Sam Kapon [CV]

Visiting Research Scholar in Economics, Princeton University

Research Fields: Microeconomic theory, Experimental economics

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Published and Accepted Papers

Prior-Free Dynamic Allocation Under Limited Liability
with Sylvain Chassang, Accepted, Theoretical Economics, 2021


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 the pivot VCG mechanism using limited liability detail-free mechanisms that selectively ignore reports from participants who cannot make their promised payments. A complementary use of cautiousness and forgiveness achieves approximate renegotiation-proofness. We emphasize the use of prior-free online optimization techniques to approximate aggregate incentive properties of the pivot mechanism.

Working Papers

Dynamic Amnesty Programs [online appendix]
R&R, American Economic Review, 2022


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.

Using Divide and Conquer to Improve Tax Collection: Theory and Laboratory Evidence
with Sylvain Chassang and Lucia Del Carpio, 2022


We consider a government collecting taxes from a large number of tax-payers using limited enforcement capacity. Under random enforcement, limited capacity results in multiple equilibria: if most agents comply, limited enforcement is sufficient to dissuade individual misbehavior; if most agents do not comply, enforcement capacity is overstretched and fails to dissuade misbehavior. In settings without behavioral frictions, prioritized enforcement strategies can implement high collection as the unique rationalizable outcome. We investigate both theoretically and experimentally the extent to which this insight extends to environments with incomplete information and bounded rationality.

Work in Progress

Hard and Soft Information in Repeated Interaction: An Experiment
with Guillaume Fr├ęchette

Using Divide and Conquer to Improve Tax Collection: Field Evidence from Peru
with Sylvain Chassang, Lucia Del Carpio

Designing Randomized Controlled Trials with External Validity in Mind
with Sylvain Chassang