The Effects of Comparable-Case Guidance on Awards for Pain and Suffering and Punitive Damages: Evidence from a Randomized Controlled Trial
54 Pages Posted: 9 Jan 2017 Last revised: 24 May 2019
Date Written: January 23, 2018
Damage awards for pain and suffering and punitive damages are notoriously unpredictable. Courts provide minimal, if any, guidance to jurors determining these awards, and apply similarly minimal standards in reviewing them. Lawmakers have enacted crude measures, such as damage caps, aimed at curbing award unpredictability, while ignoring less drastic alternatives that involve guiding jurors with information regarding damage awards in comparable cases (“comparable‐case guidance” or “prior‐award information”). The primary objections to the latter approach are based on the argument that, because prior‐award information uses information regarding awards in distinct cases, it introduces the possibility of biasing the award, or distorting the award size, even if prior-award information reduces the variability of awards. This paper responds to these objections. It reports and interprets the results of a large randomized controlled trial designed to test juror behavior in response to prior‐award information and, specifically, to examine the effects of prior-award information on both variability and bias under a range of conditions related to the foregoing objections. We conclude that there is strong evidence that prior‐award information improves the “accuracy” of awards—that it significantly reduces the variability of awards, and that any introduction of bias, or distortion of award size, is minor relative to its beneficial effect on variability. Furthermore, we conclude that there is evidence that jurors respond to prior‐award information as predicted in recent literature, and in line with the “optimal” use of such information; and that prior‐award information may cause jurors to approach award determinations more thoughtfully or analytically.
Keywords: Jury Behavior, Juror Behavior, Experiment, Randomized, Prior-Award Information, Pain and Suffering, Punitive Damages, Unpredictability, Variability, Accuracy
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