Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing

This study proposes two new dynamic assignment algorithms to match refugees and asylum seekers to geographic localities within a host country. The first, currently implemented in a multi-year pilot in Switzerland, seeks to maximize the average predicted employment level (or any measured outcome of interest) of refugees through a minimum-discord online assignment algorithm. Although the proposed algorithm achieves nearoptimal expected employment compared to the hindsight-optimal solution (and improves upon the status quo procedure by about 40%), it results in a periodically imbalanced allocation to the localities over time. This leads to undesirable workload inefficiencies for resettlement resources and agents. To address this problem, the second algorithm balances the goal of improving refugee outcomes with the desire for an even allocation over time. The performance of the proposed methods is illustrated using real refugee resettlement data from a large resettlement agency in the United States. On this dataset, we find that the allocation balancing algorithm can achieve near-perfect balance over time with only a small loss in expected employment compared to the pure employment-maximizing algorithm. In addition, the allocation balancing algorithm offers a number of ancillary benefits compared to pure outcome-maximization, including robustness to unknown arrival flows and greater exploration.