Gabriel Lopez-Moctezuma, CalTech
Abstract:
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)