As several earlier posts on this blog have discussed (see, for example, here, here, and here), collusion and corruption in public procurement is a significant problem, one that is extremely difficult to detect and combat. The nature of public procurement markets makes collusion easier to sustain, as pay-offs are higher (demand is often inelastic due to the auction mechanisms used), administrative costs increase entry barriers, and the transparency of procurement contract awards–often intended as an anticorruption device–can actually make it easier for cartel members to monitor one another and punish cheating. Law enforcement agencies have tried various techniques for breaking these cartels, for example by offering leniency to the first company that “defects” on the other cartel members by exposing the collusive arrangement. However, although leniency policies have sometimes proven to be an effective tool to fight coordinated company behavior, the efficacy of this approach is limited given the relative unlikelihood that the government will ever acquire convincing evidence of collusion absent such a defection by an insider. Hence, there is great need for alternative methods to identify collusive rings and guide tradition investigation.
In many markets, using quantitative indicators to detect collusion has not been feasible, as gathering meaningful tender-level data (or even market-level data) is too costly, or simply impossible. However, in the case of public procurement markets, there is a huge amount of publicly available data, which makes the use of “Big Data” techniques to pinpoint collusion-related irregularities more feasible. Indeed, in collaboration with our colleagues at CRCB, we have developed a simple, yet novel approach for detecting collusive behavior. Continue reading