Leveraging Uncertainties to Infer Preferences: Robust Analysis of School Choice

Abstract

Inferring applicant preferences is fundamental in many analyses of school-choice data. Application mistakes make this task challenging. We propose a novel approach to deal with the mistakes in a deferred-acceptance matching environment. The key insight is that the uncertainties faced by applicants, e.g., due to tie-breaking lotteries, render some mistakes costly, allowing us to reliably infer relevant preferences. Our approach extracts all information on preferences robustly to payoff-insignificant mistakes. We apply it to school-choice data from Staten Island, NYC. Counterfactual analysis suggests that we underestimate the effects of proposed desegregation reforms when applicants' mistakes are not accounted for in preference inference and estimation.

Tasks

Reproductions