Combining individual- and population-level data to develop a Bayesian parity-specific fertility projection model

Publication type

Journal Article

Authors

Publication date

March 15, 2024

Summary:

Fertility projections are vital to anticipate demand for maternity and childcare services, among other uses. Models typically use aggregate population-level data alone, ignoring the richness of individual-level data. We hence develop a Bayesian parity-specific projection model combining such data sources. We apply our method to England and Wales, using individual-level data from Understanding Society. Fitting generalised additive models gives smooth projections across age, cohort, and time since last birth. We also incorporate prior beliefs about the relative importance of the data sources. Our approach generates plausible forecasts by individual-level variables including educational qualification, despite their absence in the population-level data.

Published in

Journal of the Royal Statistical Society Series C (Applied Statistics)

Volume and page numbers

Volume: 73 , p.275 -297

DOI

https://doi.org/10.1093/jrsssc/qlad095

ISSN

359254

Subjects

Notes

© The Royal Statistical Society 2023.

Open Access

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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