We study a dynamic principal–agent relationship with adverse selection and limited commitment. We show that when the relationship is subject to productivity shocks, the principal may be able to improve her value over time by progressively learning the agent's private information. She may even achieve her first‐best payoff in the long run. The relationship may also exhibit path dependence, with early shocks determining the principal's long‐run value. These findings contrast sharply with the results of the ratchet effect literature, in which the principal persistently obtains low payoffs, giving up substantial informational rents to the agent.

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.

We study how incentive constraints can be relaxed by randomization in a repeated-game setting. Our study is motivated by the workings of a detected bidding cartel that adopted a protocol of keeping the winning bid secret from the designated losers when defection was a concern. Keeping the winning bid secret makes accurately undercutting the winning bid more difficult and makes defection less attractive as potential defectors risk not winning the auction even if they deviate. We formalize these ideas in the context of a repeated-game setting and show that a cartel can attain higher payoffs by having the pre-selected winner randomize its bid and keep it secret from other members. Calibration of the model to the bid data of the cartel suggests that randomization may increase firms’ profits by about 56%.

Cartels participating in procurement auctions frequently use bid rotation or prioritize incumbents to allocate contracts. However, establishing a link between observed allocation patterns and firm conduct has been difficult: there are cost-based competitive explanations for such patterns. We show that by focusing on auctions in which the winning and losing bids are very close, it is possible to distinguish allocation patterns reflecting cost differences across firms from patterns reflecting non-competitive environments. We apply our tests to two datasets: the sample of Ohio milk auctions studied in Porter and Zona (1999, “Ohio School Milk Markets: An Analysis of Bidding”, RAND Journal of Economics, 30, 263–288), and a sample of municipal procurement auctions from Japan.

We document a novel bidding pattern observed in procurement auctions from Japan: winning bids tend to be isolated, and there is a missing mass of close losing bids. This pattern is suspicious in the following sense: its extreme forms are inconsistent with competitive behavior under arbitrary information structures. Building on this observation, we develop systematic tests of competitive behavior in procurement auctions that allow for general information structures as well as nonstationary unobserved heterogeneity. We provide an empirical exploration of our tests, and show they can help identify other suspicious patterns in the data.

Cartels and bidding rings are often facilitated by intermediaries, who recommend prices/bids to firms and can impose penalties (such as reverting to competitive behavior in future interactions) if these recommendations are not followed. Motivated by such cases, we study correlated equilibria in first-price procurement auctions with complete information, where bidders who disobey their recommendations are penalized. Cartel-optimal profit is greater when more information about submitted bids is disclosed at auction and when the maximum penalty is larger. When only the winner’s identity is disclosed (or the winner’s identity and bid), cartels do not benefit from mediation. Our main result characterizes the cartel-optimal equilibrium with two symmetric bidders when both bids are disclosed. The optimal equilibrium involves extensive randomization and displays tied bids and high winning bids with positive probability, even when the maximum penalty is very small. The stationary mediation schemes we consider are always more profitable for the cartel than bid rotation.