Some Good News and Bad News About Transparency International’s Interpretation of its Latest Corruption Perceptions Index

In my post last week, I fired off a knee-jerk reaction to Transparency International’s latest Corruption Perceptions Index (CPI). My message of that post was simple and straightforward: We shouldn’t attach much (or perhaps any) importance to short-term changes in any individual country or region’s CPI score, and the bad habit of journalists—and to some extent TI itself—of focusing on such changes is both misleading and counterproductive.

Since I was trying to get that post out quickly, so as to coincide with the release of the CPI, I published it before I’d had a chance to read carefully all of the material TI published along with the new CPI, and I promised that once I’d had a chance to look at those other materials, I would follow up if I had anything else to say. I’ve now had that chance, and I do have a few additional thoughts. The short version is that the way TI itself chose to present and discuss the implications of the 2018 CPI, in the accompanying materials, is both better and worse than I’d originally thought.

So, first, the bad news: Continue reading

A Reminder: Year-to-Year CPI Comparisons for Individual Countries are Meaningless, Misleading, and Should Be Avoided

Today, Transparency International released its new Corruption Perceptions Index (CPI) for 2018. At some point, hopefully soon, I’ll have time to look closely at the new data and accompanying materials, and if I have something to say about it, I’ll post it here. But that will probably take a while, and since the media coverage of the CPI is usually pretty intense in the first few days after the release, and dissipates in a week or two, I wanted to get out at least one post right now, on the day of the release, with a plea to everyone out there–especially journalists, but civil society activists and others as well:

DO NOT COMPARE ANY GIVEN COUNTRY’S CPI SCORE TO LAST YEAR’S SCORE TO MAKE CLAIMS ABOUT WHAT’S HAPPENING IN THE FIGHT AGAINST CORRUPTION.

Just don’t do it. Don’t. I know the temptation can seem overwhelming. Who’s up? Who’s down? Things are getting better! Things are getting worse! Nothing is changing! So many stories can be written based on these changes (or non-changes).

But these sorts of comparisons are virtually all completely useless, and probably counterproductive. Continue reading

Guest Post: Towards an African Voice on Anticorruption

Today’s guest post is from Selemani Kinyunyu, Senior Policy Officer for Political and Legal Matters at the African Union Advisory Board on Corruption. The views expressed in this post are his own.

The African Union (AU) has declared the year 2018 is the African Anti-Corruption Year, and the fight against corruption was a central focus of the 31st Summit of the AU, which was held this past July 1 and 2 in Mauritania. The Summit, along with other recent developments, have made clear that there is an emerging African voice on this issue, one that emphasizes certain issues of pressing importance and that articulates a distinctive perspective on these issues. The AU Summit in particular highlighted four notable issues: Continue reading

Guest Post–Assessing Corruption with Big Data

Today’s guest post is from Enestor Dos Santos, principal economist at BBVA Research.

Ascertaining the actual level of corruption is not easy, given that it is usually a clandestine activity, and much of the available data is not comparable across countries or across time. Survey data on corruption experience can be helpful, but it is often limited to very specific kinds of corruption (such as petty bribery). Researchers and analysts have therefore, quite reasonably, tended to rely on subjective corruption perception data, such as Transparency International’s well-known Corruption Perceptions Index (CPI). (The CPI aggregates corruption perception data from a variety of other sources, mostly expert assessments.) But conventional corruption perception measures (including those use to construct the CPI) have well-known problems, including limited coverage (with respect to both years and countries) and relatively low frequency (usually annual). And they rely on the perceptions of a handful of experts, which may not necessarily be representative. These limitations mean that while traditional perception measures like the CPI may be useful for some purposes, they are not as helpful for others, such as measuring the impact of individual events or news reports on corruption perceptions, or how changes in corruption perceptions affect government approval ratings.

