Entry Barriers in Provider Markets: Evidence from Dialysis Certificate-of-need Programs

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.

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Estimating event studies when units experience multiple events

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.

[link here]

Selected work in progress

Shutting the door behind you: certificate-of-need programs and pre-emption in the U.S. dialysis industry

Is it time yet? Measuring the negative externalities of ambulance diversions

What happens when resources move by quotas don't? (joint work with Bozidar Plavsic)

Do mandatory minimum standards influence charity care expenditures? Evidence from Illinois