Jeremy A. Rosen

This paper uses graduate admissions to study dynamic selection under costly evaluation. With a synthetic data protocol to protect applicants' privacy, I relate acceptance to measures of success, such as job placements. I then estimate structural models of an admissions committee's decision problem that capture first-round filtering and waitlists, and I develop statistical tests of whether observed decision rules are optimal given time and effort constraints. To determine which if any applicant characteristics are incorrectly valued, I quantify deviation gains from adopting counterfactual decision rules. This framework is applicable to multiple settings where decision-makers face uncertain outcomes and evaluation costs.