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Open Access Publications from the University of California

Predicting Economic Incorporation Among Newly Resettled Refugees in the United States: A Micro-Level Statistical Analysis

  • Author(s): Arafah, Rami B.
  • Advisor(s): Gilbert, Neil
  • et al.

The United States plays a central role in the global response to individuals displaced by violence and persecution, offering permanent resettlement and a pathway to citizenship to more refugees than any other nation. Upon arrival in the US, refugees face a number of challenges in adjusting to their new lives and achieving the goal of economic self-sufficiency laid out by the Federal Resettlement Program. Across the country, recently resettled refugees’ employment and economic standings fall below those of the general public, as well as other immigrant populations.

While some scholars have attempted to gain an understanding of refugee economic incorporation (e.g., employment status, income) and its determinants, existing studies have fallen short of reliably doing so. The existing literature is limited by outdated findings, small non-representative samples, vague operationalization of outcomes, and a general lack of theoretical underpinning. This study serves to address these gaps in academic scholarship, leveraging inferential statistical analysis to identify factors that predict refugee economic incorporation across four measurable outcomes: binary employment status, elapsed time to first employment, hours worked per week, and hourly wage.

This study utilizes nonpublic survey data secured from the federal Office of Refugee Resettlement (ORR). Collected via the ORR’s Annual Survey Questionnaire of Refugees, the data captures a wide range of information on respondents’ lives, including demographic information, pre-resettlement experiences, post-resettlement activities, household characteristics, and economic performance. The survey data—compiled in 2013—was gathered through a random stratified sampling scheme, ensuring national representativeness. Multi-level logistic and ordinary least squares regression modeling was performed to identify predictors of the four economic outcomes listed above.

Regression modeling results suggest a high degree of predictive power at the demographic level; that is, the collection of non-modifiable traits and factors refugees bring with them to the resettlement process. These factors include gender, marital status and region of origin, among others. In addition, a number of post-resettlement factors were found to be predictive of employment and economic outcomes, including participation in job training and improvements in English language proficiency.

This study’s results yield a number of implications for future research, resettlement practice, and policy. Moving forward, research on the incorporation of refugees in permanent resettlement contexts should incorporate both quantitative and qualitative methods, as well as a holistic view of study outcomes beyond traditional economic indicators. In this vein, results from the study can be utilized to begin development of a modern, evidence-informed resettlement practice framework that integrates multi-faceted assessment, diverse service planning, and rigorous program evaluation. In the policy realm, findings call for a reassessment of the rapid-employment resettlement model, as well as a renewed focus on identifying and accommodating particularly vulnerable refugee subpopulations.

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