In a previous article, I applied simple rational choice theoretic rules to explain why corruption thrives in Nigeria. The theory was also used to identify some critical areas where public policy efforts should be focused if corruption is to be tamed. At the time, I had made assumptions about certain important variables used in the models. Now, those assumptions can be replaced with data that is now available from the EFCC, ICPC and other sources.
Background
Rational Choice Theory is the standard approach used by economists and other social scientists to explain why individuals make the decisions that they do. The field of rational choice theory is one of the most intensely researched in the social and behavioral sciences ad has spawned several Nobel Laureates, and a healthy body of real world experience has developed which validate its views of human behavior. The behavior of stockholders, economic cycles of boom and bust, criminal behavior, and a host of everyday phenomena are explained by this theory.
At its core, rational choice theory is based on simple and intuitive concepts. It assumes fundamentally that humans make decisions based on certain rules, that the benefits and costs of choices are evaluated, and that the choices that are selected are those that maximize value. How do we weigh these benefits? Given an option, W, which has a probability of occurrence, P, the expected benefit from making a choice is given by the multiplication of the option, W and the probability P. In simple mathematical terms, we would say that:
Expected Benefit = W x P.
All choice options have corresponding costs. Therefore, given a Cost, C with a probability of occurrence P', the expected cost of the option is given as:
Expected Cost = C x P'.
The Expected Net Benefit is the difference between the expected benefit and the expected cost. In effect, the expected net benefit of any option W with probability of occurrence P, and a cost C with probability of occurrence P' is given as:
Expected Net Benefit = Expected Benefit - Expected Cost = W x P - C x P'.
Whenever the Expected Net Benefits are greater than zero, i.e., non-negative, the choice is one which a rational decision maker is expected to make. If the Expected Net Benefits are negative, the rational thing to do is to turn down the option.
Corruption Cost Probabilities in Nigeria- What the Data Says
The cost of corruption derives exclusively from administrative, disciplinary or legal prosecutorial action taken against the offender. For an offender to pay a price for engaging in corrupt practices, three things must happen. (1) The offender must be reported or discovered (2) the offender must be prosecuted and (3) a conviction must be secured against the offender.
These three prerequisites for establishing a cost or penalty for corruption have associated probabilities.
Probability of discovery (1.68%): The ICPC or EFCC require that petitions be written against offenders before they act. We can therefore define the probability of discovery as the number of reported corruption cases as a fraction of the total incidents of corruption committed. How do we establish the number of corrupt incidents in Nigeria? We will not attempt to sum up every incident of bribe taking at check-points and the millions of illegal payments that occur daily, and focus instead on corporate bribery and corruption and sundry financial crimes cases involving public officials. These are the types of cases that the ICPC and EFCC are charged with addressing. Because these two agencies and their actions (or inactions) determine the public perception of the seriousness with which corruption is being addressed, it is appropriate to focus on them.
In a recent survey jointly commissioned by the UNODC, EU and EFCC, about 9% of Nigerian enterprises indicated that they routinely give bribes to public officials. There are about 398,453 registered companies in Nigeria. If we conservatively estimate that each corporate enterprise that reports that it bribes public officials records only one bribery incident per year, in the twelve years since both the ICPC and EFCC have been around, there would have been about 430,329 (i.e., 398,453 enterprises x 9% bribery rate x 1 bribery incident per year x 12 years) cases of bribery in Nigeria. Since inception in 2003 the EFCC has received at least 5,400 petitions, while the ICPC (established in 2000) has received at least 1,846 petitions, making a total of 7,246. As previously defined, the probability of discovery is therefore given as the reported cases (petitions) divided by total corrupt occurrences, i.e., 7,246/430,329 = 1.68%.
Probability of prosecution (4.8%): No readily available dataset provides prosecution rates up to the current time. Historically (as of 2008), the EFCC had prosecuted 300 cases from 5,400 petitions, while the ICPC had prosecuted 49 of 1,846 cases. This implies that a total of 349 cases were prosecuted, from a base of 7,246 petitions, giving a Probability of prosecution of 4.8 % (i.e., 349/7,246). It is possible that this number might vary, since both agencies continue to investigate and prosecute past petitions. However, since the number of new petitions would have increased over the same period, the variation from the values estimated here are unlikely to be significant.
Probability of conviction (47.3%): Of 300 arraignments that were made, the EFCC secured 145 convictions, while the ICPC secured 20 convictions from 49 cases. This implies that 165 convictions were secured from 349 prosecuted cases, giving a Probability of conviction of 47.3% (i.e., 165/349). This number might be on the high side since the convictions reported are for individuals prosecuted. Some petitions and cases have multiple defendants, and those will bias the conviction rate upwards since each individual conviction will be counted.
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