Improving refugee integration through data-driven algorithmic assignment-Reference-Cited by-同舟云学术

Improving refugee integration through data-driven algorithmic assignment

Published:2018-01-19 Issue:6373 Volume:359 Page:325-329
ISSN:0036-8075
Container-title:Science
language:en
Short-container-title:Science

Author:

Bansak Kirk¹²^ORCID,Ferwerda Jeremy²³,Hainmueller Jens¹²⁴^ORCID,Dillon Andrea²^ORCID,Hangartner Dominik²⁵⁶^ORCID,Lawrence Duncan²^ORCID,Weinstein Jeremy¹²^ORCID

Affiliation:

1. Department of Political Science, Stanford University, Stanford, CA 94305, USA.

2. Immigration Policy Lab, Stanford University, Stanford, CA 94305, USA, and ETH Zurich, 8092 Zurich, Switzerland.

3. Department of Government, Dartmouth College, Hanover, NH 03755, USA.

4. Graduate School of Business, Stanford University, Stanford, CA 94305, USA.

5. Center for Comparative and International Studies, ETH Zurich, 8092 Zurich, Switzerland.

6. Department of Government, London School of Economics and Political Science, London WC2A 2AE, UK.

Abstract

Data-driven refugee assignment The continuing refugee crisis has made it necessary for governments to find ways to resettle individuals and families in host communities. Bansak et al. used a machine learning approach to develop an algorithm for geographically placing refugees to optimize their overall employment rate. The authors developed and tested the algorithm on segments of registry data from the United States and Switzerland. The algorithm improved the employment prospects of refugees in the United States by ∼40% and in Switzerland by ∼75%. Science , this issue p. 325

Funder

Ford Foundation

Carnegie Corporation of New York

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

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