Strategic Opinion-Writing on Appellate Courts

Abstract: Ruling on thousands of cases each year, U.S. federal courts of appeals make some of the most impactful decisions in modern society. I study how polarized partisan environments affect consensus-building using quasi-random three-judge panels on these courts from 1970–2013, documenting a novel pattern in dissenting opinions. Compared to party-unanimous panels, party-mixed panels cause all judges to dissent more often, and at equal rates: median and majority-party judges are just as driven to dissent as their more politically extreme colleagues. This result is incompatible with classical models of judicial politics and is unique to partisanship. To explain my results, I introduce a theoretical framework where judges’ favored coalitions are more homogeneous along both partisan and non-partisan dimensions. Using judge metadata, I find suggestive evidence for the model’s result that polarization increases dissents by judges of panel-minority law school or gender. With state-of-the-art machine learning tools from natural language processing, I generalize beyond dissents, showing that those same features drive differences in opinion text even when rulings are unanimous. My findings show that partisanship has a powerful and complex effect on agreement and illustrate the need for new tools to capture its consequences in this opaque yet high-stakes environment.

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