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Biases in health expectancies due to educational differences in survey participation of older Europeans: It’s worth weighting for

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  • Sonja Spitzer

    (International Institute for Applied Systems Analysis (IIASA))

Abstract

Health expectancies are widely used by policymakers and scholars to analyse the number of years a person can expect to live in good health. Their calculation requires life tables in combination with prevalence rates of good or bad health from survey data. The structure of typical survey data, however, rarely resembles the education distribution in the general population. Specifically, low-educated individuals are frequently underrepresented in surveys, which is crucial given the strong positive correlation between educational attainment and good health. This is the first study to evaluate if and how health expectancies for 13 European countries are biased by educational differences in survey participation. To this end, calibrated weights that consider the education structure in the 2011 censuses are applied to measures of activity limitation in the Survey of Health, Ageing and Retirement in Europe. The results show that health expectancies at age 50 are substantially biased by an average of 0.3 years when the education distribution in the general population is ignored. For most countries, health expectancies are overestimated; yet remarkably, the measure underestimates health for many Central and Eastern European countries by up to 0.9 years. These findings highlight the need to adjust for distortion in health expectancies, especially when the measure serves as a base for health-related policy targets or policy changes.

Suggested Citation

  • Sonja Spitzer, 2020. "Biases in health expectancies due to educational differences in survey participation of older Europeans: It’s worth weighting for," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(4), pages 573-605, June.
    • Handle: RePEc:spr:eujhec:v:21:y:2020:i:4:d:10.1007_s10198-019-01152-0
    DOI: 10.1007/s10198-019-01152-0
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    References listed on IDEAS

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    Cited by:

    1. Claudia Reiter & Sonja Spitzer, 2021. "Well-being in Europe: decompositions by country and gender for the population aged 50+," Vienna Yearbook of Population Research, Vienna Institute of Demography (VID) of the Austrian Academy of Sciences in Vienna, vol. 19(1), pages 383-415.
    2. André Grow & Daniela Perrotta & Emanuele Del Fava & Jorge Cimentada & Francesco Rampazzo & Sofia Gil‐Clavel & Emilio Zagheni & René D. Flores & Ilana Ventura & Ingmar Weber, 2022. "Is Facebook's advertising data accurate enough for use in social science research? Insights from a cross‐national online survey," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 343-363, December.
    3. Tarek Ben Hassen & Hamid El Bilali & Mohammad S. Allahyari & Islam Mohamed Kamel & Hanen Ben Ismail & Hajer Debbabi & Khaled Sassi, 2022. "Gendered Impacts of the COVID-19 Pandemic on Food Behaviors in North Africa: Cases of Egypt, Morocco, and Tunisia," IJERPH, MDPI, vol. 19(4), pages 1-13, February.
    4. Dimiter Philipov & Sergei Scherbov, 2020. "Subjective length of life of European individuals at older ages: Temporal and gender distinctions," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-14, March.
    5. André Grow & Daniela Perrotta & Emanuele Del Fava & Jorge Cimentada & Francesco Rampazzo & B. Sofia Gil-Clavel & Emilio Zagheni & René D. Flores & Ilana Ventura & Ingmar G. Weber, 2021. "How reliable is Facebook’s advertising data for use in social science research? Insights from a cross-national online survey," MPIDR Working Papers WP-2021-006, Max Planck Institute for Demographic Research, Rostock, Germany.

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    More about this item

    Keywords

    Activity limitations; Education and inequality; Health expectancies; Survey participation; Iterative proportional fitting (IPF); Survey of Health; Ageing and Retirement in Europe (SHARE);
    All these keywords.

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being
    • J14 - Labor and Demographic Economics - - Demographic Economics - - - Economics of the Elderly; Economics of the Handicapped; Non-Labor Market Discrimination

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