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Experimental Tests of Self-Selection and Screening in Insurance Decisions

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Experimental Tests of Self-Selection and Screening in Insurance Decisions The Geneva Papers on Risk and Insurance Theory, 24: 139–158 (1999) c 1999 The Geneva Association Experimental Tests of Self-Selection and Screening in Insurance Decisions ZUR SHAPIRA Stern School of Business, New York University, New York, NY 10012-1126 [email protected] ITZHAK VENEZIA School of Business, Hebrew University, Jerusalem, Israel [email protected] Abstract A major characteristic of insurance markets is information asymmetry that may lead to phenomena such as adverse selection and moral hazard. Another aspect of markets with asymmetric information is self-selection, which refers to the pattern of choices that individuals with different personal characteristics make when facing a menu of contracts or options. To combat problems of asymmetric information, insurance firms can use screening. That is, they can offer the clients a menu of choices and infer their characteristics from their choices. This article reports the results of several studies that examined the degree to which people behave according to the notions of self-selection and screening. Subjects played the role of either insurance buyers or sellers. The results of these studies provide partial support for the hypothesis that subjects use self-selection and screening in insurance markets. Our study also points at the importance of learning in experimental studies. In one-stage experiments where subjects did not get feedback, screening was not detected. When multistage experiments were conducted, and the subjects learned from experience and were also taught the relevant theories, their decisions were more aligned with screening. Key words: self-selection, screening, information asymmetry, insurance markets 1. Introduction Adverse selection is an incentive problem that emerges from informational asymmetry. It refers to the conjecture that people who purchase insurance are not a random sample, but rather a group of individuals with private information about their personal situations that may lead them to obtain higher than average benefits from the insurer under the policy.1 The consequences of adverse selection may be detrimental to competitive markets (cf. Riley [1979], Rothschild and Stiglitz [1976], Spence [1973], Stiglitz and Weiss [1981]). Akerlof’s [1970] classical paper demonstrated that adverse selection, caused by asymmetric information, can eliminate markets. Since then, economists have searched for ways to minimize the handicaps of asymmetric information. For instance, the use of reputation or warranties can help in used car markets, while the introduction of deductibles may reduce adverse selection in insurance markets. In some markets, however, this problem is hard to overcome. Milgrom and Roberts [1992] employ a health care example, that of pregnancy and delivery. If such an item was offered in an insurance policy, consumers most likely to purchase it would be those planning to bear children in the near future. The unobserved 140 ZUR SHAPIRA AND ITZHAK VENEZIA characteristics (or private information) of these potential consumers may have large effects on the cost of insurance policies. Indeed, private insurance companies in the U.S. rarely offer such policies. Several authors (Beliveau [1984], Dionne and Doherty [1991], Jaynes [1978], Venezia [1991], Wilson [1977]) discussing equilibrium models of insurance markets have suggested that adverse selection can be mitigated by using screening. That is, the insurer can provide a menu of alternative contracts (differing in prices and deductibles) to potential insureds in order to induce self-selection. The insurer then infers the category of the insureds by their selection of a particular policy.2 For screening to work, some self-selection constraints are necessary (see Milgrom and Roberts [1992]). These should be designed to render it disadvantageous to riskier insureds to buy the same policies as do less risky insureds. Clearly, the behavior of economic agents under the self-selection constraints is rational. In the analysis of insureds’ behavior, an assumption is usually made that the insureds under- stand these constraints, or that they behave as if they understand them. Is this assumption of rationality borne out by empirical findings on behavior in insurance markets? The answer to this question is not entirely clear. For example, in a survey of over 3000 homeowners living in either flood-prone or earthquake-prone areas of the United States, Kunreuther et al. [1978] found several striking facts that contradict rationality as a basis for insureds’ behavior. Following Kunreuther and Slovic [1978], one can argue that a better understanding of the market failure phenomenon in insurance may be achieved by examining both the attitudes and the information-processing limitations of agents in insurance markets. The present article reports the findings of a few experiments focusing on the issues of self-selection and screening. The purpose of these experiments was to investigate whether people behave in a way that reflects the instrumentality of these concepts. If people’s behavior does not reveal an understanding of and a belief in the instrumentality of self-selection and screening, perhaps other means for fighting market failure may be needed. The structure of the article is as follows. In Section 2 we provide a methodological overview of the design of the screening tests. In Section 3 we present some pilot studies, which were performed both for testing our hypotheses and for the main study. The main experiments are described in Section 4, and a conclusion follows in the last section. 