What Matters: Agreement Between U.S. Courts of Appeals Judges

Daniel L. Chen, Xing Cui, Lanyu Shang and Junchao Zheng
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Daniel L. Chen: CNRS - Centre National de la Recherche Scientifique, TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement
Xing Cui: NYU - New York University [New York] - NYU - NYU System
Lanyu Shang: NYU - New York University [New York] - NYU - NYU System
Junchao Zheng: NYU - New York University [New York] - NYU - NYU System

Working Papers from HAL

Abstract: Federal courts are a mainstay of the justice system in the United States. In this study, we analyze 387,898 cases from U.S. Courts of Appeals, where judges are randomly assigned to panels of three. We predict which judge dissents against co-panelists and analyze the dominant features that predict such dissent with a particular attention to the biographical features that judges share. Random forest, a method developed in Breiman (2001), achieves the best classification. Dissent is predominantly driven by case features, though personal features also predict agreement.

Date: 2024-06-28
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Persistent link: https://EconPapers.repec.org/RePEc:hal:wpaper:hal-04155933

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