A Bayesian Model to Estimate Male and Female Fertility Patterns at a Subnational Level

Accurate subnational fertility estimates are crucial for shaping policy decisions across diverse sectors, including education, health care, and social welfare. However, these estimates are difficult to obtain in small populations, in which data on births classified by maternal and paternal ages may be lacking or inadequate. In this paper, we describe a Bayesian model tailored to estimate the Total Fertility Rates (TFR) for both men and women at a subnational level. The model relies on population counts from age-sex pyramids and jointly models mortality and fertility patterns while accounting for uncertainty and spatio-temporal dependencies. Testing the model with simulated data that mimic Australian regions, as well as with real data from US counties, demonstrates its ability to generate reasonable TFR estimates. The estimates produced have straightforward applications to the study of subregional reproductive disparities within the US, and the proposed modeling framework can be extended to investigate subnational fertility patterns across other countries with limited birth data.