(), Lars Ehlers
() and Alessandro Martinello
Tommy Andersson: Department of Economics, Lund University, Postal: Department of Economics, School of Economics and Management, Lund University, Box 7082, S-220 07 Lund, Sweden
Lars Ehlers: Département de sciences économiques, Université de Montréal, Postal: Département de sciences économiques,, Université de Montréal, , Montréal, , Québec H3C 3J7, , Canada
Alessandro Martinello: Department of Economics, Lund University, Postal: Department of Economics, School of Economics and Management, Lund University, Box 7082, S-220 07 Lund, Sweden
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.
29 pages, March 27, 2018
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