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 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.

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

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

61 pages, First version: October 3, 2018. Revised: March 20, 2020.

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