评估立法者透露的偏好差异:对第107届美国参议院的案例研究

Chelsea Lofland, Abel Rodríguez, Scott Moser
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引用次数: 6

摘要

点名数据被广泛用于评估立法者的偏好和意识形态,以及检验立法行为理论。特别是,点名数据经常被用来确定立法者的公开偏好是否受到诸如政党压力、少数民族地位或程序规则等外部力量的影响。本文描述了一个贝叶斯层次模型,该模型扩展了现有的空间投票模型,使用与这些假设相关的后验概率来检验关于偏好差异的尖锐假设。我们使用我们的模型来调查第107届美国参议院政党多数地位的变化对参议员透露的偏好的影响。这一分析提供的证据表明,政党归属的变化可能会影响立法者的显示偏好,但没有提供证据表明多数地位对立法者的显示偏好的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessing differences in legislators' revealed preferences: A case study on the 107th U.S. Senate
Roll call data are widely used to assess legislators' preferences and ideology, as well as test theories of legislative behavior. In particular, roll call data is often used to determine whether the revealed preferences of legislators are affected by outside forces such as party pressure, minority status or procedural rules. This paper describes a Bayesian hierarchical model that extends existing spatial voting models to test sharp hypotheses about differences in preferences the using posterior probabilities associated with such hypotheses. We use our model to investigate the effect of the change of party majority status during the 107th U.S. Senate on the revealed preferences of senators. This analysis provides evidence that change in party affiliation might affect the revealed preferences of legislators, but provides no evidence about the effect of majority status on the revealed preferences of legislators.
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