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Age groups and the measure of population aging

Author

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  • Hippolyte d'Albis
  • Fabrice Collard

Abstract

Background: Measures of population aging are important because they shape our perception of demographic trends. Indicators of aging based on fixed ages contributed to a dramatic portrayal of demographic evolutions, some of which were associated with the myth of decline. Objective: We propose a new measure of population aging, based on the relative age of each individual in the population. Our approach builds on previous work by Aghevli and Mehran (1981) and relies on optimal grouping techniques that are used to determine the various age groups within a population. The cutoff ages for these groups, such as the age from which an individual is considered to be an older person, are then endogenous variables that depend on the entire population age distribution at any given moment. Methods: We show how to apply optimal grouping techniques to age distributions and how to calculate various indicators of aging, which are invariant with respect to proportional rescaling of distributions. We compute these indicators for the US, and a sample of 13 other industrialized countries. Results: We find that, contrary to common arguments for an aging population, the share of elderly individuals within the total population has not increased much, and has remained stable in these countries. These results complement and reinforce the earlier findings of Sanderson and Scherbov (2005, 2007) who also reassessed the aging phenomenon.
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Suggested Citation

  • Hippolyte d'Albis & Fabrice Collard, 2013. "Age groups and the measure of population aging," PSE - Labex "OSE-Ouvrir la Science Economique" hal-01015396, HAL.
    • Handle: RePEc:hal:pseose:hal-01015396
    DOI: 10.4054/DemRes.2013.29.23
    Note: View the original document on HAL open archive server: https://hal.archives-ouvertes.fr/hal-01015396
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    References listed on IDEAS

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    1. Joan Esteban & Carlos Gradín & Debraj Ray, 2007. "An Extension of a Measure of Polarization, with an application to the income distribution of five OECD countries," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 5(1), pages 1-19, April.
    2. Warren C. Sanderson & Sergei Scherbov, 2007. "A new perspective on population aging," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 16(2), pages 27-58.
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    8. repec:dau:papers:123456789/11049 is not listed on IDEAS
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    12. Davies, J. B. & Shorrocks, A. F., 1989. "Optimal grouping of income and wealth data," Journal of Econometrics, Elsevier, vol. 42(1), pages 97-108, September.
    13. Warren C. Sanderson & Sergei Scherbov, 2005. "Average remaining lifetimes can increase as human populations age," Nature, Nature, vol. 435(7043), pages 811-813, June.
    14. Ambra Poggi, 2005. "Endogenous population subgroups: the best population partition and optimal number of groups," Working Papers wpdea0508, Department of Applied Economics at Universitat Autonoma of Barcelona.
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    Citations

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    Cited by:

    1. Florian Bonnet & Hippolyte d'Albis, 2020. "Spatial Inequality in Mortality in France over the Past Two Centuries," Population and Development Review, The Population Council, Inc., vol. 46(1), pages 145-168, March.
    2. Hassan Belkacem Ghassan & Hassan Rafdan Al-Hajhoj & Faruk Balli, 2019. "Bi-Demographic Changes and Current Account using SVAR Modeling: Evidence from Saudi Economy," Working Papers hal-01742574, HAL.
    3. Hippolyte d’Albis & Ikpidi Badji, 2017. "Intergenerational inequalities in standards of living in France," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 491-492, pages 71-92.
    4. Hippolyte d'Albis & Ikpidi Badji, 2017. "Les inégalités de niveaux de vie entre les générations en France," PSE-Ecole d'économie de Paris (Postprint) halshs-01524882, HAL.
    5. Roberta Paroli & Guido Consonni & Alessandro Rosina, 2020. "The Measure of Population Aging in Different Welfare Regimes: A Bayesian Dynamic Modeling Approach," European Journal of Population, Springer;European Association for Population Studies, vol. 36(2), pages 363-385, April.
    6. Hippolyte d'Albis & Fabrice Collard, 2013. "Age groups and the measure of population aging," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(23), pages 617-640.
    7. Douglas, Evan J. & Shepherd, Dean A. & Venugopal, Vidhula, 2021. "A multi-motivational general model of entrepreneurial intention," Journal of Business Venturing, Elsevier, vol. 36(4).
    8. Hassan B. Ghassan & Hassan R. Al-Hajhoj & Faruk Balli, 2018. "Bi-Demographic Changes and Current Account using SVAR Modeling," Papers 1803.11161, arXiv.org, revised Mar 2019.
    9. N Renuga Nagarajan & Mineko Wada & Mei Lan Fang & Andrew Sixsmith, 2019. "Defining organizational contributions to sustaining an ageing workforce: a bibliometric review," European Journal of Ageing, Springer, vol. 16(3), pages 337-361, September.
    10. Ghassan, Hassan & Alhajhoj, Hassan R. & Balli, Faruk, 2018. "Bi-Demographic Changes and Current Account using SVAR Modeling: Evidence from Saudi Arabia," MPRA Paper 93013, University Library of Munich, Germany, revised 01 Feb 2019.

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    JEL classification:

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

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