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

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  • Juergen Jung

    (Department of Economics, Towson University)

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 transition probability matrices between health states over the lifecycle from age 20–95. We compare non-parametric counting methods and parametric methods where we control for individual characteristics as well as time and cohort effects. We align two year transition probabilities from HRS with one year transition probabilities in MEPS using a stochastic root method assuming a Markov structure. 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, the Markov assumption, and other modeling issues that can arise with categorical outcome variables.

Suggested Citation

  • Juergen Jung, 2020. "Estimating Transition Probabilities Between Health States Using U.S. Longitudinal Survey Data," Working Papers 2020-06, Towson University, Department of Economics, revised Jun 2021.
    • Handle: RePEc:tow:wpaper:2020-06
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    More about this item

    Keywords

    Lifecycle profiles of health transition probabilities; Medical Expenditure Panel Survey (MEPS); Health and Retirement Study (RAND-HRS); Markov health transition matrices; conditional health transition probabilities; age-time-cohort effects.;
    All these keywords.

    JEL classification:

    • I10 - Health, Education, and Welfare - - Health - - - General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • D15 - Microeconomics - - Household Behavior - - - Intertemporal Household Choice; Life Cycle Models and Saving

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