Polarization of U.S. Circuit Court Judges: A Machine Learning Approach

23 Pages Posted: 28 Jun 2017 Last revised: 29 Jun 2017

See all articles by Elliott Ash

Elliott Ash

ETH Zürich

Daniel L. Chen

Directeur de Recherche, Centre National de la Recherche Scientifique, Toulouse School of Economics, Institute for Advanced Study in Toulouse, University of Toulouse Capitole, Toulouse, France

Wei Lu

University of Toulouse 1 - Toulouse School of Economics (TSE)

Date Written: June 27, 2017

Abstract

We employ a supervised learning approach to measure the polarization of U.S. Circuit Court judges using text data of court opinions from 1890s to 2010s. Our results show persistent but low partisanship of court opinions in the past century, but whether the partisanship has been increasing over the years is yet to be discussed. We also visualize voting networks of Circuit Court judges, and we find that judges are not polarized, although the graph exhibits clustering within the courts. We proceed to study the behavior of Circuit Court judges during Supreme Court vacancies. Our findings indicate that judges who were candidates of nomination actually chose to write less separate opinions if the senate was controlled by the opposing party.

Suggested Citation

Ash, Elliott and Chen, Daniel L. and Lu, Wei, Polarization of U.S. Circuit Court Judges: A Machine Learning Approach (June 27, 2017). Available at SSRN: https://ssrn.com/abstract=2993009 or http://dx.doi.org/10.2139/ssrn.2993009

Elliott Ash

ETH Zürich ( email )

Rämistrasse 101
ZUE F7
Zürich, 8092
Switzerland

Daniel L. Chen (Contact Author)

Directeur de Recherche, Centre National de la Recherche Scientifique, Toulouse School of Economics, Institute for Advanced Study in Toulouse, University of Toulouse Capitole, Toulouse, France ( email )

Toulouse School of Economics
1, Esplanade de l'Université
Toulouse, 31080
France

Wei Lu

University of Toulouse 1 - Toulouse School of Economics (TSE) ( email )

Place Anatole-France
Toulouse Cedex, F-31042
France

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