Can incorporating parity information improve the reliability of fertility projections? Insights from a Bayesian generalized additive model approach
Bibliographic record
Abstract
Fertility projections inform population projections and are used to plan for the future provision of vital services such as maternity care and schooling. Existing fertility forecasting models tend to use aggregate births data indexed by age and time alone, thereby neglecting to include information about parity, i.e. the number of previous live-born children. This omission risks ignoring a crucial mechanism of fertility dynamics and consequently producing biased predictions. We propose a Bayesian parity-specific fertility projection model within a generalized additive model (GAM) framework. The use of GAMs enables a smooth age-cohort rate surface to be estimated for each parity simultaneously. We constrain our model using aggregate data and additionally introduce random walk priors on completed family size and parity progression ratios, which are summary fertility measures known to change relatively slowly over time. Using Hamiltonian Monte Carlo methods, we fit our model to Canada, Czechia, England & Wales and the Netherlands. We compare our forecasts with the best-performing existing models to quantify the impact of including the parity dimension on predictive accuracy. Our findings indicate that a parity-specific approach could lead to more plausible and reliable fertility projections, aiding government planners in their decision-making and enabling more tailored policy solutions. Code to replicate all results in this paper can be accessed at https://doi.org/10.5281/zenodo.10535250.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".