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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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    1. Bell, Andrew, 2014. "Life-course and cohort trajectories of mental health in the UK, 1991–2008 – A multilevel age–period–cohort analysis," Social Science & Medicine, Elsevier, vol. 120(C), pages 21-30.
    2. Reither, Eric N. & Hauser, Robert M. & Yang, Yang, 2009. "Do birth cohorts matter? Age-period-cohort analyses of the obesity epidemic in the United States," Social Science & Medicine, Elsevier, vol. 69(10), pages 1439-1448, November.
    3. Stephen E. Fienberg & James S. Hodges & Liying Luo, 2015. "Letter To the Editor," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 457-457, March.
    4. Leckie, George & Charlton, Chris, 2013. "runmlwin: A Program to Run the MLwiN Multilevel Modeling Software from within Stata," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i11).
    5. Louis Chauvel & Martin Schr der, 2014. "Generational Inequalities and Welfare Regimes," LIS Working papers 606, LIS Cross-National Data Center in Luxembourg.
    6. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    7. Manfred Grotenhuis & Ben Pelzer & Liying Luo & Alexander W. Schmidt-Catran, 2016. "The Intrinsic Estimator, Alternative Estimates, and Predictions of Mortality Trends: A Comment on Masters, Hummer, Powers, Beck, Lin, and Finch," Demography, Springer;Population Association of America (PAA), vol. 53(4), pages 1245-1252, August.
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    Cited by:

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    2. 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.
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    4. 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.
    5. Ferruccio Biolcati & Francesco Molteni & Markus Quandt & Cristiano Vezzoni, 2022. "Church Attendance and Religious change Pooled European dataset (CARPE): a survey harmonization project for the comparative analysis of long-term trends in individual religiosity," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(3), pages 1729-1753, June.
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