Abstract
Anecdotal evidence suggests that intermediaries are ubiquitous in corrupt activities; however, empirical evidence on their role as facilitators of corrupt transactions is scarce. This paper asks whether intermediaries facilitate corruption by reducing the moral or psychological costs of possible bribers and bribees. We designed bribery lab experiment that simulates petty corruption transactions between private citizens and public officials. The experimental data confirm that intermediaries lower the moral costs of citizens and officials and, thus, increase corruption. Our results have implications with respect to possible anti-corruption policies targeting the legitimacy of the use of intermediaries for the provision of government services.
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Notes
A recent survey of Norwegian exporting firms (Søreide 2006) shows widespread use of intermediaries to by-pass anti-corruption regulations; when asked about the most important quality of an intermediary, 50 % of the firms pointed at the intermediary’s ties with relevant decision makers. For a legal perspective on the issue of intermediaries used by firms in foreign countries, and related case studies, see Bray (2004).
Coffman (2011) finds similar results using a standard (i.e. non-binary choice) dictator game, where the dictator could choose how much to allocate to the recipient or “sell” the decision right to a delegee.
See Abbink and Serra (2012) for the latest survey of experimental studies on corruption that have clear policy implications.
Although repeated corrupt exchanges relying on trust and reciprocation, and usually taking place between public officials and businesses, are certainly important, many corrupt transactions take place only once between public officials and ordinary citizens. For an example of experimental studies of repeated corrupt transaction relying on trust and reciprocity see Abbink et al. (2002).
Armantier and Boly (2012) show that corruption “can be studied in the lab” by comparing individuals’ behavior and responses to incentives in a bribery experiment and in an actual field experiment, in which participants, unaware of being part of a study on corruption, had to decide whether or not to engage in bribery. Both experiments were conducted in Burkina Faso. The results show that individuals’ propensities to engage in bribery were virtually identical in the lab and in the field, when controlling for individual characteristics.
We assume here and implement in the experiment that the official cannot accept the bribe but not deliver the service. If this were possible, the intermediary would then have another role of ensuring the delivery of the service as mentioned in the introduction.
The uniform distribution is assumed only to obtain simple closed-form solutions. None of the results qualitatively depends on this assumption.
This assumption (and analogous assumptions further) ensures that the probability of corruption is always less than one which simplifies expressions without any qualitative effects.
To see that, compute first the probability that corruption takes place: \(\Pr \{ m_{c}+m_{p}\leq v \} ={\int_{0}^{v}} \frac{1}{\overline{m}_{p}}{\int_{0}^{v-m_{p}}} \frac{1}{\overline{m}_{c}}dm_{c}dm_{p}=\frac{v^{2}/2}{\overline{m}_{p}\overline{m}_{c}}\). Then, the probability density function of m p ∣m c ≤v−m p is \(\frac{\Pr \{ m_{p}\mid m_{c}\leq v-m_{p} \} }{\Pr \{ m_{c}+m_{p}\leq v \} }=\frac{\frac{1}{\overline{m}_{p} }\frac{v-m_{p}}{\overline{m}_{c}}}{\frac{v^{2}/2}{\overline {m}_{p}\overline {m}_{c}}}=\frac{v-m_{p}}{v^{2}/2}\). Finally, \(E[m_{p}\mid m_{c}+m_{p}\leq v]=\int_{0}^{v}m_{p}\frac{v-m_{p}}{v^{2}/2}dm_{p}=\frac{v}{3}\).
This result relies on the distribution being uniform and the effect of the intermediary being proportionate.
In the Uncertainty treatment these are the citizens who offered a bribe that was accepted by the matched officials. In the No Uncertainty Intermediary treatments these are the citizens who paid a bribe equal to the MAB of the matched officials.
The additional subject in the intermediary treatment makes the average payoff higher; therefore, any gain generated by a corrupt transaction represents a smaller deviation from the average payoff, making both citizen and official more willing to engage in corruption. However, the ERC model would not predict the size of the behavioral change that we observe in the presence of the intermediary. Indeed, given our parameterization, discussed in the next session, the difference in average payoffs between the treatments with and without intermediary is very small and is equal to 0.42 ECU or $0.10, which is a 1 % shift in the average. Therefore, any increase in corrupt behavior that we observe in the presence of the intermediary is unlikely to be driven by such a small difference in average payoffs.
