2021/01/15 by Christian S. Schmid, Ted Hsuan Yun Chen, Schmid, Christian S. +3
Social Sciences · #Applications (stat.AP) #Digital Libraries (cs.DL) #FOS: Computer and information sciences #FOS: Physical sciences #Judicial and Constitutional Studies #Physics and Society (physics.soc-ph)
paper · pdf · doi:10.48550/arxiv.2101.07197
openalex publication_date 2021/01/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The significance and influence of US Supreme Court majority opinions derive\nin large part from opinions' roles as precedents for future opinions. A growing\nbody of literature seeks to understand what drives the use of opinions as\nprecedents through the study of Supreme Court case citation patterns. We raise\ntwo limitations of existing work on Supreme Court citations. First, dyadic\ncitations are typically aggregated to the case level before they are analyzed.\nSecond, citations are treated as if they arise independently. We present a\nmethodology for studying citations between Supreme Court opinions at the dyadic\nlevel, as a network, that overcomes these limitations. This methodology -- the\ncitation exponential random graph model, for which we provide user-friendly\nsoftware -- enables researchers to account for the effects of case\ncharacteristics and complex forms of network dependence in citation formation.\nWe then analyze a network that includes all Supreme Court cases decided between\n1950 and 2015. We find evidence for dependence processes, including\nreciprocity, transitivity, and popularity. The dependence effects are as\nsubstantively and statistically significant as the effects of exogenous\ncovariates, indicating that models of Supreme Court citation should incorporate\nboth the effects of case characteristics and the structure of past citations.\n