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The SSRC Library allows visitors to access materials related to self-sufficiency programs, practice and research. Visitors can view common search terms, conduct a keyword search or create a custom search using any combination of the filters at the left side of this page. To conduct a keyword search, type a term or combination of terms into the search box below, select whether you want to search the exact phrase or the words in any order, and click on the blue button to the right of the search box to view relevant results.

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  • Individual Author: Mullins, Brett; Rider, Mark; Sjoquist, David; Wallace, Sally
    Reference Type: Report
    Year: 2015

    This brief focuses on trends in participation of Georgia residents in the SNAP and TANF programs over a 14-year period to better understand the dynamics of these programs. In the next section, a brief summary of these two programs is provided. The third section shows participation trends in these two programs in Georgia. (Author abstract)

    This brief focuses on trends in participation of Georgia residents in the SNAP and TANF programs over a 14-year period to better understand the dynamics of these programs. In the next section, a brief summary of these two programs is provided. The third section shows participation trends in these two programs in Georgia. (Author abstract)

  • Individual Author: O'Leary, Christopher J.
    Reference Type: Report
    Year: 2015

    In this paper I examine the rates at which adults in households recently receiving Temporary Assistance to Needy Families (TANF) become jobless, apply for and receive unemployment insurance (UI) benefits, and participate in publicly funded employment services. I also investigate the correlation of UI and employment services receipt with maintenance of self-sufficiency through return to work and independence from TANF. The analysis is based on person-level administrative program records from four of the nine largest states between 1997 and 2003. Evidence suggests that three-quarters of new TANF leavers experience joblessness within three years, and one-quarter of the newly jobless apply for UI benefits. About 87 percent of UI applicants have sufficient prior earnings to qualify for UI benefits; however, only about 44 percent qualify based on their job separation reasons. Among all UI applicants, TANF leavers were found to have much higher rates of voluntary quits and employer dismissals than non-TANF leavers. Nonetheless, 50 percent of TANF leavers who apply for UI ultimately...

    In this paper I examine the rates at which adults in households recently receiving Temporary Assistance to Needy Families (TANF) become jobless, apply for and receive unemployment insurance (UI) benefits, and participate in publicly funded employment services. I also investigate the correlation of UI and employment services receipt with maintenance of self-sufficiency through return to work and independence from TANF. The analysis is based on person-level administrative program records from four of the nine largest states between 1997 and 2003. Evidence suggests that three-quarters of new TANF leavers experience joblessness within three years, and one-quarter of the newly jobless apply for UI benefits. About 87 percent of UI applicants have sufficient prior earnings to qualify for UI benefits; however, only about 44 percent qualify based on their job separation reasons. Among all UI applicants, TANF leavers were found to have much higher rates of voluntary quits and employer dismissals than non-TANF leavers. Nonetheless, 50 percent of TANF leavers who apply for UI ultimately receive benefits. Public employment services are used by one-quarter of newly jobless TANF leavers. Among UI applicants, more than 75 percent use public employment services whether they receive UI benefits or not, while only 14 percent of newly jobless TANF leavers who do not apply for UI choose to use public employment services. Among TANF leavers who become jobless and apply for UI, the rate of return to TANF is lower for those who receive UI benefits. Rates of return to TANF are highest among nonbeneficiary UI applicants and non-UI applicants with low recent earnings. (Author abstract)

  • Individual Author: Short, Vanessa L.; Oza-Frank, Reena; Conrey, Elizabeth J.
    Reference Type: Journal Article
    Year: 2012

    To compare preconception health indicators (PCHIs) among non-pregnant women aged 18–44 years residing in Appalachian and non-Appalachian counties in 13 U.S. states. Data from the 1997–2005 Behavioral Risk Factor Surveillance System were used to estimate the prevalence of PCHIs among women in states with ≥1 Appalachian county. Counties were classified as Appalachian (n = 36,496 women) or non-Appalachian (n = 88,312 women) and Appalachian counties were categorized according to economic status. Bivariate and multivariable logistic regression models examined differences in PCHIs among women by (1) Appalachian residence, and (2) economic classification. Appalachian women were younger, lower income, and more often white and married compared to women in non-Appalachia. Appalachian women had significantly higher odds of reporting <high school education (adjusted odds ratio (AOR) 1.19, 95 % confidence interval (CI) 1.10–1.29), fair/poor health (AOR 1.14, 95 % CI 1.06–1.22), no health insurance (AOR 1.12, 95 % CI 1.05–1.19), no annual checkup (AOR 1.12, 95 % CI 1.04–1.20), no recent Pap...

