Supreme Court Citations Follow Network Dynamics, Not Just Case Traits

The authority of U.S. Supreme Court majority opinions rests largely on their use as precedents. Existing studies of citation patterns face two key limitations: dyadic citations are usually aggregated to the case level, and citations are treated as if they arise independently. This paper presents a method that addresses both issues and models citations at the dyadic, network level.

🗂️ A New Dyadic Network Approach

The citation exponential random graph model (citation ERGM) is introduced as a way to treat citations between opinions as a network tie rather than independent events. User-friendly software accompanies the model, enabling researchers to simultaneously estimate:

• the effects of observable case characteristics on citation formation, and
• complex forms of network dependence that shape citation behavior.

📚 What Was Analyzed: All Supreme Court Opinions, 1950–2015

• Network data include every Supreme Court case decided between 1950 and 2015.
• Citations are modeled at the dyadic (opinion-to-opinion) level rather than aggregated to cases.
• The accompanying software implements the citation ERGM for applied use.

🔍 Key Findings

• Strong evidence of network dependence processes in citation formation, including reciprocity, transitivity, and popularity.
• These dependence effects are both substantively and statistically as important as traditional exogenous covariates (case characteristics).
• Treating citations as independent or aggregating dyads to cases can obscure these network processes.

💡 Why This Matters

• Models of Supreme Court citations should incorporate both case attributes and the structure of past citations to accurately capture precedent formation.
• The citation ERGM and its software provide a practical tool for researchers interested in the generative dynamics of legal citations and the social structure of judicial precedent.