An official website of the United States government
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.
more »
« less
The problem of the future in the spacetime of resettlement: Iraqi refugees in the U.S.
How do the lost futures of forced displacement converge with the impasse of being resettled to a “post-future” society such as the U.S.? Based on interviews conducted between 2016 and 2019 with resettlement agents, service providers and Iraqis resettled in the U.S., we argue that the condemnation of “expectations” (that is, realistic hope) coupled with the demand for refugees’ gratitude means that Iraqis resettled to the U.S. are asked to sustain a “hope against hope” for the fullness of American futurity, even in the face of its collapse. We argue that this prescribed structure of feeling distorts the affective realities of those for whom resettlement has meant at once the loss of past futures (e.g. professional qualifications, career trajectories, social status, or intergenerational cycles of care) and the running aground of capacities for futurity – especially as these capacities are bound up with transnationally stretched and reconfigured familial relations. What is at stake is the recognition of the crisis of futurability in the spacetime of resettlement and the rightfulness of refugee expectations for a more humane and fulfilling resettlement.
more »
« less
- Award ID(s):
- 1461615
- PAR ID:
- 10547634
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Environment and Planning D: Society and Space
- Volume:
- 40
- Issue:
- 3
- ISSN:
- 0263-7758
- Format(s):
- Medium: X Size: p. 508-527
- Size(s):
- p. 508-527
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)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.more » « less
-
Nations in the Global South have increasingly embraced large hydropower. Hydropower development typically involves the displacement and resettlement of entire communities and has a range of social and ecological impacts. Some communities become the operational center for the dam construction, as well as host new neighborhoods of resettlers. One of the less-studied impacts of dams is the potential loss of social capital both in resettled and host communities. Here, we ask how the Belo Monte dam in the Amazon is associated with social capital in a resettled group and a non-resettled population that, while not experiencing resettlement, nevertheless was impacted by the dam as well. We use measures of cognitive and structural social capital. Results suggest that resettlers have lower structural social capital across two proxy indicators, whereas the host community has lower cognitive social capital. Future research and social impact assessments should pay more attention to how hydropower impacts both kinds of social capital.more » « less
-
null (Ed.)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.more » « less
-
Abstract Flood risks are rising across the United States, putting the economic and social values of growing numbers of homes at risk. In response, the federal government is funding the purchase and demolition of housing in areas of greatest jeopardy, tacitly promoting residential resettlement as a strategy of climate adaptation, especially in cities. Despite these developments little is known about where people move when they engage in such resettlement or how answers to that question vary by the racial and economic status of their flood-prone neighborhoods. The present study begins to fill that gap. We introduce a new typology for classifying environmental resettlement along two socio-spatial dimensions of community attachment: (a) distance moved from one’s flood-prone home; and (b) average distance resettled from similarly relocated neighbors. Next, we analyze data from 1,572 homeowners who accepted government-funded buyouts across 39 neighborhood areas in Harris County, Texas – Houston’s urban core. Results indicate that homeowners from more privileged neighborhoods resettle closer to their flood-prone homes and to one another, thus helping to preserve the social and economic value of their homes; homeowners from less privileged areas end up farther away from both. Implications for understanding social inequities in government-funded urban climate adaptation are discussed.more » « less
