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A Common-Space Scaling of the American Judiciary and Legal Profession

  • Adam Bonica (a1) and Maya Sen (a2)
Abstract

We extend the scaling methodology previously used in Bonica (2014) to jointly scale the American federal judiciary and legal profession in a common space with other political actors. The end result is the first dataset of consistently measured ideological scores across all tiers of the federal judiciary and the legal profession, including 840 federal judges and 380,307 attorneys. To illustrate these measures, we present two examples involving the U.S. Supreme Court. These data open up significant areas of scholarly inquiry.

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Copyright
Corresponding author
* Email: bonica@stanford.edu
Footnotes
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Authors’ note: Replication materials are available online as a dataverse repository (Bonica and Sen 2016, dx.doi.org/10.7910/DVN/RPZLMY). Many thanks to Adam Chilton, Tom Clark, Andy Hall, Tom Miles, and Arthur Spirling for helpful conversations on this project. This project has also benefited from feedback garnered at workshops or conferences at Cornell Law School, Harvard Kennedy School, Harvard Law School, University of Rochester, and University of California-Berkeley.
Contributing Editor: R. Michael Alvarez
Footnotes
References
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Bailey, Michael A. 2013. Is today’s court the most conservative in sixty years? Challenges and opportunities in measuring judicial preferences. The Journal of Politics 75:821834.
Bonica, Adam. 2013. Database on ideology, money in politics, and elections. Public version 1.0 [Computer file]. Stanford, CA: Stanford University Libraries. http://data.stanford.edu/dime.
Bonica, Adam. 2014. Mapping the ideological marketplace. American Journal of Political Science 58(2):367387.
Bonica, Adam, Chilton, Adam S., and Sen, Maya. 2016. The political ideologies of American lawyers. Journal of Legal Analysis 8(2):277335.
Bonica, Adam, and Sen, Maya. 2015. The politics of selecting the bench from the bar: The legal profession and partisan incentives to politicize the judiciary. HKS Working Paper No. RWP15-001.
Bonica, Adam, and Sen, Maya. 2016. Replication data for: A common-space scaling of the American judiciary and legal profession. doi:10.7910/DVN/RPZLMY, Harvard Dataverse, V1 [UNF:6:u625R9kG2CfhwTQ0Ibz0ug==].
Bonica, Adam, and Woodruff, Michael. 2015. A common-space measure of state supreme court ideology. Journal of Law, Economics and Organization 31(3):472498.
Boyd, Christina L.2011. Federal district court judge ideology data. University of Georgia.
Epstein, Lee, Martin, Andrew D., Segal, Jeffrey A., and Westerland, Chad. 2007. The Judicial Common Space. Journal of Law, Economics, and Organization 23(2):303325.
Giles, Micheal W., Hettinger, Virginia A., and Peppers, Todd. 2001. Picking federal judges: A note on policy and partisan selection agendas. Political Research Quarterly 54(3):623641.
Honaker, James, King, Gary, and Blackwell, Matthew. 2011. Amelia II: A program for missing data. Journal of Statistical Software 45(7):147. http://www.jstatsoft.org/v45/i07.
Lauderdale, Benjamin E., and Clark, Tom S.. 2014. Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3):754771.
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):134153.
Martin, Andrew D., Quinn, Kevin M., and Park, Jong Hee. 2011. MCMCpack: Markov Chain Monte Carlo in R. Journal of Statistical Software 42(9):121.
Poole, Keith T., and Rosenthal, Howard. 2007. Ideology and Congress . 2nd rev. ed New Brunswick: Transaction Publishers.
Spaeth, Harold J., Epstein, Lee, Martin, Andrew D., Segal, Jeffrey A., Ruger, Theodore J., and Benesh, Sara C.. 2015. The Supreme Court Database. http://supremecourtdatabase.org.
Windett, Jason H., Harden, Jeffrey J., and Hall, Matthew E. K.. 2015. Estimating dynamic ideal points for state supreme courts. Political Analysis 23(3):461469.
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Political Analysis
  • ISSN: 1047-1987
  • EISSN: 1476-4989
  • URL: /core/journals/political-analysis
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