IDEAS home Printed from https://ideas.repec.org/a/spr/demogr/v56y2019i5d10.1007_s13524-019-00801-6.html
   My bibliography  Save this article

Bounding Analyses of Age-Period-Cohort Effects

Author

Listed:
  • Ethan Fosse

    (University of Toronto)

  • Christopher Winship

    (Harvard University)

Abstract

For more than a century, researchers from a wide range of disciplines have sought to estimate the unique contributions of age, period, and cohort (APC) effects on a variety of outcomes. A key obstacle to these efforts is the linear dependence among the three time scales. Various methods have been proposed to address this issue, but they have suffered from either ad hoc assumptions or extreme sensitivity to small differences in model specification. After briefly reviewing past work, we outline a new approach for identifying temporal effects in population-level data. Fundamental to our framework is the recognition that it is only the slopes of an APC model that are unidentified, not the nonlinearities or particular combinations of the linear effects. One can thus use constraints implied by the data along with explicit theoretical claims to bound one or more of the APC effects. Bounds on these parameters may be nearly as informative as point estimates, even with relatively weak assumptions. To demonstrate the usefulness of our approach, we examine temporal effects in prostate cancer incidence and homicide rates. We conclude with a discussion of guidelines for further research on APC effects.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:demogr:v:56:y:2019:i:5:d:10.1007_s13524-019-00801-6
    DOI: 10.1007/s13524-019-00801-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13524-019-00801-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13524-019-00801-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Andrew Bell & Kelvyn Jones, 2015. "Bayesian informative priors with Yang and Land’s hierarchical age–period–cohort model," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(1), pages 255-266, January.
    2. Manski, Charles F, 1990. "Nonparametric Bounds on Treatment Effects," American Economic Review, American Economic Association, vol. 80(2), pages 319-323, May.
    3. Wenjiang Fu, 2016. "Constrained Estimators and Consistency of a Regression Model on a Lexis Diagram," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 180-199, March.
    4. Chen, X. & Li, G. & Unger, J.B. & Liu, X. & Johnson, C.A., 2003. "Secular Trends in Adolescent Never Smoking from 1990 to 1999 in California: An Age-Period-Cohort Analysis," American Journal of Public Health, American Public Health Association, vol. 93(12), pages 2099-2104.
    5. repec:bla:ecinqu:v:51:y:2013:i:3:p:1651-1681 is not listed on IDEAS
    6. Qiang Fu & Kenneth Land, 2015. "The Increasing Prevalence of Overweight and Obesity of Children and Youth in China, 1989–2009: An Age–Period–Cohort Analysis," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 34(6), pages 901-921, December.
    7. Charles F. Manski, 2003. "Identification Problems in the Social Sciences and Everyday Life," Southern Economic Journal, John Wiley & Sons, vol. 70(1), pages 11-21, July.
    8. 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.
    9. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1, January.
    10. 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.
    11. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 531-542.
    12. 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.
    13. Stephen Fienberg, 2013. "Cohort Analysis’ Unholy Quest: A Discussion," Demography, Springer;Population Association of America (PAA), vol. 50(6), pages 1981-1984, December.
    14. 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.
    15. O'Malley, P.M. & Bachman, J.G. & Johnston, L.D., 1984. "Period, age, and cohort effects on substance use among American youth, 1976-82," American Journal of Public Health, American Public Health Association, vol. 74(7), pages 682-688.
    16. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Enrique Acosta & Alyson van Raalte, 2019. "APC curvature plots: Displaying nonlinear age-period-cohort patterns on Lexis plots," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(42), pages 1205-1234.
    2. Mason, Joyce & Laporte, Audrey & McDonald, James Ted & Kurdyak, Paul & Fosse, Ethan & de Oliveira, Claire, 2024. "Assessing the “healthy immigrant effect” in mental health: Intra- and inter-cohort trends in mood and/or anxiety disorders," Social Science & Medicine, Elsevier, vol. 340(C).
    3. Wiedner, Jonas, 2022. "Political and Social Consequences of Qualification Mismatches: A Bounding Approach to Status Inconsistency," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 101(1), pages 150-175.
    4. Robert M. O’Brien, 2023. "Setting bounds on age, period, and cohort effects using observed data," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2841-2857, June.
    5. O'Keefe, Patrick & Wänström, Linda & Rodgers, Joseph Lee, 2023. "Reframing the clouded scientific spectacles of the Flynn effect: A view through two lenses," Intelligence, Elsevier, vol. 97(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. 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.
    3. 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.
    4. Ryan Masters & Robert Hummer & Daniel Powers & Audrey Beck & Shih-Fan Lin & Brian Finch, 2014. "Long-Term Trends in Adult Mortality for U.S. Blacks and Whites: An Examination of Period- and Cohort-Based Changes," Demography, Springer;Population Association of America (PAA), vol. 51(6), pages 2047-2073, December.
    5. Shih-Yung Su & Wen-Chung Lee, 2019. "Age-period-cohort analysis with a constant-relative-variation constraint for an apportionment of period and cohort slopes," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-21, December.
    6. 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.
    7. Card, David & Rothstein, Jesse, 2007. "Racial segregation and the black-white test score gap," Journal of Public Economics, Elsevier, vol. 91(11-12), pages 2158-2184, December.
    8. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    9. Guido W. Imbens, 2022. "Causality in Econometrics: Choice vs Chance," Econometrica, Econometric Society, vol. 90(6), pages 2541-2566, November.
    10. Guido W. Imbens, 2020. "Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics," Journal of Economic Literature, American Economic Association, vol. 58(4), pages 1129-1179, December.
    11. Linus Holtermann & Christian Hundt, 2018. "Hierarchically structured determinants and phase related patterns of economic resilience. An empirical case study for European regions," Working Papers on Innovation and Space 2018-02, Philipps University Marburg, Department of Geography.
    12. 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.
    13. Bryan S. Graham & Guido W. Imbens & Geert Ridder, 2014. "Complementarity and aggregate implications of assortative matching: A nonparametric analysis," Quantitative Economics, Econometric Society, vol. 5, pages 29-66, March.
    14. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    15. Qiang Fu & Kenneth Land, 2015. "The Increasing Prevalence of Overweight and Obesity of Children and Youth in China, 1989–2009: An Age–Period–Cohort Analysis," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 34(6), pages 901-921, December.
    16. 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.
    17. Chakraborty, Tanika & Schüller, Simone & Zimmermann, Klaus F., 2019. "Beyond the average: Ethnic capital heterogeneity and intergenerational transmission of education," Journal of Economic Behavior & Organization, Elsevier, vol. 163(C), pages 551-569.
    18. Arkadiusz Wiśniowski & Peter Smith & Jakub Bijak & James Raymer & Jonathan Forster, 2015. "Bayesian Population Forecasting: Extending the Lee-Carter Method," Demography, Springer;Population Association of America (PAA), vol. 52(3), pages 1035-1059, June.
    19. van der Klaauw, Bas, 2014. "From micro data to causality: Forty years of empirical labor economics," Labour Economics, Elsevier, vol. 30(C), pages 88-97.
    20. Arthur Lewbel, 2019. "The Identification Zoo: Meanings of Identification in Econometrics," Journal of Economic Literature, American Economic Association, vol. 57(4), pages 835-903, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:demogr:v:56:y:2019:i:5:d:10.1007_s13524-019-00801-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.