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Explaining Deviations from the Fifty-Percent Rule: A Multimodal Approach to the Selection of Cases for Litigation

Kessler, Daniel, Meites, Thomas, Miller, Geoffrey·1996·The Journal of Legal Studies

Abstract

In their 1984 article, Priest and Klein show that a simple divergent expectations model of the decision to litigate leads to a plaintiff success rate at trial that approaches 50 percent as the fraction of cases going to trial approaches zero. However, an extensive empirical literature has documented that plaintiffs win far fewer than half of their cases. As Priest and Klein observe, this conflict between the predictions of the model and the empirical literature may be attributable to violations in the data of the assumptions behind the simple model. Based on data from 3,529 cases, we find that "multimodal" case characteristics associated with violations of these assumptions cause plaintiff win rates to deviate from the 50-percent baseline in the manner that simple law-and-economics models would suggest. In other words, among cases that conform more closely to the assumptions underlying the simple divergent expectations model, the plaintiff win rate is closer to 50 percent.

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Metadata

Authors
Kessler, D., Meites, T., & Miller, G.
Venue
The Journal of Legal Studies
Published
1996-01
Volume
25
Issue
1
Pages
233-259
ISSN
  • 0047-2530 (ISSN-L)
  • 1537-5366
OpenAlex
W2042916377
Cited by
374
References
0
FWCI
59.39
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Explaining Deviations from the Fifty-Percent Rule: A Multimodal Approach to the Selection of Cases for Litigation. https://app.cochat.ai/papers/10.1086/467977
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Kessler, D., Meites, T., & Miller, G. (1996). Explaining Deviations from the Fifty-Percent Rule: A Multimodal Approach to the Selection of Cases for Litigation. The Journal of Legal Studies. https://doi.org/10.1086/467977

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.