Arbeitspapier

Accuracy of self-reported private health insurance coverage

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.

Sprache
Englisch

Erschienen in
Series: GLO Discussion Paper ; No. 1215

Klassifikation
Wirtschaft
Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
Health Insurance, Public and Private
Thema
Health Insurance
Measurement Error
Administrative Data
Survey Misreporting
Linked Data
Australia

Ereignis
Geistige Schöpfung
(wer)
Nguyen, Ha Trong
Le, Huong Thu
Connelly, Luke
Mitrou, Francis
Ereignis
Veröffentlichung
(wer)
Global Labor Organization (GLO)
(wo)
Essen
(wann)
2022

Handle
Letzte Aktualisierung
10.03.2025, 11:42 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Nguyen, Ha Trong
  • Le, Huong Thu
  • Connelly, Luke
  • Mitrou, Francis
  • Global Labor Organization (GLO)

Entstanden

  • 2022

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