Incentivizing Exploration and Stopping (Job Market Paper)
(Coming Soon)
Description: We study a dynamic principal-agent problem where a principal seeks to acquire information about a risky project, and contracts with an agent to conduct the experiments. While the principal can commit to flexibly designing the experiments, the agent can stop at any time and is constrained by their technology, which limits how much information they can generate. Despite allowing history-dependence, we show that there exists an optimal contract with a simple structure: the principal acquires information via breakthroughs, offers a deterministic and increasing transfer schedule, and recommends stopping after any breakthrough realization. This result is important because it reduces the search for an optimal solution to a simple class of contracts and highlights the key roles of both risk-loving time preferences and differing risk-attitudes over money.
More Dependence and Correlation
(Coming Soon) With Hector Chade
Description: We study a principal multi-agent model with moral hazard to investigate a suitable notion of dependence over agents' stochastic outputs. We use this notion to analyze the following comparative statics question: how does an increase in dependence affect the principal's expected profit? We provide two broad cases where we show that the principal prefers more dependent outputs. These questions are theoretically challenging because more dependent outputs need not imply more informative ones. Our results also shed light on how organizations can optimally sort agents in teams through the dependence structure.
How Clinicians' Decisions Affect Recipient Life-Years from Transplantation (LYFT)
(Coming Soon) With Tomas Larroucau, Ellen Green, E Glenn Dutcher, Jesse D Schold, and Darren Stewart
Description: Within the allocation of deceased donor kidneys, clinicians play a key role in making acceptance decisions on behalf of recipients in the waitlist. However, as the literature documents, it is unclear why there is substantial clinician-level variation in decisions, what channels drive it, and how it impacts recipients' survival outcomes. Using administrative data, this paper studies these questions to evaluate the effect of clinicians' decisions on recipient life-years from transplantation (LYFT). We exploit the exogenous variation of on-call data to identify clinician-specific unobservables which induce selection on both their acceptance decisions and each recipient's survival outcomes. We build a structural model of clinician's acceptance decisions which reflect arbitrary correlation structure between recipient, donor, clinician, and match-specific unobservables. Our goal is to estimate the model and conduct counterfactual exercises to assess how several types of clinicians affect assignment outcomes.