Measuring Issue- and Time-Variation in Supreme Court Justice Preferences

Abstract

While unidimensional preference estimates for the U.S. Supreme Court exist in both constant and time-varying forms, estimating variation in preferences across areas of the law has been difficult because multidimensional scaling models perform poorly in the context of a nine-member court. We introduce a new approach to recovering estimates of judicial preferences that are localized to particular legal issues as well as periods of time. Using multiple indices of substantive similarity among cases, we apply a kernel-weighted optimal classification estimator to analyze how justices' preference vary across both areas of the law and time. We find that incorporating variation in substantive area significantly improves the predictive power of estimated preference orderings over a model that only allows for variation in preferences over time. We also find substantial variation in the identity of the median justice across areas of the law during most periods of the modern court, suggesting a need to reconsider empirical and theoretical research that hinges on the existence of a unitary and well-identified median justice.