Annie™ MOORE is the first software to optimise resettlement outcomes for refugees. So far it has been used in the resettlement of over 1,000 refugees resettled in the US, improving outcomes for both their families and their host communities.

The refugee “resettlement gap” continues to be a global problem. Of the estimated 1,200,000 refugees in need of resettlement in 2018, only 55,692 (4.7%) were actually resettled. 

Annie™ MOORE was developed by Alexander Teytelboym (Associate Professor, Department of Economics) along with researchers from the United States (Andrew Trapp and Narges Ahani at WPI) and Sweden (Tommy Andersson and Alessandro Martinello from Lund University) to optimise resettlement outcomes for refugees by matching the needs of refugees and their families with the service capacities of hosting communities. 

It has been used by US resettlement agency HIAS since May 2018 for all refugee placements. As well as improving the employment outcomes and quality of refugee integration within communities, Annie™ MOORE has boosted HIAS’s capacity to handle cases by increasing its operational efficiency.

Levaraging economic theory to improve outcomes for resettled refugees - Social Science Divisio

A two-sided approach

Teytelboym together with Will Jones (RHUL), David Delacretaz (Manchester) and Scott Kominers (Harvard) worked on a theory of matching refugees to local communities. In their model, refugees are matched to local communities taking into account the preferences and needs of refugees, and the provision of states and local communities.

Similar matching mechanisms are used for matching children to public schools, or junior doctors to hospitals. However, as refugees are resettled as families and localities vary enormously, Teytelboym and his collaborators had to extend existing models in a way that took into account these additional complexities.

Unfortunately, preferences of refugees are currently still not collected. Instead, governments try to optimise refugee outcomes directly, for example by trying to improve employment. Such objectives serve as a measure of the quality of the match between refugees and local areas and became the foundation for Annie™ MOORE.

Annie™ MOORE: Improving refugee resettlement from Refugees AI on Vimeo.

Download the Video Transcript


Annie™ MOORE in action

The team developed Annie™ MOORE using machine learning methods to assess the quality of a match between a refugee family and a locality based on the probability of employment for the newly arrived refugee.  Using data from HIAS, the researchers discovered that if Annie™ MOORE had been used on the 496 refugees the agency resettled during 2017, the percentage of refugees gaining employment within a 90-day period would have increased somewhere between 22% and 38%.

The software was rolled out at HIAS in 2018 and has gone on to match these predictions, with early results increasing the likelihood of employment by at least 20%. Annie™ MOORE has since helped resettle over 1,000 refugees in the US. It has played a key part in eliminating mismatch between refugees and the services provided within communities, such as single parent support and language services.

Additionally, Annie™ MOORE has helped HIAS streamline and improve services. The software allows HIAS workers to spend less time on matching conventional cases, so they can focus on families with additional needs.

HIAS is in conversation with other resettlement agencies in the US about adoption of Annie™ MOORE. Meanwhile, the researchers are in discussions with other bodies around the world, including the UK Home Office, about the potential use of the software in their own jurisdictions.

Alex Teytelboym was Highly Commended in the Policy Engagement category at the 2020 Vice-Chancellor’s Innovation Awards for his work on Annie™ MOORE.


Visit Refugees.AI



Delacrétaz, D., S. Duke Kominers and A. Teytelboym (2019)

“Matching Mechanisms for Refugee Resettlement”

Jones, W. and A. Teytelboym (2018), “The local refugee match: Aligning refugees' preferences with the capacities and priorities of localities”, Journal of Refugee Studies.

Nguyen, H., T Nguyen and A Teytelboym (2019). “Stability in Matching Markets with Complex Constraints” 

Trapp, A.C., A. Teytelboym, A. Martinello, T.Andersson and N. Ahani (2018) “Placement Optimization in Refugee Resettlement” Working Paper 2018:23 Department of Economics School of Economics and Management. Lund University