Hybrid Format
Leveraging Crowdsourced Online Genealogical Data to Study the Evolution of Fertility
Riccardo Omenti
Research Group: Kinship Inequalities, March 15, 2023
Riccardo Omenti from the University of Bologna talked about crowdsourced online genealogies, a promising new data source for demographic analysis.
Abstract
Crowdsourced online genealogies are a promising new data source for demographic analysis. This data source incorporates both individual-level demographic data, e.g. place and date of birth and/or death, as well as information about kinship ties that span over multiple centuries and countries.
This presentation aims at exploring the potential use of crowd-sourced online genealogies to study the fertility transition in Northern Europe and North America during the historical period 1700-1920. The analysis relies on FamiLinx, a novel historical dataset crowdsourced from the public available genealogies on the website geni.com. He explores the evolution of fertility by constructing country-specific time series of total fertility rates (TFR) estimated using a novel Bayesian Statistical method developed by Schmertmann & Hauer (2019). Based upon the preliminary results of the analysis, he carries out an overall critical assessment about the extent to which online genealogies can be used as an historical repository to document stylized facts from Demographic Theory.
Reference
Schmertmann, C. P., & Hauer, M. E. (2019). Bayesian estimation of total fertility from a population's age–sex structure. Statistical Modelling, 19(3), 225-247.
About
Riccardo Omenti is a PhD student in Statistics at the University of Bologna. His research interests include the employment of advanced techniques arising from both Statistics and Formal Demography to analyze demographic processes using Digital Data. He is currently working with online genealogical data to gain new insights about the potential use of this data source to investigate the evolution of fertility in Europe and North America during the first demographic transition.