Empirical Analysis of Corruption

Abstract

Although corruption is considered a significant problem in much of the devel-oping world, for many years there was virtually no hard economic data on it. Instead, economic studies for the most part relied on cross-country datasets consisting of businessmen's general per-ceptions of the relative corruption lev-els of different countries.' The lack of data meant that it was difficult to esti-mate the true costs of corruption, to test which theories of corruption were borne out in the data, and to understand what approaches might be most effective in reducing corruption. In recent years, a variety of approaches have been taken to ferret out more accu-rate indicators of corrupt activity. My recent empirical work on corruption examines how this improved data can be used to answer three questions: what are the costs of corruption; how can corrup-tion be ameliorated; and, what theories of corruption best match the data? The Costs of Corruption Corruption may matter for economic efficiency if theft of government resources increases the cost of government activity. Then, otherwise worthwhile government projects — such as redistribution schemes or public works projects — will no longer be cost effective. I examine this possibil-ity2 in my study of a large Indonesian anti-poverty program that distributed subsi-dized rice to poor households. I estimate the extent of corruption in the program by comparing administrative data on the amount of subsidized rice distributed in a given region with survey data on the amount of the subsidized rice actually received by households in that region. The central estimates suggest that, on average, at least 18 percent of the rice appears to have disappeared. I show statistically that the "missing rice" was much more concen-trated in particular regions than would be predicted by random chance. Therefore, it looks as though in some regions much of the rice was not distributed at all, rather than there simply being misreporting in the survey data. In the same paper, I construct a wel-fare calculation ofthe benefits ofthe pro-gram, both as it was implemented and using a counterfactual with the same tar-geting of beneficiaries but without cor-ruption. I estimate that the welfare losses from this "missing rice" may have been large enough to offset the potential wel-fare gains from the program's redistribu-tion. In other words, the program without corruption might have been cost-effective but, in the presence of corruption, it likely was not. These estimates suggest that cor-ruption can be costly enough to substan-tially impede redistribution. Corruption also may lead to ineffi-ciency if it undoes the government's abil-ity to correct an externality. For example, if someone can bribe a police officer or judge instead of paying an official fine, then the marginal cost of breaking the law is reduced from the official fine to the amount of the bribe. Even worse, if the police officer extracts the same bribe regardless of whether the person has bro-ken the law, then the marginal cost of breaking the law falls to zero and the law ceases to have a disincentive effect altogether. Patrick Barron and I examine this possibility in a paper on trucking in Indonesia.^ We had surveyors travel with truck drivers on 304 trips to and from the Indonesian province of Aceh, recording data on more than 6,000 illegal payments made at police and military checkpoints and at weigh stations. We believe that this represents the first large-scale survey that has ever directly observed actual bribes in the field. Using these data, we examine what happens when these trucks stop at weigh stations. Driving an overweight truck is a classic example of an activity that gen-erates an externality. While there can be benefits to a trucker from loading on additional weight, the damage the truck does to the road rises very rap-idly with the truck's weight. For this rea-son, governments around the world weigh trucks and impose fines on trucks that are overweight. In our data, we find that virtually all of the trucks in our sample were sub-stantially over the weight limits — and, in fact, 42 percent of trucks were more than 50 percent over the legal weight limit. The data also suggest that