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Title: Multiple Ideal Points: Revealed Preferences in Different Domains
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 more » 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. « less
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Award ID(s):
1740850 2114729 1738053
Publication Date:
Journal Name:
Political Analysis
Page Range or eLocation-ID:
139 to 166
Sponsoring Org:
National Science Foundation
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