Research

Working Papers

Abstract: I consider a mechanism design approach to innovation adoption and show how it is optimal for the principal to induce artificial scarcity to speed it up. Take-up of a new product generates information about its value for others, so agents want to free-ride before irreversibly adopting it themselves. This causes a time-delay externality that a principal seeking to achieve an adoption target as quickly as possible (for example, a government trying to reach herd immunity through vaccination while agents are uncertain of their personal vaccination benefits, not internalizing the positive externality of reaching the adoption target)  seeks to avoid. Scarcity speeds up learning because it limits free-riding. I show that the possibility of imposing supply restrictions is always beneficial compared to free supply. I also show that optimal supply plans are simple in that there is a batched supply release with fewer batches than agents' value types. I fully characterize such optimal plans for settings with up to three types and show that (non-optimal) supply plans may be Pareto improving.

Presented at (includes upcoming): 3B Theory Workshop, Brown Theory Lunch (Fall 2023), LACEA-LAMES 2023, SBE 2023, EWMES 2023

Abstract: School-choice clearinghouses often advise students to "rank their true preferences" despite not allowing students to express preferences over peers. We evaluate the consequences of doing so. Empirically, we find students have preferences over relative peer ability in the college admissions market in New South Wales, Australia. Theoretically, we show stable matchings exist even with peer preferences under mild conditions, but finding one via one-shot mechanisms is unlikely. The status quo procedure frequently employed by clearinghouses is to inform applicants about the assignment of students in the previous cohort, inducing a tâtonnement process which potentially provides useful information about likely peers in the current cohort. We theoretically argue this process likely leads to an unstable outcome, and we find instability in our empirical setting. We propose a mechanism that yields stability and incentivizes truthful reporting in the presence of peer preferences.

Paper Presented at: Brown Theory Lunch (Fall 2020), Brown Applied Micro Lunch (Spring 2021), Matching in Practice Workshop 2021*, SAET 2021*, NBER Summer Institute 2021(Education)*, LACEA-LAMES 2021, SBE 2021, EC'22*, Stony Brook 2022*, NASMES 2022*,  ASSA 2023*, SITE 2024*

*Presented by coauthor

Abstract: Guided by matching theory, school choice markets are designed to generate stable matchings. The entry and exit of educational programs poses a barrier to stability if a long horizon is required for students to learn their preferences. In this paper, we study how entry and exit affect learning about a payoff relevant feature of educational programs: student quality. Theoretically, we show how entry and exit can inhibit stability. Empirically, using data from the college admissions market in New South Wales, Australia, we find gradual within-program convergence to stability and show how the persistent churn of programs in this marketplace inhibits overall-market convergence, leading to an unstable matching. This instability is primarily experienced by lower-ability students and those from marginalized groups, thus potentially increasing inequality.

Abstract: Decision-makers often need advice from specialists, who can offer a more precise assessment of the state of the world, but also be biased towards one action. One such situation is the decision to fund scientific research by NIH/NSF officers with the help of a peer review system. The present paper studies how a principal can fight these types of biases by committing to future decisions that affect the payoff of the expert in an environment in which transfers are not allowed. In particular, it establishes that a particularly simple type of mechanism that works by randomizing “Grim-trigger”-type strategies, in which a principal never again listens to an expert after a bad recommendation, is optimal.

Work in Progress