  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 randomized control trial 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. The performance of this algorithm is tested on real refugee
resettlement data from both the US and Switzerland, where we find that it is
able to achieve near-optimal expected employment compared to the
hindsight-optimal solution, and is able to improve upon the status quo
procedure by 40-50%. However, pure outcome maximization can result in a
periodically imbalanced allocation to the localities over time, leading to
implementation difficulties and an undesirable workflow for resettlement
resources and agents. To address these problems, the second algorithm balances
the goal of improving refugee outcomes with the desire for an even allocation
over time. We find that this algorithm can achieve near-perfect balance over
time with only a small loss in expected employment compared to the
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.
