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Estimating Preferences of Circuit Judges: a Model of Consensus Voting created by Joshua B. Fischman

By: Material type: TextTextSeries: Journal of Law and Economics ; Volume 54, number 4, part 1Chicago: University of Chicago Press, 2011Content type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISSN:
  • 00222186
Subject(s): LOC classification:
  • HB73 JOU
Online resources: 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
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Item type Current library Call number Copy number Status Notes Date due Barcode
Journal Article Journal Article Main Library - Special Collections HB73 JOU (Browse shelf(Opens below)) SP12291 Not for loan For In House Use Only

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

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