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

Riccardo Omenti, University of Bologna
Monica Alexander, University of Toronto
Nicola Barban, University of Bologna

Accurate subnational fertility estimates are crucial for shaping policy decisions across diverse sectors, including education, health care, and social welfare. One of the major challenges in producing these estimates is the presence of small populations, in which information about birth counts stratified by the age at childbearing and fatherhood may be lacking or inadequate. In this research paper, we describe a Bayesian model tailored to estimate the period Total Fertility Rates (TFR) for both men and women at a subnational level. Building on previous work by Schmertmann and Hauer (2019), the model utilizes population counts from age-sex pyramids and models age-specific mortality and fertility patterns accounting for uncertainty and allowing for spatial and 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 proposed model exhibits significant potential for the examination of male and female fertility behaviors across various subregions and time frames in multiple countries.

Keywords: Bayesian methods , Small area estimation, Fertility, Spatial Demography

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