Digitale und Computergestützte Demografie

Auf einen Blick Projekte Publikationen Team


Das Messen und Modellieren des Migrationsverhaltens und der Mobilität von Wissenschaftler*innen

Aliakbar Akbaritabar, Asli Ebru Sanlitürk, Xinyi Zhao, Tom Theile, Emilio Zagheni; in Zusammenarbeit mit Samin Aref (University of Toronto, Kanada), Jevin West (University of Washington, Seattle, Vereinigte Staaten), Andrea Miranda-González (University of California, Berkeley, Vereinigte Staaten), Alexander Subbotin (Lomonosov Moscow State University, Russland), Francesco Billari (Bocconi University, Milan, Italien), Guy Stecklov (University of British Columbia, Vancouver, Kanada)

Ausführliche Beschreibung

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.


Bildung und Wissenschaft, internationale Migration, ethnische Minderheiten, interne Migration, Wohnsituation, Urbanisierung, Migration, Statistik und Mathematik

Schlagworte (Region):

Großbritannien, Mexico, Russland, Welt


Zhao, X.; Aref, S.; Zagheni, E.; Stecklov, G.:
In: 18th International Conference on Scientometrics and Informetrics ISSI 2021: 12-15 July 2021, KU Leuven, Belgium; proceedings, 1369–1380. Leuven: ISSI. (2021)    
Zhao, X.; Aref, S.; Zagheni, E.; Stecklov, G.:
Abstracts of the International Cartographic Association 3:327, 1–2. (2021)    
Miranda-González, A.; Aref, S.; Theile, T.; Zagheni, E.:
EPJ Data Science 9. (2020)    
Abel, G. L.; Muttarak, R.; Bordone, V.; Zagheni, E.:
European Journal of Population 35:3, 543–562. (2019)
Alburez-Gutierrez, D.; Aref, S.; Gil-Clavel, B. S.; Grow, A.; Negraia, D. V.; Zagheni, E.:
In: Smart statistics for smart applications : book of short papers SIS2019, 23–30. Pearson. (2019)    
Aref, S.; Zagheni, E.; West, J.:
In: Social Informatics 11th International Conference, SocInfo 2019, Doha, Qatar, November 18–21, 2019, Proceedings, 50–65. Cham: Springer. (2019)
Das Max-Planck-Institut für demografische Forschung (MPIDR) in Rostock ist eines der international führenden Zentren für Bevölkerungswissenschaft. Es gehört zur Max-Planck-Gesellschaft, einer der weltweit renommiertesten Forschungsgemeinschaften.