Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing

TL;DR

This paper introduces two dynamic refugee assignment algorithms that optimize employment outcomes while maintaining balanced allocations across localities, demonstrated through real-world data from Switzerland and the US.

Contribution

It presents novel algorithms for refugee assignment that balance outcome maximization with allocation fairness, tested on real data and shown to improve efficiency and robustness.

Findings

Outcome-maximizing algorithm achieves near-optimal employment levels.

Allocation balancing algorithm maintains near-perfect balance with minimal employment loss.

Algorithms outperform current procedures by 40-50% in employment outcomes.

Abstract

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.