Empirical Applications of Contract Theory: the Case of Insurance Contracts

University Of Chicago, Chicago IL

Investigators

Abstract

In the last twenty years, contract theory has developed at a rapid pace. But, until recently at least, empirical applications have lagged behind. This project is aimed at filling this gap. It is based on the view that insurance provides a nearly ideal field for empirical work on contracts. Individual insurance contracts (automobile, housing, health, life, etc.) are largely standardized. The insurer's information is accessible, and can generally be summarized through a reasonably small number of quantitative or qualitative indicators. The 'performance' - whether it represents the occurrence of an accident, its cost, or some level of expenditure - is very precisely recorded in the firms' files. Finally, insurance companies frequently use data bases containing several millions of contracts, which enables testing of most predictions of contract theory in a detailed way, using standard econometric tools. Three directions will be investigated. The theory has emphasized the role of information asymmetries in the design of optimal contracts, and more specifically the distinction between adverse selection and moral hazard. Adverse selection arises when one party to the contractual relationship - say, the subscriber to an insurance contract - has better information than the other party - say, the insurer - about some parameters that are relevant for the relationship. Moral hazard occurs when the accident probability is not exogenous, but depends on some decision (e.g., effort of prevention) that is made by the subscriber but cannot be monitored by the insurer. In general, different insurance contracts provide different incentives, hence result in different observed accident rates. The empirical distinction between moral hazard and adverse selection is crucial, in particular because the implications in terms of welfare and regulation are totally different. However, in many cases, moral hazard and adverse selection are very difficult to disentangle. The first aim of the present project is to show how the empirical distinction between moral hazard and adverse selection can be implemented when data relative to the dynamics of the relationship are available. A second goal is to study the structure of insurance pricing in the industry. Given the data available, we shall be able to estimate the total risk distribution of each insuree, as a function of her characteristics (age, type of car, location, etc.). This involves not only the probability of an accident, but also its expected severity and the corresponding costs. This regression, in other words, should provide a very accurate description of the 'product' sold by the insurance company. In a second stage, it can be related to the pricing policy. One may check whether the premium charged to a particular consumer only depends on her risk, or whether it may for any given level of risk vary with other characteristics, such as age, sex, etc. Conclusions can be drawn on the industrial organization of the field. The third research direction adopts a more normative viewpoint. A striking feature of modern biology is the increasing ability to identify the genes that either are responsible for or tend to create predispositions to various diseases. This possibility will lead to a spectacular amelioration of prevention and treatments. However, the availability of more precise information on the risk destroys insurance possibilities, which is welfare decreasing. A first task is to obtain a first evaluation of the associated welfare loss. This requires, in particular, an evaluation of the benefit provided by insurance coverage. One purpose of the study (and a quite difficult one) is to provide an preliminary evaluation of this order of magnitude. The most radical solution proposed involves a regulation that would strictly prohibit the use of genetic testing by insurance companies. Such a proposal however requires a thorough investigation. From an economist's point of view, it amounts to introducing a strong adverse selection component. Agents will presumably be informed of their risk, at least if (as it will probably be the case) individuals have free access to genetic testing. The problem, now, is to assess the impact of this asymmetry on the market for health or life insurance. This is a crucial issue, if only because the solution might well reveal worse than the initial problem. If the final outcome is a global collapse of the insurance markets at stake, everybody (including the population at risk) will end up in a much worse situation. The last goal of the project is only to provide some preliminary elements for assessing the scope and