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Placement Optimization in Refugee Resettlement

Author

Listed:
  • Narges Ahani

    (Data Science Program, Worcester Polytechnic Institute, Worcester, Massachusetts 01609)

  • Tommy Andersson

    (Department of Economics, Lund University, Lund SE-220 07, Sweden)

  • Alessandro Martinello

    (Danmarks Nationalbank, 2100 Copenhagen Ø, Denmark)

  • 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)

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.

Suggested Citation

  • Narges Ahani & Tommy Andersson & Alessandro Martinello & Alexander Teytelboym & Andrew C. Trapp, 2021. "Placement Optimization in Refugee Resettlement," Operations Research, INFORMS, vol. 69(5), pages 1468-1486, September.
    • Handle: RePEc:inm:oropre:v:69:y:2021:i:5:p:1468-1486
    DOI: 10.1287/opre.2020.2093
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    Cited by:

    1. Magnus Lodefalk & Fredrik Sjöholm & Aili Tang, 2022. "International trade and labour market integration of immigrants," The World Economy, Wiley Blackwell, vol. 45(6), pages 1650-1689, June.
    2. P'eter Bir'o & M'arton Gyetvai, 2021. "Online voluntary mentoring: Optimising the assignment of students and mentors," Papers 2102.06671, arXiv.org.
    3. Biró, Péter & Gudmundsson, Jens, 2021. "Complexity of finding Pareto-efficient allocations of highest welfare," European Journal of Operational Research, Elsevier, vol. 291(2), pages 614-628.
    4. Girum Abebe & Marcel Fafchamps & Michael Koelle & Simon Quinn, 2019. "Learning Management Through Matching: A Field Experiment Using Mechanism Design," CSAE Working Paper Series 2019-11, Centre for the Study of African Economies, University of Oxford.
    5. Maximilian Kasy & Alexander Teytelboym, 2020. "Adaptive Combinatorial Allocation," Papers 2011.02330, arXiv.org.

    More about this item

    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;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C78 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Bargaining Theory; Matching Theory
    • F22 - International Economics - - International Factor Movements and International Business - - - International Migration
    • J61 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Geographic Labor Mobility; Immigrant Workers

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