Job Market Paper: "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. Using quasi-random three-judge panels on these courts from 1970--2013, I study the effect of partisanship on consensus among judges. While bipartisan panels cause a roughly 25% increase in dissenting opinions over party-unanimous panels, I document a novel pattern in dissenter identity: the most politically extreme judge is no more likely to dissent than their colleagues. This result is incompatible with classical models of judicial politics and is unique to partisanship; other judge characteristics produce smaller increases in dissents which are more concentrated on outlier judges. To explain my results, I introduce a theoretical framework where favored coalitions contain the most similar judges along both partisan and non-partisan dimensions. Using judge metadata, I find suggestive evidence for the model's result that partisanship increases disagreements 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 while partisanship has minimal effects. My findings show that partisanship has a powerful and complex effect on consensus-building and illustrate the need for new tools to capture the subtle effects of disagreement in this opaque yet high-stakes environment