We study the mechanics of cartel enforcement and its interaction with bidding constraints in the context of repeated procurement auctions. Under collusion, bidding constraints weaken cartels by limiting the scope of punishment. This yields a test of collusive behavior exploiting the counterintuitive prediction that introducing minimum prices can lower the winning-bid distribution. The model’s predictions are borne out in Japanese procurement data, where we find evidence that minimum prices weakened collusion. A robust design insight is that setting a minimum price at the bottom of the observed winning-bid distribution necessarily improves over a minimum price of zero.

We model criminal investigation as a principal-agent-monitor problem in which the agent can bribe the monitor to destroy evidence. Building on insights from Laffont and Martimort’s 1997 paper, we study whether the principal can profitably introduce asymmetric information between agent and monitor by randomizing the monitor’s incentives. We show that it can be the case, but the optimality of random incentives depends on unobserved preexisting patterns of private information. We provide a data-driven framework for policy evaluation requiring only unverified reports. A potential local policy change is an improvement if, everything else equal, it is associated with greater reports of crime.