Accuracy of self-reported private health insurance coverage - Ha Trong Nguyen; Huong Thu Le; Luke Connelly; Francis Mitrou (Global Labor Organization (GLO), 2022)
Studies on health insurance coverage often rely on measures self-reported by respondents, but the accuracy of such measures has not been thoroughly validated. This paper is the first to use linked Australian National Health Survey and administrative population tax data to explore the accuracy of self-reported private health insurance (PHI) coverage in survey data. We find that 9% of individuals misreport their PHI coverage status, with 5% of true PHI holders reporting that they are uninsured and 16% of true non-insured persons self-identifying as insured. Our results show reporting errors are systematically correlated with individual and household characteristics. Our evidence on the determinants of errors is supportive of common reasons for misreporting. We directly investigate biases in the determinants of PHI enrolment using survey data. We find that, as compared to administrative data, survey data depict a quantitatively different picture of PHI enrolment determinants, especially those capturing age, language proficiency, labour force status or the number of children. We also show that PHI coverage misreporting is subsequently associated with misreporting of reasons for purchasing PHI, type of cover and length of cover.
Keywords: C81; I13; 330; Health Insurance; Measurement Error; Administrative Data; Survey Misreporting; Linked Data; Australia