2. Methodological overview We first tested whether or not subjects self-select. We chose to begin with this test because self-selection is a necessary requirement for screening. Sellers will not initiate screening unless they believe that buyers will self-select. We presented subjects with the task of purchasing an insurance policy. They were pro- vided with information classifying them into two subgroups, namely, L and H (for Low and High risk, respectively). These subgroups were distinguished by differing probability distributions of damages. We then offered the subjects two alternative insurance policies, one with a deductible and one providing full coverage (i.e., without a deductible). Subjects were requested to buy one policy. The policies and distribution of claims were so designed that the L subjects would prefer the policy with a deductible and the H ones the no-deductible policy, SELF-SELECTION AND SCREENING IN INSURANCE DECISIONS 141 and so that any risk-averse person would prefer buying one of the policies to remaining without insurance. Since an L client is less likely to suffer a loss, his/her chances of having to “pay” the deductible are lower than those of an H client. Therefore, the L client should be less willing to pay the extra price needed to purchase the full coverage policy instead of the policy with a deductible. We then examined whether or not the subjects indeed self-selected. To test for screening, we presented subjects with the task of selling an insurance policy. Each subject played the role of an insurer (seller of insurance). The sellers were different subjects from those in the buying experiment so as not to give the (self-selection) idea away. The insurer knew the probability distribution of claims of each type of buyer, as well as the proportion of each type of buyers in the particular market. The insurer, however, could not identify a priori which potential insured was L and which was H. The insurer could offer two insurance policies: one with a deductible (predetermined by us) and one without a deductible. The insurer’s task was to price these policies. This is a simplified version of a situation where screening can be employed. In this experiment, we constructed the distributions of claims so that self-selection would be easier to detect than in the buying experiment. We simplified the screening task; had we not done so, the screening task here would have been much more difficult than in the buying experiment, since the screening task already includes an extra step (i.e., the seller must conjecture that the buyers will self-select, and price accordingly). We tested the screening hypothesis in two ways. First, we randomly assigned each subject to one of two groups, or “markets.” These two groups were presented with descriptions of similar clients, L and H. The two groups differed, however, in the proportions of these two types in their markets. One group (Group A) had predominantly type H clients (75% type H clients and 25% type L clients), and the other group (Group B) had predominantly type L clients (75% type L and 25% type H clients). Under complete screening, prices should be the same in the two groups. This occurs since with complete separation only type L clients will buy the policy with a deductible, and only type H clients will buy the full coverage policy. The insurer in any group will therefore price the full coverage policy according to projected losses and demand of the type H clients only. Similarly, the deductible policy will be priced according to the projected losses and demand of type L clients only. The percentage of clients of each type in the group should therefore not affect pricing. We thus tested for screening by checking whether prices differed between the two groups, thereby analyzing whether the proportions of each type of clients affected pricing.3 This method may be quite a stringent test, since it requires the assumption of complete self-selection. Sellers, however, may expect that some of the buyers will not detect the self- selection opportunity and that both policy F (full coverage) and policy D (with a deductable) will be purchased by the two types of clients (that is, a pooling equilibrium may emerge). In this case, the prices will depend on the proportions of type L and type H clients in the market. Another way of testing the screening hypothesis is by examining how many sellers choose prices that induce self-selection.
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