Bargaining and learning in medical malpractice disputes.

摘要

Lengthy legal procedures and high legal costs are among the main drawbacks of the current litigation system in the United States. This dissertation studies the dispute resolution process with special emphasis on the dynamic patterns of litigation and settlement as well as the legal costs incurred and associated payments. I propose a dynamic bargaining model of dispute resolution with learning, estimate the model using micro data on medical malpractice disputes, and use the estimated model to assess the impact of proposed tort reforms. In my model, the plaintiff and the defendant do not have a common prior belief on the probability of winning court judgment and learning takes place as they bargain. In the equilibrium, a trade-off between the extra legal costs of delaying agreement and the possibility of learning new information determines the dynamic patterns of litigation and settlement. Estimation results show that the model fits all aspects of the data well and that learning plays a quantitatively important role in explaining the dynamic patterns of litigation and settlement. Using the estimated model, I conduct policy experiments to assess three of the proposed tort reforms. I find that capping jury awards or eliminating the contingency fee rule significantly shortens the expected time to resolution and lowers the expected total legal costs. On the other hand, the "loser-pay-all" allocation of legal fees delay resolution and increase costs. Because shorter legal procedures would reduce congestion in the legal system and savings of legal costs would lower the deadweight loss of litigation, I conclude that these reforms could have important welfare implications.