Replication Data for: Estimating Dynamic Ideal Points for State Supreme Courts

Courts of last resort in the American states offer researchers considerable leverage to develop and test theories about how institutions influence judicial behavior. One measure critical to this research agenda is the individual judges’ preferences, or ideal points, in policy space. Two main strategies for recovering this measure exist in the literature: Brace, Langer, and Hall’s (2000) Party-Adjusted Judge Ideology (PAJID) and Bonica and Woodruff’s (2015) judicial CFscores. Here we introduce a third measurement strategy that combines CFscores with item response (IRT) estimates of judicial voting behavior in all 52 state courts of last resort from 1995–2010. We show that leveraging two distinct sources of information (votes and CFscores) yields a superior estimation strategy. Specifically, we highlight several key advantages of the combined measure: (1) it is estimated dynamically, allowing for the possibility that judges’ ideological leanings change over time and (2) it maps judges into a common space. In a comparison against existing measurement strategies, we find that our measure offers superior performance in predicting judges’ votes. We conclude that it is a valuable tool for advancing the study of judicial politics.美国各州终审法院为研究者构建和检验“制度如何影响司法行为”的相关理论提供了重要研究便利。本研究方向的核心衡量指标之一，是法官个体在政策空间中的偏好（或称理想点）。现有文献中主要存在两类获取该指标的研究方法：一是Brace、Langer与Hall（2000）提出的党派调整法官意识形态指数（Party-Adjusted Judge Ideology, PAJID），二是Bonica与Woodruff（2015）提出的司法CF评分。本文提出第三种测量策略：将CF评分与1995至2010年间全美52个州终审法院的法官投票行为项目反应理论（Item Response Theory, IRT）估计值相结合。研究表明，融合投票数据与CF评分两类不同来源的信息，可得到更具优势的估计方法。具体而言，该复合测量指标具备两项核心优势：其一，采用动态估计方式，可容纳法官意识形态倾向随时间演变的情形；其二，能够将所有法官映射至同一政策空间之中。通过与现有测量策略的对比验证，本文发现该指标在预测法官投票行为上表现更优。综上，该测量方法可为司法政治学研究的深化提供极具价值的研究工具。