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Insurance without Commitment: Evidence from the ACA Marketplaces (with Rebecca Diamond, Tim McQuade, Petra Persson)
Abstract: We study the dynamics of participation and health care consumption in the Affordable Care Act’s health insurance marketplaces. Unlike other health insurance contexts, we find individuals commonly drop coverage midyear; roughly 30% of enrollees exit within nine months of sign-up. These dropouts spend more on health care while covered than in the months before sign-up or after exit. We model the consequences of drop-out on equilibrium premiums and consumer welfare. While dropouts generate a type of adverse selection, the welfare effect from their participation is ambiguous and depends on the relative spending per month of part-year vs. full-year enrollees. Using our empirical model, we quantify changes in premiums and welfare after the imposition of penalties targeting drop-out. We find that overall welfare declines with a ban on drop-out: young and healthy consumers---those who can more easily re-time their health spending, as well as those who value the option to exit---choose to forego coverage entirely, leading to higher average costs among the insured population and thus higher premiums.
Efficient Provision of Experience Goods: Evidence from Antidepressant Choice
Abstract: In the market for medical care, physicians often face uncertainty about how a newly diagnosed patient will respond to available treatments. I design a framework to analyze how price and promotion influence the learning process as the patient and physician jointly search for the most effective treatment. The dynamic model I employ accommodates large choice sets and permits learning to be correlated within clusters of choices. Applying this model to depression care, I ask how the design of a health insurance plan, including the required patient out-of-pocket costs by drug, might interact with the physician’s learning process. In the data, patient costs largely correspond to the drug’s wholesale cost. In contrast, I design a new drug pricing schedule that lowers the patient cost for those drugs that the model suggests are best to sample early in the search process. By using these price incentives to redirect the search process, I find physicians identify the optimal treatment faster, leading to lower overall costs, improved adherence, and ultimately better patient health.
Physician vs. Patient Incentives in Prescription Drug Choice
Revise and resubmit, American Economic Journal: Economic Policy
Abstract: In response to rising health spending, public and private insurers use two mechanisms to direct spending toward more valuable treatments: “demand-side” incentives, which impose costs on the patient to limit moral hazard, and “supply-side” incentives, which adjust the physician’s compensation to discourage spending. Using variation in patients’ and physicians’ exposure to incentives, I identify important differences in cost and health outcomes under these two mechanisms. Demand-side cost-sharing discourages both initial treatment and later adherence. Payment reforms drive physicians to substitute drug care and specialist referrals for office visits. I discuss the implications of these outcomes for optimal insurance design.
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.