In many environments, including credit and online markets, records about participants are collected, published, and erased after some time. We study the effects of erasing past records in a dynamic market where the quality of sellers follows a Markov process, and buyers leave feedback about sellers to an information intermediary. When the average quality of sellers is low, unlimited records lead to a market breakdown in the long run. We consider the information design problem and characterize information policies that can sustain trade and that maximize social welfare. These policies hide some information from the market in order to foster socially desirable experimentation. We show that these outcomes can be implemented by appropriately deleting past records. Crucially, positive and negative records play opposite roles with different intensities and must have different lengths: negative records must be deleted sufficiently late, and positive ones sufficiently early.

We examine whether the potential for costly sabotage is a deterrent to firms' use of relative performance evaluation (“RPE”) in CEO pay plans. We exploit illegal cartel membership as a source of variation in the potential for costly sabotage and document that firms are more likely to use RPE if they are currently cartel members. Moreover, firms frequently drop RPE from their CEOs’ pay plans immediately after their cartels are detected, dissolved and punished. We further provide suggestive evidence that the potential for costly sabotage explains these patterns; cartel membership severs the empirical association between RPE and competitive aggression.

We study social dilemmas in (quasi-) continuous-time experiments, comparing games with different durations and termination rules. We discover a stark qualitative contrast in behavior in continuous time as compared to previously studied behavior in discrete-time games: cooperation is easier to achieve and sustain with deterministic horizons than with stochastic ones, and end-game effects emerge, but subjects postpone them with experience. Analysis of individual strategies provides a basis for a simple reinforcement learning model that proves to be consistent with this evidence. An additional treatment lends further support to this explanation.

This paper estimates the impact of competition policy on total factor productivity growth for 22 industries in twelve OECD countries over 1995 to 2005. We find a positive and significant effect of competition policy as measured by created indexes. We provide results based on instrumental variables estimators and heterogeneous effects to support the causal nature of the established link. The effect is particularly strong for specific aspects of competition policy related to its institutional setup and antitrust activities. It is also strengthened by good legal systems, suggesting complementarities between competition policy and the efficiency of law enforcement institutions.