To address these concerns, a recent study by BBVA Research, entitled Assessing Corruption with Big Data, offered an alternative, complementary type of corruption perceptions measure, based on Google web searches about corruption. To construct this index, we examined all web searches classified by Google Trends in the “Law and Government” category for individual countries, and calculated the proportion of those searches that contain the word “corruption” (in any language and including its misspellings and synonyms). Our index, which begins in 2004, covers more than 190 countries and, unlike traditional corruption indicators, is available in real-time and with high-frequency (monthly). Moreover, it can be reproduced very easily and at very low cost.

Here are some of our main findings: Continue reading

On the Political Subtext of Definition Debates, Part 2: Measurement or Moralism?

In my last post, I conjectured that a great deal of what would seem like a dry methodological question—How should we define and measure corruption?—is actually shot through with political-ideological considerations. The reason, I further conjectured, is that “corruption” is both (1) a descriptive sociological term, used to categorize a set of related behaviors, and (2) an evaluative moral term, used to characterize certain behaviors (or people or governments or institutions or countries) as “bad” or “blameworthy.” The fact that the same term has these different functions, coupled with the fact that the word “corruption” is particularly (though not uniquely) ambiguous and open-ended, means that attempts to come up with definitions and measurements that are appropriate for some purposes may seem to others wrongheaded, even offensive.

My illustration of this difficulty in the my last post concerned debates over whether “corruption” should be defined (say, by advocacy organizations or researchers) principally as “the abuse of public power for private gain,” or instead should be defined to include purely private sector corruption (“abuse of entrusted power for private gain”). My admittedly speculative conjecture was that many (not all) who argue for the latter position do so not so much because of (plausible) arguments for analytical equivalence, but rather due to an implicit—and in my view incorrect—belief that focusing on public sector corruption suggests a neoliberal/libertarian skepticism of activist government.

Here I want to suggest a similar sort of ideological subtext in debates over whether the definition of corruption (and the sorts of corruption that the leading indicators should seek to capture) ought to be limited to what we might think of as the “direct” or “first-order” dishonest acts by the responsible officials (such as taking bribes or embezzling funds), or whether measures of corruption should also incorporate the activities that facilitate corruption (such as providing safe havens for stolen assets), as well as the ways in which the rich and powerful seek to influence public policy through legal means (such as lobbying and campaign donations). This has come up more than a couple of times in the last few months at various conferences and roundtable discussions I’ve attended. The context is typically a criticism—often impassioned—of Transparency International’s Corruption Perceptions Index (CPI) and the associated graphics (such as the color-coded country map) that are used to illustrate the index results. The criticism usually runs as follows (and here I’m paraphrasing, but I think fairly and accurately): Continue reading

More on the 2017 Corruption Perceptions Index, and the Relationship Between Media/Civil Society Freedom and Corruption

The rest of the anticorruption commentariat (and the mainstream media) may have already moved on from the publication of Transparency International’s 2017 Corruption Perception Index (CPI), but I wanted to follow up on my other posts from earlier this month (here and here) to discuss one other aspect of the new CPI. The general overview, press release, and other supporting materials that accompanied the latest CPI stress as their main theme the importance of a free press and a robust, independent civil society in the fight against corruption. As TI states succinctly in the overview page for the 2017 CPI, “[A]nalysis of the [CPI] results indicates that countries with the least protection for press and non-governmental organisations (NGOs) also tend to have the worst rates of corruption.” And from this observation, TI argues that in order to make progress in the fight against corruption, governments should “do more to encourage free speech, independent media, political dissent and an open an engaged civil society,” and should “minimize regulations on media … and ensure that journalists can work without fear of repression or violence.” (TI also suggests that international donors should consider press freedom relevant to development aid or access to international organizations, a provocative suggestion that deserves fuller exploration elsewhere.)

