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Multiple Ideal Points: Revealed Preferences in Different Domains

Published online by Cambridge University Press:  09 March 2021

Scott Moser Open the ORCID record for Scott Moser [Opens in a new window] ,
Abel Rodríguez Open the ORCID record for Abel Rodríguez [Opens in a new window]  and
Chelsea L. Lofland
Scott Moser*
Affiliation:
Associate Professor, School of Politics and International Relations, University of Nottingham, Nottingham, UK. [email protected], URL: http://www.nottingham.ac.uk/~ldzsm2/
Abel Rodríguez
Affiliation:
Professor, Department of Statistics, University of California, Santa Cruz, 1156 High Street, Mailstop SOE2, Santa Cruz, CA95064, USA. [email protected], URL: http://soe.ucsc.edu/~abel
Chelsea L. Lofland
Affiliation:
Department of Statistics, University of California, Santa Cruz, 1156 High Street, Mailstop SOE2, Santa Cruz, CA95064, USA. [email protected], URL: https://ams.soe.ucsc.edu/people/clofland
*
Corresponding author Scott Moser
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Abstract

We extend classical ideal point estimation to allow voters to have different preferences when voting in different domains—for example, when voting on agricultural policy than when voting on defense policy. Our scaling procedure results in estimated ideal points on a common scale. As a result, we are able to directly compare a member’s revealed preferences across different domains of voting (different sets of motions) to assess if, for example, a member votes more conservatively on agriculture motions than on defense. In doing so, we are able to assess the extent to which voting behavior of an individual voter is consistent with a uni-dimensional spatial model—if a member has the same preferences in all domains. The key novelty is to estimate rather than assume the identity of “stayers”—voters whose revealed preference is constant across votes. Our approach offers methodology for investigating the relationship between the basic space and issue space in legislative voting (Poole 2007). There are several methodological advantages to our approach. First, our model allows for testing sharp hypotheses. Second, the methodology developed can be understood as a kind of partial-pooling model for item response theory scaling, resulting in less uncertainty of estimates. Related, our estimation method provides a principled and unified approach to the issue of “granularity” (i.e., the level of aggregation) in the analysis of roll-call data (Crespin and Rohde 2010; Roberts et al. 2016). We illustrate the model by estimating U.S. House of Representatives members’ revealed preferences in different policy domains, and identify several other potential applications of the model including: studying the relationship between committee and floor voting behavior; and investigating constituency influence and representation.

Keywords

ideal point estimationdimensionalitynon-parametric Bayesian methodsroll-callsmultiple comparisons
Type
Article
Information
Political Analysis , Volume 29 , Issue 2 , April 2021 , pp. 139 - 166
Copyright
© The Author(s) 2021. Published by Cambridge University Press on behalf of the Society for Political Methodology

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Footnotes

Edited by Jeff Gill

Author’s note: Earlier version of this work were presented at the 2017 Midwest Political Science Association Annual Conference under the title “Comparing Revealed Preferences Across Multiple Types of Motions in the 83rd to 112th, U.S. House of Representatives.” This work has benefited from helpful comments from Marc Ratkovic, as well as from three anonymous referees. Results presented here can be reproduced at https://codeocean.com/capsule/5298256/.

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