Private information in healthcare utilization: specification of a copula-based hurdle model

type="main" xml:id="rssa12065-abs-0001"> We study whether individuals' private information on health risk affects their medical care utilization. The presence of such information asymmetry is critical to the optimal payment design in healthcare systems. To do so, we examine the relationship between self-perceived health status and healthcare expenditures. Because of simultaneity, we employ a copula regression to model jointly the mixed outcomes, with the association parameter capturing the residual dependence conditional on covariates. The semicontinuous nature of healthcare expenditures leads to a two-part interpretation of private health information: the hurdle component assesses its effect on the likelihood of using medical care services, and the conditional component quantifies its effect on the expenditures given consumption of care. The methodology proposed is applied to a survey data set of a sample of the US civilian non-institutionalized population to test and quantify the effects of private health information. We find evidence of adverse selection in the utilization of various types of medical care services.