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Endogenous Jurisprudential Regimes

Published online by Cambridge University Press:  04 January 2017

Xun Pang*
Affiliation:
School of Humanities and Social Sciences, 314 Min Zhai Hall, Tsinghua University, Beijing, China 100084
Barry Friedman
Affiliation:
New York University School of Law, 40 Washington Square South, 317, New York, NY 10012. e-mail: barry.friedman@nyu.edu
Andrew D. Martin
Affiliation:
Washington University School of Law, Campus Box 1120, One Brookings Drive, St. Louis, MO 63130. e-mail: admartin@wustl.edu
Kevin M. Quinn
Affiliation:
University of California—Berkeley School of Law, 490 Simon #7200, Berkeley, CA 94720-7200. e-mail: kquinn@law.berkeley.edu
*
e-mail: xpang@tsinghua.edu.cn (corresponding author)

Abstract

Jurisprudential regime theory is a legal explanation of decision-making on the U.S. Supreme Court that asserts that a key precedent in an area of law fundamentally restructures the relationship between case characteristics and the outcomes of future cases. In this article, we offer a multivariate multiple change-point probit model that can be used to endogenously test for the existence of jurisprudential regimes. Unlike the previously employed methods, our model does so by estimating the locations of many possible change-points along with structural parameters. We estimate the model using Markov chain Monte Carlo methods, and use Bayesian model comparison to determine the number of change-points. Our findings are consistent with jurisprudential regimes in the Establishment Clause and administrative law contexts. We find little support for hypothesized regimes in the areas of free speech and search-and-seizure. The Bayesian multivariate change-point model we propose has broad potential applications to studying structural breaks in either regular or irregular time-series data about political institutions or processes.

Type
Research Article
Copyright
Copyright © The Author 2012. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Authors' note: The authors thank Bert Kritzer, Mark Richards, and Jeff Segal for sharing replication data; Jude Hayes and Robert Walker, along with seminar participants at SLAMM 2010, NYU, and USC for helpful comments; Katie Schon, Jee Seon Jeon, and Rachael Hinkle for their research assistance; and the Filomen D'Agostino and Max E. Greenberg Research Fund at NYU School of Law, the Center for Empirical Research in the Law at Washington University, the Wang Xuelian Fund at Tsinghua University, and the National Science Foundation for supporting our research. The editor, R. Michael Alvarez, and two anonymous referees made suggestions that improved the article significantly. For replication data and code, see Pang et al. (2012). Supplementary materials for this article are available on the Political Analysis Web site.

