a simple average rather than using the WGI.

These composite indicators have a number of well-known problems, which I won’t bother going into here. Rather, the main purpose of this post is to introduce readers to an alternative index, developed by Samuel Standaert at Ghent University, which he calls the “Bayesian Corruption Index” (BCI). Standaert introduced the BCI in a 2015 article, but so far as I can tell it has not attracted much attention. The BCI certainly doesn’t solve all the problems of the traditional aggregated corruption perceptions indicators (more on this below), but it’s definitely an improvement, and deserves wider use. Let me first say a bit about how the BCI differs from the WGI, why I think it’s an advance over the WGI and CPI, and what some of its limitations are. Continue reading