Most judicial decisionmaking papers can be criticized for inadequately controlling for variation in "case complexity." Lingering disputes over what "complexity" even means in terms of litigated cases only increase the degree of difficulty in constructing such a control. And even if a consensus on what "case complexity" means existed, constructing a reliable, consistent, and stable measure introduces additional challenges.
Difficulties, conceptual and mechanical, aside, a recent paper, Measuring Supreme Court Case Complexity, makes a helpful contribution. In it, Greg Goelzhauser (Utah State--Poli Sci) et al., both critique an array of earlier efforts and advance their own Bayesian measurement model to construct a latent (Supreme Court) case complexity measure that exploits issue and provision counts from the cases' merits briefs. By measuring case complexity from pre-decision data (merits briefs) the paper argues that its ex poste approach "diminishes endogeneity and post-treatment bias concerns when studying merits stage outcomes." To develop its measure, the authors draw from issue and provision (hand-coded) counts extracted from merits briefs filed in the 1954 through 2017 Court Terms, incident to Supreme Court Rules 24(1)(a) and 24(1)(f).
While perhaps limited to Supreme Court cases, new approaches to a difficult challenge can make important methodological contributions. The paper's abstract follows.
"Case complexity is central to the study of judicial politics. The dominant measures of Supreme Court case complexity use information on legal issues and provisions observed post-decision. As a result, scholars using these measures to study merits stage outcomes such as bargaining, voting, separate opinion production, and opinion content introduce post-treatment bias and exacerbate endogeneity concerns. Furthermore, existing issue measures are not valid proxies for complexity. Leveraging information on issues and provisions extracted from merits briefs, we develop a new latent measure of Supreme Court case complexity. This measure maps with the prevailing understanding of the underlying concept while mitigating inferential threats that hamper empirical evaluations. Our brief-based measurement strategy is generalizable to other contexts where it is important to generate exogenous and pre-treatment indicators for use in explaining merits decisions."
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