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Another 'futile quest'? A simulation study of Yang and Land's Hierarchical Age-Period-Cohort model

Author

Listed:
  • Andrew Bell

    (University of Sheffield)

  • Kelvyn Jones

    (University of Bristol)

Abstract
Background: Whilst some argue that a solution to the age-period-cohort (APC) 'identification problem' is impossible, numerous methodological solutions have been proposed, including Yang and Land's Hierarchical-APC (HAPC) model: a multilevel model considering periods and cohorts as cross-classified contexts in which individuals exist. Objective: To assess the assumptions made by the HAPC model, and the situations in which it does and does not work. Methods: Simulation study. Simulation scenarios assess the effect of (a) cohort trends in the Data Generating Process (DGP) (compared to only random variation), and (b) grouping cohorts (in both DGP and fitted model). Results: The model only works if either (a) we can assume that there are no linear (or non-linear) trends in periods or cohorts, (b) we control any cohort trend in the model's fixed part and assume there is no period trend, or (c) we group cohorts in such a way that they exactly match the groupings in the (unknown) DGP. Otherwise, the model can arbitrarily reapportion APC effects, radically impacting interpretation. Conclusions: Since the purpose of APC analysis is often to ascertain the presence of period and/or cohort trends, and since we rarely have solid (if any) theory regarding cohort groupings, there are few circumstances in which this model achieves what Yang and Land claim it can. The results bring into question findings of several published studies using the HAPC model. However, the structure of the model remains a conceptual advance that is useful when we can assume the DGP has no period trends.

Suggested Citation

  • Andrew Bell & Kelvyn Jones, 2014. "Another 'futile quest'? A simulation study of Yang and Land's Hierarchical Age-Period-Cohort model," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 30(11), pages 333-360.
    • Handle: RePEc:dem:demres:v:30:y:2014:i:11
    DOI: 10.4054/DemRes.2014.30.11
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    References listed on IDEAS

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    Cited by:

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    4. Reither, Eric N. & Masters, Ryan K. & Yang, Yang Claire & Powers, Daniel A. & Zheng, Hui & Land, Kenneth C., 2015. "Should age-period-cohort studies return to the methodologies of the 1970s?," Social Science & Medicine, Elsevier, vol. 128(C), pages 356-365.
    5. Douglas D Gunzler & Adam T Perzynski & Neal V Dawson & Kelley Kauffman & Jintao Liu & Jarrod E Dalton, 2019. "Risk-period-cohort approach for averting identification problems in longitudinal models," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-17, July.
    6. 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.
    7. Emma Mishel & Paula England & Jessie Ford & Mónica L. Caudillo, 2018. "Increases in Sex with Same-Sex Partners Across U.S. Cohorts Born 1920-1998: A Race-Gender Intersection," Working Papers 20180014, New York University Abu Dhabi, Department of Social Science, revised Mar 2018.
    8. Fabian Kratz & Alexander Patzina & Corinna Kleinert & Hans Dietrich, 2019. "Vocational Education and Employment: Explaining Cohort Variations in Life Course Patterns," Social Inclusion, Cogitatio Press, vol. 7(3), pages 224-253.
    9. Thomson, Rachel M. & Katikireddi, Srinivasa Vittal, 2018. "Mental health and the jilted generation: Using age-period-cohort analysis to assess differential trends in young people's mental health following the Great Recession and austerity in England," Social Science & Medicine, Elsevier, vol. 214(C), pages 133-143.
    10. Aistov, Andrey, 2018. "Age-earnings profiles of different generations," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 50, pages 23-42.
    11. Delaruelle, Katrijn & Buffel, Veerle & Bracke, Piet, 2015. "Educational expansion and the education gradient in health: A hierarchical age-period-cohort analysis," Social Science & Medicine, Elsevier, vol. 145(C), pages 79-88.
    12. Minton, Jonathan, 2017. "Lexis Surface Visualisation Workflow," OSF Preprints ntz72, Center for Open Science.
    13. Zhenkun Wang & Jinyao Wang & Junzhe Bao & Xudong Gao & Chuanhua Yu & Huiyun Xiang, 2016. "Temporal Trends of Suicide Mortality in Mainland China: Results from the Age-Period-Cohort Framework," IJERPH, MDPI, vol. 13(8), pages 1-10, August.
    14. Jean M. Twenge & Ryne A. Sherman & Julie J. Exline & Joshua B. Grubbs, 2016. "Declines in American Adults’ Religious Participation and Beliefs, 1972-2014," SAGE Open, , vol. 6(1), pages 21582440166, March.
    15. Liliya Leopold & Thomas Leopold, 2016. "Education and Health across Lives and Cohorts: A Study of Cumulative Advantage in Germany," SOEPpapers on Multidisciplinary Panel Data Research 835, DIW Berlin, The German Socio-Economic Panel (SOEP).
    16. Eszter Siposné Nándori & Zsuzsanna Dabasi-Halász & Csaba Ilyés, 2018. "“Action and Reaction” – the Impact of European Youth Mobility on the Economy and the Labour Market," Eastern European Business and Economics Journal, Eastern European Business and Economics Studies Centre, vol. 4(1 - speci), pages 56-78.
    17. Bizet, Romain & Bonev, Petyo & Lévêque, François, 2022. "Are Older Nuclear Reactors Less Safe? Evidence from France," Economics Working Paper Series 2216, University of St. Gallen, School of Economics and Political Science.
    18. Gábor Hajdu & Endre Sik, 2018. "Age, Period, and Cohort Differences in Work Centrality and Work Values," Societies, MDPI, vol. 8(1), pages 1-33, February.
    19. Patrick Denice, 2017. "Back to School: Racial and Gender Differences in Adults’ Participation in Formal Schooling, 1978–2013," Demography, Springer;Population Association of America (PAA), vol. 54(3), pages 1147-1173, June.
    20. Ethan Fosse & Christopher Winship, 2019. "Bounding Analyses of Age-Period-Cohort Effects," Demography, Springer;Population Association of America (PAA), vol. 56(5), pages 1975-2004, October.
    21. Maarten J. Bijlsma & Rhian Daniel & Fanny Janssen & Bianca De Stavola, 2016. "An assessment and extension of the mechanism-based approach to the identification of age-period-cohort models," MPIDR Working Papers WP-2016-005, Max Planck Institute for Demographic Research, Rostock, Germany.

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    More about this item

    Keywords

    simulation; Markov chain Monte Carlo (MCMC); Yang and Land; identification problem; age-period-cohort models;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

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