IDEAS home Printed from https://ideas.repec.org/a/nse/ecosta/ecostat_2020_520d_3.html
   My bibliography  Save this article

Bayesian Probabilistic Population Projections for France

Author

Listed:
  • Vianney Costemalle

Abstract

[eng] Population projections are performed regularly by national statistics institutes. In France, the most recent projections were produced by Insee in 2016 using a deterministic approach based on 27 different scenarios. In this article, we propose a new approach which combines probabilistic population projections and a greater use of the Bayesian paradigm in order to quantify the uncertainty of future population levels without resorting to scenarios. Using the components method, the mortality rate, fertility rate and net migration are projected independently by sex and age. These three components are modelled, taking account of registry data (number of births and deaths) and net migration data series. The results reveal that the population of metropolitan France will continue to grow, reaching a level of between 66.1 million and 77.2 million inhabitants in 2070, with a probability of 95%.

Suggested Citation

  • Vianney Costemalle, 2020. "Bayesian Probabilistic Population Projections for France," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 520-521, pages 29-47.
    • Handle: RePEc:nse:ecosta:ecostat_2020_520d_3
    DOI: https://doi.org/10.24187/ecostat.2020.520d.2031
    as

    Download full text from publisher

    File URL: https://www.insee.fr/en/statistiques/fichier/4997859/03-ES-520-521_Costemalle-EN.pdf
    Download Restriction: no
    File URL: https://libkey.io/https://doi.org/10.24187/ecostat.2020.520d.2031?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
    ---><---

    References listed on IDEAS

    as
    1. Wolfgang Lutz & Warren Sanderson & Sergei Scherbov, 2001. "The end of world population growth," Nature, Nature, vol. 412(6846), pages 543-545, August.
    2. Booth, Heather, 2006. "Demographic forecasting: 1980 to 2005 in review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 547-581.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    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. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    2. Niall Newsham & Francisco Rowe, 2021. "Projecting the demographic impact of Syrian migration in a rapidly ageing society, Germany," Journal of Geographical Systems, Springer, vol. 23(2), pages 231-261, April.
    3. Osman Gulseven, 2016. "Forecasting Population and Demographic Composition of Kuwait Until 2030," International Journal of Economics and Financial Issues, Econjournals, vol. 6(4), pages 1429-1435.
    4. Demirel, Duygun Fatih & Basak, Melek, 2019. "A fuzzy bi-level method for modeling age-specific migration," Socio-Economic Planning Sciences, Elsevier, vol. 68(C).
    5. Dunstan Kim & Ball Christopher, 2016. "Demographic Projections: User and Producer Experiences of Adopting a Stochastic Approach," Journal of Official Statistics, Sciendo, vol. 32(4), pages 947-962, December.
    6. Daniel Rozell, 2017. "Using population projections in climate change analysis," Climatic Change, Springer, vol. 142(3), pages 521-529, June.
    7. Joanne Ellison & Erengul Dodd & Jonathan J. Forster, 2020. "Forecasting of cohort fertility under a hierarchical Bayesian approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 829-856, June.
    8. repec:hum:wpaper:sfb649dp2009-008 is not listed on IDEAS
    9. Grippo, Valeria & Vastola, Antonella, 2018. "Evaluation Of Bran Use Through Multi Criteria Analysis," 2018 Seventh AIEAA Conference, June 14-15, Conegliano, Italy 275646, Italian Association of Agricultural and Applied Economics (AIEAA).
    10. Booth, Heather, 2006. "Demographic forecasting: 1980 to 2005 in review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 547-581.
    11. Alfred Michael Dockery & Mark N. Harris & Nicholas Holyoak & Ranjodh B. Singh, 2021. "A methodology for projecting sparse populations and its application to remote Indigenous communities," Journal of Geographical Systems, Springer, vol. 23(1), pages 37-61, January.
    12. Juha Alho & Nico Keilman, 2010. "On future household structure," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(1), pages 117-143, January.
    13. O'Neill, Brian C. & Desai, Mausami, 2005. "Accuracy of past projections of US energy consumption," Energy Policy, Elsevier, vol. 33(8), pages 979-993, May.
    14. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    15. Fang, Lei & Härdle, Wolfgang Karl, 2015. "Stochastic population analysis: A functional data approach," SFB 649 Discussion Papers 2015-007, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    16. Alho, Juha, 2008. "Aggregation across countries in stochastic population forecasts," International Journal of Forecasting, Elsevier, vol. 24(3), pages 343-353.
    17. Monika Papież & Sabina Denkowska, 2010. "The Analysis of Mortality Changes In Selected European Countries in the Period 1960–2006," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 11(3), pages 563-584, December.
    18. Warren C Sanderson & Sergei Scherbov & Patrick Gerland, 2017. "Probabilistic population aging," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-12, June.
    19. David Lam, 2011. "How the World Survived the Population Bomb: Lessons From 50 Years of Extraordinary Demographic History," Demography, Springer;Population Association of America (PAA), vol. 48(4), pages 1231-1262, November.
    20. Malte Meinshausen, 2007. "Stylized Emission Path," Human Development Occasional Papers (1992-2007) HDOCPA-2007-51, Human Development Report Office (HDRO), United Nations Development Programme (UNDP).
    21. Clotilde Grandval & Jean-Christophe Bureau & Herve Guyomard & Laurence Roudart, 2006. "Panorama des analyses prospectives sur l'évolution de la sécurité alimentaire mondiale à l'horizon 2020-2030," Working Papers hal-02819396, HAL.

    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts
    • J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth
    • F22 - International Economics - - International Factor Movements and International Business - - - International Migration

    Statistics

    Access and download statistics

    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:nse:ecosta:ecostat_2020_520d_3. 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: Veronique Egloff (email available below). General contact details of provider: https://edirc.repec.org/data/inseefr.html .

    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.