A Bayesian parametric model to estimate and reconstruct male age-specific fertility rates

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 data from Demographic and Health Surveys (DHS) from various countries. The statsitical model we propose, whch centers on a skewed-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. The approach also allows reconstructing estimates for years without data and incorporating sampling errors from surveys. 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.