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Dynamic Refugee Matching

Author

Listed:
  • Andersson, Tommy

    (Department of Economics, Lund University)

  • Ehlers, Lars

    (Département de sciences économiques, Université de Montréal)

  • Martinello, Alessandro

    (Department of Economics, Lund University)

Abstract

Asylum seekers are often assigned to a locality in their host country directly upon arrival based on some type of uninformed dynamic matching system which does not take the background of the asylum seekers into consideration. This paper proposes an informed, intuitive, easy-to-implement and computationally efficient dynamic mechanism for matching asylum seekers to localities. This mechanism can be adopted in any dynamic refugee matching problem given locality-specific quotas and that asylum seekers can be classified into specific types. We demonstrate that any matching selected by the proposed mechanism is Pareto efficient and that envy between localities is bounded by a single asylum seeker. Via simulation, we evaluate the performance of the proposed mechanism in settings that resemble the US and the Swedish situations, and show that our mechanism outperforms uninformed mechanisms even in presence of severe misclassification error in the estimation of asylum seeker types.

Suggested Citation

  • Andersson, Tommy & Ehlers, Lars & Martinello, Alessandro, 2018. "Dynamic Refugee Matching," Working Papers 2018:7, Lund University, Department of Economics.
  • Handle: RePEc:hhs:lunewp:2018_007
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    References listed on IDEAS

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    Cited by:

    1. P'eter Bir'o & M'arton Gyetvai, 2021. "Online voluntary mentoring: Optimising the assignment of students and mentors," Papers 2102.06671, arXiv.org.
    2. Mehic, Adrian, 2019. "Immigration and Right-Wing Populism: Evidence from a Natural Experiment," Working Papers 2019:5, Lund University, Department of Economics.
    3. Francesco Campo & Sara Giunti & Mariapia Mendola & Giulia Tura, 2023. "Political Backlash to Refugee Settlement: Cultural and Economic Drivers," Working Papers 522, University of Milano-Bicocca, Department of Economics.
    4. Hagen, Martin, 2022. "Tradable immigration quotas revisited," Journal of Public Economics, Elsevier, vol. 208(C).
    5. Gina Galindo & Jose Navarro & Jhonattan Reales & Jhoan Castro & Daniel Romero & Sandra Rodriguez A. & Daniel Rivera-Royero, 2022. "Immigrants resettlement in developing countries: A data-driven decision tool applied to the case of Venezuelan immigrants in Colombia," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-16, January.
    6. Biró, Péter & Gyetvai, Márton, 2023. "Online voluntary mentoring: Optimising the assignment of students and mentors," European Journal of Operational Research, Elsevier, vol. 307(1), pages 392-405.

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    More about this item

    Keywords

    forced migration; market design; refugee matching; dynamics; envy; efficiency;
    All these keywords.

    JEL classification:

    • C71 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Cooperative Games
    • C78 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Bargaining Theory; Matching Theory
    • D71 - Microeconomics - - Analysis of Collective Decision-Making - - - Social Choice; Clubs; Committees; Associations
    • D78 - Microeconomics - - Analysis of Collective Decision-Making - - - Positive Analysis of Policy Formulation and Implementation
    • F22 - International Economics - - International Factor Movements and International Business - - - International Migration

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