Learning and Bargaining in Dispute Resolution: Theory and Evidence from Medical Malpractice Litigation

Lengthy legal procedures and high legal costs are among the main drawbacks of the current litigation system in the United States. This paper 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 on the probability of winning court judgment and learning takes place as they bargain. In the equilibrium, a trade-off between the 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 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 some 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. Since 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. On the other hand, "loser-pay-all" allocation of legal fees delay resolution and increase costs.