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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.

Writing a paper? Working on a literature review? Citing research in a funding proposal? Use the SSRC Citation Assistance Tool to compile citations.

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The SSRC Library includes resources which may be available only via journal subscription. The SSRC may be able to provide users without subscription access to a particular journal with a single use copy of the full text.  Please email the SSRC with your request.

The SSRC Library collection is constantly growing and new research is added regularly. We welcome our users to submit a library item to help us grow our collection in response to your needs.


  • Individual Author: Stacy, Christina; Craigie, Terry-Ann; Meixell, Brady; MacDonald, Graham; Zheng, Sihan Vivian; Davis, Christopher; Baird, Christina; Chartoff, Ben; Hinson, David; Lei, Serena
    Reference Type: Dataset
    Year: 2019

    In many cities, low-income residents live far from available jobs, and employers can’t find people to fill open positions. Economists call this “spatial mismatch”—a mismatch between where jobs are located and where job seekers live, which can cause high unemployment rates and lead to longer spells of joblessness. Data from Snag, the largest online marketplace for hourly jobs, show us that this is true for job seekers who use their platform. Snag data capture a large number of low-wage job seekers in each metropolitan statistical area (MSA). Looking at 2017, the most recent year of data, we analyzed the distance between every job seeker and the jobs they applied for, allowing us to map out spatial mismatch. And we talked to local government and workforce officials in two regions to learn what they’re doing to overcome this problem. (Author introduction modified)

    In many cities, low-income residents live far from available jobs, and employers can’t find people to fill open positions. Economists call this “spatial mismatch”—a mismatch between where jobs are located and where job seekers live, which can cause high unemployment rates and lead to longer spells of joblessness. Data from Snag, the largest online marketplace for hourly jobs, show us that this is true for job seekers who use their platform. Snag data capture a large number of low-wage job seekers in each metropolitan statistical area (MSA). Looking at 2017, the most recent year of data, we analyzed the distance between every job seeker and the jobs they applied for, allowing us to map out spatial mismatch. And we talked to local government and workforce officials in two regions to learn what they’re doing to overcome this problem. (Author introduction modified)

  • Individual Author: Heffernan, Christine; Goehring, Benjamin; Hecker, Ian; Giannarelli, Linda; Minton, Sarah
    Reference Type: Dataset, Report
    Year: 2018

    The purpose of this publication—the Welfare Rules Database’s annual Databook—is to provide researchers and policymakers with easy access to detailed information on how states provide cash assistance under the Temporary Assistance for Needy Families (TANF) program. The dozens of tables in this book collectively describe how states determine eligibility for TANF benefits, how they compute program benefits for eligible families, and the work requirements and time limits that they impose. In Federal Fiscal Year (FFY) 2017, 1.095 million families received cash aid from TANF in the average month.

    This publication presents the key policies that each state used to determine cash aid under the TANF program as of July 2017. The Databook also provides longitudinal tables describing various state policies for selected years between 1996 and 2017. All the tables in this publication are based on the information in the Welfare Rules Database (WRD), a publicly available, online database funded by the Department of Health and Human Services and developed and maintained by the Urban...

    The purpose of this publication—the Welfare Rules Database’s annual Databook—is to provide researchers and policymakers with easy access to detailed information on how states provide cash assistance under the Temporary Assistance for Needy Families (TANF) program. The dozens of tables in this book collectively describe how states determine eligibility for TANF benefits, how they compute program benefits for eligible families, and the work requirements and time limits that they impose. In Federal Fiscal Year (FFY) 2017, 1.095 million families received cash aid from TANF in the average month.

