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Boosting Employment of Resettled Refugees Whether a resettled refugee finds employment in the United States depends in no small part on which host community they are first welcomed to. Every week, resettlement agencies are assigned a group of refugees who they are required to place in communities around the country. In “Dynamic Placement in Refugee Resettlement,” Ahani, Gölz, Procaccia, Teytelboym, and Trapp develop an allocation system that recommends where to place an incoming refugee family with the aim of boosting the overall employment success. Should capacities in high-employment areas be used up as quickly as possible, or does it make sense to hold back for a perfect match? The simple algorithm, based on two-stage stochastic programming, achieves over 98% of the hindsight-optimal employment, compared with under 90% for the greedy-like approaches that were previously used in practice. Their algorithm is now part of the Annie™ MOORE optimization software used by a leading American refugee resettlement agency. 

Every year, tens of thousands of refugees are resettled to dozens of host countries. Although 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™ Matching and Outcome Optimization for Refugee Empowerment (Annie™ Moore), that assists a U.S. 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 back testing indicates that Annie™ can improve short-run employment outcomes by 22%–38%. We conclude by discussing several directions for future work. 

Employment outcomes of resettled refugees depend strongly on where they are placed inside the host country. While the United States sets refugee capacities for communities on an annual basis, refugees arrive and must be placed over the course of the year. We introduce a dynamic allocation system based on two-stage stochastic programming to improve employment outcomes. Our algorithm is able to achieve over 98 percent of the hindsight-optimal employment compared to under 90 percent of current greedy-like approaches. This dramatic improvement persists even when we incorporate a vast array of practical features of the refugee resettlement process including indivisible families, batching, and uncertainty with respect to the number of future arrivals. Our algorithm is now part of the Annie™ MOORE optimization software used by a leading American refugee resettlement agency. The full version of this paper is available at https://arxiv.org/pdf/2105.14388.pdf.