نقش حل اختلاف جایگزین غیر الزام آور در شکایت های قانونی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Economic Behavior & Organization, Volume 42, Issue 1, May 2000, Pages 75–96
This paper analyzes pretrial mediation, in which a panel estimates the value of the plaintiff’s claim. Each party must decide whether to accept or reject the resulting award. If either party rejects, the case proceeds on toward trial; however, most cases are ultimately settled. We develop and then test a model in which each party has private information about the claim, but the mediation panel does not. The model predicts (1) the probability that each party accepts or rejects the mediation award, and (2) if the award is rejected and the case is later settled, how the settlement payment compares to the mediation award.
In recent years there has been a rapid growth of interest in, and use of, various methods of alternative dispute resolution. These methods include arbitration, mediation, abbreviated trial procedures, and even rent-a-judge programs. The common characteristic of these methods is that a third party offers an opinion or provides information about a dispute to the litigants. This paper analyzes pretrial mediation. In this procedure a mediation panel offers an opinion of the value of the plaintiff’s claim. The rules of mediation examined here apply to medical malpractice litigation in Michigan. As explained below, the essential features of these rules are used in civil litigation of all kinds, in many State and federal courts.1 After a legal claim is filed in court, it must go through mediation before it can go to trial. Each party, plaintiff and defendant, presents its case to a panel of five persons, which evaluates the claim and proposes an appropriate award (if any) for the plaintiff. Each party then decides whether to accept or reject the award. If they both accept, the case is resolved, and the defendant pays the plaintiff the amount of the award. If either party rejects, the case proceeds on toward trial.2 It should be noted that each party must submit its response to the mediation award without knowing the response of the other party (a failure to respond within the prescribed period is considered a rejection).3 An important feature of this procedure is that a penalty is imposed on a party who rejects the mediation award unless the party is able to improve its position at trial. A party who rejects the mediation award must pay the costs of the opposing party unless the trial verdict turns out to be ‘more favorable’ to the rejecting party than the mediation award.4 The verdict is considered ‘more favorable’ to the plaintiff if it is more than 10 percent above the mediation award, and ‘more favorable’ to a defendant if it is more than 10 percent below the mediation award. The basic features of Michigan’s mediation program are found in many court-annexed arbitration programs in both state and federal district courts. In courts which have such programs, claims generally must be submitted to nonbinding arbitration before a trial can be requested. Typically, as in Michigan, the arbitration hearing is much briefer and more informal than a trial; the usual rules of evidence do not apply. Either party can, by requesting a trial, decline to accept the arbitration award. In many jurisdictions, as in Michigan, the arbitration panel’s decision is not admissible at trial. Often a penalty is imposed on a party who has requested a trial if he fails to improve his position at trial.5 Thus the analysis of Michigan’s mediation program has implications for court-annexed arbitration programs throughout the United States. One objective of this paper is to develop a theoretical model which will predict (1) how often each party will accept or reject the mediation award, and (2) assuming the case is subsequently settled, how large the settlement payment is in comparison to the mediation award. Our primary objective, however, is to test the predictions of the model against data, and to consider possible explanations for the manner in which the data diverge from the predictions of the model. We have a data set that reports for each of 477 cases the mediation award, the responses of both parties to the mediation award, and the settlement payment or trial verdict, so we are able to compare the theory with the evidence. In our model the parties to a lawsuit each have private information about the value of the claim. Each party must decide whether to accept the award of a mediation panel (which makes its decision without such information), or instead proceed on toward trial. In making this decision, each party takes into account the possibility of paying or being paid a penalty that is imposed on a party who makes an unwarranted rejection of the mediation award (our model incorporates the actual rule of State law which imposes this penalty). In the event the mediation award is not accepted by both parties, the case proceeds on toward trial. We assume, however, that before the trial can occur, all information about the claim becomes public knowledge, and the case is then settled. Our model yields a predicted distribution of expected court awards and resulting settlement payments given each of the three permutations (plaintiff accepts, defendant rejects), (plaintiff rejects, defendant accepts), and (plaintiff rejects, defendant rejects). Each such distribution can then be compared to the mediation award made in the case. Very little work seems to have been done in this area. There is a literature that examines the social costs and benefits of alternative dispute resolution.6 For the most part this literature concerns the effect of these procedures on settlement, or more broadly, on the total costs of litigation (a subsidiary theme is their effects on nuisance suits and small claims). For example Shavell (1995) examines the effects of required nonbinding alternative dispute resolution on the frequency of lawsuits, on whether the parties to lawsuits settle or go to trial, and on the aggregate costs of litigation. The thrust of this paper is quite different. Our objective is to arrive at a better understanding of the bargaining process between the parties, in litigation in which there is required but nonbinding alternative dispute resolution. Toward that end, we have developed a model that yields implications about the relation between mediation awards and settlement payments, given the response of each party to the mediation award. Finally, we examine how the data diverge from the predictions of the model, and consider several alternative possible explanations for these disparities. Bernstein (1993) examines a variety of factors affecting the outcome of nonbinding alternative dispute resolution, including risk aversion, the effects of fee and cost-shifting provisions of these programs, the effects of lawyers’ fee arrangements, and whether parties have an incentive to manipulate the process, that are relevant to our later discussion of why the data diverge from the predictions of the model. Another study related to this paper is an empirical analysis of medical malpractice litigation by Farber and White (1991). As part of this study, they provide information on the parties’ responses to the mediation award. They also compared mediation awards to settlement payments, and found a positive correlation of 0.94 in levels and 0.81 in logs. We are not aware of any previous work that predicts a conditional distribution of court awards, given the response by the parties to a proposed award made by a third party, such as a mediation or pretrial screening panel.
