Article Details

Research Database: Article Details

Citation:  Wong, S. (2016). Geographies of medicalized welfare: Spatial analysis of supplemental security income in the U.S., 2000-2010. Social Science & Medicine, 160 9-19.
Title:  Geographies of medicalized welfare: Spatial analysis of supplemental security income in the U.S., 2000-2010
Authors:  Wong, S.
Year:  2016
Journal/Publication:  Social Science & Medicine
Publisher:  Elsevier
DOI:  https://doi.org/10.1016/j.socscimed.2016.05.018
Full text:  http://www.sciencedirect.com/science/article/pii/S0277953616302398   
Peer-reviewed?  Yes
NIDILRR-funded?  Not reported

Structured abstract:

Background:  Since 1996 and the reductions and restrictions of public support, disability assistance has become an important source of government aid for low-income residents of the U.S. Working-age individuals with disabilities must have little income and resources, and provide medical documents that confirm they are unable to work due a disability in order to quality for Supplemental Security Income (SSI). This means that in order to qualify for welfare benefits, a medical diagnosis of disability must be made.
Purpose:  The authors examines the interconnections between disability, welfare, and poverty, and look at the changing spatial patterns of SSI participation in 2000 and 2010.
Data collection and analysis:  The authors examine county-level data from the American Community Survey and the Social Security Administration.
Findings:  Results from spatial analyses find variation in SSI prevalence based on geography, with higher than average SSI participation in the southeast and Appalachian regions of the U.S. and in northern California. Multiple linear regression model results reveal that SSI participation is significantly correlated with disability, poverty, race, family type, and education level across all years.
Conclusions:  The authors find that disability, poverty, and underemployment prevail in largely rural areas. They discuss the potential socioeconomic implications of long-term SSI clustering on localities and residents.

Populations served:  Rural and remote communities
SSI and SSDI recipients
Interventions:  Long-term supports (Medicaid waivers, etc.)