Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-26T22:18:11.771Z Has data issue: false hasContentIssue false
  • English
  • Français

Unpredictable Voters in Ideal Point Estimation

Published online by Cambridge University Press:  04 January 2017

Benjamin E. Lauderdale
Benjamin E. Lauderdale*
Affiliation:
Department of Politics, Corwin Hall, Princeton University, Princeton, NJ 08544-1013
*
e-mail: blauderd@princeton.edu (corresponding author)
Get access Rights & Permissions [Opens in a new window]

Abstract

Ideal point estimators are typically based on an assumption that all legislators are equally responsive to modeled dimensions of legislative disagreement; however, particularistic constituency interests and idiosyncrasies of individual legislators introduce variation in the degree to which legislators cast votes predictably. I introduce a Bayesian heteroskedastic ideal point estimator and demonstrate by Monte Carlo simulation that it outperforms standard homoskedastic estimators at recovering the relative positions of legislators. In addition to providing a refinement of ideal point estimates, the heteroskedastic estimator recovers legislator-specific error variance parameters that describe the extent to which each legislator's voting behavior is not conditioned on the primary axes of disagreement in the legislature. Through applications to the roll call histories of the U.S. Congress, the E.U. Parliament, and the U.N. General Assembly, I demonstrate how to use the heteroskedastic estimator to study substantive questions related to legislative incentives for low-dimensional voting behavior as well as diagnose unmodeled dimensions and nonconstant ideal points.

Type
Research Article
Information
Political Analysis , Volume 18 , Issue 2 , Spring 2010 , pp. 151 - 171
Copyright
Copyright © The Author 2010. Published by Oxford University Press on behalf of the Society for Political Methodology 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Author's note: The author thanks Chris Achen, Scott Ashworth, Michael Bailey, Larry Bartels, Charles Cameron, Brandice Canes-Wrone, Joshua Clinton, Kosuke Imai, John Londregan, Nolan McCarty, Adam Meirowitz, Kevin Quinn, Aaron Strauss, and several anonymous reviewers for their comments and suggestions.

References

Alvarez, R. Michael, and Brehm, John. 1995. American ambivalence towards abortion policy: Development of a heteroskedastic probit model of competing values. American Journal of Political Science 39(4): 1055–82.Google Scholar
Bafumi, Joseph, Gelman, Andrew, Park, David K., and Kaplan, Noah. 2005. Practical issues in implementing and understanding Bayesian ideal point estimation. Political Analysis 13(2): 171–87.Google Scholar
Bafumi, Joseph, and Herron, Michael C. 2007. Preference aggregation, representation, and elected American political institutions. Working paper.Google Scholar
Canon, David T. 1999. Race, redistricting, and representation. Chicago: University of Chicago Press.Google Scholar
Clinton, Joshua D., Jackman, Simon, and Rivers, Douglas. 2004a. The statistical analysis of roll call data. American Political Science Review 98(2): 355–70.Google Scholar
Clinton, Joshua D., Jackman, Simon, and Rivers, Douglas. 2004b. The most liberal senator? Analyzing and interpreting congressional roll calls. Political Science & Politics 37: 805–11.Google Scholar
Geelan, Agnes. 1975. The Dakota Maverick: The political life of William Langer, also known as “Wild Bill” Langer. Fargo: Kaye's Printing Company.Google Scholar
Grose, Christian R., and Yoshinaka, Antoine. 2006. Legislative voting in 3D: Are some U.S. senators Mavericks, flip-floppers, or simply uncertain about their constituents’ preferences? Annual meeting of the American Political Science Association, Philadelphia, PA.Google Scholar
Haberman, Shelby J. 1977. Maximum likelihood estimates in exponential response models. The Annals of Statistics 5(5): 815–41.Google Scholar
Heckman, James J., and Snyder, James M. 1997. Linear probability models of the demand for attributes with an empirical application to estimating the preferences of legislators. RAND Journal of Economics 28(0): S142–89.Google Scholar
Hix, Simon, Noury, Abdul, and Roland, Gerard. 2006. Dimensions of politics in the European parliament. American Journal of Political Science 50(2): 494–511.Google Scholar
Hix, Simon, Noury, Abdul, and Roland, Gerard. 2007. Democratic politics in the European parliament. New York: Cambridge University Press.Google Scholar
Huitt, Ralph K. 1961. The outsider in the senate: An alternative role. American Political Science Review 55(3): 566–75.Google Scholar
Jackman, Simon. 2001. Multidimensional analysis of roll call data via Bayesian simulation: Identification, estimation, inference and model checking. Political Analysis 9(3): 227–41.Google Scholar
Kirkpatrick, Samuel A., and McLemore, Lelan. 1977. Perceptual and affective components of legislative norms: A social-psychological analysis of congruity. Journal of Politics 39(3): 685–711.Google Scholar
Lee, Frances E., and Oppenheimer, Bruce I. 1999. Sizing up the senate: The unequal consequences of equal representation. Chicago: University of Chicago Press.Google Scholar
Londregan, John. 2000. Estimating legislators’ preferred points. Political Analysis 8(1): 35–56.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: 134–53.CrossRefGoogle Scholar
Martin, Andrew D., and Quinn, Kevin M. 2009. R package “MCMCpack”. http://mcmcpack.wustl.edu/ (accessed December 19, 2009).Google Scholar
McCarty, Nolan, Poole, Keith T., and Rosenthal, Howard. 2001. The hunt for party discipline in congress. American Political Science Review 95: 673–87.Google Scholar
Patterson, Samuel C. 1961. The role of the deviant in the state legislative system: The Wisconsin assembly. The Western Political Quarterly 14(2): 460–72.Google Scholar
Poole, Keith T. 2000. Nonparametric unfolding of binary choice data. Political Analysis 8(2): 211–37.Google Scholar
Poole, Keith T. 2001. The geometry of multidimensional quadratic utility in models of parliamentary roll call voting. Political Analysis 9(3): 211–26.Google Scholar
Poole, Keith T. 2005. Spatial models of parliamentary voting. New York: Cambridge University Press.Google Scholar
Poole, Keith T., and Rosenthal, Howard. 1985. A spatial model for legislative roll call analysis. American Journal of Political Science 29(2): 357–84.Google Scholar
Poole, Keith T., and Rosenthal, Howard. 1997. Congress: A political-economic history of roll call voting. New York: Oxford University Press.Google Scholar
Rivers, Douglas. 2003. Identification of multidimensional spatial voting models. Manuscript. Stanford University. Typescript.Google Scholar
Robinson, Elwyn. 1966. History of North Dakota. Lincoln: University of Nebraska Press.Google Scholar
Severo, Richard. 2005. William Proxmire, Maverick Democratic Senator From Wisconsin, Is Dead at 90. The New York Times, December 16.Google Scholar
Shields, Johanna Nicol. 1985. The line of duty: Maverick congressmen and the development of American political culture, 1836–1860. Westport, CT: Greenwood Press.Google Scholar
Treier, Shawn, and Hillygus, Sunshine. 2007. Front and center? The policy attitudes of ideological moderates. Working paper.Google Scholar
Voeten, Erik. 2000. Clashes in the assembly. International Organization 54(2): 185–215.Google Scholar