Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published September 20, 2017 | Submitted
Report Open

Litigation of Settlement Demands Questioned by Bayesian Defendants

Abstract

This paper analyzes a stylized model of pretrial settlement negotiations in a personal-injury case. It is assumed that the prospective plaintiff knows the severity of his injury but that the prospective defendant has incomplete information. As a result of this information asymmetry a proportion of slightly-injured plaintiffs are tempted to inflate their settlement demands and a proportion of such demands are rejected by suspicious defendants. By analogy with other models of adverse selection (e. g., Rothschild-Stiglitz (1976)), the presence of slightly-injured plaintiffs imposes a negative externality on plaintiffs with genuine severe injuries since defendant s cannot identify the severely-injured and sometimes reject their reasonable demands, forcing them into costly litigation. A filing fee imposed on minor claims is shown to displace the equilibrium but, paradoxically, to cause an increase in the frequency of litigation. This model differs from recent contributions to the literature on pretrial negotiations under incomplete information. Unlike P'ng (1983) and Bebchuk (1983), the uninformed litigant in this model learns from the observed equilibrium behavior of the informed litigant. Unlike Ordover-Rubinstein (1983) and Salant-Rest (1982), settlement demands are endogenous.

Additional Information

This paper extends the analysis in Salant and Rest (1982) by relaxing its restriction that plaintiffs must make one of two exogenous settlement demands. I would like to thank Gregory Rest for his collaboration in the earlier research effort. I would also like to express my deepest gratitude to Jonathan Cave for his many helpful suggestions.

Attached Files

Submitted - sswp516.pdf

Files

sswp516.pdf
Files (930.4 kB)
Name Size Download all
md5:26f85a6f9c146fe7f249f4f756ef0c5a
930.4 kB Preview Download

Additional details

Created:
August 19, 2023
Modified:
January 14, 2024