Placement Optimization in Refugee Resettlement

被引:30
作者
Ahani, Narges [1 ]
Andersson, Tommy [2 ]
Martinello, Alessandro [3 ]
Teytelboym, Alexander [4 ]
Trapp, Andrew C. [1 ,5 ]
机构
[1] Worcester Polytech Inst, Data Sci Program, Worcester, MA 01609 USA
[2] Lund Univ, Dept Econ, SE-22007 Lund, Sweden
[3] Danmarks Nationalbank, DK-2100 Copenhagen O, Denmark
[4] Univ Oxford, Dept Econ, Oxford OX1 3UQ, England
[5] Worcester Polytech Inst, Foisie Business Sch, Worcester, MA 01609 USA
基金
美国国家科学基金会; 英国经济与社会研究理事会;
关键词
refugee resettlement; matching; integer optimization; machine learning; humanitarian operations; INTEGRATION; CHOICE;
D O I
10.1287/opre.2020.2093
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
Every year, tens of thousands of refugees are resettled to dozens of host countries. Although there is growing evidence that the initial placement of refugee families profoundly affects their lifetime outcomes, there have been few attempts to optimize resettlement decisions. We integrate machine learning and integer optimization into an innovative software tool, Annie (TM) Matching and Outcome Optimization for Refugee Empowerment (Annie (TM) MOORE), that assists a U.S. resettlement agency with matching refugees to their initial placements. Our software suggests optimal placements while giving substantial autonomy to the resettlement staff to fine-tune recommended matches, thereby streamlining their resettlement operations. Initial back testing indicates that Annie (TM) can improve short-run employment outcomes by 22%-38%. We conclude by discussing several directions for future work.
引用
收藏
页码:1468 / 1486
页数:20
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