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Should age-period-cohort studies return to the methodologies of the 1970s?

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  • Reither, Eric N.
  • Masters, Ryan K.
  • Yang, Yang Claire
  • Powers, Daniel A.
  • Zheng, Hui
  • Land, Kenneth C.

Abstract

Social scientists have recognized the importance of age-period-cohort (APC) models for half a century, but have spent much of this time mired in debates about the feasibility of APC methods. Recently, a new class of APC methods based on modern statistical knowledge has emerged, offering potential solutions. In 2009, Reither, Hauser and Yang used one of these new methods – hierarchical APC (HAPC) modeling – to study how birth cohorts may have contributed to the U.S. obesity epidemic. They found that recent birth cohorts experience higher odds of obesity than their predecessors, but that ubiquitous period-based changes are primarily responsible for the rising prevalence of obesity. Although these findings have been replicated elsewhere, recent commentaries by Bell and Jones call them into question – along with the new class of APC methods. Specifically, Bell and Jones claim that new APC methods do not adequately address model identification and suggest that “solid theory” is often sufficient to remove one of the three temporal dimensions from empirical consideration. They also present a series of simulation models that purportedly show how the HAPC models estimated by Reither et al. (2009) could have produced misleading results. However, these simulation models rest on assumptions that there were no period effects, and associations between period and cohort variables and the outcome were perfectly linear. Those are conditions under which APC models should never be used. Under more tenable assumptions, our own simulations show that HAPC methods perform well, both in recovering the main findings presented by Reither et al. (2009) and the results reported by Bell and Jones. We also respond to critiques about model identification and theoretically-imposed constraints, finding little pragmatic support for such arguments. We conclude by encouraging social scientists to move beyond the debates of the 1970s and toward a deeper appreciation for modern APC methodologies.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:socmed:v:128:y:2015:i:c:p:356-365
    DOI: 10.1016/j.socscimed.2015.01.011
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    References listed on IDEAS

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    1. Liying Luo, 2013. "Assessing Validity and Application Scope of the Intrinsic Estimator Approach to the Age-Period-Cohort Problem," Demography, Springer;Population Association of America (PAA), vol. 50(6), pages 1945-1967, December.
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    4. Yang Yang & Kenneth Land, 2013. "Misunderstandings, Mischaracterizations, and the Problematic Choice of a Specific Instance in Which the IE Should Never Be Applied," Demography, Springer;Population Association of America (PAA), vol. 50(6), pages 1969-1971, December.
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    6. 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.
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    8. Harding, David J., 2009. "Recent advances in age-period-cohort analysis. A commentary on Dregan and Armstrong, and on Reither, Hauser and Yang," Social Science & Medicine, Elsevier, vol. 69(10), pages 1449-1451, November.
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    Cited by:

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    2. Johannes Beller, 2022. "Age-period-cohort analysis of depression trends: are depressive symptoms increasing across generations in Germany?," European Journal of Ageing, Springer, vol. 19(4), pages 1493-1505, December.
    3. Ryan Masters & Daniel Powers, 2020. "Clarifying assumptions in age-period-cohort analyses and validating results," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-21, October.
    4. 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.
    5. Ryan K. Masters & Daniel A. Powers & Robert A. Hummer & Audrey Beck & Shih-Fan Lin & Brian Karl Finch, 2016. "Fitting Age-Period-Cohort Models Using the Intrinsic Estimator: Assumptions and Misapplications," Demography, Springer;Population Association of America (PAA), vol. 53(4), pages 1253-1259, August.
    6. 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.
    7. Twenge, Jean M. & Campbell, W. Keith & Sherman, Ryne A., 2019. "Declines in vocabulary among American adults within levels of educational attainment, 1974–2016," Intelligence, Elsevier, vol. 76(C), pages 1-1.
    8. Gustavo De Santis & Massimo Mucciardi, 2017. "From Euclidean distances to APC models," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 829-846, March.
    9. Redding, Stuart & Nicodemo, Catia & Wittenberg, Raphael, 2021. "Analysis of trends in emergency and elective hospital admissions and hospital bed days 1997 to 2015," Social Science & Medicine, Elsevier, vol. 279(C).
    10. 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.
    11. Zhang, Ming & Li, Yang, 2022. "Generational travel patterns in the United States: New insights from eight national travel surveys," Transportation Research Part A: Policy and Practice, Elsevier, vol. 156(C), pages 1-13.

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