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Age Groups and the Measure of Population Aging

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Abstract

This paper proposes the use of optimal grouping methods for determining the various age groups within a population. The cutoff ages for these groups, such as the age from wich 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. This method is applied to the age distributions of some industrialized countries, for which cutoff ages as well as the main indicators of aging are calculated over the last 50 years

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  • Hippolyte d'Albis & Fabrice Collard, 2012. "Age Groups and the Measure of Population Aging," Documents de travail du Centre d'Economie de la Sorbonne 12081, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    • Handle: RePEc:mse:cesdoc:12081
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    Cited by:

    1. 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.
    2. 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.
    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. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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).
    9. 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.
    10. 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.

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    More about this item

    Keywords

    Population aging; age distributions; aging indexes; optimal grouping; old age; demographic measures;
    All these keywords.

    JEL classification:

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

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