A Bayesian Parametric Model to Estimate and Reconstruct Male Age-Specific Fertility Rates

Benjamin-Samuel Schlueter , Max Planck Institute for Demographic Research
Bruno Schoumaker, UCLouvain
Monica Alexander, University of Toronto

Research on human fertility has primarily focused on women, with male fertility remaining underexplored. The biggest differences in timing and magnitude between male and female fertility is observed in the Global South, where data on male fertility is not widely available. In this project we propose a Bayesian parametric model to estimate and reconstruct male fertility rates for countries with no civil registration and vital statistics systems for past, present, and future periods. We draw on the own-children method applied to Demographic and Health Surveys (DHS) from various countries. The statistical model we propose, which centers on a skew-normal distribution, accounts for missing data, small samples, and data quality issues. The model is flexible enough to capture variations in male age-specific fertility rates across different populations and periods, and its parameters can be interpreted in terms of fertility trends. The approach allows reconstructing estimates for years without data, combine surveys covering the same year, and incorporating sampling errors from surveys. The methodology also ensures that yearly estimated male age-specific fertility rates are coherent with yearly births estimates from the United Nations World Population Prospects. Our results suggest that there is a general convergence between male and female TFRs over time but the mean age at parenthood remains substantially higher for males compared to females in the countries studied. This research will contribute to a more comprehensive understanding of male fertility trends and provide essential inputs for modeling kinship structures, orphanhood, and conducting indirect mortality estimates.

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 Presented in Session 2. Bayesian Demographic Modeling