Beyond Truth-Telling: Preference Estimation with Centralized School Choice and College Admissions
Résumé
We propose novel approaches and tests for estimating student preferences with data from centralized matching mechanisms, like the Gale-Shapley Deferred Acceptance, when students are strictly ranked by, e.g., test scores. Without requiring truthtelling to be the unique equilibrium, we show that the matching is (asymptotically) stable, or justified-envy-free, implying that every student is matched with her favorite school/college among those she is qualified for ex post. Having illustrated the approaches in simulations, we apply them to school choice data from Paris and demonstrate evidence supporting stability but not truth-telling. We discuss when each approach is more appropriate in real-life settings.
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