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 spatiotemporal dependencies. Testing the model with simulated data that mimic Australian regions, as well as with real data from US counties, Mexican states and Nigerian regions, demonstrates its ability to generate reasonable TFR estimates. The estimates produced have straightforward applications to the study of subregional reproductive disparities, and the proposed modeling framework can be extended to investigate subnational fertility patterns across other countries with limited or fragmented birth data.

A growing number of social scientists use online genealogical data as an alternative digital census of historical populations to study past demographic dynamics. However, the non-representativeness of this data source requires the development of bias-adjusting methods to obtain accurate demographic estimates. We address this challenge by proposing an indirect estimation framework to investigate fertility trends in seven European countries and the United States of America for the historical period 1751–1910, integrating data from the big genealogical database FamiLinx with more conventional data sources. The proposed methods allow for the indirect estimation of the total fertility rate using the number of women aged 15–49 and children under age 5, while accounting for child mortality, age-specific fertility patterns, and biases. Our methodological approaches demonstrate that, when combined with reliable demographic data, online genealogical data can be fruitfully used to examine fertility patterns in countries and periods lacking well-functioning national civil registration systems.

Background: Online genealogies are promising data sources for demographic research, but their limitations are understudied. This paper takes a critical approach to evaluating the potential strengths and weaknesses of using online genealogical data for population studies. We focus on the FamiLinx dataset, which contains demographic information and kinship ties across multiple countries and centuries.

Objective: We propose novel measures to assess the completeness and quality of demographic variables in the FamiLinx data at both the individual and familial level over the 1600–1900 period. Utilizing Sweden as a test country, we investigate how the age–sex distribution and mortality levels of the digital population extracted from FamiLinx diverge from the registered population.

Methods: We employ descriptive statistics, negative binomial regression modeling, and standard life table techniques for our measures of completeness and quality.

Results: Missing values and accuracy in demographic information from FamiLinx are selective. When one demographic variable is available, researchers can effectively anticipate the availability of other demographic information. The completeness and quality of demographic variables within kinship networks are markedly higher for individuals with more complete and accurate demographic information. Populations from FamiLinx display lower mortality levels than the registered population, and their representativeness improves toward the end of the nineteenth century.

Contribution: This study sheds new light on the opportunities and challenges of harnessing online genealogies for demographic research. Although this data source offers much promise, its usability in population studies is dependent on the quality and completeness of its recorded demographic information and their selectivity.