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Placement Optimization in Refugee Resettlement


  • Trapp, Andrew C.

    (Foisie Business School, Worcester Polytechnic Institute)

  • Teytelboym, Alexander

    (Department of Economics, University of Oxford)

  • Martinello, Alessandro

    (Department of Economics, Lund University)

  • Andersson, Tommy

    (Department of Economics, Lund University)

  • Ahani, Narges

    (Foisie Business School, Worcester Polytechnic Institute)


Every year tens of thousands of refugees are resettled to dozens of host countries. While 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 Moore, that assists a US 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 backtesting indicates that Annie can improve short-run employment outcomes by 22%–38%. We conclude by discussing several directions for future work.

Suggested Citation

  • Trapp, Andrew C. & Teytelboym, Alexander & Martinello, Alessandro & Andersson, Tommy & Ahani, Narges, 2018. "Placement Optimization in Refugee Resettlement," Working Papers 2018:23, Lund University, Department of Economics, revised 20 Mar 2020.
  • Handle: RePEc:hhs:lunewp:2018_023

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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,
    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,

    More about this item


    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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