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

The Level-of-Aggregation Question in Corruption Measurement

Recently I learned that CDA Collaborative (a nonprofit organization that works on a variety of development and conflict-resolution projects) has launched a new blog on corruption. Though it’s a new platform, they already have a few of interesting posts up, and it’s worth a look.

While I’m always happy to advertise new platforms in the anticorruption blogosphere, in this post I mostly want to focus on the first entry in the CDA’s new blog, a post by Professor Michael Johnston entitled “Breaking Out of the Methodological Cage.” It’s basically a critique of the anticorruption research literature’s alleged (over-)reliance on quantitative methods, in particular cross-national regression analyses using country-level corruption indices (such at the Corruption Perceptions Index (CPI) or Worldwide Governance Indicators (WGI) graft index). There are some things in Professor Johnston’s post that I agree with, and much that I disagree with. I want to focus on one issue in particular: the question of the right unit of analysis, or level of aggregation, to use when attempting to measure corruption.

Professor Johnston has two related complaints (or maybe two variants on the same underlying complaint) regarding these national-level perceived corruption measures. First, he complains these “[o]ne dimensional indices tell us … that corruption is the same thing everywhere, varying only in amount[.]”  In other words, corruption indices lump a whole bunch of disparate phenomena together under the same umbrella term “corruption,” ignoring the internal diversity of that category. Second, he contends that “relying … on country-level data is to assume that corruption is a national attribute, like GDP per capita” when in fact “corruption arises in highly specific processes, structural niches, and relationships.” Corruption, he explains, is not an attribute of countries, but of more specific contexts, involving “real people … in complex situations[.]”

Respectfully, I think that these points are either wrong or irrelevant, depending on how they are framed. Continue reading

Innovative or Ineffective?: Performance-Based Lending as an Anticorruption Tool

The Sustainable Development Goals’ (SDGs) new focus on fighting corruption and building institutions has generated quite a stir (including on this blog – see here, here, here, and here). But the Millennium Challenge Corporation (MCC) – a U.S. agency responsible for disbursement of assistance geared toward international development targets – has long been acting against corruption through its effort to achieve the SDG precursors, the Millennium Development Goals (MDGs). Institution-building does not appear among the substantive aims of the eight MDGs. Rather, the MCC made anticorruption central to its work by introducing corruption indices into its process for competitive selection of aid recipients. In brief, the MCC Board of Directors chooses aid-eligible countries by evaluating and scoring candidates countries’ “policy performance” on a number of measures. Crucially, in order to qualify for aid, countries must score above average for their income group on the Worldwide Governance Indicators (WGI) “Control of Corruption” score. The indicator is therefore known as the “hard hurdle.” The Board also assesses corruption trends in its analysis of a country’s ability to reduce poverty and generate economic growth, which, with policy performance, comprises the overall evaluation.

This strategy is known as performance-based lending, and the MCC has employed it to award over $10 billion in grants to nearly 40 countries over the past 12 years. Is the MCC approach a good one? Many critics say no. I say yes. Although it is a strategy that is still evolving, performance-based lending—including the corruption control “hard hurdle”—is not only innovative and effective, but important.

Continue reading