Research & Analysis by Joyce Nicholas
This article examines the geographic, demographic, socioeconomic, and program-participation characteristics of initial Supplemental Security Income (SSI) and Social Security Disability Insurance (DI) applicants who faced homelessness during 2007–2017. Using Social Security Administration data, the authors chart the distribution of homeless SSI/DI applicants and beneficiaries across county-equivalent areas in the contiguous United States. They also use a text-mining method to identify 162,536 potentially homeless disability-program applicants, in addition to the 647,790 applicants identified using the standard homeless-status indicators in the administrative data. They find that homelessness among disability-program applicants was largely an urban phenomenon, with almost half (42.1 percent) of applicants living in one of 25 urban areas. Relative to their domiciled counterparts, homeless disability-program applicants were far more likely to be male, aged 18–64, and without a high school or general equivalency diploma.
Source, Form, and Amount of In-kind Support and Maintenance Received by Supplemental Security Income Applicants and Recipients
This article examines the in-kind support and maintenance (ISM) received by Supplemental Security Income (SSI) program applicants and recipients. Social Security defines ISM as unearned income received by SSI applicants and recipients in the form of food and/or shelter from anyone living within or outside their households. About 9 percent of SSI recipients have their benefit rates reduced because of ISM during any given year. Using data from the Modernized SSI Claims System, the author quantifies the source, form, and amount of ISM received by SSI recipients. The article reveals that SSI recipients are more likely to receive ISM from outside than inside their homes, receive assistance in the form of shelter rather than food, and allege assistance that is equal to or less than the current ISM caps.
This article looks at Supplemental Security Income (SSI) multirecipients. Using matched administrative and survey data, the author quantifies the prevalence of SSI recipients who live with other recipients (not including an SSI-eligible spouse). The author also conducts family- and household-level analyses to shed light on the social and economic characteristics of SSI multirecipients. The article reveals that SSI multirecipients represent about one-fifth of the SSI population and that their poverty rates vary according to family and household composition characteristics.
This article is an extension of work reported in an earlier article entitled, "Elderly Poverty and Supplemental Security Income" (Social Security Bulletin 69(1): 45–73). Like the original work, the present study looks at the consequences of obtaining estimates of the prevalence of poverty among persons aged 65 or older by using administrative data to adjust incomes reported in the Current Population Survey. The original article looked at incomes in 2002; the present one covers measures of absolute and relative poverty status of the elderly during the 2003–2005 period. Again, we find that inclusion of administrative data presents challenges, but under the methodology we adopt, such adjustments lower estimated official poverty overall and increase estimated poverty rates for elderly SSI recipients by correcting for the misreporting of SSI, OASDI, and earnings receipt by CPS respondents.
Provided here are the absolute and relative poverty status of 2002 elderly Supplemental Security Income (SSI) recipients. Official poverty estimates are generated from the Current Population Survey's Annual Social and Economic Supplement (CPS/ASEC). The poverty study presented here differs from previous studies in that it is based on CPS/ASEC income and weight records conditionally adjusted by matching Social Security administrative data. This effort improves the coverage of SSI receipt and the accuracy of SSI estimates. The adjusted CPS/administrative matched data reveal lower 2002 poverty rates among elderly persons (with and without SSI payments) than those generated from the unadjusted CPS/ASEC data.