Guest Post: Typologies of (Anti-) Corruption — How Much More Boring Can It Get? Or Maybe Not…

Dieter Zinnbauer, Senior Program Manager for Emerging Policy Issues at Transparency International, contributes the following guest post:

Remember that childhood game, you say a word over and over and it seems to lose its meaning and just dissolves into a melodic sound? I feel similarly about trying to slice up the umbrella concept of corruption and sort it into practical, reasonably comprehensive, and distinctive subcategories – an endeavor that usually gets out of hand, consumes disproportionate amounts of scarce thinking-time and energy, and eventually leaves the participants more confused and in disagreement than at the outset. Yet quite recently I have begun to change my mind a bit about the unproductiveness of typologizing (anti)corruption. In fact, I have begun to derive some surprising enjoyment and inspiration from playing around with different ways to look at and classify different types of (anti)corruption. Here three examples:

  1. First vs. second/higher order information asymmetries: One can distinguish among the different types of information problems that may characterize a specific form of corruption, in order to design more effective interventions. First order asymmetries are about differing information levels and thus different abilities of principals and agents to judge whether (for example) a contract has been honored or service has been rendered as agreed. Second or higher order information asymmetries pertain to a more complex situation where neither principals nor agents may know what the other side or their peers actually know and can thus help identify challenges such as situations of pluralistic ignorance (when peers do not have full knowledge about and cannot sanction what their peers think or do – and thus plot a self-centered course of action). Both information asymmetries can co-exist. More practically, considering both first and higher order information asymmetries and assessing specific corruption problems accordingly can help design more effective interventions to tackle them, for example when the number of people deciding to take action against corruption and complain to a hotline are made visible in aggregates and thus provide testament to the groundswell of individual opposition and thus make the seeding of collective action or proactive compliance more likely.
  2. Private vs. public harm: One can distinguish between private vs. public harms that result from specific types of corruption, in order to identify new funding strategies. Being hauled over by a corrupt police office for the sole purpose of extracting a bribe is first and foremost a private harm that directly hurts the citizen. Having a corrupt mayor that colludes with a contractor to overprice a public contract and steal the surplus is an act of corruption that primarily causes public harm, as it hurts the community budget. Using this prism of private vs. public harm may help identify different challenges, and new opportunities for funding anticorruption interventions. The risk of individual harm, for example, might be insurable (how about a micro-insurance for legal aid in the case of extortionate corruption?) whereas the risk of pubic harm may lead to collective good financing strategies, e.g. thinking about inviting in a social entrepreneur that could help produce and capture the value of the public good and share some of the gains with the community (e.g. a forensic accountant that is paid a share of stolen money that she helped to recover). I have elaborated more on some of these business model ideas here.
  3. Risk vs. uncertainty. One can distinguish between between situations in which corrupt actors face the risk of detection and situations in which they are uncertain as to whether they will be detected.In this context, drawn from Frank Knight’s classic treatment, “risk” refers to situations in which the range of possible outcomes is known and their respective likelihoods can be reasonably well estimated, while “uncertainty” refers to a situation where neither all possible outcomes nor their probability are well understood. How could this matter for anticorruption? This might actually be a useful distinction when trying to gauge the effect, relative desirability, and complementary nature of the mandated, rule-based information disclosure associated with “open government” practices vs. the effects of a flurry of massive-scale data leakages that, as unpredictable as they are individually in focus and timing, do seem as a whole to represent an enduring new trend and additional, albeit very different driver of openness. Deliberately designed and predictably implemented, conventional disclosure rules, such as new provisions to disclose lobbying practices, are more like a risk that can be managed. This type of prescribed transparency may help to eradicate some corrupt practices since they become too easy to detect and do not pay off any more, while keeping others with higher returns and still positive expected values on the playbook and also enticing the proactive search for loopholes and new practices to avoid the predictable spotlight of disclosure. In contrast to this risk management situation, massive, sudden, unpredictable data leaks however are an entirely different phenomenon. They are much more difficult to calculate with and fold into conventional approaches to risk management. How should a potentially corrupt actor behave when all its communications with governments foreign or domestic during the last two decades could end up in the public limelight, perhaps not even released not by a senior co-conspirator in the know, but by a low level disgruntled system administrator or some outside hacker or IT provider? How does this new vulnerability to “catastrophic” blanket exposure impact upon the corruption game and the prospects for integrity in the longer run? How can policies help shape this new calculus in favour of integrity? I have no answers, but it strikes me that these are very interesting questions to explore. And a risk vs. uncertainty perspective on different drivers of openness can serve as a good starting point for this journey.

To be clear: none of these three mini-typologies comes with any kind of ambition to help classify the immensely diverse (anti)corruption space in any kind of comprehensive or clear-cut manner. Yet, they help cut through some part of the thicket of (anticorruption) concepts and offer some rather interesting vantage points for further explorations.

4 thoughts on “Guest Post: Typologies of (Anti-) Corruption — How Much More Boring Can It Get? Or Maybe Not…

  1. Thanks for a provocative post that should stir much reflection and debate. One point that immediately comes to mind is the example you use to distinguish between public and private harm. Don’t be too quick to classify the corrupt traffic officer’s extortion of a single motorist as purely a private harm. There could be, as those schooled in economics are fond of reminding, spill over effects from this action. The extorted motorist tells all her friends that the police are corrupt. Her friends tell all their friends that the government is so incompetent it can’t curb corruption in the police force. The friends of friends dismiss the government as a combination of incapable and venal public servants. The legitimacy of the government suffers; more and more citizens ignore the law. The military steps in to “right” the situation.

