Migration and Mobility
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Societies are continuously facing the challenges of managing vital migration flows and integrating migrants in the context of below-replacement fertility, slow population aging, and sudden crises or shocks. While demographers have played a key role in developing migration theories and methods, a lack of adequate data generally hinders their validation and the development of new conceptual frameworks. Established data sources, typically collected from censuses, surveys, and registers, often lack sufficient spatial and temporal granularity. Definitions and modes of collection often vary across countries and can be inconsistent. Data may be partially or completely unavailable, especially in low- and middle-income countries.
The recent availability of massive amounts of digital data – as well as the increase in computing power – have opened up new opportunities for the study of migration and mobility. Harnessing the digital traces generated by human activities on social media, the Internet, and digital devices more broadly holds the key to filling data gaps and addressing classic and emerging questions in migration and mobility research.
Our main ambition is to combine established and novel data sources within a solid statistical framework, measure and predict migration outcomes and the integration of migrants, and evaluate the impact of the digitalization of life on migration and mobility. As we pursue our goals, we also aim at improving our theoretical understanding of migration and mobility processes and informing policy decisions in a world that is increasingly connected.
Current projects in this Laboratory are organized around three main research domains:
In the research area Measuring and Modeling Migration and Mobility, we advance methods to improve estimates of international and internal migration flows and stocks and to make predictions for the future. By integrating a large number of data sources, including population registers, population surveys, and digital trace data, within a Bayesian statistical framework, we aspire to develop a database of high-quality and harmonized estimates of international migration at different levels of temporal and geographic granularity. Consistent and high-quality estimates serve as the basis for testing and advancing migration theories and for rapidly assessing responses to shocks, such as natural disasters, disease outbreaks, and international conflicts.
In High-Skilled Migration, we study the migration of professionals and scholars, using data sources such as surveys, LinkedIn, and large-scale bibliometric databases. A key goal includes the development and maintenance of a comprehensive and longitudinal database of the migration of scholars at different levels of geographic granularity. This will reduce the data gap in the migration patterns of the highly educated and facilitate advances in theoretical frameworks and in the impact evaluation of policy interventions.
In Integration and Segregation, some of the key goals include measuring and understanding the dynamics of the patterns of integration and segregation in both offline and online spaces, assessing distance and similarities between countries in terms of cultural preferences revealed by digital traces — and their relationships with migration flows —, and evaluating the way in which different sociodemographic and economic groups have differential patterns of geographic mobility.