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Improving refugee integration through data-driven algorithmic assignment.

  • Author(s): Bansak, Kirk
  • Ferwerda, Jeremy
  • Hainmueller, Jens
  • Dillon, Andrea
  • Hangartner, Dominik
  • Lawrence, Duncan
  • Weinstein, Jeremy
  • et al.

Published Web Location

https://science.sciencemag.org/content/359/6373/325
No data is associated with this publication.
Abstract

Developed democracies are settling an increased number of refugees, many of whom face challenges integrating into host societies. We developed a flexible data-driven algorithm that assigns refugees across resettlement locations to improve integration outcomes. The algorithm uses a combination of supervised machine learning and optimal matching to discover and leverage synergies between refugee characteristics and resettlement sites. The algorithm was tested on historical registry data from two countries with different assignment regimes and refugee populations, the United States and Switzerland. Our approach led to gains of roughly 40 to 70%, on average, in refugees' employment outcomes relative to current assignment practices. This approach can provide governments with a practical and cost-efficient policy tool that can be immediately implemented within existing institutional structures.

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