    Maybe a single shakedown of a single motorist won’t have such an effect (unless perhaps the motorist is a well-read, well-respected journalist) but a series of shakedowns or a patter of shakedowns in certain neighborhoods (say those where one ethnic group predominates) could. Your public/private harm distinction may be useful but you need to be careful when choosing an example.

  2. You can count me among those who are generally skeptical of the values of typologies as such. There are countless ways one can sort and categorize and lump and split, and these efforts are only worthwhile if they serve some purpose. So what I like about your effort here is to connect the conceptual distinctions you’re trying to draw to practical questions about how best to respond. That said, I’m still unsure how useful these particular forms of categorization are:

    The first strikes me as promising, but as awfully abstract. I’m not sure your really need the concept of “higher-order information asymmetries” to get at the idea that transparency as to what other people are doing may somethings be helpful. Note here though that your example doesn’t actually describe a kind of corruption, but rather something about the response. So is this really a contribution to a typology of corruption? Or is it rather making a useful observation about information, transparency, coordination, etc., that is helpful even if we don’t try to sort corruption into different categories?

    On the second, I share Rick’s concern that the line between public and private harm might not always be so easy to draw, but I see what you’re getting at here. I think the more conventional distinction drawn in the literature is between the distributive effects of corruption and the efficiency effects of corruption. Some forms of corruption might have big distributional effects but minimal efficiency consequences; for others, the efficiency consequences might be much greater. This doesn’t map directly onto your public vs. private harm distinction, but it might be a bit more useful (at least from an economist’s perspective) in evaluating social consequences. But again, do we really need a typology (which implies sorting into categories) as opposed to a recognition that different corrupt acts may have different distributive and efficiency consequences?

    On the third, I’ve actually never been a big fan of the Knightian risk v. uncertainty distinction. I’m a Bayesian at heart, I guess, and I think when you press on this distinction it doesn’t really make much sense, for reasons I won’t go into now but would be happy to if this comment thread continues. But here again, I’m not sure if what you say really (1) actually depends that much on the Knightian distinction, or (2) is about typologies of corruption. It actually seems more about how information about corruption might be revealed, which seems to me like something that could quite different from the kind of corruption we’re talking about.

  3. Dear Rick and Matthew,
    Thanks so much for your very inspiring feedback on my post. This launches us as straight into the very exploratory debate that I intended to spark. In order to tease out the differences in each typology I admittedly overstretched some of the distinctions, making them appear dichotomous where in reality they will just vary in degree of composition and fall onto a continuum.

    Rick, I think you are absolutely right to point out that in many incidences of corruption both private and public harm may occur, including the example that I picked. But paying attention to this public/private distinction might still help to identify cases that contain a significant dimension of private harm and thus may come with a higher individual willingness to pay for targeted anti-corruption efforts. This may then help broaden the horizon for innovative funding approaches that directly pick up on this possibility to achieve an individual welfare gain, whereas existing funding for anti-corruption via tax or charity seems primarily tied to the public harm / public goods model. And I am not sure if the distributive/ vs. efficiency effect distinction captures this equally well, but I have admittedly not thought this through at all.

    Matthew, I also agree that the risk vs. uncertainty juxtaposition is very artificial and could more practically look like a rather continuous likelihood x damage curve. Yet the point was to speculate that the astounding scale, scope and growing frequency of this new generation of vast data leaks really seems to be introducing an entirely new level of uncertainty into the risk calculus of likelihood of detection and related costs. In my view this black-swan like case of being pulled into the limelight really sets this type of leaking apart from the usual risks associated with conventional whistle-blowing, audits, inspections or mandatory (carefully delineated and known in advance) disclosure requirements. I would love to learn about research that seeks to explore whether this stream of wiki-, offshore-, cable-, lux- and other leaks are having what kind of impact on such detection risk management strategies. And what could that then mean for anti-corruption and conventional transparency in such a world of leaks?

    Finally, re. the questionable usefulness of first, vs. higher-order information asymmetries for what seems a common-sense insight, I would beg to disagree, albeit just on the flimsy basis of my own little subjective experience. While transparency on what one’s peers are thinking or doing felt somehow very intuitive and useful to help get out of collective action dilemmas or break situation of pluralist ignorance, the idea of higher-order asymmetries really got me thinking about what other (higher-order) feedback loops could be generating interesting dynamics. So for example I am toying with the idea of what might happen if we “feed(feed-back)back”, i.e. confront potentially corrupt officials with the collective public feedback on their performance right at the spot and in the situation where a corrupt exchange might take place. What happens if they know that I know that they know that that collective anger about such corrupt behavior is coming to a boil? Could this or other strange feedback loops change the dynamics of a corrupt transaction that can often be a very fragile social exchange dependent on settled expectations on both ends? Perhaps this is intuitive to others, but for me this idea of higher-order information asymmetries really sent me down this multiple-feedback route and I am still exploring it, for example on my blog on the concept of ambient accountability (

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

This site uses Akismet to reduce spam. Learn how your comment data is processed.