نتیجه گیری انگلیسی
This paper develops a model in which the parties to a lawsuit each have private information about the value of the claim. Each party must decide whether to accept the award of a mediation panel (which makes its decision without such information), or instead proceed on toward trial. In making this decision, each party takes into account the possibility of paying or being paid a penalty which is imposed on a party who makes an unwarranted rejection of the mediation award. Since we intend to test the model against data from Michigan, the model incorporates the rule of Michigan law which imposes this penalty. Given the costs of litigation, the model predicts the probability of each permutation of possible responses of the parties to the mediation award, and the distribution of expected court awards for each possible response. We then compare the predictions of the model to the data. We make the strong, but not unreasonable, assumption that the amount paid in settlement represents the expected value of the claim in litigation. If, however, one of the parties would be liable for costs in the event of litigation, we further assume that this potential liability is reflected in the settlement. The model performs quite well in some respects but not in others. In general, we find that the data depart from the model because of asymmetries in favor of the defendant. Plaintiffs accept the mediation award far more often than defendants. In addition, defendants obtain settlements more favorable than a mediation award they accepted more than twice as often as plaintiffs do. Of all the possible explanations for these results, the most promising candidates are that (1) plaintiffs are much more risk-averse than defendants, or (2) since lawyers for plaintiffs, but not those for defendants, work under a contingent fee agreement, they have a greater incentive to encourage their clients to accept the mediation award. One reason why defendants might be less risk-averse than plaintiffs is moral hazard; in cases where a defendant physician has the right to control the litigation, he can make decisions without having to bear the consequences, which will be visited on his insurance company. Another asymmetry in the results arises because defendants tend to do better than plaintiffs in the settlements of cases in which they have previously rejected the mediation award. While this might be explained by factors (1) or (2), it could also reflect the fact that defendants, but not plaintiffs, have an incentive to manipulate the mediation process, simply because the threat of sanctions for rejecting the mediation award is more significant to defendants than it is to plaintiffs. Finally, in a comparison of trial verdicts and mediation awards, set forth in Table 4, we found that plaintiffs generally did worse at trial than they did in settlement agreements, relative to the mediation award. One possible explanation is that defence lawyers are more apt to underachieve at the mediation hearing in cases which they believe are most likely to go to trial, in order to avoid the penalty for an unwarranted rejection of the mediation award. Table 4 also shows that in 70 percent of the cases where the plaintiff is liable for the penalty, there is no recovery, making it unlikely that the defendant will be able to collect the penalty. In addition, the table shows that it is not especially unusual for the plaintiff to obtain a positive recovery that is less than the mediation award. This suggests that either (1) claims that go to trial are greatly overvalued by mediation panels, or (2) the courts are apt to award the plaintiff something like the expected value of his claim rather than full damages, as would be required by a literal interpretation of the law. Our model does not allow for differences in risk preferences, agency problems, or for difficulties in collecting from a party the penalty for collecting the mediation award. Consequently this model cannot explain the asymmetries in the data described above. The contribution of our model is that it offers an explanation for those cases in which the party obtains a settlement on terms more favorable than those he has already accepted in mediation. According to the model, this outcome occurs not because new information has come to light, but rather because the settlement reflects the penalty that would be imposed on the party who rejected the mediation award. However, the principal contribution of this paper is in showing how the data depart from the predictions of the model, and considering the possible reasons for those deviations.