Multiple Ideal Points: Revealed Preferences in Different Domains
Multiple Ideal Points: Revealed Preferences in Different Domains
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DOI：
10.1017/pan.2020.21
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发表时间：
2019-03
影响因子：
5.4
通讯作者：
Scott Moser;Abel Rodríguez;Chelsea Lofland
中科院分区：
文献类型：
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作者：
Scott Moser;Abel Rodríguez;Chelsea Lofland
摘要：我们扩展了经典的理想点估计，允许选民在不同领域投票时有不同的偏好，例如，在农业政策投票时与国防政策投票时不同。我们的缩放程序会在通用尺度上得出估计的理想点。因此，我们能够直接比较成员在不同投票领域（不同动议组）中所显示的偏好，以评估成员是否对农业动议投票比对国防议案更加保守。通过这样做，我们能够评估单个选民的投票行为与一维空间模型的一致程度——如果一个成员在所有领域都具有相同的偏好。关键的新颖之处在于估计而不是假设“停留者”的身份，即在投票过程中所显示的偏好保持不变的选民。我们的方法提供了研究立法投票中基本空间和问题空间之间关系的方法（Poole 2007）。我们的方法有几个方法论优势。首先，我们的模型允许测试尖锐的假设。其次，所开发的方法可以理解为一种用于项目响应理论缩放的部分池模型，从而减少估计的不确定性。相关的是，我们的估计方法为点名数据分析中的“粒度”（即聚合水平）问题提供了一种原则性的、统一的方法（Crespin 和 Rohde，2010 年；Roberts 等人，2016 年）。我们通过估计美国众议院议员在不同政策领域所揭示的偏好来说明该模型，并确定该模型的其他几个潜在应用，包括：研究委员会与全体投票行为之间的关系；调查选民的影响力和代表性。
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.