Research
Working papers
Provider payment incentives: evidence from the U.S. hospice industry [new] [with Norma B. Coe]
Key subjects: provider-induced demand; gaming; consolidation; hospice; Medicare; policy; non-linear program design; health care utilization
Abstract: Moral hazard and provider-induced demand may contribute to overutilization of scarce health care resources. The U.S. health care system includes several compensatory cost-containment mechanisms, but their effects depend on how patients and providers respond. We investigate hospice programs' responses to a cap in the Medicare hospice benefit on their average annual payments per patient. We estimate their intensive margin responses to the cap by leveraging variation in cap-related financial incentives generated by the policy’s nonlinear design and the transition between fiscal years. We find that programs on track to exceed the cap in the last three months of a fiscal year raise their enrollment rates by 5.7% and their live discharge rates by 4.3% on average, reducing their cap liabilities. The marginal enrollees have longer average remaining lifetimes and are less likely to have been recently hospitalized. Their hospice spells are also more likely to be fragmented by subsequent live discharges. On the extensive margin, we find that cap liabilities are associated with terminations of Medicare provider certification numbers, suggesting that the cap impacts market structure. Current policy discussions about reducing the cap should consider its potential effect on market structure.
[manuscript here (NBER Working Paper #31691)]
Entry Barriers in Provider Markets: Evidence from Dialysis Certificate-of-need Programs
Key subjects: entry barriers; competition; pre-emption; incumbent advantage; certificate-of-need; dialysis; access-to-care
Abstract: Can entry barriers in health care provider markets raise welfare? In the U.S., proponents of regulatory entry barriers called CON programs claim that they reduce waste by limiting "unnecessary" entry. I examine CON programs in the dialysis industry, where their effects on market structure, access, health, costs, and welfare are poorly understood, and where patients are sensitive to access and quality. I combine quasi-experimental policy variation in low population areas with a structural model of patient preferences to find that marginal entrants improved access significantly, reduced hospitalization rates, and generated for patients the utility value of traveling 275-344 fewer miles per month; but there is evidence that they contributed even more to fixed costs. Using policy variation throughout North Carolina, I also find evidence that the NC dialysis CON program created a mechanism through which incumbents could block potential entrants by expanding in tandem with their local patient populations. Taken together, my findings suggest that stronger regulatory entry barriers in low population areas may raise total welfare at patients' expense—but they also amplify concerns that CON programs dampen competition statewide.
Estimating event studies when units experience multiple events
Key subjects: Econometric theory; difference-in-differences; event study; multiple events; matching
Abstract: An event study is an empirical framework for measuring the impact of an event over time using observational data. Under no anticipation and parallel trends assumptions, difference-in-differences are known to identify the event's average treatment effect on the treated when units experience one event at most. In this paper, I introduce a new event study framework to accommodate settings where units may experience multiple events. I introduce a matching estimator which consistently and transparently estimates the average treatment effect on the treated of a single event under generalizations of the conventional no anticipation and parallel trends assumptions. I show that the matching estimator is equivalent to a weighted least squares estimator for a particular set of weights. I also introduce a parallel pre-trends test which can be used to scrutinize these assumptions in the usual sense. Finally, I demonstrate in a series of Monte Carlo simulations that the estimator and parallel pre-trends test work well for a wide range of treatment effects, including dynamic, non-stationary, and history-dependent treatment effects.