Beyond Truth-Telling: Preference Estimation with Centralized School Choice and College Admissions

Gabrielle Fack, Julien Grenet and YingHua He

No 10907, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: 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 truth-telling to be the unique equilibrium, we show that the matching is (asymptotically) stable, or justi?ed-envy-free, implying that every student is matched with her favorite school/college among those she is quali?ed 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

Keywords: College admissions; Gale-shapley deferred acceptance mechanism; Stable matching; School choice; Student preferences (search for similar items in EconPapers)
JEL-codes: C78 D47 D50 D61 I21 (search for similar items in EconPapers)
Date: 2015-10
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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Related works:
Working Paper: Beyond Truth-Telling: Preference Estimation with Centralized School Choice and College Admissions (2020)
Working Paper: Beyond Truth-Telling: Preference Estimation with Centralized School Choice and College Admissions (2020)
Journal Article: Beyond Truth-Telling: Preference Estimation with Centralized School Choice and College Admissions (2019)
Working Paper: Beyond Truth-Telling: Preference Estimation with Centralized School Choice and College Admissions (2019)
Working Paper: Beyond Truth-Telling: Preference Estimation with Centralized School Choice and College Admissions (2019)
Working Paper: Beyond Truth-Telling: Preference Estimation with Centralized School Choice and College Admissions (2017)
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