Elisabeth C. Paulson (Harvard Business School)
Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing
Abstract: This work 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 near-optimal 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—currently being piloted in the US—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 Switzerland and the United States.
For those who cannot attend in-person, the Zoom link is as usual: https://harvard.zoom.us/j/95184948637?pwd=bXBIc2U5MEZ0QmRUb01WQ0o0SXRCdz09 (password: econcs)
LOCATION:SEC 1.413, & streamed via Zoom at: https://harvard.zoom.us/j/95184948637?pwd=bXBIc2U5MEZ0QmRUb01WQ0o0SXRCdz09 STATUS:CONFIRMED DTSTART:20230324T170000Z DTEND:20230324T180000Z END:VEVENT END:VCALENDAR