Many immigrant families face an array of barriers to enrolling in safety net programs like Medicaid and the Supplemental Nutrition Assistance Program (SNAP). Some are universal structural barriers that many program applicants confront navigating complex enrollment systems. Others are unique challenges around immigration concerns and language access. Understanding who these families are is the first step for community leaders, including state and local policymakers and health and human services agencies, to ensure access to programs for those who are eligible and to provide culturally and linguistically responsive assistance to support immigrants and their children.
In new Urban research based on interviews with more than 40 state- and county-based stakeholders and four multilingual focus groups with immigrants, we identify common barriers to safety net participation for immigrant families in North Carolina and challenges faced by immigrant-serving organizations and health and human services agencies. We recommend solutions to create a more inclusive safety net for North Carolina’s immigrant families. And we provide supplemental fact sheets that describe specific lessons for supporting Spanish, Hmong, and Swahili speakers.
Below, we’ve mapped North Carolina counties to spotlight characteristics of immigrant residents to inform more effective policy and practice.
Resources
Report
Supporting North Carolina’s Immigrant Families
Fact Sheets
Spanish-Speaking
- Access to Safety Net Programs for North Carolina’s Spanish-Speaking Immigrant Families: Perspectives from Focus Group Participants
- Acceso a los programas de la red de seguridad para familias inmigrantes de habla hispana en Carolina del Norte
Hmong-Speaking
- Access to Safety Net Programs for North Carolina’s Hmong Immigrant Families: Perspectives from Focus Group Participants
- Kev Mus Cuag Tau Cov Khoos Kas Saftey Net rau Cov Tsev Neeg Hmoob Kev Tsiv Teb Tsaws Chaw Nyob North Carolina
Swahili-Speaking
Data for this feature are drawn from data tables produced by the US Census Bureau using five-year American Community Survey (ACS) data for 2015–19. The ACS is a nationally representative survey of over two million sample members per year. ACS response rates for 2015–19 ranged between 86.0 and 95.8 percent.
Total populations include people of all ages; children are under 18. Immigrants are people who were born abroad and include both naturalized US citizens and non-US citizens. Children born abroad to citizen parents are grouped with those who are US-born. Families with low incomes are those with incomes below 200 percent of the federal poverty level. The ACS asks about speaking a language other than English at home, then asks those who speak another language to specify which language and to self-report how well they speak English. We define people with limited English proficiency as those whose survey responses indicate they (1) speak a language other than English at home, and (2) do not speak English “very well” (that is, they speak English only “well,” “not very well,” or “not at all”) consistent with ACS definitions.
All rates are estimated as the ratio of weighted counts for the numerator and denominator of the rate. State- and county-level rates include approximate margins of error (MOEs) to indicate sampling error. (The approximation is based on a Census Bureau approximation for calculating the MOE of a rate based on the MOEs of the numerator and denominator.) Estimates +/- MOEs approximate 90 percent confidence intervals. Estimates are suppressed for counties with populations of interest (denominator for rates) below 2,000. Thus, availability of information varies for counties across the state. Only limited information is available for counties with small implied samples of residents who are immigrants, while additional information is available for counties with larger numbers of residents who are immigrants. We chose reporting thresholds to suppress estimates with large relative MOEs often based on few interviews and reported estimates for a reasonable share of counties in the state. Readers are advised to use caution when relying on estimates with large MOEs.
This project was funded by the Kate B. Reynolds Charitable Trust. The project team included Hamutal Bernstein and Jennifer M. Haley (co-PIs), Diana Guelespe (project manager), Luis Gallardo, and Sofia Hinojosa at the Urban Institute and Krista Perreira, Hannah Gill, and Lisa Carlson at the University of North Carolina. Doug Wissoker and Soumita Bose contributed to ACS data analysis. Ben Kates, Brittney Spinner, Kayla Snow, and Wesley Jenkins contributed to the creation of this data visualization. View this project on GitHub.