Speaking in broad terms, I agree with TI’s position, and I’m heartened to see TI making an effort to use the publicity associated with the release of the CPI to push for concrete improvements on a particular area of importance, rather than simply stressing the bad effects of corruption (such as the alleged adverse impacts on inequality and poverty), or devoting undue attention to (statistically meaningless) movements in country scores from previous years. Whether TI succeeded in leveraging the CPI’s publicity into more attention to the freedom of the media and civil society is another story, but the effort is commendable.

That said, I spent a bit of time digging into the supporting research documents that TI provided on this issue, and I find myself in the uncomfortable position of finding the proffered evidentiary basis for the link between a free press/civil society and progress in the fight against corruption problematic, to put it mildly—even though my own reading of the larger academic literature on the topic makes me think the ultimate conclusion is likely correct, at least in broad terms. That latter fact, coupled with my recognition that the materials I’m evaluating are advocacy documents rather than academic research papers, makes me reluctant to criticize too harshly. Nonetheless, on the logic that it’s important to hold even our friends and allies accountable, and that in the long term promoting more careful and rigorous analysis will produce both more suitable policy prescriptions and better advocacy, I’m going to lay out my main difficulties with TI’s data analysis on the press freedom-corruption connection: Continue reading

Adjusting Corruption Perception Index Scores for National Wealth

My post two weeks ago discussed Transparency International’s newly-released 2017 Corruption Perceptions Index (CPI), focusing in particular on an old hobby-horse of mine: the hazards of trying to draw substantive conclusions from year-to-year changes in any individual country’s CPI score. Today I want to continue to discuss the 2017 CPI, with attention to a different issue: the relationship between a country’s wealth and its CPI score. It’s no secret that these variables are highly correlated. Indeed, per capita GDP remains the single strongest predictor of a country’s perceived corruption level, leading some critics to suggest that the CPI doesn’t really measure perceived corruption so much as it measures wealth—penalizing poor countries by portraying them as more corrupt, when in fact their corruption may be due more to their poverty than to deficiencies in their cultures, policies, and institutions.

This criticism isn’t entirely fair. Per capita income is a strong predictor of CPI scores, but they’re far from perfectly correlated. Furthermore, even if it’s true that worse (perceived) corruption is in large measure a product of worse economic conditions, that doesn’t mean there’s a problem with the CPI as such, any more than a measure of infant mortality is flawed because it is highly correlated with per capita income. (And of course because corruption may worsen economic outcomes, the correlation between wealth and CPI scores may be a partial reflection of corruption’s impact, though I doubt there are many who think that this relationship is so strong that the causal arrow runs predominantly from corruption to national wealth rather than from national wealth to perceived corruption.)

Yet the critics do have a point: When we look at the CPI results table, we see a lot of very rich countries clustered at the top, and a lot of very poor countries clustered at the bottom. That’s fine for some purposes, but we might also be interested in seeing which countries have notably higher or lower levels of perceived corruption than we would expect, given their per capita incomes. As a crude first cut at looking into this, I merged the 2017 CPI data table with data from the World Bank on 2016 purchasing-power-adjusted per capita GDP. After dropping the countries that appeared in one dataset but not the other, I had a 167 countries. I then ran a simple regression using CPI as the outcome variable and the natural log of per capita GDP as the sole explanatory variable. (I used the natural log partly to reduce the influence of extreme income outliers, and partly on the logic that the impact of GDP on perceived corruption likely declines at very high levels of income. But I admit it’s something of an arbitrary choice and I encourage others who are interested to play around with the data using alternative functional forms and specifications.)

This single variable, ln per capita GDP, explained about half of the total variance in the data (for stats nerds, the R2 value was about 0.51), meaning that while ln per capita GDP is a very powerful explanatory variable, there’s a lot of variation in the CPI that it doesn’t explain. The more interesting question, to my mind, concerns the countries that notably outperform or underperform the CPI score that one would predict given national wealth. To look into this, I simply ranked the 167 countries in my data by the size of the residuals from the simple regression described above. Here are some of the things that I found: Continue reading