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Improving methodology for fertility forecasting through the incorporation of individual-level data and population-level parity information

Joanne Ellison, University of Southampton
Ann M. Berrington, University of Southampton
Erengul Dodd, University of Southampton
Jonathan J. Forster, University of Warwick
Jakub Bijak, University of Southampton

Fertility projections are a key determinant of population projections; they are also vital to anticipate demand for maternity and childcare services. It is standard practice for fertility projection models to use aggregate population-level data alone, e.g. from vital registration - this ignores the rich inferences that individual-level data can provide. Existing models also neglect to include information about birth order (parity), despite parity-specific data being collected by many countries and the evidence supporting greatly differing determinants of childbearing by parity. To this end, in this paper we develop fertility forecasting methodology that incorporates individual-level data sources and/or population-level parity-specific fertility data in a Bayesian framework. We have applied Generalized Additive Models to data from the UK Household Longitudinal Study, with parity-specific fertility rates from vital registration incorporated via a marginalisation process. This weights the contributions of the data sources according to our beliefs about their relative importance to overall inference, while retaining individual-level covariates such as qualification. Preliminary results from an alternative approach indicate that constraining completed family size within the modelling process can improve forecast precision and accuracy. Our findings have the potential to lead to more reliable fertility projections, aiding government policymakers and planners in their decision-making.

Keywords: Bayesian methods / estimation, Population projections, forecasts, and estimations, Fertility and childbirth, Methodology

See paper.

  Presented in Session 39. Fertility and Sexual and Reproductive Health: New Methods