Propinquity and Family Dynamics of Kin Living Abroad: Historical Insights from Online Genealogies

Andrea Colasurdo , Max Planck Institute for Demographic Research
Diego Alburez-Gutierrez, Max Planck Institute for Demographic Research

Kinship networks are central in the migration process and the impact of kin networks on migration behavior should be analyzed to better understand the decisions of migrants and their trajectories. Existing studies that evaluate the impact of family ties on geographical mobility usually focus on residential movements within a country or some specific transnational migrations. However, there is still a substantial lack of knowledge about how these family networks have changed over time and have involved extended families and different countries. Thanks to online genealogies and historical datasets demographic and kinship information are available across multiple centuries and generations. Using the online crowdsourced genealogy FamiLinx, a database extracted from Geni.com, this research will shed light on the dispersion of transnational kin networks across countries and their development over time. This dataset contains information on transnational movements and transnational kin ties over time, rather than having movements and networks restricted by country borders. Preliminary results show that the percentage of descendants who lived abroad with respect to a focal seems to be higher among individuals living in European countries, while the percentage of transnational ancestors seems to be larger among US-born profiles. The observed trends over time reflect the historical migration patterns experienced in those areas. Our findings will help to understand contemporary and future trends and shed light on the actual dispersion of kin networks and predict potential migration behaviors. In further developments of the project, we will examine trends in the experience of relatives’ loss and birth occurring abroad.

See extended abstract

 Presented in Session 8. Harnessing the Power of Genealogical Data: Opportunities and Challenges