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Estimating Dynamic Ideal Points for State Supreme Courts

Published online by Cambridge University Press:  04 January 2017

Jason H. Windett ,
Jeffrey J. Harden  and
Matthew E. K. Hall
Jason H. Windett*
Affiliation:
Department of Political Science, Saint Louis University, 127 McGannon Hall, 3750 Lindell Blvd, St Louis, MO 63108, USA
Jeffrey J. Harden
Affiliation:
Department of Political Science, University of Colorado Boulder, 416 Fleming, UCB 333, Boulder, CO 80309, USA. e-mail: jeffrey.harden@colorado.edu
Matthew E. K. Hall
Affiliation:
Department of Political Science, University of Notre Dame, 217 O'Shaughnessy Hall, Notre Dame, IN 46556, USA. e-mail: matt.hall@nd.edu
*
e-mail: jwindett@slu.edu (corresponding author)
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Abstract

Courts of last resort in the American states offer researchers considerable leverage to develop and test theories about how institutions influence judicial behavior. One measure critical to this research agenda is the individual judges' preferences, or ideal points, in policy space. Two main strategies for recovering this measure exist in the literature: Brace, Langer, and Hall's (2000, Measuring preferences of state supreme court judges, Journal of Politics 62(2):387–413) Party-Adjusted Judge Ideology and Bonica and Woodruff's (2014, A common-space measure of state supreme court ideology, Journal of Law, Economics, & Organization, doi: 10.1093/jleo/ewu016) judicial CFscores. Here, we introduce a third measurement strategy that combines CFscores with item response (IRT) estimates of judicial voting behavior in all fifty-two state courts of last resort from 1995 to 2010. We show that leveraging two distinct sources of information (votes and CFscores) yields a superior estimation strategy. Specifically, we highlight several key advantages of the combined measure: (1) it is estimated dynamically, allowing for the possibility that judges' ideological leanings change over time and (2) it maps judges into a common space. In a comparison against existing measurement strategies, we find that our measure offers superior performance in predicting judges' votes. We conclude that it is a valuable tool for advancing the study of judicial politics.

Type
Letters
Information
Political Analysis , Volume 23 , Issue 3 , Summer 2015 , pp. 461 - 469
Copyright
Copyright © The Author 2015. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Authors' note: The measures described here as well as complete replication materials are available at the Political Analysis Dataverse (Windett, Harden, and Hall 2015). This article is part of a larger research agenda by the authors on representation and state supreme courts. The ordering of names reflects a principle of rotation. We thank Mike Alvarez, Fred Boehmke, Jake Bowers, Paul Brace, David Nickerson, and Steve Rogers for helpful comments. We also thank Elizabeth Alberty, Eric Behna, Meaghan Gass, and Chad Williams for research assistance. Supplementary materials for this article are available on the Political Analysis Web site.

References

Armstrong, David A., Bakker, Ryan, Carroll, Royce, Hare, Christopher, Poole, Keith T., and Rosenthal, Howard. 2014. Analyzing spatial models of choice and judgment with R. Boca Raton, FL: CRC Press.Google Scholar
Berry, William D., Ringquist, Evan J., Fording, Richard C., and Hanson, Russell L. 1998. Measuring citizen and government ideology in the American states, 1960–93. American Journal of Political Science 42(1): 327–48.Google Scholar
Bonica, Adam. 2013. Ideology and interests in the political marketplace. American Journal of Political Science 57(2): 294–311.Google Scholar
Bonica, Adam. 2014. Mapping the ideological marketplace. American Journal of Political Science 58(2): 367–86.Google Scholar
Bonica, Adam, and Woodruff, Michael J. 2015. A common-space measure of state supreme court ideology. Journal of Law, Economics, & Organization, doi: 10.1093/jleo/ewu016.Google Scholar
Brace, Paul, Langer, Laura, and Gann Hall, Melinda. 2000. Measuring preferences of state supreme court judges. Journal of Politics 62(2): 387–413.Google Scholar
Canes-Wrone, Brandice, Clark, Tom S., and Kelly, Jason P. 2014. Judicial selection and death penalty decisions. American Political Science Review 108(1): 23–39.Google Scholar
Emmert, Craig F., and Ann Traut, Carol. 1994. The California supreme court and the death penalty. American Politics Quarterly 22(1): 41–61.Google Scholar
Epstein, Lee, Hoekstra, Valerie, Segal, Jeffrey A., and Spaeth, Harold J. 1998. Do political preferences change? A longitudinal study of U.S. Supreme Court justices. Journal of Politics 60(3): 801–18.Google Scholar
Hall, Matthew E.K., and Windett, Jason H. 2013. New data on state supreme court cases. State Politics & Policy Quarterly 13(4): 427–45.Google Scholar
Heinze, Georg, Ploner, Daniela Dunkler, Meinhard, and Southworth, Harry. 2013. logistf: Firth's Bias Reduced Logistic Regression. R package version 1.21. http://CRAN.R-project.org/package=logistf.Google Scholar
Martin, Andrew D., and Quinn, Kevin M. 2002. Dynamic ideal point estimation via Markov chain Monte Carlo for the U.S. Supreme Court, 1953–1999. Political Analysis 10(2): 134–53.Google Scholar
Segal, Jeffrey A., and Spaeth, Harold J. 2002. The Supreme Court and the attitudinal model revisited. New York: Cambridge University Press.Google Scholar
Shor, Boris, and McCarty, Nolan. 2011. The ideological mapping of American legislatures. American Political Science Review 105(3): 530–51.Google Scholar
Ulmer, S. Sidney. 1962. The political party variable in the Michigan supreme court. Journal of Public Law 11:352–62.Google Scholar
Windett, Jason H., Harden, Jeffrey J., and Hall, Matthew E. K. 2015. Replication data for: Estimating dynamic ideal points for state supreme courts, Political Analysis Dataverse. http://dx.doi.org/10.7910/DVN/PPPKMF.Google Scholar