Bayesian Indirect Estimation of Historical Fertility in Europe and US Using Online Genealogical Data

Riccardo Omenti, Monica Alexander and Nicola Barban

No ygt2k_v1, OSF Preprints from Center for Open Science

Abstract: A growing number of social scientists use online genealogical data as an alternative digital census of historical populations for the examination of 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 traditional data sources. The proposed methods produce total fertility rate (TFR) estimates using minimal data, specifically women aged 15-49 and children under age 5, while accounting for child mortality, age-specific fertility patterns, and biases inherent in online genealogical data. 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 historical periods lacking well-functioning national civil registration systems.

Date: 2025-07-22
New Economics Papers: this item is included in nep-evo and nep-his
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:ygt2k_v1

DOI: 10.31219/osf.io/ygt2k_v1

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