Matching refugees to host country locations

My political science colleagues at Stanford have been thinking fruitfully about how to match refugees to locations in the countries to which they have been granted asylum:

Matching Refugees to Host Country LocationsBased on Preferences and Outcomes
∗ Avidit Acharya† Kirk Bansak‡ Jens Hainmueller§ February 21, 2019

Abstract: Facilitating the integration of refugees has become a major policy challenge in many host countries in the context of the global displacement crisis. One of the first policy decisions host countries make in the resettlement process is the assignment of refugees to locations within the country. We develop a mechanism to match refugees to locations in a way that takes into account their expected integration outcomes and their preferences over where to be settled. Our proposal is based on a priority mechanism that allows the government first to specify a threshold g for the minimum level of expected integration success that should be achieved. Refugees are then matched to locations based on their preferences subject to meeting the government’s specified threshold. The mechanism is both strategy-proof and constrained efficient in that it always generates a matching that is not Pareto dominated by any other matching that respects the government’s threshold. We demonstrate our approach using simulations and a real-world application to refugee data from the United States.
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Here's an earlier paper by a group including some of the same authors

Science. 2018 Jan 19;359(6373):325-329. doi: 10.1126/science.aao4408.

Improving refugee integration through data-driven algorithmic assignment.

Bansak K1,2, Ferwerda J2,3, Hainmueller J1,2,4, Dillon A2, Hangartner D2,5,6, Lawrence D2, Weinstein J1,2.

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.