Health care utilization and self-assessed health: specification of bivariate models using copulas

The discernment of relevant factors driving health care utilization constitutes one important research topic in Health Economics. This issue is frequently addressed through specification of regression models for health care use (y – often measured by number of doctor visits) including, among other covariates, a measure of self-assessed health (sah). However, the exogeneity of sah has been questioned, due to the possible presence of unobservables influencing y and sah, and because individuals’ health assessments may depend on the quantity of medical care received. This paper circumvents the potential endogeneity of sah and its associated consequences within conventional regression models (namely the need to find valid instruments) by adopting a full information approach, with specification of bivariate regression models for the discrete variables (sah,y). The approach is implemented with copula functions, which enable separate consideration of each variable margin and their dependence structure. Estimation of these models is through maximum likelihood, with cross-section data from the Portuguese National Health Survey of 1998/99. Results indicate that estimates of regression parameters do not vary much between different copula models. The dependence parameter estimate is negative across joint models, which suggests evidence of simultaneity of (sah,y) and(This abstract was borrowed from another version of this item.)