IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/3m6va_v1.html
   My bibliography  Save this paper

The Global Burden of Disease fertility forecasts: Summary of the approach used and associated statistical concerns

Author

Listed:
  • Alkema, Leontine

Abstract

BACKGROUND The Global Burden of Disease (GBD) project’s forecasts up to 2100 suggest fertility drops will be even greater in sub-Saharan Africa than the UN Population Division (UNPD) has predicted. OBJECTIVE This reflection summarizes the main assumptions used in the GBD fertility forecasts. I assess the methods used, focusing on high fertility countries and the use of met need for contraceptives as a predictor. RESULTS Based on GBD’s forecasting method, I draw two conclusions. Firstly, GBD fertility forecasts are based on unvalidated assumptions about increasing met need for contraception and may overestimate decreases in fertility in countries with low levels of modern contraceptive use. Secondly, the GBD forecast model for fertility is not a causal model for predicting changes. Claims GBD researchers make about the effect of changing access to family planning on fertility are not informative for guiding policy. Based on the GBD validation exercise, I conclude that the GBD study did not check the performance of the method for predicting left-out fertility data. Also the approach used to compare the predictive performance of UNPD and GBD forecasting methods may give the GBD method an inherent advantage. CONCLUSIONS Communication regarding the GBD method and its findings must avoid causal language and acknowledge the method’s limitations. Future research should examine the performance of the method, especially for countries with low modern contraceptive use. CONTRIBUTION This paper summarizes the GBD fertility forecasting method and indicates three areas of concern about it and its use.

Suggested Citation

  • Alkema, Leontine, 2020. "The Global Burden of Disease fertility forecasts: Summary of the approach used and associated statistical concerns," OSF Preprints 3m6va_v1, Center for Open Science.
  • Handle: RePEc:osf:osfxxx:3m6va_v1
    DOI: 10.31219/osf.io/3m6va_v1
    as

    Download full text from publisher

    File URL: https://osf.io/download/5f3ec80bbacde8009a33c5a9/
    Download Restriction: no

    File URL: https://libkey.io/10.31219/osf.io/3m6va_v1?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. Leontine Alkema & Adrian Raftery & Patrick Gerland & Samuel Clark & François Pelletier & Thomas Buettner & Gerhard Heilig, 2011. "Probabilistic Projections of the Total Fertility Rate for All Countries," Demography, Springer;Population Association of America (PAA), vol. 48(3), pages 815-839, August.
    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. Heer, Burkhard & Polito, Vito & Wickens, Michael R., 2020. "Population aging, social security and fiscal limits," Journal of Economic Dynamics and Control, Elsevier, vol. 116(C).
    2. Mei Sang & Jing Jiang & Xin Huang & Feifei Zhu & Qian Wang, 2024. "Spatial and temporal changes in population distribution and population projection at county level in China," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    3. Dominik Paprotny, 2021. "Convergence Between Developed and Developing Countries: A Centennial Perspective," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 153(1), pages 193-225, January.
    4. Afua Durowaa-Boateng & Anne Goujon & Dilek Yildiz, 2023. "A Bayesian model for the reconstruction of education- and age-specific fertility rates: An application to African and Latin American countries," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 49(31), pages 809-848.
    5. Tom Wilson & Irina Grossman & Monica Alexander & Phil Rees & Jeromey Temple, 2022. "Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(3), pages 865-898, June.
    6. Meng Xu & Helge Brunborg & Joel E. Cohen, 2017. "Evaluating multi-regional population projections with Taylor’s law of mean–variance scaling and its generalisation," Journal of Population Research, Springer, vol. 34(1), pages 79-99, March.
    7. Maria Tzitiridou-Chatzopoulou & Georgia Zournatzidou & Michael Kourakos, 2024. "Predicting Future Birth Rates with the Use of an Adaptive Machine Learning Algorithm: A Forecasting Experiment for Scotland," IJERPH, MDPI, vol. 21(7), pages 1-13, June.
    8. Kevin Rennert & Brian C. Prest & William A. Pizer & Richard G. Newell & David Anthoff & Cora Kingdon & Lisa Rennels & Roger Cooke & Adrian E. Raftery & Hana Sevcikova & Frank Errickson, 2021. "The Social Cost of Carbon: Advances in Long-Term Probabilistic Projections of Population, GDP, Emissions, and Discount Rates," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 52(2 (Fall)), pages 223-305.
    9. Vanella, Patrizio, 2017. "Age- and Sex-Specific Fertility in Germany until the Year 2040 - The Impact of International Migration," Hannover Economic Papers (HEP) dp-606, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    10. Daphne H. Liu & Adrian E. Raftery, 2020. "How Do Education and Family Planning Accelerate Fertility Decline?," Population and Development Review, The Population Council, Inc., vol. 46(3), pages 409-441, September.
    11. Adrian Raftery & Jennifer Chunn & Patrick Gerland & Hana Ševčíková, 2013. "Bayesian Probabilistic Projections of Life Expectancy for All Countries," Demography, Springer;Population Association of America (PAA), vol. 50(3), pages 777-801, June.
    12. Patrizio Vanella & Max J. Hassenstein, 2023. "Stochastic Forecasting of Regional Age-Specific Fertility Rates: An Outlook for German NUTS-3 Regions," Mathematics, MDPI, vol. 12(1), pages 1-19, December.
    13. Asako Ohinata & Dimitrios Varvarigos, 2020. "Demographic Transition and Fertility Rebound in Economic Development," Scandinavian Journal of Economics, Wiley Blackwell, vol. 122(4), pages 1640-1670, October.
    14. Michael Pearce & Adrian E. Raftery, 2021. "Probabilistic forecasting of maximum human lifespan by 2100 using Bayesian population projections," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 44(52), pages 1271-1294.
    15. Guy Abel & Jakub Bijak & Jonathan J. Forster & James Raymer & Peter W.F. Smith & Jackie S.T. Wong, 2013. "Integrating uncertainty in time series population forecasts: An illustration using a simple projection model," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(43), pages 1187-1226.
    16. Liu Qiang & Fernando Rios-Avila & Han Jiqin, 2020. "Is China's Low Fertility Rate Caused by the Population Control Policy?," Economics Working Paper Archive wp_943, Levy Economics Institute.
    17. Jane N. O’Sullivan, 2023. "Demographic Delusions: World Population Growth Is Exceeding Most Projections and Jeopardising Scenarios for Sustainable Futures," World, MDPI, vol. 4(3), pages 1-24, September.
    18. Carl P. Schmertmann & Marcos R. Gonzaga, 2018. "Bayesian Estimation of Age-Specific Mortality and Life Expectancy for Small Areas With Defective Vital Records," Demography, Springer;Population Association of America (PAA), vol. 55(4), pages 1363-1388, August.
    19. Eduardo A. Castro & Zhen Zhang & Arnab Bhattacharjee & José M. Martins & Taps Maiti, 2013. "Regional fertility data analysis: A small area Bayesian approach," SEEC Discussion Papers 1302, Spatial Economics and Econometrics Centre, Heriot Watt University.
    20. Phoebe Koundouri & Georgios I. Papayiannis & Achilleas Vassilopoulos & Athanasios Yannacopoulos, 2022. "A general framework for the generation of probabilistic socioeconomic scenarios and risk quantification concerning food security with application in the Upper Nile river basin," DEOS Working Papers 2203, Athens University of Economics and Business.

    More about this item

    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:osf:osfxxx:3m6va_v1. 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: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    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.