Reproducing scientists’ mobility: a data-driven model

Abstract High skill labour is an important factor underpinning the competitive advantage of modern economies. Therefore, attracting and retaining scientists has become a major concern for migration policy. In this work, we study the migration of scientists on a global scale, by combining two large d...

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Autores principales:	Giacomo Vaccario, Luca Verginer, Frank Schweitzer
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5e9031a7ca7d4a0791049bacc1f6d9d4
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id	oai:doaj.org-article:5e9031a7ca7d4a0791049bacc1f6d9d4
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spelling	oai:doaj.org-article:5e9031a7ca7d4a0791049bacc1f6d9d42021-12-02T14:42:21ZReproducing scientists’ mobility: a data-driven model10.1038/s41598-021-90281-92045-2322https://doaj.org/article/5e9031a7ca7d4a0791049bacc1f6d9d42021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90281-9https://doaj.org/toc/2045-2322Abstract High skill labour is an important factor underpinning the competitive advantage of modern economies. Therefore, attracting and retaining scientists has become a major concern for migration policy. In this work, we study the migration of scientists on a global scale, by combining two large data sets covering the publications of 3.5 million scientists over 60 years. We analyse their geographical distances moved for a new affiliation and their age when moving, this way reconstructing their geographical “career paths”. These paths are used to derive the world network of scientists’ mobility between cities and to analyse its topological properties. We further develop and calibrate an agent-based model, such that it reproduces the empirical findings both at the level of scientists and of the global network. Our model takes into account that the academic hiring process is largely demand-driven and demonstrates that the probability of scientists to relocate decreases both with age and with distance. Our results allow interpreting the model assumptions as micro-based decision rules that can explain the observed mobility patterns of scientists.Giacomo VaccarioLuca VerginerFrank SchweitzerNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Giacomo Vaccario Luca Verginer Frank Schweitzer Reproducing scientists’ mobility: a data-driven model
description	Abstract High skill labour is an important factor underpinning the competitive advantage of modern economies. Therefore, attracting and retaining scientists has become a major concern for migration policy. In this work, we study the migration of scientists on a global scale, by combining two large data sets covering the publications of 3.5 million scientists over 60 years. We analyse their geographical distances moved for a new affiliation and their age when moving, this way reconstructing their geographical “career paths”. These paths are used to derive the world network of scientists’ mobility between cities and to analyse its topological properties. We further develop and calibrate an agent-based model, such that it reproduces the empirical findings both at the level of scientists and of the global network. Our model takes into account that the academic hiring process is largely demand-driven and demonstrates that the probability of scientists to relocate decreases both with age and with distance. Our results allow interpreting the model assumptions as micro-based decision rules that can explain the observed mobility patterns of scientists.
format	article
author	Giacomo Vaccario Luca Verginer Frank Schweitzer
author_facet	Giacomo Vaccario Luca Verginer Frank Schweitzer
author_sort	Giacomo Vaccario
title	Reproducing scientists’ mobility: a data-driven model
title_short	Reproducing scientists’ mobility: a data-driven model
title_full	Reproducing scientists’ mobility: a data-driven model
title_fullStr	Reproducing scientists’ mobility: a data-driven model
title_full_unstemmed	Reproducing scientists’ mobility: a data-driven model
title_sort	reproducing scientists’ mobility: a data-driven model
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5e9031a7ca7d4a0791049bacc1f6d9d4
work_keys_str_mv	AT giacomovaccario reproducingscientistsmobilityadatadrivenmodel AT lucaverginer reproducingscientistsmobilityadatadrivenmodel AT frankschweitzer reproducingscientistsmobilityadatadrivenmodel
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Reproducing scientists’ mobility: a data-driven model

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