The Affordable Care Act (ACA) established health insurance marketplaces where consumers can buy individual coverage. Leveraging novel credit card and bank account micro-data, we identify new enrollees in the California marketplace and measure their health spending and premium payments. Following enrollment, we observe dramatic spikes in individuals' health care consumption. We also document widespread attrition, with roughly half of all new enrollees exiting coverage before the end of the plan year. Some enrollees who drop out re-time discretionary health spending to the months of insurance coverage. This drop-out behavior can generate a new type of adverse selection: insurers face high costs relative to the premiums collected when they enroll strategic consumers. We develop a model to illustrate how this pattern of attrition can undermine market stability and lead to substantial price increases, even absent differences in enrollees' underlying health risks. Further, using data on plan price increases, we show that insurers largely shift the costs of attrition to non-drop-out enrollees, whose inertia generates low price sensitivity. Our results suggest that campaigns to improve use of social insurance may be more effective when they jointly target take-up and attrition.
Forthcoming, Quarterly Journal of EconomicsRead More
Revise and resubmit, Quarterly Journal of EconomicsRead More
Revise and resubmit, American Economic Journal: Economic PolicyRead More
Abstract: How do physicians learn about new treatments? Using the setting of antipsychotic treatment choice, we measure the relative importance of two key pathways: observational learning, in which physicians update their knowledge from public signals, and learning-by-doing, in which the physician relies on her own private experience treating patients. To do so, we exploit two sources of exogenous shocks to physicians’ information. First, in 2007, regulators issued new guidance in the antipsychotic market, approving one drug as a secondary treatment for depression and warning that another posed a substantial risk of side effects. Second, in 2006, the introduction of Medicare Part D shocked the typical physician’s patient composition, with more patients obtaining private insurance coverage. Examining the time periods surrounding the drug advisories, we find physicians with greater patient volume and with more specialized training learn about product quality sooner. Public warnings primarily affect the decisions of the least experienced and least specialized physicians. Importantly, among physicians seeing few patients, recent graduates react more quickly and robustly to the advisories following their publication. We further show that exploiting variation in experience stemming from Medi- care’s insurance expansion is necessary to distinguish the effect of volume from unobserved factors, such as physician quality.
American Economic Review: Papers & Proceedings 2015, 105(5): 120–125Read More
Abstract: Many economic decisions involve a binary choice - for example, when consumers decide to purchase a good or when firms decide to enter a new market. In such settings, agents’ choices often depend on imperfect expectations of the future payoffs from their decision (expectational error) as well as factors that the econometrician does not observe (structural error). In this paper, we show that expectational error, under an assumption of rational expectations, is a source of classical measurement error, and we propose a novel moment inequality estimator that accounts for both expectational error and structural error in a binary choice model. With simulated data and Chilean firm-level customs data, we illustrate the identifying power of our inequalities and show the biases that arise when one ignores either source of error. We use the customs data to estimate the fixed costs exporters face when entering a new market.