Endogenous Health Groups and Heterogeneous Dynamics of the Elderly

Health dynamics and its associated medical and care costs have been identified by the macro literature as a major concern of the elderly. Due to its multidimensionality, however, a dicult task faced by researchers is to summarize health parsimoniously into a single state variable. We propose a panel Markov switching model to identify patterns of health heterogeneity where individuals can move across health groups as they age. To estimate the model, we use Markov chain Monte Carlo techniques to exploit information from both the crosssectional and time series dimensions. We identify health groups for individuals in the Health and Retirement Survey for the US. Results show that there exists four clearly diVerentiated groups depending on individual’s physical and mental disabilities. Furthermore, we show that health groups outperform other measures of health commonly used in the literature at explaining the variance in the use of nursing homes, home health care, out of pocket medical expenses and predicted mortality.