Faculty of Business and Economics, University of Basel, 4002 Basel, Switzerland.
Laboratory of Fertility and Well-Being, Max Planck Institute for Demographic Research, 18057 Rostock, Germany.
Proc Natl Acad Sci U S A. 2022 Mar 8;119(10):e2120455119. doi: 10.1073/pnas.2120455119. Epub 2022 Mar 1.
Crowdsourced online genealogies have an unprecedented potential to shed light on long-run population dynamics, if analyzed properly. We investigate whether the historical mortality dynamics of males in familinx, a popular genealogical dataset, are representative of the general population, or whether they are closer to those of an elite subpopulation in two territories. The first territory is the German Empire, with a low level of genealogical coverage relative to the total population size, while the second territory is The Netherlands, with a higher level of genealogical coverage relative to the population. We find that, for the period around the turn of the 20th century (for which benchmark national life tables are available), mortality is consistently lower and more homogeneous in familinx than in the general population. For that time period, the mortality levels in familinx resemble those of elites in the German Empire, while they are closer to those in national life tables in The Netherlands. For the period before the 19th century, the mortality levels in familinx mirror those of the elites in both territories. We identify the low coverage of the total population and the oversampling of elites in online genealogies as potential explanations for these findings. Emerging digital data may revolutionize our knowledge of historical demographic dynamics, but only if we understand their potential uses and limitations.
众包在线族谱如果经过适当分析，具有揭示长期人口动态的空前潜力。我们研究了 familinx 中男性的历史死亡率动态是否代表了总人口，或者它们是否更接近两个地区的精英亚群。第一个地区是德国帝国，其族谱覆盖率相对于总人口规模较低，而第二个地区是荷兰，其族谱覆盖率相对于人口较高。我们发现，对于接近 20 世纪之交的时期（有基准国家生命表可供参考），familinx 中的死亡率始终低于总人口，而且更加均匀。在那个时期，familinx 的死亡率水平类似于德国帝国精英的死亡率水平，而与荷兰的国家生命表更接近。对于 19 世纪以前的时期，familinx 中的死亡率水平反映了两个地区的精英的死亡率水平。我们将在线族谱中总人口覆盖率低和精英抽样过多视为这些发现的潜在解释。新兴的数字数据可能会彻底改变我们对历史人口动态的认识，但前提是我们了解其潜在用途和局限性。
