Insider Imitation, with Erik Madsen (paper)

(Forthcoming, Journal of Political Economy 2023)

We study how regulating data usage impacts innovation in digital markets.  Platforms commonly use proprietary data about third-party sellers to inform their own competing offerings, dampening incentives for innovation.  We model this interaction and characterize how data usage restrictions reshape these incentives.  An outright ban on data usage may boost or curtail innovation, depending upon the thickness of the right tail of demand for new products.  More flexible rules controlling when and what data is made available can always improve the effectiveness of regulation.  Our results contribute to an ongoing policy discussion regarding competition in digital markets. 

Past and Future: Backward and Forward Discounting, with Debraj Ray and Ruqu Wang (paper)

( Journal of the European Economic Association 2023)

We study a model of time preference in which both current consumption and the memory of past consumption enter “experienced utility” — or the felicity — of an individual. An individual derives overall utility from her own felicity and the anticipated felicities of future selves. These postulates permit an agent to anticipate future regret in current decisions, and generate a set of novel testable implications in line with empirical evidence. The model can be applied to disparate phenomena, including present bias, equilibrium savings behavior, anticipation of regret, and career concerns.

Optimal Vaccine Policies: Spillovers and Incentives, with Joshua Weiss (paper)

(Covid Economics 2021)

We offer a novel theoretical framework to analyze the behavioral implications of vaccination policies. The key features of the model are that agents: 1) differ both in their potential exposure (x) to others and vulnerability (y) to severe illness, 2) exert negative externalities through interaction, and 3) can take voluntary preventative measures, for instance self-isolation. Our main result is a complete characterization of the second-best policy. Three striking features emerge. First, it is non-monotone -- people with intermediate y are vaccinated more than those with either low or high y. Second, it exhibits an exposure premium among those who do not self-isolate -- people with higher x require lower overall risk, xy, to be vaccinated. Third, for those who voluntarily self-isolate, it is invariant to y, depending only upon x. Mandatory lockdown (first-best) policies differ qualitatively and can be implemented using a flat interaction tax. Numerical results demonstrate that policies vaccinating only the most vulnerable perform significantly worse than other simple heuristics, especially when supplies are limited.

Privacy, Personalization and Price Discrimination, with Sinem Hidir  (paper, online appendix)

(Journal of the European Economic Association, 2020)

We study a bilateral trade setting in which a buyer has private valuations over a multi-product seller's inventory. We introduce the notion of an incentive-compatible market segmentation (IC-MS) - a market segmentation compatible with the buyer's incentives to voluntarily disclose their preferences. Our main result is a characterization of the buyer-optimal IC-MS. It is partially revealing, comprised primarily of pooling segments wide enough to keep prices low but narrow enough to ensure trade over relevant products. We use our results to study a novel design problem in which a retail platform seeks to attract consumers by calibrating the coarseness of its search interface. Our analysis speaks directly to the debate regarding product steering versus price discrimination in online retail. 

Working Papers