Abstract

I consider the problem of evaluating the effect of a health care reform on the demand for doctor visits when the effect is potentially different in different parts of the outcome distribution. Quantile regression is a useful technique for studying such heterogeneous treatment effects. Recent progree has been made to extend such methods to applications with a count dependent variable. An analysis of a 1997 health care reform in Germany shows the benefit of the approach: lower quantiles, such as the 25 percent quantile, fell by substantially larger amounts than what would have been predicted based on Poisson or negative binomial models.