Generative Dynamics of Supreme Court Citations: Analysis with a New Statistical Network Model

TL;DR

This paper introduces a new statistical network model to analyze Supreme Court citation patterns at the dyadic level, revealing significant dependence effects like reciprocity and transitivity that influence how opinions are cited as precedents.

Contribution

The paper develops and provides software for a citation exponential random graph model that accounts for network dependence and case characteristics in Supreme Court citation analysis.

Findings

Evidence of reciprocity, transitivity, and popularity in citation networks

Dependence effects are as significant as case characteristics in citation formation

Modeling citations as a network improves understanding of precedent influence

Abstract

The significance and influence of US Supreme Court majority opinions derive in large part from opinions' roles as precedents for future opinions. A growing body of literature seeks to understand what drives the use of opinions as precedents through the study of Supreme Court case citation patterns. We raise two limitations of existing work on Supreme Court citations. First, dyadic citations are typically aggregated to the case level before they are analyzed. Second, citations are treated as if they arise independently. We present a methodology for studying citations between Supreme Court opinions at the dyadic level, as a network, that overcomes these limitations. This methodology -- the citation exponential random graph model, for which we provide user-friendly software -- enables researchers to account for the effects of case characteristics and complex forms of network dependence in citation formation. We then analyze a network that includes all Supreme Court cases decided between 1950 and 2015. We find evidence for dependence processes, including reciprocity, transitivity, and popularity. The dependence effects are as substantively and statistically significant as the effects of exogenous covariates, indicating that models of Supreme Court citation should incorporate both the effects of case characteristics and the structure of past citations.