Causal Inference in Matching Markets: Cutoff Mechanisms

29 Pages Posted: 21 Jan 2020

See all articles by Jiafeng Chen

Jiafeng Chen

Harvard University, Faculty of Arts and Sciences, Students; Harvard University, Harvard College, Students

Date Written: December 29, 2019

Abstract

We formalize an econometric model for two-sided matching mechanisms in a school choice context, where identification exploits similarity between students who just qualify for a school and those who just fail to qualify for a school. We discuss estimation and inference of the locally linear regression estimator for such a setting, using theoretical results developed in Chen (2019b). Our approach allows us to clarify and relax the simplifying large-market assumption made in earlier work (AbdulkadirogĖ†lu et al., 2017, 2019), and we show that classical regression discontinuity procedures extend to settings where the discontinuity cutoff is endogenously chosen. Our results provide a rigorous statistical basis for causal inference and program evaluation in a number of settings where treatment assignment is complex.

Keywords: Matching, Deferred acceptance algorithm, Econometrics, Program evaluation, Regression discontinuity

JEL Classification: I2, C10, C70, D47

Suggested Citation

Chen, Jiafeng, Causal Inference in Matching Markets: Cutoff Mechanisms (December 29, 2019). Available at SSRN: https://ssrn.com/abstract=3510897 or http://dx.doi.org/10.2139/ssrn.3510897

Jiafeng Chen (Contact Author)

Harvard University, Faculty of Arts and Sciences, Students ( email )

Cambridge, MA
United States

Harvard University, Harvard College, Students ( email )

Cambridge, MA
United States

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