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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: Hodges, Leslie; Men, Fei
    Reference Type: Conference Paper
    Year: 2018

    In February of 2018, 6.7 million American workers were unemployed. Of these workers, one in four had been unemployed for more than half a year (BLS, 2018). Unemployment has been linked to numerous negative outcomes, including increased risk of poverty and of material hardships. A major goal of the Federal-State Unemployment Compensation Program (UI) is to protect individuals and their households against the economic risks associated with unemployment. By providing weekly cash benefits to displaced workers while they search for new jobs, we expect that UI would help households to meet basic needs and act as a buffer against economic hardships. However, with a few exceptions, the prior literature has not paid a great deal of attention to the effects of UI on poverty and material well-being.

    One reason for this lack of attention is that studies interested in identifying optimal benefit levels and optimal program size have primarily focused on how UI affects the behaviors of workers and firms. Another possible reason is that UI is not targeted towards the poor, and helping...

    In February of 2018, 6.7 million American workers were unemployed. Of these workers, one in four had been unemployed for more than half a year (BLS, 2018). Unemployment has been linked to numerous negative outcomes, including increased risk of poverty and of material hardships. A major goal of the Federal-State Unemployment Compensation Program (UI) is to protect individuals and their households against the economic risks associated with unemployment. By providing weekly cash benefits to displaced workers while they search for new jobs, we expect that UI would help households to meet basic needs and act as a buffer against economic hardships. However, with a few exceptions, the prior literature has not paid a great deal of attention to the effects of UI on poverty and material well-being.

    One reason for this lack of attention is that studies interested in identifying optimal benefit levels and optimal program size have primarily focused on how UI affects the behaviors of workers and firms. Another possible reason is that UI is not targeted towards the poor, and helping workers and their households reach or maintain a certain level of economic well-being is not an explicit goal. As a result, there may be less scrutiny of whether the UI program makes participants better off compared to means-tested programs such as SNAP and TANF.

    Nevertheless, from our perspective, poverty and material hardship measures are particularly appealing for examining the effects of UI participation. First, determining optimal benefit levels requires identifying behavioral distortions and identifying what prior studies call the “beneficial insurance effect," such as knowing how UI receipt affects household income and household consumption of goods and services. Second, UI participation among individuals in or near poverty has received greater attention following welfare reform in the mid-90s and following historical rates of unemployment during the Great Recession. However, by focusing only on the poverty effects of UI, we would be ignoring the effects that the program might have on the economic well-being of households who are not in poverty, and we would be assuming that having a certain income level is synonymous with being able to meet basic needs.

    In order to examine whether receipt of UI benefits reduces poverty and material hardships, we use data from the Survey of Income and Program Participation (SIPP) and bivariate probit regression analysis to model jointly the probability of UI benefit receipt and the probability of experiencing poverty and of experiencing housing, utility, food, and medical hardships. In order to account for unobserved differences between individuals who receive UI benefits while unemployed and those who do not, our models include state UI policies as instrumental variables. Similar to prior studies, our preliminary results suggest that UI receipt has a substantial negative effect on poverty, and that UI receipt reduces food insecurity, but not other hardships. By examining UI's effects on economic well-being this study contributes to current understanding of how the program is meeting the needs of workers in the modern economy. (Author abstract)

  • 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: Strawn, Julie
    Reference Type: Stakeholder Resource
    Year: 2018

    More than ever, States need to improve their capacity to collect and report high quality data for their SNAP E&T programs. Such forces as new federal reporting requirements, an expanded focus on program accountability, and a growing number of third-party partnerships all create a need for more and better SNAP E&T outcome data. This brief addresses the following: 1) Why States should collect and analyze data; 2) What States must collect and what additional data they may find useful to collect; 3) What resources are available to States to support data collection and reporting; and 4) Examples of how States are capturing, measuring, and reporting data. The brief also includes examples of commonly used workforce development metrics and best practices for creating metrics. (Author introduction)

    More than ever, States need to improve their capacity to collect and report high quality data for their SNAP E&T programs. Such forces as new federal reporting requirements, an expanded focus on program accountability, and a growing number of third-party partnerships all create a need for more and better SNAP E&T outcome data. This brief addresses the following: 1) Why States should collect and analyze data; 2) What States must collect and what additional data they may find useful to collect; 3) What resources are available to States to support data collection and reporting; and 4) Examples of how States are capturing, measuring, and reporting data. The brief also includes examples of commonly used workforce development metrics and best practices for creating metrics. (Author introduction)

  • Individual Author: Yang, Edith; Hendra, Richard
    Reference Type: Journal Article
    Year: 2018

    Background: The high costs of implementing surveys are increasingly leading research teams to either cut back on surveys or to rely on administrative records. Yet no policy should be based on a single set of estimates, and every approach has its weaknesses. A mixture of approaches, each with its own biases, should provide the analyst with a better understanding of the underlying phenomenon. This claim is illustrated with a comparison of employment effect estimates of two conditional cash transfer programs in New York City using survey and administrative unemployment insurance (UI) data. Objectives: This article explores whether using administrative data and survey data produce different impact estimates and investigates the source of differential effects between data sources. Research design: The results of a survey nonresponse bias analysis and an analysis of characteristics of non-UI-covered job characteristics using data collected on 6,000 families who enrolled in either the Family Rewards or Work Rewards evaluation are...

    Background: The high costs of implementing surveys are increasingly leading research teams to either cut back on surveys or to rely on administrative records. Yet no policy should be based on a single set of estimates, and every approach has its weaknesses. A mixture of approaches, each with its own biases, should provide the analyst with a better understanding of the underlying phenomenon. This claim is illustrated with a comparison of employment effect estimates of two conditional cash transfer programs in New York City using survey and administrative unemployment insurance (UI) data. Objectives: This article explores whether using administrative data and survey data produce different impact estimates and investigates the source of differential effects between data sources. Research design: The results of a survey nonresponse bias analysis and an analysis of characteristics of non-UI-covered job characteristics using data collected on 6,000 families who enrolled in either the Family Rewards or Work Rewards evaluation are presented. Results: In both evaluations, survey data showed positive employment effects, while administrative data showed no statistically significant employment effects. Family Rewards increased employment mostly in non-UI-covered jobs, while the positive survey impact estimates in Work Rewards were partially due to survey nonresponse bias. Conclusions: Despite cost pressures leading researchers to collect and analyze only administrative records, the results suggest that survey and administrative records data both suffer from different kinds of sample attrition, and researchers may need to triangulate data sources to draw accurate conclusions about program effects. Developing more economical data collection practices is a major priority (Author abstract).

  • Individual Author: Lee, Joanne; Needels, Karen; Nicholson, Walter
    Reference Type: Report
    Year: 2017

    This is a study of short- and medium-term adjustments that recipients of unemployment insurance (UI) make after a job loss in two regions in California. The study uses data from a two-wave longitudinal survey and UI administrative records to focus on such issues as how recipients’ job search strategies change over time, the role of UI benefits and other strategies unemployed workers use to cope with financial hardships, and UI recipients’ satisfaction with the program. (Author abstract)

    This is a study of short- and medium-term adjustments that recipients of unemployment insurance (UI) make after a job loss in two regions in California. The study uses data from a two-wave longitudinal survey and UI administrative records to focus on such issues as how recipients’ job search strategies change over time, the role of UI benefits and other strategies unemployed workers use to cope with financial hardships, and UI recipients’ satisfaction with the program. (Author abstract)

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