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Irrational Economist_ Making Decisions in a Dangerous World - Erwann Michel-Kerjan [73]

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The View from One Year Out.” Risk Management and Insurance Review 11, no. 1: 51-63.

Kunreuther, H., and C. K. Hsee (2000). “The Affection Effect in Insurance Decisions.” Journal of Risk and Uncertainty 20, no. 2: 149-159.

Kunreuther, H., and M. V. Pauly (2000). NBER Reporter, March 22.

Pagán, J. A., and M. V. Pauly (2006). “Community-Level Uninsurance and Unmet Medical Needs of Insured and Uninsured Adults.” Health Services Research 41, no. 3: 788-803.

Schlesinger, H., and N. Doherty (1985). “Incomplete Markets for Insurance: An Overview.” Journal of Risk and Insurance 52: 402-423.

17

The Hold-Up Problem

Why It Is Urgent to Rethink the Economics of Disaster Insurance Protection

W. KIP VISCUSI

As other contributors to this book have suggested, how people make decisions involving risk and uncertainty and how economists think people should make these decisions are often quite different matters.

For many decisions that we make the stakes are modest, so whether we stray a bit from economic efficiency norms may be of professional interest to economists but of little societal import. When the stakes are large, however, the soundness of decisions truly matters. In situations involving risks posed by disasters and catastrophic events, mistaken choices may impose considerable costs both on the individual and on society.

For example, although for decades economists have devoted substantial attention to the economic evaluation of the merits of flood control projects and other public works designed to offer protection from natural disasters, comparatively little attention has been given to individual decisions that affect the losses these disasters impose. Some economists, armed with expected utility theory and the economic analysis of insurance (both of which are discussed in previous chapters of this book), seem to rest assured that people perceive the risks accurately and make sound decisions regarding insurance and self-protection.

THE CASE OF FLOOD INSURANCE DECISIONS


It was in this context that Howard Kunreuther and his colleagues produced a landmark empirical investigation in the 1970s that documented the failures of individual insurance decisions.1 I would like to use this study as an illustrative example and source of information here. The original study focused on risks posed by earthquakes and floods. These hazards pose potentially large losses for which rational people should find actuarially fair insurance policies attractive. Because disaster insurance is heavily subsidized by the government, purchasing such insurance should be especially attractive. However, even under these favorable cost conditions, people often chose not to purchase the insurance. And indeed, they usually didn’t even know the basic parameters of the available insurance opportunities. In the case of flood insurance, a 1976 study found that for those who were insured, 17 percent of consumers did not know the cost of their flood insurance and 44 percent didn’t know the size of their deductible. Among those who were not insured, 68 percent didn’t know the cost of the insurance and 82 percent didn’t know the possible choices of the deductible. The failure of people to purchase insurance, coupled with this failure of knowledge that is counter to the usual economic assumption of perfect information, led to this strong conclusion: “The expected utility model, as traditionally used by economists, provides relatively little insight into the individual choice process regarding the purchase of insurance.”2

Economists’ expected utility theory predicts that the subsidized insurance should be more attractive for people who expect greater financial damage from a severe flood. Among those who believed their damage claims would be zero, 24 percent purchased insurance.3 Overall, 46 percent of those expecting finite damage claims not exceeding $10,000 purchased insurance, with the percentages rising to 61 percent for those expecting a loss of between $10,001 and $30,000 and to 67 percent for those expecting a loss above $30,000. Likewise,

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