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Can Incorporating Parity Information Improve the Reliability of Completed Cohort Fertility Projections? Insights From
Joanne Ellison1, Jakub Bijak2, Erengul Dodd3
1Department of Social Statistics and Demography, University of Southampton, Southampton, UK.
Demography
|April 13, 2026
Summary
This study introduces a new Bayesian fertility projection model that accounts for the number of previous children (parity). This parity-specific approach improves the accuracy and reliability of fertility forecasts for better population planning.
Area of Science:
- Demography
- Population Studies
- Statistical Modeling
Background:
- Fertility projections are crucial for population forecasts and planning essential services like maternity care and schooling.
- Current models often overlook parity (number of previous live births), a key factor in fertility dynamics.
Purpose of the Study:
- To propose a novel Bayesian parity-specific fertility projection model.
- To improve the accuracy and reliability of fertility forecasting by incorporating parity information.
Main Methods:
- Developed a Bayesian parity-specific fertility projection model within a generalized additive model framework.
- Utilized aggregate birth data, random walk priors on completed family size and parity progression ratios.
- Employed Hamiltonian Monte Carlo methods and data from the Human Fertility Database for fitting the model to 16 countries.
Main Results:
- The parity-specific model enables simultaneous estimation of smooth age-cohort rate surfaces for each parity.
- Comparison with existing models demonstrates improved predictive accuracy when including the parity dimension.
- Findings suggest parity-specific projections are more plausible and reliable.
Conclusions:
- Incorporating parity into fertility models leads to more accurate and dependable population projections.
- This approach aids government planners in decision-making and developing tailored policy solutions.
- The study highlights the importance of parity in understanding and forecasting fertility dynamics.
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