Allocation Rules and the Stability of Mass Tort Class Actions

This paper studies the effects of allocation rules on the stability of mass tort class actions. I analyze a two-stage model in which a defendant faces multiple plaintiffs with heterogeneous damage claims. In stage 1, the plaintiffs play a noncooperative coalition formation game. In stage 2, the class action and any individual actions by opt-out plaintiffs are litigated or settled. I examine how the method for allocating the class recovery interacts with other factors---the shape of the damage claims distribution, the scale benefits of the class action, and the plaintiffs\u27 probability of prevailing at trial and bargaining power in settlement negotiations---to determine the asymptotic stability of the global class. My results suggest criteria to attorneys and courts for structuring and approving efficient allocations plans in mass tort class actions and for evaluating the requirements for class certification in mass tort cases

Asymmetric Empirical Similarity

The paper offers a formal model of analogical legal reasoning and takes the model to data. Under the model, the outcome of a new case is a weighted average of the outcomes of prior cases. The weights capture precedential influence and depend on fact similarity (distance in fact space) and precedential authority (position in the judicial hierarchy). The empirical analysis suggests that the model is a plausible model for the time series of U.S. maritime salvage cases. Moreover, the results evince that prior cases decided by inferior courts have less influence than prior cases decided by superior courts

Different Contexts, Different Risk Preferences?

We examine the stability of risk preferences across contexts involving different stakes. Using data on households\u27 deductible choices in three property insurance coverages and their limit choices in two liability insurance coverages, we assess the stability across the five contexts in the ordinal ranking of the households\u27 willingness to bear risk. We find evidence of stability across contexts involving stakes of the same magnitude, but not across contexts involving stakes of very different magnitudes. Our results appear to be robust to heterogeneity in wealth and access to credit, complicating seemingly ready explanations

Tort Liability and Unawareness

Unawareness is a form of bounded rationality where a person fails to conceive all feasible acts or consequences or to perceive as feasible all conceivable act-consequence links. We study the implications of unawareness for tort law, where relevant examples include the discovery of a new product or technology (new act), of a new disease or injury (new consequence), or that a product can cause an injury (new link). We argue that negligence has an important advantage over strict liability in a world with unawareness—negligence, through the stipulation of due care standards, spreads awareness about the updated probability of harm

Computational Complexity and Tort Deterrence

Standard formulations of the economic model of tort deterrence constitute the injurer as the unboundedly rational bad man. Unbounded rationality implies that the injurer can always compute the solution to his care-taking problem. This in turn implies that optimal liability rules can provide robust deterrence, for they can always induce the injurer to take socially optimal care. In this paper I examine the computational complexity of the injurer\u27s care-taking problem. I show that the injurer\u27s problem is computationally tractable when the precaution set is unidimensional or convex, but that it is computationally intractable when the precaution set is multidimensional and discrete. One implication is that the standard assumptions of unidimensional and convex care, though seemingly innocuous, are pivotal to ensuring that tort law can provide robust deterrence. It is therefore important to recognize situations with multidimensional discrete care, where robust tort deterrence may not be possible

The Cost of Legal Restrictions on Experience Rating

We investigate the cost of legal restrictions on experience rating in auto and home insurance. The cost is an opportunity cost as experience rating can mitigate the problems associated with unobserved heterogeneity in claim risk, including mispriced coverage and resulting demand distortions. We assess this cost through a counterfactual analysis in which we explore how risk predictions, premiums, and demand in home insurance and two lines of auto insurance would respond to unrestricted multiline experience rating. Using claims data from a large sample of households, we first estimate the variance-covariance matrix of unobserved heterogeneity in claim risk. We then show that conditioning on claims experience leads to material refinements of predicted claim rates. Lastly, we assess how the households’ demand for coverage would respond to multiline experience rating. We find that the demand response would be large

The Nature of Risk Preferences: Evidence from Insurance Choices

The authors use data on insurance deductible choices to estimate a structural model of risky choice that incorporates standard risk aversion (diminishing marginal utility for wealth) and probability distortions. They find that probability distortions--characterized by substantial overweighting of small probabilities and only mild insensitivity to probability changes--play an important role in explaining the aversion to risk manifested in deductible choices. This finding is robust to allowing for observed and unobserved heterogeneity in preferences. They demonstrate that neither Kőszegi-Rabin loss aversion alone nor Gul disappointment aversion alone can explain our estimated probability distortions, signifying a key role for probability weighting

Estimating Risk Preferences in the Field

We survey the literature on estimating risk preferences using field data. We concentrate our attention on studies in which risk preferences are the focal object and estimating their structure is the core enterprise. We review a number of models of risk preferences—including both expected utility (EU) theory and non-EU models—that have been estimated using field data, and we highlight issues related to identification and estimation of such models using field data. We then survey the literature, giving separate treatment to research that uses individual-level data (e.g., property insurance data) and research that uses aggregate data (e.g., betting market data). We conclude by discussing directions for future research

Distinguishing Probability Weighting from Risk Misperceptions in Field Data

The paper outlines a strategy for distinguishing rank-dependent probability weighting from systematic risk misperceptions in field data. Our strategy relies on singling out a field environment with two key properties: (i) the objects of choice are money lotteries with more than two outcomes and (ii) the ranking of outcomes differs across lotteries. We first present an abstract model of risky choice that elucidates the identification problem and our strategy. The model has numerous applications, including insurance choices and gambling. We then consider the application of insurance deductible choices and illustrate our strategy using simulated data

The Nature of Risk Preferences: Evidence from Insurance Choices

We use data on households' deductible choices in auto and home insurance to estimate a structural model of risky choice that incorporates "standard" risk aversion (concave utility over final wealth), loss aversion, and nonlinear probability weighting. Our estimates indicate that nonlinear probability weighting plays the most important role in explaining the data. More specifically, we find that standard risk aversion is small, loss aversion is nonexistent, and nonlinear probability weighting is large. When we estimate restricted models, we find that nonlinear probability weighting alone can better explain the data than standard risk aversion alone, loss aversion alone, and standard risk aversion and loss aversion combined. Our main findings are robust to a variety of modeling assumptions.