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

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.