The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Portrait of Tommy Andersson. Photo.

Tommy Andersson

Professor

Portrait of Tommy Andersson. Photo.

Placement Optimization in Refugee Resettlement

Author

  • Andrew C. Trapp
  • Alexander Teytelboym
  • Alessandro Martinello
  • Tommy Andersson
  • Narges Ahani

Summary, in English

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.

Department/s

  • Department of Economics

Publishing year

2018

Language

English

Publication/Series

Working Papers

Issue

2018:23

Document type

Working paper

Topic

  • Economics

Keywords

  • Refugee Resettlement
  • Matching
  • Integer Optimization
  • Machine Learning
  • Humanitarian Operations
  • C44
  • C55
  • C61
  • C78
  • F22
  • J61

Status

Published