I am a PhD Candidate at the New York University Department of Economics on the 2020/21 job market.
Primary fields: Micro Theory, Experimental
Secondary field: Industrial Organization
In my job market paper, I study the design of dynamic incentives to report crime.
A regulator faces a stream of agents each engaged in crime with stochastic returns. The regulator designs an amnesty program, committing to a time path of penalty reductions for criminals who self-report before they are detected. In an optimal time path, the intertemporal variation in the returns from crime can generate intertemporal variation in the generosity of amnesty. I construct an optimal time path and show that it exhibits amnesty cycles. Amnesty becomes increasingly generous over time until it hits a bound, at which point the cycle resets. Agents engaged in high return crime self-report at the end of each cycle, while agents engaged in low return crime self-report always. An extension to multi-agent organizations, like price-fixing cartels, is examined, in which a preemption motive can magnify the effect of time varying amnesty.
Limits on a government’s capacity to enforce laws can result in multiple equilibria. If most agents comply, limited enforcement is sufficient to dissuade isolated agents from misbehaving. If most agents do not comply, overstretched enforcement capacity has a minimal impact on behavior. We study the extent to which divide-and-conquer enforcement strategies can help select a high compliance equilibrium in the presence of realistic compliance frictions. We study the role of information about the compliance of others both in theory and in lab experiments. As the number of agents gets large, theory indicates that providing information or not is irrelevant in equilibrium. In contrast, providing individualized information has a first order impact in experimental play by increasing convergence to equilibrium. This illustrates the value of out-of-equilibrium information design.
Prior-Free Dynamic Allocation Under Limited Liability
with Sylvain Chassang, R&R, Theoretical Economics, 2020
A principal seeks to efficiently allocate a productive public resource to a number of possible users. Vickrey-Clarke-Groves (VCG) mechanisms provide a detail-free way to do so provided users have deep pockets. In practice however, users may have limited resources. We study a dynamic allocation problem in which participants have limited liability: transfers are made ex post, and only if the productive efforts of participants are successful. We show that it is possible to approximate the performance of VCG using limited liability detail-free mechanisms that selectively ignore reports from participants who cannot make their promised payments. We emphasize the use of prior-free online optimization techniques to approximate aggregate incentive properties of VCG.
Work in Progress
Hard and Soft Information in Repeated Interaction: An Experiment
with Guillaume Fréchette, 2020
Multilateral Reputational Bargaining
with Rumen Kostadinov, 2018