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A Mechanism-Based Approach to the Identification of Age–Period–Cohort Models

Author

Listed:
  • Christopher Winship

    (Harvard University, Cambridge, Massachusetts, cwinship@wjh.harvard.edu)

  • David J. Harding

    (University of Michigan, Ann Arbor)

Abstract

This article offers a new approach to the identification of age–period–cohort (APC) models that builds on Pearl's work on nonparametric causal models, in particular his front-door criterion for the identification of causal effects. The goal is to specify the mechanisms through which the age, period, and cohort variables affect the outcome and in doing so identify the model. This approach allows for a broader set of identification strategies than has typically been considered in the literature and, in many circumstances, goodness of fit tests are possible. The authors illustrate the utility of the approach by developing an APC model for political alienation.

Suggested Citation

  • Christopher Winship & David J. Harding, 2008. "A Mechanism-Based Approach to the Identification of Age–Period–Cohort Models," Sociological Methods & Research, , vol. 36(3), pages 362-401, February.
    • Handle: RePEc:sae:somere:v:36:y:2008:i:3:p:362-401
    DOI: 10.1177/0049124107310635
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    Citations

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    Cited by:

    1. Maarten J. Bijlsma & Rhian M. Daniel & Fanny Janssen & Bianca L. De Stavola, 2017. "An Assessment and Extension of the Mechanism-Based Approach to the Identification of Age-Period-Cohort Models," Demography, Springer;Population Association of America (PAA), vol. 54(2), pages 721-743, April.
    2. Liying Luo & James Hodges, 2019. "The Age-Period-Cohort-Interaction Model for Describing and Investigating Inter-Cohort Deviations and Intra-Cohort Life-Course Dynamics," Papers 1906.08357, arXiv.org.
    3. 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.
    4. Dong Zhou, 2016. "The Long-term Impacts of the Cultural Revolution: A Micro-Analysis," LABOUR, CEIS, vol. 30(3), pages 285-317, September.
    5. Dregan, Alex & Armstrong, David, 2009. "Age, cohort and period effects in the prevalence of sleep disturbances among older people: The impact of economic downturn," Social Science & Medicine, Elsevier, vol. 69(10), pages 1432-1438, November.
    6. Keyes, Katherine M. & Utz, Rebecca L. & Robinson, Whitney & Li, Guohua, 2010. "What is a cohort effect? Comparison of three statistical methods for modeling cohort effects in obesity prevalence in the United States, 1971-2006," Social Science & Medicine, Elsevier, vol. 70(7), pages 1100-1108, April.
    7. Yu-Han Jao & Jui-Chung Allen Li, 2011. "Trends in the Employment of Married Mothers of Preschool-Aged Children in Taiwan," Working Papers WR-850, RAND Corporation.
    8. 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.
    9. Whittaker, William & Birch, Stephen & MacKenzie, Adrian & Murphy, Gail Tomblin, 2016. "Cohort effects on the need for health care and implications for health care planning in Canada," Health Policy, Elsevier, vol. 120(1), pages 81-88.
    10. 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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