    To compare preconception health indicators (PCHIs) among non-pregnant women aged 18–44 years residing in Appalachian and non-Appalachian counties in 13 U.S. states. Data from the 1997–2005 Behavioral Risk Factor Surveillance System were used to estimate the prevalence of PCHIs among women in states with ≥1 Appalachian county. Counties were classified as Appalachian (n = 36,496 women) or non-Appalachian (n = 88,312 women) and Appalachian counties were categorized according to economic status. Bivariate and multivariable logistic regression models examined differences in PCHIs among women by (1) Appalachian residence, and (2) economic classification. Appalachian women were younger, lower income, and more often white and married compared to women in non-Appalachia. Appalachian women had significantly higher odds of reporting <high school education (adjusted odds ratio (AOR) 1.19, 95 % confidence interval (CI) 1.10–1.29), fair/poor health (AOR 1.14, 95 % CI 1.06–1.22), no health insurance (AOR 1.12, 95 % CI 1.05–1.19), no annual checkup (AOR 1.12, 95 % CI 1.04–1.20), no recent Pap test (AOR 1.20, 95 % CI 1.08–1.33), smoking (AOR 1.08, 95 % CI 1.03–1.14), <5 daily fruits/vegetables (AOR 1.11, 95 % CI 1.02–1.21), and overweight/obesity (AOR 1.05, 95 % CI 1.01–1.09). Appalachian women in counties with weaker economies had significantly higher odds of reporting less education, no health insurance, <5 daily fruits/vegetables, overweight/obesity, and poor mental health compared to Appalachian women in counties with the strongest economies. For many PCHIs, Appalachian women did not fare as well as non-Appalachians. Interventions sensitive to Appalachian culture to improve preconception health may be warranted for this population. (Author abstract)

  • Individual Author: Allard, Scott W.
    Reference Type: Report
    Year: 2007

    Several research questions emerge as we consider the challenges of administering social service programs to poor populations. Where do our communities provide assistance to poor and near-poor households? Do gaps or mismatches in access to social services exist in our communities? How do providers finance services for low-income populations and do these revenue streams shift frequently? How often do cuts in funding lead to instabilities or inconsistencies in service delivery?

    To begin to answer these questions, this chapter examines data from the Multi-City Survey of Social Service Providers (MSSSP) and the Rural Survey of Social Service Providers (RSSSP), which I conducted with social service providers helping low-income populations in three metropolitan areas and four multi-county rural sites respectively between November 2004 and June 2006. Working from a detailed database of service providers in each site, trained interviewers conducted over 2,200 telephone interviews with program managers and executive directors. Each survey contains detailed geographically-sensitive...

    Several research questions emerge as we consider the challenges of administering social service programs to poor populations. Where do our communities provide assistance to poor and near-poor households? Do gaps or mismatches in access to social services exist in our communities? How do providers finance services for low-income populations and do these revenue streams shift frequently? How often do cuts in funding lead to instabilities or inconsistencies in service delivery?

    To begin to answer these questions, this chapter examines data from the Multi-City Survey of Social Service Providers (MSSSP) and the Rural Survey of Social Service Providers (RSSSP), which I conducted with social service providers helping low-income populations in three metropolitan areas and four multi-county rural sites respectively between November 2004 and June 2006. Working from a detailed database of service providers in each site, trained interviewers conducted over 2,200 telephone interviews with program managers and executive directors. Each survey contains detailed geographically-sensitive information on services provided, clients served, funding, and organizational characteristics from a range of governmental, nonprofit, and faith-based social service providers.  

    This chapter will proceed as follows. First, I briefly present a history of the American safety net that explains how social service programs have become central components within our local safety nets. Next, I explain how the current service-based safety net is more sensitive to the spatial location of service agencies than is typically understood.  In addition, I discuss how funding for social service programs is less counter-cyclical and more volatile than aggregate federal expenditure data would suggest. Drawing upon data from the MSSSP and RSSSP, I explore social service provision within several different rural and urban settings.  In particular, I focus upon mismatches and instabilities within the provision of social service programs. Finally, I conclude by discussing the implications of a patchworked and volatile service-based safety net for future social welfare policymaking. (author introduction)

  • Individual Author: Alden Dinan, Kinsey; Cauthen, Nancy K.; Fass, Sarah
    Reference Type: Stakeholder Resource
    Year: 2004

    About 85 percent of low-income children have parents who work, and most have at least one parent working full-time, year-round. Nonetheless, many of these parents are unable to afford basic necessities for their families, such as food, housing, and stable child care. Even a full-time job is not always enough to make ends meet, and many parents cannot get ahead simply by working more. As earnings increase—particularly as they rise above the official poverty level—families begin to lose eligibility for work supports. At the same time, work-related expenses, such as child care and transportation, increase. This means that parents may earn more without a family experiencing more financial security. 1 In some cases, earning more actually leaves a family with fewer resources after the bills are paid.

    The Family Resource Simulator, developed by the National Center for Children in Poverty, illustrates how this happens. This web-based tool calculates resources and expenses for a hypothetical family that the user “creates” by selecting city and state, family characteristics, income...

    About 85 percent of low-income children have parents who work, and most have at least one parent working full-time, year-round. Nonetheless, many of these parents are unable to afford basic necessities for their families, such as food, housing, and stable child care. Even a full-time job is not always enough to make ends meet, and many parents cannot get ahead simply by working more. As earnings increase—particularly as they rise above the official poverty level—families begin to lose eligibility for work supports. At the same time, work-related expenses, such as child care and transportation, increase. This means that parents may earn more without a family experiencing more financial security. 1 In some cases, earning more actually leaves a family with fewer resources after the bills are paid.

    The Family Resource Simulator, developed by the National Center for Children in Poverty, illustrates how this happens. This web-based tool calculates resources and expenses for a hypothetical family that the user “creates” by selecting city and state, family characteristics, income sources, and assets. The user also selects which public benefits the family receives when eligible and makes choices about what happens when the family loses benefits (e.g., does the family seek cheaper child care after losing a subsidy?).

    The result is a series of charts that show the hypothetical family’s total income from various sources as earnings rise, as well as the cost of basic family expenses. Using the Simulator, this report describes the experiences of two hypothetical families in the workforce. (Author introduction)

     

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