References

Bai, Jushan, and Perron, Pierre. 2003. Computation and analysis of multiple structural change models. Journal of Applied Econometrics 18: 122.Google Scholar
Baldez, Lisa, Epstein, Lee, and Martin, Andrew D. 2006. Does the U.S. Constitution need an ERA? Journal of Legal Studies 35: 243–83.Google Scholar
Barry, Daniel, and Hartigan, J. A. 1993. A Bayesian analysis for change-point problems. Journal of the American Statistical Association 88: 309–19.Google Scholar
Brandt, Patrick T., and Sandler, Todd. 2010. What do transmational terrorists target? Has it changed? Are we safe? Journal of Conflict Resolution 54: 214–36.CrossRefGoogle Scholar
Carlin, Bradley P., Gelfand, Alan E., and Smith, Adrian F. M. 1992. Hierarchical Bayesian analysis of change-point problems. Applied Statistics 41: 389405.CrossRefGoogle Scholar
Carlin, Bradley P., and Chib, Siddhartha. 1995. Bayesian model choice via Markov chain Monte Carlo methods. Journal of the Royal Statistical Society B 57(3): 473–84.Google Scholar
Carrubba, Clifford J., Friedman, Barry, Martin, Andrew D., and Vanberg, Georg. 2012. Who controls the content of Supreme Court opinions? American Journal of Political Science 56: 400–12.CrossRefGoogle Scholar
Chib, Siddhartha. 1995. Marginal Likelihood from the Gibbs Output. Journal of the American Statistical Association 90: 1313–21.Google Scholar
Chib, Siddhartha. 1996. Calculating posterior distributions and model estimates in Markov mixture models. Journal of Econometrics 75: 7998.Google Scholar
Chib, Siddhartha. 1998. Estimation and comparison of multiple change-point models. Journal of Econometrics 86: 221–41.Google Scholar
Clark, Tom S., and Lauderdale, Benjamin. 2010. Locating Supreme Court opinions in doctrine space. American Journal of Political Science 54: 871–90.Google Scholar
Corley, Pamela C. 2008. The Supreme Court and opinion content: The influence of parties’ briefs. Political Research Quarterly 61: 468–78.Google Scholar
Davis, Richard A., Lee, Thomas C. M., and Rodriguez-Yam, Gabriel A. 2006. Structural break estimation for nonstationary time-series models. Journal of the American Statistical Association 101: 223–39.Google Scholar
Friedman, Barry. 2006. Taking law seriously. Perspectives on Politics 4: 261–71.Google Scholar
George, Tracey E., and Epstein, Lee. 1992. On the nature of Supreme Court decision-making. American Political Science Review 86: 323–37.Google Scholar
Green, Peter J. 1995. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82: 711–32.Google Scholar
Hawkins, Douglas M. 2001. Fitting multiple change-point models to data. Computational Statistics & Data Analysis 37: 323–41.Google Scholar
Hill, Jennifer L., and Kriesi, Hanspeter. 2001. Classification by opinion-changing behavior: A mixture model approach. Political Analysis 9: 301–24.Google Scholar
Jacobi, Tonja, and Tiller, Emerson H. 2007. Legal doctrine and political control. Journal of Law, Economics, and Organization 23: 326–45.Google Scholar
Kass, Robert E., and Raftery, Adrian E. 1995. Bayes factors. Journal of the American Statistical Association 90: 773–95.Google Scholar
Kornhauser, Lewis A. 1992a. Modeling collegial courts. I: Path dependence. International Review of Law and Economics 12: 169–85.Google Scholar
Kornhauser, Lewis A. 1992b. Modeling collegial courts. II: Legal doctrine. Journal of Law, Economics, and Organization 8: 441–70.Google Scholar
Kritzer, Herbert M., and Richards, Mark J. 2003. Jurisprudential regimes and Supreme Court decision-making: The Lemon regime and Establishment Clause cases. Law & Society Review 37: 827–40.Google Scholar
Kritzer, Herbert M., and Richards, Mark J. 2005. The influence of law in the Supreme Court's search-and-seizure jurisprudence. American Politics Research 33: 3355.Google Scholar
Lavielle, Marc. 2005. Using penalized contrasts for the change-point problem. Signal Processing 85: 1501–10.Google Scholar
Lax, Jeffrey R. 2011. The new judicial politics of legal doctrine. Annual Review of Political Science 14: 131–57.Google Scholar
Lax, Jeffrey R., and Rader, Kelly T. 2010. Legal constraints on Supreme Court decision-making: Do jurisprudential regimes exist? Journal of Politics 72: 273.Google Scholar
Maltzman, Forest, Spriggs, James F. II, and Wahlbeck, Paul J. 2000. Crafting law on the Supreme Court. New York: Cambridge University Press.Google Scholar
Miles, Thomas J., and Sunstein, Cass R. 2006. Do judges make regulatory policy? An empirical investigation of Chevron. University of Chicago Law Review 73: 823–81.Google Scholar
Minin, Vladimir N., Dorman, Karin S., Fang, Fang, and Suchard, Marc A. 2005. Dual multiple change-point model leads to more accurate recombination detection. Bioinformatics 21: 3034–42.Google Scholar
Olshen, Adam B., and Venkatraman, E. S. 2004. Circular binary segementation for the analysis of array-based DNA copy number data. Bioinformatics 5: 557–72.Google Scholar
Pang, Xun, Friedman, Barry, Martin, Andrew D., and Quinn, Kevin M. 2012. Replication data for: endogenous jurisprudential regimes. IQSS Dataverse Network V1 [Version]. http://hdl.handle.net/1902.1/18328 (accessed June 4, 2012).Google Scholar
Park, Jong Hee. 2010. Structural change in the U.S. Presidents’ use of force abroad. American Journal of Political Science 54: 766–82.Google Scholar
Park, Jong Hee. 2011. Change-point analysis of binary and ordinal probit models: An application to bank rate policy under the interwar gold standard. Political Analysis 19: 188204.Google Scholar
Pierce, Richard J. Jr. 1999. Is standing law or politics? North Carolina Law Review 77: 1741–89.Google Scholar
Qu, Zhongjun, and Perron, Pierre. 2007. Estimating and testing structural changes in multivariate regressions. Econometrica 75: 459502.CrossRefGoogle Scholar
Richards, Mark J., and Kritzer, Herbert M. 2002. Jurisprudential regimes in Supreme Court decision-making. American Political Science Review 96: 305–20.Google Scholar
Richards, Mark J., Smith, Joseph L., and Kritzer, Herbert M. 2006. Does Chevron matter? Law & Policy 28: 444–69.Google Scholar
Scott, Kevin M. 2006. Reconsidering the impact of jurisprudential regimes. Social Science Quarterly 87: 380–94.Google Scholar
Segal, Jeffrey A. 1986. Supreme Court Justices as human decision-makers: An individual-level analysis of the search-andseizure cases. American Journal of Political Science 48: 938–55.Google Scholar
Segal, Jeffrey A., and Cover, Albert D. 1989. Ideological values and the votes of U.S. Supreme Court Justices. American Political Science Review 83: 557–65.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
Siegmund, David. 2004. Model selection in irregular problems: Applications to mapping quantitative trait loci. Biometrika 91: 785800.Google Scholar
Spirling, Arthur. 2007. Bayesian approaches for limited dependent variable change-point problems. Political Analysis 15: 387405.Google Scholar
Staudt, Nancy C. 2004. Modeling standing. New York University Law Review 79: 612–84.Google Scholar
Zhang, Nancy R., and Siegmund, David. 2007. A modified Bayes information criterion with applications to the analysis of comparative genomic hybridization data. Biometrics 63: 2232.Google Scholar
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