Split-Award Tort Reform and Settlement Outcomes: An Experimental Investigation
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.032 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".