Research
Research
Working Papers
Working Papers
Rising competition in imperfect markets pushes agents to invest in costly signals that differentiate themselves. Such investments can mitigate unraveling and improve matching efficiency, but also generate rat races that reallocate resources towards relative standing. I develop an empirical framework to quantify how competition affects signal adoption in matching markets and its welfare consequences, applying it to the role of pre-Ph.D. experiences—master’s and predoctoral programs—in Ph.D. admissions. These experiences help programs screen applicants and provide research training. Yet when capacity is limited and grade inflation reduces informativeness, students pursue additional research experience to stand out. Using LinkedIn data on Economics and Business Ph.D.s, I find that pre-Ph.D. experience improves admission outcomes, with 54% of the gain attributable to signaling and 46% to training. While signaling restores about half of the matching efficiency lost under pooling, its opportunity costs exceed benefits, yielding a 15% net welfare loss. Benefits are concentrated among economics majors from top colleges; other groups are worse off. Grade inflation explains roughly one-quarter of the rise in pre-Ph.D. experience.
2024 International Conference on Game Theory, Stony Brook
We study durable-good selling with adverse selection when a seller commits to a complete future price plan that may expire in any subsequent period. We show that arbitrary within-plan screening cannot improve the seller's equilibrium payoff: a canonical equilibrium posts one screening price whenever discretion returns and, after rejection, no further attractive price before expiration. The seller's continuation payoff is unique across all perfect Bayesian equilibria whenever she is free to choose a plan. Finally, greater enforcement persistence can reduce both seller profit and total welfare by delaying rescreening. The characterization holds under stochastic price plans and private expiration.
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2023 International Conference on Game Theory, Stony Brook
I study a disclosure game in which an investigator chooses state-contingent probability of finding evidence to reveal state, a gatekeeper may suppress any evidence found. The investigator values the publication, whereas the gatekeeper benefits from receiver's belief of high state. Covert acquisition uses a constant discovery probability wherever evidence would be disclosed, while overt acquisition searches more intensively at higher states. While high states are more likely be reported, silence induces a lower posterior, reducing the return to suppression and expanding disclosure. Investigator and receiver strictly prefer overt acquisition. The gatekeeper prefers overt when its payoff is convex, and covert when concave.
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2025 ASSA Annual Meeting
I build and test a dynamic model of learning and moral hazard in drug innovation. Firms receive noisy trial outcomes, update beliefs about quality, and decide whether to continue or terminate development. Financing structure shapes continuation incentives: large pharmaceutical firms self-finance and internalize costs and rewards, while venture-backed biotechs face milestone-based contracts that attenuate downside risk and distort attrition. The model predicts biotechs are more likely to continue after unfavorable signals because continuation signals competence to investors. Using a panel of 12,000+ clinical projects from~3,000 firms (1990 — 2020), I find that, conditional on negative signals, biotech projects are 12% less likely to be discontinued than those of large pharmaceutical companies. Contractual agency problems thus distort dynamic learning and selection, with implications for innovation efficiency and policy design.
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