Research Fields
Political Economy, Economic Theory, Behavioral EconomicsI am a PhD student in the Department of Economics on the 2024-2025 job market. My research is in political economy, combining economic theory with tools from machine learning and natural language processing.
See the pages above for more information about my research and teaching, as well as all my job market materials.
Job Market Paper: “Strategic Opinion-Writing on Appellate Courts”
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.