Sam Kapon [CV]

Post-Doctoral Research Scholar, Princeton University

Research Fields: Microeconomic theory, Experimental economics

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Published and Accepted Papers

Dynamic Amnesty Programs [online appendix]
Accepted, 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 punishments 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.

Prior-Free Dynamic Allocation Under Limited Liability
with Sylvain Chassang, Theoretical Economics, 2022


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

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.

Using Divide and Conquer to Improve Tax Collection: Evidence from the Field
with Sylvain Chassang and Lucia Del Carpio, 2022


In the context of collecting property taxes from 13432 households in a district of Lima (Peru), we investigate whether prioritized enforcement can improve the effective use of limited enforcement capacity. We randomly assign households to two treatment arms: one replicating the city’s usual collection policy, and one implementing a prioritized enforcement rule in which households are ordered according to a suitable rank and sequentially issued clear short-term promises of collection if they fail to make minimum tax payments. Raw findings show that prioritized enforcement improved tax collection by increasing tax revenue, and decreasing the number of costly collection actions taken. We identify an important friction ignored by existing theory: tax-payers' response to incentives is slow, which changes the optimal management of collection promises. Finally, we estimate a model of tax-payer behavior and use it to produce counterfactual treatment estimates for other collection policies of interest. In particular, we estimate that, keeping the number of collection actions fixed, prioritized enforcement would increase tax revenue over 5 months by 11.3%.

Work in Progress

Hard and Soft Information in Repeated Interaction: An Experiment
with Guillaume Fréchette

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