A Sharp Test for the Judge Leniency Design

Fuente: arXiv
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Hauptverfasser: Coulibaly, Mohamed, Hsu, Yu-Chin, Mourifié, Ismael, Wan, Yuanyuan
Format: Preprint
Veröffentlicht: 2024
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author Coulibaly, Mohamed
Hsu, Yu-Chin
Mourifié, Ismael
Wan, Yuanyuan
author_facet Coulibaly, Mohamed
Hsu, Yu-Chin
Mourifié, Ismael
Wan, Yuanyuan
contents We propose sharp testable implications and tests to jointly assess the random assignment, exclusion, and monotonicity assumptions in judge leniency designs. Our procedures accommodate various data scenarios in which the number of defendants handled by a judge may be either small or large, and allow for discrete or continuous instrumental variables. When the validity of the design is rejected, a variant of the marginal treatment effect can be identified under weaker assumptions. We apply our test to the Philadelphia court data studied by Stevenson (2018) and demonstrate that it outperforms non-sharp joint tests by significant margins in simulation studies
format Preprint
id arxiv_https___arxiv_org_abs_2405_06156
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle A Sharp Test for the Judge Leniency Design
Coulibaly, Mohamed
Hsu, Yu-Chin
Mourifié, Ismael
Wan, Yuanyuan
Econometrics
We propose sharp testable implications and tests to jointly assess the random assignment, exclusion, and monotonicity assumptions in judge leniency designs. Our procedures accommodate various data scenarios in which the number of defendants handled by a judge may be either small or large, and allow for discrete or continuous instrumental variables. When the validity of the design is rejected, a variant of the marginal treatment effect can be identified under weaker assumptions. We apply our test to the Philadelphia court data studied by Stevenson (2018) and demonstrate that it outperforms non-sharp joint tests by significant margins in simulation studies
title A Sharp Test for the Judge Leniency Design
topic Econometrics
url https://arxiv.org/abs/2405.06156