For simplicity we did not include K and E in our theoretical framework. Their inclusion does not alter the comparative statics and our general predictions.
Whether and to what extent the strategy elicitation affects observed behavior is the subject of an ongoing debate. The empirical evidence is mixed. Güth et al. (2001), Schotter et al. (1994) and Brosig et al. (2003) find that the strategy elicitation induces a significantly different behavior as compared to the direct elicitation. Using different experimental designs, Cason and Mui (1998), Brandts and Charness (2000) and Oxoby and McLeish (2004) find no differences. The complexity of the experiment may be a crucial factor: the difference increases with the complexity of the game (Brandts and Charness 2000). Our game is simple so any effect is likely to be small. For a recent survey of experimental comparisons of strategy versus direct-response method, see Brandts and Charness (2011).
Withholding information is common practice in experimental economics and it is not considered deception. As stated by Hey (1998), “there is a world of difference between not telling subjects things and telling them the wrong things. The latter is deception, the former is not.” This element of our design is similar in purpose to a block design seen in other experimental research. In these, subjects play several multi-period stages and learn the instructions for each stage only when the stage is reached. Examples include, but are not limited to, Brandts and Charness (2000) and Hamman et al. (2011).
Of the many papers doing something similar to what we did, we can cite Ellingsen et al. (2010), which elicit recipient beliefs in a Dictator Game, a Trust game and a hidden action Trust game, and communicate these beliefs to the dictator/trustor before the dictator/trustor makes his or her allocation decision. In order to elicit truthful beliefs, Ellingsen et al. (2010) do not inform the recipients that their beliefs will be communicated to the dictator. Our design and purpose are very similar: in order to elicit MABs that reflect truthful moral costs associated with corruption (without confounds generated by strategic motives), we do not inform the public officials that their MABs will be communicated to the citizens.
Even though demographics are balanced across treatments, we include them in our empirical specifications because they might affect individuals’ propensities to engage in corruption differently in the different treatments. In other words, demographics might interact with our treatments when determining individuals’ willingness to bribe or accept a bribe. For instance, in our sample we find that under Uncertainty the propensity to offer a bribe increases with age; however, age has no effect on bribery under No Uncertainty, and it actually decreases individuals’ propensity to bribe in the Intermediary treatment.
This is exactly what we observe in panel 2 of Table 2.
The average bribe offered under Uncertainty by all citizens, including those whose bribes were rejected, is 8.75.
We also administered a post-experiment questionnaire asking participants to rate the fairness of their own actions and that of other roles. We found that citizens (but not officials) who acted corruptly in the game were significantly more likely to see their decisions as fair in the presence of the intermediary than in both the Uncertainty and No Uncertainty treatments. This reinforces our argument that intermediation lessens the psychological cost of corrupt behavior. Interestingly, OMS participants also judged corrupt citizens significantly less harshly when an intermediary was used.
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We thank Abigail Barr, David Cooper, Dann Millimet, Ernesto Reuben, Tim Salmon and two anonymous referees for valuable insights and suggestions. We also thank participants at the 2010 SEA Meeting, the FSU experimental reading group, the CESS (Oxford) workshop, the 4th NYU-CESS Experimental Political Science conference, the 7th IMEBE meeting and the 2011 Lisbon Meeting in Institutions and Political Economy for useful comments. Mikhail Drugov gratefully acknowledges the financial support of the Spanish Ministry of Education and Science under grants ECO2008-03516 and ECO2011-30323-C03-03. Danila Serra acknowledges financial support from the program for the Study of Political Economy and Free Enterprise at Florida State University.
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Drugov, M., Hamman, J. & Serra, D. Intermediaries in corruption: an experiment. Exp Econ 17, 78–99 (2014). https://doi.org/10.1007/s10683-013-9358-8
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DOI: https://doi.org/10.1007/s10683-013-9358-8