With a rise in global trade and a focus on international development, the past two decades have seen increased attention to the worldwide problem of corruption. With that attention have come efforts to measure the degree of corruption from one country to another.
A great deal hinges on these assessments — practical matters such as investment decisions and access to financing, but also collective narratives about different parts of the world, the challenges they face, and the risks they present.
Given the importance of this issue, our current tools for measuring corruption leave something to be desired. The most prominent corruption indexes make use of an “aggregation” technique in which a number of individual country-level assessments are gathered, their scores adjusted to a common scale, and the average of those adjusted score taken to yield each country’s rating. Some models ascribe greater or lesser weight to certain inputs, but the idea is the same: the overall level of corruption is gauged by adding together individual indicators of corruption.
We can think of this as a linear model of corruption: as any given indicator goes up or down, so does the total score. But corruption isn’t necessarily a linear phenomenon. For one thing, there are different kinds of corruption — ranging from petty bribe demands directed at the populace to sweetheart deals and “back-scratching” to grand kleptocracy — each with its own set of potential drivers and enablers.
Different orders of corruption interact in various ways — corruption at the head of government fostering a corrupt environment throughout; mid-level officials demanding a cut of bribe payments extracted by low-level officials under pressure to meet their “quotas” — and will be subject to differing political and social dynamics. Just asking “how corrupt” a country is may not be the best approach to understanding these dynamics.
At the same time, in thinking about what causes corruption it is easy to slip into certain normative assumptions. From the perspective of liberal democracy, the struggle against corruption is made possible by a free and active press, transparency in government, and robust civic engagement. But if an authoritarian regime decides that it will not tolerate bribery of its officials, its deterrence measures may be every bit as effective (if not more so) as those liberal institutions.
These observations are at best anecdotal. In terms of rigorous analysis, our understanding of corruption and how to measure it remains underdeveloped. Improving that understanding requires an interdisciplinary approach, drawing upon economics, law, political science, sociology, statistics, and data science.
In producing its annual Bribery Risk Matrix, TRACE aims to utilize not only the best data, but also the most nuanced and current thinking about the causes and indicators of bribery risk. To that end, we have convened a working group of leading anticorruption scholars — political scientists Alexander Cooley, Dan Hough, and Michael Johnston, economist and statistician Enrico Giovannini, and professor of law and economics Tina Søreide. Meeting monthly over the course of a half-year, the group works to study, discuss, and model the societal, political, and economic factors that contribute to bribery risk.
While our initial aim in convening the working group is to develop incremental improvements to the Bribery Risk Matrix methodology, our overall goal is longer-term. Whether in media discussions or in academic literature, we rely too much on simplistic assessments of corruption, and we can be overly preoccupied with the “horse race” aspect of country rankings. By initiating a deeper inquiry into questions of measurement, comparison, and how we talk about corruption, we hope to contribute to improving public discourse, private-sector engagement, and scholarly analysis.