The effects of self-assessed health: Dealing with and understanding misclassification bias

Abstract

Self-assessed health (SAH) is often used in health econometric models as the key explanatory variable or as a control variable. However, there is evidence questioning its test-retest reliability, with up to 30% of individuals changing their response. Building on recent advances in the econometrics of misclassification, we develop a way to consistently estimate and account for misclassification in reported SAH by using data from a large representative longitudinal survey where SAH was elicited twice. From this we gain new insights into the nature of SAH misclassification and its potential for biasing health econometric estimates. The results from applying our approach to nonlinear models of long-term mortality and chronic morbidities reveal that there is substantial heterogeneity in misclassification patterns. We find that adjusting for misclassification is important for estimating the impact of SAH. For other explanatory variables of interest, we find significant but generally small changes to their estimates when SAH misclassification is ignored.

Keywords: Chronic conditions; Discrete and limited dependent variables; Measurement error; Misreporting; Mortality; Multinomial regressor; Subjective health.