Abstract: Resettlement Cities: A Mixture Model Analysis of the Dispersion Strategy of US Refugee Policy in the Post 9/11 Era (Society for Social Work and Research 25th Annual Conference - Social Work Science for Social Change)

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Resettlement Cities: A Mixture Model Analysis of the Dispersion Strategy of US Refugee Policy in the Post 9/11 Era

Wednesday, January 20, 2021
* noted as presenting author
Richard Smith, PhD, Associate Professor, Wayne State University
Odessa Gonzalez Benson, PhD, MSW, Assistant Professor, University of Michigan-Ann Arbor
Chiho Song, PhD, Postdoctoral Fellow, University of Washington, Seattle, WA

The annual placement of refugees into thousands of cities across the country has historically been contentious, from refugees of World War II and the Vietnam War to refugees of the post-9/11 era. Theoretically, refugee placement presents with scalar tensions in governance as the federal government determines refugee placement into cities and states largely without a process that is systematic, deliberative, nor defined in law. Meanwhile, refugee placement has become a politicized. State governors and city officials in the US are divided on resettling refugees in their jurisdictions. In this study, we examine the refugee dispersion strategy of the U.S. Department of State’s Reception and Placement Program (R&P Program) in the post-9/11 era of refugee policy. Specifically, we examine initial conditions in US cities where refugees have been placed. Our study aim is to develop a latent typology of resettlement cities, in order to uncover contradictions inherent to federal resettlement policy, including structural discrimination that heretofore has been undetected.


We used publicly available U.S. Department of State data on the number of refugee placements or arrivals (n = 548,198) in each resettlement city (n = 2,170) from 2008 to 2015. We then merged resettlement data with city-level variables, on the economic (e.g., labor market conditions) and demographic (e.g., total population, immigrant population change) variables from the US Census. We also include socio-political variables on political affiliations; and a typology derived from a local government immigrant friendly policy scan. Because we had a mix of continuous and binary variables, we used a mixed data mixture model (MixAll) to create clusters. The best fitting model was selected using AIC, BIC, and Integrated Classification Likelihood (ICL).


The best fitting model had eight clusters: 1) Small Southern Low Immigration Cities with Poverty (n=79); 2) Immigrant Deflection Boomtowns (n=70); 3) Southern & Central Construction Boomtowns with Poverty (n=713); 4) Large Wealthy Coastal Liberal Immigrant Integrators (n=709); 5) Legacy Cities Midwest & South Welcoming Immigrants (n=113); 6) Western Immigrant Gateway Boomtowns (n=94); 7) Small Conservative Immigrant Gateways with Poverty (n=82); and 8) Liberal Wealthy Low Immigration Legacy Cities (n=310). Findings show how relevant dimensions characterize resettlement cities: converging, but also overlapping in divergent and unexpected ways. Results also show how labor market conditions in three clusters were not always favorable for refugee employment (e.g., Clusters 1, 5, 3). Finally, refugees are placed into localities with low or declining immigrant populations that lack immigrant friendly policies (e.g., Clusters 1,2,8). On the other hand, refugees are also placed into localities with fast-growing immigrant populations (e.g., Clusters 4, 6, 7) counter to the intent of dispersion policies.

Conclusion and Implications:

Our findings inform policy and practice with refugees. First, poor labor market conditions in resettlement cities reveal how placement strategies do not necessarily place refugees in places where they may obtain jobs as soon as possible. Findings also raise questions about local sentiments towards refugees. These multiscalar tensions suggest federal refugee policy should incorporate analysis of the local economy and policies of state and local governments.