Leveraging Uncertainties to Infer Preferences: Robust Analysis of School Choice

TL;DR

This paper introduces a robust method for inferring applicant preferences in school choice data by leveraging uncertainties like tie-breaking lotteries, improving accuracy in preference estimation and policy impact analysis.

Contribution

It presents a novel approach that accounts for application mistakes caused by uncertainties, enhancing preference inference in deferred-acceptance matching environments.

Findings

Robust preference inference improves accuracy.

Underestimation of reform effects without accounting for mistakes.

Method applied successfully to NYC school-choice data.

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.