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Improving refugee integration through data-driven algorithmic assignment
Science ( IF 44.7 ) Pub Date : 2018-01-18 , DOI: 10.1126/science.aao4408
Kirk Bansak 1, 2 , Jeremy Ferwerda 2, 3 , Jens Hainmueller 1, 2, 4 , Andrea Dillon 2 , Dominik Hangartner 2, 5, 6 , Duncan Lawrence 2 , Jeremy Weinstein 1, 2
Affiliation  

Data-driven refugee assignment 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. 325 A machine learning–based algorithm for assigning refugees can improve their employment prospects over current approaches. 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.

中文翻译:

通过数据驱动的算法分配改善难民融合

数据驱动的难民分配 持续的难民危机使政府有必要找到在收容社区重新安置个人和家庭的方法。班萨克等人。使用机器学习方法开发了一种算法,用于按地理位置安置难民,以优化他们的整体就业率。作者开发并测试了来自美国和瑞士的注册数据段的算法。该算法将美国难民的就业前景提高了约 40%,将瑞士的难民就业前景提高了约 75%。科学,这个问题 p。325 基于机器学习的难民分配算法可以改善他们的就业前景。发达的民主国家正在安置越来越多的难民,其中许多人面临融入东道国社会的挑战。我们开发了一种灵活的数据驱动算法,可以在重新安置地点之间分配难民,以改善融合结果。该算法结合使用监督机器学习和最佳匹配来发现和利用难民特征和安置地点之间的协同作用。该算法在来自美国和瑞士这两个具有不同分配制度和难民人口的国家的历史登记数据上进行了测试。相对于当前的分配做法,我们的方法使难民的就业结果平均提高了大约 40% 到 70%。这种方法可以为政府提供一种实用且具有成本效益的政策工具,可以在现有制度结构内立即实施。
更新日期:2018-01-18
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