Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/260252 
Erscheinungsjahr: 
2020
Schriftenreihe/Nr.: 
Working Paper No. 2018:23
Verlag: 
Lund University, School of Economics and Management, Department of Economics, Lund
Zusammenfassung: 
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.
Schlagwörter: 
Refugee Resettlement
Matching
Integer Optimization
Machine Learning
Humanitarian Operations
JEL: 
C44
C55
C61
C78
F22
J61
Dokumentart: 
Working Paper

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