Visualizing, modeling and forecasting age distribution of fertility

The study of fertility differences has often focused on average fertility levels, such as age-specific fertility rates, while neglecting the age distribution of fertility. We introduce three novel summary statistics to analyze fertility distributions: we identify the modal age of women for each year; we use Wasserstein distance to find the medoid density; and we apply Taylor’s power law to assess regional heterogeneity in the age distribution of fertility. Then, we present two methods for modeling and forecasting state-wise fertility gaps. A compositional data analytic framework, particularly a centered log-ratio transformation, is employed to generate out-of-sample density forecasts. The cumulative distribution function supports visualization and comparison of fertility densities. Applied to Australian birth counts from 1975 to 2024, our results show a decline in births along with continued postponement of childbearing, with Victoria projected to experience greater delays than New South Wales and Queensland.

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Publication Details

Journal
Mathematical Population Studies
Published
2026-10-15
DOI
https://doi.org/10.1080/08898480.2026.2747887
Primary Topic
Insurance, Mortality, Demography, Risk Management
Type
article
Field-Weighted Citation Impact
0.00
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article

Visualizing, modeling and forecasting age distribution of fertility

Han Lin Shang, Yang Yang
Mathematical Population Studies
Insurance, Mortality, Demography, Risk Management
article

Visualizing, modeling and forecasting age distribution of fertility

Han Lin Shang, Yang Yang
article en

Abstract

The study of fertility differences has often focused on average fertility levels, such as age-specific fertility rates, while neglecting the age distribution of fertility. We introduce three novel summary statistics to analyze fertility distributions: we identify the modal age of women for each year; we use Wasserstein distance to find the medoid density; and we apply Taylor’s power law to assess regional heterogeneity in the age distribution of fertility. Then, we present two methods for modeling and forecasting state-wise fertility gaps. A compositional data analytic framework, particularly a centered log-ratio transformation, is employed to generate out-of-sample density forecasts. The cumulative distribution function supports visualization and comparison of fertility densities. Applied to Australian birth counts from 1975 to 2024, our results show a decline in births along with continued postponement of childbearing, with Victoria projected to experience greater delays than New South Wales and Queensland.

Mathematical Population Studies
University of Newcastle Australia (AU), Macquarie University (AU)
Reduced inequalities
Openalex Percentile: Top 5%
Insurance, Mortality, Demography, Risk Management
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