Flexing Judicial Muscle

Empirical scholarship about judicial activism has focused on the United States Supreme Court, relied upon subjective coding of individual cases as “activist” or “restrained,” and only examined instances when the federal judiciary invalidated legislative, executive, and state actions. This article contends that such limitations have yielded an extremely narrow and flawed perspective concerning judicial activism and decision-making. In contrast, this article introduces a study of the United States Courts of Appeals that evaluates judicial activism based upon the appellate review of all types of district court judgments using an objective measure that does not rely on coding individual opinions as “activist” or “restrained.” Activism, at its core, is about judges flexing their metaphorical muscles by elevating their judgment above other constitutionally significant actors. By analyzing how individual judges respect both deferential and non-deferential standards of review in reviewing district court judgments, this study captures, in the aggregate, the degree to which judges privilege their judgment. The study utilizes a new dataset that includes over 30,000 judicial votes by judges sitting on the United States Courts of Appeals from 2008 in cases in which a standard of review was applied. The study finds that there is no evidence of a statistically significant correlation between the activism of judges and their political ideology (regardless of how it is measured). However, the study does find that the Courts of Appeals vary substantially in their levels and variations of judicial activism in a statistically significant manner. Further, in reviewing the validity of this study, the article explores in greater detail the judicial activism measurements of four notable jurists: Judges Frank Easterbrook, Richard Posner, (now Justice) Sonia Sotomayor, and J. Harvie Wilkinson III. The article also includes Activism Scores for the 142 judges serving on the Courts of Appeals who had a sufficient number of qualified votes in the dataset.