In 2017, the Republic of Georgia sent $272 million in exports to its neighbor, Azerbaijan. The same year, Azerbaijan reported receiving $74 million—that’s not a typo—in imports from Georgia. Goods worth $198 million seemingly disappeared before they reached Azerbaijani customs. The gap is a big deal. Azerbaijan taxes imports just above 5% on average (weighted for trade), which means its treasury missed out on collecting roughly $10 million in tariffs—0.1% of all government spending in that year—from just a single trading partner.
Many factors could explain the gap (see, for example, here, here, and here). Shippers might have rerouted goods to other destinations, the two countries’ customs offices might value goods differently, or the customs offices could have erred in reporting results or converting them to dollars. But one reason Azerbaijan’s reported imports are so low—not only here, but systemically across trade partners and years—is corruption and associated tariff evasion. Many traders likely undervalue and/or underreport their imports when going through Azerbaijani customs, and the sheer magnitude of the trade gap suggests the complicity or collusion of the authorities. The corruption involved might be petty (e.g., an importer bribing a customs officer to look the other way, or a customs officer pocketing the tax and leaving it off the books) or grand (e.g., a politician with a side business using her influence to shield imports from inspection; see here). A similar dynamic might also be at work in exporting countries: companies may undervalue exports to limit their income tax liability, possibly paying bribes to avoid audits.
Though Azerbaijan may be an extreme case, it is not unique. Economists have examined these export gaps (sometimes called “mirror statistics”) and have found similar discrepancies in, for example, Hong Kong’s exports to China, China’s exports to the United States, and Cambodia’s imports from all trading partners. Most recently, economists Derek Kellenberg and Arik Levinson compared trade data across almost all countries over an eleven-year time period, finding that “corruption plays an important role in the degree of misreports for both importers and exporters.” For lower-income countries, Professors Kellenberg and Levinson showed a positive relationship between a country’s level of perceived corruption, as measured by Transparency International’s Corruption Perceptions Index (CPI), and its underreporting of imports. The authors also showed a strong positive relationship between perceived corruption and the underreporting of exports across all countries.
Mirror statistics are an imperfect measure of customs corruption, to be sure, but they can serve two useful purposes in fighting this sort of corruption, and anticorruption reformers should pay more attention to this type of data. Continue reading
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
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
Early last month, UN Secretary General António Guterres delivered some brief opening remarks to the Security Council at a meeting on the relationship between corruption and conflict. In these remarks, Secretary General Guterres cited a couple of statistics about the economic costs of corruption: an estimate, attributed to the World Economic Forum (WEF), that the global cost of corruption is $2.6 trillion (or 5% of global GDP), as well as another estimate, attributed to the World Bank, that individuals and businesses cumulatively pay over $1 trillion in bribes each year. And last week, in her opening remarks at the International Anti-Corruption Conference, former Transparency International chair Huguette Labelle repeated these same figures.
Those statistics, as I’ve explained in prior posts (see here and here) are bogus. I realize that Secretary General Guterres’ invocation of those numbers shouldn’t bother me so much, since these figures had no substantive importance in his speech, and the speech itself was just the usual collection of platitudes and bromides about how corruption is bad, how the international community needs to do more to fight it, that the UN is a key player in the global effort against corruption, blah blah blah. Ditto for Ms. Labelle–her speech used these numbers kind of like a rhetorical garnish, to underscore the point that corruption is widespread and harmful, a point with which I very much agree. But just on principle, I feel like it’s important to set the right tone for evidence-based policymaking by eschewing impressive-sounding numbers that do not stand up to even mild scrutiny. Just to recap: Continue reading
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
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