Working Papers

Take-Up, Drop-Out, and Spending in ACA Marketplaces (with Rebecca Diamond, Tim McQuade, Petra Persson)

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 penalties targeted at drop-out consumers, in addition to an individual mandate, can affect premiums and participation in the individual market.


NBER Working Paper No. 24668

Accounting for Structural and Measurement Error in Binary Choice Problems: A Moment Inequality Approach (with Eduardo Morales)

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.

Online Appendix
Matlab code for simulation exercise