Gabriel Lopez-Moctezuma, CalTech

"Sequential Voting in the U.S. Supreme Court”



We quantify the extent to which justices in the U.S. Supreme Court learn from

each other when voting on the merits of cases the Court reviews. We analyze justices’

conference votes, which have been historically cast behind closed doors in order of

seniority. We provide causal evidence that junior justices systematically incorporate

the votes of their senior colleagues when voting at conference, while accounting for both observed and unobserved heterogeneity. To assess the

extent and the mechanisms through which learning occurs, we develop an empirical

model of sequential voting in the Court. In the model, justices make decisions under

incomplete information and incorporate their preferences, public and private

information, as well as the choices of previous justices in the voting sequence. Given

the parameter estimates from the sequential model, we show that the median justice

in the Court is willing to change her vote in approximately 30% of cases after incorporating

the voting history. We assess the effect of sequential voting by seniority on

the Court’s probability of mistakes and compare it to alternative voting mechanisms

such as simultaneous and anti-seniority voting.


Joint work with Benjamin Johnson (Penn State Law School)