Working papers

When Colleges Compete: Signals, Noise, & Equilibrium Outcomes joint with S. Nageeb Ali & Ran I. Shorrer 

Abstract: This paper proposes a framework of college admissions in which colleges choose who to admit based on noisy signals, leading to a winner's curse. We find conditions for a unique equilibrium in threshold strategies. Our main results evaluate how correlation in colleges' signals affects matching and welfare. Although each applicant may favor a more independent draw, greater correlation benefits applicants overall: in equilibrium, a larger fraction then obtains a higher ranked choice. Weaker applicants gain unambiguously whereas stronger ones risk going unmatched. Greater correlation reduces assortative matching and makes colleges worse off. These findings speak to the debate on standardized testing by highlighting how public signals benefit applicants. We use this framework to also study early decision policies, offering an informational rationale for why colleges favor those applying early.

Revealing Compatible Choice    [PDF] 

Abstract: This paper presents a framework for multi-agent settings where each agent must select an action compatible with the choices of others. Agents face a technological constraint that dictates which alternative profiles are feasible, and each agent chooses her preferred option from those available, given the others' choices. I distinguish between two cases: rival and non-rival alternatives. For a generic problem, I introduce a key axiom, Acyclicity, that characterizes the decision-making process. In the non-rivalrous case, Acyclicity can be further decomposed into two properties: WARP-CC and Joint Acyclicity. Additionally, I propose revealed preference relations to conservatively identify players’ preferences and assess compatibility based on observable, potentially limited choice data. Importantly, the construction and the results presented on this paper do not rely on access to the complete choice observations. 

Work-in-Progress

How Intermediaries Shape Labor Market Outcomes

Abstract: Labor markets today use digital platforms for workers to learn about positions and for firms to fill their vacancies. These platforms routinely charge fees to one or both sides of the market and choose a recommendation policy that makes some workers more visible than others. This paper develops a model for how a profit-maximizing intermediary would make these choices and their effects on the labor market. In this model, the intermediary observes workers' abilities and chooses between selling firms perfect information or promoting lower ability workers to firms but at a fee. We characterize conditions under which each regime is optimal.