Reproducing scientists' mobility: a data-driven model

doi:10.1038/s41598-021-90281-9

Return

Reproducing scientists' mobility: a data-driven model

2021-05-24

Giacomo Vaccario

Luca Verginer

Frank Schweitzer *

DOI：10.1038/s41598-021-90281-9

Original

Save

View PDF

Abstract

En 中文

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.

Keywords:

BRAIN-DRAIN

COLLABORATION

NETWORKS

EMIGRATION

INNOVATION

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.