Placement Optimization in Refugee Resettlement

Narges Ahani (), Tommy Andersson, Alessandro Martinello, Alexander Teytelboym () and Andrew C. Trapp ()
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Narges Ahani: Data Science Program, Worcester Polytechnic Institute, Worcester, Massachusetts 01609
Alexander Teytelboym: Department of Economics, University of Oxford, Oxford OX1 3UQ, United Kingdom
Andrew C. Trapp: Foisie Business School and Data Science Program, Worcester Polytechnic Institute, Worcester, Massachusetts 01609

Operations Research, 2021, vol. 69, issue 5, 1468-1486

Abstract: 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 ™ Matching and Outcome Optimization for Refugee Empowerment ( Annie ™ 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 ™ can improve short-run employment outcomes by 22%–38%. We conclude by discussing several directions for future work.

Keywords: programming: integer; statistics: regression; networks/graphs: matchings; information systems: decision support systems; Policy Modeling and Public Sector OR; refugee resettlement; matching; integer optimization; machine learning; humanitarian operations (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Working Paper: Placement Optimization in Refugee Resettlement (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:69:y:2021:i:5:p:1468-1486

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