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Area of Science:

  • Sociology of Science
  • Migration Studies
  • Network Science

Background:

  • High-skilled labor, particularly scientists, is crucial for economic competitiveness.
  • Attracting and retaining scientists is a key policy concern globally.

Purpose of the Study:

  • To analyze the global migration patterns of scientists using publication data.
  • To reconstruct scientists' geographical career paths and analyze the global mobility network.
  • To develop and validate an agent-based model explaining these mobility patterns.

Main Methods:

  • Analysis of publication data for 3.5 million scientists over 60 years.
  • Reconstruction of geographical career paths and network analysis of city-to-city scientist mobility.
  • Development and calibration of a demand-driven agent-based model.

Main Results:

  • The probability of scientists relocating decreases with both age and distance.
  • The model successfully reproduces empirical findings at both individual and network levels.
  • Mobility patterns are explained by micro-based decision rules within a demand-driven academic hiring system.

Conclusions:

  • Scientist migration is a complex phenomenon influenced by age and distance.
  • Agent-based modeling provides a powerful tool for understanding scientist mobility.
  • The findings offer insights for migration policies aimed at attracting and retaining scientific talent.