Digital and Computational Demography
At a Glance
Modeling and Analysis of Migration and Mobility among Scholars
Aliakbar Akbaritabar, Asli Ebru Sanlitürk, Xinyi Zhao, Tom Theile, Emilio Zagheni; in Collaboration with Samin Aref (University of Toronto, Canada), Jevin West (University of Washington, Seattle, USA), Andrea Miranda-González (University of California, Berkeley, USA), Alexander Subbotin (Lomonosov Moscow State University, Russian Federation), Francesco Billari (Bocconi University, Milan, Italy), Guy Stecklov (University of British Columbia, Vancouver, Canada)
The migration of the highly skilled has important consequences for innovation and economic growth in sending and receiving countries. Understanding the processes that drive migration flows and the impacts of incentives or economic change is key to designing effective policies that address high-skilled migration and its consequences.
In the past, it was difficult to conduct research on highly skilled migrants, in part because of a lack of data. The recent availability of large-scale digital trace data from professional networking sites and from large collections of scholarly publications has enabled researchers to study the population dynamics of professionals. In particular, digital libraries such as Web of Science or Scopus offer the unique opportunity to follow career trajectories of scholars and their networks of co-authors over time and space. In this project, bibliometric data are used to contribute to the development of migration theories, including relationships between internal and international migration.
As part of this project, we used longitudinal data from Web of Science over the period 1956–2016 to study international movements of researchers around the world, tracked through changes in their institutional affiliation addresses. Web of Science offers a database equivalent to a “linked census” of publications that can be harnessed to follow changes in affiliations over time. In an article published in 2019, we focused on a highly selective group of research-active scholars who have published with their main affiliation addresses from at least three different countries. In particular, we analyzed the common features of these scholars, referred to as “super-movers” or “peripatetic scientists”, using newly developed variables on academic age, origin, and destination.
In related work, we used bibliometric data from Scopus over the 1996–2019 period to analyze the internal mobility of scholars within Mexico. In work published in 2020, we focused on mobility across 32 states of Mexico, looking for regularities across migration patterns over time. By doing so, we identified the development of geographic states of academic attraction and obtained net migration rates based on movements of scholars in Mexico. Analyzing internal migration required identifying these states from affiliation data. This spurred methodological innovation, including the development of a neural network model that predicts states of academic affiliation with a high level of accuracy.
As we develop our analyses, our ambition is to produce a dataset that maps the global migration of scholars at different levels of geographic and temporal granularity. This data will be used to assess the impact of policy changes on flows and on the network structure of migrants, and to improve our theoretical understanding of the relationships between internal and international migration. Moreover, we expect that combining individual-level longitudinal data about scholars with aggregate-level rates will help us to improve our understanding of the micro-macro dynamics in migration and population change.
Education and Science, Internal Migration, Housing, Urbanisation, International Migration, Ethnic Minorities, Migration, Statistics and Mathematics
Mexico, Russian Federation, United Kingdom, World
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