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While the topics of risk aversion and utility theory have been discussed extensively in the academic literature on risk and insurance, this literature does not include a pedagogical discussion that is widely accessible for classroom use. This article provides a practical introduction to risk aversion that is designed for readers with little prerequisite course work in economics or statistics. We describe a simple model of insurance demand that can be applied to the property, liability, life, and health insurance markets. We also demonstrate how risk aversion affects a variety of real-life insurance decisions made under conditions of uncertainty, including how much the market will bear to pay for insurance administrative expenses and how demand varies for different types of auto insurance coverage. Exercises and practice problems are provided so that readers can test their mastery of the concepts presented in the article. An instructional note on using this article to teach risk aversion in the classroom is also provided.

We demonstrate how innovations in insurance risk classification can lead to adverse selection, or cream skimming, against insurers that are slow to adopt such pricing innovations. Using a model in which insurers with insufficient pricing data cannot differentiate between low-and high-risk policyholders and therefore charge both the same premium, we show how innovative insurers develop new risk classification data to identify overcharged low-risk policyholders and attract them from rival insurers with reduced prices. Less innovative insurers thus insure a growing percentage of high-risk customers, resulting in adverse selection attributable to their informational disadvantage. Next, we examine two cases in which "Big Data" innovations in risk classification led to concerns about cream skimming among U.S. auto insurers. First, we track the rapid adoption of credit-based insurance scores as pricing variables in personal auto insurance markets. Second, we examine the growing popularity of usage-based insurance programs like telematics, plans in which insurers use data on policyholders' actual driving behavior to set prices that attract low-risk customers. Issues associated with the execution of such pricing strategies are discussed. In both cases, we document how rival insurers quickly adopt successful innovations to reduce their exposure to adverse selection.David Cather is in the

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