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Agenda Constrained Legislator Ideal Points and the Spatial Voting Model

Published online by Cambridge University Press:  04 January 2017

Joshua D. Clinton and
Adam Meirowitz
Joshua D. Clinton
Affiliation:
Department of Political Science, Stanford University, Stanford, CA 94305. e-mail: jclinton@stanford.edu
Adam Meirowitz
Affiliation:
Graduate School of Business, Stanford University, Stanford, CA 94305. e-mail: ameirow@stanford.eduDepartment of Politics, Princeton University, Princeton, NJ 08544
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Abstract

Existing preference estimation procedures do not incorporate the full structure of the spatial model of voting, as they fail to use the sequential nature of the agenda. In the maximum likelihood framework, the consequences of this omission may be far-reaching. First, information useful for the identification of the model is neglected. Specifically, information that identifies the proposal locations is ignored. Second, the dimensionality of the policy space may be incorrectly estimated. Third, preference and proposal location estimates are incorrect and difficult to interpret in terms of the spatial model. We also show that the Bayesian simulation approach to ideal point estimation (Clinton et al. 2000; Jackman 2000) may be improved through the use of information about the legislative agenda. This point is illustrated by comparing several preference estimators of the first U.S. House (1789–1791).

Information

Type
Research Article
Information
Political Analysis , Volume 9 , Issue 3 , January 2001, pp. 242 - 259
Copyright
Copyright © 2001 by the Society for Political Methodology 

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References

Bowling, Kenneth R. 1968. Politics in the First Congress, 1789–1791, Ph.D. dissertation. Madison: University of Wisconsin.Google Scholar
Bowling, Kenneth R. 1971. “Dinner at Jefferson's: A Note on Jacob E. Cooke's ‘The Compromise of 1790.’” William and Mary Quarterly 28: 629–648.CrossRefGoogle Scholar
Clinton, Joshua D., Jackman, Simon, and Rivers, Douglas. 2000. “The Statistical Analysis of Legislative Behavior: A Unified Approach,” Stanford University typescript. Palo Alto, CA: Stanford University.Google Scholar
Cooke, Jacob E. 1970. “The Compromise of 1790.” William and Mary Quarterly 27: 523–545.CrossRefGoogle Scholar
DeSarbo, Wayne, and Cho, Jaewun. 1989. “A Stochastic Multidimensional Scaling Vector Threshold Model for the Spatial Representation of Pick Any/N Data.” Psychometrika 54: 105–129.CrossRefGoogle Scholar
Durrett, Richard. 1995. Probability: Theory and Examples, 2nd ed. Belmont, CA: Duxbury Press.Google Scholar
Haberman, S. J. 1977. “Maximum Likelihood Estimates in Exponential Response Models.” Annals of Statistics 5: 815–841.CrossRefGoogle Scholar
Heckman, James, and Snyder, James. 1997. “Linear Probability Models of the Demand for Attributes with and Empirical Application to Estimating the Preferences of Legislators.” Rand Journal of Economics 28: 142–189.CrossRefGoogle Scholar
Jackman, Simon. 2000. “Estimation and Inference Are Missing Data Problems: Unifying Social Science Statistics via Bayesian Simulation.” Political Analysis 8: 307–322.CrossRefGoogle Scholar
Jackman, Simon. 2001. “Estimation and Inference for Legislative Ideal Points by Bayesian Simulation: Extensions and Elaborations,” Stanford University typescript. Palo Alto, CA: Stanford University.Google Scholar
Jefferson, Thomas. 1829. Memoir, Correspondence and Miscellanies, from the Papers of Thomas Jefferson, ed. Jefferson Ranoolph, Thomas. Charlottesville, VA: F. Case and Co.Google Scholar
Kiefer, J., and Wolfowitz, J. 1956. “Consistency of the Maximum Likelihood Estimator in the Presence of Infinitely Many Incidental Parameters.” Annals of Mathematical Statistics 27: 887–896.CrossRefGoogle Scholar
Krehbiel, Keith, and Rivers, Doug. 1988. “An Analysis of Committee Power: An Application to Senate Voting on the Minimum Wage.” American Journal of Political Science 32: 1151–1174.CrossRefGoogle Scholar
Londregan, John. 2000a. “Estimating Legislator's Preferred Points.” Political Analysis 8: 35–56.CrossRefGoogle Scholar
Londregan, John. 2000b. Legislative Institutions and Ideology in Chile. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Miller, Gary J. 1993. “Formal Theory and the Presidency.” In Researching the Presidency, eds. Edwards, George C. III et al. Pittsburgh: University of Pittsburgh Press.Google Scholar
O’Dwyer, Margaret M. 1964. “A French Diplomat's View of Congress 1790.” William and Mary Quarterly 21: 408–444.CrossRefGoogle Scholar
Poole, Keith T. 2000. “Nonparametric Unfolding of Binary Choice Data.” Political Analysis 8: 211–237.CrossRefGoogle Scholar
Poole, Keith, and Rosenthal, Howard. 1996. Congress: A Political-Economic History of Roll Call Voting. New York: Oxford Press.Google Scholar
Risjord, Norman K. 1976. “The Compromise of 1790: New Evidence on the Dinner Table Bargain.” William and Mary Quarterly 33: 309–314.CrossRefGoogle Scholar
Wald, A. 1948. “Estimation of a Parameter When the Number of Unknown Parameters Increases Indefinitely with the Number of Observations.” Annals of Mathematic Statistics 19: 220–227.CrossRefGoogle Scholar