Using data on essentially every U.S. Supreme Court decision since 1946, we estimate a model of peer effects on the Court. We estimate the impact of justice ideology and justice votes on the votes of their peers. To identify the peer effects, we use two instruments that generate plausibly exogenous variation in the peer group itself, or in the votes of peers. The first instrument utilizes the fact that the composition of the Court varies from case to case due to recusals or absences for health reasons. The second utilizes the fact that many justices previously sat on Federal Circuit Courts, and justices are generally much less likely to overturn decisions in cases sourced from their former “home” court. We find large peer effects. For example, we can use our model to predict the impact of replacing Justice Ginsburg with Justice Barrett. Under the the assumption that Justice Barrett's ideological position aligns closely with Justice Scalia, for whom she clerked, we predict that her influence on the Court will increase the Conservative vote propensity of the other justices by 4.7 percentage points. That translates into 0.38 extra conservative votes per case on top of the impact of her own vote. In general, we find indirect effects are large relative to the direct mechanical effect of a justice's own vote.

中文翻译：

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对美国最高法院的同行影响

基本上使用自 1946 年以来美国最高法院每一项裁决的数据，我们估计了对法院的同伴效应模型。我们估计正义意识形态和正义投票对其同行投票的影响。为了识别同伴效应，我们使用两种工具在同伴群体本身或同伴的投票中产生看似合理的外生变化。第一种文书利用了这样一个事实，即法院的组成因案件而异，因健康原因而缺席或缺席。第二个利用了许多法官以前在联邦巡回法院任职的事实，而且法官通常不太可能推翻来自他们以前的“家乡”法院的案件的决定。我们发现了很大的同伴效应。例如，我们可以使用我们的模型来预测用巴雷特法官取代金斯伯格法官的影响。假设巴雷特大法官的意识形态立场与她担任书记的斯卡利亚大法官密切相关，我们预测她对法院的影响将使其他大法官的保守党投票倾向增加 4.7 个百分点。除了她自己投票的影响之外，这意味着每个案例额外获得 0.38 票。一般来说，我们发现相对于法官自己投票的直接机械效应，间接效应更大。