An engaging and accessible examination of what ails insurance markets—and what to do about it—by three leading economists.

Why is dental insurance so crummy? Why is pet insurance so expensive? Why does your auto insurer ask for your credit score? The answer to these questions lies in understanding how insurance works. Unlike the market for other goods and services—for instance, a grocer who doesn’t care who buys the store’s broccoli or carrots—insurance providers are more careful in choosing their customers, because some are more expensive than others.

Unraveling the mysteries of insurance markets, Liran Einav, Amy Finkelstein, and Ray Fisman explore such issues as why insurers want to know so much about us and whether we should let them obtain this information; why insurance entrepreneurs often fail (and some tricks that may help them succeed); and whether we’d be better off with government-mandated health insurance instead of letting businesses, customers, and markets decide who gets coverage and at what price. With insurance at the center of divisive debates about privacy, equity, and the appropriate role of government, this book offers clear explanations for some of the critical business and policy issues you’ve often wondered about, as well as for others you haven’t yet considered.

Researchers use Nielsen Homescan data, which provide detailed food-purchase information from a panel of U.S. households, to address a variety of important research topics. However, some question the credibility of the data since the data are self-recorded and the recording process is time-consuming. Matching purchase records from 2004 Homescan data with data obtained from a large grocery retailer, it is evident that quantities purchased are reported more accurately in Homescan than are prices. Many of the price differences may be driven by the way Nielsen imputes prices: when available, Nielsen uses store-level prices instead of the actual price paid by the household. There are also differences by household type in the tendency to make mistakes that are correlated with demographic variables. However, the fraction of variance explained by the documented recording errors is in line with other research data sets for which cross-validation studies have been conducted.