Helping Consumers Know Themselves

In standard models with asymmetric information, the parties involved are assumed to have private information about their own characteristics. In the health insurance market, for example, customers are typically assumed to know more about their health status than insurers do, and if the customers use this information in deciding whether to buy insurance, we have a classic case of adverse selection. Modern data-gathering technologies, however, can reverse this situation. For example, because cell-phone providers keep and analyze detailed records, they can know more about a consumer’s expected usage than the customer herself does. Similarly, a credit card company may know more about a customer’s probability of incurring a late fee than the customer herself. We explore the consequences of this reversal in the information asymmetry.1 A bare-bones model examines how providing consumers with information about their own usage affects prices and welfare. We first establish the rather obvious result that, taking prices as given, consumers benefit from having better information about their own usage: they can choose the pricing plans that are more suitable for them. Recent work on Medicare Part D has shown this effect can be sizable (Jeffrey Kling, Sendhil Mullainathan, Eldar Shafir, Lee Vermuelen, and Marian Wrobel, 2010). When seniors were given information about plan prices based on their own prior drug utilization, the rate of plan switching went up by 20% and expenditures went down by at least 14% per switcher. ∗Kamenica: University of Chicago Booth School of Business, 5807 S. Woodlawn Ave, Chicago, IL 60637, [email protected]. Mullainathan: Harvard University Economics Department, 1805 Cambridge Street, Cambridge, MA 02138, [email protected]. Thaler: University of Chicago Booth School of Business, 5807 S. Woodlawn Ave, Chicago, IL 60637, [email protected]. We thank David Laibson for helpful comments. 1Meglena Jeleva and Bertrand Villeneuve (2004) examine a model where insurance companies are better at predicting risk than consumers, but consumers have residual private information about their risk. Their analysis, however, is quite different from ours: they focus on identifying the properties of a second-best screening contract that could not arise without firm’s private information.