Placement Optimization in Refugee Resettlement

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 2018:23

Classification and Topics

• Classification: Wirtschaft ; Operations Research; Statistical Decision Theory ; Large Data Sets: Modeling and Analysis ; Optimization Techniques; Programming Models; Dynamic Analysis ; Bargaining Theory; Matching Theory ; International Migration ; Geographic Labor Mobility; Immigrant Workers
• Subject: Refugee Resettlement ; Matching ; Integer Optimization ; Machine Learning ; Humanitarian Operations

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Trapp, Andrew C. ; Teytelboym, Alexander ; Martinello, Alessandro ; Andersson, Tommy ; Ahani, Narges
• Event: Veröffentlichung
• (who): Lund University, School of Economics and Management, Department of Economics
• (where): Lund
• (when): 2020

Further information

• Handle: http://hdl.handle.net/10419/260252
• Last update: 10.03.2025, 11:43 AM CET