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Estimating trends in the total fertility rate with uncertainty using imperfect data

Author

Listed:
  • Leontine Alkema

    (University of Massachusetts Amherst)

  • Adrian E. Raftery

    (University of Washington)

  • Patrick Gerland

    (United Nations)

  • Samuel J. Clark

    (Ohio State University)

  • Francois Pelletier

    (Statistics Canada)

Abstract

Background: Estimating the total fertility rate is challenging for many developing countries because of limited data and varying data quality. A standardized, reproducible approach to produce estimates that include an uncertainty assessment is desired. Methods: We develop a method to estimate and assess uncertainty in the total fertility rate over time, based on multiple imperfect observations from different data sources including surveys and censuses. We take account of measurement error in observations by decomposing it into bias and variance and assess both by linear regression on a variety of data quality covariates. We estimate the total fertility rate using a local smoother, and assess uncertainty using the weighted likelihood bootstrap. Results: We apply our method to data from seven countries in West Africa and construct estimates and uncertainty intervals for the total fertility rate. Based on cross-validation exercises, we find that accounting for differences in data quality between observations gives better calibrated confidence intervals and reduces bias. Conclusions: When working with multiple imperfect observations from different data sources to estimate the total fertility rate, or demographic indicators in general, potential biases and differences in error variance have to be taken into account to improve the estimates and their uncertainty assessment.

Suggested Citation

  • Leontine Alkema & Adrian E. Raftery & Patrick Gerland & Samuel J. Clark & Francois Pelletier, 2012. "Estimating trends in the total fertility rate with uncertainty using imperfect data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 26(15), pages 331-362.
  • Handle: RePEc:dem:demres:v:26:y:2012:i:15
    DOI: 10.4054/DemRes.2012.26.15
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    References listed on IDEAS

    as
    1. Tom Moultrie & Rob Dorrington, 2008. "Sources of error and bias in methods of fertility estimation contingent on the P/F ratio in a time of declining fertility and rising mortality," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 19(46), pages 1635-1662.
    2. MacKinnon, James G. & White, Halbert, 1985. "Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties," Journal of Econometrics, Elsevier, vol. 29(3), pages 305-325, September.
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    Citations

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    Cited by:

    1. Finlay, Jocelyn E., 2021. "Women’s reproductive health and economic activity: A narrative review," World Development, Elsevier, vol. 139(C).
    2. Nina Cesare & Hedwig Lee & Tyler McCormick & Emma Spiro & Emilio Zagheni, 2018. "Promises and Pitfalls of Using Digital Traces for Demographic Research," Demography, Springer;Population Association of America (PAA), vol. 55(5), pages 1979-1999, October.
    3. 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.
    4. Isabel Günther & Kenneth Harttgen, 2016. "Desired Fertility and Number of Children Born Across Time and Space," Demography, Springer;Population Association of America (PAA), vol. 53(1), pages 55-83, February.
    5. Ann Garbett & Brienna Perelli‐Harris & Sarah Neal, 2021. "The Untold Story of 50 Years of Adolescent Fertility in West Africa: A Cohort Perspective on the Quantum, Timing, and Spacing of Adolescent Childbearing," Population and Development Review, The Population Council, Inc., vol. 47(1), pages 7-40, March.

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    More about this item

    Keywords

    retrospective surveys; Demographic and Health Surveys (DHS); Bayesian inference; local smoother; United Nations; weighted likelihood bootstrap; variable selection;
    All these keywords.

    JEL classification:

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

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