Split-Award Tort Reform and Settlement Outcomes: An Experimental Investigation
Notice bibliographique
Résumé
In an attempt to reduce the liability insurance costs of firms, several US states have implemented many different kinds of tort reform. Some reforms take the form of caps or limits on punitive damage awards while others, called “split-awards†, have mandated that a proportion of the award be allocated to the plaintiff with the remainder going to the state. Split-awards do not affect the payment of the defendant (firm) at trial but reduce the plaintiff's award in case of trial. Then, plaintiffs are more willing to accept lower settlement offers and therefore, the firm's expected litigation loss is lowered under this statute. It is important to note, however, that the reduction in the firm's expected litigation loss will affect its expenditures on accident prevention (level of care) and therefore, the probability of accidents. In this paper, we first construct a strategic model of litigation under asymmetric information to investigate the effect of the split-award reform on the level of care that firms choose in an effort to prevent accidents and lawsuits, and the probability that lawsuits proceed to the award stage of a trial. Consistent with Daughety and Reinganum (2003), we predict that a decrease in the plaintiff's share of the award decreases the probability of trial. Given that the split-award statute applies only when the case is settled in court, the parties have an incentive to settle out of court in order to cut out the state. In addition, we find that a decrease in the plaintiff's share of the award increases the probability of accidents. This arises because a decrease in the plaintiff’s share reduces expected litigation costs. The firm reacts to these lower expected costs by reducing expenditures on safety. We then report the findings of an experiment designed to assess the effects of changes in the plaintiff's share of the award on firm's level of care and the probability of trial. We use a within-subject design with two treatments, corresponding to two levels of the plaintiff's share of the punitive award, zero (no split-award condition) and fifty percent (split-award condition). In each experimental session, subjects are assigned the role of Player A (the plaintiff) or Player B (the defendant), play one of the two versions of the game, and are paid according to their performance. The game consists of an individual decision-making problem (firm’s choice of level of care) and a bilateral (pre-trial) bargaining game between a plaintiff and a defendant. Subjects’ understanding of the game is facilitated by having them play the game several times against a computer partner before playing the actual game against a human partner. We run 8 80-minute sessions of 8 to 12 subjects each (a pool of 70 undergraduate students in total) at the experimental laboratory of the University of Alberta Business School. Consistent with our qualitative predictions, our findings indicate that settlement rates are significantly higher when bargaining is performed under the split-award institution. Defendant's litigation expenses and plaintiff's net compensation are significantly lower under the split-award statute. The examination of the individual data suggests risk-aversion and strategic behavior of the subjects within the limits of their computational ability.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,037 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,006 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,032 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».