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.
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.
2023 International Conference on Game Theory, Stony Brook
I study a disclosure environment with a sender, a receiver, and a mediator who can exert costly effort to acquire verifiable evidence about an unknown state. The receiver’s payoff rises with action accuracy; the sender always prefers a high action. The mediator chooses an effort policy over the state space and discloses any evidence to the sender, who then decides whether to pass it on. With covert acquisition, the equilibrium replicates standard voluntary disclosure: disclosure above a cutoff, zero effort below. With observable acquisition, the mediator can bias effort toward higher states, making silence a stronger bad-news signal and inducing the sender to disclose more. I characterize the optimal biased acquisition policy and show that it increases equilibrium disclosure and receiver welfare relative to the covert benchmark. Biased but transparent information intermediaries can thus improve welfare under asymmetric information.
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2024 International Conference on Game Theory, Stony Brook
We analyze a dynamic durable-goods game in which the seller’s commitment to future prices decays randomly over time and is publicly observed. Partial commitment induces strategic purchase delays and endogenous information revelation about buyer types, producing non-monotonic welfare effects: welfare initially falls as commitment rises from period-by-period pricing to partial commitment, then improves as commitment approaches full enforcement, where all trade occurs immediately. Intermediate commitment can delay transactions more than in the no-commitment benchmark, and welfare losses from delay can outweigh screening gains, ultimately harming consumers