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Bayesian population reconstruction of female populations for less developed and more developed countries

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  • Mark C. Wheldon
  • Adrian E. Raftery
  • Samuel J. Clark
  • Patrick Gerland

Abstract

We show that Bayesian population reconstruction, a recent method for estimating past populations by age, works for data of widely varying quality. Bayesian reconstruction simultaneously estimates age-specific population counts, fertility rates, mortality rates, and net international migration flows from fragmentary data, while formally accounting for measurement error. As inputs, Bayesian reconstruction uses initial bias-reduced estimates of standard demographic variables. We reconstruct the female populations of three countries: Laos, a country with little vital registration data where population estimation depends largely on surveys; Sri Lanka, a country with some vital registration data; and New Zealand, a country with a highly developed statistical system and good quality vital registration data. In addition, we extend the method to countries without censuses at regular intervals. We also use it to assess the consistency of results between model life tables and available census data, and hence to compare different model life table systems.

Suggested Citation

  • Mark C. Wheldon & Adrian E. Raftery & Samuel J. Clark & Patrick Gerland, 2016. "Bayesian population reconstruction of female populations for less developed and more developed countries," Population Studies, Taylor & Francis Journals, vol. 70(1), pages 21-37, March.
    • Handle: RePEc:taf:rpstxx:v:70:y:2016:i:1:p:21-37
    DOI: 10.1080/00324728.2016.1139164
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    1. Rowland, Donald T., 2003. "Demographic Methods and Concepts," OUP Catalogue, Oxford University Press, number 9780198752639.
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    Cited by:

    1. Wazir Asif & Goujon Anne, 2021. "Exploratory Assessment of the Census of Pakistan Using Demographic Analysis," Journal of Official Statistics, Sciendo, vol. 37(3), pages 719-750, September.
    2. Raftery, Adrian E. & Ševčíková, Hana, 2023. "Probabilistic population forecasting: Short to very long-term," International Journal of Forecasting, Elsevier, vol. 39(1), pages 73-97.

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