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37 Yale L. & Pol'y Rev. 405 (2018-2019)
The Effects of Comparable-Case Guidance on Awards for Pain and Suffering and Punitive Damages: Evidence from a Randomized Controlled Trial

handle is hein.journals/yalpr37 and id is 429 raw text is: YALE LAW & POLICY REVIEW The Effects of Comparable-Case Guidance on Awards for Pain and Suffering and Punitive Damages: Evidence from a Randomized Controlled Trial Hillel. BavFr & Reagan Mozer 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 a ward 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 ifprior- 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- a ward 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 * Assistant Professor, SMU Dedman School of Law; Fellow, Harvard Institute for Quantitative Social Science. ** Ph.D. Candidate, Department of Statistics, Harvard University. This paper benefited immensely from the guidance of Professors Donald Rubin, Eric Posner, Michael Saks, Richard Lempert, David Rosenberg, Christopher Robertson, and James Greiner, and from support provided by the Institute for Quantitative Social Science and the Center for American Political Studies. 405