Early Predictability of Asylum Court Decisions

Daniel Chen, Matt Dunn, Levent Sagun and Hale Sirin

No 17-781, TSE Working Papers from Toulouse School of Economics (TSE)

Abstract: Early Predictability of Asylum Court Decisions with M. Dunn and L. Sagun In the United States, foreign nationals who fear persecution in their home country can apply for asylum under the Refugee Act of 1980. Unfortunately, over the past decade, legal scholarship has uncovered significant disparities in asylum adjudication by judge, by region of the United States in which the application is filed, and by the applicant’s nationality. These disparities raise concerns about whether applicants are receiving equal treatment before the law. Using machine learning to predict judges’ decisions, we document another concern that may violate our notions of justice: significant variation among the degree of predictability of judges at the time the case is assigned to a judge. Highly predictable judges are those who almost always grant or deny asylum. Our predictive model corroborates prior work as the final outcome of the case is overwhelmingly driven by the adjudicating judge and the applicant’s nationality. We are able to predict the final outcome of a case with 80% accuracy at the time the case opens. Additionally, this study shows that highly predictable judges tend to make use of fewer hearing sessions before making their decision. The contribution of this study is twofold. First, early prediction of a case with 80% accuracy could assist asylum seeker in their process of application. Secondly, by demonstrating the variation of predictability among the judges, based solely on a minimal subset of case information, this study raises questions about whether the specifics of each case are being given their due weight in asylum adjudications.

Date: 2017-03
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