Estimating Markov Transition Probabilities Between Health States Using U.S. Longitudinal Survey Data

Abstract

We use data from two representative U.S. household surveys, the Medical Expenditure Panel Survey (MEPS) and the Health and Retirement Study (Rand-HRS) to estimate Markov transition probability matrices between health states over the lifecycle from age 20–95. We use non-parametric and parametric methods and control for individual characteristics such as age, gender, race, education, income as well as cohort effects. We align two year transition probabilities from HRS with one year transition probabilities in MEPS using a stochastic root method. We find that the non-parametric counting method and the regression specifications based on ordered logit models produce similar results over the lifecycle. However, the counting method overestimates the probabilities of transitioning into bad health states. In addition, we find that young women have worse health prospects than their male counterparts but once individuals get older, being female is associated with transitioning into better health states with higher probabilities than men. We do not find significant differences of the conditional health transition probabilities between African Americans and the rest of the population. We also find that the lifecycle patterns are stable over time. Finally, we discuss issues with controlling for time effects, sample attrition, and other modeling issues that can arise with categorical outcome variables.