Quantitative assessments of the relationship between health and medical treatment are of great importance to policy makers. To overcome endogeneity problems we formulate and estimate a tractable dynamic factor model where observed health outcomes are driven by the individual's latent health. The dynamics of latent health reflects both exogenous health deterioration and endogenous health investments. Our model allows us to investigate the effect of medical treatment on current health, as well as on future medical treatment and health outcomes. We estimate the model by maximum simulated likelihood and minimum distance methods using a rich longitudinal data set from Italy obtained by merging a number of administrative archives. These data contain detailed information on medical drug purchase, hospitalization, and mortality for a representative sample of elderly hypertensive patients. Our findings show that the observed autocorrelation in medical treatment reflects both permanent and time-varying observed and unobserved heterogeneity. They also show that medical drug purchase significantly maintains future health levels and prevents transitions to worse health. This suggests that policies aimed at increasing the awareness and the compliance of hypertensive patients help reduce cardiovascular risks and consequent hospitalization and mortality.
Keywords: dynamic panel data models; factor models; health dynamics; latent variable models; maximum simulated likelihood; minimum distance method.
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