    This publication presents the key policies that each state used to determine cash aid under the TANF program as of July 2017. The Databook also provides longitudinal tables describing various state policies for selected years between 1996 and 2017. All the tables in this publication are based on the information in the Welfare Rules Database (WRD), a publicly available, online database funded by the Department of Health and Human Services and developed and maintained by the Urban Institute. The Databook summarizes the more detailed information in the WRD. Users interested in more information than is provided in this Databook are encouraged to use the full database, available at https://wrd.urban.org. This site includes a point-and-click interface, as well as extensive documentation. (Edited author introduction)

  • Individual Author: Demyan, Natalie; Passarella, Letitia
    Reference Type: Dataset, Report
    Year: 2018

    These snapshots for Maryland and each of the 24 jurisdictions in the state provide demographic information about noncustodial parents, their employment, and their child support orders and payments. Additionally, comparisons are made between noncustodial parents earning full-time minimum wage or less and those earning a living wage. (Author description)

     

    These snapshots for Maryland and each of the 24 jurisdictions in the state provide demographic information about noncustodial parents, their employment, and their child support orders and payments. Additionally, comparisons are made between noncustodial parents earning full-time minimum wage or less and those earning a living wage. (Author description)

     

  • Individual Author: Romich, Jennifer; Long, Mark; Allard, Scott; Althauser, Anne
    Reference Type: Conference Paper, Dataset
    Year: 2018

    This paper describes a uniquely comprehensive database constructed from merged state administrative data.  State Unemployment Insurance (UI) systems provide an important source of data for understanding employment effects of policy interventions but have also lack several key types of information: personal demographics, non-earnings income, and household associations.  With UI data, researchers can show overall earnings or employment trends or policy impacts, but cannot distinguish whether these trends or impacts differ by race or gender, how they affect families and children, or whether total income or other measure of well-being change. This paper describes a uniquely comprehensive new administrative dataset, the Washington Merged Longitudinal Administrative Database (WMLAD), created by University of Washington researchers to examine distributional and household economic effects of the Seattle $15 minimum wage ordinance, an intervention that more than doubled the federal minimum wage.

    WMLAD augments UI data with state administrative voter, licensing, social service,...

    This paper describes a uniquely comprehensive database constructed from merged state administrative data.  State Unemployment Insurance (UI) systems provide an important source of data for understanding employment effects of policy interventions but have also lack several key types of information: personal demographics, non-earnings income, and household associations.  With UI data, researchers can show overall earnings or employment trends or policy impacts, but cannot distinguish whether these trends or impacts differ by race or gender, how they affect families and children, or whether total income or other measure of well-being change. This paper describes a uniquely comprehensive new administrative dataset, the Washington Merged Longitudinal Administrative Database (WMLAD), created by University of Washington researchers to examine distributional and household economic effects of the Seattle $15 minimum wage ordinance, an intervention that more than doubled the federal minimum wage.

    WMLAD augments UI data with state administrative voter, licensing, social service, income transfer, and vital statistics records. The union set of all individuals who appear in any of these agency datasets will provide a near-census of state residents and will augment UI records with information on age, sex, race/ethnicity, public assistance receipt, and household membership. In this paper, we describe 1.) our relationship with the Washington State Department of Social and Health Services that permits this data access and allows construction of this dataset using restricted personal identifiers; 2.) the merging and construction process, including imputing race and ethnicity and constructing quasi-households from address co-location; and 3.) planned benchmarking and analysis work. (Author abstract)

     

  • Individual Author: Bureau of Labor Statistics
    Reference Type: Dataset, Report
    Year: 2018

    From April to July 2018, the number of employed youth 16 to 24 years old increased by 2.0 million to 20.9 million, the U.S. Bureau of Labor Statistics reported today. This year, 55.0 percent of young people were employed in July, little changed from a year earlier. (The month of July typically is the summertime peak in youth employment.) The unemployment rate for youth was 9.2 percent in July, also little changed from July 2017. (Because this analysis focuses on the seasonal changes in youth employment and unemployment that occur each spring and summer, the data are not seasonally adjusted.) (Author introduction)

    From April to July 2018, the number of employed youth 16 to 24 years old increased by 2.0 million to 20.9 million, the U.S. Bureau of Labor Statistics reported today. This year, 55.0 percent of young people were employed in July, little changed from a year earlier. (The month of July typically is the summertime peak in youth employment.) The unemployment rate for youth was 9.2 percent in July, also little changed from July 2017. (Because this analysis focuses on the seasonal changes in youth employment and unemployment that occur each spring and summer, the data are not seasonally adjusted.) (Author introduction)

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