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Improving refugee integration through data-driven algorithmic assignment.

Kirk Bansak1,2, Jeremy Ferwerda2,3, Jens Hainmueller1,2,4

  • 1Department of Political Science, Stanford University, Stanford, CA 94305, USA.

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A new algorithm improves refugee integration by matching individuals to resettlement locations using machine learning. This data-driven approach significantly boosts employment outcomes, offering a practical policy tool for governments.

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Area of Science:

  • Computational social science
  • Machine learning applications
  • Sociology of migration

Background:

  • Developed democracies are resettling increasing numbers of refugees.
  • Refugee integration into host societies presents significant challenges.
  • Current resettlement assignment practices may not optimize integration outcomes.

Purpose of the Study:

  • To develop and evaluate a data-driven algorithm for assigning refugees to resettlement locations.
  • To improve refugee integration outcomes, particularly employment.
  • To provide a practical and cost-efficient policy tool for governments.

Main Methods:

  • Developed a flexible, data-driven algorithm combining supervised machine learning and optimal matching.
  • Leveraged refugee characteristics and resettlement site synergies.
  • Tested the algorithm on historical registry data from the United States and Switzerland.

Main Results:

  • The algorithm demonstrated significant improvements in refugee employment outcomes, ranging from 40% to 70% on average.
  • These gains were observed relative to existing assignment practices in the studied countries.
  • The approach proved effective across different assignment regimes and refugee populations.

Conclusions:

  • The developed algorithm offers a practical and cost-efficient method to enhance refugee integration.
  • The data-driven approach can be readily implemented within existing governmental structures.
  • This tool has the potential to substantially improve employment prospects for resettled refugees.