Roll Call Data and Ideal Points
AbstractWe show that, in the absence of symmetry or other parametric restrictions on legislators’ utility functions, roll call voting records cannot be used to estimate legislators’ ideal points unless we complement these data with information on the location of the alternatives being voted upon by the legislature. Without such additional information, the roll-call data cannot differentiate between distinct, arbitrary, sets of ideal points for the legislators no matter how large the roll call record or how low the number of policy dimensions. On the other hand, when the location of voting alternatives is known, we derive simple testable restrictions on the location of legislators’ ideal points from the roll call data.
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Bibliographic InfoPaper provided by University of Rochester - Wallis Institute of Political Economy in its series Wallis Working Papers with number WP42.
Length: 40 pages
Date of creation: Aug 2006
Date of revision:
Contact details of provider:
Postal: University of Rochester, Wallis Institute, Harkness 109B Rochester, New York 14627 U.S.A.
Ideal Point Estimation; Rationalizable Choice; Roll Call Voting Record.;
Find related papers by JEL classification:
- D7 - Microeconomics - - Analysis of Collective Decision-Making
- D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-08-12 (All new papers)
- NEP-CDM-2006-08-12 (Collective Decision-Making)
- NEP-POL-2006-08-12 (Positive Political Economics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Varian, Hal R, 1982. "The Nonparametric Approach to Demand Analysis," Econometrica, Econometric Society, vol. 50(4), pages 945-73, July.
- Richter, Marcel K. & Wong, K.-C.Kam-Chau, 2004. "Concave utility on finite sets," Journal of Economic Theory, Elsevier, vol. 115(2), pages 341-357, April.
- de Leeuw, Jan, 2006. "Principal component analysis of binary data by iterated singular value decomposition," Computational Statistics & Data Analysis, Elsevier, vol. 50(1), pages 21-39, January.
- Tasos Kalandrakis, 2008.
Wallis Working Papers
WP51, University of Rochester - Wallis Institute of Political Economy.
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