Daniel L. Chen, Xing Cui, Lanyu Shang, Junchao Zheng
{"title":"What Matters: Agreement between U.S. Courts of Appeals Judges","authors":"Daniel L. Chen, Xing Cui, Lanyu Shang, Junchao Zheng","doi":"10.2139/ssrn.2816492","DOIUrl":null,"url":null,"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.","PeriodicalId":382898,"journal":{"name":"CELS 2016 11th Annual Conference on Empirical Legal Studies (Archive)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"CELS 2016 11th Annual Conference on Empirical Legal Studies (Archive)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.2816492","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
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.