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The hierarchical age–period–cohort model: Why does it find the results that it finds?

Author

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  • Andrew Bell

    (University of Sheffield)

  • Kelvyn Jones

    (University of Bristol)

Abstract

It is claimed the hierarchical-age–period–cohort (HAPC) model solves the age–period–cohort (APC) identification problem. However, this is debateable; simulations show situations where the model produces incorrect results, countered by proponents of the model arguing those simulations are not relevant to real-life scenarios. This paper moves beyond questioning whether the HAPC model works, to why it produces the results it does. We argue HAPC estimates are the result not of the distinctive substantive APC processes occurring in the dataset, but are primarily an artefact of the data structure—that is, the way the data has been collected. Were the data collected differently, the results produced would be different. This is illustrated both with simulations and real data, the latter by taking a variety of samples from the National Health Interview Survey (NHIS) data used by Reither et al. (Soc Sci Med 69(10):1439–1448, 2009) in their HAPC study of obesity. When a sample based on a small range of cohorts is taken, such that the period range is much greater than the cohort range, the results produced are very different to those produced when cohort groups span a much wider range than periods, as is structurally the case with repeated cross-sectional data. The paper also addresses the latest defence of the HAPC model by its proponents (Reither et al. in Soc Sci Med 145:125–128, 2015a). The results lend further support to the view that the HAPC model is not able to accurately discern APC effects, and should be used with caution when there appear to be period or cohort near-linear trends.

Suggested Citation

  • Andrew Bell & Kelvyn Jones, 2018. "The hierarchical age–period–cohort model: Why does it find the results that it finds?," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(2), pages 783-799, March.
    • Handle: RePEc:spr:qualqt:v:52:y:2018:i:2:d:10.1007_s11135-017-0488-5
    DOI: 10.1007/s11135-017-0488-5
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    References listed on IDEAS

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    3. Sumaira Mubarik & Fang Wang & Saima Shakil Malik & Fang Shi & Yafeng Wang & Nawsherwan & Chuanhua Yu, 2020. "A Hierarchical Age–Period–Cohort Analysis of Breast Cancer Mortality and Disability Adjusted Life Years (1990–2015) Attributable to Modified Risk Factors among Chinese Women," IJERPH, MDPI, vol. 17(4), pages 1-13, February.
    4. Esperanza Vera-Toscano & Elena C. Meroni, 2021. "An Age–Period–Cohort Approach to the Incidence and Evolution of Overeducation and Skills Mismatch," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 153(2), pages 711-740, January.
    5. Robert Bozick, 2021. "Age, period, and cohort effects contributing to the Great American Migration Slowdown," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 45(42), pages 1269-1296.
    6. Yiwan Ye & Xiaoling Shu, 2022. "Lonely in a Crowd: Cohort Size and Happiness in the United Kingdom," Journal of Happiness Studies, Springer, vol. 23(5), pages 2235-2257, June.
    7. Rainer Reile & Aleksei Baburin & Tatjana Veideman & Mall Leinsalu, 2020. "Long-term trends in the body mass index and obesity risk in Estonia: an age–period–cohort approach," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 65(6), pages 859-869, July.
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    9. Myck, Michał & Oczkowska, Monika, 2022. "Healthier over time? Period effects in health among older Europeans in a step-wise approach to identification," Social Science & Medicine, Elsevier, vol. 297(C).

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