Unobserved Health: The Impact of Reporting Error on Health Dynamics

This paper examines the limitations of using self-reported health status (SRHS) as a direct measure of true health in dynamic economic models. Motivated by empirical evidence from major panel datasets (MEPS, HRS, and PSID) showing duration dependence and violations of the Markov property in SRHS transitions, we introduce a latent health model that accounts for transitory reporting error and individual heterogeneity. The model treats health as a continuous latent variable following an autoregressive process with mixture-distributed shocks, mapped to discrete SRHS outcomes through individual-specific reporting thresholds. Estimation results reveal strong persistence in latent health (ρ ≈ 0.9) and systematic reporting heterogeneity: older and less-educated individuals report worse SRHS for identical latent health levels. The model outperforms standard Markov specifications in capturing observed transition dynamics and demonstrates strong predictive validity through external validation using mortality data and labor force outcomes. Our framework enables more accurate policy simulations for social insurance programs and provides a template for handling noisy ordinal outcomes in other domains of applied microeconomics.