In most US health insurance markets, plans face strong incentives to "upcode" the patient diagnoses they report to the regulator, as these affect the risk-adjusted payments plans receive. We show that enrollees in private Medicare plans generate 6% to 16% higher diagnosis-based risk scores than they would under fee-for-service Medicare, where diagnoses do not affect most provider payments. Our estimates imply that upcoding generates billions in excess public spending and significant distortions to firm and consumer behavior. We show that coding intensity increases with vertical integration, suggesting a principal-agent problem faced by insurers, who desire more intense coding from the providers with whom they contract.

We show in two natural experiments that default rules in Medicare Part D have large, persistent effects on enrollment and drug utilization of low-income beneficiaries. The implications of this phenomenon for welfare and optimal policy depend on the sensitivity of passivity to the value of the default option. Using random assignment to default options, we show that beneficiary passivity is extremely insensitive, even when enrolling in the default option would result in substantial drug consumption losses. A third natural experiment suggests that variation in active choice is driven by random transitory shocks rather than the inherent attentiveness of some beneficiaries.

Insurance markets often feature consumer sorting along both an extensive margin (whether to buy) and an intensive margin (which plan to buy). We present a new graphical theoretical framework that extends a workhorse model to incorporate both selection margins simultaneously. A key insight from our framework is that policies aimed at addressing one margin of selection often involve an economically meaningful trade-off on the other margin in terms of prices, enrollment, and welfare. Using data from Massachusetts, we illustrate these trade-offs in an empirical sufficient statistics approach that is tightly linked to the graphical framework we develop.