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The Aggregate Dynamics of Lower Court Responses to the US Supreme Court

Published online by Cambridge University Press:  21 October 2022

Ali S. Masood*
Affiliation:
Rhodes College
Benjamin J. Kassow
Affiliation:
University of North Dakota
Donald R. Songer
Affiliation:
University of South Carolina
*
Contact the corresponding author, Ali Masood, at masooda@rhodes.edu.
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Abstract

We argue that given finite resources to review the large number of lower court decisions, Supreme Court justices should primarily be interested in aggregate responses to their precedents. We offer a theory in which the US Supreme Court drives aggregate responses to its decisions by signaling the utility of its precedents to judges on the lower courts. Specifically, we argue that lower court judges have a greater propensity to rely on a Supreme Court decision when the justices explicitly direct a lower court to consider a formally argued decision in its summary decisions. Our results demonstrate that such signals significantly increase the frequency with which the lower courts adopt the precedents of the US Supreme Court. We corroborate the causality of these links through qualitative analyses, distance matching methods, and simultaneous sensitivity analysis. Our study offers new and important insights on judicial impact and decision-making behavior in the American courts.

Information

Type
Research Article
Copyright
© 2019 by the Law and Courts Organized Section of the American Political Science Association. All rights reserved.
Figure 0

Table 1.Multilevel Negative Binomial Regression of Lower Court Citations and Positive Treatments of Supreme Court Precedent

Figure 1

Figure 1.Impact of summary decisions on lower court citations. The log number of summary decisions is plotted on the x-axis. Plot based on the average of the predicted counts across all real values in the data. Solid line, predicted number of citations. Shaded area, 95% confidence intervals.

Figure 2

Figure 2.Impact of summary decisions on lower court positive treatments. The log number of summary decisions is plotted on the x-axis. Plot based on the average of the predicted counts across all real values in the data. Solid line, predicted number of positive treatments. Shaded area, 95% confidence intervals.

Figure 3

Table 2.Fractional Logistic Regression of Lower Court Citations of Supreme Court Precedent That Are Positive Treatments

Figure 4

Table 3.Mahalanobis Distance Matching on Degree of Influence of Summary Decisions (SD) on Citations and Positive Treatments

Figure 5

Table 4.Rosenbaum Test for Impact of Summary Decisions on Citations and Positive Treatments

Supplementary material: File

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