Date of Award

5-2011

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Major

Political Science

Major Professor

John M. Scheb

Committee Members

Otis Stephens, James Black, Anthony Nownes

Abstract

My dissertation will seek to explain the voting behavior of judges in state courts of last resort in death penalty appeals cases. To do this, I have constructed a dataset that encompasses all death penalty appeals cases in 30 states during the period 2000-2006. The dependent variable in my quantitative analyses is the vote rendered by each judge in each case, and can take on two values: a vote to uphold the sentence of death, or a vote to reverse or vacate the sentence of death. Drawing from the judicial literature, my independent variables will include personal factors, institutional factors, and environmental factors. Personal factors include the gender and race of the judge, which the literature suggests are related to differences in judicial behavior. I will also use ideology scores developed by Brace et al. (2000), but only for a subset of cases for which those scores are available. Institutional factors include the party identification of the governor at the time the judge was appointed or elected, the party identification of the governor at the time the case was decided, and the party composition of the state House and Senate at the time the case was decided. Environmental factors include the state murder rate, the number of executions since 1976, and the number of inmates on death row at the time of the decision. The theoretical underpinning of this research is derived from the new institutionalism, which posits that judges’ decisions are shaped not only by judicial attitudes and strategic considerations, but by a variety of institutional and environmental factors. I hypothesize that the institutional and environmental factors previously enumerated will have a significant impact on the voting behavior of state high court judges in death penalty appeals. To test my hypotheses, I will use logistic regression to construct models incorporating all of the previously mentioned variables.

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