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Estimating Preferences of Circuit Judges: A Model of Consensus Voting

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  • Joshua B. Fischman

Abstract

This paper develops a consensus voting model for estimating preferences of federal circuit court judges. Unlike standard ideal point models, which assume that judges vote sincerely for their preferred outcomes, the consensus model accounts for the norm of consensus in the courts of appeals by including a cost of dissent in the judicial utility function. A test of the consensus voting model on a data set of asylum appeals demonstrates that it provides a substantially better fit than a comparable sincere voting model and also generates more accurate predictions of voting probabilities. The model generates credible estimates of the impact of panel composition on case outcomes, which is surprisingly large in the asylum cases. Even though 95 percent of these decisions were unanimous, roughly half of the cases could have been decided differently if assigned to different panels.

Suggested Citation

  • Joshua B. Fischman, 2011. "Estimating Preferences of Circuit Judges: A Model of Consensus Voting," Journal of Law and Economics, University of Chicago Press, vol. 54(4), pages 781-809.
  • Handle: RePEc:ucp:jlawec:doi:10.1086/661512
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    References listed on IDEAS

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    1. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," Review of Economic Studies, Oxford University Press, vol. 60(3), pages 531-542.
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    5. Martin, Andrew D. & Quinn, Kevin M., 2002. "Dynamic Ideal Point Estimation via Markov Chain Monte Carlo for the U.S. Supreme Court, 1953–1999," Political Analysis, Cambridge University Press, vol. 10(02), pages 134-153, March.
    6. Londregan, John, 1999. "Estimating Legislators' Preferred Points," Political Analysis, Cambridge University Press, vol. 8(01), pages 35-56, January.
    7. Bernheim, B. Douglas & Peleg, Bezalel & Whinston, Michael D., 1987. "Coalition-Proof Nash Equilibria I. Concepts," Journal of Economic Theory, Elsevier, vol. 42(1), pages 1-12, June.
    8. Butler, J S & Moffitt, Robert, 1982. "A Computationally Efficient Quadrature Procedure for the One-Factor Multinomial Probit Model," Econometrica, Econometric Society, vol. 50(3), pages 761-764, May.
    9. Voeten, Erik, 2007. "The Politics of International Judicial Appointments: Evidence from the European Court of Human Rights," International Organization, Cambridge University Press, vol. 61(04), pages 669-701, October.
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    Cited by:

    1. Álvaro Bustos & Tonja Jacobi, 2014. "A Theory of Judicial Retirement," Documentos de Trabajo 451, Instituto de Economia. Pontificia Universidad Católica de Chile..
    2. Guimarães, Bernardo & Salama, Bruno Meyerhof, 2017. "Contingent judicial deference: theory and application to usury laws," Textos para discussão 440, FGV/EESP - Escola de Economia de São Paulo, Getulio Vargas Foundation (Brazil).
    3. Chen, Daniel L. & Levonyan, Vardges & Yeh, Susan, 2016. "Policies Affect Preferences: Evidence from Random Variation in Abortion Jurisprudence," IAST Working Papers 16-58, Institute for Advanced Study in Toulouse (IAST).
    4. Shamena Anwar & Patrick Bayer & Randi Hjalmarsson, 2015. "Politics in the Courtroom: Political Ideology and Jury Decision Making," NBER Working Papers 21145, National Bureau of Economic Research, Inc.
    5. repec:kap:ejlwec:v:43:y:2017:i:3:d:10.1007_s10657-015-9512-1 is not listed on IDEAS
    6. repec:eee:irlaec:v:52:y:2017:i:c:p:16-28 is not listed on IDEAS
    7. Guimaraesy, Bernardo & Meyerhof Salama, Bruno, 2017. "Contingent judicial deference: theory and application to usury laws," LSE Research Online Documents on Economics 86146, London School of Economics and Political Science, LSE Library.
    8. David S. Abrams & Marianne Bertrand & Sendhil Mullainathan, 2012. "Do Judges Vary in Their Treatment of Race?," The Journal of Legal Studies, University of Chicago Press, vol. 41(2), pages 347-383.

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