Scandinavian Working Papers in Economics

Working Papers,
Lund University, Department of Economics

No 2018:23: Placement Optimization in Refugee Resettlement

Andrew C. Trapp (), Alexander Teytelboym (), Alessandro Martinello (), Tommy Andersson () and Narges Ahani ()
Additional contact information
Andrew C. Trapp: Foisie Business School, Worcester Polytechnic Institute, Postal: Foisie Business School, Worcester Polytechnic Institute, 100 Institute Rd., Worcester, MA 01609, USA
Alexander Teytelboym: Department of Economics, University of Oxford, Postal: Manor Road Building, Manor Road, Oxford OX1 3UQ, United Kingdom
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
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
Narges Ahani: Foisie Business School, Worcester Polytechnic Institute, Postal: Foisie Business School, Worcester Polytechnic Institute, 100 Institute Rd., Worcester, MA 01609, USA

Abstract: Every year 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 destinations. We integrate machine learning and integer optimization technologies into an innovative software tool that assists a resettlement agency in the United States with matching refugees to their initial placements. Our software suggests optimal placements while giving substantial autonomy for the resettlement staff to fine-tune recommended matches. Initial back-testing indicates that Annie can improve short-run employment outcomes by 22%-37%. We discuss several directions for future work such as incorporating multiple objectives from additional integration outcomes, dealing with equity concerns, evaluating potential new locations for resettlement, managing quota in a dynamic fashion, and eliciting refugee preferences.

Keywords: Refugee Resettlement; Matching; Integer Optimization; Machine Learning; Humanitarian Operations

JEL-codes: C44; C55; C61; C78; F22; J61

36 pages, October 3, 2018

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