TY - JOUR T1 - Improving refugee integration through data-driven algorithmic assignment JF - Science JO - Science SP - 325 LP - 329 DO - 10.1126/science.aao4408 VL - 359 IS - 6373 AU - Bansak, Kirk AU - Ferwerda, Jeremy AU - Hainmueller, Jens AU - Dillon, Andrea AU - Hangartner, Dominik AU - Lawrence, Duncan AU - Weinstein, Jeremy Y1 - 2018/01/19 UR - http://science.sciencemag.org/content/359/6373/325.abstract N2 - The continuing refugee crisis has made it necessary for governments to find ways to resettle individuals and families in host communities. Bansak et al. used a machine learning approach to develop an algorithm for geographically placing refugees to optimize their overall employment rate. The authors developed and tested the algorithm on segments of registry data from the United States and Switzerland. The algorithm improved the employment prospects of refugees in the United States by ∼40% and in Switzerland by ∼75%.Science, this issue p. 325Developed 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. ER -