Characterizing High-Skilled Mobility Patterns in Europe from Social Media

Daniela Perrotta , Max Planck Institute for Demographic Research
Diego Alburez-Gutierrez, Max Planck Institute for Demographic Research
Carlos Callejo PeƱalba, Aalto University
Kiran Garimella, MIT Institute for Data, Systems, and Society
Tom Theile, Max Planck Institute for Demographic Research
Ingmar Weber, Qatar Computing Research Institute
Emilio Zagheni, Max Planck Institute for demographic Research

International high-skilled migration represents an increasingly important component of migration streams with a significant impact on the global flow of skills and on migration policies. Population movements of high-skilled workers are difficult to measure and model. In this study, we use a largely untapped data source, LinkedIn data for Advertisers, in order to understand the network of flows of professionals in Europe. Indeed, digital data from services like LinkedIn represent a relatively low-cost resource to identify migration patterns and draw a high-resolution picture of human mobility patterns at an unprecedented scale. Here we report some preliminary results on the mobility network that can be reconstructed from the retrieved data from LinkedIn Ads. We aim to develop methods based on gravity-type migration models to assess the extent to which there are imbalances in migration flows across countries and by socio-demographic characteristics, including age and gender, or by industry of occupation. As we develop our models we expect to be able to evaluate the relative importance of socio-economic, political, and geographical factors in shaping flows of professionals in Europe.

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 Presented in Session P3. Poster Session Migration, Economics, Environment, Methods, History and Policy