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Abstract:
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The significance and influence of US Supreme Court majority opinions derive in large part to 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 independent of one another. We make the case that studying Supreme Court citations as a dynamic citation network overcomes both of these limitations. We present a methodology for studying citations between US Supreme Court opinions at the dyadic level, as a network. This methodology---the citation temporal exponential random graph model---enables researchers to account for the effects of case characteristics and complex forms of network dependence in citation formation. We apply this methodology to a network that includes all Supreme Court cases decided between 1937 and 2013 and find evidence for dependence processes, including reciprocity, transitivity, popularity, and activity.
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